Learning geology before the
earthquake – a follow-up
The influence of faultlines in two-tier boards of
directors on post-M&A firm value and performance
Author: Nouredyn el Sawy 1st supervisor: L. Dam
2nd supervisor:
University of Groningen – Faculty of economics and business MSc Finance
Word count (excluding appendices): 25,505 (19,692) Abstract: Faultlines are hypothetical dividing lines in a team, that, when activated, may have communication-disturbing repercussions on a team, as subgroup forming. The present study investigates the influence of faultlines in a dualistic board system on M&A success; faultlines in supervisory boards and executive boards are investigated separately. The methodology presents a walkthrough of the faultline calculations and the event studies. The results are in line with typical faultline research. I find a significant relation between strong faultlines and a lower post-M&A firm value for both types of boards. Especially gender and experience faultlines portray this effect. Moreover, faultlines are particularly influential in smaller executive boards, whereas board size did not matter in supervisory boards. post-M&A firm success can be improved by composing boards in such a way as to decrease faultlines.
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Table of contents
1. Introduction 3
2. Theoretical background and hypotheses 6
o 2.1 Boards of directors 6
o 2.2 Group diversity and faultlines 7
o 2.3 Mergers and acquisitions 10
o 2.4 Hypotheses 11
3. Methodology 12
o 3.1 Research process 12
3.1.1 Sample and data collection 12
3.1.2 Variables 15
3.1.3 Control variables 16
o 3.2 Calculating the faultline strength 17
3.2.1 Differences faultline strength measurements 17 3.2.2 The five steps to calculating faultline strength 19
3.2.2a Determining attributes and categories 19
3.2.2b Calculating the IA and CG 22
3.2.2c Overall faultline strength calculations 22
o 3.3 Firm performance measurement 23
3.3.1 The event study 24
o 3.4 Validity and reliability 26
4. Results 28
o 4.1 Descriptive statistics 28
o 4.2 Regression analysis and results 30
4.2.1 Supervisory boards without nationality faultline attributes 31 4.2.2 Supervisory boards with nationality faultline attributes 33 4.2.3 Executive boards without nationality faultline attributes 35 4.2.4 Executive boards with nationality faultline attributes 38
5. Discussion 40
o 5.1 Theoretical implications 40
o 5.2 Managerial implications 44
o 5.3 Limitations and future research 46
6. Conclusion 47
7. Bibliography 48
Appendices
o Appendix A – Descriptive faultline strength calculations 53 o Appendix B – List of internal alignment calculations 64 o Appendix C – Faultline strength normal distributions per dataset 66
3 1. Introduction
A sizable amount of research has been devoted to diversity research and its influence on team performance (e.g. Jehn, Northcraft & Neale, 1999; Garton, 1992). Though a source for task- and relational conflict, it is a great basis for creativity and creative thinking, as many different ideas and thought processes collide. Boards of directors are no exception to these effects, and its influence can be seen to affect the firm in its entirety (Carpenter, Geletkaycz & Sanders, 2004). In particular, a decision that involves all the major senior executives and affects the whole firm (e.g. an M&A decision) is likely to be influenced by board structure to a greater extent (Hambrick et al, 1996). Thus, the proficiency of senior executives in making a solid M&A decision, affecting the value of the firm in an immediately measurable fashion, is likely influenced by the degree of diversity in boards.
However, unless a team is perfectly diverse, people who are similar to some extent gravitate towards each other. This subgroup-forming phenomenon is caused by a concept dubbed and introduced in 1998 by Lau & Murnighan: faultlines. The term, based on geological faults (fractures in the earth’s crust), is explained as possibly unnoticed breaking points in a team that have the potential to crack, or ‘activate’, when exposed to certain external factors; not unlike an earthquake. Once activated, faultlines can cause subgroups to emerge within a team, which may lead to conflict or power plays by larger subgroups, but may also facilitate the decision making process in certain contexts. Research done on faultlines is mostly based on demographic attributes, as attributes based on personality traits are simply too difficult to find and analyse perfectly.
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faultlines in two-tier boards of directors on a firm’s post-M&A abnormal returns, through the governing role these boards play in the decision making process of top management and compare it to its effects on one-tier boards. Thus, with this thesis, I will attempt to fill the research gap by answering the following research question:
How do demographic faultlines in two-tier boards affect abnormal returns from mergers and acquisitions?
In this, it is hypothesized that faultlines in two-tier boards have a negative effect on post-M&A firm value and performance, as exemplified by previous work on faultlines in upper echelon teams (e.g. Lau & Murnighan, 2005; Molleman, 2005; Thatcher et al, 2003). To answer the research question, focus will be put on demographic attributes as gender, age, title, experience and nationality and faultline strength is computed accordingly. These attributes will not be treated as single demographic characteristics, but will be viewed collectively, taking into consideration how their alignment as a whole potentially divides a team in subgroups. An event study is performed to calculate abnormal returns from all M&As performed with the firms’ current board. Ultimately, the faultline data is combined with the abnormal returns in several multivariate regressions, to find potential faultline influence on post-M&A firm success.
To accomplish this, demographic attributes from 765 firms across European countries with two-tier boards were collected. Countries included in the study are France, Germany, the Netherlands and all Scandinavian countries but Finland. Firm data was collected from all industries, so as to have enough data and more broadly applicable results.
As such, this project contributes to the literatures on strategic management, group diversity and mergers and acquisitions and is relevant to academics and practitioners. In addition, it will contribute to the area of psychology, as faultline theory has an inherently psychological background.
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theoretical and managerial implications are presented in the discussion section, after which some suggestions for further research are given.
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2. Theoretical background and hypotheses
2.1 Boards of directors
A corporation’s board of directors is a team of senior managers, who are responsible for the governance of the firm. These members can be either elected or appointed, and can be either from inside the company (insider directors) or from outside the company (e.g. independent or outside directors). Insider directors in this context are translated into all directors who in any way are directly related to the corporation in question. This can be as an employee, major shareholder, or any other member who represents one of the firm’s stakeholders (e.g. labour unions). Contrarily, outsider directors are directors who do not have a direct involvement with the firm and are usually from another company in a different industry.
Boards are not particularly different from regular teams when studying its diversity. However, its influence can be studied in relationship with the performance of the organization as a whole. Important aspects of that performance are influenced directly by the top management team, but equally important, influenced indirectly by the board’s governing powers (Carpenter et al, 2004). Corporate law grants directors the formal authority to approve management initiatives, to evaluate managerial performance, and to allocate rewards and penalties to management on the basis of criteria that are supposed to reflect shareholders' interests (Fama & Jensen, 1983; Plessis, 2004).
