When Birds of a Feather Don’t Flock Together: The Influence of Post-
M&A Faultlines in Top Management Teams on Financial Performance
Neela PirwitzUniversity of Groningen
Supervised through Dr. Marvin Hanisch, co-assessed through Michelle Weck
Department of Innovation Management and Strategy, University of Groningen, the Netherlands
E-Mail: neelazpirwitz@gmail.com Word Count: 13147
Final Version: 22. July 2020
Abstract
The failure rate for mergers and acquisitions (M&A) is known to be very high (Calipha, Tarba & Brock, 2010; Christensen, Alton, Rising & Waldeck, 2011). In this research I investigated in how far faultline consolidation in newly built Top Management Teams (TMTs) could be the reason for this. Here fore, I considered the effect of several CEO-based faultlines on Post M&A Financial
Performance. The research is based on 151 observations of the biopharmaceutical industry, between the years of 1994-2016. I predicted that several faultlines in the TMT, measured through the CEO, would have significant negative effects on Post M&A Financial Performance. This could contribute to the knowledge of why the failure rate of M&As is this high. Although I could not find significant support for any of my hypotheses, there were significant relationships between Post M&A Financial Performance and several control variables. These should be considered in future research. I contribute to the current literature by furthering the integration of faultline theory into the M&A context and considering team-disintegration not only in a team of a single company but in the context of a newly built team, which is made up of two previously cohesive, existing teams.
Acknowledgement
I would like to thank Marvin Hanisch for the great supervision and invaluable feedback during the entire process of writing this thesis. Your feedback from the introduction meeting to the final deadline were always of great added value. I genuinely appreciate the time you took to answer E-Mails, explain concepts and have video calls with us whenever we needed and gave us as much guidance as you could.
Table of Contents
1. Introduction ………...4
2. Theory ………...6
3. Hypotheses Development ………...8
3.1 The Effect of Gender Faultlines on Firm Performance ………..9
3.2 The Effect of Experience Related Faultlines on Firm Performance ………..10
3.3 The Effect of Cultural Faultlines on Firm Performance ………10
4. Methods and Data ………..12
4.1 Analytical Model ………...12
4.2 Data Collection and Sample ………..12
4.3 Measurements ………....13 4.3.1 Dependent Variable ………..13 4.3.2 Independent Variables ………..14 4.3.3 Control Variables ……….14 4.4 Analytical Model ………...18 5. Results ……….18
5.1 Descriptive Statistics and Correlations ……….…18
5.2 Regression Analysis ………..26
5.3 Additional Analysis ………...29
6. Discussion ………30
6.1 Overview of the Study ………...30
6.2 Theoretical Implications ………....30
6.3 Managerial Implications ………31
6.4 Limitations and Future Research ………...32
7. Conclusion ………...33
References ………34
1. INTRODUCTION
Many firms engage in Mergers and Acquisitions (M&A) in an attempt to increase firm
performance (Christensen, Alton, Rising & Waldeck, 2011). These deals are especially popular in the pharmaceutical industry (Armaghanian, 2017). If a pharmaceutical firm is unable to develop new drugs on its own, it is attractive for them to engage in a M&A deal to stay relevant in the market (Richman, Mitchell, Vidal & Schulman, 2017). In this way, they attempt to remain financially successful. To be successful on the long-term, the merging firms need to cooperate impeccably (Melkonian, Monin & Noorderhaven, 2011). However, this is not always an easy task. A popular metaphor for M&A deals is the concept of marriage (Rottig, 2013). As in any marriage the two partners will benefit from compatible character traits which will enable them to cooperate and build a united front. Since the two firms will need to adjust to structures, procedures, habits and values, conflict might occur. Through different ideologies or characteristics, sub-groups dividing members of the newly joint firms could arise, leading to increased conflict within the group (Thatcher, Jehn & Zanutto, 2003). This could be especially problematic in the Top Management Teams (TMTs) of the respective firms, as they often have a role model function for employees. Hence, subgroup formation in TMTs can influence the success, of the entire firm (Li & Hambrick, 2005). It is therefore important to consider whether division caused by these subgroups can be strong enough to hamper the
performance of the firms, rather than amplifying it.
Diversity literature thoroughly examined the impact of disintegration within a team (Brewer & Brown, 1998; van Knippenberg et al., 2004; Lincoln & Miller, 1979). An often-found conclusion is, that diversity can lead to increased conflict in a group and is not necessarily beneficial (Choi & Sy, 2010; Abrams, et al., 1990). Hence, the relationship between diversity and performance could be negative. It is a known fact that 70-90% of M&A deals fail (Christensen, Alton, Rising & Waldeck, 2011), and the sub-groups built through diversity might be partially responsible for this. Even though the failure rates for M&As is this high, about 49,000 M&A deals valued at 3,9 billion USD were completed in 2018 alone (IMAA Institute, n.a.).
organizational performance (van Knippenberg, West & Homan, 2011), this could particularly be the case if faultlines occur in a newly found top management team. Prior research investigated faultlines based on e.g. gender, tenure or cultural differences within TMTs (Choi & Sy, 2010; Ndofor, Sirmon & He, 2015; Jiang, Jackson, Shaw & Chung, 2012). However, to the best of my knowledge, they did not yet consider how TMT sub-group formation influences the M&A process if there is a high level of diversity between teams. It is of great importance to investigate this, as this could be a meaningful step forward in discovering the reasons of extremely high M&A failure rates and potentially decreasing those in the future. Consequently, the investments made into M&As would be more beneficial and could lead to firms achieving their goal of increasing firm performance more often. As this is a core reason why many firms enter in a M&A deal (Christensen, Alton, Rising & Waldeck, 2011), this would be of great value.
