“Organizational Learning and Performance of International
Acquisitions: The Moderating Role of Institutional
Distance”
Paul Bakker University of Groningen Faculty of Economics and Business
Riouwstraat 19
9715 BS Groningen, The Netherlands Paulrb25@hotmail.com
Study: International Economics & Business Course: Master Thesis
Abstract
Organizational learning literature has offered diverse explanations of the relationship between organizational learning and acquisition performance. Similarly, scholars have investigated the cultural effect on cross-border acquisitions performance and have offered contradictory arguments. This thesis tries to integrate both approaches and examines whether organizational learning is conditional on environmental factors; we suggest that applicability of experimental acquisition knowledge varies in respect to the extent of acquirers’ experience and differences in national institutional frameworks which in turn affects post-acquisition performance. The database includes 289 cross-border acquisitions launched by European firms during the period 1999-2003. To examine whether the relationship between experience and acquisition performance is conditional on cross-country institutional differences, interaction terms are used to test the moderating effect of cultural and institutional distance on organizational learning. The results show differences in regulations, power distance and uncertainty avoidance moderate the relationship between organizational learning and acquisition performance.
Contents
1. Introduction 4
2. Theory and hypotheses 8
2.1 Related Acquisitions 8
2.2 Unrelated Acquisitions 9
2.3 The Political Economy of Institutions 11
2.4 Organizational Learning and Post-acquisition Performance: The Moderating
Effect of Cultural Distance 14
2.5 Organizational Learning and Post-Acquisition Performance: The Moderating
Effect of Institutional Distance 19
3. Data and Methods 23
1. Introduction
Since the 1990s, the popularity of international expansion via mergers and acquisitions (M&A) dramatically increased. M&As are an incremental part of the corporate strategy of many global companies and refer to activities such as the buying, selling, and combining of different companies. The key principle of M&As is to create shareholders value over and above the sum of the two merged companies (one plus one is three principle). In addition, as international acquisitions are a quick way to establish foreign presence (King et al., 2004) worldwide acquisition activity reached a record high of $4.5 trillion in 2007 (Reus & Lamont, 2009). However, about 70% of all acquisitions do not bring about financial gains in the five years following completion (King et al., 2004). As an attempt to solve the puzzle, M&A researchers have examined factors that may impact M&A performance; the M&A performance predictor that received the most attention is organizational (experimental) learning (Hitt et al., 1998; Haleblian & Finkelstein, 1999; Vermeulen & Barkema, 2001).
1997). Haleblian and Finkelstein (1999) reported evidence for a non-linear, U-shaped, relationship, highlighting possible negative learning effects in unrelated domains for inexperienced firms; acquirers inappropriately apply lessons learned in past experiences to inherently different contexts. In a similar vein, Hayward (2002) finds no linear impact of prior acquisition experience on short-term stock price reactions, while Zollo and Reuer 2003) report evidence of non-linear experience effects in multi-task contexts (i.e., spillovers of alliance experience on acquisition performance).
Despite the contradictory results, these past studies have one common trait. They all, similar to other M&A research not focused on organizational learning, understate the importance of the institutional environment (Dikova & Witteloostuijn, 2007; Peng, Wang, & Jiang, 2008; Lin, Peng Yang & Sun, 2009). However, both formal laws and regulations and informal norms and values—collectively known as an institutional framework—have been suggested to influence firms’ M&A choices (North, 1990; Peng & Heath, 1996; Lin et al., 2009). Lin et al. (2009) stress on the importance of comparing and contrasting the learning drivers of international M&As between different levels of institutions as this would help highlight the importance of institutions typically omitted in previous M&A research (Meyer et al., 2009; Meyer and Peng, 2005; Wright et al., 2005).
costs of integration (Stahl & Voigt, 2005), on the other hand cultural distances may enhance performance by providing the acquirer with diverse set of new routines and repertoires necessary to operate successfully in a new environment (Morrosini, Shane, & Singh, 1998; Chakrabarti et al. 2009). Slangen’s (2006) proposition that culture (or cultural distance) is perhaps not a main factor affecting post-acquisition performance provides a possible explanation of the conflicting results. The mixed findings of past research are caused by the fact that the effect of national cultural distance on cross-border acquisition performance is neither consistently negative nor consistently positive, but contingent on the effect of idiosyncratic organizational practices (Slangen, 2006: 161).
study post acquisition performance, we combine micro-level organizational learning with macro-level contingencies such as formal and informal national institutions.
The research question posed here is: Is the effect of learning from past acquisitions on post-acquisition performance different for acquisitions launched in environmentally distant settings from the effect on acquisitions set up in more proximate environments? To address this question, two specific research questions are proposed: the first one examines the moderating effects of informal institutional (cultural) distance (between the home countries of the acquirer and the acquisition target) on the relation between experimental learning and post-acquisition performance and the second specific research question examines the moderating effect of formal institutional (regulative) distance (between the home countries of the acquirer and the target) on the relation between experimental acquisition learning and post-acquisition performance.