Some organization theorists argue that because the board possesses these powers, they set the premises of managerial decision making by the top management team (e.g., Mizruchi, 1983). That is, chief executive officers (CEOs), who are a part of any executive board, as well as any top management team, learn what the frame of mind of the board is, conduct themselves in a manner compatible with these dispositions, and implement decisions that correlate with the board's concepts of strategy. The relevant aspect of performance to this research, which is indirectly influenced by the board of directors, is the performance directly related to M&A decisions. The change in the firm’s value after making an M&A investment is assessed in relation to the board’s composition. Forbes and Milliken (1999) propose a model of strategic decision-making effectiveness in boards of directors that argues the importance of boards’ cohesiveness. As will become apparent in the section on group diversity and faultlines, group cohesiveness suffers significantly from strong faultlines.
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Netherlands take a two-tier approach, which employs the dualism of an executive management board (EB) and a separate supervisory board (SB) (Jungmann, 2006).
As such, supervisory board members are elected by shareholders in the general meeting. Their main task is to monitor the executive board actively and thoroughly. Accordingly, the SB has the right to be informed completely and correctly by the EB on all business matters (Plessis, 2004). The SB composition is redefined each year, with a chance of re-election for each member. Re-election chances are increased by fulfilling their main task proficiently. After its election, the supervisory board nominates the CEO and the executive board members. Each year the EB members are evaluated and face re-election. In addition, the SB has the right to dismiss executive board members, if poor firm performance appears to be related to unsatisfactory management. However, this measure is usually reserved as a last resort (Jungmann, 2006).
The supervisory board has additional tasks aside from electing and monitoring the EB. Firstly, the SB sets the overall goals of the firm and supervises the way in which it is conducted. Moreover, the SB has the power to veto relevant, important decisions. Thus, where the EB conducts the day-to-day management, the SB supervises managers’ activities and monitors their capabilities (Belot et al, 2014).
“The process of information elaboration is essential to performance in teams dealing with complex problems and decisions, non-routine challenges and a great variety of complex information” (van Knippenberg et al, 2010). Therefore, good communication to facilitate this process is of great importance to any higher level management team. However, group diversity has the potential to hinder communication and cause inter-member conflicts.
2.2 Group diversity and faultlines
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(van Knippenberg & Schippers, 2007; Jiang et al, 2012). In an attempt to open up diversity research and look at it from a different dimension, Lau & Murnighan developed the term group faultlines.
Group faultlines, or simply called faultlines, are hypothetical dividing lines that may split a diverse group into subgroups based on one or more attributes of the group members (Lau & Murninghan, 1998). It is a relatively new term, as it was introduced in 1998 by Lau & Murnighan, who published an article on the dynamics of subgroup forming in the development of organizational groups. Faultlines can be formed on the basis of many different kinds of attributes, the most prominent and easiest to analyse of which are demographic attributes. Age, sex, race and job tenure are all examples of attributes on which demographic faultlines can be based. Another demographic attribute that is sometimes used in research as a potential cause of faultline forming is formal education. However, as reasoned by Barkema (2007), by the time managers reach higher echelons in their corporation, they have gained so much experience in different work settings that their formal education, which typically took place decades before, is no longer a good proxy for differences in cognitive characteristics. When they tested it they indeed found no evidence of faultlines based on formal education.
An alignment of multiple demographic attributes may cause social categorization and intergroup relationships within a team. The most likely demographic attributes favouring a division into subgroups are those which are beyond the control of the people themselves, as gender, race, age, tenure and experience (Pelled et al, 1999). Although tenure, experience and age do change over time, it is impossible for people to return to a previous stage, making it beyond their control as well (Pelled et al, 1999). Faultlines may also be based on non-demographic characteristics, like personality traits and other social features of a person’s character. However, because of the high complexity associated with finding such personality traits in a high number of people, the focus of this study will be on demographic attributes.
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also the alignment of those attributes among the members, and the number of potentially homogeneous subgroups (Thatcher et al, 2003).
In theory, faultlines can only exist in teams that are moderately diverse, as teams with no diversity whatsoever will form one cohesive (uncreative) group, whereas groups that are completely diverse will have no attributes to base subgroups on (Lau & Murnighan, 1998). In practice however, inactivated faultlines are always there, as no person is perfectly the same, nor perfectly different. A team could be perfectly diverse in terms of demographic characteristics, but for other characteristics, based on personality traits, there will always be some similarity on which faultlines can be based. The chance that these dormant faultlines will be activated and cause subgroups to emerge depends on the strength of the faultline.
Group faultlines are relevant for all sorts of group performance, because it hampers creativity and communication. This causes important decisions to be made with less premeditation, which is an impermissible problem in the complex decision-making process of boards of directors. Lau & Murnighan (2005) suggest that the most important negative effect of faultlines is likely to be communication. With strong faultlines, communication between subgroups can generate conflict, scorn, and poor performance; with weak faultlines, communication should improve performance. This theory has been tested often, with mostly similar results (among others, Thatcher et al, 2003; Molleman, 2005). Only rare cases have concluded differently, as with Van Knippenberg et al. (2010), who found that faultlines may have either positive or negative influences, depending on how highly shared the corresponding case’s objective is. A highly shared objective can capitalize on faultlines, whereas faultlines may be absolutely detrimental for a hardly shared objective.
When subgroups are formed, people expect support from the members of their subgroup. Thus, fewer ideas are thrown in the group, as they will be pitched per subgroup, not per individual. Individuals become biased toward their subgroup’s members. Therefore, each subgroup’s position will be strengthened, making disagreements and other conflicts within the entire group more difficult to solve (Lau & Murnighan, 1998). Strong emotional subgroup attachments may then become potential sources for interpersonal or relationship conflict (Jehn, 1995).
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be inclined to speak up and voice their disagreement. Moreover, smaller subgroups may be more likely to use covert power tactics, whereas larger subgroups may be more likely to use overt power tactics. These differences between subgroups of different sizes cause the larger subgroup not to notice that the team is not as much in agreement as initially seems on the surface. Thus, when these disagreements eventually come to light, they may seem unexpected and last longer because of a lack of understanding among the members of the subgroups (Lau & Murnighan, 1998).
However, none of the aforementioned literature has focused on faultlines in boards of directors. El Sawy (2014) states that faultlines have a slight positive effect on M&A decision making. Though it is still uncertain what causes this, it was theorized that this positive effect may be caused by three things. Firstly, Knippenberg et al. (2010) state that faultlines may have a positive effect on highly shared objectives in the firm, under which M&As would classify. Secondly, board members’ large amount of experience may prevent them from having petty conflicts. Finally, since within the scope of M&As the main role of boards of directors is to govern the decision making process of other upper echelon management teams, the division of a board in subgroups may be positive. Arguably, having two or three viewpoints (a viewpoint for each subgroup) instead of a viewpoint for each team member may facilitate the governing process, as decisions are gained faster and more easily, without many conflicts arising for the aforementioned reasons.