Therefore, the purpose of this study is to provide insights into how diversity based faultlines impact firm success. In line with prior findings (Van Peteghem, Bruynseels, & Gaeremynck, 2018; van Knippenberg, Dawson, West & Homan, 2011), I predict a negative relationship between
faultlines and firm performance. I argue that as diversity increases, and faultlines get stronger, conflict will become more frequent and hence Post-M&A Firm Performance will decrease. Thereby, I will focus on faultline occurrence based on gender, tenure and cultural differences, as these have frequently been found in prior literature (Pearsall, Ellis & Evans, 2008; Choi & Sy, 2010; Ndofor, Sirmon & He, 2015; Jiang, Jackson, Shaw & Chung, 2012; Hutzschenreuter & Horstkotte, 2013). I will consider these based on the CEO-level of the firm, as CEOs make up a key part of the TMT and are believed to have especially large impacts on the behavior thereof (Ling, Simsek, Lubatkin & Velga, 2008), and aim to answer the following research question: “Does TMT disintegration based on faultlines lead to decreasing firm performance following a M&A deal? “
I test my hypotheses based on 151 M&A contracts of the biopharmaceutical industry, between the years of 1994 and 2016. To test the hypotheses, I conducted an OLS regression. I used several control variables on the dyad, firm and CEO level to control for confounding variables. Unfortunately, I did not find significant results. Consequently, I am unable to state that gender-, tenure- or culturally based faultlines in the TMT have a significant effect on Post-M&A Firm Performance. Even after I performed an additional robustness test using Tobin’s Q, I could not find significant relations between the dependent- and independent variables.
considered in the context of TMTs before, there is little research implementing it into the M&A context. Even though the results of this study are insignificant, they provide a basis from which future research can be carried out from. Moreover, I hope to provide more awareness on the influence of diversity on conflict and following decreases in firm performance, for TMT members and especially CEOs. This awareness might lead to more empathy between CEOs and TMTs, as they are more conscious about the changes and pressures they each go through in an attempt to make this new “marriage” successful. Hence, less conflict could occur and potential faultlines could be weakened. Accordingly, the chances of the M&A deal being successful could increase.
2. THEORY SECTION
M&As have become increasingly popular throughout the world. One of the most commonly named factors for a successful M&A deal is “orchestrating and executing the integration process” (Calipha, Tarba & Brock, 2010, p. 3; Christensen, Alton, Rising & Waldeck, 2011). This means, that one of the key conditions for a successful M&A deal is the perfect coordination of different elements of the companies. If the acquiring company can achieve this, and the same dedication is brought to the deal as to projects the companies usually handle alone, a M&A deal can become a competitive
advantage for the firms (Ferrer, Uhlaner & West, 2013). Additionally, firms can enhance their R&D capabilities if they close a M&A deal with a compatible firm (Cassiman, Colombo, Garrone & Veugelers, 2005), which can also contribute to the establishment of a competitive advantage. This speaks for M&As having the potential of fulfilling the goal of enhancing firm growth and creating sustainable value for the companies (Calipha, Tarba & Brock, 2010).
considering the new, combined firm goals. This could then lead to inefficiencies and ultimately firm failure.
Applicable to this context is the Faultline Theory, which I will use as focal theory in this study. According to Lau and Murninghan (1998), faultline theory considers how different combinations of team members and their characteristics can influence a team’s performance and behavior. This transpires through the creation of “faultlines”, which are defined as imaginary borders, that divide the team into a certain number of sub-groups, based on common attributes of the team members
(Bezrukova et. al, 2009). These faultlines can occur based on surface level (e.g. gender or age) and deep level (e.g. values or personality traits) characteristics.
We can additionally distinguish between strong and weak faultlines. If a team develops few subgroups, it has strong faultlines, and if all team members either have relatively homogenous traits or many different sub-groups were developed, it has weak faultlines. Hereby, strong faultlines are more likely to lead to conflict and mistrust, which might result in team members refusing to share their knowledge with each other (Gratton, Voigt & Erickson, 2007). Hence, the strength of faultlines is as important to consider as their origin. Depending on whether the faultlines are strong or weak, a newly formed team could be successful or not. This also means, that the mere existence of faultlines is not a sufficient reason for them to have negative consequences for cooperation within the team and the success it will show. However, Bezrukova et al. (2009) showed, that negative effects of faultlines are amplified with increasing differences between sub-groups, and according to Henry, Arrow & Carini (1999), an increase in team performance occurs depending on the level at which group members identify themselves with their team. Hence, if team members identify with the team on a low level, the team will experience lower levels of performance.
As a result of a merger or acquisition, TMT dynamics will be interrupted when a new TMT needs to be formed based on the two former TMTs. Thus, the TMT faces great amounts of change following a M&A deal. According to psychological reactance theory (Brehm, 1966), individuals who are threatened with losing part of their behavioral control through a form of reduction will experience enhanced motivation to regain these behavioral freedoms. Hence, the TMT members of each original firm might show resistance to change and difficulties adjusting to new regulations might occur while team members try to cling to the responsibilities and leadership positions they had before.
Consequently, the solidification of faultlines is encouraged.
in leading the company prior to the merger, it is not a given that they will be equally successful in leading a newly combined team. They might be overwhelmed by the fact that rather than focusing on managerial decisions they now also have to focus on inter-personal relations within the new team. This might fuel the creation of faultlines and lead to a shift in focus from the company to the TMT only. Consequently, firm performance might be jeopardized, and the merger or acquisition could fail. Hence, CEOs are an incredibly important part of the TMT to examine.
In this study I will focus on faultlines based on gender, tenure and nationality. These
characteristics are especially prone to conflict (Abrams et al, 1990; Choi and Sy, 2010) and therefore might have a particularly large impact on the success of a M&A deal. If these faultlines were to occur followed by a new constellation of a TMT, they are likely to cause conflict through for example mistrust and discrimination based on gender, experience or culture. As a result of these conflicts, the TMT might invest most of their energy into solving those conflicts, which would take the focus off restructuring the company to ensure a successful future. It is therefore indispensable to understand whether these faultlines trigger M&A failure.
3. HYPOTHESES DEVELOPMENT
Diversity is often praised as a tool to increase innovation rates and aid creativity (Janmohamed, 2019). Yet, by itself, diversity is no guarantee for a group to function more effectively. Especially when considering faultlines, diversity might be more of a curse than a blessing. If faultlines are strong, due to many different sub-groups being formed based on different characteristics of the team members, the likelihood for conflict increases (Thatcher, Jehn & Zanutto, 2003). Hence, diversity would increase the potential for faultlines being formed, and therefore for conflict.
