The Effects of differences between acquirer and
target on acquisition performance.
Nick Masius S1773399
International business management
Rijksuniversiteit Groningen, Faculty of Business and Economics, Broerstraat 4, 9712 CP Groningen, the Netherlands
Supervised by Rian Drogendijk January 15th 2016
2 Abstract
3 Table of contents
Chapter: Paragraph: Page:
1. Introduction 4
2. Literature review 2.1 (Cross-border) Acquisitions 2.2 Cultural distance
2.3 Industry effects 2.4 Relative company size 2.5 Acquisition experience 7 8 9 10 10
3. Hypothesis 3.1 H1: Cultural distance 3.2 H2: Industry difference 3.3 H3: Size difference
13 13 14
4. Method 4.1 Data & sample selection 4.2 Measurement of variables 4.2.1 Acquisition performance 4.2.2 Cultural distance 4.2.3 Industry difference 4.2.4 Size difference 4.2.5 Acquisition experience 4.2.6 Company size 4.3 Analysis 15 17 17 18 21 21 22 22 22 5. Results 5.1 Descriptive statistics
4 1. Introduction
Company acquisitions play an important role in todays’ economic world. To give an impression: in 2006, the worldwide acquisition activity got up to an all-time record high of 3.79 trillion U.S. dollars (Thomson Financial, 2007), and also the volume of cross-border acquisitions has been growing rapidly worldwide, from 23% of total merger volume in 1998 to 45% in 2007 (Erel, Liao & Weisbach, 2012). At the same time, the total merger volume has increased rapidly in the last two decades as well. The cross-border mergers and acquisitions (M&A) amount to a large share of transnational investments. In years of merger waves, cross-border M&A flows amounted to up to 80% of global FDI and the total value of worldwide cross-border M&A flows amounted to over 1 trillion U.S. dollars in 2007 (Stiebale & Trax, 2011). This means that cross-border (or international) acquisitions are constituting to a larger share of an increasing total volume in M&A activity, and are playing an increasingly large role in todays’ economy.
Even though acquisitions are playing such a big role, many of the multidisciplinary literature, including two meta-analyses (Datta, Pinches & Narayanan, 1992; King, Dalton, Daily & Covin, 2004) imply that most acquisitions fail. The motives for M&A are a suggested cause of this failure. According to Budznski & Kretschmer (2009), there are motives for M&A that do not necessarily imply efficiency increases. In fact, M&As have been suggested to arise out of managers’ utility maximization , which includes preferences for expansion or ‘empire-building’ motives (Shleifer & Vishny, 1988). These motives are less likely to result in an efficiency increase after the deal (Stiebale & Trax, 2011).
5 an acquisition also gets bigger the larger the target company is, as the integration process becomes more difficult for bigger targets (King, Slotegraaf & Kesner, 2008).
The goal of this research is to provide an answer to the question whether these three sources of differences between the acquirer and target company have an impact on the success of a cross-border acquisition. If so, this might enable managers and policy makers to make better informed decisions in the future, to in turn increase the rate of successful acquisition hopefully. Thus to be clear, this research focuses on the effect that cultural distance, industry difference and size difference between acquirer and acquisition target have on the performance of the acquisition with the aim to explain better why it is that some acquisitions are successful while others are not.
Moreover, this research will focus on acquisitions made by companies from Germany that acquire a company from another country. The reason to focus on acquiring companies from only Germany is mostly because the results are more reliable when there is a single country that the targeted companies’ countries are being compared against on cultural distance, and fortunately this is also less of an effort compared to having to compare all the cultures of the targets’ home countries to the acquiring companies’ home countries. Germany has been chosen as the base country for the acquiring companies for a number of reasons. Looking into the literature on international acquisitions, it quickly became clear that most research focussed on the U.S., which sparkled an interest to find out whether the findings would be different in another country. When looking for another country to research I figured that the next best data source would be from a European country because of the high quality of institutional regulations. Within Europe, Germany is one of the countries with the highest number of international acquisitions by its companies and has to my knowledge not been researched yet in this field and context. Another big economy that could have been chosen to research is the United Kingdom, however I believe that the U.K. is more alike the U.S. than Germany is, which makes it more interesting to see whether the results in Germany differ from those in the U.S. Another, yet subjective, reason to choose Germany was that I personally find it the most interesting big country, because Germany is a neighbouring country and is also the biggest trade partner of the Netherlands.