2.3 Mergers and acquisitions
Mergers and acquisitions, also commonly referred to as M&As, are a type of external expansion investment that grows a business overnight, as opposed to gradually, through corporate combinations (Kalra, 2013). Though mergers and acquisitions are usually used interchangeably, they mean slightly different things. When a firm purchases and takes over another company, it is called an acquisition. The target company no longer exists from a legal point of view. With a merger, two firms go forward as one, forming a new entity.
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Hambrick et al. (1996) argued that a decision about an expansion may involve all the firm’s senior executives, as opposed to other decisions that may involve only a subset of the top team. This makes the choice of M&A decisions a particularly appropriate setting for this research.
2.4 Hypotheses
In light of previous work on board faultlines (el Sawy, 2014), the current study is directed to assemble a new database and to test whether faultlines in two-tier boards have similar effects on M&A success as faultlines in one-tier boards. In doing so, faultlines are expected to exhibited negative effects, similar to findings of related previous research (e.g. Knippenberg et al, 2010; Lau & Murnighan, 2005; Shaw, 2004). In spite of relevant research to the contrary (el Sawy, 2014), it was decided to hypothesize negative effects, as this is more strongly empirically established.
Hypothesis 1: Ceteris paribus, demographic faultline strength in supervisory boards of directors will negatively moderate the success of mergers and acquisition decisions made, leading to lower post-M&A abnormal returns.
Hypothesis 2: Ceteris paribus, demographic faultline strength in executive boards of directors will negatively moderate the success of mergers and acquisition decisions made, leading to lower post-M&A abnormal returns.
This research will attempt to investigate these hypotheses as completely as possible. The next section demonstrates the research process. Below, a conceptual model of the study is presented.
Figure 1: Visual representation of the hypothesized relationships
Level of proficiency in
making M&A decisions Post-M&A firm value
12 3. Methodology
3.1 Research process
Much theory has been developed on (high ranking management team) faultline influences, the most prominent of which were done by Lau and Murnighan in 1998 and 2005. However, there are still many areas in which these developments can be tested, as they are very broad. For example, the theories have been tested on firm performance through return on assets (Hutzschenreuter & Horstkotte, 2013; Knippenberg et al., 2010; Thatcher, Jehn & Zanutto, 2003), but we cannot be sure to get the same result when tested on other aspects of firm performance, as merger & acquisition decision success rates. In addition, research on faultlines in upper echelon management typically investigates the effects of top management team decisions on performance, as opposed to those of boards of directors. Furthermore, many of these papers have used a logarithm developed by Thatcher et al. (2003) to compute the faultline strength (FLS). However, I believe this method to be inferior to that derived by Shaw (2004), which will be elaborated upon in later sections of the methodology. Therefore, the theory testing approach was applied in this research. Following the work of el Sawy (2014) on one-tier board faultlines in pharmaceutical firms in the United States, the current study focuses on two tier boards in European countries (e.g. Germany, the Netherlands)
3.1.1 Sample and data collection
Data was collected on three levels. Firms were identified and collected, from which demographic
board data was taken, using the BoardEx database. Finally, these were combined with mergers & acquisitions data in an event study. In the following section the data collection will be discussed more
elaborately.
Firms
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database, in all ranges of industries, from Denmark, France, Germany, the Netherlands, Norway and Sweden, were included in the initial dataset of the research.
Using BoardEx, 1207 firms with a two-tier board were identified. For the 1183 supervisory boards, 630 firms had no missing values in age, of which 599 had 3 members or more. This is a prerequisite, as no faultlines can exist in a team with 1 or 2 members. Of these initial 1183 supervisory boards, 193 firms had no missing values in either age or nationality, meaning all categories can be utilized. Of these, 180 firms had 3 members or more.
For the 984 executive boards, 730 boards had no missing age values, of which 308 boards had more than 2 members. Moreover, 332 boards had no missing values in age or nationality, with a total of 93 teams with 3 members or more. As evident from these numbers, there is a rather low degree of executive teams with more than 2 members, as opposed to the much higher degree in supervisory boards. This is due to executive boards consisting merely of executive directors (e.g. the CEO, CFO, COO, etc. and a chairman), limiting the amount of possible members to eleven in Germany and a mere seven members in the remaining participating nations (Belot et al, 2014). This is in direct contrast with supervisory boards, which can have up to 18 members (Belot et al, 2014). This inconsistency in the amount of initial supervisory boards and executive boards is caused by some missing data in the BoardEx database. However, as this study does not aim to observe differences in supervisory boards and executive boards per firm, but merely to investigate the relation between board composition and M&A proficiency on a general level, this is not an issue.
A dilemma arose from these numbers. As stated, when only taking into account the firms without missing values in age, the amount of boards for the sample is satisfactorily large. However, because of the large account of individuals without a known nationality, the amount of firms without any missing values in age or nationality is quite limited. This constituted a trade-off between a larger sample and a somewhat smaller, but more interesting sample. It was difficult to decide whether the smaller sample size is large enough for a statistically proof research. Therefore, it was decided to perform four event studies: two on supervisory boards; with and without the nationality attributes and two on executive boards; with and without the nationality attributes. This made it possible to observe whether the decrease in the sample size causes any changes in the results. If this is the case, the nationality-related attributes are dropped.
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Boards
Of these firms, outgoing directors that constitute the sample of this research were identified through BoardEx. BoardEx is an international database on demographic board data, which contains timely and correct information on boards of directors. Therefore, the precision of the information is ensured. Demographic data was collected and board members were divided into supervisory boards and executive boards, as made possible by the BoardEx search engine. The BoardEx database contains the most recent information, since it is updated daily. As the board demographics were collected on April 22nd of 2015, no M&As after this date were included in the event study. The exported dataset was inspected both manually and automatically on abnormalities in Excel, to validate its precision. Finally, missing data points, which were especially evident in age and nationality, were filled up as proficiently as possible using annual reports and Google searches to validated websites. Boards with more than one member with missing data in the same category (e.g. two members with unknown nationalities) were excluded from the study. Although this exclusion is not absolutely necessary in a faultline study according to several scholars (Shaw, 2004; Thatcher et al., 2003), doing so secures a much higher accuracy in the calculations.
