Socio-demographic characteristics and firm
performance: Examining the Chief Supply Chain
Officer-CEO interface
Master thesis
University of Groningen
Faculty of Business and Economics
MSc Supply Chain Management
June 22, 2020
Student: Jurgen Nijmeijer
Student number: S2959038
E-mail: j.nijmeijer.2@student.rug.nl
University supervisor: dr. X. Tong
Second assessor: dr. C. Xiao
Acknowledgements:
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TABLE OF CONTENTS
1. INTRODUCTION ... 4
2. LITERATURE REVIEW AND HYPOTHESIS DEVELOPMENT ... 6
2.1 What CSCOs are and what they do ... 6
2.2 Upper echelon theory ... 7
2.3 Diversity between CEO and CSCO in TMTs ... 7
2.3.1 Educational background diversity ... 8
2.3.2 Age diversity ... 9 2.3.3 Nationality diversity ... 11 2.3.4 Career variety ... 12 2.4 Conceptual model ... 13 3. METHODOLOGY ... 14 3.1 Sample ... 14 3.2 Measurement ... 15 3.2.1 Dependent variable ... 15 3.2.2 Independent variables ... 15 3.2.3 Control variables ... 16 3.3 Data analysis ... 16 4. FINDINGS ... 17 4.1 Education diversity ... 18 4.2 Age diversity ... 19 4.3 Nationality diversity ... 20 4.4 Career variety ... 21 5. DISCUSSION ... 23
5.1 Findings of the hypotheses ... 23
5.2 Managerial implications ... 26
5.3 Limitations ... 27
6. CONCLUSION AND FUTURE RESEARCH ... 28
7. REFERENCES ... 29
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ABSTRACT
This thesis aims to contribute to the current literature by filling the gap within the domain of personal characteristics of the CEO and Chief Supply Chain Officer (CSCO) as members of the top management teams (TMT). More specifically, the differences in socio-demographic characteristics such as age, nationality, educational background and career variety will be investigated in the CSCO-CEO interface and their impact on firm performance. The Execucomp, Boardex and Compustat databases are used to obtain the data from the S&P 1500 companies for the characteristics of CSCOs and CEOs and financial data of the firms. The results of the regression analysis showed that two of the four formulated hypotheses in this thesis are supported. Age difference between the CSCO and CEO is negatively related to firm performance. Career variety in the CSCO-CEO interface has a positive effect on firm performance. Education difference and nationality difference do not show a significant relationship with firm performance and therefore these hypotheses cannot be accepted.
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1. INTRODUCTION
There has been a trend in recent years that more and more companies are appointing a chief supply chain officer (CSCO) to their top management teams (TMT) (Hendricks, Hora and Singhal, 2015). Firms are changing the composition of their top management teams with a member who is involved in supply chain-related activities and who is able to directly report to the CEO. One of the tasks that CSCOs engage in is strategic sourcing and building stable relationships with the suppliers (Groysberg, Kelly and MacDonald, 2011). Twenty years ago, supply chain positions were generally not represented in a TMT (Groysberg et al., 2011). Since many firms expanding their operations to other countries, the complexity and challenges in managing subunits of firms all over the world increased, and also the need for a supply chain management position became clearer.
The fact that more firms are appointing CSCOs to their TMTs and the important role of a CSCO as responsible function for maintaining the entire supply chain, more research is needed about the personal characteristics of CSCOs in comparison with a CEO as member of a TMT. According to Hambrick and Mason (1984), personal characteristics of members of the TMTs are important partial predictors for organizational outcomes. They call this the upper echelon theory. These characteristics include the socio-demographic characteristics such as age, nationality, educational background and career variety. The fact that these socio-demographic characteristics of the top managers have an impact on the performance (Tsui, Egan and O’Reilly, 1992), more research is needed about this topic.
5 performance. These results are all focused on firm-level characteristics. However, it remains unclear what the influence is of the differences in personal characteristics of the CSCO-CEO interface on the firm performance. Differences in personal characteristics are already investigated in extant research, but the focus of these literature is all about TMT-level. Little is known about the socio-demographic characteristics of the upper echelon theory investigate in the CSCO-CEO interface, as the CSCO is only recently getting more attention in business.
