The Impact of Leader Experience on Organizational Success Master Thesis

ALL ABOUT THE COACH - THE IMPACT OF LEADER EXPERIENCE ON ORGANIZATIONAL SUCCESS

Felix Anton RHOMBERG Student registration number: 01515047 Strategic Management

MASTER THESIS

Submitted in partial fulfillment of the requirements for the degree in Strategic Management

At the Leopold-Franzens-Universität Innsbruck

Supervisor: Univ.-Prof. Mag. Mag. Dr. Julia Rapp-Hautz

Department of Strategic Management, Marketing, and Tourism

Innsbruck, 25.05.2021

I. Abstract

Over the last four decades, there has been a growing body of literature on the Upper Echelons Theory. Research in the field has found a plethora of CEO characteristics which influence firm performance. However, there is still scant knowledge about the different types of CEO experience. To analyze the influence of leaders' experience on organizational performance, this master thesis uses a dataset consisting of soccer coaches in the top10 soccer leagues of Europe over ten consecutive seasons. The results show that a diverse career path within the organization improves team performance, whereas longer CEO organizational job tenure leads to decreased team performance.

II. Table of content

I. Abstract ...... I

II. Table of content ...... II

III. List of figures ...... IV

IV. List of tables ...... V

V. List of Abbreviations ...... VI

1. Introduction ...... 1

1.1. Research Gap ...... 1

1.2. Method ...... 2

1.3. Structure of the master thesis ...... 3

2. Upper Echelons Theory ...... 4

2.1. Types of characteristics...... 7

2.1.1. Observable characteristics ...... 8

2.1.2. Underlying characteristics ...... 9

2.1.3. Interactions with others ...... 10

2.2. Levels of influences ...... 10

2.3. CEO experience ...... 11

2.4. CEO experience in Upper Echelons Theory ...... 14

2.4.1. CEO age ...... 20

2.4.2. CEO tenure ...... 21

2.4.3. CEO prior career path ...... 24

2.5. Sport industry ...... 27

3. Hypothesis development ...... 29

3.1. Amount ...... 29

3.2. Time ...... 30

3.3. Type ...... 31

4. Empirical Research ...... 33

4.1. Data collection...... 33

4.2. Sample description ...... 34

4.3. Research model ...... 37

4.4. Measurements ...... 40

4.4.1. Dependent variable ...... 40

4.4.2. Independent variables ...... 41

4.4.3. Control variables ...... 44

4.4.4. Correlation after Pearson ...... 46

5. Data analysis ...... 48

5.1. Regression analysis ...... 48

5.2. Summary of results ...... 51

6. Discussion ...... 55

6.1. Implications...... 55

6.2. Limitation and further research suggestions ...... 61

7. Conclusions ...... 67

I. References ...... I

II. Appendix ...... XII

II.I. Web scraping code used in R ...... XII

II.II. List of all observed coaches ...... XLIX

III. Affidavit ...... LIV

III

III. List of figures

Figure 1: Structure of this master thesis; Source: Own elaboration ...... 3 Figure 2: Strategic Choice Under Conditions of Bounded Rationality; Source: Hambrick & Mason (1984) p. 195 ...... 5 Figure 3: An Upper Echelons Perspective of Organizations; Source: Hambrick & Mason (1984) p. 198 ...... 6 Figure 4: An Updated Upper Echelons Research Perspective; Source: Bromiley & Rau (2016) p. 177 ...... 7 Figure 5: A Conceptual Framework of Work Experience Measures; Source: Quinones et al. (1995) p. 892 ...... 13 Figure 6: Research model; Source: Own elaboration ...... 40 Figure 7: Breusch and Pagagan Lagrange Multiplier Test; Source: Own elaboration ...... 49 Figure 8: Hausman Test; Source: Own elaboration ...... 49

IV. List of tables

Table 1: An integrated framework of work experience; Source: Quinones et al. (1995) and Tesluk and Jacobs (1998) ...... 14 Table 2: Summarizing table of research in the are of CEOs’ prior work experience; Source: Own elaboration ...... 19 Table 3: Summary of league attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de ...... 35 Table 4: Summary of team attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de ...... 36 Table 5: Summary of coach attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de ...... 37 Table 6: Work experience framework related to this master thesis; Source: Own elaboration based on the dataset gathered from transfermarkt.de ...... 39 Table 7: Correlation matrix; Source: Own elaboration ...... 47 Table 8: Results of regression analysis; Source: Own elaboration ...... 50 Table 9: Summary table of Hypotheses and their support; Source: Own elaboration ...... 54 Table 10: Descriptive statistics of CEOs' organizational job tenure and organizational success; Source: Own elaboration ...... 63 Table 11: Proposal for CEO prior work experience framework for future studies; Source: Own elaboration ...... 65 Table 12: List of all observed coaches; Source: Own elaboration ...... LIII

V. List of Abbreviations

CEO Chief Executive Officer e.g. exempli gratia / for example

EO entrepreneurial orientation et al. et alii / and others i.e. id est / that is

Inc. Incorporated

OLS Ordinary Least Square

R&D Research & Development

TMT Top Management Team

UER Upper Echelons Research

UET Upper Echelons Theory

1. Introduction

This master thesis analyzes the link between CEOs’ prior work experience and organizational performance. The first section will introduce the topic of the UET and pinpoint the research gap. Based on the shortcomings of previous literature, this section will then develop the research question. This will be followed by a summary of the research method applied in this study. At the end of this section, the structure of the master thesis will be outlined and illustrated in greater detail.

1.1. Research Gap

Elon Musk – CEO of Tesla Inc., SpaceX, and many other companies – is a perfect example of how strong CEOs can impact organizational success. By now, he has established a huge fan base, which he keeps posted via his Twitter account. Therefore, he reaches over 45 million people worldwide every day (Musk, 2021). His media presence is one reason he does not plan for a marketing budget in his companies. In his opinion, his name is advertisement enough for the company (Koch, 2020). His rise to one of the richest men in the world proves him right (Handelsblatt, 2021). What this example shows us was already described in theory by Hambrick and Mason (1984) years earlier. They developed the UET, thereby attempting to explain firm performance based on CEOs’ background characteristics.

