Cross-border Acquisition Completion or Abandonment

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Abandonment

The effect of the ease of doing business on turning cross-border acquisition deal

announcements into completion

Master’s Thesis International Business & Management

Mike Enting s2324458

m.e.enting@student.rug.nl

Thesis Supervisor Co-Assessor

P.J. (Paulo) Marques Morgado dr. G. (Gjalt) de Jong

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Abstract

This thesis explores the relationship between the ease of doing business and the likelihood of completing cross-border acquisition deals. Based on the Ease of Doing Business Index provided by the World Bank, we investigate whether the ease of doing business in a country affects the relationship between the acquisition stake sought by a target company and the amount of acquisition deals to be completed after public announcement. Additionally, we look whether the ease of doing business has an effect on the amount of time between deal announcement and completion. We performed a binary logistic regression analysis and a regression analysis with a sample of 246 Dutch acquisitions targeting foreign small and medium-sized enterprises in the time period of 2010-2014. The results show that our hypothesis are not significantly supported, but do show an increase in variation. This indicates that future research can use our results and investigate more deeply to what extend the ease of doing business may affect deal completion and time until deal completion.

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Introduction

In the 2015 global market, one acquisition was completed every 12 minutes (IMAA, 2016). A widely accepted failure rate of 50% (Papadakis & Thanos, 2010) implies one acquisition failed every 24 minutes. This intuitive contradiction set for many researchers in the field of International Business to investigate cross-border acquisitions as a form of internationalization. These staggering numbers, however, only show the failure of acquisitions after firms have completed their deals. In the present literature, we know much less about the failures of cross-border acquisitions during the acquisition process, meaning failure between the time of announcement and deal completion. This under researched area did however show surprising outcomes in the past. Empirical research by Holl & Kyriazis showed that firms abandon up to 25% of acquisitions at some point in the pre-completion phase (Holl & Kyriazis, 1997). According to Hotchkiss et al., reasons for deal termination include external factors such as adverse rulings by courts or regulatory agencies (Hotchkiss, Qian, & Song, 2005).

A recent news article published by Fortune magazine titled: ‘2016 is turning into a record year for broken deals in global M&A’ (Reuters, 2016). As they described, firms abandoned a wave of transactions due to concerns over regulatory and tax risks, as well as national security. Deal termination is often very costly, especially for the acquiring company, as it incurred substantial costs in preparing the initial offer. These cost include identifying an appropriate target (Bainbridge, 1990) and cost associated with the due diligence (Officer, 2003). Additional costs include the hiring of external accountants, and financial and legal advisors to facilitate the deal. Moreover, terminating a deal after public announcement harms a company’s reputation and credibility (Luo, 2005). Therefore, companies that once placed a public announcement do strive to complete the deal. We aim to get a deeper understanding of the period between deal announcement and deal completion of cross-border acquisition deals.

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This present study tries to add to the existing literature by looking at another external factor that may explain some of the variance in deal completion. Here we use a relatively new measure that focuses on the ease of doing business in over 190 countries. The World Bank’s Ease Of Doing Business Index (EDBI) has grown in popularity since its first issue in 2006, and is used by researchers in over 100 articles and 50 journals. Among those studies is a research done by Dinuk Jayasuriya, in which he investigates whether improvements in ranking generate greater foreign direct investment flows (2011). His results show that the relationship is significant for the average country. This indicates that scoring higher on the EDBI helps countries attract foreign direct investment, underlining the significant importance of this newly developed index to the modern business world.

For businesses, it is important to look for measures that explain what factors influence the probability to complete an acquisition, and the time it takes from deal announcement until completion. Following the logics of transaction cost economics (Williamson, 1985), firms let transaction costs determine whether production or services are outsourced (market), internalized (hierarchy) or a hybrid structure in the form of strategic alliances or sub-contracting. A higher probability of firms to complete an acquisition deal after announcement, and in a minimum time to do so, will lead to lower costs of acquisitions as a form of governance. Lower costs will make this form of governance more attractive for firms, hence lead to more acquisitions in the future. It is therefore important to get a comprehensive idea of what factors help predict the likelihood that firms complete an acquisition deal following deal announcement.

According to existing literature, a firm’s prior acquisition experience plays a crucial role in determining success and failure (Barkema & Schijven, 2008). A look at Dutch beer brewing giant Heineken, for example, shows us the many acquisitions they made in the past help them in the process of approaching new acquisition targets. The literature on acquisition experience has, however, only focused on the period after deal completion. A similar logic holds for the pre-deal completion phase of the acquisition process; that is, more experience in acquiring firms will help firms in the pre-deal completion phase of the acquisition process. Other than acquisition experience, scholars have investigated cultural and institutional effects to have an effect on acquisitions (Barkema & Schijven, 2008). A wide body of research has found significant results for both a positive (Morosini, Shane, & Singh, 1998) and negative relationship (Papadakis & Thanos, 2010) between cultural distance and acquisition performance. From this we can conclude that national cultures and institutions matter for cross-border acquisitions.

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announced cross-border acquisition deal will be completed. Therefore, we propose the following research question:

Does the ease of doing business matter for cross-border acquisition completion?