This research aims to contribute to the existing literature of CSCOs by filling the gap regarding to the differences in personal characteristics between CSCOs and CEOs as members of TMTs related to firm performance. In order to study the personal characteristics of the above-mentioned TMT members, the upper echelons characteristics of the upper echelon theory by Hambrick and Mason (1984) are used to describe personal characteristics of executives, as these characteristics greatly influence the decisions in the firm and, in turn, the firm performance. Specifically, the socio-demographic characteristics will be used in this research.
This leads to the following research question: ‘Do differences in socio-demographic
characteristics of the CSCO and CEO improve firm performance?’ The socio-demographic
characteristics that will be investigated are age, nationality, educational background and career variety of CSCOs and CEOs. Firm performance will be measured with the variable operating cycle. To answer the research question, a regression analysis will be performed. The study will use data from the S&P 1500 companies from the Execucomp, BoardEx and Compustat databases.
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2. LITERATURE REVIEW AND HYPOTHESIS
DEVELOPMENT
In this chapter the literature will be reviewed and a hypothesis is proposed for each variable. Because the focus of the research is on the CSCO-CEO interface and due to the fact that CSCOs are relatively unknown, first the role of CSCOs will be elaborated on. Then, the upper echelon theory, diversity and characteristics of the upper echelon theory are described.
2.1 What CSCOs are and what they do
A CSCO “is the highest executive with designated responsibility for SCM” (Wagner and Kemmerling, 2014, p. 157). He is the representative of the supply chain management department in the TMT and is responsible for supply chain management activities for the entire firm (Roh et al., 2016). During the 1960s – 1980s, due to globalization, outsourcing and changing business models, manufacturing executives had to control less resources, because many manufacturing capabilities were outsourced. Executives that represented the manufacturing operations in TMTs were removed (Womack, Jones and Roos, 2007). However, as Skinner (1969) and Wheelwright (1984) predicted in their research that top-tier operations managers have an important role in creating competitive advantages, the emergence of SCM-executives in TMTs has risen in the later years. Over the last decades, research has shown that supply chain functions are of great value in TMTs (Fawcett, Magnan and McCarter, 2008).
There are three main tasks where the CSCO is involved in according to Mentzer, Stank and Esper (2008) and Wagner and Kemmerling (2014). The first task is leading the supply chain management organization; this includes managing the supply chain activities and developing capabilities that are in line with the strategy of the firm.
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2.2 Upper echelon theory
This study draws on the upper echelon theory by Hambrick and Mason (1984). The upper echelon theory argues that the decisions of the top executives influence the firm actions and the firm performance. The theory states that managers of the TMT have unique characteristics and interpretations that guide their decisions regarding the actions and the strategy of the company. Interpretations of the top managers are influenced by experiences, values and the personalities (Hambrick, 2007). The upper echelon theory is based on the constraints of bounded reality, which means that companies are restricted in their way they gather and process information in order to make the right decisions (Cyert & March, 1963; March & Simon, 1958). Top managers therefore filter and interpret the information they get based on their cognitive bases and values, which can be indicated by the observable characteristics such as age, education, career variety and nationality. These characteristics in turn influence the performance of the firm (Figure 1). Hambrick and Mason (1984) also stated that characteristics of a team predict stronger explanations of organizational outcomes than a top executive alone, because leadership of an organization is a joint activity and the interpretations of all TMT members influence the behaviour and performance of the firm. Other researchers concluded that demographic characteristics of both individual executives and TMTs are highly related to firm performance (D’Aveni, 1990; Eisenhardt and Schoonhoven, 1990; Boeker, 1997).
2.3 Diversity between CEO and CSCO in TMTs
The upper echelon study predicts that the socio-demographic characteristics of TMTs are related to firm performance (Hambrick and Mason, 1984; Papadakis and Barwise, 2002). Several studies have shown that the composition of TMTs have an impact on the strategic change and the ability to survive in turbulent environments (Golden and Zajac, 2001; Stewart,
8 2006). They claim that more heterogeneous characteristics (different (educational) backgrounds and competences) between managers, are more likely to succeed than managers with homogenous characteristics. Based on this literature, Naranjo-Gil, Hartmann and Maas (2008) argue that teams consisting of heterogeneous managers have more chance to succeed in operational performance when engaging in strategic change than homogenous teams do, because managers with heterogeneous characteristics have more diversified set of skills, experiences and competences on board. This diversity is important in decision making processes and creative thinking as it broadens the horizon of the information collection (Pitcher and Smith, 2001). However, Naranjo-Gil et al. (2008) predict that this only applies to job-related dimensions of heterogeneity. Job-job-related dimensions are characteristics that are directly relevant for the job, such as educational and functional background. Other socio-demographic variables such as age have an opposite effect with regard to group diversity and performance. The following subsections explain for each variable how diversity affects the firm performance. For some variables are contrary hypotheses proposed.
