The impact of CEO-CSCO differences in education and experience
on firm performance
MSc Thesis
University of Groningen, Faculty of Economics and Business
Student name: Mario-Costin Andrei Student e-mail: m.c.andrei@student.rug.nl
Student number: S3111644 Supervisor: Dr. X. (Bruce) Tong
2 Table of contents Abstract ... 4 1. INTRODUCTION ... 5 2. THEORETICAL BACKGROUND ... 9 2.1. Education ... 9 2.2. Experience ... 10 2.3. Supporting theories ... 11 3. HYPOTHESES DEVELOPMENT ... 11
3.1. Education and firm performance ... 12
3.2. Experience and firm performance... 13
3.3. The theory-practice interplay ... 14
3.4. Conceptual model ... 15
4. METHODS ... 16
4.1. Research design ... 16
4.2. Sample and data collection ... 17
4.3. Variables ... 19 4.3.1. Dependent variable ... 19 4.3.2. Independent variables ... 19 4.3.3. Control variables ... 20 4.4. Analysis ... 21 5. RESULTS ... 23
5.1. Individual effects of executives on performance ... 24
3
6. DISCUSSION ... 29
6.1. Noteworthy findings ... 29
6.2. Limitations and future research ... 32
7. CONCLUSION ... 33
8. REFERENCES ... 35
APPENDIX A. PSEUDO-CODE FOR THE DATABASE MATCHING ... 39
4 Abstract
Chief Supply Chain Officers (CSCOs) have recently become increasingly important in the composition of a top management team (TMT). They oversee the supply chain and the logistical resources and, as a result, contribute to the financial health of a company. The increase of globalization has led to more diversification among TMT members and, therefore, individual characteristics of executives are playing a more important role. Investigating the impact of differences in educational backgrounds and experiences within the CEO-CSCO arrangement on firm performance adds to the current growing body of literature. The reasoning is based on the upper echelon theory and the demography theory. The hypotheses formulated state that the differences in educational backgrounds and experience negatively impact firm performance due to the heterogeneity of individuals. Additionally, I hypothesize that the differences in experience also act as a moderator in the relationship between the differences in educational backgrounds and firm performance. An econometric analysis is conducted on a sample of 578 firm years of 175 S&P North American firms extracted from the Wharton University of Pennsylvania’s databases (Execucomp, BoardEx, Compustat). The results show that heterogeneity in experience positively impacts firm performance while heterogeneity in education has no impact. The moderating effect is, however, not statistically supported. These results reveal which individual characteristics are interesting to look at when an organization assigns executives to their TMT order to ensure synergy between them. Therefore, this study opens future avenues for further research into which additional characteristics might play a role between executives.
Keywords: Chief Supply Chain Officer, Chief Executive Officer, Education, Experience,
5 1. INTRODUCTION
The ever-increasing globalization has brought along many benefits to modern society. Companies and people alike are now more interconnected than ever due to advancements in technology and internet infrastructure. However, with this phenomenon, challenges follow along too (Intriligator, 2004). Modern organizations now have the opportunity to assign people from various cultural backgrounds and nationalities to their top management teams (TMTs). That, in turn, means that selection criteria also need to be taken into account more rigorously now than ever before. There are many factors that could lead to the downfall of a company be them external (i.e. political risks) or internal factors (i.e. improper management) (Philip, 2010). For instance, if the TMT is unfit to run the company, no matter how well the lower tier employees perform, it does not make a difference in the foreseeable future since the company will suffer anyway.
Commonly, the TMTs have several popular executive roles that are present in almost every board, namely: chief information officer (CIO), chief financial officer (CFO), chief marketing officer (CMO) and the chief executive officer (CEO), just to name a few. However, one role that has only recently been discussed by academics and companies alike is the chief supply chain officer (CSCO) (Roh, Krause, & Swink, 2016). The link between supply chain management (SCM) practices and organizational performance has been studied in the literature previously (Ellinger et al., 2011; Hsu, Tan, Kannan, & Keong Leong, 2009; Li, Ragu-Nathan, Ragu-Nathan, & Subba Rao, 2006; Tan, Kannan, Handfield, & Ghosh, 1999; Wagner, Grosse-Ruyken, & Erhun, 2012). Therefore, the role of the CSCO and its importance for the performance of organizations becomes more predominant in current times (Wagner & Kemmerling, 2014). All of the board members have to interact with each other almost on a daily basis because their decisions impact not only the company, but also the external environment of the company (Hallin & Holmström Lind, 2012). As a result, it becomes apparent that the way these TMT members interact is of paramount importance. That is because their decisions are closely related to organizational outcomes, as it has been extensively argued by Hambrick and Mason (1984) in their upper echelon theory.
6 increasing in importance and the individuals assigned to these roles are spearheading the progress and performance of the company (Hendricks, Hora, & Singhal, 2015; Roh et al., 2016). Specifically, the differences in demographic characteristics of TMTs is an area that lacks attention. Educational background differences are one such characteristic and it is virtually inexistent in the current body of literature. Heterogeneity of education has been previously discussed by scholars with the aid of the demography theory which relates the individual characteristics of executives to firm performance (Wiersema & Bantel, 1992). Due to the differences that might appear in cognitive capabilities stemming from formal education between TMT members, performance might also be impacted. This could be due to the discrepancies in decision making and cognitive abilities that come from having different educational backgrounds. Differences in experience are another aspect that has not received enough attention. Most studies have focused on organization-level experience (i.e. experience accumulated at the firm organization-level) and have omitted studying the individual experience of TMT members (Nielsen, 2010). However, the experience of executives could prove to be a valuable asset in influencing company performance. Therefore, while academic knowledge accumulated from educational trajectory is indisputably of major importance, in a fast-changing era such as the one we live in, hands-on experience plays a vital role. As a result, these particular demographic characteristics, namely education and experience, are worth investigating further in order to advance the current body of literature.