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Literature Review

Foreign Direct Investment and Cross-Border Acquisitions

Globalization and technological development have contributed to the popularity of cross-border acquisitions as an internationalization strategy. According to the World Investment Report (UNCTAD, 2016), global Foreign Direct Investment (FDI) has gone up 38% to $1,76 trillion, which is the highest level since the economic and financial crisis that started in 2008. Cross-border acquisitions form a big part of this total sum of FDI, growing from $432 billion in 2014 to $721 billion a year later. These cross-border acquisitions come with great challenges, in particular at the post-acquisition stage (UNCTAD, 2016). Research on post-acquisition firm performance shows that nearly 50% of acquisitions fail and destroy shareholder value (Child, Faulkner, & Pitkethly, 2001). This contradiction in growing popularity of cross-border acquisitions as a foreign entry mode strategy and its high failure rate has let many researchers to investigate on this field. However, researchers examining the field of cross-border acquisitions mostly focus on the financial post-acquisition firm performance (Papadakis & Thanos, 2010); Ramaswamy, 1997). Although such research is very relevant, less is known on the pre-deal completion phase of the acquisition process. Empirical research shows that up to 25% of acquisitions are abandoned at some point in the pre-completion phase (Porth, 1992). In the following section, we take a closer look at the acquisition process of cross-border acquisitions.

The Acquisition Process

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Figure 1 Merger and Acquisition Process (Boone & Mulherin, 2007)

This second stage of the acquisition process contains of the acquiring company performing additional objective analysis (due diligence). Because this period can take several months to complete, there is the possibility of new information arising that was not available at the time of the public announcement. Research, mainly published in the financial or legal literatures, shows that new information released in this second stage significantly affects the risk and return of merger arbitrage (Holl & Kyriazis, 1997). The fact that renegotiation is inevitable after new information becomes available to the acquisition parties, the ability to renegotiate the previously agreed upon contract determines whether or not the acquisition will be completed. Deal completion is most important for the acquiring company, since it incurred substantial costs in preparing the initial offer. These cost include identifying an appropriate target (Mitchell, Pulvino, & Stafford, 2004) and cost associated with the due diligence (Bainbridge, 1990). Additional costs include the hiring of external accountants, and financial and legal advisors to facilitate the deal. Because of the substantial costs incurred during the acquisition process and the high failure rate during this time period, we would like to investigate what is it that affects the likelihood that a deal announcement will be converted into a deal completion.

Foreign Business Environment

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According to this previous literature, it is logical to assume that the foreign business environment has an effect on the acquisition process. Other than the Institutional perspective, research done by Morosini et al. (1998) investigates the relationship between national cultural distance and cross-border acquisition performance. They test the alternative hypothesis that national cultural distance enhances cross-border acquisition performance by providing access to the target’s unique resources embedded in national culture, and conclude with results showing a positive relationship between national cultural distance and cross-border acquisition performance. Although in the current research we do not wish to investigate acquisition performance, it is interesting to see how foreign business environments do not only have a detrimental effect on business practices.

In the present research, we want to add to the existing literature by investigating the effects of the foreign business environment measured through a relatively new scale named the Ease of Doing Business Index (EDBI) (World Bank, 2017). This measure looks at various indexes and indicators to measure the ease of doing business on a country level. Although within country variations do exist, there are strong forces that do create and maintain a national culture (Hofstede, 1983), making this measure to be an appropriate index to use in the present research. Factors such as a country’s political system, education, religion, and language can together create a burden for Multinational Enterprises (MNE) to operate in a foreign country (Schwartz, 1999). The following section will introduce this measure.

Ease of Doing Business Index

In order to measure the business environment in a country, we use the Ease of Doing Business Index (World Bank, 2017). This index has been published since 2006, and ranks economies on their ease of doing business, from 1-190. A high ease of doing business means that _{“the regulatory environment is} more conducive to the starting and operation of a local firm” (World Bank, 2017). The index is an aggregated score on eleven topics (including Starting a Business, Trading Across Borders, Paying Taxes,

etc.) that contain a total of 45 various indicators. All indicators are of equal weight and the aggregate forms the Ease of Doing Business Index. For an increasing amount of countries, subnational doing business data is also available, covering for within-country differences of the business environment.

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used the Index, suggesting that the EDBI is a reliable source to work with. Additionally, continuous feedback provided by critics, professionals and policymakers have made the Ease of Doing Business Index undergo continuous improvements, including the measurement of within-country differences for the largest economies. Among studies that have used the EDBI is a research done by Dinuk Jayasuriya, in which he investigates whether improvements in ranking generate greater foreign direct investment flows (2012). His results show that the relationship is significant for the average country. This indicates that scoring higher on the EDBI can help countries attract foreign direct investment, underlining the significant importance of this newly developed index to the modern business world.

Despite its significance and recent popularity among researchers, no research yet has attempted to investigate a possible relation between the ease of doing business as measured by the EDBI and acquisition deal completion. This research tries to fill this gap by looking at Dutch cross-border acquisitions and the likelihood of deal completion.