We find that in institutionally similar settings, with increasing levels of acquisition experience acquirers benefit from their accumulated skills but at a decreasing rate. In institutionally different settings, acquirers with little acquisition experience face difficulties in appropriately applying lessons learned from past acquisitions to inherently different contexts (Haleblian & Finkelstein, 1999) and therefore suffer from negative learning effects on post-acquisition performance; the best performing acquirers are those with the most acquisition experience which they apply to the widest range of institutional contexts.
relation between organizational learning and post-acquisition performance. In section three the data and methods are described, followed by a discussion of the results in section four. At the end the conclusions, limitations, and suggestions for further research are given in section four.
2. Theory and Hypotheses
2.1. Related acquisitions
Acquisitions are related when both the acquirer and the target firm operate in similar markets and distribution channels, or use similar production technologies (Rumelt et al. 1974). In related acquisitions, firms are able to exploit their resource similarity and accomplish economies of scale and scope. These resources are more likely to be applicable, better utilized and valued in related business. Furthermore, in related acquisitions firms can exercise market power because the acquirer’s size increases relative to its competitors and therefore these firms have the ability to influence prices and quantities (Singh & Montgomery, 1987). Besides these mechanisms that cause two firms to be potentially better off joined in one organization rather than operating as separate entities, related acquisitions result in management effectiveness (Salter et al.,1979) because the acquiring firm is able to employ its dominant business logic (King et al., 2004).
combine their resources; this leads to value creation. Building on this research, Palich et al. (2000) focused on the diversification strategies of firms. The authors describe three types of strategies; single business, related diversification, and unrelated diversification. Using data from 55 previously published studies, their meta-analysis provide support for an inverted-U shaped model, were financial performance increases as firms shift from a single business strategy to related diversification but financial performance decrease as firms change from related diversification to unrelated diversification. According to the author, related diversifiers have the advantage of sharing resources and achieving economies of scale and scope, whereas unrelated diversifiers face difficulties in transferring competencies and activities across units. A single-business does not employ economies of scope. In sum, related acquisitions create an advantage by sharing resources and achieving economies of scale and scope. In addition, related acquisitions facilitate organizational learning by allowing an application of accumulated experience. In section 2.3 these learning effects will be further discussed.
2.2 Unrelated acquisitions
strain on decision making, control, and governance (Palich et al. 2000). Furthermore, effort losses and diseconomies increase the costs of unrelated acquisitions.
Learning effects are also not apparent in unrelated acquisitions since past experience is not directly applicable in the new setting (i.e. new industry). Cohen and Levinthal (1990) show that learning effects are the greatest when new knowledge that is learned is related to the current individual- or firm’s experience. Organizational learning mainly depends on a firm absorptive capacity, which in turn depends on the prior related knowledge of the firm. For instance, an economics student may not be able to interpret a new financial model without first having an understanding of basic mathematics, yet a student trained in biology will be greatly challenged trying to interpret any financial model. In the paper by Lane and Lubatkin (1998), the results, based on 196 M&A, showed that firms cannot apply their experience in unrelated acquisitions; an attempt to do so lead to negative transfer effects due to the increased control and governance costs.
is only possible in related acquisitions, we will investigate the learning effects in related acquisitions only.
2.3. The Political Economy of Institutions
North (1990: 73) describes organizations as "purposive entities designed by their creators to maximize wealth, income, or other objectives defined by the opportunities afforded by the institutional structure of the society". North states that in order to be competitive and playing by the rules set by societal institutions, organizations need many skills, mostly acquired in a learning-by-doing manner; firms develop organizational skills and routines that work as a consequence of repeated transactions (interactions) with other firms and with societal institutions. The type of information and knowledge required by organizations are determined by a particular institutional context, because "maximizing behavior by the firm can take the form of making choices within the existing set of (institutional) constraints" (North, 1990: 79).
organizational structures that are not impervious to change, firms may originate from national cultures that are very different creating an additional obstacle to integration and organizational change. Such institutionally determined inability or difficulty to change may imply that it is rather challenging (if at all possible) for the acquirer to integrate the acquired firm within the existing structures of the firm.