As is required for any faultline study, demographic data on gender and age were collected for all members. For supervisory boards, additional data on title, nationality and experience were acquired, whereas for executive boards only additional data on nationality and experience were included. This exclusion of the title category is because of the nature of both this category and the executive boards. In this context, the title category is seen as a team member’s group position; what functions do they fulfil and how are they positioned in the team? For this, a distinction between inside directors, outside (independent) directors and leading directors is made. Although an observable distinction in supervisory boards, executive boards consist merely of leading directors, making FLS with title as basis always amount to zero, hence the exclusion. As advised by Lau & Murnighan (1998; 2005), job tenure was included into the study as a faultline attribute. However, it was renamed to experience, as that brings out the essence of why this attribute is added more clearly, which is to match people that have worked alongside each other for an extended amount of time. The nationality category was difficult to collect, causing the dataset to shrink considerably. As mentioned before, it was therefore decided to perform four event studies. More on the demographic decisions made and their categorization is stated in further sections.
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Thomson SDC, the faultline information was then matched with relevant M&A data over the past 19 years. The companies were categorized on their least experienced member. Thus, for example, if a team consists of five members with more than 10 years of experience and one member with only 1 year of experience, the entire board is categorized as having 1 year of experience, as the entire faultline dynamic may be changed by the addition of a new member (Lau & Murnighan, 1998). Then, the event dates of the M&As were matched with the board information, and if an event occurred with a different board than the one today, this event was excluded from the research (e.g. if the acquisition was in 2011, but the board last changed in 2012, the board data from 2015, which is the information obtained, is irrelevant). This represents a limitation in the research, which could not be overcome due to data restrictions, as historical data on board composition was not readily available.
Mergers and acquisitions
Finally, DataStream was used to find firm-level data to analyse firm performance. Performance was primarily assessed through stock prices, as this is the most prominent measure of firm performance (Zollo & Meier, 2008). Of the 765 companies for which FLS was computed, 247 companies had performed M&As with their current supervisory board, with 1178 mergers and acquisitions. Moreover, 135 firms had performed an M&A with their current executive board, with a total of 1081 M&As. The effect of these M&As on firm value was calculated by means of an event study, more on which will be discussed in section 3.3.1. A regression analysis was performed on the post-M&A abnormal returns and the FLS per board, to investigate whether a relation between FLS and M&A performance could be found. The process and the outcomes are stated in the results section.
3.1.2 Variables
The current study empirically investigates how demographic faultlines influence the making of M&A decisions under the management of executive boards of directors and the boards by which they are governed and supervised. Accordingly, I measure the relation between both executive and a supervisory boards’ faultline strength and the correlating firm’s performance value differentiation in the form of abnormal returns as a direct result from a merger or acquisition.
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acquisition, measured by comparing differentiation in stock price with the predicted returns. In addition to analyzing these abnormal returns against faultline strength, they were transformed into a dichotomous state, indicating either a positive or a negative abnormal return, as a robustness check. This will be explained in further detail in the results section.
The independent variable was board faultline strength in boards of directors, computed using an algorithm developed by Shaw (2004). It takes into consideration how multiple demographic characteristics and their alignment may divide a team into subgroups when combined, as opposed to single demographic attributes individually.
3.1.3 Control variables
Because M&A success may be caused indirectly by several firm characteristics, control variables were used to test the relative impact of faultline strength more accurately. Faultlines are hypothesized to impair efficiency. Therefore, one must accommodate for differences in firm efficiency in the sample. Hence, the lagged return on assets (ROA) was used as a control variable, as the ROA can be seen as an indication of efficiency. Furthermore, each firm’s leverage, or debt-to-equity ratio was used, as a firm’s financial structure may influence M&A results. Below, all variables were condensed into a table, specifying variable types, scale types and operationalization.
Table 1: Overview of relevant variables
Variable Variable type Scale type Operationalization
Faultline strength Independent Ratio The probability a faultline will be activated. Cumulative abnormal
returns (continuous)
Dependent Ratio Stock price differentiation within the event window, as compared to before the event.
Cumulative abnormal returns (dichotomous)
Dependent Categorical Cumulative abnormal returns, categorized into two different values, indicating either a profit or a loss.
Lagged Return on assets
Control Ratio An indication of a firm’s profitability. Calculates how much net income was generated from invested capital at the end of the year previous to the event.
Lagged debt-to-equity ratio (leverage)
17 3.2 Calculating the faultline strength
The next step in the process is to calculate the faultline strength (FLS) between the members of the identified boards of directors. The FLS is the cornerstone of this research, as it is ultimately coupled with all future measures of performance. In their article from 1998, Lau & Murnighan presented a simplified measure of FLS, with which they identified the strength in ranges, from non-existent and very low to very strong, by means of intuitive classification (Shaw, 2004). Though ground breaking at its time, this measure is too simplistic to get a useable variable for this research. Fortunately, scholars as Shaw (2004) and Thatcher et al. (2003) have found other measures of FLS since then, which obtain useable measures of faultline strength in percentages
3.2.1 Different faultline strength measurements
Some differences in measurement exist between these scholars’ methodologies. Thatcher’s method has been used widely (e.g. Molleman, 2005; Hutzschenreuter & Horstkotte, 2013), as it is a quick way of determining the FLS. However, it only takes relatively small groups into consideration of approximately 4-6 members, with only two possible subgroups, because of the limitations of the method. If a team would consist of more than 6 members, it is a reasonable assumption the group might split into more than 2 subgroups (Thatcher et al., 2003). However, measuring group ‘splits’ with more than two subgroups would require a process that is too computationally complex for their algorithm. Their algorithm only accounts for the strongest group split, dividing the team into two subgroups (Thatcher et al., 2003). This would create a problem in this research, as many of the boards reach more than 10 members, some of which have as many as 20 members.
Furthermore, Thatcher’s method does not take all possible combinations of internal alignment and cross-subgroup alignment into consideration, but merely identifies the strongest possible split and looks at the potential breaking chance from there. Therefore, using thatcher’s algorithm, you can always only account for the emerging of a faultline based on the one most likely attribute. Thus, the nature of its calculations makes Thatcher’s method less thorough. It has the potential to lose reliability in the outcome of the strength measurement, as more potential subgroup splits reside in other attribute combinations and therefore the results cannot be trusted fully.
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subgroup alignment (CG) of all combinations with all possible attributes as basis to calculate the chance of a faultline emerging.
In our example, a faultline could perhaps be based on gender. This means that the subgroups are divided into male groups and female groups. If males are very similar to one another with regard to the other attributes, the faultline is stronger. Naturally, the same goes for the female group. We calculate the internal alignment of males and age, males and education and males and nationality and do the same for the alignment of the females with age, education and nationality. Thus, we calculate the internal alignment by looking at similarities in attribute composition in males and in females. Furthermore, if males are different from females with regard to all other attributes, the faultline is stronger as well. Thus, we calculate the cross-subgroup alignment by looking at (the lack of) similarities in attribute composition between males and females. So far, Thatcher and Shaw’s algorithm are approximately equally useful.