Dutch Acquisitions

In the present research, we will focus on Dutch firms as the acquiring firm in cross-border deals. We believe the Dutch market is very interesting, because it has shown some significant increases of outward FDI in recent years. According to a recent report by the OECD (2016), The Netherlands as one of the major OECD investors, has shown an increase in outward FDI to reach levels of 113 billion USD in 2015. From these numbers we can conclude that The Netherlands are an active and important player on the cross-border acquisition market. Additionally, The Netherlands is currently ranked 28th on the Ease of Doing Business Index (World Bank, 2017). As it is neither scoring very high or low on the scale, it could be possible to generalize our findings to countries that score close to The Netherlands on the EDBI Rank.

Institutional Theory

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environment imposes significant social pressures for firms to justify their strategic actions and outputs. In order to do so, firms would engage in isomorphic behavior (DiMaggio & Powell, 1983). Isomorphic behavior is the tendency of firms to be homogenous because of the social pressures. It is a necessary mechanism for a firm to be recognized by stakeholders as a legitimate player (Tseng & Chou, 2011). DiMaggio & Powell introduced three forms of isomorphism:

● Normative Isomorphism: The conformance to normative standards established by external institutions

● Coercive Isomorphism: Formal pressure from other organizations

● Mimetic Isomorphism: Imitation of structures by other organizations in response to pressure.

As we have mentioned before, the institutional complexity surrounding cross-border acquisition deals is quite high. After deal announcement, deals are subject to regulations, political influences and private benefits such as protecting local firms (Bittlingmayer & Hazlett, 2000). Therefore, the institutional environment is likely to have a substantial effect on cross-border acquisitions and the post-acquisition announcement failure rate of such deals. In their research, Dikova et al. (2010) found significant results for the effects of national culture on acquisition deal completion success. In the intermediary phase of a cross-border acquisition (Figure 1), there is a great deal of uncertainty and complexity as two major procedural hurdles need to be passed (Dikova et al., 2010):

1) Compliance with domestic and international regulations, such as antitrust laws, merger/acquisition procedure evaluation, etc.

2) Implementation of announcement strategies at the firm level, including activities such as providing managers with background material, develop a plan for delivering sensitive news to employees, etc.

We believe the ease of doing business as measured by the world bank’s EDBI could possibly explain some of the variance in complexity a cross-border acquisition might face during these two procedural hurdles. More complex institutions will lower the chance of acquisition deal completion, and extend the time period needed to reach completion for those deals that do succeed.

Transaction Cost Economics

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operation of market mechanisms is not without costs. He stated that the governance structure a firm uses in a given situation is dependent on the comparative transaction costs. Transaction cost economics argues that the assumptions of perfect knowledge and perfect enforcement do not apply in the real world. Markets are never fully efficient because if natural market imperfections exist, market transaction costs arise. These costs include both ex ante costs, including search and negotiation costs, and ex post costs including monitoring and enforcement costs (Williamson, 1985). MNE’s can be seen as institutions that attempt to bypass these imperfections and ultimately minimize transaction costs (Chiles & McMackin, 1996).

In transaction cost theory, foreign direct investment and the choice for various entry modes is based on the concepts of bounded rationality and opportunism. Williamson (1973) defined opportunism as “an effort to realize individual gains through a lack of candor or honesty in transactions”. It can arise from asymmetrically distributed information, the inability of firms to write complete contracts or flaws in the monitoring process. The concept of bounded rationality can be classified more as a contextual uncertainty rather than a behavioural uncertainty, referring to the limited cognitive capabilities of the decision makers (Williamson, 1989). The third dimension of transaction is asset specificity, defined as “the degree to which an asset can be redeployed to alternative uses and by alternative users without sacrifice of productive value” (Williamson, 1989)

Transaction cost theory suggests that the governance structure an MNE chooses is driven by the desire to minimize transaction costs (Williamson, 1985). According to this theory, firms let transaction costs determine whether production or services are outsourced (market), internalized (hierarchy) or a hybrid structure in the form of strategic alliances or sub-contracting. Whether or not a firm chooses to internalize production or services through the form of an acquisition thus depends on the relative costs of this form of governance compared to the other forms. One can imagine that costs incurred in countries with stronger institutions are likely to be lower than the costs incurred in countries with weaker institutions. Also, if the acquisition process takes longer in one country than the other, this could have an effect on the costs incurred in completing a deal in that environment.

Acquisition Percentage

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announcement. In line with this thinking, a higher stake associating high importance, one could assume the acquiring company is interested in completing the acquisition process in the shortest amount of time possible. Following the rationale of transaction cost economics mentioned earlier, a long acquisition process leads to higher costs, decreasing the attractiveness of acquiring as a form of governance.

From the targets perspective, contrarily, a higher degree of control might lead to more aversion towards the acquisition deal. The target company may have high demands during the negotiation stage, as it will have less control after the acquisition deal has been completed. Especially small and medium sized enterprises (SMEs) as target companies, as investigated in the present research, might show a high degree of aversion towards an acquisition deal by a foreign company. SMEs are more likely to be still owned by the entrepreneur or entrepreneurs family, possibly less willing to sell their life’s work. Additionally, a high degree of acquisition stake sought by the acquiring company may lead to more requirements and regulations the companies have to abide to. Moreover, we believe that the high aversion of the target company will lead to longer acquisition processes.

