08
Fall
Master Thesis
by
N. Duygu Durmusoglu
S2831473
University of Groningen
Faculty of Economics and Business
MSc. International Business and Management
18 January 2016
Supervisor: Paulo Marques Morgado
Referent: Ad Visscher
Women Representation and Age
Diversity in Executive Teams, and
Their Relationship with Firm
Abstract
In this master thesis women representation and diversity of age in executive teams were researched in the context of emerging markets, and their relationship with firm performance was demonstrated. Although executive teams are historically associated with old men’s network, women have started to become highly qualified and thus more active in corporate world in emerging markets. Moreover, different age groups are likely to appear in the executive teams due to massively young but increasingly ageing population. Based on the insights of previous literature, two hypotheses were built. Data of 118 firms were collected in total from Brazil, Russia, India, China and South Africa. It was revealed that both women representation and age diversity are very low in executive teams. As a result of the regression analysis, no significant effects of women representation and age diversity in executive teams on firm performance were found. However, it was found that women representation has a significant positive effect on firm performance in China. Moreover, the relationship between age diversity in executive teams and firm performance in South Africa was almost
significant. Finally, results of the research, managerial implications and limitations were discussed.
Table of Contents
Abstract ... 3
1. Introduction ... 6
2. Literature Review ... 9
2.1. Firms in Emerging Markets ... 9
2.2. BRICS ... 10
2.3. Resource Based View ... 11
2.4. Upper Echelon Theory ... 12
2.5. Executive Team Diversity in General ... 12
2.5.1. Women Representation ... 16
2.5.2. Diversity of Age ... 21
2.6. Conceptual Model ... 24
3. Methodology ... 26
3.1. Time Horizons ... 26
3.2. Data Sources and Collection ... 27
3.3. Data Sample ... 28
3.4. Variables ... 28
3.5. Data Analysis ... 29
4. Results ... 31
4.1. Descriptive Analyses ... 31
4.1.1. Descriptive Analyses for Female Representation ... 31
4.1.2. Descriptive Analyses for Age Diversity ... 33
4.2. Pearson Correlations ... 35
4.3. Multiple Linear Regression Analyses ... 36
5. Discussion ... 42
5.1. Discussion on Female Representation in Executive Teams ... 43
5.2. Discussion on Age Diversity in Executive Teams ... 48
6. Conclusion ... 51
6.1. Theoretical and Managerial Implications ... 51
References ... 55
Appendices ... 62
Table of Figures, Graphs and Tables Figure 1: Growth of emerging market and international firms in terms of revenue ... 10
Figure 2: Diversity in executive teams of Fortune 100 companies in 2014 ... 13
Figure 3: Women executives in Fortune 500 companies from 2009 to 2013 (%) ... 17
Figure 4: Women contribution to global and regional GDP ... 19
Table 1: Summary of previous research and findings on women participation ... 20
Figure 5: Population aged over 65 in emerging markets ... 21
Table 2: Summary of previous research and findings on age diversity ... 24
Figure 6: Conceptual Model ... 25
Graph 1: Female representation across the executive teams ... 31
Graph 2: Female representation in executive teams per country ... 32
Graph 3: Female representation in executive teams per industry ... 32
Graph 4: Age average across the executive teams ... 33
Graph 5: Age diversity across the executive teams ... 34
Graph 6: Age diversity in executive teams per country ... 34
Graph 7: Age diversity in executive teams per industry ... 35
Table 3: Pearson correlation matrix ... 36
Table 4: Regression analyses of Model 1, Model 2 and Model 3 ... 38
Table 5: Regression analysis of Model 4 ... 39
Table 6: Regression analysis of Model 5 ... 40
Table 7: Regression analysis of Model 6 ... 41
Figure 7: Gender parity scores and inequality levels at work ... 43
Table 8: Comparison of female executives per industry between this study and U.S. .... 44
1. Introduction
Emerging economies are over 80% of the population of the world and their share in world gross domestic product (GDP) is 45% (European Central Bank, 2015). They have great opportunities that stem from huge customer base waiting to be served, cheap labor and material, and many other occasions that promote growth and profitability. Thus, scholars have paid close attention to those economies in recent years. BRICS countries, namely Brazil, Russia, India, China and South Africa have been regarded as a powerful bloc of emerging markets. They constitute a prominent sample to do research about emerging markets.
Emerging markets have growth opportunities and huge potentials, contrary to saturated developed economies. This makes the environment highly competitive. Not only local firms but also multinational companies (MNCs) are competing against each other in order to stay profitable and ensure their survival in those competitive but promising environments. Both domestic firms and MNCs have their own advantages in the competition in emerging markets. Tangible and intangible resources that firms have are the sources of competitive advantage. Hoskisson, Eden, Lau and Wright (2000) argue that human capital is an effective intangible resource to compete against rivals. Therefore, it is important to manage human resources in emerging markets effectively in order to gain a competitive advantage.
This paper focuses on the executive team composition of firms in emerging economies. More specifically, the research investigates the effects of women representation and diversity of age in executive teams on firm performance in BRICS countries. Even though previous research showed that profitability in emerging markets is highly related with business strategies and innovative business models (London & Hart, 2004; Eyring, Johnson & Nari, 2011), this thesis concerns whether or not executive team composition in terms of women representation and age diversity affects firm performance.
companies in member states in order to increase number of women in their corporate boards by 40% in 2020 (European Commission, 2013).
Women at top positions is a recent topic for emerging markets. As women have become more educated, they have started to participate more in business as employees, entrepreneurs or executives (Khalaf, Tsang, Wilson, Gorst, & Zalewski, 2014). Therefore, women in emerging markets are gradually acquiring skills and knowledge such that firms can benefit. With the rise of women in business in emerging markets, particularly in BRICS, it would be interesting to further investigate whether women as executives contribute to firm performance.
Furthermore, diversity of age in emerging markets is another interesting area to do research. When distribution of population in BRICS is observed, statistics show that countries have relatively young but increasingly ageing populations (Brazilian Institute of Geography and Statistics, 2014). This paves the way for age diversity in firms where young and old people work together. Therefore, it is noteworthy to reveal any potential relationship between age diversity and firm performance in emerging markets.