2.3.1 Educational background diversity
The educational background is a characteristic that forms a cognitive base of a top manager (Hambrick and Mason, 1984). The level of education says something about the capacity for information processing, which is higher at a higher level of education (Schroder, Driver and Streufert, 1967). Since the CSCO directly report to the CEO in the TMT (Roh et al., 2016), there is regular interaction between the CSCO and CEO, the information processing capacity is important to make quick decisions during meetings. Gottesman and Moorey (2006) found that firms managed by CEOs with MBA or law degrees do not have a better performance than firms with a CEO that does not have a degree. However, this is only about the education of the CEO and it does not conclude anything about differences in education between CEO and CSCO, which is the aim of this study.
9 influence, as the top managers such as CSCO and CEO jointly make the decisions for the supply chain operations of the firm, which ultimately impact the entire firm performance. Similarly, Bantel and Jackson (1989) argued that higher levels of education of the managers lead to more comprehensive decisions, what results in greater innovation. Thus, teams with a diversity in higher education have a broader set of skills, are more productive and is associated with diversity in managers’ perspectives, what benefits the performance. Diversity in educational backgrounds may contribute to problem solving and decision making in dynamic environments. One may argue that differences in educational background lead to a negative effect on firm performance, such as misunderstandings. However, most research on this topic argues for a positive effect on educational background diversity and performance. Therefore, this thesis follows the leading opinion of a positive effect on firm performance.
According to the previous arguments, the following hypothesis is proposed:
H1: Differences in educational background in the CSCO-CEO interface is positively related
to firm performance.
2.3.2 Age diversity
10 younger managers follow another strategy about diversification than older managers would do, which can affect the performance.
The above describes the differences of individuals, but the research question is about differences between CSCO and CEO as TMT members. One of the roles of a CSCO is representing the SCM-department in the TMT. In this capacity, the CSCO justifies the decisions made within the department and discusses these with the CEO (Roh et al. 2016). Managers such as CSCOs and CEOs, who are in the same age group usually have similar experiences and values. The behaviour of the managers is almost the same since the grew up in the same generation and it is easy to communicate with each other (Zhang, 2007). This is contrary to managers with a big age difference, as they tend to have more conflicting values, ideas and power struggles (Pfeffer, 1983; Zhang, 2007). The coherence and cooperation between two members are better when they have similar ages, which is beneficial for the firm’s performance (Zhang, 2007). Therefore, the following hypotheses related to the differences in age between the CSCO and CEO has been drawn up:
H2a: Differences in age in the CSCO-CEO interface is negatively related to firm performance.
However, there are also situations possible that differences in ages between the CSCO and CEO positively contributes to firm performance. This can be explained by the fact that CSCOs and CEOs in different age groups bring in cognitive diversity due to their experiences in different business environments in different time periods (Kim and Rasheed, 2014). In addition, age differences between managers are also likely to lead to diversity in decision-making styles and risk-taking behaviour, which promote the performance with regard to the supply chain operations and the overall firm performance (Hitt and Tyler, 1991). It can influence the supply chain operations, because the CSCO – who is responsible for the SCM-department – can decide to do business with one cheap supplier (riskier) instead of a more expensive option such as having two suppliers (Pillania and Kan, 2008). The result is this is that some components are not available on time in the first case, but in the latter case that some components are double available. Both cases have consequences for the supply chain department performance and in turn for the firm performance (Pillania and Kan, 2008). The CSCO is responsible for the SCM-department, but the final responsibility lies with the CEO and therefore the differences in age do matter for the (risky) decisions that have to be made. The following competing (to H2a) hypothesis is proposed based on the above:
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2.3.3 Nationality diversity
Over the last decades, TMTs consisted mostly of local managers. Due to globalisation and the rise of emergent markets, the number of other nationalities in the upper echelons of a firm is increasing, which makes nationality an important aspect dimension in the CEO-CSCO interface (Staples, 2007). A CSCO acts as a boundary spanner between the different units and the CEO (Roh et al., 2016). This means that he has contact with the CEO relatively often. A CSCO is also the point of contact between the different actors of a supply chain. In order to realize effective flows through the supply chain, coordination between the actors is important (Cao, Zhang, To and Po Ng, 2008). The CSCO has to share this information with the CEO as the decisions influence the performance of the supply chain department and as a result, the performance of the firm. During these moments, communication is essential between the CSCO and CEO. However, differences in nationality between the CSCO and CEO could disturb this, because differences in nationality between top managers may increase communication issues, misunderstandings and conflicts between the managers (Cox, Jr, 1991; Elron, 1997; Lehman and Dufrene, 2008). This fact may negatively influence the performance of the supply chain department and in turn, the firm performance. Therefore, this study proposes the following hypothesis:
H3a: Differences in nationality in the CSCO-CEO interface is negatively related to firm performance.