Although a high acquisition stake might imply importance to complete the deal to the acquiring company, we believe the aversive feelings from the SME target will play a more substantial role in the acquisition deal completion, and therefore expect a negative relationship between the acquisition stake sought and the acquisition deal completion and time-to-reach completion.

Hypotheses

As we discussed before, the acquisition process is a complex one. The acquisition stake sought by the acquiring company may lead to more aversion from the target company towards completing the deal. Due to institutional variance among countries, one can imagine that acquisitions can be more easily completed in some countries than others. We try to make a comparison among countries and their business environment by making use of the Ease of Doing Business Index provided by the Doing Business initiative from the World Bank. Countries scoring higher on this index would imply a higher ease of doing business. We want to investigate whether there is a moderating effect from the target country’s EDBI score between the acquisition stake sought by the acquiring company and acquisition deal completion. Thus, our first hypothesis is formed as

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The time between public announcement of an acquisition deal and deal completion can take up to several months, and is costly due to substantial costs of due diligence (Dikova et al., 2010). If the time between deal announcement and deal completion is longer, the cost of the acquisition process are set to increase. The transaction cost theory as introduced by Williamson (1985) is used to explain different organizational arrangements and modes of governance. According to this theory, an acquisition will only be the most attractive option if the transaction costs of the acquisition are lower than a market or hybrid solution. Hence, high costs during the acquisition process make the acquisition as a mode of governance less appealing.

The ease of doing business in a country is likely to affect the relationship between acquisition stake sought and the time between announcement and completion. More specifically, a country scoring higher on the EDBI should positively influence the relationship between the degree of acquisition stake sought and the time period between the announcement date and date of completion. Thus, our second hypothesis is formed as

H2: A low degree of acquisition stake leads to faster cross-border acquisition deal completion when the Ease of Doing Business Index is higher.

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Methodology

The purpose of the research is to investigate the relationship between the Ease of Doing Business Index and cross-border acquisition deal completion. The research asks for an appropriate research design. Research Design

The research design is based on the philosophy of positivism, mainly focusing on observable reality in order to pursue generalizability. Because of this positivism as a research philosophy, we perform a deductive research approach in order to form and subsequently test our hypothesis based on the observations and data we collected.

Research strategy and time horizon

We will use a quantitative research approach to test our hypothesis on the relationship between the ease of doing business and cross-border acquisition deal completion. In order to conduct this quantitative research, we need to collect the relevant data from an existing database. We will use this data to test the hypothesis that we formed as a result of our deductive research approach. Only quantitative research will be used, hence our research can be labeled as a mono-method research.

Considering the time horizon of our research, we will investigate Dutch cross-border acquisitions in the time period between 2010 and the end of 2014. This can be labeled as a longitudinal time-horizon, since we look at the development of several economies over various years. As stressed in the article by Dikova et al. (1985), the public takeover process can take several months to complete. Therefore, we cannot use any more recent examples, because the acquisition announcement may have not had the time to be completed yet. The time frame of four years was also needed to create a sample size sufficiently large to perform our analysis.

Sample and data collection

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increases in ownership and are only looking at acquisitions that went from a minority to a majority stake in the target company. This left our first raw sample to a total of 368 cases. After filtering out double cases, non-Dutch acquisitions, domestic acquisitions and cases with missing values we were left with a final sample size of 246. The distribution of countries in our sample can be found in Table 1 below.

Table 1 Overview of sample target countries

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Measurement of Variables

Cross-border acquisition deal completion

Our first dependent variable is the cross-border deal completion. This variable is measured as a dummy, taking on the value of 1 if the acquisition deal is completed, and 0 if the deal is abandoned. We use the Zephyr online database to look for information on ‘deal status’. Deals that are labeled to be ‘completed’ are considered to be completed, hence receive the value of 1. Deals that are labeled to be ‘withdrawn’ or ‘expired’ are considered to be abandoned, hence receive the value of 0. Our sample had no missing values (N=246) and showed a mean of 0,86 (Std = 0,35).

Table 2 Descriptive statistics of variables

Time to completion

To test our second hypotheses, regarding the effect of the EDBI on the time-span between acquisition deal announcement and completion, we subtract the announcement date from the date on which the deal was completed. This measures the time in days from the day the acquisition was announced until completion. Data on both the announced and completed day can be found in the online database Zephyr.

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Acquisition Percentage

Our independent variable is the percentage of ownership the acquiring company is attempting to acquire of the target company. This data is retrieved from the Zephyr online database. In the research conducted by Dikova et al. (2010), ‘percentage sought’ (ownership sought by the acquirer in the target) is used as a control variable. Their results show that when there is a greater percentage sought, the transaction becomes more important to the acquirer, and therefore the acquirer may hasten the completion process to preclude any third-party interventions or defensive moves. However, as we argued before, we are looking at SME target companies, and expect a high acquisition stake to also go with more aversion towards the deal from the target companies.