The focus of this research is on the effects of women representation and age diversity in executive teams on firm performance. Thus, field of interest is whether executive teams with higher females and age diversity drive higher profitability in BRICS. It might be reasonable to expect influences of females and age diversity in executive teams on firm performance because differences in cognitive skills, perceptions and points of view between males and females and between younger and older directors may directly affect the inputs in their decision making process. Therefore, it would be interesting to research whether those differences between executive team members affect firm performance in emerging markets, specifically in BRICS.
not perform well due to lack of communication and consensus among team members (Barkema & Shvyrkov, 2007).
Although previous studies provided valuable insights about the topic in general, they were mainly conducted in developed economies in which corporate governance systems are well established and the economy is stable. Diversities within firms are more likely to occur in the developed world because firms have become more open and familiar to diversities over the years. As representation of women in corporate world of emerging markets is still a new phenomenon, there is not much research about the effects of women executives on firm performance. Further, age diversity impacts on firm performance in emerging economies have not been discussed yet. Therefore, the research question of this thesis is defined as:
Do women representation and diversity of age influence executive teams in order to drive higher firm performance in emerging markets?
2. Literature Review
Referring to the past studies, importance of emerging markets as a context and effects of diverse executive team member characteristics on firm performance were reviewed by focusing particularly on female representation and age diversity effects. Based on the insights obtained from previous literature, hypotheses were formulated.
2.1. Firms in Emerging Markets
Emerging markets, where the environment is highly competitive and complex, it is utmost important to have a competitive advantage. Local firms and MNCs try to outcompete each other by exploiting their own advantages. MNCs have already established reputations (Chong, 1973), substantial resources, credible brands and products, and superior technology (Dawar & Frost, 1999). On the other hand, domestic firms’ one of the biggest advantages is their familiarity with the environment and institutions (Bhattacharya & Michael, 2008; Khanna & Palepu, 2006). Therefore, while MNCs hold strong financial and physical assets, locals enjoy the knowledge of unstable and turbulent environments.
Integration of peripheral economies into global market as well as removal of the trade barriers have supported the importance of emerging markets (Atsmon, Child, Dobbs, & Narasimhan, 2012). Emerging markets show promise for firms because late urbanization has stirred up not only growth opportunities but also rise of new consumer class waiting to be served (Atsmon et al., 2012). Since developed economies are saturated, growth opportunities are not as much as in emerging markets. A study conducted on 720 companies from 1999 to 2008 shows that firms of emerging markets grow faster than those of developed economies (McKinsey&Company, 2012a) as it can be seen in Figure 1.
In spite of growth opportunities and huge potentials, emerging markets are known as their weakly institutionalized and turbulent environments. Formal institutions are weak and thus informal institutions such as interpersonal relationships play crucial roles in business transactions (Peng, Wang, & Jiang, 2008). Economic and political systems are unstable (Hoskisson, Eden, Lau, & Wright, 2000) and corporate governance is weak (Peng et al., 2008). Therefore it is two-edged sword to do business in emerging markets.
more willing to work in local companies (Atsmon et al., 2012). Therefore both local and international firms in emerging markets should find a way to address the human resources challenges. For example, a private employer in India has overcome the manager gap issue by hiring young managers and designing development programs for them (Atsmon et al., 2012).
Figure 1: Growth of emerging market and international firms in terms of revenue (McKinsey&Company, 2012a).
2.2. BRICS
BRICS, as a whole, is 42% of the world’s population, 26% of the world’s land territory, 27% of the world’s GDP (BRICS Summit Russia, 2015). Export and import numbers of BRICS as a percentage of GDP can be found in Appendix A. It is estimated that BRICS share will be 37% and 45% of the world’s GDP by 2020 and 2030 respectively (BRICS Summit Russia, 2015). This implies that BRICS, as a bloc, is a powerful driving force on the global economy.
the sake of rapid emergence of small and medium sized enterprises (SMEs) unemployment issue has been addressed and favorable environment for growth and labor has been provided (Olawale & Garwe, 2010). Furthermore, South Africa has been chosen as an entry point to Africa by many international firms (Games, 2012).
Those countries provide with an attractive area to do research on women representation and diversity of age in executive teams. According to the Global Gender Gap Report, women constitute more than half of the college and university graduates in Brazil, Russia and China (World Economic Forum, 2013). Moreover, global gender gap index of South Africa in economic participation and opportunity has been largely improved since 2006 (World Economic Forum, 2013). It was revealed that share of women in senior management roles was 23% in Brazil, 19% in India, 51% in China and 28% in South Africa in 2013 (Catalyst, 2014a).
Age pyramid in Brazil, Russia, India, China and South Africa shows that majority of the population is in between 25 and 59 year-old people, and second greatest population is under 15 except Russia (Brazilian Institute of Geography and Statistics, 2014). Detailed statistics can be found in Appendix B. This shows the fact that population is relatively young but ageing in BRICS. Overall situation of the population might be reflected on corporations which makes it likely to see age diversity in firms as well as in executive teams.
2.3. Resource Based View
Resource based view is one of the most prominent theories when competitive advantage of firms is considered. Resource based view argues that firms’ tangible and intangible assets provide with competitive advantage and lead to better performance (Barney, 1991; Prahalad & Hamel, 1990; Wernerfelt, 1984). Therefore, firms with superior resources can outperform their competitors. Referring to resource based view, firms in emerging markets exploit their own strengths through their unique resources because they are the key to the success in emerging markets.
performance because diversity brings different skills, points of view and values through which information processing might be enhanced and this unique asset might be turned into profit (Li et al., 2011; Richard, 2000). Therefore, executive directors, who are one of the most essential assets of firms, might be source of competitive advantage as well as superior performance in emerging markets. This master thesis reveals whether gender and age diversity in executive teams are sources of better performance in emerging markets.
2.4. Upper Echelon Theory
Origins of upper echelon theory come from the arguments of Cyert and March (1963) who discussed that conflicting interests and goals, bounded rationality and different aspiration levels affect managerial choices and strategic decisions. Decision makers, who have bounded rationality, should act in multi-goal and conflicting environments in order to make economically rational decisions (Cyert & March, 1963). Based on this ‘behavioral theory of a firm’ upper echelon theory has emerged. Upper echelon theory has been associated with TMT compositions, diversity among them and their influence on firm performance.
The main idea behind upper echelon theory is that top executives of a firm perceive the situations through their own perspectives (Hambrick & Mason, 1984). The theory argues that organizations are the reflection of their top management teams, meaning that organizational decisions are shaped by the dominant members of the team (Hambrick & Mason, 1984). This indicates that top managers act as a leading force behind firm performance due to their strategic choices and individual characteristics (Laible, 2013). Hambrick and Mason (1984) suggested that socioeconomic background, gender, age, education, knowledge and years of experience in the company are some of TMT characteristics that have an influence on firm profitability because organizational decisions are affected by those characteristics of the dominant actors in TMTs.