In addition, it may be demanding to work out how the resources of the acquiring firm should be deployed to the target firm in order to put them into their most productive uses (Meyer & Altenborg, 2008). Often, knowledge assets are involved in post-acquisition transfers; post-acquisition problems may arise because the quality of such assets is more difficult to observe or measure than the quality of tangible assets, and because the acquirer cannot be certain what assets can be successfully transferred to and absorbed by the acquired firm (Coff, 1999). The more differences there are in the institutional frameworks of the two M&A partners the harder it is to specify how much intangible assets will be transferred, and whether such assets can be deployed in a new setting (Haspeslagh & Jemison, 1991).
transactions) encounter environmental complexity that cannot be fully deciphered by relying solely on knowledge and skills developed in the home institutional environment (Dikova et al., 2009). Furthermore, as organizational activity is regulated (or constrained) by nation-specific set of rules and regulations and because acquisition deals are typically idiosyncratic in nature (that is, the organizational knowledge created is specific to the deal), the applicability of past experimental acquisition knowledge may be limited to certain foreign (i.e. very similar) locations. In other words, if an international acquisition is launched in an institutionally very different setting, past experience with selecting suitable acquisition targets, the negotiation of acquisition deals and the successful integration of acquired firms may not be relevant or useful.
transformation of the merging firms in one new entity, etc. However, we suggest that the institutional environment of the acquisition (i.e. the difference between national formal and informal institutions of the acquisition partners) may affect the extent of the applicability of acquisition experience on the focal acquisition and subsequently affect its performance. However, this may vary across firms because firms differ in the extent of their past acquisition experience. Detailed propositions are developed in detail below.
2.4 Organizational Learning and Post-Acquisition Performance: The Moderating Effect of Cultural distance
broadens a firm’s knowledge base and therefore improve the success rate of future expansions.
(an inverted U-shape) relation between past acquisition experience and post-acquisition performance in culturally similar locations.
Nadolska and Barkema (2007) note that it is wrong to assume that knowledge and routines that apply to acquisitions in one foreign country or a region can routinely be used in any other foreign country. Under ambiguity, experience may not lead to learning; experience provides information about what did or did not work in the past acquisitions, but does not necessarily provide guidance of what will work in the future (Srikanth, 2005) as it may be unclear whether acquirer deficiencies caused the problem or rather the specific context of a deal. In addition, literature has shown that experience does not necessarily lead to a change in mental models (March & Olsen, 1975; Levitt & March, 1988). Often firms misapply learning; it could be due to either superstitious learning, which occurs in situations when compelling subjective experience is used to derive learning without understanding the action-outcome linkages (Levitt & March, 1988) or due to application of learned routines to problems with only superficial similarity (Pinder, 1984; Leonard-Barton, 1992).
firms with little acquisition experience because “the more dissimilar a previously learned situation is to a current situation, the less appropriate it becomes to transfer prior behavior to the new situation” (Finelstein & Haleblian, 2002: 38).
In addition, conflicts between the acquisition partners stemming from ambiguous language in the acquisition agreement can trigger disputes between the partners in the integration phase of the acquisition (Dikova et al., 2009). Buono and Bowditch (1989) argue that because employees are strongly embedded in their own culture the interfirm acquisition contract often leads to misunderstandings and misattribution of motives and intentions all of which impedes smooth interactions of people originating from different cultures (Olie, 1996). The problematic interactions cause the workforce uncertainty, confusion, distress and hostility among others (Elsass & Veiga, 1994; Hofstede, 2001). These feelings can be termed “accultrative stress” which is the disruption tension that is felt by the members of a culture when they are required to interact with representatives of another culture (Very et al., 1996).
Experienced acquirers are likely to have greater cultural sensitivity in resolving incompatibilities (Morosini, et al., 1998; Very et al., 1997). Therefore we argue that only firms with significant experience in international acquisitions can benefit from an exposure to “diverse routines and repertoires embedded in unique cultures that were not previously available to the acquirer” (Reus & Lamont, 2009: 1299); only acquirers which have developed abilities and refined routines to successfully integrate foreign units can efficiently deal with accultrative stress and benefit from the access to new foreign assets. Differences in cultures can indeed help companies develop richer knowledge structures (Barkema & Vermeulen, 1998) but only experienced firms can correctly apply these knowledge structures in new culturally different settings.
acquisitions) can be applied to the new acquisition launched in a culturally very different location, they are likely to misapply lessons subsequently affecting acquisition performance in a negative way; performance picks up at higher levels of acquisition experience when positive transfers are possible. We propose the following hypothesis:
Hypothesis 1: Cultural distance moderates the relationship
between acquisition experience and acquisition performance in
such a way that,
a) at lower levels of cultural distance performance increases
with acquisition experience at a decreasing rate;
b) at higher levels of cultural distance, there is U shape relation
between experience and performance: performance
decreases at lower levels of acquisition experience and
increases at higher levels of acquisition experience.
2.5 Organizational Learning and Post-Acquisition Performance: The Moderating Effect of Regulatory Institutional Distance.
acquisition in an institutionally similar environment can easily understand legitimacy requirements such as regulations concerning HRM activities (rules for retraining personnel, regulated obligations for letting people go, employment structures, etc.). Similar to the hypothesized effect of acquisition experience on performance in culturally proximate locations, an increased acquisition experience is likely to have a positive effect on acquisition performance in similar formal institutional settings; we propose a curvilinear (an inverted U-shape) relation because firms learn from their previous experiences at a decreasing rate (Barkema et al., 1996).