Developed forty years ago, this theory and the underlying topic is still fascinating and crucial to understand business success. Consequently, countless researchers have undertaken research in the field over the past four decades. To this end, they focused on the CEO's observable characteristics and analyzed how and to what extent those variables help describe firm strategic actions and firm success. Among others, they investigated observable facts such as CEO age (Grimm & Smith, 1991; Serfling, 2014), CEO origin (Karaevli, 2007; Karaevli & Zajac, 2013; Zhang & Rajagopalan, 2010), CEO formal education (Wang, Holmes Jr., Oh, & Zhu, 2016; Barker & Mueller, 2002; Wally & Baum, 1994; Thomas, Litschert, & Ramaswamy, 1991), CEO gender (Dixon-Fowler, Ellstrand, & Johnson, 2013; Smith, Smith, & Verner, 2013), and CEO prior work experience (Crossland, Zyung, Hiller, & Hambrick, 2014; Fern, Cardinal, & O'Neill, 2011; Mackey, 2008; Herrmann & Datta, 2006).

Although the CEOs’ prior career experience is well understood, Wang et al. (2016) stated a need for a more diverse focus on this specific CEO characteristic. Therefore, the emphasis should be on different types of prior work experiences and how they influence organizational performance. To address this need, the work experience framework introduced by Quinones et al. (1995) and Tesluk and Jacobs (1998) will be used. This framework is a matrix with four different levels of specificity, namely (1) industry experience, (2) organizational experience, (3) job experience, and (4) task experience, and three levels of measurement mode, which are (1) amount, (2) time, and (3) type.

Moreover, this master thesis attempts to investigate the UET in a to-date hardly untapped field, namely the sports industry. In recent years this sector attracts increasing attention, mainly due to its uniqueness and simplicity in measuring organizational performance, as every sporting event always has a winner. However, we are still far away from theoretical saturation, and more research is needed to support the hitherto scarce literature in this specific field. Thus, the underlying research questions of this master thesis are:

RQ1: How does leaders' prior work experiences influence organizational performance within a sport setting?

RQ2: Is there a difference when leaders' prior work experience is divided into four groups, namely (1) industry experience, (2) organizational experience, (3) job experience, and (4) task experience?

1.2. Method

To answer the underlying research questions, a quantitative approach, more precisely, a panel data regression, is conducted. Data from all employed coaches in the top10 European soccer leagues (UEFA, 2021) from the 2010/11 season until the 2019/20 season was gathered. The online platform “www.transfermarkt.de” is indisputably the most prominent database concerning international soccer. A web scraping code was performed using the programs “R” and “RStudio” to download the data entailed on this webpage. With the limitation of data availability, the investigated dataset consists of 770 coaches and 2,177 observations, and 16 variables. For data analysis, a fixed-effects regression model was performed. The

investigation of this model was confirmed by a Breusch and Pagagan Lagrange Multiplier Test and a Hausman Test. CEO characteristics were combined with team characteristics to provide an all-encompassing picture.

1.3. Structure of the master thesis

Theoretical Empirical Introduction Data analysis Discussion Conclusion background research •Research •Upper •Data •Regression •Implications gap and Echelons collection analysis to theory and research Theory •Sample •Summary of management question •CEO description results •Limitations •Method experience •Research and further •Sport model research settings •Measure- suggestions •Hypothesis ments development

Figure 1: Structure of this master thesis; Source: Own elaboration

As illustrated in Figure 1, this master thesis is structured as follows: First, a short introduction to the UET will provide a profound theoretical basis. Then, the previously mentioned prior work experience framework by Quinones et al. (1995) and Tesluk and Jacobs (1998) will be explained. Furthermore, more in-depth insights into the previously researched areas of the CEO experience concerning the UET will be given to highlight and explain the importance of the correct categorization. Moreover, as this thesis attempts to analyze a unique industry, selected studies on this industry will be presented to highlight its uniqueness. Finally, hypotheses will be generated based on the current state of knowledge.

Having outlined the theoretical background, the empirical analyzes will follow. The first part of this section will describe the data collection process and the sample. In a second step, the research design is illustrated and explained. This will give more insights into how the dataset will be analyzed. In that regard, the specific variables will be highlighted and explained in greater detail. Furthermore, the variables will be tested using a correlation test. Subsequently, several panel regression models will be calculated, and the results will be summarized. The final section will point out the limitations of this master thesis and the further avenues of research.

2. Upper Echelons Theory

Why do some companies outperform others? Researchers have stated that the industry has a considerable impact (Elango, 1998). Nevertheless, even when considering this fact, there are some companies in each industry that are still better than others. So, is it pure luck, or might it be the companies’ characteristics? Hambrick and Mason (1984) attempted to answer this question, and in their research, they stated that it is entirely different. They established the "Upper Echelons Theory" (UET) and thus changed the way researchers considered top managers. In this theory, Hambrick and Mason (1984) stated that managerial background characteristics highly influence organizational output.

Hambrick and Mason (1984) support their theory with two primary principles. First, top executives matter, and second they demonstrated their human limitations through the bounded rationality principle. Even though some older researchers have stated that large companies run themselves (Hall, 1977), Hambrick and Mason (1984) are convinced that top managers highly influence the organizational outcome – no matter how big a company is. They base their reasoning on Cyert and March (1963). They confirmed that a company's performance results from the top managers' decisions. Second, the bounded rationality principle states that every human is limited in accessing information, processing it, and using the available information (Simon, 1957; Holmes Jr., Bromiley, Devers, Holcomb, & McGuire, 2011). Hambrick and Mason (1984), in their work, called these three steps "Limited Field of Vision", "Selective Perception", and "Interpretation". These steps are also shown in Figure 2. For a better understanding, all terms used by Hambrick and Mason (1984) in their model will be shortly explained in the introductory paragraph.