7 certain settings. This “theory-practice” dilemma has been highlighted in society recently and it poses a very important point of discussion (Bromme & Tillema, 1995). As a result, it might be that experience not only influences company performance directly, but it might also moderate the relationship between formal education and the performance of an organization. Implying that the addition of different experience levels into the already different educational backgrounds will exacerbate the negative impact of heterogeneity of education on firm performance. Thus, lowering the performance even more.
One proper starting point would be the relationship between the CEO and the CSCO in order to bridge this missing, conflicting gap in the literature. The choice for these two specific executives was made because they have roles that are of paramount importance to the company. The decisions of the CEOs are undoubtedly crucial for the company since they act as the heads of the boards of executives (Carmeli, Schaubroeck, & Tishler, 2011). On the other hand, the CSCOs have had an increasingly important role in companies. Nowadays, supply chain capabilities could be a source of competitive advantage (Krause, Youngdahl, & Ramaswamy, 2014). Additionally, this role seems to be more widely adopted by organizations and fulfills many core functions (Roh et al., 2016). Without a CSCO, it could be that the company has no one to oversee and coordinate the entirety of the supply chain which could prove detrimental to the overall performance. It has been found empirically that companies overlooking supply chain resources in a competitive market are underperforming (Ellram, Tate, & Feitzinger, 2013). A good relationship between these two executives that fulfill extremely important roles could prove to be a long-term competitive advantage for the company.
This thesis is using the upper echelon theory and the demography theory to explore the intricate relationship between differences in educational backgrounds and experience levels and firm performance. A potential moderating effect of the difference in experience between CEOs and CSCOs is also taken into account. That is because heterogeneity in experience might exacerbate the negative impact of educational differences and firm performance due to the more practical side of experience. As a result, the research question guiding this study is as follows:
8 A quantitative analysis is performed on a sample of 175 North American S&P 1500 firms that contains information about both the CEOs and the CSCOs of the respective companies. The personal data of executives has been extracted from Wharton Research Data Services’ BoardEx database. The database has been used in the past by scholars to investigate heterogeneity because it contains highly useful information regarding TMTs’ members individual information (Fang, Francis, & Hasan, 2012). The recorded presence of CSCOs in the TMT of companies has been derived from Execucomp. In total, 578 firm years (containing both sample and control firms) are analyzed. The dataset includes information about the maximum level of education achieved by the executives (e.g. BSc, MSc, MBA and PhD) and information about their prior work experience in terms of past companies and industries. Additionally, the financial data regarding the performance of each company has been collected using Compustat and it has been cross-matched with the respective firm years where both the CSCO and CEO coexisted.
By analyzing the differences between the CEO and the CSCO in terms of educational backgrounds and experience, this thesis aims to shed light on the interaction between two extremely important executive roles in the success of every company. Since there are many internal factors that could potentially influence the performance of a company, narrowing down said factors to statically meaningful ones should allow companies to make the right decisions in terms of selection criteria when picking their TMT members.
9 2. THEORETICAL BACKGROUND
Given the broad definitions of education and experience, the upcoming section aims to place the concepts in a context useful towards answering the aforementioned research question. By explaining the variables and their intricacies, the development of hypotheses is more comprehensive and the interaction between education, experience and firm performance becomes clearer.
2.1. Education
10 education could play a crucial role in the decisions of executives and as an extension, on the performance of a company (Bhagat et al., 2012).
2.2. Experience
11 2.3. Supporting theories
Upper echelon theory relates the backgrounds of the top managers of an organization to
the performance of said organization (Hambrick & Mason, 1984). Since within a TMT there are individuals with various characteristics, their way of thinking or doing, could be a reflection of the organization (Hambrick & Mason, 1984). A supplementary perspective to the upper echelon theory could be the theory of bounded rationality. It is a phenomenon that every person is confronted with. It states that we, as humans, cannot make the optimal decisions because we do not possess all the information (Simon, 1991). Therefore, the executives within a company try, to the best of their ability, to make financially sound decisions. These strategic choices have an impact on the company performance (Child, 1972). However, their backgrounds and the limited about of information they possess usually influence those decisions.
A potentially complementary theory that similarly relates the demographic characteristics of executives and firm performance is the demography theory (Stinchcombe, McDill, & Walker, 1968). Whenever a decision is made that impacts the entirety of the organization, all executives should provide their input. Therefore, the way the members of the board interact, is crucial. The personal characteristics they have could prove to be too heterogenous and throw the whole group into disarray. If the differences are too broad, making decisions might prove to be more difficult (Wiersema & Bantel, 1992). Group dynamics, consensus and within-group communication are all important elements of a decision making process (Wiersema & Bird, 1993). The composition of a TMT could contribute towards understanding the outcomes of certain decisions and how it impacts firm performance (Pfeffer, 1983).
3. HYPOTHESES DEVELOPMENT
12 3.1. Education and firm performance
Formal education of executives has been previously related to firm performance in extant literature (Bhagat et al., 2012; Darmadi, 2013). The general logic of this connection relates to the fact that education improves cognitive abilities, innovation capabilities and responsiveness to change (Bantel & Jackson, 1989; Hitt & Tyler, 1991; Kimberly & Evanisko, 1981). Additionally, organizations are now able to choose among candidates more freely due to globalization. This implies that highly educated executives might have priority over their less educated counterparts. Even so, when talking about educational differences between TMT members and their implications for the firm, there is not much literature to be found. Aside from a few studies, such as the one of Darmadi (2013) that investigates the relationships between board members’ education and firm performance, individual level differences between executives have been virtually ignored, especially between the CEO and the CSCO of an organization.