Legal requirements are more difficult to understand and apply when the legal environment of the host country is very different. It takes time to learn a new set of legislation requirements which may slow down the integration process and negatively affect post acquisition performance in the short run. Firms have to learn how to operate in institutional different settings (Vermeulen & Barkema, 2001). In cross-border M&A, the differences in country-specific regulative institutions can determine how costly the deal is (Dikova et al., 2009). Less experienced acquirers must devote time and resources to learn and comply to a new set of regulations, because the knowledge gathered from limited acquisition experiences in different regulative contexts may not be directly applicable to the new setting; all this increases the costs of post-acquisition integration and is likely to have a negative effect on post acquisition performance (Dikova et al. 2009).
protection of investors' interests and legal enforceability, but many countries in continental Europe and Latin America use a civil law system that offers less protection of ownership rights, and "tends to broaden the range of events that justify non-compliance" (Reuer et al., 2004: 24). Investing time and managerial attention into mechanisms securing contractual compliance will divert the acquirer’s attention from pending integration issues with direct effect on post-acquisition performance.
integration and ultimately affects acquisition performance in a positive way. Hence, we propose the following hypothesis:
Hypothesis 2: Formal institutional distance moderates the
relationship between acquisition experience and acquisition
performance in such a way that,
a) at lower levels of formal institutional distance performance
increases with acquisition experience at a decreasing rate;
b) at higher levels of formal institutional distance, there is a U
shape relationship between experience and performance:
performance decreases at lower levels of acquisition
experience and increases at higher levels of acquisition
experience.
The research model is shown in figure 1.
Figure 1. Research Model
3. Data and Methods
3.1 Data collection
The acquisition- and financial data are collected from the websites Zephyr1 and Amadeus2 which are part of Bureau van Dijken Electronic Publishing. The data contains information of a four year time period from January 1999 till December 2003. The data consists of only completed cross-border acquisitions; the home countries of all acquirers are located in Europe. All acquisitions are above the 1 million deal value. Furthermore, the acquisition stakes of the acquirers in the target firms range from 51% to 100%.
Initially the data consisted of 596 acquisitions launched in the period 1999-2002; however, 307 of these 596 acquisitions were excluded from the sample because they represented unrelated expansion of European firms. In this paper, we only examine related acquisitions, selected if the three-digit US Standard Industrial Classification (SIC) codes of the acquirer and the target firm matched. Table 1 shows the number of the acquiring and target nations. Most cross-border acquisitions are between European firms and target firms located in the United States of America.
[Insert table 1 about here]
1http://zephyr.bvd.com contains information of over 600,000 transactions of M&A, IPO, and venture
3.2 Measures
The dependent variable in this study is (post) Acquisition Performance. We employ a financial performance measure, using accounting return on assets (ROA) corrected for industry average ROA (Ooghe et al., 2006). According to Chatterjee and Meeks (1996) ROA one year after the acquisition event is an appropriate measure for acquisition performance as it captures the direct effect of the acquisition (a larger observation window creates possibilities for capturing information not-specific to the event). Since the data consists of both privately and publicly traded companies, accounting data is suitable to measure post-acquisition performance (Ooghe et al., 2006). The ROAs are denoted as end-of-the year book value. The performance variable is corrected for industry average ROA. Amadeus database was used to generate a list ROA of companies (around 2000) in the same SIC industry for every given year (for all relevant industries). The industry average ROA was than calculated by dividing the total amount of ROA by the total number of firms. To calculate the change in ROAs, it is necessary to consider the acquirer firm-level ROA measured at the end of the year before the acquisition year. The data are obtained from the Amadeus database. A positive change score indicates an improvement of the post-acquisition period relative to the pre-acquisition period of the acquirer. A negative change score indicates a decrease of post-acquisition performance relative to the pre-acquisition period. The formula, taken from Haleblian & Finkelstein (1999), is denoted as:
Acquisition Performance=(ROA(t+1)a – ROA (t+1)i) – (ROAta –ROAti)
We employ several independent variables. The first one, Acquisition Experience, captures the extent of organizational learning from prior international acquisitions. Acquisition Experience is measured as the number of related international acquisitions made by the acquirer 3 years prior to the focal acquisition (Collins et al., 2008). Acquisition Experience ranges from 0 to 37. The squared term of Acquisition experience is applied to reflect the suggestion that firms learn from their prior acquisition experience at a decreasing rate (Barkema et al., 1996). The number of prior international acquisitions was derived from the Zephyr Bureau van Dijken database.
uncertainty avoidance and power distance. The study by Barkema and Vermeulen (1998) provides us with another reason to examine separately the effects of cultural distance indexes; in the context of international JV survival, the authors demonstrate that differences in uncertainty avoidance rather than differences in power distance have a negative impact on IJV survival. Studies like Morosini et al. (1998), Barkema et al., (1996), and Teerikangas & Very (2006) state that individual cultural distance indexes may influence acquisition performance differently; Chakrabart et al. (2009) call for better understanding of the relative effect of the different cultural dimensions.