2.5. Executive Team Diversity in General
and to what extent it is useful for firms. As a result, insights and conclusions of past studies were adapted to this research in order to build up hypotheses.
Previous research largely investigated how firm performance is affected when there are diverse executive team member characteristics. Past studies came up with mixed results, meaning that while some of them found evidence for the positive relationship, others remained inconclusive. Previous research that found positive relationship was mostly conducted on companies from developed economies. Figure 2 shows the executive team diversity of Fortune 100 companies in 2014.
It was suggested by the previous research that the executive team diversity has positive effects on firm performance however this relationship differs across environmental contexts. Keck (1997) found evidence that heterogeneous firms show better performance in turbulent contexts because environmental complexities require different perspectives and problem solving skills. On the other hand homogenous teams perform better in stable environments (Keck, 1997). Moreover, some researchers found evidence that diversity among TMT members affects firm performance in a positive way as environmental uncertainty increases (Cannella, Park, & Lee, 2008). Given the fact that emerging markets are economically and politically unstable environments, firms with diverse executive team member characteristics in emerging markets might perform better.
Diversity among executive team members allows different perspectives and interpretations to arise which increases the quality of decision making process (Nielsen, 2010). In addition, those differences help to understand the complexity of the environment better (Nielsen, 2010). The novelty of ideas and several interpretations that stem from the diversity of the members possibly lead to have competitive advantage and stronger firm performance in emerging markets whose competitive environments force firms to make competitive moves. Thus it is reasonable to expect the executive teams with different characteristics to perform better in emerging markets.
Furthermore, some researchers argued that diversity in executive team characteristics is advantageous as long as it does not create subgroups within the teams because subgroups hamper communication and decrease the effectiveness of strategic decisions (Barkema & Shvyrkov, 2007). Since demographic characteristics are the most distinguishable traits at the first place, they may create categorizations among team members which in turn causes subgroups (Barkema & Shvyrkov, 2007). However, it was argued that subgroups are more likely to be formed if several demographic characteristics; such as age, tenure, gender and educational background all at once come together (Barkema & Shvyrkov, 2007). Those characteristics that can create subgroups called strong faultlines (Lau & Murnighan, 1998, 2005). Thus, in this master thesis women representation or age diversity alone is not likely to cause subgroups and thus lower firm performance.
representative upper echelon (Lewis & Tatli, 2015). Otherwise, diversities would be no more than symbolic tokens and thus the impacts cannot be seen.
There are also some previous studies that found no effects or negative effects of TMT diversity. For example, it was revealed that decision making process of diversified teams is not different than that of homogenous teams (Kilduff et al., 2000). This finding might imply that there are certain roles of directors whose onus is to fulfill the requirements so their demographic differences do not matter much in decision making.
Bottom line of the previous research that found a negative relationship is that diversity occurs at the expense of coherence, social integration and communication. It was indicated that although heterogeneous teams have greater propensity of competitive actions, diversities might slow down the decision making process and execution of the actions (Hambrick, Cho, & Chen, 1996). The authors argued that heterogeneity might result in slower actions and responses to competitors’ initiatives due to internal strains (Hambrick et al., 1996).
Hambrick et al. (1996) indicated that heterogeneity might create acrimony and distrust as widely diverse groups have dissimilar values and vocabularies. TMT diversity can also trigger internal communication breakdowns and conflict among team members (Greening & Johnson, 1997; O’Reilly, Snyder, & Boothe, 1993). Furthermore, diversity was demonstrated as a reason for turnover rates by accounting for more variance in executive team turnover (Wiersema & Bird, 1993). Higher turnover can hamper firm performance because each newcomer executive potentially needs a period of time for adaptation that may decrease effectiveness of decision making. In case of emerging markets, communication problems, slower decision making and conflict among team members might create severe consequences for firms because institutional instability and highly competitive environment require coherence and collaboration along with fast decision making.
perspectives and cognitive resources enhance firm performance. Therefore, as long as diversities are not dominated and the integration is sustained, it is expected that women representation and age diversity provide with competitive advantage and thus lead to better performance in emerging markets.
2.5.1. Women Representation
Gender is a more complex demographic variable than other demographic variables as it affects socio-cognitive base of individuals (Krishnan & Park, 2005). Women and men have inherently distinct characteristics which may lead to different information processing, consideration of different views and coming up with varied solutions. For example, it was discussed that men are more impatient and take more risks than women (Frederick, 2005). Some researchers also found evidence that under controlled economic conditions women do not give less risky decisions than men but under more abstract financial conditions men tend to be more risky than women (Schubert, Brown, Gysler, & Brachinger, 1999). It was also suggested that men executives show more overconfidence in significant corporate decisions compared to women (Huang & Kisgen, 2013), while women executives make decisions which are more in favor of shareholders through increased monitoring and incentive alignment (Adams & Ferreira, 2009; Huang & Kisgen, 2013). Thanks to gender based differences, a number of females in a group of males can bring social and cognitive diversity to the environment. Thus, women representation in top management has been subject to several discussions on whether sex matters in corporate affairs and if it does, to what extent it creates a difference in firm performance.
Figure 3: Women executives in Fortune 500 companies from 2009 to 2013 (%)
It is another highly debated topic whether women lead to powerful financial performance or powerful companies hire women executives. For example, General Motors whose CEO is a woman had $3,949 millions profit, Facebook whose COO is a woman had $2,940 millions profit and Hewlett-Packard whose CEO is a woman had $5,013 millions profit last year (Reingold et al., 2015). It is evident that financially strong companies have women in their executive teams and none of them are newcomers. Thus, those women have a role in the firms’ superior performance. On the other hand, powerful women are mostly in developed world where they have been able to benefit from social and economic opportunities for many years. For example, Forbes magazine published a list of most powerful women in the world, 42 of whom are working in U.S. companies out of 60 when celebrities, politicians and women in NGOs are excluded (Forbes, 2015).