However, rather than diversity causing difficulties in itself, it is the way diversity is organized between the members of a group that can cause hardships and influence a group’s performance (Thatcher, Jehn & Zanutto, 2003). This speaks for the company culture having an effect on firm performance. This might especially be the case once the original company culture gets disrupted, and new team members join the TMT, as for example in a merger or acquisition. As faultlines could be the result of poorly organized diversity, unproductiveness, conflict or resentment among team members, might be the consequence.
means, that homogenous teams might function more seamlessly than diverse teams, when the consequence of diversity is a split of the group into subgroups. Therefore, diversity, seems to be a double-edged sword: on the one hand, it can increase productivity and idea novelty, on the other hand it might lead to faultlines and therefore performance difficulties and conflict.
Li and Hambrick (2005) specifically find that the magnitude of faultlines increases in situations of factional faultlines. These occur when new members of a group act as representatives for e.g. a company, rather than as individuals, like they would in the context of a merger or an acquisition. If the small groups that are formed through faultlines experience negative emotions like stereotyping or feel disintegrated overall, cooperation will be threatened. The extent of the resulting dysfunction is directly related to the strength of existing demographic faultlines. These negative emotions and behaviors are especially evident in interactions with few participants, as in for example email
correspondence. Even though, these interactions are seemingly small and uninfluential, they are often the origin of the “culture clash” between new partner companies. Failing mergers due to poor
performance of the new team might be the consequence. Based on these findings, Li & Hambrick (2005) concluded that a lot of attention needs to be payed to these small groups, rather than only determining whether two firms are compatible on first sight. This approach would be too broad and will likely result in failure of a deal. They use these findings to offer an explanation for the high failure rate of international joint ventures. Since joint ventures seems to have similar problems as M&As, it is expected that their findings would fit the M&A context too, further supporting the argument of this study.
As prior research showed the existence of gender, tenure and culturally based faultlines (Fiske, 1998; Ndofor, Sirmon & He, 2015; Hutzschenreuter & Horstkotte, 2013), I will consider these in greater detail for this study.
3.1. The Effect of Gender Faultlines on Firm Performance
Top management teams can reap financial benefits from gender diversity, given that the organizational structure promotes integration between team members (Opstrup & Villadsen, 2014). However, gender is associated with highly shared stereotypic beliefs (Fiske, 1998), where additional sexist beliefs further increase this categorization (Van Knippenberg, Haslam and Platow, 2007). This should allow for consolidation and strengthening of faultlines.
Additionally, a study by Schwab et al. (2015) found that high levels of managerial gender diversity can hinder performance benefits. Consequently, high gender diversity should lead to a decrease in firm performance. The results of these articles mirror the theory of this study well and seem to suggest that gender faultlines decrease firm performance.
Therefore, I hypothesize:
H1: The effect of gender faultlines on firm performance is negative.
3.2. The Effect of Experience Related Faultlines on Firm Performance
Executives with substantial experience often identify more intensely with a certain company and its strategy. This might lead to less effective decision-making and by extension a decrease in firm performance (Schwenk, 2020). Difficulties in the integration process between members of two TMTs, and hence, faultlines, could be the consequence. Groups experiencing strong faultlines involving tenure or experience, may experience increased levels of task conflict, based on the fact that they tend to engage in more sharing of information midst group members (Choi & Sy, 2010). This first and foremost affects gender-tenure faultlines, demonstrating that the effect is especially strong when sub-groups formed through tenure are also differentiated due to gender (Choi & Sy, 2010). Supporting this finding, Ndofor, Sirmon and He (2015) found, that strong tenure-based faultlines in TMTs led to an increase in competitive actions. These competitive actions could in turn lead to a decrease in firm performance if team members focus more on competition than their mutual goals.
Furthermore, Kaczmarek et al. (2012) state, that task-related faultlines have a strong negative effect on firm performance. This is especially the case, if the CEOs have tenure, so are very
experienced. Since the effect of task-related faultlines on firm performance seems to be worse if the TMT is busy, we can assume that it is just as bad or potentially even worse if it is going through a M&A, a situation in which the CEOs are likely exposed to high cognitive loads and therefore levels of stress .
Therefore, I hypothesize:
H2: The effect of tenure related faultlines on firm performance is negative.
3.3. The Effect of Cultural Faultlines on Firm Performance
affected group into sub-groups (Moran, Abramson and Moran, 2014). Consequently, in line with this study’s argumentation, team performance, and therefore firm performance could decrease.
Furthermore, a study by Jiang, Jackson, Shaw and Chung (2012) showed that strong
nationality faultlines have a negative relationship to social interactions. If social interactions decrease based on nationality related faultlines, it is likely that productivity in the affected team will decrease too. Additionally, conflict might increase due to both sub-groups having different expectations to the other sub-group but not communicating them. If this were to be the case in a TMT following a merger or acquisition, it is likely that not only team performance would decrease, but that firm performance would decrease too.
Moreover, Hutzschenreuter and Horstkotte (2013) uncovered that nationality based faultlines decrease the top-management team’s ability to manage difficulties, due to interventions in
information processing as a result of these faultlines. The authors believe this might be due to known stereotypes and biases which might cause conflict and consequently demand managerial attention, leaving them unable to focus on the company’s goal.
Therefore, I hypothesize:
H3: The effect of culture related faultlines on firm performance is negative.
Gender Faultlines Tenure- Based Faultlines Cultural Faultlines Firm Performance H1 - H2 - H3 -
Figure 1: Conceptual Model
CEO Age Firm Size CEO Breadth of Firm Age Education Past Firm Performance
4. METHODS AND DATA
This section will provide a comprehensive overview over the sample and data used in this study. Moreover, the variables used in this study, and the way they were measured will be discussed. Lastly, the way of analysis will be elaborated on. A cross-sectional approach was used to test the hypotheses.
4.1 Empirical Setting
This empirical study is set in the world-wide pharmaceutical industry. The pharmaceutical industry was chosen as M&A proceedings are deeply entrenched in this industry’s culture
(Armaghanian, 2017). M&As are commonly used in this industry, to allow a firm to sell newly discovered pharmaceuticals once the patens of their own products are about to expire and the acquiring company is not developing a profitable drug themselves at that moment (Richman, Mitchell, Vidal & Schulman, 2017). Consequently, more deals have been completed in this industry than in any other industry (Alvaro, Branch and Challener, 2020), making it safe to assume that past experiences with M&A deals provide good conditions for successful M&As. These factors make the industry an interesting and relevant field of study, allowing for high quality research and a proficient assessment of the findings of this study.