Uncertainty avoidance involves the extent to which ambiguous situations are threatening to individuals, rules and order are preferred, and uncertainty is tolerated in a society (House et al., 2004). In countries with high uncertainty avoidance, the process of planning is considered very important as it is viewed as a way to anticipate uncertainties. In countries scoring high on uncertainty avoidance, decision-making is slow because detailed contingency plans are drawn to deal with unexpected deviations from prescribed behavior (Dikova et al., 2009). Beatty and Gordon (1988) demonstrate empirically that senior managers show reluctance to take on new projects with uncertain outcomes. By contrast, “the management style in countries with low uncertainty avoidance is more flexible, because people in such societies are typically of the opinion that uncertainties cannot be controlled and planned” (Dikova et al., 2009:10).
scoring high on power distance, the manager has substantially more power compared with subordinates; in countries scoring low on power distance index, individual autonomy tends to be of greater importance (Cartwright & Cooper, 1996). This is why organizations in countries scoring low on power distance are typically flat, with a limited number of supervising personnel; in such organizations managers typically make the final decision concerning their subordinates’ working conditions only after a consultation with their subordinates (Hofstede, 1991). Reversely, in countries scoring high on power distance index, organizations are hierarchical, and subordinates expect to be told what to do because they are used to extensive supervision at various hierarchical levels.
To create the measure suitable for testing our first hypothesis, we first take the distance (difference) between the scores of both uncertainty avoidance and power distance of the home countries of the acquirer and the target firm in the focal acquisition. Then we multiply the absolute values of our two distance measures with the acquisition experience variable and its squared term (Barkema & Vermeulen, 1998). For every country used in the data, the scores on the two culture dimensions were obtained from Hofstede’s website4. To avoid multicollinearity problems, all interaction variables are centered before multiplication.
Our third independent variable is Formal institutional distance; to capture this distance we use data from ‘the ease of doing business indexes’5 collected by the World Bank Group and ranking each country on a scale from 1 to 181. Eden & Millar (2004) emphasize that the costs of doing business abroad is an appropriate measure for
institutional distance. Higher rankings (a lower value) indicate simpler regulations and stronger protection of property rights. In our model we used the index with the most relevance to the context at hand; we chose the Enforcing Contracts indicator because this is one of the most important indicators that can influence post acquisition performance. Enforcing contracts index contains the procedures, time, and costs needed to resolve a commercial dispute, an important factor in the integration phase of an acquisition. To create the measure suitable for testing our second hypothesis, we first take the distance (difference) between the scores of the Enforcing contracts index of the home countries of the acquirer and the target firm in the focal acquisition. Then we multiply the absolute value of this distance measures with the acquisition experience variable and its squared term (Barkema & Vermeulen, 1998). To avoid multicollinearity problems, the interaction variables are centered before multiplication.
Control Variables. We employ several control variables in this study. Our first control is
Our second control variable is Acquirer firm size. Research shows that large firms perform better than small firms (Kusewitt, 1985; Kitching, 1973; Haleblian & Finkelstein, 1999), and firm size influences acquisition intensity (Hoskisson et al., 1994). Acquirer firm size is measured as the number of employees of the acquiring firm. According to Gulati (1993), the number of employees provides a good measure of the resource sufficiency of the firm which in turn affects firm performance.
Our third control is Industry technological intensity because accounting practices and other financial variables are not homogeneous within industries (Cochran & Wood, 1984). Furthermore, investments in technology are important determinants of firm performance (OECD, 2007). Industry technological intensity is measured using the ‘OECD Technology and Industry scoreboard 2007’. The industry differences are captured using a dummy variable which takes a value of 1 if the focal acquisition was launched in a high-technology industry, 2 if the acquisition was in a medium-high technology industry, 3 if the acquisition was in a medium-low technology industry, 4 if the acquisition was in low technology industry, and 5 if it was in another industry not matching the OECD technology classification. In table 2 the number of acquisitions per industries are given and their percentage of the total sample.
Our fourth control variable, Governance distance, is a composite measure of the governance indexes by Kaufmann et al.(2002). Each of these dimensions capture different aspects of governance such as (1) Voice and Accountability, (2) Political Stability and Absence of Violence, (3) Government Effectiveness, (4) Regulatory Quality, (5) Rule of Law, and (6) Control of Corruption. This control variable is measured using the Kogut and Singh (1988) index, which generates one value reflecting the governance distance between the home countries of the acquirer and the target firms on the 6 dimensions. The formula applied is
GDj = ∑i=1{Gij –Giu) 2
/Vi} 6
Where Gij stands for the index for the ith governance dimension and jth country (the home of the acquirer), Vi is the variance of the index of the ith dimension, u indicates the home country of the target, and GDj is the governance difference of the jth country from the u country.