It has been highly encouraged by scholars, economists as well as the media that women should more actively participate in the workforce because their contribution to the economy is invaluable. It is estimated that if women are more equal players so that the gender gap is closed in the workplace, $12 trillion to $28 trillion additional GDP might be delivered to global economy in 2025 (McKinsey&Company, 2015). This shows how much importance women have for the workforce so that their participation at every level should be highly supported.
and women may lead to dissimilar effects on corporates. For example, it was argued that women are more associated with leadership activities in the environments where social interaction is required (Kent & Moss, 1994). Social interaction is highly crucial in emerging markets because institutional voids are substituted by interpersonal relationships (Peng & Heath, 1996; Peng et al., 2008). In addition, social interaction is inevitable in globalized markets (Kent & Moss, 1994). Emerging markets are increasingly globalized where many domestic firms and MNCs compete against each other. Therefore, in line with the argument of Kent and Moss (1994), women might be likely to perform leadership skills in emerging markets.
A study conducted on top management teams of S&P 1500 firms found a positive relationship between firm performance and representation of women in executive teams which improves managerial task performance and thus firm performance (Ross & Dezso, 2012). Authors revealed that, ceteris paribus, a firm makes over $40 million more economic value on average with at least one female in the TMT than others with no female executives (Ross & Dezso, 2012).
Gender diversity can be a source of both supportiveness and conflict which is dependent on the context (Earley & Mosakowski, 2000). In line with this arguments, an interesting study showed that gender diversity has a positive effect only if the corporate governance system is weak because women increase the monitoring activities in corporate boards which leads to better performance (Adams & Ferreira, 2009). Given the weak institutional environment and corporate governance systems of emerging markets, positive impacts of female executives might be revealed.
Figure 4: Women contribution to global and regional GDP (McKinsey&Company, 2015).
Moreover, several previous research found inconclusive or negative effects of women directors on firm performance. Rose (2007) could not find any significant relationship between firm performance and female representation at the corporate boards. This result is surprising because the study was conducted in Denmark which is known as highly liberal in terms of gender policies (Rose, 2007). Another research revealed that women might have a short term positive effect on firm performance but at the long run, there is not any significant relationship (Marimuthu & Kolandaisamy, 2009). A study conducted in Germany found a negative relationship between gender diversity in executive teams and firm performance (Laible, 2013). Finally, effects of women directors in Swedish firms were investigated and it was found that the firms with women underperform (Du Rietz & Henrekson, 2000). Possible explanations of these results could be that majority of the corporations, especially top positions, are still under domination of old, white men which causes women to take a backseat or behave like other men and thus impedes them to have an influential role on firm performance (Rose, 2007). Additionally, women in executive teams might be a source of conflict and slow down decision making process (Laible, 2013).
more females in the executive teams do not necessarily have to perform better. There are certain roles that have been defined and executives have to implement what they are supposed to do regardless of their sexes. This implies that firm performance is not affected whether or not there are female executives.
It was also suggested that ‘one-size-fits-all’ approach is not applicable to the relationship between firm performance and gender diversity meaning that there are many other dynamics to be considered through this relationship such as environmental context, industry and task requirements (Kravitz, 2003). Laible (2013) indicated that the link between gender diversity in executive teams and firm performance has multiple causes and effects that are ambiguous on the context and firm’s environment. Therefore it makes no sense to strongly expect positive or negative results because this research is different than past studies and has its own characteristics.
Table 1: Summary of previous research and findings on women participation
performance in BRICS. However, previous studies have enlightened the rationale so that it is plausible to expect a positive relationship in BRICS.
Hypothesis 1: Do executive teams in emerging markets drive higher performance when there is higher women percentage?
2.5.2. Diversity of Age
Age diversity has become a crucial research topic in the recent years. Population is ageing worldwide (United Nations, 2013) so as the workforce which paves the way for age diversities in corporations. Figure 5 below demonstrates the ageing in population in underdeveloped, developing and developed world from 1990 to 2013. Specifically, in the context of BRICS age distribution is favorable for creating age diversity in the workforce so as in executive teams because population is ageing although there are still considerable amount of young people (see Appendix B for age distribution in BRICS).
Ageing in emerging markets brings some challenges. Developed world first became rich and then old but emerging markets are ageing while still economically struggling (The Emerging Markets Symposium, 2015). In order to overcome the potential issues regarding ageing population, younger population comes into prominence in emerging markets because it becomes the driving force of the economy. Moreover, it was suggested that working lives in emerging markets have been extended which prevents labor shortage (Jackson, Howe, & Nakashima, 2011) and enables younger employees to benefit from the experiences of the elder ones (The Emerging Markets Symposium, 2015). Thus, it is likely to encounter with different age groups in the labor force. Age diversity in labor force potentially reflects on corporations as well as in executive teams.
Different age groups have different capabilities and characteristics which are reflected in the way they execute a task, and in their decision making. In turn, this can affect overall firm performance. The only demographic measure that affects firm performance positively was found to be age diversity (Kilduff et al., 2000). The authors suggested that the greater the age diversity, the better the firms perform (Kilduff et al., 2000). Moreover, it was argued that team members with different ages can complement each other within a task which results in better performance (Ilmakunnas & Ilmakunnas, 2011).
Young people are associated with being more creative, innovative, energetic and flexible while older people are more experienced and knowledgeable, and have higher levels of responsibility and commitment (Beaver & Hutchings, 2005). When these diverse characteristics are balanced, higher performance is achieved (Li et al., 2011). Therefore, age diversity brings different points of view, skills and insights that might result in better decision making and problem solving capabilities and thus better firm performance.
Age diversity is an important resource because coexistence of younger and older employees constitutes a unique value for firms (Li et al., 2011). For the sake of employees at diverse ages, firms obtain invaluable assets that are unique and difficult to imitate (Li et al., 2011). Therefore, age diversity potentially brings competitive advantage to the firms and better performance compared to age homogenous firms.
However, there are also several previous studies that could not find significant support for the positive effects of age diversity on firm performance. A research revealed that globally the least variation exists in the age of directors than any other aspects of diversity (PricewaterhouseCoopers, 2015). Furthermore, age diversity is not yet clearly defined as a strategy within organizations as currently it is not a priority for the employers (Roundtree, 2011). These might imply that age diversity is not regarded as a factor that improves firm performance.
Stereotyping is an issue that can affect team performance negatively. Age based stereotypes exist such as elder employees are mostly associated with not keeping up with the developments while younger employees are assumed to be more likely to make mistakes (Parry & Tyson, 2009). Those might hamper teamwork and thus results in negative team performance.