In our sample, the majority of the acquisitions were full acquisitions (stake sought = 100%): 211 out of the 246 cases (Mean = 96,05%, Std Deviation = 11,68%). We expected this number to be high, as all target companies are SMEs and often not listed. The remaining 35 cases in our sample were acquisitions between 50 and 99%, as we excluded all minority acquisitions when computing our sample. In our research, we will only include acquisitions that have reached the stage of public announcement (Figure 1) and information available on acquisition stake and either acquisition completion or abandonment. This information can be retrieved from the Zephyr online database.

Ease of doing Business Index

The ease of doing business, our moderating variable, is measured through the Ease of Doing Business Index, retrieved from the World Bank’s ‘Doing Business’ website (World Bank, 2017). The Index is build up out of 11 topics, namely: starting a business, dealing with construction permits, getting electricity, registering property, getting credit, protecting minority investors, paying taxes, trading across borders, enforcing contracts, resolving insolvency and labor market regulation. A detailed description of each of these variables is provided in table A1 in the appendix.

This index has been published since 2006, and ranks economies on their ease of doing business, from 1-190. An addition that has been made to the Index in recent years is the within-country differences that are present in researched economies. For example in China, Beijing and Shanghai give different scores for certain measures in the EDBI. For the purpose of this research, we do not focus on within-country varieties in scores, but only look at the average national scores of economies.

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Doing Business Rank’ is only available on the two most recent years (i.e. 2016 and 2017). Since our sample looks at the time period between 2010 and 2014, this data is not useful in our analysis. Therefore, we look at at the ‘Distance to Frontier’ score.

The ‘Distance to Frontier’ score helps to assess the absolute level of regulatory performance over time. This score measures the distance of each country to the ‘frontier’, the best performer observed on each of the indicators across all countries. This distance to frontier is scaled from 0 to 100, where 0 represents the lowest performance and 100 represents the frontier. To illustrate; a score of 65 in 2009 means a country was 35 percent points away from the frontier. A score of 70 in 2010 would mean the country is improving.

Control Variables

We chose industry type as a control variable, to check whether it has a significant effect on deal completion. For this, we looked at the sic-codes of the acquiring and target companies. We looked at whether the industry of target and acquirer was different (coded 0) or the same (coded 1). Deals between companies in the same industry are often in pursuit of economies of scale. Out of the 246 cases, 157 acquisitions were executed between firms in different industries (0) and 89 within the same industry (1).

Our second control variable is the target company size in terms of number of employees. Even though our sample only includes small and medium sized target companies (employees < 250), there still might be a size effect within the sample itself. In our sample, the minimum amount of employees was 1, and maximum was 247 (Mean = 50,80, Std. Deviation 51,98).

Type Variable Description Source

DV Cross-border Acquisition Deal Completion

Completed / Non-Completed Zephyr

DV Time to Completion Time between announcement and completion

Zephyr

IV Acquisition Percentage Percentage sought by acquirer Zephyr

MV Ease of Doing Business EDBI score Doing Business Project from the World Bank

CV Industry SIC-Code Zephyr

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Data Analysis Hypothesis 1

The statistical analysis we use to test our first hypothesis, with acquisition completion as dependent variable, is the ‘binomial logistic regression analysis’, or simply ‘logistic regression’. This test is a variation on the ordinary least square (OLS) regression analysis, as it does not look to predict any continuous variable. The logistic regression is used to predict the probability that an observation falls into one of two categories (0 or 1), in our case whether or not an acquisition will be completed. From our sample, we take all cases in which the acquisition was completed as 1 (completed). All other cases, such as withdrawn or expired, were coded as 0 (abandoned). The binary logistic regression method is in line with previous methods used in other papers that also investigated determinants of acquisition completion (Aguilera & Dencker, 2008; Dikova et al., 2010).

The assumptions to conduct a logistic regression analysis are somewhat different from a regression analysis (Robert and Richard Burns, 2012). Firstly, a linear relationship between the dependent and independent variable is not assumed in logistic regression. Hence, we do not need to test for linearity in this particular test. Secondly, the dependent variable must be a dichotomy, in our case completed and non-completed. Thirdly, the independent variable need not be interval, normally distributed, linearly related or of equal variance within each group. Fourthly, all cases must be mutually exclusive and exhaustive, meaning a case can only be in one group and every case must be a member of one of the groups. In our research, this means that all cases must be either be marked completed or uncompleted. Lastly, at least fifty cases per predictor are needed as an appropriate sample size. Since we only use one predicting variable, our sample size of N=246 is sufficient. For our first test, all assumptions are met, and do not form any obstacle to conduct our analysis.

The logistic regression model can be expressed as follows:

P(Acquisition Completion) = 1(1+e^-Zi)

Here Z represents the interaction variable of _{Acquisition Stake and EDBI rank:}

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In this formula, β0 is the intercept, β1is the regression coefficient, εi is the error term and ‘i’ refers to ith deal of our 246 acquisition deals taken into account.

After checking for all assumptions, a logistic regression analysis was performed to predict completion or withdrawal of 246 Dutch acquisitions using the interaction between acquisition stake and the EDBI Distance to frontier as a predictor. We tested two different models. In the first model, we selected ‘completed dummy’ as our dependent variable, taking on values of either 0 for non-completion or 1 for deal completion. Then, we only included the control variables, namely industry and target employees. Industry was coded as a dummy variable, where non-similar industries between acquirer and target were coded as 0, and a similar industry was coded as 1. Employees represented the number of employees working at the target firm. The second model included our moderation analysis, namely the interaction term between acquisition stake and the ease of doing business index. Our descriptives show that there is no missing data in our sample, hence all 246 cases are analyzed.