In their model, Hambrick and Mason (1984) used the phrase "Strategic Choice", referring back to Child’s (1972) terminology. According to Child (1972), "Strategic Choice" is an all-encompassing phrase that consists of formally made choices, informal decisions, and indecisions. Furthermore, the term "strategy" stands for a more complex problem, which means that it can not be solved by entering numbers in a mathematical formula (Hambrick & Mason, 1984). Hence, March and Simon (1958) posit that the top managers' cognitive base strongly influences the person in charge of such complex decisions.

Figure 2 describes the principles of the cognitive base and bounded rationality in detail. There might be countless possible solutions, yet, based on the top managers’ cognitive base and values, many of them are excluded. This phenomenon is primarily due to the fact that top managers are not aware of all the possible alternatives or estimate the consequences of the alternatives wrong (Hambrick & Mason, 1984). Hambrick and Snow (1977) already mentioned that no matter how big a team of managers is, they will never grasp the complete picture of possible solutions and consequences for the organization and its environment. However, Hambrick (2007) mentioned that the top managers' experiences and background characteristics are core drivers of top managers' cognitive base, meaning that different top managers' background characteristics tend towards distinct cognitive biases. The top managers' biases due to the cognitive base can be seen at the "Limited Field of Vision", where only some stimuli reach this step. Moreover, within each bounded rationality step, the top managers exclude more and more stimuli. The leaders' background experience influences their selective perception and interpretation of the stimuli. In the end, this turns into one single strategic choice, which is also in line with the top managers' values (Hambrick & Mason, 1984).

Figure 2: Strategic Choice Under Conditions of Bounded Rationality; Source: Hambrick & Mason (1984) p. 195

With this model, Hambrick and Mason (1984) described how important the top managers' background experience is for the cognitive process of strategic decision making. Furthermore, the focus on observable facts should deliver faster, more relevant data, especially in this early stage of the theory. Moreover, as most top managers reject psychological analysis, it would be challenging and time-consuming to study a large group of top executives. Therefore, it is more easily approachable to collect observable facts than data regarding the top managers'

psychological biases. Hence, Hambrick and Mason's (1984) first potential research draft is presented in Figure 3.

Figure 3: An Upper Echelons Perspective of Organizations; Source: Hambrick & Mason (1984) p. 198

In their illustration, Hambrick and Mason (1984) put particular focus on the socio-demographic characteristics of top managers. Such demographic variables include "Age, Functional tracks, Other career experiences, Education, Socioeconomic roots, Financial background, and Group characteristics". Furthermore, Hambrick and Mason (1984) proposed analyzing different strategic choices, such as product innovation, unrelated and related diversification, and many more. Moreover, all the analyses should consider organizational performance as the dependent variable. Still, Hambrick and Mason (1984) suggest different ways of controlling the performance variable, such as profitability, growth, or variations in profitability.

Similar to Hambrick and Mason’s (1984) request, a growing number of researchers have concentrated their research on top managers' demographic characteristics, thereby analyzing observable facts, such as age (Grimm & Smith, 1991; Serfling, 2014), origin (Karaevli, 2007; Karaevli & Zajac, 2013; Zhang & Rajagopalan, 2010), formal education (Wang, Holmes Jr., Oh, & Zhu, 2016; Barker & Mueller, 2002; Wally & Baum, 1994; Thomas, Litschert, & Ramaswamy, 1991), gender (Dixon-Fowler, Ellstrand, & Johnson, 2013; Smith, Smith, & Verner, 2013) and prior work experience (Crossland, Zyung, Hiller, & Hambrick, 2014; Fern, Cardinal, & O'Neill, 2011; Mackey, 2008; Herrmann & Datta, 2006). However, in recent years, there is a growing body of literature on top managers' personalities (Billett & Qian, 2008; Chatterjee & Hambrick, 2011; Fanelli & Misangyi, 2006; Nadkarni & Herrmann, 2010;

Peterson, Walumbwa, Byron, & Myrowitz, 2009). Consequently, Bromiley and Rau (2016) tried to structure the UER in a new, more compelling way.

Figure 4: An Updated Upper Echelons Research Perspective; Source: Bromiley & Rau (2016) p. 177

2.1. Types of characteristics

As shown in Figure 4, Bromiley and Rau (2016) structured the existing research in the field of the UET into three different approaches. Every approach has its specific focus with regard to Hambrick and Mason's (1984) model. The first category is "observable characteristics" of the top managers. This category is easily comparable to Hambrick and Mason's (1984) primary research model, where they stated that researchers should focus on demographic characteristics, i.e., prior work experience, age, tenure, origin, or gender. The second group is "underlying characteristics". Hambrick and Mason (1984) had already mentioned this category implicitly in their first paper, but as they assumed that observable facts would yield quicker and more compelling results, they did not include it in their model. Nowadays, there are different ways of analyzing someone's personality. Therefore, Bromiley and Rau (2016) added it explicitly to the UER perspective. This category consists of measures, such as personality, values, charisma, 7

humility, or the leadership style the CEO follows. Finally, the third category is referred to as "interaction with others". Hambrick and Mason (1984) had initially characterized this category as an observable characteristic. However, Bromiley and Rau (2016) maintained that observable and underlying characteristics influence the top managers' interaction with others. As a result, it would be desirable that researchers study this category separately and in-depth. In this group, researchers control how the CEO and other stakeholders interact, for example, the CEOs’ power over others.

2.1.1. Observable characteristics

As described in the previous chapter, this characteristic is comparable with the first research proposal by Hambrick and Mason (1984). In their reasoning, they cited Weick (1969), who demanded that researchers stop trying to find psychological explanations to all matter but keep research as simple as possible. Thereby, researchers should focus on observable individual facts of the researched personas.

To the time Hambrick and Mason (1984) introduced this theory, this was not a groundbreaking approach in research. Nonetheless, it was innovative in the specific context. For example, researchers had already studied demographic characteristics as indicators of alcohol abuse (Boscarino, 1979) or jury behavior (Mills & Bohannon, 1980). Furthermore, demographic characteristics had been linked to participation in volunteer work (Schram & Dunsing, 1981) and job involvement (Sekaran & Mowday, 1981) before Hambrick and Mason (1984) introduced this concept to leaders' performance research.