6 difference and size difference together will result in new insights concerning their effect on acquisition performance. By putting these three independent variables together in one model they are also controlling for the effects that each of them has. At the same time, the acquisition experience of the acquiring firm is being controlled for as well in the analysis. The data needed to conduct this research was found via the databases of Zephyr and Orbis and analysed through linear regression analyses.
7 2. Literature review
Relevant articles for the several topics in this research are being reviewed in this chapter to provide a clear image of the situation in which this research is placed and to provide insights into the underlying arguments for the hypotheses that are stated in the next chapter.
2.1 (Cross-border) Acquisitions
Acquisitions are more commonly discussed and researched as a part of the construct mergers and acquisitions (M&A), which entails a whole range of possible governance structures in which two companies decide to work together or one buys the other company (or at least a controlling stake). The main motives to engage in M&As are the strengthening of market power (Kamien & Zang, 1990) and the realization of efficiency gains. Efficiency gains might stem from technology transfer within the merged entity (Roller, Stennek & Verboven, 2001), the reallocation of production and technology to more efficient use (Javanovic & Rousseau, 2008), or the exploitation of complementary assets in acquirer and target firm (Javanovic & Braguinsky, 2004).
Previous studies have shown that successful M&As can benefit from economies of scale or economies of scope, but diversification for other reasons (like empire-building or entrenching motives), have a tendency to be less successful (Shleifer & Vishny, 1989; Denis & McConnell, 2003; Besanko et al., 2004; Cole et al., 2006; Hitt et al., 2009). Other survey research has concluded that the returns of the target firm are on average significantly positive after a merger or acquisition, while the evidence on the acquiring company is far less conclusive (e.g., Datta et al., 1992; Bruner, 2002; Campa & Hernando, 2004).
8 complementarities are probably more important for cross-border acquisitions than differences in the costs of production factors.
On average, cross-border acquisitions are much larger than domestic ones and more often target listed firms (Grimpe & Hussinger, 2008). Cross-border acquisitions are also associated with higher uncertainty, higher risk of failure (Bertrand & Zuniga, 2006; Harris & Ravenscraft, 1991), and higher (transaction) costs due to the larger cultural distance and institutional differences (Di Giovanni, 2005). Due to these added difficulties acquirers may require a higher expected return from cross-border acquisitions than from domestic ones (Stiebale & Trax, 2011).
2.2 Cultural distance
As the previous paragraph started to give a hint about cultural distance causing higher uncertainty, higher risk of failure and higher transaction costs, this paragraph will delve deeper into the effects of cultural distance on acquisitions.
9 distance has a positive or negative effect on international acquisition performance has got to do with whether there are integration capabilities in place with the acquirer. They argue that when there are integration capabilities in place, the positive view occurs, and that cultural distance means bigger potential for learning about the local routines and repertoires. When these integration capabilities are not in place, however, cultural distance inhibits the development of integration capabilities of international acquisitions. Even though a lot has been researched and said about cultural distance, due to the uniqueness of different cultures it is hard to come to generalizable conclusions. For instance, Newman & Nollen (1996) found that different cultures require different approaches to optimize performance.
2.3 Industry effects
10 2.4 Relative company size
The next difference between the acquiring and targeted company that is being looked at is the relative company size. One would expect the size of the acquisition to play a role in the performance of an international acquisition, when you consider that a deal of relatively large value to the acquirer would also have a greater effect on its stock returns. For this reason, the acquirer is likely to be more committed (not just financially, but time and human resource wise as well) to make sure that the acquisition is a success. Finkelstein & Haleblian (2002) did some research on the matter, and found that large acquisitions are likely to affect performance simply due to their size, either positive or negative. More interesting are the results of the research by Harrison et al. (1991). They found that differences in resources between the acquiring firm and the target were positively related to the performance. A larger difference in this case implying that the acquirer is larger than the targeted company. A possible explanation of these findings could be that relatively smaller acquisitions are easier to manage since they often involve fewer employees and there might be less of a power struggle in top management when the acquiring company is so obviously bigger. This is partially the same explanation as given by King, Slotegraaf & Kesner (2008). They state that the effect of company size on acquisition performance is likely to result from the effectiveness of the integration process, with the integration being more difficult for larger acquisitions. However, the target company must be large enough to have an impact on the acquiring firm’s performance.