13 heterogeneity could lead to a decrease in performance (Wiersema & Bantel, 1992). This leads to the construction of the following hypothesis:
Hypothesis 1 (H1): The difference in educational backgrounds between the CEO and the CSCO negatively influences firm performance.
3.2. Experience and firm performance
Seasoned executives have encountered more unpredictable, novel situations than their less experienced counterparts and this has helped them build a considerable amount of tacit knowledge. This type of knowledge cannot be easily transferred and mostly resides within the individual (Collins, 2013). Unlike explicit knowledge gained from manuscripts (i.e. books read at the university), tacit knowledge is highly valuable and rare. Different levels of experience of executives derived from previous work lead to implementing so called routines into their current management practices (Miner, Gong, Baker, & O’Toole, 2011). In line with the upper echelons theory, the experiences of TMT members have an impact on the strategic choices and, as a result, on outcomes in terms of performance (Hambrick & Mason, 1984). According to Bromme and Tillema (1995), having worked in different industries or in a vast number of companies shapes ones vision and thinking.
14 As a result, while the CEO and CSCO both have their specific roles, their decisions undoubtedly affect the financial performance of the company. Decisions are often shaped by previous experience and they can lead to either a positive outcome or to a less positive one. A CEO faces tough decisions on a daily basis and by using his/her routines and intuition derived from experience he/she makes strategic choices for the organization. Analogously, the CSCO has the same attributes, but at a supply chain level. If the decisions of the two executives are misaligned because of increased heterogeneity in their experience levels, the performance of the company suffers. Consequentially, this leads to the conception of the following hypothesis:
Hypothesis 2 (H2): The difference in experience between the CEO and the CSCO negatively influences firm performance.
3.3. The theory-practice interplay
Both education and experience have very important roles for an individual’s executive performance and, as an extension, for a company’s performance. However, these two concepts are not mutually exclusive and there is a definite interplay between them. Bromme & Tillema (1995) suggest that there is a gap in terms of real-life application between the two and that education might be trumped by experience in a practical context. While formal education is derived from an extensive study of academic literature and it is mostly explicit, experience acquired through practice is more tacit, pragmatic and specific (Leinhardt, Young, & Merriman, 1995). That being said, education and experience have a synergistic effect that impact the way a manager makes decisions. Education offers a broad set of schematics that can aid towards finding a solution. On the other hand, experience shapes the theoretical knowledge that stems from education and makes it more specific and applicable in a practical setting (Bromme & Tillema, 1995).
15 TMT. On the other hand, the CSCO oversees the supply chain of the company and his/her decisions have been shown to have an impact on firm performance (Roh et al., 2016). If these two individuals cannot collaborate because of their heterogenous characteristics in terms of education and experience, the results could be catastrophic. A more seasoned, highly educated CEO will most likely have a hard time collaborating with a less experienced, less academically inclined CSCO. This also applies if the CSCO is the more experienced, academically endowed individual. If there is increased heterogeneity in the decision-making capabilities and intuitions, it will undoubtedly take a toll on the organization as a whole. Therefore, based on these arguments, the following hypothesis has been constructed:
Hypothesis 3 (H3): The CEO-CSCO difference in experience will exacerbate the relationship between the CEO-CSCO difference in educational backgrounds and firm performance
3.4. Conceptual model
16 4. METHODS
The next section defines the data used by this thesis and also describes the data collection process in detail. Additionally, the independent, dependent and control variables are operationalized. Thereafter, the section concludes by describing the analysis procedure used.
4.1. Research design
This thesis uses archival data to investigate the relationships between the variables. Existing secondary data collected from a reputable database fits the methodological approach of this thesis in several different ways. First and foremost, the CEO-CSCO differences need to be examined over a longer period of time, not just in one specific time setting. Therefore, unlike primary data which is usually collected by the researchers themselves (i.e. surveys), archival data contains a larger backlog of observations that spans across multiple years. This is of great importance for the analysis of this research question because it allows a wider and more reliable view of the relationships between the variables. Secondly, for the limited scope of this thesis,
17 research resources are rather scarce and the convenience and availability of secondary data such as the database from the Wharton University of Pennsylvania provides an appropriate data source. For instance, Execucomp, one of the databases provided by Wharton, provides extensive information regarding the TMT for each year. Lastly, existing papers investigating the relationships between CSCOs and firm performance also relied on secondary data such as the one provided on Compustat for their analysis (Bhagat et al., 2012; Roh et al., 2016; Wagner & Kemmerling, 2014). As a result of these precedents, it is only reasonable to also make use of the same data type for the scope of this thesis. In light of the aforementioned arguments for using archival data and the hypotheses developed in the previous section, this thesis uses a deductive econometric analysis approach.