However, we cannot always know which attribute will eventually be the basis for the faultline, should the group be broken into subgroups. Therefore, to fully capture the likelihood that a faultline emerges, we need to calculate the IA for all possible combinations with all possible attributes as basis. This means the IA of all groups over education, all groups over nationality and all age-groups over gender must be calculated to measure the IA with age as basis. The same goes for all areas of education and all nationalities that are considered in the particular research to calculate the IA with education and nationality as basis respectively. Moreover, we need to calculate the cross-subgroup alignment; if people in the male group are similar to people in the female group on other attributes (males have approximately the same age, education and nationality as females), the likelihood of a faultline emerging is smaller than it would be with less or no attribute overlap (males differ in age, education and nationality from females). The cross-subgroup alignment measurement must be done for all possible category combinations. As Thatcher’s method merely considers the strongest group-split to calculate FLS, whereas Shaw considers all possible splits, Shaw’s measure is far superior in its reliability.
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applied in this thesis, and are therefore not merely presented in general, but specifically as how they were applied in this research.
3.2.2 The five steps to calculating faultline strength
3.2.2a Determining attributes and categories
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It signifies to some extent, for as far as it is possible, a potential division based on personality traits. This is because the essence of the experience attribute’s inclusion lies in that people who have worked together for a longer period of time are likely to know each other personally, and may form subgroups on the basis of that interpersonal knowledge, as opposed to people who do not have that knowledge and will therefore constitute the other subgroup. Thus, when new people join the group after some years of having the same board, it is likely the relationship between these new and old members will form a faultline (Lau & Murnighan, 1998).
After deciding on which attributes the FLS will be calculated, the next step is to code them into categories, so that they can be used in the upcoming calculations. Naturally, one must be careful to categorize the attributes into categories that properly reflect and represent the potential dividing lines among group members. For this research, the aforementioned attributes were categorized as follows. Gender (two levels, coded as male = 1; female = 2), age (four levels, coded as below 50 = 1; 50 to 59 = 2; 60 to 67 = 3; 68 or above = 4), title (three levels, coded as leading directors = 1; Inside directors = 2; Outside directors = 3), nationality (seven levels, coded as Scandinavian = 1; Anglo-Saxon = 2; Dutch and Belgian = 3; Germanic = 4; Spanish-speaking and Italian = 5; (Colonial-) French = 6; rest of the world = 7), native (two levels, coded as native = 1; foreign = 2) and years of experience (four levels, coded as below 2 years = 1; 2 to 4 years = 2; 4 to 8 years = 3; over 8 years = 4). According to Shaw (2004), an approach for determining the number of perceived attribute categories is to examine taxonomic research related to the attributes that are being investigated. For these attributes, a combination of this (e.g. for age and experience) and a categorization through logical thinking (e.g. for title and nationality) was used to decide on the categories, whereas the categorization of gender and native was dichotomous. Below, the thought processes are being elaborated on further.
As seen above, age is coded into four unevenly distributed levels. Directors below the age of 50 were used as the youngest group, because this is typically considered to be a very young age for board members. Moreover, people over the age of 67 were used for the oldest group, because this is the retirement age in all of the participating countries, as stated by the projection-ageing report of the European Union (2009). It is reasonable to expect the quality of being retired (of regular duties besides being a board director) to potentially be a significant cause for subgroup forming. The third group, 60 to 67, is seen as the oldest non-retired members, thus putting the three boarders at 50, 60 and 67 years of age.
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directors, founders and presidents. They constitute a category because their superior level of influence separates them from the group, which makes them more likely to vary from the rest dynamically, and potentially stick together in case of a title-related group split. As seen above, another division is set between the ‘regular’ directors, on the difference between insiders and outsiders. An inside director is someone who is directly connected to the organization, either as an employed executive, a major shareholder or a representative of other stakeholders. Outside directors are, contrarily, members who are not otherwise engaged with the organization. Outsiders usually have their primary affiliation with another organization and serve on the board on merely a part-time basis (Forbes & Milliken, 1999). Therefore, they have limited direct exposure to the firm and the other (inside) directors. Because of this limited exposure, it is assumable that inside directors and outside directors represent a potential faultline basis.
Nationality was coded into seven levels, based on cultural similarities. These similarities were mostly based on language, as it is reasonable to assume that people speaking the same native language are more likely to spend time together in a social manner outside of the workplace. As such, subgroups may be formed. Four of these groups were formed around the firms’ countries of base operations (Scandinavian, Dutch and Belgian, Germanic and (Colonial-) French). Anglo-Saxon and Spanish-speaking countries were added because of their frequent occurrence in the dataset. Finally, the rest of the nationalities were placed in a collective ‘rest of world’ category, as they existed of too many infrequently occurring cultures.
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3.2.2b Calculating the internal alignment and cross-subgroup alignment
The third step contains the calculation of the internal alignment; the first series of calculations in determining the FLS. Every faultline is based on one attribute, and the IA calculates “the extent to which members within a particular subgroup are similar to one another on all other relevant attributes” (Shaw, 2004). As mentioned above, it is impossible to predict which attribute will form the basis of the faultline, should it emerge. Therefore, to calculate faultline strength it is necessary to calculate the possibility of a faultline to emerge from every possible attribute as base. Though it is a calculation with relatively simple steps, it can be overwhelming to do it manually. The necessity to combine all categories makes it a very elaborate process. The process is presented in appendix A. The next step in determining the FLS is calculating the cross-subgroup alignment over the attributes. This is necessary, because apart from the similarity between people that form a subgroup, it is important to consider the similarity of those people with the other subgroups, as cross-group similarities could greatly reduce the significance of the internal alignment, should it exist. Males can be very similar to each other in other attributes, but if the females are equally as similar in these features, there will be no reason for subgroup forming. Fortunately, the calculation of the CG is slightly more straightforward than that of the IA. As with the IA, the general calculations will be explained, after which one real-life example will be demonstrated to clarify the process. The calculations of the cross-subgroup alignment are presented in appendix A as well.
3.2.2c Overall faultline strength calculations
The fifth and final step is to calculate the overall faultline strength, by combining the internal alignment and cross-subgroup alignment. These methods are constructed such as to allow for the outcomes to be used in multiple ways. The FLS can be assessed relative to a single attribute (e.g. gender), or the overall FLS can be obtained by combining all outcomes, as illustrated in appendix A. To leave room for additional uses for the data, the former is used. Since a strong FLS is characterized by a high IA and a low CG, the reciprocal of the CG index was used to calculate the overall FLS, making the formula for faultline strength as follows:
𝐹𝐿𝑆 = 𝐼𝐴 × (1 − 𝐶𝐺)
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composition per attribute. For example, in an example with three attributes, the calculations for FLS with attribute 1 as basis would look as follows:
𝐹𝐿𝑆1 =𝐼𝐴12 + 𝐼𝐴13
2 × (1 −
𝐶𝐺12 + 𝐶𝐺13
2 )
These computed FLSs can then be averaged to get the overall FLS, from which more relations and conclusions can be drawn.