Diagnostic checks
Diagnostic checks are necessary to check the validity of the model and the relationship between the variables. Several diagnostic tests are performed.
Multicollinearity
relationship or parameters of interest. We test for multicollinearity by including a correlation matrix. In this correlation matrix the magnitudes of the linear relationship between the variables are show, which range from 0 to +/-1. There is a strong linear relationship when the correlation is above 0.8, in the table below, it shows that our data does not suffer from multicollinearity6.
Heteroskedasticity
Heteroskedasticity often arises in the analysis of cross-sectional data. Heteroskedasticity arises when the variance of for all observations are not the same. Cross-sectional data sets typically explain only a small fraction of the observed variation. A White test is conducted to asses the existence of heteroskedasticity. Since our model has a fair amount of explanatory variables, the test is performed without the moderating variables. The White test is not able to reject the null hypothesis in model 2 of no heteroskedasticity. To control for heteroskedasticity, a generalized least squares (GLS) regression can be used. Different error variances are used to reweigh the observations so that each has the same variance. When using GLS, the moderating effect of power distance becomes insignificant, the rest of the moderating effects (formal institutional distance and uncertainty avoidance) stays significant.
Total 162.88 64 0.0000 Kurtosis 7.36 1 0.0067 Skewness 20.69 9 0.0141 Heteroskedasticity 134.82 54 0.0000 Source chi2 df p Cameron & Trivedi's decomposition of IM-test
Prob > chi2 = 0.0000 chi2(54) = 134.82
against Ha: unrestricted heteroskedasticity White's test for Ho: homoskedasticity
. estat imtest, white
When plotting the residuals along the independent variables, we see that heteroskedasticity occurs (e.g. the scatter of the residuals on experience shown below). However, it is difficult to point out the nature of heteroskedasticity in our model. GLS is only efficient when the nature of heteroskedasticity is clear. If not, the estimators of the GLS are still consistent but the standard errors will be wrong. If the nature isn’t clear, it is better to use robust standard errors. By using a OLS regression with robust standard errors, we group heteroskedasticity and therefore control for it. Furthermore, according to Thursby (1987), OLS is almost always preferred compared to GLS when doing cross-sectional analysis. According to his analysis, OLS will result in less inconsistency and is less biased compared to GLS.
-5 0 0 5 0 R e s id u a ls 0 10 20 30 40 exp Normal Distribution
Hypothesis tests and interval estimates rely on the assumption that errors are normally distributed. To test for the normality of distribution a Jarque-Bera test is conducted. This test is based on the skewness (how symmetric the residuals are around zero) and kurtosis (the peakedness of the distribution). For a normal distribution, the kurtosis value is 3, perfectly symmetric residuals will have a skewness of zero.
Applying the Jarque-Bera statistic for model 2 we reject the hypothesis at a 5% level that residuals are normally distributed. Removing some outliners does improve normality.
Statistical Analysis
The majority of literature investigating short-term acquisition performance uses cross-sectional analysis (Fowler & Schmidt, 1989; Morosini et al., 1998). The advantage of using cross-sectional data is that the direct effect of the acquisition is measured. These studies are the best way to determine prevalenceand are useful at identifying associations such as the effect of institutions on the relation between organizational learning and performance. Furthermore, when investigating the role of institutions, the explanatory variables do not change over short period of time (culture and institutional distance). The disadvantage of these kinds of studies is that the data suffers from more multicollinearity7, less degrees of freedom, and less variation as with e.g. panel data.
To determine our regression model, we refer to the papers of Fowler & Schmidt (1989) and Reus & Lamont (2009). These two papers use stepwise multiple regression analysis8 to determine the model. The appropriate model was chosen using a F-test.
Although past research on acquisition performance has emphasized the effects of learning and cultural distance, to the best of our knowledge there is no attempt to investigate the moderating effects of cultural and institutional distance on the relation between organizational learning and cross-border acquisition performance. This makes it difficult to refer to past literature in choosing the methodology. However, Reus & Lamont (2009)
investigated the moderating effects of culture on understandability and communications. Interaction terms were used to understand these moderating effects. To get a clear view of these interactions, the linear effects are graphed on high (the mean + standard deviation) and low (mean – standard deviation) values of culture. However, since our data includes a curvilinear effect, the graphing has to be done by using the technique of Barkema & Vermeulen (1998). In this paper, the authors use squared interaction terms and generate a 3-dimensional graph to get a better understanding of these effects. In our paper we generate these squared interaction terms and plot a 3-dimensional graph, which is generated using the significant interaction as analytic function that is plotted using the program Graphis9.