On the other hand, nationality diversity in TMTs can also bring cultural insights and competitive advantage to the firm, for instance by international networks that managers possess (Oxelheim and Randøy, 2003). Diversity in nationality will lead to higher firm performance, because managers have access to different sources of relevant information and are able to process it more thoroughly (Nielsen and Nielsen, 2013). Similarly, Ruigrok and Kaczmarek (2008) concluded that nationality diversity of the board and management team members has a positive impact to financial performance in the UK, the Netherlands and Switzerland for the above-mentioned reasons. Due to the fact that nationality diversity can both positively and negatively influence the firm performance, this study makes the following hypothesis contrary to H3a:
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2.3.4 Career variety
Career variety can be defined as “the array of distinct professional and institutional experiences an executive had prior to becoming a specific function” (Crossland, Zyung, Hiller and Hambrick, 2014). The amount of different experiences an executive had in their career, shows the cognitions of the person. Crossland et al. (2014) state for instance that CEOs with a high variety in their career track prefer experimentation and change, while CEOs with a low variety prefer stability and incrementalism in their career. Different extant research concluded that accumulation of career variety widens the cognitive breadth of an individual (Hambrick, Geletkanycz and Fredrickson, 1993; Dragoni, Oh, Vankatwyk and Tesluk, 2011). Variety in the career background of managers also lead to better evaluation of the alternatives and solutions to solve difficult problems. Managers in teams with a functional background heterogeneity are more adaptive in uncertain environments than managers in homogenous teams (Cannella, Park and Lee, 2008). Furthermore, Crossland et al. (2014) claim that other research has concluded that a higher career variety led to a diverse social and professional network. This can be beneficial for CSCOs, because they are responsible for creating and maintaining strategic relationships with other organizations (Wagner and Kemmerling, 2014). Another advantage of diversity in the career background has to deal with information sharing; a higher diversity leads to better information sharing, when team members are more experienced in different functional areas (Bunderson and Sutcliffe, 2002). Additionally, heterogeneity in the career background can improve the TMT’s competence to respond to environmental changes and opportunities, which is indirectly related to the performance (Cannella et al., 2008). Like the hypothesis about educational background diversity, for this hypothesis there is no competing hypothesis proposed, because the arguments and results in literature are one-sided and all predict a positive relationship (which are mentioned here). Therefore, the following hypothesis related to career variety in the CSCO-CEO interface will be hypothesized:
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2.4 Conceptual model
The hypotheses are shown in the conceptual model in figure 2. This model depicts the four upper echelon characteristics as described in the literature part in relation to the firm performance. These variables are related with each other, because they are all socio-demographic characteristics from the upper echelon theory by Hambrick and Mason (1984).
Differences in educational background
CSCO-CEO interface Differences in age
CSCO-CEO interface Differences in nationality CSCO-CEO interface Differences in career variety CSCO-CEO interface Firm performance
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3. METHODOLOGY
This chapter describes the sample used for this research and the source where the data is obtained from. Next to this, it elaborates on the dependent, independent and control variables and how these are measured. It also discusses the type of analysis that is used to perform this research and why this type is chosen.