In the meantime, countless researchers have analyzed and confirmed the influence of observable facts like age (Grimm & Smith, 1991; Serfling, 2014), origin (Karaevli, 2007; Karaevli & Zajac, 2013; Zhang & Rajagopalan, 2010), formal education (Wang, Holmes Jr., Oh, & Zhu, 2016; Barker & Mueller, 2002; Wally & Baum, 1994; Thomas, Litschert, & Ramaswamy, 1991), gender (Dixon-Fowler, Ellstrand, & Johnson, 2013; Smith, Smith, & Verner, 2013) and prior work experience (Crossland, Zyung, Hiller, & Hambrick, 2014; Fern, Cardinal, & O'Neill, 2011; Mackey, 2008; Herrmann & Datta, 2006) on strategic action and firm performance. However, in recent years, research also attempted to identify new socio-demographic indicators. For example, Carpenter (2011) analyzed to what extent military

service or governance of non-profit organizations prior to the managerial position impacts strategic decisions.

2.1.2. Underlying characteristics

Assumingly, Hambrick and Mason (1984) already knew that future research would focus on these parameters. Nevertheless, they did not explicitly mention them in their first research paper, mainly since researchers back then did not know how to measure underlying characteristics, such as personality or values. The invention of the Five-Factor Model – Big Five (Barrick & Mount, 1991) and the Myers-Briggs Type Indicator (Gardner & Martinko, 1996) led to a major turn in the field. In the Five-Factor Model, every factor describes a unique set of psychological traits. The five factors are (1) conscientiousness, (2) emotional stability, (3) agreeableness, (4) extraversion, and (5) openness to experience (Boudreau, Boswell, Judge, & Bretz Jr., 2001). Likewise, the Meyers-Briggs Type Indicator tests four different dimensions: First, whether the person tends towards Extraversion or Introversion. Second, whether the person is sensing for information or gathering their information from intuition. People who tend towards sensing for information preferably gain hands-on experience and focus on facts, whereas people who tend towards intuition are likely to imagine alternative settings and pasts. The third dimension compares whether people reason and ground their decisions in facts or make their decisions simply based on feelings. Finally, the fourth category controls how the person deals with the outside world. When people have a strong inclination towards structure, they are judging personalities, whereas perceiving personalities are more open-minded and flexible. Out of these four dimensions, 16 different personas are formed, each with specific values, ranging from the Inspector (who is an introvertive, sensing, thinking, and judging person) to the Champion (who is an extrovertive, intuitive, and feeling-based perceiving person) (McCaulley, 1990). Since then, a growing number of researchers have tried to link underlying characteristics to strategic decisions and business success and explain the relation between them (Abatecola & Cristofaro, 2020).

Over the past decades, much research was done in that field. For example, researchers analyzed the influence of the CEO personality (Park & Gould, 2017) based on the Five-Factor Model (Nadkarni & Herrmann, 2010) and by opposing positive and negative personality dimensions (e.g., charisma vs. overconfidence) (Chatterjee & Hambrick, 2011). Furthermore, researchers

analyzed to what extent the CEO leadership style influences firm performance (Resick, Whitman, Weingarden, & Hiller, 2009; Ling, Simsek, Lubatkin, & Veiga, 2008). Besides, CEO and TMT values were linked to strategic decisions and firm performance (Waldman, et al., 2006; Wally & Baum, 1994; Baum, Bird, & Singh, 2011; Chin, Hambrick, & Trevino, 2013).

2.1.3. Interactions with others

Researchers in that area argue that CEOs are greatly influenced in their decision-making by other personas inside and outside the organization. The main focus is on the CEO's power over other stakeholders (Dowell, Shackell, & Stuart, 2011; Haynes & Hillman, 2010; Miller, Le Breton-Miller, Minichilli, Corbetta, & Pittino, 2014) and social ties of the CEO to board members and other stakeholders (Westphal & Deephouse, 2011; McDonald & Westphal, 2011). However, Crossland and Hambrick (2011) interpreted this area in a slightly different way. They analyzed whether social ties influence the CEOs' underlying characteristics, and in a further step, whether the former influences CEOs' decision-making. Furthermore, researchers were interested in social ties concerning the TMT (Westphal & Clement, 2008).

Even though all of these approaches could deliver remarkable results, this master thesis will focus on the first approach, namely the "observable facts". This is due to several reasons. The primary reason is that these characteristics are observable and measurable characteristics, which make them central to the first intention of the UET (Hambrick & Mason, 1984). Furthermore, they are among the most frequently studied characteristics in the UET literature (Finkelstein, Hambrick, & Cannella Jr., 2009). Therefore, it is of particular interest to see whether and to what extent the findings in the new setting are consistent with previous literature.

2.2. Levels of influences

Apart from the differentiation into different levels of characteristics, as shown in Figure 4, Bromiley and Rau (2016) further divided their updated research perspective into three levels of influences. Within the first group, researchers are concerned about the CEO characteristics. In comparison, in the second group, the researchers analyze the TMT characteristics. Hambrick and Mason (1984) did not differ in their primary research model between CEO and TMT; instead, they mentioned all top managers. However, Bromiley and Rau (2016) realized that 10

most researchers differ in their approaches, and therefore they added this categorization scheme. Finally, Bromiley and Rau (2016) also added a third level, namely the CEO and TMT interface. Within this level, researchers analyze whether or not it is valuable or hindering that CEO characteristics and TMT characteristics differ.

In this setting, all the above-mentioned research areas could deliver unique and fascinating results. However, as the field of this research is still young on shaky grounds, it would be beyond the remit of this thesis to consider all aspects. Therefore, the focus of this thesis will be on analyzing the CEO. Hence, the focus of this master thesis will be on the observable characteristics of the CEO. The main emphasis will be on the CEO experience, as the next chapter will describe in greater detail.

2.3. CEO experience

"The only source of knowledge is experience."

- Albert Einstein (German Theoretical-Physicist 1879-1955)

As Albert Einstein already realized, earlier life experience is best suited to explain knowledge and skills (Einstein, 2021). Therefore, most human resource concepts are grounded in those premises. Prior work experience is the indicator that human resource managers look for first when it comes to hiring, firing, promoting (Fisher, Cunningham, Kerr, & Allscheid, 2017), training (Ford, Quinones, Sego, & Sorra, 1992), or compensating (Medoff & Abraham, 1980).