Age diversity can cause social fragmentation because age based subgroups of the team might discriminate against others (Kunze, Boehm, & Bruch, 2013). As age diversity increases, perceived negative age discrimination environment within the team also increases due to the social fragmentation (Kunze et al., 2013). Members of an executive team might intuitively prefer to work and socialize with peers at similar ages which might cause negative impacts on the decision making process and thus firm performance. Additionally, evidence was found that demographic diversity is negatively related to the strategic consensus within the team (Knight et al., 1999). Age diversity, which is one of the most distinctive demographic characteristics, might ruin the consensus of the executive team and cause lower firm performance. Even though one can argue that different knowledge and points of view that stem from age diversity are useful in decision making in general, it may also result in lack of agreement in executive teams (Knight et al., 1999).
Age is one of the most clearly visible aspects of diversity so it might cause sub-grouping within teams (van der Walt & du Plessis, 2010). Referring to the similarity attention theory, close-aged team members attract each other so they are split into groups based on similar stages of their lives; such as growing up in the internet era or having young children (Lawrence, 1988). Lawrence (1988) claimed that this social categorization can further trigger strong bonds within subgroups and might harm firm performance.
Table 2: Summary of previous research and findings on age diversity
Hypothesis 2: Do executive teams in emerging markets drive higher performance when there is higher diversity of age?
2.6. Conceptual Model
Figure 6: Conceptual Model
Executive teams in
local firms
Firm performance
Women representation
3. Methodology
This master thesis demonstrates a causal relationship and deals with the operationalized concepts that affect this causality. It depends on quantifiable observations and facts without any intervention of the researcher (Thomas, 2004). Therefore, the study has a positivist philosophy.
The research has a deductive approach that hypotheses were developed referring to the existing theories and previous studies. Deductive research starts with a theory and continues with formulating hypothesis (Jonker & Pennink, 2010). After concepts are converted into variables, data is collected in order to test the hypothesis (Jonker & Pennink, 2010). Deductive approach leads to quantitative methodology. Thus, in this master thesis after formulating hypotheses based on existing theories and literature, quantitative data regarding gender and age composition of executive teams in BRICS were collected and the impact of these variables on firm performance was demonstrated by using statistical methods. Furthermore, after the data collection and analyses of the results, several interviews were done with female representatives from BRICS who are aiming for careers in top positions. Interviews provide with a qualitative approach and add value to the study. Mixing of qualitative and quantitative methods is a way to ensure triangulation which enhances internal validity of the research (Thomas, 2004).
3.1. Time Horizons
The most recent available data was used for this master thesis. Majority of the executive team information provided by the databases belongs to year 2014; therefore, the performance measures of the corresponding firms were collected for 2014.
Data sample of this thesis consists of gender and age information of executive teams and performance measures of several companies from BRICS for 2014. The cross sectional time horizon provides a ‘snapshot’ of that specific year (Flick, 2011). Therefore, this master thesis analyzed how female representation and diversity of age in executive teams in BRICS affected the firm performance in 2014.
3.2. Data Sources and Collection
Secondary data was used for this research. Demographic information of executive team members has already existed in a database and thus relevant data was collected from that database. Using secondary data has several advantages. For example, secondary data can be used immediately but obtaining data by oneself can be costly (Punch, 1998). Moreover, quality of the data from a secondary source can be superior to anything that a researcher could have obtained on her/his own (Punch, 1998).
BoardEx is a database where demographic information of board members and senior executives around the world can be found. It is widely acknowledged and credible so it is assumed that the data is reliable. Information is collected from reliable sources and verified from multiple other sources before entering the database (BoardEx, 2014). Unlike other corporate governance related databases, advantage of using this database is that it includes not only data of U.S. based and Western European countries but also that of rest of the world. It is convenient to gather information from this database for emerging markets. Therefore, executive teams’ gender and age information for BRICS was collected from BoardEx database.
Furthermore, in order to see the impacts of executive team characteristics on firm performance company key financials were needed. Return on asset values were used as an indicator of firm performance. Financial information of companies was collected from Orbis database where data of over 175 million companies worldwide exists (Bureau van Dijk, 2015). When return on asset information was not available on Orbis, Financial Times database was used which can be accessed via Internet. Financial Times is the leading business news and information organization whose authority and accuracy are internationally recognized (Financial Times, 2015).
3.3. Data Sample
To determine a sufficient sample size is a challenging issue in a research. As a rule of thumb it should be “as few as you must, as many as you can” (Thomas, 2004). There are several different opinions about the optimal sample size. While some researchers argue that around 200 units usually give sufficient data for analysis (Thomas, 2004), others give a minimum bound that ranges between 30 and 100 in order to conduct a reliable analysis (Bailey, 1994).
It is not easy to find data about emerging market firms since their executive team characteristics are not available on many databases. Thus, information regarding demographics of executive team members in emerging markets is not easily accessible. After required data was found in BoardEx, key financials were collected from Orbis which caused exclusion of some companies due to non availability of financial information of the corresponding firms.
As a result of first dump from BoardEx, 1139 firms’ executive team characteristics were found in total in which only 215 firms had women. It is essential that the data sample should be representative. When distribution of 1139 companies among industries, and female percentage in the executive teams were analyzed, it was decided to focus on banks and capital intensive sectors. Capital intensive sectors (a.k.a. high capital industries) included 12 industries which are oil and gas, electricity, construction and building materials, pharmaceuticals and biotechnology, diversified industrials, chemicals, mining, engineering and machinery, steel and other metals, real estate, automobiles and parts, and finally transport. In those industries there were enough companies to conduct an analysis. Moreover, females did exist in the executive teams even though they were not many in number.
Subsequently, companies whose executive teams consist of 3 or less people were excluded from the sample in order to analyze the diversity of age which was calculated based on the age variance. Finally, companies which had missing information in the variables were excluded from the sample. Final sample consisted of 118 companies, 45 of which had at least 1 woman in the executive team. List of companies can be found in Appendix C.
3.4. Variables
Independent Variables: Female percentage was used as one of the independent
varied across the sample, meaning that larger executive teams were more likely to have more women. Thus, using share of women in the team is more accurate than using the absolute value. Female share was calculated as number of females divided by total number of executive directors. Furthermore, age diversity was calculated by age variance of the executive team members instead of age average. Age variance focuses on the age spread of directors therefore is better in explaining diversity than the average value.