2.5 Acquisition experience
Another concept that is being examined for its role concerning the acquisition performance is the experience that the acquiring company already has in acquiring companies prior to the one examined.
11 traditional learning perspective, which says that higher levels of overall acquisition experience are associated with superior performance.
The way in which higher levels of overall acquisition experience can result in a greater acquisition performance, is through the development of routines that guide firms in the various facets of the acquisition process (e.g., target evaluation and due diligence, negotiations, and post-acquisition assimilation) in following acquisitions (Finkelstein & Haleblian, 2002; Vermeulen & Barkema, 2001). This is as it turns out quite difficult as research suggests that the majority of acquisitions continue to fail (King et al., 2004). At least one of the reasons why it is so difficult to learn to acquire successfully, is because acquisitions are far more complex than activities at the operating level. There are many interdependent sub activities in the acquisition process like due diligence, negotiation, financing and integration, which are all complex in themselves (Hitt et al., 2001). An example of a company that has applied its previous acquisition experience is General Electric, that has managed to routinize its acquisition process to the point where it can effectively integrate most of its acquisitions within 100 days (Ashkenas, DeMonaco & Francis, 1998).
As stated, this research is particularly interested in international acquisitions. The international or ‘cross-border’ part brings about another level of complexity as previously stated as well. One of the most important challenges that the acquiring companies have to deal with in foreign countries comes from being an outsider in local market networks and relative lack of knowledge of business conditions therein (Chakrabarti et al., 2009). For these reasons Chakrabarti et al. (2009) expect prior acquisition experience to be a more important factor in the context of cross-border acquisitions compared to acquisitions undertaken in the domestic context.
12 not related to the current acquisition can contribute to poor decisions based on wrong assumptions by the acquiring companies’ managers (Salomon & Perkins, 1989).
13 3. Hypotheses
In this chapter the hypotheses resulting from the literature review are stated and elaborated upon.
3.1. Cultural distance
As stated in the literature review, a greater cultural distance between countries brings about more complexities, uncertainty and information asymmetry that stem from the liability of foreignness (Zaheer, 1995) and outsidership (Johanson & Vahlne, 2009). A larger cultural distance between the acquiring and targeted company thus leads to more complex acquisitions. And even though Morosini et al. (1998) found that there are also positive effects possible due to cultural distance, I think that due to bounded rationality constraints (Hambrick et al., 2005; Mintzberg, 1973) these positive effects occur less often and to a lesser extent than the negative consequences of cultural distance. These considerations have led me to the following hypothesis concerning cultural distance and acquisition performance:
Hypothesis 1: The higher the cultural distance between the different countries the acquirer and target company are in, the lower the performance of the acquisition.
3.2 Industry differences
When acquiring a company in the same industry, it is easier for a company to leverage its capabilities onto the targeted company and companies that manage to leverage their capabilities in M&A’s have shown to have better acquisition performances than those that do not (Neely, Jullens & Krings, 2015). Aside from this, companies in the same industry are also more likely to have a higher technological relatedness, which Frey and Hussinger (2006) found to be a significant determinant of international acquisitions, likely because managers find it to be important for the success of the acquisition. Therefore:
14 3.3 Size differences
Even though the research by Finkelstein & Haleblian (2002) states that large deals affect the performance of the acquisition more, simply because that due to their size they have a larger effect on the stock of the acquiring company, this hypothesis assumes that this relation does not have to be a positive one. The larger effect that large acquisitions have can just as well pan out to be negative for the acquiring company when unsuccessful. Therefore, here I base my hypothesis on the findings by Harrison et al. (1991) and King, Slotegraaf & Kesner (2008), who found that the larger the acquiring company is compared to the targeted
company, the better the acquisition performance. This is because the effect of company size on acquisition performance results from the effectiveness of the integration process. The integration process, in turn, is more difficult for relatively larger targets. Therefore:
15 4. Method
In this chapter of the paper the methods used to execute the research are being elaborated upon. First the data collection is being discussed, which data sources were used and which data has been derived from them and how. Next, the way in which these results are being used to conduct the statistical analysis will be discussed.