As a result, it is not surprising that the effects of CEO experience are one of the most studied fields within the UET (Finkelstein, Hambrick, & Cannella Jr., 2009). Researchers found several connections to CEO experience. For example, CEO experience seems to influence the knowledge (e.g., about the industry), skills (e.g., how to do the job properly), social capital (e.g., getting to know customers or suppliers), information processing (e.g., knowing what to do next and from where to get help), and many more (Dearborn & Simon, 1958). Most research in this area has focused on the tenure and age of the top managers as an indicator of CEO experience (Bromiley & Rau, 2016). Consequently, Wang et al. (2016) stated that a more diverse focus on this observable characteristic should bring more in-depth results.

Quinones, Ford, and Teachout (1995) introduced a work experience framework. Figure 5 shows this framework, which should guide future research in the field. The researchers stated that many scholars analyzed top managers' experience, with the limitation that they all used different metrics to measure it. For example, most used tenure (Wu, Levitas, & Priem, 2005) defined as years in a specific position (e.g., CEO) (McDaniel, Schmidt, & Hunter, 1988) or years in an organization (McEnrue, 1988). However, others also defined it as the number of times to perform a particular task (Vance, Coovert, MacCallum, & Hedge, 1989) to measure work experience. Even though both are measuring almost the same, researchers have stated that two individuals with the same job tenure can differ drastically in the number of tasks they performed (Ford, Quinones, Sego, & Sorra, 1992). Quinones et al. (1995) stated that many researchers used the term "work experience" in the same way but referred to totally different measurement methods. Thus, they pointed out inconsistencies within this research area.

As illustrated in Figure 5, Quinones et al. (1995) used two dimensions for their work experience framework: first, the level of specificity, and second the measurement mode. Within both dimensions, there are three levels. Consequently, they introduced a 3 x 3 categorization scheme for prior work experience. In this manner, they divided the level of specificity into (1) task experience, (2) job experience, and (3) organizational experience. Furthermore, they divided the measurement mode into (1) amount, (2) time, and (3) type.

In their work, Quinones et al. (1995) described prior task experience as the experience of doing a specific thing. For example, experience in product development or production optimization. Concerning prior job experience, they termed it as the experience of executing this specific role. This was measured based on, for example, how often and for how long the CEO was CEO in one or the other firm. Finally, they described the prior organizational experience as the experience within the organization before becoming its CEO, for example, in other roles, such as employee or member of the TMT. Quinones et al. (1995) described the measurement modes as follows. The amount is defined as the number of times the studied manager has carried out a specific task, the number of jobs the manager has had in this position, or the current organization. Time refers to time-based measures, such as tenure; put differently, for how long the manager has been working in this organization (organizational tenure) or this job (job tenure). Moreover, Type is a rather qualitative approach to investigate how many different jobs and complexity levels the manager has done previously.

Figure 5: A Conceptual Framework of Work Experience Measures; Source: Quinones et al. (1995) p. 892

Furthermore, Quinones et al. (1995) tested their work-experience framework in meta-analyses. They found that each categorization can deliver exceptional results. Hence, they displayed the importance of this conceptualization and differentiation in various types of work experience.

Additionally, to the conceptual framework by Quinones et al. (1995), Tesluk and Jacobs (1998) included a fourth level of specificity, i.e., the industry experience of the CEO. The researchers note that this level is missing in Quinones et al.' framework. Nevertheless, the industrial experience of a CEO can deliver unique and valid information. Table 1 demonstrates the integrated framework of work experience. As can be seen, the four levels of specificity are (1) CEO prior industry experience, (2) CEO prior organizational experience, (3) CEO prior job experience, and (4) CEO prior task experience. Besides the previously explained levels of specificity, the prior industry experience can be explained as the CEO's experience in firms in 13

the same product or service sector as the current company. Therefore, industry experience includes experience as a CEO and experience in such companies as an employee or top manager.

Industry Number of jobs in this Industrial tenure Type of jobs in this industry

experience industry Organizational Number of jobs in this Organizational Type of jobs in this experience organization tenure organization Job experience Number of CEO jobs Job tenure Job complexity

Task experience Number of tasks Time on task Task difficulty / complexity / Level of Specificity performed criticality Amount Time Type Measurement Mode Table 1: An integrated framework of work experience; Source: Quinones et al. (1995) and Tesluk and Jacobs (1998)

As the conceptualization in Table 1 shows, CEO experience is highlighted from different angles, starting from counting how many jobs the CEOs had prior to the current position to the time they spent in this position and analyzing the CEOs’ prior career steps. Furthermore, all these aspects will be analyzed on several levels. For better understanding, the literature will be reviewed in the following sections.

2.4. CEO experience in Upper Echelons Theory

As already stated, CEOs’ prior work experience is one of the most studied fields of the UET (Finkelstein, Hambrick, & Cannella Jr., 2009). However, the results are highly contradictory. Some researchers found positive effects of prior work experience (Wang, Holmes Jr., Oh, & Zhu, 2016; Crossland, Zyung, Hiller, & Hambrick, 2014; Zhang & Rajagopalan, 2010), whereas others found adverse effects (Matta & Beamish, 2009; Hambrick, Geletkanycz, & Fredrickson, 1993; McClelland, Liang, & Barker, 2010). Some researchers even observed mixed effects within one study (Geletkanycz & Black, 2001; Naseem, Lin, Rehman, Ahmad, & Ali, 2019). Noteworthily, further studies found an inverted U-shape in their data, meaning that in the beginning, the CEO’s prior work experience has an advantage, but too much prior work experience affects performance negatively (Henderson, Miller, & Hambrick, 2006). Furthermore, other researchers found no significant influence of the CEOs’ prior work 14

experience on organizational performance (Barker & Mueller, 2002; Orens & Reheul, 2013). Wang et al. (2012) thought that the different measurement methods would help explain why the results of the studies are contradictory. Hence, this section and Table 2 will provide an overview to explain and compare these studies concerning their measuring mode.