The classification table of block 0 shows results for the model in which the coefficients for all the independent variables are 0. This block 0 model is a baseline model and shows that 86,2% can be predicted correctly. This number indicates that if we knew nothing about our variables and guessed that an acquisition would be completed, we would be correct 86,2% of the time. This number is already quite high, because most of the deals included in our sample are completed after announcement. This block 0 model will be used to compare our own model against after adding the variables of interest.

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After adding the control variables, Model 1 shows the omnibus tests of model coefficients is not significant (p = .463, Chi-square 1.540, df2). This indicates that this model is not a better model than our block 0 model without any predictor variables, meaning our control variables do not predict the outcome of our dependent variable any better than our constant. The Nagelkerke R Square shows a value of .011, indicating that this model only including the control variables explains 1,1% of the variation. Since this number is very low, the controls are not likely to affect the outcome of our test significantly. The Hosmer and Lemeshow test tests the hypothesis that the data is not a good fit for our model. Our analysis shows a significance of .372 on this test (Chi-square 8.661, df8), indicating the test is not significant (p >.05), hence our data does seem to be a good fit for our model. The classification table of model 1 shows that after adding the control variables, 86,2% of the cases are predicted correctly. This is the same amount as we predicted in our block 0 that only included our constant. Therefore, we can conclude that adding the control variables to our model is not an improvement on our model that only included the constant. From the variables in the equation table, we can see that both control variables individually are also not statistically significant (Industry Dummy p = .428, Target number of employees p = .382).

In Model 2, we added our independent variable ( _{Acquisition Percentage), our moderator (EDBI}

General_{) and the interaction term between the two variables (Acquisition Percentage * EDBI General).}

The omnibus tests of model coefficients shows that our model including these terms is significantly better (p = .000, Chi-Square 37.452, df3) at predicting the outcome of our dependent variable than the model not including any variables (block 0). Model 2 then shows a Nagelkerke’s R2 of .265, meaning that the model describes 26,5% of the variation in the data. The Hosmer and Lemeshow test shows a significance of .542 (Chi -square 6.954, df8), indicating that the model does seem to be a good fit for our data. From the classification table, we can see that now 86,6% of the cases are predicted correctly. This is an increase from our basic block 0 of 0,4%, meaning our model including the variables predicts the outcome of our dependent variable 0,4% better than the model only including the constant.

From the variables in the equation table, we can see that the interaction term of acquisition stake andEDBI General is not statistically significant (p = .724), and does not have a significant impact on the accuracy of the model. Also, the main effects of _{Acquisition Percentage and (p = .643) and Target EDBI}

General (p= .871) are not statistically significant. From our analysis we see that the interaction effect

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As stressed before, The Ease of Doing Business Index is made up out of eleven different indicators. However, because one indicator has been added recently (_{labor market regulation), it is not} available for all our data, and we only include the first ten indicators as can be found in the Appendix (Table A1). We can see if any of the indicators correlates more with the dependent variable than others. This correlation table can be found below.

Table 5 Correlations for Completed and EDBI variables

Interesting to see is that this table shows the highest significant correlation of .489 (p < 0.01) between _{Target EDBI Trading Across Borders and our dependent variable Completed Dummy.} Considering our research question, it is interesting to see that the indicator Trading Across Borders correlates most with our dependent variable. To find out whether this variable may influence the interaction between independent variableAcquisition Percentage and the dependent variable Completion

Dummy_{, we conduct another logistic regression analysis.}

As with the previous analysis, we make two separate models; the first including our control variables_{industry dummy and target employees, and the second model including Acquisition Stake, EDBI}

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Table 6 Trading across borders and acquisition completion

The first block (block 0) shows the same results as in our previous analysis, meaning with no variables added to the equation, 86,2% of the observations are predicted correctly. The results in block 1 are also the same as in our previous analysis, as we add the same control variables at this stage. Hence, we can conclude again to say that adding the controls does not leave us with a better model than the model only including the constant.

In block 2, we add our variables of interest, namely our independent variable ( Acquisition

Percentage_{), our moderator (EDBI trading across borders) and the interaction term between them}

(_{Acquisition Percentage * EDBI trading across borders). The omnibus tests of model coefficients shows} that our model including these terms is significantly better (p = 0.000, Chi-square 47,165, df3) at predicting the outcome of our dependent variable than the model not including any variables (block 0). Nagelkerke’s R2 shows a value of .325, meaning that the model describes 32,5% of the variation in the data. The Hosmer and lemeshow test shows a significance of .096 (Chi-square 13.497, df8), indicating that the model only just seems to be a good fit for our data. From the classification table, we can see that now 87,4% of the cases are predicted correctly, indicating an increase of 1,2% over our block 0 model that predicted 86,2% of the cases correctly. This mean that our current model including all variables predicts the outcome of our dependent variable correctly 87,4% of the time.