4. Results
Table 3 provides the descriptive statistics of all the variables included in the study as well as their correlations; an examination of the descriptive output reveals high variability in our measures and there appears to be no indication of multicollinearity problems.
[Insert table 3 about here]
As in past studies we performed OLS regressions and used STATA 10.0; independent variables with positive regression coefficients indicate a positive impact on
acquisition performance. Table 4 presents the regression results of cross-border acquisition performance regressed on our predictors and the control variables.
[Insert table 4 about here]
In the first model, only control variables are included. From our controls two variables are significant; contrary to theory predictions, the coefficient of Acquirer’s past performance (p<0.01) has a negative sign indicating a negative relation to performance. This result does not confirm past findings that acquisitions launched by previously well performing acquirers perform well in the short-run; perhaps the positive relation between an acquirer’s performance prior to the focal acquisition and the post-acquisition performance is more relevant in the long run. Inline with theory, Acquirer firm size control is significant (p<0.05) and positively related to post-acquisition performance.
All the interaction terms are significant providing support to both hypotheses; the interaction between the squared term of acquisition experience and uncertainty avoidance difference is significant at 0.05 level, the interaction term between acquisition experience and power distance difference is significant at 0.01 level, and the interaction between the squared term of acquisition experience and formal institutional difference is significant at 0.1 level.
To help interpret the moderating results in model 2 we plotted how our moderating variables influenced acquisition performance as a function of acquisition experience (Brambor, Clark and Golder, 2006). We kept all other variables constant at their mean value. The software we used is Graphis.
[Insert Figures 1, 2 and 3 about here]
the inverted U shape turns into U shape at high levels of uncertainty avoidance. This provides support to our theoretical predictions that in such locations the international acquisitions of low experienced firms (1-10 prior acquisitions) show a negative relation to performance because of misapplied lessons or superficial learning; reversely, very experienced firms (more than 11 international acquisitions) are able to benefit from positive transfers and exhibit superior acquisition performance.
Figure 2 shows the estimated relationships between past acquisition experience, power distance differences and acquisition performance. As predicted by theory, Figure 1 shows a curvilinear (inverted U shape) relationship between acquisition experience and acquisition performance of international acquisitions launched in environments with no differences in power distance. However, contrary to theory predictions of a change of curvilinearity shape, it becomes more pronounced inverted U shape. Figure 2 suggests that at high levels of power distance differences (in the range of 150 point differences) firms with the lowest and the highest acquisition experience have badly performing acquisitions (in the short run). Acquisitions launched by intermediately experienced firms (between 10 and 15 international acquisitions) in different power distance cultures perform quite well.
regulative institutions. The inverted U shape disappears at higher levels of regulative distance (around 50 points difference) which suggests that performance of acquisitions, launched at locations with slightly different regulative institutions, is hardly affected by acquirer’s acquisition experience. Figure 3 suggests that the inverted U shape turns into U shape at high levels of regulative distance. This provides support to our theoretical predictions that in locations with significantly different regulative institutions, little acquisition experience shows a negative relation to acquisition performance because of misapplied lessons; reversely, intermediately and very experienced firms (13 to 25 prior acquisitions) are able to benefit from positive transfers and exhibit high acquisition performance.
5. Discussion
conflicting results; some found positive others found negative relation between national culture and acquisition performance.
Reversely, acquisitions launched in institutionally different locations face different set of rules set by national societal institutions; in a dissimilar context the acquirer may inappropriately generalize acquisition experience potentially leading to negative performance outcomes (Haleblian & Finkelstein, 1999). Acquirers with limited acquisition experience may be unable to realize what part of their accumulated skills are applicable in the new situation, they may misapply lessons or experience superficial learning all of which negatively affect short-term acquisition performance. Our results support the argument that unlike acquisition novices, experts have the ability to perceive differences between dissimilar events decreasing the likelihood of making inappropriate generalization errors and increasing the likelihood of appropriate discrimination (Haleblian & Finkelstein, 1999).
outcome has a range of contingency plans and can accomplish successful integration in likely cooperation with a flexible target, positively affecting acquisition performance.
Our results reveal a similar relation between acquisition experience, regulative distance and acquisition performance. Experienced acquirers with developed routines how to address very different regulative requirements typically rely on their rich international business network to secure informed external consultants who pursue the timely enforcement of contracts and handle diverse legal requirements such as tax, employment and corporate issues (Dikova et al., 2009). In such a manner, various demands of alien regulative framework are satisfied, timely integration is achieved and the short term acquisition performance is positively affected.