As displayed in Table 2, researchers used three different methods to measure CEO prior work experience, namely (1) CEO age, (2) CEO tenure, and (3) CEO prior career path. Furthermore, even within each cluster, researchers applied different methodological approaches. For example, CEO tenure was measured as (1) industrial tenure (Richard, Wu, & Chadwick, 2009; Hambrick, Geletkanycz, & Fredrickson, 1993; Geletkanycz & Black, 2001), (2) organizational tenure (Naseem, Lin, Rehman, Ahmad, & Ali, 2019; McClelland, Liang, & Barker, 2010; Hambrick, Geletkanycz, & Fredrickson, 1993), (3) organizational job tenure (Wang, Holmes Jr., Oh, & Zhu, 2016; Richard, Wu, & Chadwick, 2009; McClelland, Liang, & Barker, 2010; Matta & Beamish, 2009; Henderson, Miller, & Hambrick, 2006; Barker & Mueller, 2002; Damanpour & Schneider, 2006; Yim, 2013; Orens & Reheul, 2013), (4) or job tenure (Damanpour & Schneider, 2006).

Paper Measurement Most interesting finding mode Barker and Mueller (2002) CEO tenure Barker and Mueller (2002) stated that CEO tenure (organizational job) does not significantly influence companies' R&D CEO characteristics and firm investments. R&D spending CEO age However, they found CEO age to have a significant adverse effect on R&D investments.

CEO prior career Further, they found that CEOs with experience in path output functions (e.g., marketing and sales, or (throughput vs. engineering and R&D) invest significantly more in output) R&D. In comparison, CEOs with experience in throughput functions (such as finance and accounting or administrative) do not differ significantly in R&D investments from CEOs without experience in output functions.

Crossland, Zyung, Hiller, and CEO prior career Crossland et al. (2014) found significant Hambrick (2014) path correlations between CEO career variety and (career variety) strategic change, resource reallocation, strategic CEO career variety: effect on distinctiveness, and TMT turnover. firm-level strategic and social novelty

Damanpour and Schneider CEO age Damanpour and Schneider (2006) found no (2006) significant influence of CEO age on any innovation phase. According to Damanpour and Phases of the adoption of Schneider (2006), the three innovation phases are innovation in organizations: initiation, adoption (decision), and effects of environment, implementation. organization and top managers CEO tenure However, they observed that there is a significant (organizational job, positive correlation between CEO job tenure and and job) all three innovation phases. At the same time, CEO organizational job tenure positively influences the adoption (decision) phase.

Geletkanycz and Black (2001) CEO prior career Geltkanycz and Black (2001) found a significant path negative influence of CEO prior career experience Bound by the past? Experience- (career variety) on CEO commitment to the status quo. Meaning, based effects on commitment to the more different functions the CEOs had before the strategic status quo becoming CEO, the more willing to act actively they are.

CEO tenure Furthermore, the study revealed a positive (industrial) correlation between CEO industrial tenure and the commitment to the status quo.

Hambrick, Geletkanycz, and CEO tenure Hambrick et al. (1993) showed that organizational Fredrickson (1993) (organizational and tenure does not significantly correlate with CEOs' industrial) commitment to the status quo. However, they Top executive commitment to the stated that CEOs’ industrial tenure positively status quo: some tests of its influences the CEOs' commitment to the status determinants quo, meaning, the higher the CEOs’ industrial tenure, the higher their commitment to the status quo.

Henderson, Miller, and CEO tenure Their study revealed an inverted U-shape effect on Hambrick (2006) (organizational job) firm profitability within a stable industry. However, in an unstable industry, the firm How quickly do CEOs become profitability is declining immediately and steadily. obsolete? Industry dynamism, CEO tenure, and company performance

Matta and Beamish (2008) CEO tenure Matta and Beamish (2008) found a significant (career horizon - positive effect of CEO career horizon on firm The accentuated CEO career time to retirement) strategic actions, defined as international horizon problem: evidence from acquisitions. Meaning, the longer the CEOs have international acquisitions to work until they can retire, the more willing they are to take risks.

CEO tenure However, the researchers found neither a (organizational job) significant influence of CEOs’ organizational job tenure on international acquisitions.

CEO prior career Nor did they found a significant effect of CEOs’ path international experience on international (international) acquisitions.

CEO prior career Matta and Beamish (2008) further stated that path CEOs coming from an output function are (throughput vs. significantly more willing to make international output) acquisitions than CEOs coming from a throughput function.

McClelland, Liang, Barker CEO tenure McClelland et al. (2010) found a significantly (2010) (organizational job positive correlation to the CEOs’ commitment to and organizational) the status quo at higher CEO organizational job CEO commitment to the status tenure, while there was no significant influence of quo: replication and extension CEO organizational tenure on the CEOs' using content analysis commitment to the status quo.

CEO age Moreover, they stated that CEOs' age positively influences the CEOs’ commitment to the status quo.

Naseem, Lin, Rehman, Ahmad, CEO tenure Naseem et al. (2019) found a negative correlation and Ali (2019) (organizational) between CEO organizational tenure and firm performance. Does capital structure mediate the link between CEO CEO age Further, this study found a significant positive characteristics and firm impact from CEO age on firm performance; performance? however, there is a limit where it starts dropping.

Orens and Reheul (2013) CEO age Orens and Reheul's (2013) study indicates that older CEOs hold more money as a reserve than Do CEO demographics explain younger CEOs. This reserve serves mainly to cash holdings in SMEs? support their company through economically challenging times.

CEO tenure However, researchers did not found any significant (organizational job) relationship between CEOs' organizational job tenure and CEOs' cash holdings.

CEO prior career Orens and Reheul further stated that CEOs with path other-industry experience tend to invest more (same-industry vs. money. Therefore, they significantly have fewer other-industry) cash holdings than CEOs without other-industry experience. However, there is no difference in cash holdings between CEOs with same-industry experience and those without same-industry experience.