3.1 Sample
This research uses secondary (archival) panel data which is collected from the Execucomp, BoardEx and Compustat databases. The collection of the data was done jointly with the supervisor. The supervisor collected the variables related to the CSCO and the financial data. The individual characteristics of the CEO are collected by a team of six students. The reason why we chose to use data from these databases is that it can be obtained from a reliable source and that it is publicly available. The sample is constructed form the constituents of the S&P 1500 firms ranging from 1992 to 2018. The financial firms are not included in this research, because these firms differ in accounting and regulative requirements with regard to firms in other sectors. The unit of analysis in this research is the firm year, since the research is based on panel data that contain observations over multiple time periods for the same individual. The Execucomp database is used to gather the basic information of the TMT within each firm year. In order to obtain the individual characteristics of the CSCOs, we used the BoardEx database. This database contains information about individual characteristics of all members in TMTs of public firms in the United States since 1933 and is used by many researchers that studied team composition topics (e.g. Ferreira and Kirchmaier, 2013). From this database the variables for TMT members such as educational background, number of firms worked, number of sectors worked and age can be extracted. In addition, the financial data (e.g. operating cycle) is gathered from the Compustat database.
15 collecting the individual characteristics, such as age, nationality, education and work experience from the BoardEx database for both the CSCO and CEO. Due to the fact that ExecuComp and BoardEx are two separate databases, the two databases are combined, where the same firm and individual are matched. This is done by matching the same firm out of the two databases by using the common ID (CUSIP). Thereafter, the variables for the analysis are created and collected. Finally, the collected data about the CEO variables are checked by other students from the team in other to guarantee the correctness of the data.
3.2 Measurement
3.2.1 Dependent variable
The hypotheses in this study predict one dependent variable: the firm performance. Firm performance is measured by using the operating cycle of a company. The operating cycle is closely linked to supply chain and CSCOs and is therefore chosen as dependent variable instead of for example return on assets (ROA), which is a more common variable to measure firm performance. The operating cycle refers to the time a company requires to convert its inventories to cash (Majeed, Makki, Saleem and Aziz, 2013). The necessary variables that are used to calculate the operating cycle are obtained from the Compustat database. Operating cycle is calculated using the formula as also used by Lo, Yeung and Cheng (2009). This formula is shown in Appendix A. For this research I take the natural log of the operating cycle to avoid skewness in the results.
3.2.2 Independent variables
The independent variables in this research that predict firm performance are differences in educational background between CSCO-CEO, differences in age between CSCO-CEO, differences in nationality between CSCO-CEO and differences in career variety between CSCO-CEO. All these individual characteristics are obtained from Boardex. Differences in
educational background in the CSCO-CEO interface are measured by a binary variable; if
CSCOs and CEOs have the same level of degree, this will be coded as 1, and 0 otherwise. The levels of degree used in this research are a bachelor degree, a master degree, MBA & EMBA degree and PHD&JD degree. The difference in age between CSCO and CEO is measured by using their dates of birth. The dates of birth of both the CSCO and CEO are subtracted from each other, which gives the difference in age between these two members. The nationality
16 CSCO and CEO, and 0 otherwise. The last variable is about the difference in career variety between the CSCO and CEO. For both TMT members the number of firms they worked before they joined the current firm is counted. These two numbers are subtracted from each other, which gives the difference in career variety between CEO and CSCO.
3.2.3 Control variables
There are three control variables included in this research, namely TMT size, firm size and a variable about the acquisition of a firm. In line with Roh et al. (2016), the first control variable I use is TMT size. Larger TMTs might have relatively unknown positions such as a CSCO in their TMT than smaller TMTs. Furthermore, TMT size is included as important control variable in the research of Carpenter et al. (2004) in the upper echelons research. In order to avoid skewness, I use the natural log of TMT size. Another variable I control for is the firm size, as larger firms are more likely to have a CSCO on board. The firm size is measured by taking the natural log of the total assets. The third control variable in this study is about a firm’s acquisition of another firm in a given year, similar to Roh et al. (2016) in their study. An acquisition can play a role in having a CSCO in the TMT, because acquisitions could lead to complex supply chain processes (Naor, Linderman and Schroeder, 2010). This variable is binary; 1 means that the firm has engaged in a merge and acquisition in a given year, 0 otherwise.