4.2 Data Collection and Sample
This study was executed aided by a dataset that was created on the basis of 1454 M&A contracts provided through the Faculty of Economics and Business at the University of Groningen. The contracts were taken from the biopharmaceutical industry in the time period of 1994-2016. Data on several aspects of the firm, dyad and CEO level were documented by a group of six students. This made it possible for the dataset to be used for a multitude of studies.
The data was collected through multiple sources, depending on the variable. Data on the deal type was collected through the contract or press release of a specific deal. The data collected on the firm level was collected through the annual reports (form 10-k), found on the U.S Security and Exchange (SEC) website, or the company’s website, of both acquiring and targeted firms. These included the firm assets and income. The values for three years prior and after the deal, as well as the deal year itself were of relevance. Information on the founding date of the companies were found through Google (e.g. Wikipedia pages of the firms). The geographical distance between headquarters of acquiring and targeted firm was measured (in kilometers) through the website
into Google, which allowed to find information on the education of the CEO, showed whether they were a co-founder of the company, their education and tenure. Often this information was taken from LinkedIn or Bloomberg.com.
The final dataset was downloaded as an Excel spreadsheet. The deals that showed a 1 in the drop column were deleted from the dataset using the filter function. This reduced the original 1454 observations to 958 observations. Additionally, two more variables were created manually, which displayed the breadth of education of the acquiring and target CEOs. This could not have been done in StataSE16 and therefore needed to be done prior to importing the dataset into the program. The finalized Excel spreadsheet was then imported into StataSE16 for further analyzation, where more deals needed to be removed based on missing values in several variables that were essential for further calculations. This resulted in a final sample of 151 observations. Reasons for a strongly reduced sample size include several contracts appearing more than once in the original sample, several deals not being completed, time constraints in collecting the data, and missing information, as the firms are not obliged to disclose all the information needed for this dataset. To ensure no
confounding variables would influence significance, nine control variables on firm-, dyad and CEO level were taken into account. Before conducting calculations, I tested the sample of this study for normality. As the sample’s distribution was close to normal, an Ordinary Least Squares (OLS) Regression was carried out on its basis.
4.3 Measurements
In the following the different variables used for this study and their measurements will be described. All variables originate from the dataset that was specifically created for the purpose of this study. A short description of all variables can be found in Table 1.
4.3.1. Dependent Variable
Post M&A Financial Performance. Financial ratios are a popular tool to calculate firm performance (Barnes, 1987). Here, financial performance was measured through a cross-sectional comparison of income before and after the merger. Here fore, a variable averaging the income of the targeting firm three years prior to the deal was calculated. The same was done for the three years post the deal. By finding the difference of these two newly created variables a third variable, showing the ratio, can be manufactured. Negative values in this variable will show a decrease in financial
4.3.2 Independent Variables
The TMT of a company is composed of a multitude of members. One key member is the CEO of the company. Since they are the member of highest rank in a firm and have the strongest influence on the TMT (Simsek, 2007), faultlines will be measured based on CEO data.
CEO Gender. Gender was coded with 1 = male and 0 = female. To enable a better measurement of faultlines, the absolute difference between acquiring and target CEO data was calculated. This led to a new variable, which can be used in the OLS calculations.
CEO Tenure. CEO tenure will be measured in months. A new variable, measuring the absolute difference between the tenure values of the acquiring and target CEO was created, to enable a better measurement of faultlines. This variable will be used in the regression calculations.
CEO Nationality. In this study, CEO Culture is determined through the nationality of the CEO. Since this is mirrored in the form of a categorical variable, a meaningful way of converting the categories into numbers was needed. Therefore, values of Hofstede’s power-distance index were used. Power distance describes the degree of hierarchy a culture is used to, whereby lower numbers reflect a lower degree of power distance, i.e. the lower the number, the less important is hierarchy for a
country (Hofstede, 2010). The exact index values were taken from a calculation function on Hofstede’s website (hi.hofstede-insights.com) and attributed to the corresponding country in
StataSE16. As for the other independent variables, a new variable was created measuring the absolute difference between the values of power distance of the acquirer and target CEO. The absolute
difference was then used in the OLS regression.
4.3.3 Control Variables
Control Variable on the Dyadic Level
Deal Type. As deal type is a categorical variable, dummy codes were assigned to the different categories.
Control Variables on the Firm Level
Firm Age. As older companies are likely to be more experienced in merging and acquiring practices, they likely have an easier time cooperating and compromising with new TMT members. Since this variable is categorical, new numbers were assigned to each founding year. This enables a clear interpretation of results. Firm Age was shown in years.
Firm Size. Here, company assets will be used to control for income data, which is used to measure the dependent variable. To measure this, the amount of assets at t-1 of both acquiring and target company were used.
Geographical Distance. Geographical Distance might have an influence on the culture of the company and the TMT. It is likely that, should the geographical distance between headquarter of the firms be low, TMT disintegration will be kept to a minimum. If the geographical distance is high, it is more likely that the two TMTs operate under different cultural norms, having a negative impact on the cooperation between the members of the new TMT. This might cause misunderstandings and frustration, which could eventually lead to a decrease in performance, including financial
performance.
Control Variables on the CEO Level
CEO Age. The age of the CEO can have an influence on the relationship between tenure and financial performance, as older CEOs have more experience. Therefore, the experience faultline would not only be strengthened through differences in tenure but also through differences in age. CEO Age is measured in years.
CEO Motivation. To control for the level of motivation a CEO shows to make the deal a success, I considered whether the CEO was a co-founder of the firm. This variable was dummy coded, with 1 = co-founder and 0 = not a co-founder.
Same University. For CEOs having been educated at the same university might create a feeling of cohesion and connection. This could increase the amount of positive feelings towards the CEO of the second company, leading to more cooperation and understanding. As a result, TMT disintegration might be low and financial performance either unaffected or increasing. To do this, dummy variables were assigned, were 1 = same university and 0 = different universities.
Same Firm. Similarly, to CEOs having attended the same university, CEOs having worked at the same company in the past might create a feeling of cohesion. CEOs might find each other more sympathetic and trust the second CEO more leading to lower levels of TMT disintegration and no differences in, or higher levels of, firm performance. Here too, dummy variables were created. A value of 1 was assigned if the CEOs had worked in the same company before, 0 was assigned if this was not the case.