4.1 Data and sample selection
The research that has been conducted for this paper is based on data from online databases. No surveys or interviews have been conducted or used. The reason behind the choice in data resource is that the information needed to conduct this research has already been collected by other organizations and it would be very hard to find the data first hand considering the time and resource constraints this research faced. Furthermore, this allowed the author to use a much larger data sample since it was relatively easy to come across information about acquisitions and the companies involved.
The first database used in this research is Zephyr by Bureau van Dijk; this is a comprehensive M&A database with integrated detailed company information. This source of information made it possible to find a large number of M&A deals. Because there is such a large amount of information available on M&A deals it was possible to focus the research on a more specific part of M&A deals. Only international acquisitions were taken into account, since one of the differences between the acquiring and targeted firm that is being researched is the cultural distance between the countries they are from. Another point of focus was to look at 100% acquisitions only, since different types of mergers or acquisitions could have different variables, with different importance, that determine the performance of the merger or acquisition, it is useful to focus on one type acquisition and exclude that reason for a bias in the results. The reason to choose 100% acquisitions, instead of another form of M&A, was because it is such a clear distinction with other acquisitions which makes it more practical to research, and because these acquisitions are often most important to be successful for the acquiring company since it is fully committed.
16 highest number of M&A’s. In turn, this made me wonder whether the results from studies conducted in the U.S. are also representative for other countries. And so it was decided to put it to the test and to choose a different sample country. Germany has been chosen as the base country for the acquiring companies for a number of reasons. When looking for another country to research I figured that the next best data source would be from a European country because of the high quality of institutional regulations. Within Europe, Germany is one of the countries with the highest number of international acquisitions by its companies. Another big economy that could have been chosen to research is the United Kingdom, however I believe that the U.K. is more alike the U.S. than Germany is, which makes it more interesting to see whether the results differ in Germany from the U.S. Another, yet subjective, reason to choose Germany was that I personally find it the most interesting big country, because Germany is a neighbouring country and is also the biggest trade partner of the Netherlands.
With the criteria set for the research of the paper, the search for data could begin in Zyphor. There were still so many deals included in the results that I decided to narrow it down to deals made in the last five years, in order to have the most up-to-date data included which is the most relevant for anyone interested in the results of this research. The goal was to find 100 deals that were 100% acquisitions, made by German companies, and for which the buyer stock prices were available in Zephyr in order to use these to estimate the performance of the acquisition. Sorting through the search results from large deals to smaller deals, I found 45 deals that met the search criteria before the deals were becoming smaller and there were little deals with the stock prices of the buyer company known. At this point I decided to expand the search criteria to include deals from ten to five years ago. This search resulted in another 57 acquisition deals that met the search criteria, before the acquisition deals were getting so small that it became hard to find deals with all the data needed. Together, these results added up to a total of 102 acquisition deals in the data set. However, due to some of the needed information not being available for some of the variables tested, the usable sample boiled down to 61 acquisitions.
17 4.2 Measurement of variables
4.2.1 Acquisition performance
18 4.2.2 Cultural distance
The cultural distance was calculated through an alternative version of Kogut & Singh’s (1988) cultural distance index. Instead of calculating the cultural distance using the cultural dimensions and country culture scores by Hofstede (2001), the cultural dimensions and country culture scores by the GLOBE project (House et al., 2004) was used. As Reus & Lamont (2009) argue as well, even though a countries’ culture is deeply rooted and does not change a lot, the data on which Hofstede’s scores is based are from 1970 and represent a single company, whereas the data from the GLOBE project has been collected in the 1990s and contains a large number of companies. Another difference between the two data sources on culture is that Hofstede uses six dimensions of culture while the GLOBE projects’ data differentiates between nine different dimensions of culture. So the GLOBE projects’ data on culture was preferred, because it is more recent, involves a higher number of companies and has a broader database of culture with the higher number of cultural dimensions.