3.3 Data analysis
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4. FINDINGS
The hypotheses are tested by means of a regression analysis. The natural log of the operating cycle is used as the dependent variable and the different variables of the hypotheses as independent. For each hypothesis, the statistics of two models are calculated. The first model includes all control variables. The second model includes all control variables and the independent variable. By comparing these two models, one can assess whether the independent variable will improve the model. In order to test the hypothesis, the following model is used:
𝐹𝑖𝑟𝑚 𝑝𝑒𝑟𝑓𝑜𝑟𝑚𝑎𝑛𝑐𝑒 = 𝛼 + 𝛽1 ∗ 𝐼𝑛𝑑𝑒𝑝𝑒𝑑𝑒𝑛𝑡 𝑣𝑎𝑟𝑖𝑎𝑏𝑙𝑒 𝑜𝑓 𝑡ℎ𝑒 ℎ𝑦𝑝𝑜𝑡ℎ𝑒𝑠𝑖𝑠 + 𝛽2 ∗
𝑇𝑀𝑇 𝑠𝑖𝑧𝑒 + 𝛽3∗ 𝐴𝑐𝑞𝑢𝑖𝑠𝑖𝑡𝑖𝑜𝑛 + 𝛽4 ∗ 𝐶𝑜𝑚𝑝𝑎𝑛𝑦 𝑠𝑖𝑧𝑒
The descriptive statistics and correlations of all variables used in the regression analysis are presented in table 1. There is no multicollinearity between the variables, because none of the variables exceed 0.8, which is the upper level. From this it can be concluded that the correlations will not cause problems in the analysis.
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Table 1: Descriptive statistics and correlations
** Correlation is significant at the 0.01 level (2-tailed).
4.1 Education diversity
The first hypothesis is about the education difference in the CSCO-CEO interface and firm performance. The sample consists of 355 firm-year observations, after the missing values are deleted. The results of this test are shown in table 2. Both models are significant. Model 1 is significant and a F-value of 5,230 and significance of ,002. The R2 is ,043 and the adjusted R2 is ,035. Model 2 has a F-value of 3,916 and a significance of ,004. The R2 is the same as in model 1 and the adjusted R2 is ,032, so model 2 does not improve compared to model 1. The independent variable in this hypothesis has a positive relationship with regard to the operating cycle (β = ,011). This means that it has a negative effect on firm performance, because a higher operating cycle is worse for the firm. However, the test shows that this independent variable is not significant (p = 0,888). The control variable company size in both models is significant and this also applies to the control variable TMT size to a lesser extent.
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Table 2: Regression analysis education diversity hypothesis (sample: 355 observations)
* significant at p<0,05 ** significant at p<0,01
4.2 Age diversity
The second hypothesis is about the difference in age in the CSCO-CEO interface and firm performance. This sample is built on 503 observations. The results are depicted in table 3. Model 1 is significant with a F-value of 6,242 and a significance of ,000. The R2 is ,036 and the adjusted R2 is ,030, which is not very high. Model 2 is also significant with a F-value of 6,064 and significance of ,000. The R2 and adjusted R2 are higher than in model 1, model 2 improves compared to model 1. Model 2 shows that adding the independent variable, age difference, has a positive effect on the operating cycle (β = ,013) and that the variable age difference itself is significant (p < 0,05). Since it positively predicts operating cycle, it means that it has a negative effect on firm performance, because the operating cycle will increase. Also, it turns out that the control variable company size in both models is very significant and to a lesser extent the control variable TMT size in the second model. Company size is an important determinant in predicting the firm performance.
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Table 3: Regression analysis age diversity hypothesis (sample: 503 observations)
Model 1 Model 2 Variables Coefficients Std. Error Std. Error of the estimate Coefficients Std. Error Std. Error of the estimate (Constant) 4,743** ,336 4,626** ,338 Age difference ,013* ,005 TMT size -,399 ,162 -,434** ,162 Acquisition ,075 ,100 ,062 ,099 Company size ,066** ,020 ,078** ,020 F-value 6,242** 6,064** R2 ,036 ,655 ,046 ,653 Adjusted R2 ,030 ,039 * significant at p<0,05 ** significant at p<0,01 4.3 Nationality diversity
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Table 4: Regression analysis nationality diversity hypothesis (sample: 172 observations)
Model 1 Model 2 Variables Coefficients Std. Error Std. Error of the estimate Coefficients Std. Error Std. Error of the estimate (Constant) 3,982** ,596 3,945** ,600 Nationality difference -,171 ,266 TMT size -,254 ,295 -,249 ,295 Acquisition -,183 ,184 -,181 ,185 Company size ,113* ,038 ,117* ,039 F-value 3,230* 2,518* R2 ,055 ,675 ,057 ,676 Adjusted R2 ,038 ,034 * significant at p<0,05 ** significant at p<0,01 4.4 Career variety
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Table 5: Regression analysis career variety hypothesis (sample: 491 observations)
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5. DISCUSSION
The literature that investigates CSCOs in TMTs in organisations shows that CSCOs have gained in popularity in recent years. Several big companies have appointed a CSCO to their TMTs and this is still growing (Hendricks et al., 2015; Wagner and Kemmerling, 2014). This thesis aims to provide insights in the socio-demographic characteristics between the CSCO and CEO and the relationship with firm performance. The results show that diversity in socio-demographic characteristics of the upper echelon theory impact the firm performance, however this does only apply to age diversity and career variety. The other two (education and nationality diversity) tested characteristics do not improve firm performance.