Table 1: Overview of Variables
Variable Type Variable Name Definition Dependent
Variable
Post M&A Financial Performance
The ratio between the firm income of the acquirer pre- and post-deal. The pre-performance value was adjusted through adding the target’s performance.
Independent Variable
CEO Gender Measured through Dummy Codes, where 1 = male and
0 = female.
CEO Tenure The time frame a CEO worked in the firm that took
part in the deal, measured in months. This value was standardized.
Control Variable on the Dyadic Level
Deal Type Dummy Variables were created for the Deal Types of
“Merger”, “Acquisition”, “Minority Stock Purchase”, “Majority Stock Purchase” and “All Stock Purchase”.
Control Variables on the Firm Level
Firm Age The Firm Age was measured in years. Here fore, the
founding years were subtracted from the current year, 2020. The value was standardized.
Firm Size The assets of acquiring firm and target firm at t-1 were used to control for income used in the dependent variable. The value was standardized.
Geographical Distance
Geographical Distance between headquarters of acquiring and target firms were measured in kilometers. This value was standardized.
Control Variables on the CEO Level
CEO Age The CEO Age is measured in years. The year of birth
was subtracted from the current year, 2020. This value was standardized.
CEO Breadth of Education
This variable was constructed manually by counting the number of degrees of each CEO in the final sample. Dummy Codes reaching from 0-5 were attributed, 0 = no degrees and 5 = five degrees.
CEO Motivation The CEO Motivation was measured based on dummy
variables determining whether the CEO is also founder of the firm. 1 = founder, 0 = not a co-founder.
Same Firm A dummy variable with 1 = same firm and 0 = not the
same firm was used to determine if the CEOs had worked in the same firm prior to the deal.
4.4. Analytical Model
Since the dependent variable, Post M&A Financial Performance, is continuous, all
hypotheses were tested through an OLS regression. The assumptions of linearity, random sampling, multicollinearity, zero conditional mean, heteroscedasticity and normality of error term were tested and allowed for the use of the OLS regression. The Breusch-Pagan/Cook-Weisberg Test displayed a Chi2-Value of 3.21 and a p-value of 0.36. As these values are both high, heteroscedasticity is confirmed, and the sample shows robust standard errors. The issue of multicollinearity will be discussed in the following section. Relevant graphs, showing the results of the tests of assumptions can be found in Appendix A.
To test the effects of gender-, tenure- and cultural faultlines on Post M&A Financial Performance, the model Post M&A Financial Performance = b1CEO Gender + b2CEO Tenure + b3CEO Culture + b4Controls was used.
The results of the OLS Regression will be further elaborated upon in the following section.
5. RESULTS
This section will provide the results of the statistical analyses made to test the hypotheses. First, I will discuss the descriptive statistics and correlations between variables. Then, I will elaborate on the outcomes of the OLS regression analysis and finally I will discuss an additional robustness test.
5.1. Descriptive Statistics and Correlations
mean of 6.199 (S.D. = 12.018). For this value to make sense, we need to subtract it from the maximum value (55), as it was constructed through the absolute difference between power distance scores and is therefore deceptive. When subtracting the mean value from the maximum, we find a value of 48.801. This is logical, as the power distance score for the US = 40, and a majority of the firms in this sample are from the United States. As evident through the standard deviation, there is quite some variation in the power distance scores.
A final interesting finding to consider is, that 90.7% of deals were either mergers or acquisitions. Since this is the vast majority, it might be interesting to consider the differences of faultlines in different deal types in the future.
Table 2: Descriptive Statistics. This table shows the descriptive values (number of observations,
mean, standard deviation, minimum and maximum value) of the final data set. The descriptions of the single variables and the way I measured them can be found in Table 1.
All Stock Purchase 151 0.007 0.081 0 1 Firm Age Acquirer 151 69.96 70.701 4 352 Firm Age Target 151 33.629 24.955 5 218 Firm Size Acquirer 151 20700000000 31300000000 30000000 111000000000 Firm Size Target 151 1530000000 333000000 5737484 12300000000 Geographical Distance 151 3022.088 2916.415 0 11341.23 CEO Age Acquirer 151 67.96 8.019 50 88 CEO Age Target 151 66.974 8.938 49 100 CEO Breadth of Education Acquirer 151 2.132 0.914 0 5 CEO Breadth of Education Target 151 2.026 0.791 0 5 Co-Founder Acquirer 151 0.325 0.469 0 1 Co-Founder Target 151 0.192 0.395 0 1 Same Firm 151 0.07 0.081 0 1 Same University 151 0.026 0.161 0 1
Table 3 displays the correlation matrix. For research considering personal characteristics, as done by this study, correlation coefficients of 0.2-0.4 are considered to be moderate, while
interpretation of this matrix. Overall the correlations are low to moderate. The correlation between Post M&A Financial Performance and CEO Gender (r = 0.08) is positive but not significant. This is opposite to the negative correlation I predicted and would mean that independent variable 1 has a slight positive, if not significant, association with Post M&A Financial Performance. The correlation between the second independent variable, CEO Tenure, and Post M&A Financial Performance was positive and insignificant too (r = 0.06). Hence, an increase in tenure seems to be associated with higher Post M&A Financial Performance. Lastly, CEO Nationality shows an insignificant negative correlation (r = 0.06) with the dependent variable. Therefore, there seems to be a negative association between differences in culture on the dependent variable, as predicted. The dependent variable additionally shows significant correlations for several control variables. As shown in the table, there is a negative significant relationship between the dependent variable and Majority Stock Purchase (r = -0.19, p<0.05). Hence, this deal type seems to have a negative association with Post M&A Financial Performance. Additionally, Firm Age Target has a highly significant, negative correlation with the dependent variable (r = -0.27, p<0.001). Lastly, the relationship between Firm Size, of both acquiring and target firms, show a highly significant, negative relationship (acquirer: r = -0.21, p<0.001; target: r = 0.3, p<0.01) with the dependent variable. Since the overall correlations between variables are low to moderate, they do not pose a threat to further calculations and their interpretations. Nevertheless, there are some high correlations that should be noted. The relationship between Merger and
Acquisition is negative and significant (r = -0.78, r<0.001) and very strong. The relationship between Firm Size Acquirer and Firm Age Acquirer is very high and significant too (r = 0.62, r<0.001). This indicates, that an increase in firm size is strongly related to higher firm age. The same is true for the target side (r = 0.45, p<.001). The remaining correlations are mainly low to moderate, and/or
insignificant. The highly correlated, significant relationships could influence further calculations and should therefore be taken into account, and avoided, in future research.