A different approach yielding almost identical results is to perform the FLS equation on each attribute combination individually and then averaging the result. Thus, with the same example of three attributes, the calculations for FLS with attribute 1 as basis looks as follows:
𝐹𝐿𝑆1 =[𝐼𝐴12 × (1 − 𝐶𝐺12)] + [𝐼𝐴13 × (1 − 𝐶𝐺13)] 2
The first approach was chosen, because the second approach does not set off values against each other, which sometimes results in a FLS of zero, even though no perfect homogeneity or diversity exists in the team.
As can be derived by nature of the formula, if either IA equals 0 or CG equals 1, the faultline strength will be 0. The index varies in size from 0.0 to 1.0, where 0.0 indicates non-existing faultline strength, meaning likely no subgroups will form. A score nearing 1.0 indicates a very high possibility of a subgroup emerging. These extremes are unlikely to occur though, as they require perfect diversity or homogeneity.
This concludes Shaw’s five steps to calculating the FLS. As evident by the process, calculating the faultline strength for a team is an elaborate process to perform on a large amount of teams. These calculations were coded into SAS, so that they may be applied automatically on an unlimited amount of teams. The FLS was coded using a program created by scholars Y. Chung, J.B. Shaw and S.E. Jackson in 2006, which can be found online. A link will be provided in the bibliography. In order to use this program, all attribute data must be categorized, coded and sorted sequentially in Excel, by company ID and member ID. Table 16 (appendix A) is an example of what the sorted data of one team looks like.
3.3 Firm performance measurement
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event on the performance value of a firm. In this research, the event is an investment in the form of a merger or acquisition. The goal is then to create an estimate on what the firm performance would have looked like without the investment, to compare with what happened with the investment. The initial task is to define the events and identify the event window, which is the period over which security prices of the firms will be examined (MacKinlay, 1997). After establishing the event period, it is necessary to determine from which index the independent variable will be drawn. Finally, the event’s impact is measured by means of the firm’s abnormal return, which is drawn by comparing the predicted returns with the actual returns in the event window (MacKinlay, 1997).
3.3.1 The event study
The events constitute all M&As before April 22nd 2015, within the scope of the particular firm’s board. Thus, for example, if a board’s least experienced member joined in 2011, all M&As between 2011 and April 22nd 2015 for that board’s firm are used. As before, ‘experience’ refers to one’s time spent on this particular board. This way, it is ensured that each board’s M&As correlate with the right faultline strength.
DataStream was used to collect the variables for the event study. The company’s SEDOL codes were used to identify companies. Firms whose SEDOLs could not be identified by DataStream were deleted from the study; this came down to two firms for both of the supervisory board datasets and none for the executive boards. The dependent variable constitutes closing stock price data during the estimation window on all firms that had at least one merger or acquisition within the period between April 22nd 2015 and the year the least experienced member joined the team. Two events for each event study were dropped because there was no stock value available from when the event took place. This was caused by the event’s date falling on a non-trading day. Furthermore, daily local market indices were collected, and used to compute the independent variable: market return. These were collected for firms from Denmark, France, Germany, the Netherlands, Norway and Sweden with market returns from the C20, CAC40, DAX, AEX, OBX and the OMX30 respectively.
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values, it is much higher for boards with nationality values. This is caused in part due to some outliers in the data becoming much more prominent with the smaller sample size. In addition, it could be caused by the ‘with nationality’ datasets containing larger firms. The sample sizes are reduced for these datasets, because they are dependent on directors’ willingness to make private information (as country of origin) public. Perhaps it is the directors from larger firms that are more willing to make this private information public. This issue is discussed in greater detail in the results section.
Table 2: Descriptive statistics on the way events are performed
Supervisory boards Supervisory boards with nationality Executive boards Executive boards with nationality Amount of events 1178 470 1058 401 Events performed by parent firm (%) 63.91 50 53.75 50.37 Events performed domestically (%) 47.28 48.94 46.44 42.89 Events acquiring 100% of shares (%) 75.2 73.7 69.75 69.34 Average transaction value ($mil) 673.21 1481.52 619.7 1064.11
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Figure 2: Illustration of the estimation window and event window
These results are then combined to calculate the abnormal returns (AR), which is defined as the difference between the normal returns and the predicted returns.
𝐴𝑅𝑖,𝑡= 𝑅𝑖,𝑡− 𝑃𝑅𝑖,𝑡
Where 𝐴𝑅𝑖,𝑡 is the abnormal return of stock i on day t, 𝑅𝑖,𝑡 is the actual return of stock i on day t and
𝑃𝑅𝑖,𝑡 is the predicted return of stock i on day t. Many factors can be analyzed for their effect on abnormal returns, as for example the target location, acquisition technique or payment method. However, the current research tests for a sociological factor never applied before; the probability of subgroup forming in the supervisory and executive boards of directors. Thus, the calculated abnormal returns are regressed against faultline strength, of which the outcomes are presented in the results section.
3.4 Validity and reliability
Validity
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Internal validity was ensured by stating the limited potential of the results. The average amount of the dependent variable’s variance that could be predicted by the results is acknowledged. Furthermore, as the dependent variable’s variance was measured by merely one independent variable in all cases but the ‘combined FLS attributes regressions’, and further accompanied by only uncorrelated control variables, alternative explanations for the variance are ruled out. Regarding external validity, measurements were performed across several countries for multiple industries. Results may therefore not be explained by industry or country characteristics.
Reliability
28 4. Results
As mentioned, multiple event studies were performed. Each event study resulted in abnormal returns for the day of the M&A. These were aligned with the relevant faultline strength values. For each dataset, the dependent variable abnormal return was regressed against the independent variable of FLS as a whole (overall FLS) to investigate a possible relation between FLS and M&A success. This was done by means of a multivariate regression analysis with several control variables. Furthermore, multivariate regressions were performed with as independent variable the FLS per attribute to find which attributes have the largest impact on post-M&A firm performance. Multiple robustness checks were performed. The results will now be presented and discussed.
4.1 Descriptive statistics
Firstly, the preliminary results of the faultline analysis will be discussed. Shaw’s FLS algorithm results in several variables; the internal alignment and cross-subgroup alignment per firm and the FLS per attribute. The latter can be further averaged to get the overall FLS. The method used to accomplish this was presented in section 3.2.2c.
The descriptive statistics on overall faultline strength are presented below, for each dataset. The FLS values of both the full datasets and the datasets that were used for the research are displayed, to see if the used sets are a good representation of the total sample. As evident from the results, the means and standard deviations for these are nearly identical. Thus, the firms that performed M&As are presumably a good representative of the total amount of collected firms with regard to overall FLS.