5.1 Findings of the hypotheses
For the characteristic of the educational background diversity between CSCO and CEO, the regression analysis showed that there is no significant relationship between the education diversity and firm performance. Therefore, hypothesis H1 cannot be confirmed. As stated in the literature review part, differences in education between managers, should lead to a variety in knowledge bases and access to different sources of knowledge bases. Moreover, educational difference is expected to lead to heterogeneity in decision-making (Hitt and Tyler (1991). However, this is not the case as the hypothesis is rejected. Díaz-Fernández, González-Rodríguez and Pawlak (2014) tested the relationship between educational background diversity on the return on sales (ROS) and on return on assets (ROA) instead of operating cycle, did also found no significant effects. In the same vein, the results of Wu, Wei and Lau (2010) show that educational background diversity is not related to firm performance. However, they argue that, when CEOs exhibit high levels of empowering leadership, educational background diversity between team members and firm performance are related. So, CEO empowering leadership strengthens the relationship between educational background diversity and firm performance. CEO empowering leadership is about coaching and training other managers by the CEO, in this case the CSCO. Since there is an education difference between CEO and CSCO, the CSCO can learn from the CEO in the coaching process and hereby improving the work performance (Wu et al., 2010). This makes the relationship between educational background diversity and firm performance significant according to Wu et al. (2010).
24 firm performance, let alone diversity between CEO and CSCO in educational background. This can be explained by the fact that cognitive ability, which an individual should develop and shape after the completion of an education, is not an important factor in firm performance according to Gottesman and Moorey (2006). They argue that instead of education other personality traits such as charisma, collegiality and effort between managers influences collaboration and the firm performance.
Furthermore, another explanation why educational background diversity between CEO and CSCO do not improve firm performance, is due to the fact that human capital of both CEO and CSCO is an important factor in predicting firm performance. Human capital, one of the components of intellectual capital, next to structural and relational capital, is a more comprehensive concept than educational background. Human capital includes knowledge, professional skills, educational level and experience from different perspectives; Díaz-Fernández, González-Rodríguez and Simonetti (2015) argue that human capital contributes to the success of a firm. So, it is not just educational background diversity, but a combination covered under the term of human capital that include experience of CSCO and CEO amongst others that contribute to a firm’s success and performance (Díaz-Fernández et al., 2015; Cannella et al., 2008)
25 Mueller, 2002). So, it depends on the age of the CEO-CSCO pair what kind of strategy is executed related to the supply chain operations, as long as there is no age difference between the CEO and CSCO, because that is detrimental for the firm performance.
The third hypothesis is about nationality diversity in the CSCO-CEO interface and firm performance. The result of the regression analysis shows that the hypothesis H3a is not significant. The contrary hypothesis H3b is then also not significant. Therefore both hypotheses are rejected. Other studies, for example that of Darmadi (2011) found also no evidence that nationality diversity influences firm performance, although he studied nationality diversity of the entire TMT related to firm performance.
Nielsen (2009) examined the role of international diversification of a firm and the homogeneous/heterogeneous characteristics of managers. Nielsen (2009) claimed that the composition of managers in a team is dependent on the degree of complexity of a firm and its strategy, for example international diversification. The company’s strategic focus and global exposure are important determinants of choosing the right managers (Daily and Schwenk, 1996). Due to globalisation and working in environments with international operations, companies require executives with different backgrounds in nationality. Therefore, as firm internationalize, they like to see managers as a CEO and CSCO with different nationalities on board (Zaheer, 1995). This may explain why the hypothesis related to nationality is not supported. If the sample was based only on companies who face a high degree of international diversification, the hypothesis may be supported, based on the results of Nielsen (2009). Namely, then nationality diversity does matter for firm performance.