From the variables in the equation table, we can see that the interaction term of _acquisition

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that after analyzing theTrading across borders indicator more deeply, we do not find enough support to accept hypothesis 1.

We can still analyze one step further, as the Trading Across Border indicator of the ease of doing business index is separated into six different measures, namely; _{Documents to Export, Time to Export,} Costs to Export _{, Documents to Import, Time to Import and Costs to Import. A correlation table among} these variables looks as follows:

Table 7 Correlations for Completed and Trading Across Borders Indicators

From this, we can see that all six measures of the Trading Across Borders indicator of the Ease of Doing Business Index correlate significantly on the dependent variable. To see if there is any effect of these six measures on the relationship between the independent variable Acquisition Percentage and our dependent variable _{Completed Dummy, we take on another logistic regression analysis.}

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Table 8 Trading across borders and acquisition deal completion

In block 2, we add our variables of interest, namely our independent variable (Acquisition Percentage), our moderators (Target EDBI TAB Documents to Export, Target EDBI TAB Time to Export, Target EDBI TAB Costs to Export, Target EDBI TAB Documents to Import, Target EDBI TAB Time to Import, Target EDBI TAB Costs to Import _{) and the interaction term between our independent variable and}

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Hypothesis 2

The statistical analysis we use to test our second hypothesis is the linear regression analysis, with

Acquisition Duration_{as a dependent variable. The regression analysis we performed looks as following:}

Ln_Acquisition Duration i = β0 + β1(Industry dummy) + β2 (Target employees) + β3(Acquisition

Percentage_{) +β4 (EDBI) + β5 (Acquisition Percentage*EDBI) + εi}

In this formula, β0 is the intercept, β1,2,n are the regression coefficients, εi is the error term and ‘i’ refers to the ith deal of the 212 deals in our sample.

Descriptive Analysis

We test whether the interaction between acquisition deal stake and the ease of doing business have an influence on the time until deal completion. For this test, we only look at cases in our sample that have been marked ‘completed’.

Table 9 Descriptive Statistics of Variables

Our independent variable,_{acquisition percentage, varies from a minimum of 50 to a maximum of} 100 percent (Mean = 95,94%, Std. deviation = 12,04%), as we only included majority takeovers in our sample. 184 out of the 212 (86,8%) observations show an acquisition percentage of 100, indicating that most of the observations in our sample were full acquisitions.

The_{Ease of Doing Business} _{shows the score of the target country on the Ease of Doing Business} Index. In our sample, this score varies between a minimum of 39,69 to a maximum of 85,72 (Mean = 73,59, Std. deviation = 8,34). The countries and their frequencies included in our sample can be found in table 10 below.

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difference between the two dates of 0. We believe this is mainly due to the fact that our sample only includes SMEs, explaining that acquisition procedures can be executed relatively quickly.

Table 10 Overview of sample target countries

Correlations

The relationships between the variables were investigated using the Pearson correlation coefficient. Table 11 below shows the main correlations. The significant correlations (p <0.05) are marked with a *. From the table below, we can see that there is a significant correlation (r=.170, p<0.05) between our independent variable acquisition percentage and our moderating variable Target EDBI

General_{. There is no significant relationship between our independent variable acquisition percentage and} dependent variablecompleted - announced (r=-.035, p=.613), and also no significant relationship between our moderating variable _{Target EDBI General and our dependent variable Completed - Announced}

(r=0.29, p=.675). This indicates that the moderating effect of target EDBI general as hypothesized in H2 may not find support in the subsequent analysis.

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Multicollinearity

None of the Pearson’s correlation coefficients in table 11 above are 0.8 or higher, strongly indicating there is little chance of multicollinearity. In order to test this statistically, we conduct a Variance Inflation Factors (VIF) test. VIF scores of 5 or higher (Rogerson, 2001) indicate strong evidence of multicollinearity. As shown in table 12 below, there are no values showing to be anywhere near to 5. Hence, we can conclude that multicollinearity is not an issue for our analysis.

Table 12 Multicollinearity between independent variables

Normality and Outliers

In order to check for normality and outliers, we plotted histograms and boxplots (Appendix Figure E1-E4) for _{Acquisition Percentage, Target EDBI General and Completed - Announced.} Acquisition Percentage does not show a normal distribution and shows values that are not 100 as outliers. This is because the fast majority of all values give a value of 100 (184 out of 212 cases). Because we are interested in the difference between the cases that show a value of 100 and those that show a different value, we do not consider those cases as outliers and decided to keep them in our analysis.

From the histogram that plots our moderating variable _{Target EDBI General, we do see} something that looks like a normal distribution. The histogram shows 5 outliers. Because these scores are based on a scale (The Ease of Doing Business Index), we do not see them as outliers. Therefore, we do not exclude them from our analysis.

Lastly, we look at the distribution of our dependent variable _{Completed - Announced, the variable}

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Regression Analysis

Table 13 below shows a summary of the moderation regression analysis we performed. In the first model, we only added the two control variables,_{target employees and industry dummy. This model} shows a very low Adjusted R2 of .006, and is insignificant as can be seen from the insignificant F-value (p=.198). This means that both our moderators do not have any significant impact on our dependent variable_{Completed - Announced, the variable that measures the time between acquisition announcement}

and acquisition completion.