Dependent Variable: Considering the previous research on similar topics, return on
asset (ROA) was largely used to evaluate firm performance (Adams & Ferreira, 2009; Boone & Hendriks, 2009; Krishnan & Park, 2005; Marimuthu & Kolandaisamy, 2009; Smith et al., 2006). ROA is an efficient way of measuring financial performance because it shows whether the firm has enough returns on its assets. Therefore ROA was chosen as the dependent variable of this research. ROA is calculated as the ratio of firm’s net income to its total assets. The higher the ROA, the better the performance.
Control Variables: Based on the previous research, three variables were included as
control variables since they were likely to affect the relationship between independent variables and firm performance. Many previous research were controlled for executive team size (Adams & Ferreira, 2009; Baixauli-Soler, Belda-Ruiz, & Sanchez-Marin, 2015; Kilduff et al., 2000). It was suggested that as executive team size increases, information processing capabilities of firms increase as well, which in turn may lead to better performance (Hoffman, Lheureux, & Lamont, 1997). Executive team size was calculated as total number of executive directors. Firm size was used as another control variable since larger firms may have more resources which lead to better performance. It was also used by the previous research (Adams & Ferreira, 2009; Baixauli-Soler et al., 2015; Krishnan & Park, 2005). Firm size was calculated as total number of employees in the firm. Finally, tenure of the executive directors included in the research as a control variable. It was calculated as average time spent in the organization. Therefore, controlling the research for those variables helps to see the true relationship between the independent variables and firm performance.
3.5. Data Analysis
analysis is a way of predicting the dependent variable from two or more independent variables. First of all, effects of the control variables are analyzed in a regression analysis. After that, independent variables are added to the model. By this way, effects of each independent variable on the dependent variable can be analyzed while controlling the study for the control variables.
4. Results
4.1. Descriptive Analyses
In this section numerical statistics regarding female representation and age diversity in executive teams, which are composed of 4 or more people in high capital industries and banks, were demonstrated.
4.1.1. Descriptive Analyses for Female Representation
Total number of executive teams is 118 and that of executive directors is 600. While 544 of them are men, there are only 56 women. Thus, overall female percentage in the sample is 9.33%. Maximum number of females in an executive team is 4, while the minimum number is zero. The only executive team with 4 females belongs to a bank from Russia. 73 executive teams out of 118 have zero female which means that majority of the executive teams does not have a woman as it is shown in Graph 1. Maximum proportion of women in an executive team is 50 percent and belongs to China. This means that men and women are either equal in number, which is the best case, or women are underrepresented in the sample. There are not any cases in which number of women is greater than men.
Graph 1: Female representation across the executive teams
When average of female percentage in executive teams per country is analyzed, it is revealed that Russia is in the first place followed by China with 15% and 11% respectively. On the other hand, female executive directors are not employed at all in Brazil (see Graph 2). This implies that women executives are least likely to exist in Brazil and most likely to exist in Russia and China among BRICS.
Graph 2: Female representation in executive teams per country
When average of female percentage in executive teams per industry is analyzed, it is found out that chemicals and electricity industries do not include any women in our sample. Thus, average of female percentage is zero. Greatest amount of female percentage exists in pharmaceuticals and biotechnology with 21%. Interestingly, automotive industry is in the second place where average female percentage in executive teams is 20%.
Industry-wise comparison shows that average female percentage in banks is 13% while that in high capital industries is only 8% as demonstrated in Graph 3 below. This implies that female executive directors are more likely to be represented in financial services than in high capitals.
Graph 3: Female representation in executive teams per industry
0,00 0,11 0,05 0,15 0,09 0,00 0,05 0,10 0,15 0,20
Brazil China India Russian Federation
South Africa Average Female Percentage per Country
4.1.2. Descriptive Analyses for Age Diversity
The youngest executive director is 24 from China and the oldest is 84 from India. Regarding age average distribution among executive teams, the majority clusters at ages between 45 and 60 with the mean value of 52 as it can be seen in Graph 4. More specifically, there are 105 executive teams out of 118 whose members are between 45 and 60 years old on the average. Frequency of executive teams with the age average less than 45 and greater than 60 is relatively low. This means that the sample of this study is mainly composed of middle-aged and elderly people. The oldest executive team has the age average of 75.25 in a South African company in transport industry and the youngest executive team has the age average of 37 in a Russian company in mining industry.
Graph 4: Age average across the executive teams
Similarly, Graph 5 demonstrates the age diversity among the sample. The frequency is highest when the age variance is between 0 and 50. Frequency drops drastically as age variance gets larger. This shows that executive team members are mostly at close ages and thus age diversity is low among the sample. The most and least diverse executive teams in terms of age are Indian companies in electricity, and in oil and gas industries respectively.
Graph 5: Age diversity across the executive teams
Regarding age diversity of executive teams per country, as Graph 6 shows, South African firms have the most diverse executive teams. Brazil, on the other hand, has the lowest age diversity. Thus, it can be concluded that executive teams in South Africa are more likely to consist of directors from different age groups while executives are at more similar ages in Brazil.
Graph 6: Age diversity in executive teams per country
0 10 20 30 40 50 60 70
Brazil China India Russian
Federation South Africa
When age diversity of executive teams per industry is analyzed, most diverse industry is found as electricity. Therefore, it is more likely to encounter with relatively younger and elder directors together in executive teams in electricity industry. Conversely, oil and gas has the lowest age diversity among all industries followed by chemicals. Age variance per industry can be found in Graph 7.
Industry-wise comparison of age diversity shows that high capitals on the average are more diverse than banks. Therefore, one can argue that executive directors from different age groups are more likely to be employed in high capital industries than in financial services.
Graph 7: Age diversity in executive teams per industry
4.2. Pearson Correlations
Before analyzing the results of multiple linear regression, Pearson’s correlation test was conducted in order to see how well each of the independent variable is correlated with the dependent variable. Pearson’s correlation coefficient (i.e. r) shows the degree of correlation between variables which ranges between -1 and 1 (Field, 2009). Thus, it measures the magnitude of the relation. Values between ±.1 and ±.3 indicate a small effect, ±.3 and ±.5 indicate a medium effect and ±.5 and ±1 indicate a large effect (Field, 2009).
0 10 20 30 40 50 60 70 80 90 100
Age Diversity per Industry
Age variance of high capital
Pearson’s correlation coefficients and significance levels can be seen in Table 3. Coefficient between female percentage and return on asset is 0.125 with the p-value 0.177, and between age variance and return on asset is 0.072 with the p-value 0.438. These results state that independent variables of this study, female percentage and age diversity in executive teams, are not significantly correlated with the firm performance in BRICS. However, r values are greater than zero, which means that the correlation between independent variables and the dependent variable is positive and in the same direction so if one increases, the other one increases as well.