Richard, Wu, and Chadwick CEO tenure Richard et al. (2009) found that organizational job (2009) (organizational job, tenure correlates negatively with the connection and industrial) between CEOs’ entrepreneurial orientation and The impact of entrepreneurial firm performance. However, at the same time, the orientation on firm researchers found that this connection is positively performance: the role of CEO affected by the CEOs' industry tenure. position tenure and industry tenure

Wang, Holmes Jr., Oh, and Zhu CEO age Wang et al. (2016) did not found a significant (2016) influence on strategic action. However, they found that CEO age positively influences firm Do CEOs matter to firm strategic performance and firm profitability. actions and firm performance? A

meta-analytic investigation CEO tenure Moreover, they found a significant negative based on upper echelons theory (organizational job) influence of CEOs' organizational job tenure on strategic action, strategic risk, and strategic change. However, they still found a positive effect of CEO organizational job tenure on firm performance and firm profitability.

CEO prior career Furthermore, Wang et al. (2016) found a path significant positive correlation between the (amount) amount of CEOs' prior work experience and strategic action, strategic risk, and firm performance. However, there is no significant correlation to firm profitability.

CEO prior career Yet, the only significant positive correlation was path found between strategic action and CEOs' prior (industry, task experience. Further, only CEOs' prior industry organizational, job, experience is positively related to firm and task) performance.

Yim (2013) CEO age Yim (2013) studied that a CEO who is 20 years older is around 30% less likely to invest in an The acquisitiveness of youth: acquisition. CEO age and acquisition behavior CEO tenure Moreover, Yim (2013) observed that the (organizational job) relationship between CEO organizational job tenure and acquisition probability reveals an inverted U-shape. This means that in earlier years, CEOs are more willing to invest in acquisitions; yet, when the CEO has reached the maximum (after eight years in their dataset), the likelihood of an acquisition drops.

Zhang (2008) CEO prior career Zhang (2008) stated that CEOs without path organizational experience are fired earlier than Information asymmetry and the (organizational, CEOs with organizational experience. However, dismissal of newly appointed job) job experience does not influence the time to CEOs: an empirical investigation dismissal.

Table 2: Summarizing table of research in the are of CEOs’ prior work experience; Source: Own elaboration

2.4.1. CEO age

Many researchers consider CEO age an indicator of CEO experience (Wang, Holmes Jr., Oh, & Zhu, 2016; Damanpour & Schneider, 2006; Naseem, Lin, Rehman, Ahmad, & Ali, 2019; McClelland, Liang, & Barker, 2010; Barker & Mueller, 2002; Yim, 2013; Orens & Reheul, 2013). However, the previously explained classification model, illustrated in Table 1 by Quinones et al. (1995) and Tesluk and Jacobs (1998), states that CEO age does not indicate formal experience as age itself is not related to a person’s professional activities.

Nevertheless, earlier research revealed some interesting results. For example, Yim (2013) analyzed CEOs and their investment strategies. He showed that the number of acquisitions drops immensely as the CEO increases in age. Companies with a CEO who is 20 years older invest around 30% less in acquisitions. This finding is consistent with many other researchers who stated that younger CEOs are more inclined to risk-taking spending money on investments and R&D (Barker & Mueller, 2002; McClelland, Liang, & Barker, 2010; Orens & Reheul, 2013). However, other researchers argued that more investments do not automatically generate higher firm performance. For example, Damanpour and Schneider (2006) could not find a significant influence of CEO age on implementing investments. Naseem et al. (2019) found that companies led by a young CEO are significantly worse performers due to their limited experience. However, they also found that firm performance suffers if the CEO is too advanced in age. Contradictorily, Wang et al. (2016) did not find an inverted U-shape. However, they also found that younger CEOs lack an understanding of complex problems. Therefore, they found significant evidence that older CEOs are better performers and lead more profitable firms.

In conclusion, past research indicates that younger CEOs may be more innovative and more willing to try out new paths. However, when it comes to performance and profitability, older CEOs seem to be significantly better than younger ones, mainly because younger CEOs tend to exaggerate only to make avoidable mistakes.

2.4.2. CEO tenure

Time-based measures are commonly called tenure and are most commonly measured in years a person has held a position. Table 2 already indicates that this is one of the most studied fields in CEO experience research (Finkelstein, Hambrick, & Cannella Jr., 2009). As already outlined, different conceptualizations were tested. For example, some approaches measured tenure in an industry (Geletkanycz & Black, 2001; Hambrick, Geletkanycz, & Fredrickson, 1993; Richard, Wu, & Chadwick, 2009) or organization (Hambrick, Geletkanycz, & Fredrickson, 1993; McClelland, Liang, & Barker, 2010; Naseem, Lin, Rehman, Ahmad, & Ali, 2019), while others measured tenure in a specific position (Damanpour & Schneider, 2006), with the majority, however, measuring tenure in a specific position in a single organization (McClelland, Liang, & Barker, 2010; Barker & Mueller, 2002; Henderson, Miller, & Hambrick, 2006; Matta & Beamish, 2009; Orens & Reheul, 2013; Wang, Holmes Jr., Oh, & Zhu, 2016; Yim, 2013; Richard, Wu, & Chadwick, 2009; Damanpour & Schneider, 2006).

Findings regarding the CEOs' industrial tenure are overall consistent. Although some older researchers found a few industry experiences as an essential managerial skill (Gupta, 1984), more recent studies suggest that too much industry experience will limit the executives' vision. Geletkanycz and Black (2001) and Hambrick et al. (1993) both found that with a higher CEO industrial tenure, the CEO commitment to the status quo rises. Thus, CEOs become less innovative and focus more on stability. Nevertheless, Richard et al. (2009) showed that higher industrial tenure does not automatically mean that they are worse performers. They analyzed the connection between the firm's entrepreneurial orientation (EO) and firm performance and whether this connection is influenced by a higher industrial tenure by the CEO. Their study measured firm performance as return on equity (ROE) and return on assets (ROA). Moreover, they measured entrepreneurial orientation as the willingness to invest, innovate, and develop new products. They found out that firms led by a CEO with a long industrial tenure and a medium EO are performing much better than firms led by a CEO with a short industrial tenure and a high EO. Consequently, they showed that if CEOs with high industrial tenure overcome the risk of limited executives' vision, they can outperform the others. This is mainly true as higher-tenured CEOs have gained more experience and gained a better reputation. Through this reputation, CEOs have more social contacts with other firms within the industry, and they can generate higher knowledge and make the most suitable investments.