26 the network, which can be useful when disruptions occur in the supply chain for example. In that case, you always have a large network in reserve. This ensures that a firm can quickly switch between suppliers and that the daily operations can proceed, which may give a competitive advantage and benefits the firm performance (Adobor and McMullen, 2007; Jiang, Tao and Santoro, 2010). Furthermore, career diversity enhances the competence to respond to environmental changes, because the CEO and CSCO have experience in different functional areas, where they have gained knowledge to apply in new situations (Cannella et al., 2008).
With the new insights about whether differences in socio-demographic characteristics between CEO and CSCO influence firm performance, this thesis provides some theoretical contributions for scholars and academics. First, this thesis is an addition to the few existing papers about CSCOs. The existing literature about CSCOs described and investigated the relationship between firm-level characteristics and CSCOs. For instance, the paper of Roh et al (2016) investigated antecedents associated with CSCOs in TMTs. Wagner and Kemmerling (2014) did research to what extent SCM (CSCO) presence in TMTs differ across several industries. In contrast to this paper, this thesis zoomed in at individual level instead of industry level and therefore this is a contribution to the existing literature. Next to the addition to literature about CSCOs, this is also a contribution to the literature related to TMTs, for instance the paper of Carpenter (2002). This thesis may provide a new perspective for academics into the composition of TMTs with regard to the personal characteristics of a CSCO; hereby taking into account the heterogeneity of the characteristics in team composition to compose a CEO-CSCO pair that is able to work effectively.
5.2 Managerial implications
27 performance, therefore it pays of to look to career background when appointing either a CSCO or CEO to the TMT. The recruiting staff should compare the professional background to find out whether the CEO and CSCO fit together well, so that they are of value in the company. Age diversity between CEO and CSCO is detrimental for the performance, so when firms strive for optimal performance and only take into consideration this specific socio-demographic characteristic, it is better not to have CSCO-CEO pair with a difference in age on board in the TMT. Given the tasks and due to the fact that a CSCO and CEO work together and that they will make important decisions together, this could be a requirement for recruiting staff to choose that specific CSCO. Of course there are many other aspects that are equally important, but if the company has a choice between multiple candidates, this aspect can be taken into account when choosing the most suitable CSCO or CEO. From the perspective of a CSCO it is important that he has the right manager to work with, because when you form a good pair, then a CSCO can also shows his skills better, which promotes the collaboration and in turn the firm performance.
5.3 Limitations
28
6. CONCLUSION AND FUTURE RESEARCH
The main objective for this research and the aim of the research question is to find out if differences in socio-demographic characteristics in the CSCO-CEO interface improve the firm performance. It contributes to the relatively new research topic about CSCOs and their characteristics. This study also provides an extension to the upper echelon theory about the importance of individual characteristics and executives regarding firm performance. The regression analysis has shown that the four investigated socio-demographic characteristics from the upper echelon theory predict different outcomes. An age difference between CEOs and CSCOs is not beneficial for the firm’s performance. A variety in the career background between the CEO and CSCO on the contrary, promotes the firm performance. Other socio-demographic characteristics of the upper echelon theory such as nationality diversity and education diversity are not significant and therefore no conclusion can be drawn for these characteristics.
29
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35
APPENDIX A: Formula for calculating operating cycle
The following formula is used to calculate the operating cycle (as also used by Lo et al., 2009):
𝑂𝐶 = 𝐼 + 𝐴𝑅
𝑂𝐶 = operating cycle
𝐼 = number of inventory days
𝐴𝑅 = number of account receivable days
Number of inventory days:
𝐼 =365 IT
𝐼𝑇 = Inventory turnover ratio
𝐼𝑇 = 𝐶𝑂𝐺𝑆 Avg. Inv.
𝐶𝑂𝐺𝑆 = Cost of goods sold
Avg. Inv. = Average inventory balance
Number of account receivable days:
𝐴𝑅 = 365 ART
𝐴𝑅𝑇 = Account receivable turnover
𝐴𝑅𝑇 = 𝐶𝑆
Avg. AR 𝐶𝑆 = Credit sales