The respondents of the GLOBE projects’ research were asked to answer questions relating to cultural qualities on the basis of ‘as they are’, and values that assessed cultural qualities ‘as they should be’. Reus & Lamont (2009) argue that as-is’ differences are more likely to surface more readily than ‘as-should-be’ differences, and chose to use these scores for their research. This research is based on the same principle and the practices scores were used to calculate the degree of cultural distance. The formula that Kogut & Singh (1988) came up with has thus been adjusted to fit the 9 dimensions in the GLOBE project and is stated below in equation 1.
𝐶𝐷𝑗 = ∑ (Iij−Iig)²/𝑉𝑖
9 9
𝑖=1 (1)
19 Table 4.1.1 Home countries of targeted companies and frequencies
Country Frequency
United states of America 27
United Kingdom 18 Netherlands 7 France 6 Switzerland 6 Luxembourg 4 Italy 4 Sweden 3 Brazil 3 Norway 3 Austria 2 China 2 Poland 2 Turkey 2 Russian Federation 2 India 1 Czech Republic 1 Cyprus 1 Israel 1 Slovenia 1 Spain 1 Belgium 1 Serbia 1 Canada 1 Denmark 1 Ireland 1
20 U.K., which together amount for 45 out of 102 acquisitions already. Unfortunately, the GLOBE project (House et al., 2004) does not have the cultural value scores for all of these countries. Therefore, the cultural difference from Germany scores could not be computed for all of these countries and the ones that could not be computed, unfortunately had to be left out of the research. These were acquisitions from six countries and eleven acquisitions had to be left out because of this. The cultural difference scores computed through the formula stated in equation 1 are shown in table 4.1.2 below.
Table 4.1.2 Cultural distance of target countries from Germany
Country Cultural distance from Germany
United States of Amerika 1.306
21 4.2.3 Industry difference
While using Orbis, all the industry codes of both all the acquiring and all targeted companies were collected in order to be able to analyse whether there is a difference in acquisition performance when there is a difference in industries. The US SIC codes, NACE Rev.2 codes and NAICS 2012 codes were all collected in order to find out which type of codes were available for most companies in the data sample to then choose which type of codes were best to use for the research. The codes that had the highest availability in the data were the US SIC codes and these were thus used to conduct the research. Now that all the data had been collected, the US SIC codes of the acquiring and targeted companies were placed next to each other. Then, simply by observation, a new dummy variable was created called IndustryDifference in which an observation was scored as ‘1’ when: at least one of the US SIC codes of the acquiring company was similar to those of the targeted company, and at least the first two numbers out of four of the US SIC code were the same. Otherwise the observation was scored as ‘0’. As an example, one acquirer was given the US SIC codes 2834 and 2899 in Orbis. The target of this deal was given the US SIC code 2833. Since both the acquiring and targeted company have at least one US SIC code that starts with ‘28’ this observation is scored as a ‘1’ in the dummy variable IndustryDifference.
4.24 Size difference
22 assets-to-acquirer assets, and that is what has been done here as well. The RelativeSizeTotalAssets variable was computed by dividing the total assets of the acquirer by the total assets of the targeted company. This RelativeSizeTotalAssets is the operationalized variable to measure the size difference between the acquiring and targeted company.
4.25 Acquisition experience.
Another piece of information then gathered via Zephyr was the acquisition experience of the acquiring companies. Using Zephyr, the acquisition history of all acquiring companies was searched for up until the date of the acquisition of the observation. The amount of acquisitions made were then recorded for each of the acquiring companies.
4.26 Company size
As stated previously for the size difference between the acquiring and targeted company, the data concerning the size of the companies was collected through the Orbis database and the measure most commonly available were the total assets of the companies. These have been recorded for all acquiring and targeted companies when the data was available in Orbis.