Moreover, the Variance Inflation Factor (VIF) was calculated. With a mean of 1.54 and a maximum value of 3.9, the values lie well below the cut-off vale of 10, suggesting that
Table 3: Correlation Matrix.
Variables (1) (2) (3) (4) (5) (6) (7) (8)
(1) Post M&A Financial Performance
1.00
(2) CEO Gender Difference 0.08 1.00
(3) CEO Tenure Difference 0.06 0.00 1.00
(4) CEO Nationality Difference -0.06 0.01 -0.06 1.00
(5) Acquisition -0.07 -0.02 -0.01 0.15+ 1.00
(6) Merger 0.12 0.06 -0.00 -0.13 -0.78*** 1.00
(7) Majority Stock Purchase -0.19* -0.02 -0.07 0.1 -0.18* -0.06 1.00
(8) Minority Stock Purchase -0.00 -0.02 0.06 -0.04 -0.13 -0.04 -0.01 1.00
(9) All Stock Purchase -0.00 -0.02 -0.01 -0.04 -0.13 -0.04 -0.01 -0.01
(11) Firm Age Target -0.27*** -0.07 0.04 0.00 -0.03 -0.06 0.23*** -0.01
(12) Firm Size Acquirer -0.21** 0.02 0.00 -0.01 0.13+ -0.07 0.12 -0.05
(13) Firm Size Target -0.30*** -0.09 0.12 0.1 0.01 -0.05 0.2* -0.04
(14) Geographical Distance 0.08 -0.02 0.03 0.28*** 0.13 -0.01 -0.04 -0.08
(15) CEO Age Acquirer -0.01 0.03 -0.03 -0.09 -0.13 0.03 0.01 0.05
(16) CEO Age Target 0.06 0.04 0.1 0.03 -0.12 0.08 0.01 0.1
(17) CEO Breadth of Education Acquirer -0.01 -0.03 0.01 0.1 0.02 -0.06 -0.02 -0.01
(18) CEO Breadth of Education Target -0.01 -0.14+ -0.08 0.02 -0.11 0.11 -0.08 -0.00
(19) Co-Founder Acquirer -0.06 0.08 -0.13 -0.04 -0.14+ 0.07 0.04 -0.06
(20) Co-Founder Target -0.04 0.07 0.07 0.02 -0.12 0.13 0.09 -0.04
(21) Same Firm 0.00 -0.02 -0.07 -0.04 0.05 -0.04 -0.01 -0.01
Correlation Matrix Continued
Variables (9) (10) (11) (12) (13) (14) (15) (16)
(9) All Stock Purchase 1.00
(10) Firm Age Acquirer -0.06 1.00
(11) Firm Age Target -0.05 0.13+ 1.00
(12) Firm Size Acquirer -0.05 0.62*** 0.09 1.00
(13) Firm Size Target -0.03 0.25*** 0.45*** 0.34*** 1.00
(14) Geographical Distance 0.04 -0.09 -0.17* 0.02 0.05 1.00
(15) CEO Age Acquirer 0.09 0.12 0.13 -0.15+ -0.12 -0.22** 1.00
(16) CEO Age Target 0.05 0.08 0.22** -0.10 0.01 -0.09 0.12 1.00
(17) CEO Breadth of Education Acquirer
-0.1 -0.03 -0.04 -0.12 0.07 0.11 -0.06 0.08
Correlation Matrix Continued
Variables (17) (18) (19) (20) (21) (22)
(17) CEO Breadth of Education Acquirer
1.00
5.2. Regression Analysis
Table 5 displays the key results of the OLS regression, determining the effect of CEO Gender, CEO Tenure and CEO Nationality on Post M&A Firm Financial Performance. I used robust standard errors throughout the analysis, as some firms were part of multiple deals throughout the investigated time frame.
Model 1 shows the regression results for the main effect of Post M&A Financial Performance and the control variables. With a R2 value of 0.2004, 20.04% of the variance in the dependent variable
Post M&A Financial Performance is explained by the control variables. Several control variables show statistically significant results. Firm Age Target shows a negative significance (b = -307.09, p<0.1). This implies that an increase in Firm Age leads to a decrease in Post M&A Financial Performance. A possible explanation for this could be, that older firms can be less open to change. Since the variable is only significant for the target firm, it might mean that older, less flexible firms are less motivated to adjust to a new context and hence work on integration less, which is one of the most important factors for a successful M&A deal. Additionally, the deal types of Majority Stock Purchase (b = -1,449.77, p<0.05), Minority Stock Purchase (b = -761.06, p<0.05) and All Stock Transaction (b = -723.13, p<0.1) all show a negative and significant effect on the dependent variable. This might be due to the low number of deals of this kind and should therefore be investigated further in the future. Lastly, Firm Size of the Acquirer, measured in the amount of assets at t-1, shows a negative and highly significant relationship (b = -365.32, p<0.001). This means, that higher assets of the acquiring firm at t-1 have a significant negative impact on the dependent variable.
Model 2 displays the effect of CEO Gender, on Post M&A Financial Performance.
Hypothesis 1 predicted a negative relationship of gender faultlines (measured through CEO Gender) on firm Post M&A Financial Performance. The model also includes all control variables. The results for CEO Gender are positive and non-significant. Hence there seems to be a positive, non-significant relationship between CEO Gender and Post M&A Financial Performance, rejecting Hypothesis 2.
The value for R2 equals 0.2039, meaning 20.39% of the variation within the dependent
variable are explained by CEO Gender and the control variables. This is somewhat higher than the value that resulted from the calculations of Model 1, meaning that the variance in the dependent variable Post M&A Financial Performance cannot be further explained by CEO Gender.