In the second model, we add the standardized independent variable_{Acquisition Percentage, as} well as the standardized moderator _{Target EDBI General. This model shows an Adjusted R2 value of} .000 at a significance level of .720, indicating that this model does not explain any of the value of the dependent variable. Lastly, our third model includes the moderation variable computed from the standardized variables of _{Acquisition Percentage and Target EDBI General. This model shows an} Adjusted R2 of .007 and no significance with an F-value of .109. This indicates that our interaction term does not significantly explain any variance of our dependent variable. Therefore, we do not find enough support to accept hypothesis 2.

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Conclusion

Our thesis focused on a period of the acquisition process that is not yet fully understood by scholars and practitioners, namely the stage between acquisition announcement and completion or abandonment. We attempted to provide a better understanding of why acquisition deals may be completed or abandoned depending on the ease of doing business score in the target company's country. New knowledge on this stage of the acquisition process is worthy of attention, as terminated acquisitions do cause considerable losses, mostly for the acquiring company. We tested empirically whether the_{ease of} doing business moderated the effect of acquisition percentage and two dependent variables; acquisition

completion and _{time to completion. For the analysis on acquisition completion, we used a sample of 246} Dutch acquisitions targeting foreign small and medium-sized enterprises in the time period of 2010-2014. We did not find any significant results regarding the main analysis, but did see an increase in variance explained by our models as we looked at the _{Trading across borders variable. For the analysis on time} until completion, we did not find any significant results. However, similar to the results on our first hypothesis, we did see a slight increase in the explanatory value of our model.

Discussion

Our test for hypothesis one did not show any significant results, indicating there is no significant moderation effect of the ease of doing business of a target country on the relationship between acquisition percentage and the likelihood an acquisition deal will be completed. Our model did show a Nagelkerke’s R2 of .265, meaning that the model described 26,5% of the variation in the data. Although insignificant, this high amount of variation explained does prove there is something to be investigated.

To investigate whether we could find where this explanation came from more specifically, we did an analysis on the ten indicators that the ease of doing business is build up from. A correlation table showed a high correlation between the variable _{Trading across borders and our dependent variable}

acquisition deal completion_{. This indicator did also not significantly moderate the relationship between}

acquisition percentage and acquisition deal completion. We did, however, see an increase in the amount of variation explained by our model. Nagelkerke’s R2 showed a value of .325, indicating that the model described 32,5% of the variation in the data. This is an increase of 6% compared to our initial model that included all indicators of the ease of doing business.

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A reason for the insignificant results shown in the three analysis’ performed for Hypothesis 1 could be that many observations of the independent variable _{acquisition percentage in the sample showed} a value of 100%, i.e. full acquisition. Perhaps a sample with more variance in the independent variable could show significant results, as our models did explain more variance as we looked deeper into high correlating variables.

Our second hypothesis did not leave us with significant results, indicating there is no significant relationship between the_{ease of doing business and the time to completion. We did, however, see a small} increase (0.012) in the adjusted R2 when our moderating variable was included in the model. This showed that the interaction between the independent variable and moderating variable did explain slightly more than both variables separately.

A reason for the insignificant results for our analysis on hypothesis 2 could be that our dependent variable time to completion had many observations with value 0, i.e. the same date for acquisition announcement and deal completion. Perhaps a similar analysis conducted on a different sample could show significant results, as we did see a slight increase in the explanatory value of our models.

Theoretical Implications

Although insignificant, our results for hypothesis 1 did show an increase in variance as we looked at the trading across border variable (and its indicators) of the Ease of Doing business Index. Therefore, there are suggestions that the external environment as measured through the Ease of Doing Business Index influences the outcome of a cross-border acquisition deal announcement. Further research is encouraged to confirm and attempt to generalize these indications. For our second hypothesis regarding the time until deal completion, for small and medium sized companies in cross-border acquisitions the ease of doing business of the target country does not significantly affect this duration. This should, however, be implemented with caution, as we saw that our sample was skewed with many observations with deal announcement and deal completion being the same date.

Managerial Implications

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Limitations and Suggestions for Future Research

Limitations of our research include the fact that we only looked at acquisitions of companies whose country of origin is The Netherlands. This limits the generalizability of our results, as acquisitions from other countries may show different results. A second limitation of our research is that we used secondary data from an existing databases. Our data gathering is therefore bounded by the quality of the Zephyr online database, as well as the data gathered from the Ease of Doing Business Index. Relatedly, a limitation of the Ease of Doing Business Index is that it is only focused on small and medium sized enterprises. Results of our study can therefore not be generalized to large enterprises. Lastly, our sample showed many observations with value 100% on _{Acquisition Percentage and 0 for Acquisition duration.} This is a strong in-sample bias, which we concluded could be a possible cause for our results to be insignificant.

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Appendices

Appendix A

Table A1: What ‘Doing Business’ measures - 11 areas of business regulation

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Appendix E: Descriptive analysis Hypothesis 2

Figure E1: Visual outlier inspection

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Figure E3: Normality test Target Ease of Doing Business Index General

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