4.2. Sample and data collection
For the scope of this thesis, a population of 1500 S&P North American firms is considered. The sample that was selected out of the population consisted of organizations that appointed a CSCO to their TMT. Additionally, the sample was further reduced by excluding financial firms due to their very different accounting and regulative requirements in comparison to firms in other industries. The end result consists of a sample of 175 S&P firms that have appointed a CSCO between the years 1992 and 2018. In total, 578 firm years have been established to fulfill the aforementioned criteria. A firm year represents a calendar year wherein both a CEO and CSCO coexisted in the TMT. As a result of this, the unit of analysis of this thesis is the CEO-CSCO coexistence in the same firm year. It comes as no surprise that throughout the years, the CEO or the CSCO might change at the TMT level. Therefore, these differences have been accounted for by selecting multiple firm years regardless if the CEO or the CSCO have changed. In simpler terms, this entails that even though the CEO, the CSCO or both have changed throughout a firm’s years, the place takers of these executives are considered for analysis regardless. Logically, companies change their TMTs all the time so discarding data simply because the CEO or the CSCO has not remained the same throughout the firm years is waste of data.
18 the executives (e.g. age, educational background, professional background, gender) has been extracted using BoardEx while more general, basic information regarding the names, titles and TMT sizes has been collected using Execucomp. Furthermore, the operational performance, as measured by the operating cycle, spanning the 578 firm years has been collected from the Compustat database. The databases used by this thesis have been used in the past by other scholars exploring the TMT interactions and firm performance (Bhagat et al., 2012; Dezsö & Ross, 2012; Fang et al., 2012; Roh et al., 2016). The databases provide useful and reliable information regarding individual level characteristics of executives at the TMT level and firm key performance indicators (KPIs) such as operating cycle.
In order to correctly identify the appointment of CSCOs to a firm’s TMT, the title of their position needs to be either, “Interim CSCO”, “Chief Supply Chain Officer” or “CSCO”. This has been done because the position might be mistaken with that of the Chief Operations Officer (COO). There has to be a clear distinction made between the two executive roles because the COO generally oversees the internal operations of a company while the CSCO focuses on the supply chain level of the firm. This is in line with the identification criteria used by Roh et al. (2016) in their paper. Analogously, in order to correctly identify the CEOs that coexisted in the same firm years with the CSCO, the title of their position had to be “Interim CEO”, “Chief Executive Officer” or “CEO”. Titles such as “Division CEO” have not been taken into consideration because they have niche role specifications and do not oversee the company as a whole.
19 researchers and cross-checking has been done consistently and constantly across the entirety of the database. Therefore, the reliability of the data collection is improved.
4.3. Variables
4.3.1. Dependent variable
For the scope of this thesis, the dependent variable namely, firm performance, is operationalized through the use of the operating cycle. Another option that would have been feasible in this context would have been the return on assets (ROA). However, the latter is more general. In this particular thesis, the operating cycle is more fitting because it is more closely related to the domain of the CSCO. Additionally, it is still suitable for measuring the overall performance of the firm. The operating cycle concerns the summation of manufacturing, delivery and payment fulfillment time. This further results in the sum of the number of inventory days and the number of accounts receivable as stipulated by Lo, Yeung, and Cheng (2009). As a result, this performance measure represents an amalgamation capturing general operational performance. If the operating cycle decreases that means, in turn, that the performance of the firm increases. Appendix B shows the mathematical formula for calculating the operating cycle. The data regarding each firm year’s operating cycle has been extracted from the Compustat database. Additionally, in order to prevent skewness of the data, the natural logarithms of the values are computed.
4.3.2. Independent variables
20 CEO has superior cognitive capabilities given his higher achieved level (i.e. PhD) in comparison to the CSCO (i.e. BSc). Naturally, the numbers from 1 to 4 assigned to the education levels are labels. This is done in order to make sense of a complicated topic that is educational achievement. If such substantial differences are more common in the sample, the negative impacts of heterogeneity surely become obvious. Previous studies have taken the prestige of certain universities or degrees as a measurement of education (Bhagat et al., 2012; Jalbert, Rao, & Jalbert, 2011). However, for the scope of this thesis, the absolute values in terms of formal educational achievements could prove to be more beneficial. That is because they provide more clarity and since this division in the numerical values of the degrees is standardized, it should yield better results.
Experience is a broad notion but, as previously defined, for the scope of this thesis, it is operationalized as the past experience in terms of the number of different firms worked at previously by the CEO and CSCO. By computing the difference in the number of firms worked previously between the CEO and CSCO, it becomes clearer if their different backgrounds affect firm performance. Once again, for the sake of clarity, assuming the CEO has worked at 6 different firms before being appointed CEO at the firm where the CSCO is also present and the CSCO only worked at 3 different firms, the difference between them becomes 3. Naturally, this numerical difference means nothing unless placed in a context. Therefore, a large difference between the CEO and the CSCO of a firm in terms of firms previously worked at suggests a high level of heterogeneity in terms of intuitions and ways of approaching management issues. Should a repeated number of large differences in experience lead to a negative/reduced firm performance, then can we conclude that discrepancies in experience levels have a negative impact.
4.3.3. Control variables
21 dichotomous variable is coded as “1” and otherwise as “0”. Secondly, differences in age of the executives are controlled for. Large heterogeneity in terms of age has been shown in the past to have a negative effect on the performance of an organization by Richard and Shelor (2002). Given the broad differences in generations and mentalities it could be extrapolated that they might have a negative effect firm performance. Thirdly, the BoardEx database displays the previous functions held by the executives. Therefore, in the case of the CEO, the effect of previous experience as COO is controlled for. Even though the COO has a fundamentally different role from the CSCO, there is slight overlap between the two. Whenever the CEO has been found to have COO experience in another company, the variable took the value of “1” and if there was no prior COO experience, the variable was coded as “0”.