4.3 Analysis
Now that the data had been acquired and the variables had been operationalized, the analysis could begin using SPSS. Here is another recap of the variables used again: the dependent variable is Acquisition performance, the independent variables are Cultural distance, Industry difference and Size difference, and the control variables are Acquisition experience, Acquirer total assets and Target total assets.
24 5. Results
After having described how the data was gathered and in which way the analyses have been conducted, this chapter reveals the results of this research. First the descriptive statistics will be presented, and then the correlations between the researched variables and finally the regression results are revealed.
5.1 Descriptive statistics
In order to provide some more insights about the sample used for this research the descriptive statistics are shown below in table 5.1.1. The table shows the minimum, maximum, mean and standard deviation of the variables used. (The previous chapter explains the operationalized variables that have been chosen to represent the variables of this research and how they have been measured.) As can be seen in the table, there were 102 observations made in total, but due to incomplete available data the ‘valid N’ is 61 that remained and was used to conduct the regression analysis with.
25 variable has got such bigger numbers than those do, since the Size difference is calculated using the original numbers.
Table 5.1.1 Descriptive statistics
N Minimum Maximum Mean Std.
26 Table 5.1.2 Skewness and Kurtosis results
Skewness Kurtosis
Statistic Std. Error Statistic Std. Error
Stock difference 1.863 0.239 10.014 0.474 Cultural distance 1.959 0.253 3.339 0.500 Industry difference -0.093 0.257 -2.038 0.508 Size difference 6.798 0.289 48.757 0.570 International acquisition experience 3.197 0.239 9.164 0.474
Acquirer total assets -0.099 0.240 -0.365 0.476
Target total assets -0.821 0.289 1.310 0.570
5.2 Correlations
In order to find out more about the relations among the independent variables, as well as have a rough indication of the relation of independent and dependent variables, the correlations have been computed and stated below in table 5.2.1. As can be seen, there are three significant correlations.
27 Table 5.2.1 Correlations Stock difference Cultural distance Industry difference Size difference Acquisition experience Acquirer total assets Stock difference Cultural distance -0.061 (91) Industry difference -0.117 (88) -0.061 (80) Size difference 0.048 (69) 0.182 (61) -0.158 (69) Acquisition experience -0.120 (102) 0.047 (91) -0.122 (88) 0.032 (69) Acquirer total assets -0.046 (101) 0.120 (90) -0.147 (88) 0.032 (69) 0.606 (101)** Target total assets -0.003 (69) -0.134 (61) 0.177 (69) -0.491 (69)** 0.142 (69) 0.343 (69)** The Pearson Correlation is stated in the table with the N behind it in parentheses. ** significant at 5%, * significant at 10%
5.3 Regression results
28 The intercept shows the value of the dependent variable ‘Stock difference’ if the scores of the other variables would be zero. The first three variables then, cultural distance, industry difference and relative size are the operationalized terms for the three independent variables of the research. The other variables, international acquisition experience, acquirer total assets and target total assets are used as control variables in this research. The table then lists the adjusted R Square, which tells us to what extent the variance in the dependent variable can be explained by the variables in the regression analysis. Finally, the F-value is shown, which is a measure to control whether the regression model is statistically significant. Table 5.3.1 Regression results
Culture, Industry & size and acquisition performance VIF Intercept 12.075 (19.814) Cultural distance -1.120 (1.599) 1.057 Industry difference -11.101 (3.886)*** 1.108 Relative size 0.000 (0.000) 1.427 Acquisition experience -0.011 (0.007) 1.539 Acquirer total assets -1.397 (2.055) 1.870
Target total assets 1.553 (1.627) 1.679 Adjusted R Square 0,099 (14.317)
F 2.105**
Number of observations is 61 Standard errors are in parentheses.