In addition, explicit knowledge resulting from experience with different regulations or planning procedures needs less effort in communication to be successfully transferred from one acquisition event to another; it is more transparent, less sticky, and essentially easier to transfer at greater speed (Szulanski & Capetta, 2003). In contrast, tacit knowledge such as experience with informal communication patterns at various hierarchical organizational levels is “much more difficult to identify, evaluate, and absorb since the transfer process is plagued with inherent complexity and possible misunderstandings between the parties involved in the process” (Becerra, Lunnan & Huemer, 2008: 696). Much tacit knowledge (i.e. from experience with 20 or more international acquisitions) cannot generate positive transfers within short period of time (i.e. 3 years), hence it has a negative effect on acquisition performance.
5.1 Limitations
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Table 1. Number of acquiring and target nations of cross-border acquisitions 1999-2003
Number of acquisitions by the acquirer’s Number of acquisitions by the target’s country of origin country of origin
Country of origin acquisitions Country of origin acquisitions
United Kingdom 76 United States of America 104
France 57 United Kingdom 24
Germany 27 Canada 14 Sweden 27 Sweden 13 Finland 19 Australia 12 Netherlands 20 Netherlands 10 Italy 14 Switzerland 8 Spain 11 Brazil 9 Switzerland 11 Germany 9 Ireland 9 Spain 8
Greece 8 Czech Republic 6
Belgium 3 India 5
Portugal 3 Poland 5
Luxembourg 2 Ireland 4
Norway 1 Mexico 4
Russian Federation 1 Norway 5
Table 2. Industry technological intensity
Industry Number Percent
Table 3. Means, standard deviation, and correlations
Mean St.Deviation 1 2 3 4 5 6 7 8 9 10
1. Performance -1.52 11.67 1
2. Acquirer past performance 3.72 10.93 -0.323*** 1 3. Size of the acquirer 8.36 2.46 -0.018 0.277*** 1
4. Industry tech. intensity 4.04 1.42 -0.062 0.028 0.016 1
5. Governance distance 0.75 1.41 0.003 0.003 0.122** 0.060 1 6. Acquisition experience 6.07 5.82 0.129** -0.014 0.460*** 0.059 0.012 1 7. Acquisition experience2 69.12 142 0.088 -0.022 0.335*** -0.006 -0.004 0.906*** 1 8. Uncertainty avoidance diff. 21.10 16.28 -0.005 -0.036 -0.145** -0.023 0.159*** -0.05 -0.029 1 9. Power distance difference 12.92 13.11 -0.204** 0.081 0.068 0.012 0.523*** 0.053 0.058 0.445*** 1 10. Formal institutional distance 31.30 35.8 0.092* 0.006 0.106* 0.071 0.354*** -0.073 -0.055 0.079 0.156** 1
***
Table 4. Regression results for international acquisition performance
Model 1 Model 2
Constant -2.669 (2.657) -2.430 (3.069) Acquirer past performance -0.449 (0.055)*** -0.383 (0.054)*** Acquirer Firm Size 0.523 (0.251)** 0.018 (0.291) Industry R&D intensity -0.269 (0.409) -0.486 (0.401) Governance Distance -0.167 (0.444) 0.476 (0.530) Acquisition Experience 1.148 (0.526)** Acquisition Experience2 -0.040 (0.023)* Uncertainty avoidance differences 0.210 (0.074)*** Power distance differences -0.457 (0.095)*** Formal Institutional distance 0.0670 (0.032)** Experience x Uncertainty avoidance -0.042 (0.019)** Experience2x Uncertainty avoidance 0.002 (0.001)** Experience x Power distance differences 0.072 (0.023)*** Experience2x Power distance differences -0.003 (0.001)** Experience x Formal Institutional Distance -0.014 (0.008)* Experience2x Formal Institutional Distance 0.0008 (0.003)**
R2 0.193 0.296
∆R2 0.103
N 291 289
***
Figure 1. The relation between uncertainty avoidance differences, prior acquisition experience and
acquisition performance.
Figure 2. The relation between power distance differences, prior acquisition experience and
acquisition performance.
Figure 3. The relation between formal institutional differences, prior acquisition experience and
acquisition performance.
Appendix. Summary Statistics
Summary Statistics
Variable N Minimum Maximum Mean St.Deviation Acquirer performance 324 -68.31 73.94 -1.55 11.67 Acquirer past performance 324 -66.67 30.83 3.72 10.93 Size of the acquirer 295 0 13.08 8.36 2.46
Industry Technology 319 1 5 4.04 1.41 Governance index 324 0.02 8.3 0.75 1.41 Acquisition experience 322 0 37 6.07 5.82 Acquisition experience2 322 0 1369 69.12 142.65 UAI 324 0 77 21.10 16.28 PDI 324 0 69 12.91 13.11
Formal institutional distance 324 2 180 31.30 35.81
Experience x UAI 322 0 1479 123.05 182.57