Return on asset Female percentage Variance of age Number of employees Executive team size Avg. time spent in the organization Return on asset
Correlation coef. (r-value) 1 0,125 0,072 -0,084 -0,092 -0,081
Sig. (p-value) 0,177 0,438 0,367 0,323 0,386
Female percentage
Correlation coef. (r-value) 0,125 1 0,041 -0,103 0,04 -0,062
Sig. (p-value) 0,177 0,662 0,265 0,668 0,505
Variance of age
Correlation coef. (r-value) 0,072 0,041 1 -0,075 -0,034 0,074
Sig. (p-value) 0,438 0,662 0,418 0,713 0,426
Number of employees
Correlation coef. (r-value) -0,084 -0,103 -0,075 1 0,179 -0,007
Sig. (p-value) 0,367 0,265 0,418 0,052 0,937
Executive team size
Correlation coef. (r-value) -0,092 0,04 -0,034 0,179 1 -0,15
Sig. (p-value) 0,323 0,668 0,713 0,052 0,105
Avg. time spent in the organization
Correlation coef. (r-value) -0,081 -0,062 0,074 -0,007 -0,15 1
Sig. (p-value) 0,386 0,505 0,426 0,937 0,105
Table 3: Pearson correlation matrix
4.3. Multiple Linear Regression Analyses
which is the most commonly used value. The model that was run only with control variables is referred as Model 1 and the model that was run with control variables and independent variables is referred as Model 2.
Before analyzing the outcomes, the model was checked for the multicollinearity assumption which should not be violated for the regression to work accurately. Multicollinearity occurs when there is a strong correlation between independent variables (Field, 2009). There must not be a perfect correlation between independent variables to obtain unbiased results in a regression analysis (Field, 2009). Multicollinearity can be checked via SPSS as it creates related diagnostics. Variance Inflation Factor (VIF) and tolerance values are indicators of multicollinearity. As a rule of thumb, VIF values should be less than 10 in order to conclude that multicollinearity is not a concern for the research (O’Brien, 2007). Also, variables which have tolerance values less than 0.2 might be highly correlated so those values should be greater than 0.2 (Menard, 1995). Both Model 1 and Model 2 meet the assumption. Specifically, VIF values are around 1 and tolerance values are around 0.9 which are less than 10 and greater than 0.2 respectively for both Model 1 and Model 2. Results can be found in Appendix D.
Outcome of the multiple regression analysis of Model 1 and Model 2 is shown in Table 4. What is worth discussing regarding Model 1 is that none of the control variables are significant at 5% level in this study which means that control variables do not have an impact on return on asset of companies. However, it is not the field of concern whether the control variables are statistically significant.
When independent variables were added to the model, R square slightly increased which means that female percentage and age variance are relevant variables in assessing firm performance. Each independent variable contributes to predict return on asset values. Nevertheless, R square in Model 2 is still relatively low. More specifically, independent variables could explain 4% of the variability of the return on assets. Furthermore, neither female percentage nor age variance in executive teams created a statistically significant effect on the dependent variable because p-values of the independent variables, which are 0.219 and 0.473 respectively, are greater than 0.05. Therefore, it cannot be concluded that higher female percentage or higher age diversity in executive teams leads to better firm performance in BRICS.
are not significant, female representation and age diversity are positively related with firm performance.
Control and independent variables Model 1 Model 2 Model 3
Number of employees β -0,068 -0,05 -0,112
p-value 0,474 0,601 0,267
Executive team size β -0,094 -0,099 -0,062
p-value 0,326 0,3 0,54
Avg. time spent in the organization β p-value -0,095 0,312 -0,094 0,322 -0,12 0,224
Female percentage β 0,116 0,16
p-value 0,219 0,102
Variance of age β p-value 0,067 0,473 0,034 0,726
R square 0,022 0,04 0,058
Table 4: Regression analyses of Model 1, Model 2 and Model 3
In a linear regression analysis, outliers or influential points may bias the results of the analysis (Stevens, 1984). It is important to detect the outliers and influential points. Thus, the analysis was re-run without the outliers and influential points which is referred as Model 3. Cook’s Distance, Mahalanobis Distance and Leverage Values are three diagnostics that can detect those data points. Field (2009) indicates that Cook’s Distance measures the overall influence of a data point on the model. The data whose Cook’s Distance values are bigger than 1 can be too influential (Stevens, 1984). Mahalanobis Distance measures the distance from a data point to the means of independent variables (Field, 2009). According to the critical values table, which can be found in Appendix E, Mahalanobis Distance should not be bigger than 20 for this study when sample size and number of independent variables are considered (Barnett & Lewis, 1978). Leverage Values measure the influence of the dependent variable’s observed value on its predicted values (Field, 2009). It was suggested that the values greater than 2((k+1)/n) might be a matter of concern where k stands for the number of independent variables and n stands for the number of data (Hoaglin & Welsch, 1978). When the outcome of Model 3 is analyzed, it can be seen that R square increased to 5.8% so relevancy of the independent variables in explaining the variability of the return on assets slightly increased. However, still none of the independent variables are significant.
heavy industries differ from a financial service sector in BRICS. Industry-wise analysis is referred as Model 4. Model 4 demonstrates that neither the results of high capital industry nor those of banks are significant (see Table 5). Although results are insignificant, beta coefficients are positive in high capital industry so both female representation and age diversity are positively related with return on assets. Age diversity in banks is likely to influence firm performance negatively due to negative beta coefficient but the effect is not significant.
Control and independent
variables Banks High Capital
Number of employees β p-value 0,503 0,419 -0,055 0,585
Executive team size β -0,36 -0,064
p-value 0,474 0,533
Avg. time spent in the organization β p-value 0,306 0,55 -0,107 0,283
Female percentage β p-value 0,326 0,552 0,127 0,199
Variance of age β -0,202 0,061
p-value 0,684 0,537
R square 0,515 0,042
Table 5: Regression analysis of Model 4
Subsequently, another analysis was conducted for each country separately in order to make a comparison among BRICS. Country-wise analysis is referred as Model 5. In Model 5, Brazil was not included in the analysis due to inadequacy of data. As shown in Table 6, female percentage is significant and positively related with return on assets in China. Thus, it can be concluded that the higher female percentage, the better firm performance in high capital industries and banks in China. Furthermore, another significant result showed up in South Africa. Variance of age is almost significant with p-value 0.054 in South Africa and the relationship is in positive direction as expected. Thus, it can be concluded that diversity of age in executive teams affects South African firms’ performance positively. Results of the rest of the countries are insignificant.