According to van Knippenberg et al. (2010), R2 can be used as an indication of whether
faultlines exist. A better estimate yet, is R itself which can be calculated by taking the square root of R2. Since R always lies between 0 and 1, a R value of 0 would show no evidence of a faultline and a
As in Model 1, the control variables of Firm Age Target (-295.58, p<0.1), Majority Stock Purchase (1,442.25, p<0.05), Minority Stock Purchase (757.27, p<0.05) All Stock Transaction (b = -692.73, p<0.01 ) and Firms Size Acquirer (-369.69, p<0.001) are negative and significant.
Model 3 analyses the relationship between Post M&A Financial Performance and CEO Tenure. This relationship was also hypothesized to be negative. The R2 value of this analysis equals
0.2017, indicating that 20.17% of the variance within the dependent variable is explained by CEO Tenure and the control variables. As contrary to what was predicted, the results for CEO Tenure are positive and do not have a significant effect of Post M&A Financial Performance. Hence, I reject Hypothesis 2. As in Model 2, we can still get an indication of whether a tenure based faultline is present by calculating R (0.449). This too is an indication for a faultline of medium strength.
Again, the control variables Firm Age Target (b = -306.48, p<0.1), Majority Stock Purchase (-1,383.53, p<0.05), Minority Stock Purchase (b = -785.56, p<0.05), All Stock Transaction (-718.13, p<0.1) and Firm Size Acquirer (b = -364.33, p<0.001) are negative and significant.
In Model 4 the relationship between Post M&A Financial Performance and CEO Nationality was analyzed to test Hypothesis 3. Here too, the relationship was hypothesized to be negative. Table 5 shows, that the R2 value in this model is equal to 0.2019. The results of this analysis for CEO
Nationality do not support Hypothesis 3, as they are negative (b = -60.59) but not significant. As before, we considered the value of R to determine the strength of the culturally based faultline (R = 0.449), indicating a medium faultline.
As in the previous models control variables Firm Age Target (b = -308.83, p<0.1), Majority Stock Purchase (b = -1,394.16, p<0.05), Minority Stock Purchase (b = -761.58, p<0.05), All Stock Purchase (b = -744.16, p<0.1) and Firm Size Acquirer (b = -368.56, p<0.0001) show negative and significant relations to the dependent variable. In this model, CEO Age Target is positively significant (b =151.95, p<0.1), indicating that an increase in CEO Age of the target firm has a significant
influence on the dependent variable. A possible explanation for this might be increased experience in older CEOs, which might lead to better integration between the firms and higher Post M&A Financial Performance.
Model 5 presents the effect of all independent variables on the dependent variable,
additionally to all control variables. The R2 value for this model equals 0.2067, so 20.67%. This is the
highest R2 value out of all five models. Hence, the variation in the dependent variable is best
In this model too, the control variables Firm Age Target (b = -296.50, p<0.1), Majority Stock Purchase (b = -1,327.12, p<0.05), Minority Stock Purchase (b = -780.40, p<0.05), All Stock Purchase (b = -707.84, p<0.1) and Firm Size Acquirer (b = -371.98, p<0.001) show negative and significant relationships to Post M&A Financial Performance. Interestingly, CEO Age Target is not significant in this model, even though the independent variable CEO Nationality (Model 4) is part of this model too. Accordingly, the significance seems to be neutralized by CEO Gender and CEO Tenure.
Table 5: Results of the OLS Regression. The values of CEO Tenure Difference, CEO Nationality
Difference, Firm Age, CEO Age, Firm Size, and Geographical Distance have been standardized to allow for accurate evaluation.
OLS Regression Results
(1) (2) (3) (4) (5)
Variables Model 1 Model 2 Model 3 Model 4 Model 5
CEO Gender Difference 450.54
(641.63)
458.42 (642.13)
CEO Tenure Difference 54.97
(101.56)
50.94 (101.82)
CEO Nationality Difference -60.59 -58.40
(91.52) (93.50)
Firm Age Acquirer 55.31 58.50 56.39 53.15 57.47
(140.48) (140.93) (141.06) (141.11) (142.16) Firm Age Target -307.09+ -295.58+ -306.48+ -308.83+ -296.50+ (170.20) (173.54) (167.90) (171.36) (172.76)
CEO Age Acquirer -6.74 -11.37 -5.34 -8.70 -12.04
(126.15) (125.65) (126.68) (125.89) (125.89)
CEO Age Target 149.30+ 144.14 144.65 151.95+ 142.29
(89.32) (91.08) (88.11) (89.79) (90.54)
Acquisition -467.11 -484.19 -455.79 -444.38 -452.08
(299.93) (304.29) (300.75) (306.51) (310.97)
Merger -95.72 -122.29 -84.70 -96.96 -113.74
(372.47) (376.30) (370.22) (374.54) (375.95) Majority Stock Purchase -1,449.77* -1,442.25* -1,383.53* -1,394.16* -1,327.12*
(663.49) (657.94) (676.91) (661.60) (669.61) Minority Stock Purchase -761.06* -757.27* -785.56* -761.58* -780.40* (363.80) (365.06) (367.78) (365.37) (369.89) All Stock Transaction -723.13+ -692.73+ -718.13+ -744.16+ -707.84+ (403.07) (402.22) (407.68) (411.87) (416.36) Firm Size Acquirer -365.32*** -369.69*** -364.33*** -368.56*** -371.98***
(165.06) (164.30) (165.23) (163.92) (163.56) Co-Founder Acquirer -214.26 -229.97 -198.58 -211.31 -212.89 (272.42) (269.12) (276.11) (273.67) (274.14) Co-Founder Target -31.67 -46.37 -41.38 -23.05 -47.30 (290.11) (293.86) (289.29) (294.66) (298.24) Studies Acquirer -54.60 -49.81 -54.87 -50.50 -46.02 (130.46) (131.65) (130.53) (129.92) (130.95) Studies Target -168.38 -150.15 -161.67 -165.36 -140.70 (152.48) (156.72) (154.29) (153.11) (159.41) Geographical Distance 80.34 79.66 78.93 96.46 93.88 (109.82) (112.63) (110.39) (114.73) (118.35) Same Firm 0.10 24.10 50.29 -20.88 50.80 (326.44) (335.44) (352.19) (331.45) (367.34) Same University 763.19 765.80 767.09 718.78 726.65 (568.99) (567.32) (561.66) (581.86) (573.17) R-squared 0.2004 0.2039 0.2017 0.2019 0.2167 AIC 2622 2623 2624 2624 2627 BIC 2670 2675 2675 2675 2684 Log-likelihood -1295 -1295 -1295 -1295 -1294
Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1
Notes: n = 151
5.3. Additional Analyses
Additionally, to the OLS regression I performed a robustness test. Following the example of prior literature, (Wernerfelt & Montgomery, 1988; Hejazi, Ghanbari & Alipour, 2016) I used Tobin’s Q as an estimate for firm performance. For the calculation of the new model, I redefined the
6. DISCUSSION
This section will provide a summary of the research model, the empirical research strategy, main results and an interpretation thereof. The current results will be put into context of existing literature and several managerial implications, limitations and future research possibilities will be presented.