At the firm level, the size of the TMT is controlled for. As previously mentioned, there are many executive roles present on the board. In turn this means that as the TMT size increases, the individual weights of the decisions of each executive are diminished. Additionally, West and Anderson (1996) have found a correlation between TMT size and radical innovation. This type of innovation could prove to be beneficial for firm performance. On the other hand, a larger TMT might lead to internal conflicts due to creation of subgroups. Increased diversity could prove to be too much for some individuals so they choose to form groups where they feel more comfortable (Lau & Murnighan, 1998). For this control variable, the values are calculated as the natural logarithm. Lastly, the presence of a COO is controlled for. Due to the slight overlap of the roles, the presence of a COO might affect the performance of the CSCO (Roh et al., 2016). Therefore, if a COO is present it might be that the relationship between the CEO and the CSCO degrades more. Whenever a COO is present the dummy variable took the value of “1” and whenever it was absent it took the value of “0”.
4.4. Analysis
23 Table 1. Summary of statistics and correlation matrix
5. RESULTS
Prior to conducting the analysis, the correlations between the variables have been checked and the assumptions of the linear regression have been verified. All the assumptions of the linear regression were successfully met. For instance, the data is homoscedastic suggesting that the residuals are equal throughout the regression line. Additionally, after checking for multicollinearity, there seems to be no evidence of it since the value of the variance inflation factor (VIF) remains consistently below the value of 3. The summary of statistics and the correlation matrix is presented in Table 1.
24 Table 2. Sample distributions – Education and experience levels
5.1. Individual effects of executives on performance
As previously mentioned, before analyzing the effect of differences between the CEO-CSCO arrangement on the performance, the individual effects of the executives will be investigated. A descriptive analysis of the independent variables reveals that the CEOs and CSCOs of the companies seem to, on average, have a similar educational background and a similar level of experience. Both executives appear to have finished a MSc degree on average (i.e. M = 2.16 for CEOs, M = 2.12 for CSCOs). Similarly, both executives seem to have worked somewhere in
between 3 to 4 (i.e. M = 3.42 for CEOs, M = 3.83 for CSCOs) different firms before working in
the same company together. However, the CEOs’ experience levels seem to deviate more
compared to the CSCOs’ experience levels. (i.e. SD = 2.84 for CEOs, SD = 2.07 for CSCOs). The
sample distributions are presented in Table 2.
25
Table 3. Linear regression results – The impact of education and experience of CEOs on firm performance performance. When it comes to the control variables, there are no statistically significant findings
26
Table 4. Linear regression results – The impact of education and experience of CSCOs on firm performance
Table 5. Sample distributions – Differences in education and experience 5.2. The effect of differences on performance
When analyzing the differences in educational backgrounds and experience levels between the executives, slight discrepancies between the two become more apparent. On average, it appears as if the CEOs have slightly higher academic achievements (M = 0.1455), while the CSCOs seem to have slightly more prior experience (M = -0.4096). The small differences in experience are to be noted because, on average, the CEOs are older than the CSCOs (M = 3.16). Therefore, this means that even though the CSCOs are younger, they have worked at more firms prior to working at the firm they are working at with the CEOs analyzed. The rest of the information regarding the sample distribution of the differences is also presented in Table 6.
27
Table 6. Linear regression results – The impact of differences in education and experience between CEO-CSCO on firm performance
After regressing the differences in educational backgrounds and experience on the operating cycle the results reveal no statistically significant effect of educational differences (β = -0.056; SE = 0.044; p = 0.201) and no moderation effect (β = 0.000; SE = 0.010; p = 0.986). However, the differences in experience do seem to positively impact firm performance rather than negatively (β = -0.032; SE = 0.012; p = 0.008). In terms of control variables, none of them have any effect on company performance within the CEO-CSCO arrangement. Overall, the models are a proper fit for the data since the F-test is F (9, 359) = 2.247; p = 0.019 and the R2 = 0.053.
29 6. DISCUSSION
6.1. Noteworthy findings
The findings of this thesis reveal several interesting results within the CEO-CSCO arrangement. First of all, the individual impact of the CSCO appears to be more noticeable than that of the CEO. The higher the education level of a CSCO the more negative the impact on performance seems to be. This contradicts the link previously made between an increased educational level and firm performance. The arguments of previous scholars regarding the fact that higher educational achievements increase cognitive capabilities are still logically sound (Bantel & Jackson, 1989; Hitt & Tyler, 1991; Kimberly & Evanisko, 1981). However, it appears as if the educational level of the CSCO actually decreases performance when measured by the operating cycle. A plausible explanation for this finding might be that education might actually hinder problem solving in a practical context as Bromme and Tillema (1995) suggested. Theoretical knowledge is more abstract than its counterpart, experience. Therefore, a practical, novel problem that has not come up previously in an academic context might confuse an individual that only has theory as a point of reference. The moderating effect of experience on the relationship between educational background and firm performance is statistically supported in the case of the CSCOs because it weakens the effect of education on firm performance. This is in line with the arguments put forward by Bromme and Tillema (1995) suggesting that experience might trump education in practice. Needless to say, both variables are important but it appears as if experience moderates the relationship between the educational background and firm performance, at least in the case of the CSCO. However, in the case of the CEO, experience positively impacts performance suggesting that the routines and intuitions derived from extensive experience might make the CEO more knowledgeable and able to solve problems on the go by using his/her extensive databank of experiences. Therefore, the higher the level of experience of the CEO, the more positive the performance of the company as a whole.