*** significant at 1%, ** significant at 5%, * significant at 10%
29 performance measured as stock difference when the scores of the other variables would be zero. Then, looking at the independent variables we set out to test, we see that, out of the tested independent variables, only industry difference has got a statistically significant result. We see that the coefficient is –11.101 and that this result is significant at the 1% level. In this case, the coefficient means that for every percentage point increase in industry difference, the performance of the acquisition tends to go down by 11.101. This means that on average the stock price (one week after the acquisition) of the acquirer is €11.10 lower when acquiring another company in a different industry, than when it acquires a company in the same industry. Out of the control variables that have been used, there were none that resulted in significant results.
Another thing that is reported in this table are the VIF scores. These scores measure the robustness of the model, to see whether different variables are actually having a similar effect on the dependent variable and thus distorting the results. As can be seen in the table, all VIF scores are within one and two, which means that they are quite robust.
Industry difference is the only variable that revealed a statistically significant relationship. The results regarding hypothesis one and three were not statistically significant and these hypotheses are therefore rejected. Hypothesis two however, does have statistically significant results in the regression model to back it up. Hypothesis two: “When the acquirer and target are active in different industries, the performance of the acquisition is less
successful.” can be accepted. When the acquirer and target companies are active in the same
30 6. Discussion
So what could these results imply about the role of cultural distance, industry difference and size difference for acquirers from Germany?
Starting with cultural distance, the results regarding cultural distance were not statistically significant, which is actually in accordance with the existing literature, which is in general inconclusive. Some articles found a positive relationship, some a negative one, and there were more that ended up with inconclusive results. So one could argue that companies from Germany are not any different than companies from the other countries that had been tested in previous research, in the way cultural distance affects the performance of their acquisition. However, due to the relatively small amount of observations of this research and the results being insignificant, it would be interesting to see if the results would be different if someone with more time and resources could conduct a similar research with a larger sample. The second variable tested, industry difference, was found to statistically significantly affect the international acquisition performance. This was in line with the hypothesis made, and is also in line with the previous research conducted regarding industry differences based on leveraging capabilities and technological relatedness. So now that we know that German companies, in general, perform better when acquiring foreign companies that are active in the same industry, it would be interesting to follow this up by researching whether this holds for all industries and to what extent this varies among industries. I would expect this to be more true for highly technical products, due to the leveraging of capabilities and technical relatedness, than for less technical products that require less technology or capabilities. This could actually be (one of) the reason(s) that German companies have been found to be so successful when acquiring companies in foreign countries but in the same industry. Because German companies are known for being very technologically efficient in their electronics as well as car manufacturing.
31 with acquisitions of various sizes, where there would be issues in other countries. This is pure speculation however, further research is needed to come to a conclusion. I would again advice to use a larger sample in that case.
For as far as the limitations of this research, the relatively small sample size has already been mentioned a couple of times, but there are of course more limitations. One of these is the skewness and kurtosis of this research and of the size difference variable in particular. Since the independent variable that was the least of a skewness or kurtosis is industry difference, which is also the only statistically significant variable in the regression analysis, it could be that the skewness and kurtosis is a factor that prevented the other
variables the be statistically significant in the regression analysis. This is in part related to the sample size limitation, since a larger sample size might also decrease the skewness and kurtosis of the variables. This is thus yet another reason to conduct future research on this topic with a larger sample size. Another way to prevent this skewness and kurtosis might be to only include acquisitions that fit certain parameters, since a small number of acquisitions with very large differences can cause the skewness and kurtosis.
Although purposely done, the focus on German acquirers limits this research in the sense that the question still remains whether these results can be generalized for other countries and it would be interesting to see in future research if the results would be different in other countries.
32 7. Conclusion
To sum things up, this research set out to find out whether three differences between acquiring companies from Germany and their targeted companies in other countries, namely cultural distance, industry difference and size difference, have an effect on the international acquisition performance. In order to do this, the first thing done was to create a literature overview of the existing literature on these variables. Based on this overview, there were three hypotheses constructed to test each of the three variables earlier mentioned. A new dataset was created by looking up acquisition deal data on Zyphor and Company data on Orbis. This raw data was then processed and computed in ways to make them usable for statistical analyses. The hypotheses were tested using a linear regression model, in which all three independent variables were tested in the same model to control for their effects as well as with the control variables acquisition experience and acquirer and target total assets.
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