Table 3: Descriptive statistics on overall faultline strength per dataset – total and utilized sets
Variable Sample size Mean Std. Dev. Min Max
FLS Supervisory boards total 599 .131 .064 0 .411
FLS Supervisory boards utilized 246 .141 .065 0 .331
FLS Supervisory boards nationality total 180 .126 .053 0 .244 FLS Supervisory boards nationality utilized 95 .136 .05 .008 .244
FLS Executive boards total 308 .056 .057 0 .333
FLS Executive boards utilized 135 .055 .061 0 .333
FLS Executive boards nationality total 93 .059 .053 0 .198 FLS Executive boards nationality utilized 53 .050 .049 0 .157 Notes: Where FLS stands for faultline strength, ‘total’ refers to all gathered firms for which faultline strength was calculated
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Though not necessary for obtaining relevant findings, it is desirable to have a normally distributed FLS, so that weak and strong board faultlines are represented equally and a wide range of board compositions can be tested for its effects on firm performance. As evident from the figures in appendix C, the supervisory boards with and without the nationality values portray a near-normal distribution, with a slight skewness to the left. Therefore, firm performance can be investigated in relation to a near equal amount of weaker and stronger faultlines. However, both sets of executive boards are heavily skewed to the left, meaning they contain a much larger amount of boards with a low FLS than a high FLS. This is especially evident in the set without nationality values. There is a simple explanation for the bias towards lower FLS. Since executive boards typically contain a low amount of members, a larger chance of similarities amongst members exists, driving FLS down. This problem is thus inherent to executive boards in general. A way to obtain a normal distribution in the EB faultline strengths is to omit teams with three, or even four members. Doing so would diminish the representativeness of the sample however, and is ill advised. Another possibility is to split the sample between larger and smaller teams. This way, the executive boards can be analyzed separately, split at their member total. Particularly, this will lead to two analyses with a more normally distributed FLS, allowing for more focused regressions on executive boards with a very low amount of members (e.g. four and less) and executive boards with an average or high amount of members (e.g. more than four members). Accordingly, conclusions can be drawn on these two types of executive boards. To test for differences between executive boards with few members and with many members, this method was performed in this research.
30 4.2 Regression analyses and results
The goal of this thesis is to see whether a correlation exists between faultline strength and M&A success in two-tier boards. Moreover, to see if a measurable difference exists between the effects of FLS on supervisory boards and on executive boards. Finally, to find which faultline attributes account for the strongest relation and should thus be taken into consideration the most when constructing a team. This is accomplished by performing two sets of multivariate regression analyses per dataset, making eight in total.
For each dataset, a regression was performed with as independent variable the overall FLS and as dependent variable the abnormal returns. Furthermore, these abnormal returns were transformed into a dichotomous variable (either a post-M&A profit or a loss) and regressed against overall FLS as a robustness check. Appendix D contains all dichotomous regressions. Control variables for this regression are the lagged ROA to accommodate for firm efficiency, and the lagged leverage to accommodate for financial structure effects. The lagged values were used as opposed to the events’ (end of the) year values, as they represent the basis of what a firm gets to utilize in its operations for that year. Using the events’ end of the year values may be a misrepresentation of what the firm had to its disposal at the time of the event, particularly if it occurred early in the year. For some regressions, the control variables showed no significant relation. Therefore, as will become clearer in relevant sections, regressions were performed with and without the controls as a robustness check. Table 27 in appendix D contains all relevant non-controlled regressions.
Secondly, multiple multivariate regression analyses were performed for each dataset with as independent variables each FLS attribute individually, and once simultaneously. This way, the regression brings forward which faultline attributes affect the dependent variable in which way. As before, the dependent variable is the abnormal return as a continuous value. The same controls were used for these regressions. As with the initial overall regression, robustness checks in the form of a dichotomous dependent variable and the exclusion of control variables when significance is lacking are applied, and presented in appendix D. For all of the aforementioned regressions, it is expected to find a negative relation between higher faultline strength and M&A success.
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4.2.1 Supervisory boards without nationality faultline attributes
Table 4 illustrates the results of the first regression. The continuous abnormal returns are significantly negatively related to the overall faultline strength on the 5% level. This indicates that a higher faultline strength makes it more likely to result in a less proficient M&A. A p-value of 0.014 illustrates a 1.4% chance that the results were based on chance. Furthermore, the adjusted R-squared indicates that approximately 1.1% of the variance in abnormal returns from M&As can be predicted by overall FLS in supervisory boards. One robustness check in the form of a dichotomous dependent variable was performed. No results were found, as illustrated in appendix D, making this dataset not robust against dichotomization of the dependent variable.
Table 4: Multivariate regression to test for a relation between overall faultline strength in supervisory boards and post-M&A abnormal returns
Supervisory boards Cumulative abnormal returns
Faultline strength -0.0302** (0.0122) p-value 0.014 Lagged ROA 0.0003* (0.0001) Lagged leverage 0.000** (0.000) Observations 977 Adjusted R-squared 0.011
Notes: The regression was performed with the cumulative abnormal returns gained from an event window of one day.
Where ROA stands for return on assets. The data was collected on gender, age, title and experience from supervisory boards. For an elaboration on the data collection methods, see sections 3.1.1 and 3.2.2a. Standard errors are in
parentheses. *** p<0.01, ** p<0.05, * p<0.1
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High correlation between independent variables in a multivariate regression leads to a cannibalistic effect between variables with regard to significance. Although this affects the coefficient estimates and significance, which should thus be taken for granted, it does not reduce the reliability of the model as a whole.
Table 5: Multicollinearity in faultline strength attributes for supervisory boards Gender FLS Age FLS Title FLS Experience FLS
Gender FLS 1
Age FLS 0.2699 1
Title FLS 0.2492 0.042 1
Experience FLS 0.4301 0.3469 0.2754 1
Notes: Correlations are calculated for faultline strength attributes in supervisory boards
Table 6: Multivariate regressions, to test for a relation between individual faultline attributes in supervisory boards and post-M&A abnormal returns
Supervisory boards Cumulative abnormal returns
Gender FLS -0.0139** -0.00840 (0.0069) (0.00777) Age FLS -0.0104 -0.00351 (0.0081) (0.00883) Title FLS -0.00987 -0.00351 (0.0089) (0.00948) Experience FLS -0.0207** -0.0136 (0.0095) (0.0111) p-value 0.046 0.197 0.269 0.029 Lagged ROA 0.0002* 0.0003* 0.0003** 0.0002* 0.0002 (0.0001) (0.0001) (0.0001) (0.0001) (0.0001) Lagged leverage 0.000** 0.000** 0.000** 0.000** 0.000** (0.000) (0.000) (0.000) (0.000) (0.000) Observations 977 977 977 977 977 Adjusted R-squared 0.009 0.007 0.006 0.01 0.009
Notes: The regressions were performed with the cumulative abnormal returns gained from an event window of one day.