30 level of an organization, MBAs can be beneficial if achieved by CSCOs. This is indeed in line with the arguments put forward by Wiersema and Bantel (1992). Similarly, if a CEO has had prior COO experience, the CSCO will have a positive impact on performance. However, this contradicts the arguments of Roh et al. (2016) that state that given the overlap between the roles of COOs and CSCOs, the subsequent performance of CSCOs might be negatively affected. In this case, a plausible explanation might be that cumulative similar experience and overlap (COOs are concerned with the internal operations of the company thereby also overseeing logistics in some regard) of both the CEO and CSCO might help the company rather than damage it. Since the presence of a COO within a TMT does not seem to affect the performance of the CSCO, it can be that in actuality, the CSCO is not fazed by a COO and his/her presence within the company has no implications on how the CSCO performs. Therefore, this contradicts the arguments of Roh et al. (2016).
31 “theory-practice” dilemma put forward by Bromme and Tillema (1995) is not relevant within the CEO-CSCO arrangement. There appears to be a proper synergy between the CEO and the CSCO in all regards at least within this specific sample. Additionally, the control variables that at the independent executive level appeared to have some sort of impact on firm performance, completely lose their statistical significance when the two executives are studied together. Therefore, in line with the arguments of Hambrick and Mason (1984) on their upper echelon theory, the backgrounds of the managers do influence the organization. And, at least in the case of the CSCOs, their backgrounds appear to have more impact on the performance of the organization than the differences between them and the CEOs. This might be because the CSCOs act as boundary spanners for their organizations and they co-create the formulation of business strategies, policies and processes (Roh et al., 2016). As a result, they operate right at the edges of the organizations and they aim to achieve collaboration across strategic business units (SBUs) in order to attain goals at the group-level (Roh et al., 2016). Additionally, the operating cycle might be more related to the activities of the CSCOs, even though it is still a firm performance measurement. Similarly, the demography theory also suggests that the individual heterogenous demographic characteristics of managers can have an impact at the firm level (Wiersema & Bantel, 1992). However, at least in this case, heterogeneity in experience appears to be a positive influence on firm performance since it decreases the operating cycle.
32 companies can try to find individuals with heterogeneity in experience levels in order to prevent a negative effect on the performance of the organization. Additionally, the characteristics of CSCOs seem to have a more substantial impact on firm performance in comparison to the characteristics of the CEOs. This entails that companies should pay more attention to their supply chain executives when it comes to the financial wellbeing of the organization.
6.2. Limitations and future research
When it comes to the limitations of this thesis, there are several notable ones. Firstly, this thesis only investigates a couple of differences that exist between executives, namely: education and experience. However, there are several other differences between individuals that could potentially influence the performance of a firm. For instance, cultural diversity is one such characteristic that has been shown to influence firm performance in certain contexts (Richard, Barnett, Dwyer, & Chadwick, 2004). Secondly, the sample used for this thesis is comprised entirely out of companies in the S&P 1500. This implies that the selected organizations are all substantially bigger than an average company. Therefore, the findings of this thesis cannot be generally applicable to all companies in the world because not all are as sizeable and established as the companies in the S&P 1500. Thirdly, the final sample revealed that between the CEOs and the CSCOs, there were little to no differences in terms of educational backgrounds. The variance was rather low and the average educational achievements of both the CEOs and the CSCOs seemed to be around the MSc level. This entails that most of the executives are highly educated and there is little to no difference in the selected sample. Additionally, not only is the sample of this thesis comprised out of large companies, it is also a rather small sample. After filtering and triaging the data, the final sample ended up spanning 578 firm years. While in itself the sample is rather large, it is not substantial enough in order to make an inference about the entire population of CEOs and CSCOs. Lastly, the sample has been extracted from North American firms, thereby excluding the other companies in other parts of the world. As a result, this could also have an impact on the generalizability of the findings.
33 organization. As a result, it opens a lot of avenues for researchers to explore in the future. For instance, certain factors such as the prestige of the academic institution from which the executives obtained their diplomas might be interesting to investigate. Bhagat et al. (2012) looked into something similar and their analysis yielded compelling results. Culture is also a highly intriguing and still debated characteristic in the current body of literature. The heterogeneity in cultural backgrounds has been studied by researchers the likes of Richard et al. (2004). Their analysis revealed that heterogeneity in culture has an impact on performance, depending on the context. That being said, Hofstede’s cultural dimensions could be a potential useful tool to investigate the impact that heterogenous cultural backgrounds could have on a firm’s performance. Wharton’s database provides the country of birth of most executives and it also provides the country where the company operates. Furthermore, given the fact that the database from which the sample was extracted is available online, the replicability of this thesis’s findings is increased. This implies that future researchers can study more characteristics of executives and the impact of the differences between them alongside multiple variables of interest.
7. CONCLUSION
35 8. REFERENCES
Bantel, K. A., & Jackson, S. E. 1989. Top management and innovations in banking: Does the composition of the top team make a difference? Strategic Management Journal.
https://doi.org/10.1002/smj.4250100709.
Barro, R. J., & Lee, J. W. 2013. A new data set of educational attainment in the world, 1950-2010. Journal of Development Economics. https://doi.org/10.1016/j.jdeveco.2012.10.001. Bhagat, S., Bolton, B. J., & Subramanian, A. 2012. CEO Education, CEO Turnover, and Firm
Performance. SSRN Electronic Journal. https://doi.org/10.2139/ssrn.1670219.
Bromme, R., & Tillema, H. 1995. Fusing experience and theory: The structure of professional knowledge. Learning and Instruction, 5(4): 261–267.
Carmeli, A., Schaubroeck, J., & Tishler, A. 2011. How CEO empowering leadership shapes top management team processes: Implications for firm performance. Leadership Quarterly. https://doi.org/10.1016/j.leaqua.2011.02.013.
Child, J. 1972. Organizational Structure, Environment and Performance: The Role of Strategic Choice. Sociology. https://doi.org/10.1177/003803857200600101.