6.1 Overview of the Study
This study was performed in the field of M&As, specifically in the biopharmaceutical industry. By answering the research question “Does TMT disintegration based on faultlines lead to decreasing firm performance following a M&A deal? “, I attempted to contribute to the literature by considering how faultlines might affect TMT disintegration and consequently firm performance. Previous literature showed the existence of these specific faultlines (Pearsall, Ellis & Evans, 2008; Ndofor, Sirmon & He, 2015; Hutzschenreuter & Horstkotte, 2013) but usually considered them in a team within an established company, rather than a newly built TMT of a company that was only just integrated. Here, two very different firm cultures would clash, which could provide excellent conditions for faultline creation. I hypothesized that CEO Gender faultlines, CEO Tenure faultlines and CEO Nationality faultlines would have a negative impact on the Post M&A Financial
Performance of the firm as the TMT would be disintegrated. Since many M&A deals fail (Calipha, Tarba & Brock, 2010; Christensen, Alton, Rising & Waldeck, 2011), I aimed at contributing to the search for an explanation for this. The results did not show support for any of the hypotheses. Hence, I cannot conclude that faultlines based on CEO Gender, CEO Tenure or CEO Nationality have a significantly negative effect on Post M&A Financial Performance.
6.2 Theoretical Implications
I based my research on the dependent variable Post M&A Financial Performance, which I measured through the ratio of firm income before and after the deal for both acquiring and targeted firms. As former research considered faultlines within one team (Van Peteghem, Bruynseels, & Gaeremynck, 2018; van Knippenberg, Dawson, West & Homan, 2011; Thatcher, Jehn, & Zanutto, 2003), rather than a newly build team made up of two old, cohesive teams, it is difficult to judge whether the variable was measured in an optimal way. It is therefore possible, that the variable would have benefitted more from an efficiency related measurement, as for example innovation
performance.
presence of faultlines. Following the example of van Knippenberg et al. (2010), I calculated the R values of the models I used for the OLS regression. All of them showed moderate faultline strength. A study by Meyer & Schermuly (2012) found, that the negative effect of diversity based faultlines on group performance can be nullified if team members show positive attitudes towards diversity and task motivation. Since the sample of this study mainly made use of mega deals, there is a possibility for TMTs having a lot of experience and positive attitudes towards M&As. Hence, the impact of faultlines on Post M&A Financial Performance would be less likely, explaining my insignificant results.
Moreover, I found significant correlations of the dependent variable and several control variables, as between certain control variables. This might have led to confounding effects and misleading results.
Even though I rejected all three hypotheses, this research contributes to the literature in several ways. Firstly, it is valuable for the understanding of faultline theory and its further use and
exploration. Through calculating R, I showed that faultlines exist in newly found TMTs, after a M&A deal. Secondly, my train of thought and way of measuring variables might inspire fellow researchers to conduct similar research and integrate their own knowledge and ideas. This might lead to
significant results in the future, further helping the process of discovering why the majority of M&As fail.
6.3 Managerial Implication
The results of this study also have managerial implications. The insignificant results of faultlines based on several CEO traits might indicate that CEO characteristics do not have a significant
6.4 Limitations and Future Research
This research project is subject to several limitations. Firstly, the data collection was done by a team of six students over a limited time period. Even tough consistency checks were run, there is a possibility for coding errors.
Secondly, CEO Tenure was only considered for the firm the CEO worked in at the time of the deal. If they were CEO of a different company prior to working in the company involved in the deal, this was not documented. This might have an influence on tenure related results as a wrong
impression of experience could be given. A different scoring system could hence be beneficial and should be accounted for in future research.
Third, the majority of the deals were conducted between companies in the United States. Therefore, the majority of the CEOs were from the United States too, influencing the nationality related results to a great extent. The power distance scores were therefore the same or very similar for many deals. Future research would benefit from a more diverse sample to accurately represent cultural differences and related faultline creation.
Fourth, the dependent variable Post M&A Financial Performance is prone to be influenced by confounding variables. Hence, the study would have benefited from an endogeneity test to rule out third variables influencing the relationship between the independent variables and the control variables. These might occur when there is a certain similarity between the dependent variable and independent variables, omitted variable bias or possible measurement errors in the independent variables. This problem might be addressed by taking instrumental variables into account. However, these can be hard to find, making it difficult to follow this process in an accurate manner.
Fifth, it is possible that the variables would have been better represented through different measurement methods. It is for example possible, that the dependent variable would have been better represented by innovation performance as this would be an efficiency related measurement that might be easier to estimate.
Lastly, as mentioned before, the sample is based on mega deals. Accordingly, the firms involved in the deals might have prior experience in the field of M&As and therefore know how to avoid faultlines, or at the very least minimize their effect.
7. CONCLUSION
The goal of this thesis was to start investigating the influence of TMT faultlines on the success of M&As. To achieve this, I examined the impact of several person specific faultlines on Post M&A Financial Performance. To be specific, the impacts of CEO Gender-, CEO Tenure and CEO Nationality faultlines were examined.
The performed OLS regression was based on cross-sectional data. None of the hypotheses were supported, even after a robustness check was carried out. Nevertheless, the outcomes offer relevant insights into the field of study and provide a solid basis for future research. The limitations of the study clarify the need for future research and for the hypothesis to be tested through differently defined variables to draw concrete conclusions. Conclusively, this study contributed to the literature by offering a starting point in bringing faultline theory in combination with knowledge of the
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APPENDICES
Appendix A: Tests of Assumptions of OLS Regression
Linearity of CEO Gender Difference
Linearity of CEO Tenure Difference
Linearity of CEO Nationality Difference