Control and independent
variables Russia India China
South Africa
Number of employees β 0,294 0,145 -0,047 0,083
p-value 0,722 0,497 0,726 0,877
Executive team size β -0,272 -0,088 -0,114 -0,52
p-value 0,769 0,652 0,392 0,307
Avg. time spent in the organization β -0,669 -0,306 -0,117 -0,152 p-value 0,519 0,127 0,371 0,572 Female percentage β 0,589 0,105 0,28 -0,142 p-value 0,448 0,583 0,04* 0,599 Variance of age β 0,244 0,188 -0,034 0,645 p-value 0,723 0,34 0,789 0,054* R square 0,232 0,168 0,127 0,405
Table 6: Regression analysis of Model 5
Gender diversity report of Ernst and Young (2013) shows that female executive officers exist mostly in industries such as retail and wholesale, media and entertainment, airlines, and hospitality and leisure. Thus, new data was collected for other industries, namely beverages, clothing, leisure and personal products, food and drug retailers, food producers and processors, general retailers, leisure and hotels, media and entertainment, and wholesale where existence of women in executive teams is more likely than high capital industries. 73 companies were excluded due to missing information or selection criteria, such as executive team size. Final sample of the new industries includes 34 companies 16 of which have women in their executive teams. Results of the new sectors are demonstrated as Model 6 in Table 7.
Control and independent variables Other sectors
Number of employees β 0,174
p-value 0,351
Executive team size β -0,044
p-value 0,812 Avg. time spent in the organization β 0,202 p-value 0,294
Female percentage β 0,038
p-value 0,837
Variance of age β p-value 0,199 0,291
R square 0,124
Table 7: Regression analysis of Model 6
5. Discussion
Our data shows that women are few and ages of directors are similar in executive teams in BRICS. Several regression analyses were performed on the data and the results revealed that there are not any significant relationships between female participation and age diversity in executive teams and firm performance. Therefore, we could not find enough support to conclude that higher female percentage and age diversity in executive teams prompt to better firm performance in emerging markets, specifically in BRICS. Even though the results are insignificant, both relationships are in the positive direction, meaning that independent variables are positively related with the dependent variable.
After the overall analysis, several analyses were conducted. Among all sub-analyses, it was found that the relationship between women in executive teams and firm performance is significant and positive in China. Thus, higher female percentage in executive teams affects Chinese firms’ performance positively. Moreover, the relationship between age diversity in executive teams and firm performance is positive and almost significant in South Africa with p-value 0.054. This implies that the more diverse ages South African executive directors have, the better the firm performance.
What is striking in this research is that both number of women and age diversity in executive teams in BRICS are very low. Thus, insignificance of the regression results can be explained by the underrepresentation of women and homogeneity in ages of directors in executive teams. The scarcity of diversities in executive teams agrees with the finding of a past study which revealed that demographic diversity such as age, gender and ethnicity does not typically exist in leadership positions (Lewis & Tatli, 2015).
5.1. Discussion on Female Representation in Executive Teams
A recent study revealed that gender inequality is high in the workplace globally; especially at the leadership positions (McKinsey&Company, 2015). More specifically, 68% of the countries have extreme gender inequality in leadership positions (McKinsey&Company, 2015). For emerging market countries, situation is more severe. Figure 7 below shows the gender inequality at work and gender parity scores by regions. While gender equality is highest in North America, India brings up the rear (McKinsey&Company, 2015). In most of the emerging economies women are few at the workplace which might stem not only from culture but also from inequality of opportunities.
Figure 7: Gender parity scores and inequality levels at work (McKinsey&Company, 2015)
In our sample, highest female percentages showed up in pharmaceuticals and biotechnology, and automobiles and parts. A research conducted in U.S. in 2013 confirms our results that pharmaceutical and biotechnology, and motor vehicles and motor vehicles equipment manufacturing had the highest female executive representation among capital-intensive industries (Catalyst, 2014c; Ernst&Young, 2013). While in our sample electricity and chemicals had zero female executives, oil and gas had the smallest amount of women in the U.S. based study. Comparison between our data sample and statistics of U.S. from 2013 can be found in Table 8.
Table 8: Comparison of female executives per industry between this study and U.S.
little influence on firm performance (Kravitz, 2003). Therefore, contributions of women executive directors are not yet substantial enough to have an impact on firm performance due to their underrepresentation. In line with this argument, the insignificant results show that women’s impacts are not yet reflected on firms’ profitability in BRICS. Even though they contribute, the impacts are hidden by dominant groups and thus are not measurable via financial performance (Rose, 2007).
Alone woman in an executive team might be ignored or even excluded from some decision-making processes (Kramer, Konrad, & Erkut, 2008). Among the executive teams that include women in our sample, majority has only one woman. This is another reason that can explain insignificant results because a woman by herself might serve as a symbolic representative rather than an important decision maker. It is called tokenism and may impede women to show significant effects on decision making (Torchia, Calabro, & Huse, 2011). Therefore, in our sample women could not influence the overall management decisions and thus firm performance since they have not reached a certain mass and power in the executive teams.
Moreover, it is controversial that to what extent underrepresented groups can be effective. A study conducted on Fortune 1000 companies revealed that only if there are 3 or more women in a team, they can exert their influence on the overall group and make contributions (Kramer et al., 2008). This is known as critical mass (Kramer et al., 2008). Therefore, women who meet the critical mass in the executive teams can make significant difference. Women less than the critical mass might still be effective but they are more likely to be influenced by men (Kramer et al., 2008).
In our sample there is only one company, which is from Russia, that meets the critical mass of 3 or more women executives. Given that Russia has the highest female executive ratio on the average in our sample, it would have been reasonable to see significant results. However, statistical power of an analysis depends on the sample size (Field, 2009). When we split the overall data into countries, sample size of Russia was not enough to make certain conclusions. Therefore, it is believed that if there were more data from Russian companies, we would potentially see a significant effect on firm performance. This is our strong suggestion for future research that a similar but more thorough study might be conducted in Russia.