Collins, H. 2013. Tacit and Explicit Knowledge. Tacit and Explicit Knowledge. https://doi.org/10.7208/chicago/9780226113821.001.0001.
Darmadi, S. 2013. Board members’ education and firm performance: evidence from a
developing economy. International Journal of Commerce and Management, 23(2): 113– 135.
Dezsö, C. L., & Ross, D. G. 2012. Does female representation in top management improve firm performance? A panel data investigation. Strategic Management Journal.
https://doi.org/10.1002/smj.1955.
Dreyfus, H. L., Drey-fus, S. E., & Zadeh, L. A. 2008. Mind over Machine: The Power of Human Intuition and Expertise in the Era of the Computer. IEEE Expert.
https://doi.org/10.1109/mex.1987.4307079.
Dyke, L., & Fischer, E. 1992. An Inter-Industry Examination of the Impact of Owner Experience on Firm Performance. Journal of Small Business Management.
Ellinger, A. E., Natarajarathinam, M., Adams, F. G., Brian Gray, J., Hofman, D., et al. 2011. Supply chain management competency and firm financial success. Journal of Business
Logistics. https://doi.org/10.1111/j.2158-1592.2011.01018.x.
Ellram, L. M., Tate, W. L., & Feitzinger, E. G. 2013. Factor-Market Rivalry and Competition for Supply Chain Resources. Journal of Supply Chain Management.
https://doi.org/10.1111/jscm.12001.
Fang, Y., Francis, B., & Hasan, I. 2012. More than Connectedness – Heterogeneity of CEO Social Network and Firm Value. SSRN Electronic Journal.
36 Fingesten, P. 1968. The tacit dimension ?: Garden City, N.Y., Doubleday and Co., 1966, 108
pages. Journal of Communication Disorders. https://doi.org/10.1016/0021-9924(68)90018-X.
Georgakakis, D., Greve, P., & Ruigrok, W. 2018. Differences that matter: hiring modes and demographic (dis)similarity in executive selection. International Journal of Human
Resource Management. https://doi.org/10.1080/09585192.2018.1496126.
Gottesman, A. A., & Morey, M. R. 2011. Does a Better Education Make For Better Managers? An Empirical Examination of CEO Educational Quality and Firm Performance. SSRN
Electronic Journal. https://doi.org/10.2139/ssrn.564443.
Hallin, C., & Holmström Lind, C. 2012. Revisiting the external impact of MNCs: An empirical study of the mechanisms behind knowledge spillovers from MNC subsidiaries.
International Business Review. https://doi.org/10.1016/j.ibusrev.2010.12.003.
Hambrick, D. C., & Mason, P. A. 1984. Upper Echelons: The Organization as a Reflection of Its Top Managers. Academy of Management Review, 9(2): 193–206.
Hamori, M., & Koyuncu, B. 2015. Experience matters? The impact of prior CEO experience on firm performance. Human Resource Management. https://doi.org/10.1002/hrm.21617. Hendricks, K. B., Hora, M., & Singhal, V. R. 2015. An empirical investigation on the
appointments of supply chain and operations management executives. Management
Science. https://doi.org/10.1287/mnsc.2014.1987.
Hitt, M. A., & Tyler, B. B. 1991. Strategic decision models: Integrating different perspectives.
Strategic Management Journal. https://doi.org/10.1002/smj.4250120502.
Hsu, C. C., Tan, K. C., Kannan, V. R., & Keong Leong, G. 2009. Supply chain management practices as a mediator of the relationship between operations capability and firm performance. International Journal of Production Research.
https://doi.org/10.1080/00207540701452142.
Intriligator, M. D. 2004. Globalization of the world economy: Potential benefits and costs and a net assessment. Journal of Policy Modeling.
https://doi.org/10.1016/j.jpolmod.2004.04.004.
Jalbert, T., Rao, R., & Jalbert, M. 2011. Does School Matter? An Empirical Analysis Of CEO Education, Compensation, And Firm Performance. International Business & Economics
Research Journal (IBER). https://doi.org/10.19030/iber.v1i1.3882.
Kimberly, J. R., & Evanisko, M. J. 1981. Organizational innovation: the influence of individual, organizational, and contextual factors on hospital adoption of technological and
administrative innovations. Academy of Management Journal. Academy of Management. https://doi.org/10.2307/256170.
Krause, D., Youngdahl, W., & Ramaswamy, K. 2014. Manufacturing - Still a missing link?
Journal of Operations Management. https://doi.org/10.1016/j.jom.2014.09.001.
37 https://doi.org/10.5465/AMR.1998.533229.
Leinhardt, G., Young, K. M. C., & Merriman, J. 1995. Integrating professional knowledge: The theory of practice and the practice of theory. Learning and Instruction.
https://doi.org/10.1016/0959-4752(95)00025-9.
Levinson, H. 1993. Between CEO and COO. Academy of Management Perspectives, 7(2): 71– 81.
Li, S., Ragu-Nathan, B., Ragu-Nathan, T. S., & Subba Rao, S. 2006. The impact of supply chain management practices on competitive advantage and organizational performance. Omega. https://doi.org/10.1016/j.omega.2004.08.002.
Lo, C. K. Y., Yeung, A. C. L., & Cheng, T. C. E. 2009. ISO 9000 and supply chain efficiency: Empirical evidence on inventory and account receivable days. International Journal of
Production Economics. https://doi.org/10.1016/j.ijpe.2008.11.010.
Melone, N. P. 1994. Reasoning in the Executive Suite: The Influence of Role/Experience-Based Expertise on Decision Processes of Corporate Executives. Organization Science.
https://doi.org/10.1287/orsc.5.3.438.