Where FLS stands for faultline strength and ROA stands for return on assets. The data was collected on gender, age, title and experience from supervisory boards. For an elaboration on the data collection methods, see sections 3.1.1 and 3.2.2a.
Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
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4.2.2 Supervisory boards with nationality faultline attributes
The next dataset to be discussed consists of the supervisory boards including the nationality faultline attributes. As mentioned in section 3.2.2a, these are the nationality faultline (pertaining cultural differences) and native faultline (pertaining the quality of being either a native or a foreigner). The amount of observations is greatly reduced, as many directors do not publicly state their country of origin. As before, the overall FLS regression will first be discussed. As evident from the table below, the results are quite similar to supervisory boards excluding nationality faultlines. Looking at the abnormal returns, a significant negative relation with overall faultline strength is found. This relationship is stronger than without the nationality attribute, which may exemplify this attribute’s importance. However, the possibility should not be overlooked that this may be caused by the smaller sample size. In addition, some bias towards people with publicly available information may exist. Perhaps it is more often directors from larger and better known firms that make their private information public. Thus, when including only people that have made their nationality publicly available, this may lead to a bias towards larger firms. This idea is seconded by the much larger average deal value, as mentioned in the methodology section. However, it is beyond the scope of this research to investigate this issue in detail. For this dataset, 2.7% of the variance in the height of the abnormal returns can be explained by the overall faultline strength.
Table 7: Multivariate regression to test for a relation between overall faultline strength in supervisory boards and post-M&A abnormal returns Supervisory boards with
nationality values
Cumulative abnormal returns
Faultline strength -0.0654*** (0.0243) p-value 0.007 Lagged ROA -0.0001 (0.000170) Lagged leverage 0.000*** (0.000) Observations 429 Adjusted R-squared 0.027
Notes: The regressions were performed with the cumulative abnormal returns gained from an event window of one day.
Where ROA stands for return on assets. The data was collected on gender, age, title, experience and nationality from supervisory boards. For an elaboration on the data collection methods, see sections 3.1.1 and 3.2.2a. Standard errors are in
parentheses. *** p<0.01, ** p<0.05, * p<0.1
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found in table 9. As stated in the results, faultlines with gender and experience as basis are once again significantly related to abnormal returns. This is to be expected, as the board compositions of the partaking firms are identical to the previous dataset, and all used firms are contained in that set as well. However, there are some important identifiable differences: the gender and experience faultlines are now much stronger related, and faultlines with age as a basis are now significantly related at the 1% level as well, which is a massive disparity from before, since it did not show any relation in the larger dataset. As mentioned, this may be caused by a bias towards larger firms, as larger firms’ directors’ information may be more publicly available. This is exemplified by the fact that the average deal size for this dataset is much larger than that of the dataset containing supervisory boards without nationality faultline attributes.
Unfortunately, neither nationality faultline strength nor native faultline strength are related to M&A proficiency. Thus, it is not possible to influence M&A success by altering a supervisory board with regard to directors’ descent. Since no relation can be found and the nationality faultlines thus not add information, it is better to view the supervisory board dataset without nationality faultlines as canon, since its dataset is larger and likely more divergent with regard to firm size. This is further backed by the massive differentiation in the effects of age faultlines, as these cannot be explained as of yet.
When combined, only the experience attribute significantly influences abnormal returns. This is caused by multicollinearity amongst several of the explanatory variables. As before, many of the different attributes are highly correlated (as seen in table 8), and this is especially evident amongst gender, age and experience faultlines. Thus, it is safe to assume the experience attribute has, so to say, ‘taken over’ all significance of the other attributes and is causing the individual predictive indicators to behave erratically. Therefore, merely the predictive power of the model as a whole should be considered with the simultaneous regression. Naturally, the nationality and native attributes exhibit a very strong correlation as well.
Table 8: Multicollinearity in faultline strength attributes for supervisory boards with nationality attributes Gender FLS Age FLS Title FLS Experience FLS Nationality FLS Native FLS
Gender FLS 1 Age FLS 0.4162 1 Title FLS 0.0985 0.1518 1 Experience FLS 0.6191 0.4797 0.1084 1 Nationality FLS 0.1183 0.1426 0.1111 0.0886 1 Native FLS -0.0145 0.0359 0.1025 0.0012 0.6974 1
Notes: Correlations are calculated amongst faultline strength attributes in supervisory boards with nationality attributes.
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Table 9: Multivariate regressions, to test for a relation between individual faultline attributes in supervisory boards and post-M&A abnormal returns
Supervisory boards with nationality values
Cumulative abnormal returns
Gender FLS -0.0353*** -0.0022 (0.0128) (0.0160) Age FLS -0.046*** -0.0224 (0.0148) (0.0175) Title FLS 0.000525 0.00733 (0.0143) (0.0143) Experience FLS -0.0732*** -0.0642*** (0.0160) (0.0209) Nationality FLS 0.0135 0.0508* (0.0212) (0.0295) Native FLS 0.0002 -0.0171 (0.0112) (0.0152) p-value 0.006 0.002 0.971 0.000 0.523 0.987 Lagged ROA -0.0001 0.000 0.000 -0.0002 0.000 0.000 -0.0001 (0. 0002) (0.0002) (0. 0002) (0. 0002) (0. 0002) (0. 0002) (0.0002) Lagged leverage 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** 0.000*** (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) (0.000) Observations 429 429 429 429 429 429 429 Adjusted R-squared 0.0281 0.0325 0.0106 0.0568 0.0116 0.0106 0.0554
Notes: The regressions were performed with the cumulative abnormal returns gained from an event window of one day.
Where FLS stands for faultline strength and ROA stands for return on assets. The data was collected on gender, age, title, experience and nationality from supervisory boards. For an elaboration on the data collection methods, see sections 3.1.1
and 3.2.2a. Standard errors are in parentheses. *** p<0.01, ** p<0.05, * p<0.1
In summary, overall faultline strength in supervisory boards portrays a negative relationship with a firm’s M&A proficiency, though the effects are small. Moreover, especially faultlines in gender and experience distort a board’s functioning and age may particularly play a significant role in larger firms. However, it is beyond the scope of the current study to investigate the full truth behind this claim. Furthermore, cultural differences do not affect supervisory board collaboration in such a way so as to affect decision making about mergers and acquisitions. This is neither the case for subgroup forming between natives and foreigners.
4.2.3 Executive boards without nationality faultline attributes