Miner, A. S., Gong, Y., Baker, T., & O’Toole, J. 2011. How does TMT prior experience shape strategy? A routine-based framework based on evidence from founding teams. The
Handbook of Research on Top Management Teams.
Nielsen, S. 2010. Top management team internationalization and firm performance.
Management International Review. https://doi.org/10.1007/s11575-010-0029-0.
Pfeffer, J. 1983. Organizationla Demography. Research in Organizational Behavior.
Philip, M. 2010. Factors affecting business success of small & medium enterpresies (SMEs) Volume 1 , Issue 2 ( November , 2010 ). Apjrbm, 1(2).
Richard, O. C., Barnett, T., Dwyer, S., & Chadwick, K. 2004. Cultural diversity in management, firm performance, and the moderating role of entrepreneurial orientation dimensions.
Academy of Management Journal. https://doi.org/10.2307/20159576.
Richard, O. C., & Shelor, R. M. 2002. Linking top management team age heterogeneity to firm performance: Juxtaposing two mid-range theories. International Journal of Human
Resource Management. https://doi.org/10.1080/09585190210134309.
Roh, J., Krause, R., & Swink, M. 2016. The appointment of chief supply chain officers to top management teams: A contingency model of firm-level antecedents and consequences.
Journal of Operations Management. https://doi.org/10.1016/j.jom.2016.05.001.
Schön, D. A. 2017. The reflective practitioner: How professionals think in action. The Reflective
Practitioner: How Professionals Think in Action. https://doi.org/10.4324/9781315237473.
Simon, H. A. 1991. Bounded Rationality and Organizational Learning. Organization Science. https://doi.org/10.1287/orsc.2.1.125.
38
of Management Learning and Education, 9(3): 429–441.
Song, J. H. 1982. Diversification Strategies and the Experience of Top Executives of Large Firms. Strategic Management Journal. https://doi.org/10.1002/smj.4250030411. Stinchcombe, A. L., McDill, M. S., & Walker, D. R. 1968. Demography of Organizations.
American Journal of Sociology. https://doi.org/10.1086/224635.
Tan, K. C., Kannan, V. R., Handfield, R. B., & Ghosh, S. 1999. Supply chain management: An empirical study of its impact on performance. International Journal of Operations and
Production Management. https://doi.org/10.1108/01443579910287064.
Thomas, A. S., Litschert, R. J., & Ramaswamy, K. 1991. The performance impact of strategy ‐ manager coalignment: An empirical examination. Strategic Management Journal. https://doi.org/10.1002/smj.4250120704.
Volberda, H. W. 1997. Building Flexible Organizations for Fast-moving Markets. Long Range
Planning. https://doi.org/10.1016/s0024-6301(96)00110-0.
Wagner, S. M., Grosse-Ruyken, P. T., & Erhun, F. 2012. The link between supply chain fit and financial performance of the firm. Journal of Operations Management.
https://doi.org/10.1016/j.jom.2012.01.001.
Wagner, S. M., & Kemmerling, R. 2014. Supply chain management executives in corporate upper echelons. Journal of Purchasing and Supply Management.
https://doi.org/10.1016/j.pursup.2014.01.006.
West, M. A., & Anderson, N. R. 1996. Innovation in top management teams. Journal of Applied
Psychology. https://doi.org/10.1037/0021-9010.81.6.680.
Wiersema, M. F., & Bantel, K. A. 1992. Top Management Team Demography and Corporate Strategic Change. Academy of Management Journal. https://doi.org/10.5465/256474. Wiersema, M. F., & Bird, A. 1993. Organizational Demography in Japanese Firms: Group
Heterogeneity, Individual Dissimilarity, and Top Management Team Turnover. Academy of
39 APPENDIX A. PSEUDO-CODE FOR THE DATABASE MATCHING
Require 𝐷𝑎, the database with individuals need to be matched and 𝐷𝑏, the database with candidates.
Require T, the threshold to determine whether a and b match.
Create an empty data frame df_out to save results;
for each individual i in 𝐷𝑎 do
Get name na from individual i’s ‘peoplename_a’ attribute;
Delete invalid characters in name na and update name na;
Get firmid ida from individual i’s ‘firmid_a’ attribute;
Create a matched list L_match_result; Append individual i to L_match_result;
Create a temporary empty list L_candidates;
for each candidate j in 𝐷𝑏 do
if candidate j’s ‘firmid_b’ attribute is equal to ida then
Get name nb from candidate j’s ‘peoplename_b’ attribute;
Delete invalid characters in name nb and update name nb;
Get Lengthmax, the maximum length of namea and nameb
Calculate Lev, the Levenshtein distance between namea and nameb;
Get SimilaritySusing equation: S = 1-Lev/Lengthmax; if S is greater than threshold T then
Append candidate j to L_candidates;
end if end if
end for
Get the most similar candidate C_macthed of L_candidates using Levenshtein distance
Append the most similar candidate C_macthed to the matched list L_match_result;
Append the matched list L_match_result to result dataframe df_out;
40 APPENDIX B. MATHEMATICAL FORMULA FOR CALCULATING THE
OPERATING CYCLE (ADOPTED FROM Lo et al. (2009)
Operating cycle= Number of inventory days + Number of account receivable days OC=I+AR
Number of inventory days:
I=365 𝐼𝑇
IT= 𝐶𝑂𝐺𝑆 𝐴𝑣𝑔.𝐼𝑛𝑣.
Number of account receivable days:
AR=365 𝐴𝑅𝑇