Moreover, Richard et al. (2009) also analyzed how organizational job tenure influences the relationship between EO and firm performance. Interestingly, they found the exact opposite. CEOs with short organizational job tenure and a medium EO outperform CEOs with long organizational job tenure and the same EO. The researchers stated that CEOs new to the organization often have a more diverse focus on problems. Moreover, these CEOs are more open to change the organization and thereby create new competitive strategies. Surprisingly, the results are very inconsistent in this regard. Besides the study by Richard et al. (2009), two other studies analyzed similar aspects but with utterly different results. First, Henderson et al.’s (2006) findings indicate that the direction of influence of CEO organizational job tenure on firm profitability mainly depends on the industry. They found a positive influence of CEO organizational job tenure on organizational profitability, albeit up to a limit. Once this limit passed, firm profitability was shown to decrease. However, they stated that these results are only consistent within a stable industry. In unstable industries, a company needs more change, and thus CEO organizational job tenure is negatively correlated to organizational performance. Second, the most recent study was published by Wang et al. (2016). It emerges from their study that CEO organizational job tenure has a negative influence on strategic action, strategic risk, and strategic change. Nevertheless, they stated that these companies are more profitable and better performers despite their stronger commitment to the status quo. They referred to Damanpour and Schneider (2006), who found that organizational job tenure positively influences the CEOs' ability within the innovation process's adoption (decision) phase. Moreover, this is in line with McClelland et al. (2010), who observed that CEOs' organizational job tenure has a significant positive influence on the CEOs' commitment to the status quo. Orens and Reheul (2013) expected similar results when they analyzed whether organizational job tenure influences R&D investments. However, they could not find a significant influence. They realized that if researchers analyzed CEO age and CEO tenure, important differences emerged in the results. While tenure only looks at the time spent in an organization, it does not indicate how many years the CEO will still be working before reaching retirement age. Meaning, if a CEO started working in an organization at a very early age and has worked there for approximately 20 years, they still have to work for further 20 years. Thus, they are still willing to push further and improve firm performance. However, if a CEO is about 60 years old and only has to work for two to five years before retiring, they will not risk their legacy by risky investments. These findings are consistent with both Barker and Müller (2002) and Matta and Beamish (2008). Barker and Müller (2002) found that a higher CEO organizational job tenure does not significantly reduce R&D investments, yet increased CEO age had significant negative

influences on R&D investments. In contrast, Matta and Beamish (2008) found no significant influence of CEO organizational job tenure on international acquisitions. However, when controlling for how long the CEO still is in active working life (CEO career horizon), they found that CEOs closer to retirement invest less in international acquisitions.

Early research on CEOs' organizational tenure assumed that CEOs moving up the hierarchy are strongly committed to the status quo. They state that this mainly comes from the fact that they have always been judged by the current indicators and, therefore, typically trust in these established forms. Moreover, as the CEOs have worked in the organization for a longer time, they have become blinkered in their work. Hence, CEOs with a high organizational tenure are usually not innovative enough and risk to fail in the long term (Vancil, 1987; Miller, 1991; Wanous, 1980). However, more recent literature found no significant influence of CEO organizational tenure on the commitment to the status quo (Hambrick, Geletkanycz, & Fredrickson, 1993; McClelland, Liang, & Barker, 2010). Hambrick et al. (1993) suggested that these older studies probably examined industry tenure effects instead of organizational tenure effects, as it has already been shown that CEOs' industrial tenure has a positive influence on the CEOs’ commitment to the status quo. The authors further stated that these two variables strongly correlate as working in this organization automatically also means working in this industry. Nevertheless, Naseem et al. (2019) found that CEO organizational tenure negatively correlates with firm performance. However, they argued that this mainly results from the fact that these CEOs, over time, become selfish and put their personal needs over the company's needs. As a result, they tend to make wrong investments.

Finally, Damanpour and Schneider (2006) analyzed CEO tenure under the aspect of CEO job tenure, meaning the years the CEO has spent in the CEO position at this organization or any other organization. Thereby, they analyzed the connection between CEO job tenure and CEO effectiveness during an innovation process. According to the researchers, the innovation process is classified into the following categories: (1) initiation, (2) adoption (decision), and (3) implementation. In the first step, the CEOs identified the need for change. In this manner, they generate knowledge of already existing possibilities. In the second step, they decide which path to take. As a result, they have to allocate resources for this path – both financial and personnel resources. In the final step, it is all about the effective implementation of the path. Thereby, the researchers found that CEOs with a higher job tenure are significantly more

effective in all three innovation phases. This means that CEOs who worked as a CEO for a more extended period of time are better in identifying problems, finding solutions, allocating the right amount of resources to it and implementing those effectively and efficiently.

In summary, the results concerning CEO tenure are mixed. Even after splitting the studies into sub-groups, the results still are highly contradictory. However, most researchers tend to find a negative effect on strategic actions, investments, and firm success. Hand in hand, they find a positive relation to commitment to the status quo and rigidity (Geletkanycz & Black, 2001; Hambrick, Geletkanycz, & Fredrickson, 1993; Naseem, Lin, Rehman, Ahmad, & Ali, 2019). The studies mainly base their findings on a psychological reason: longer-tenured CEOs are more concerned about their legacy and thus do not want to risk it anyhow. Moreover, the second reason is that CEOs have more power to choose employees and board members as times go by. Once this power is established, they usually tend to surround themselves with an increasing number of so-called "Yes sayers". These are people who always agree with the CEOs' opinions, which will disallow them to see every opportunity and lead to missing new trends (Miller, 1991; Meyer, 1975; Westphal, 1999; Zajac & Westphal, 1996). Other researchers support these results but argued that the decline results from a different reason. Their reasoning is that in the beginning, the CEO fears more dismissals. Therefore, they are more willing to demonstrate their power and invest more heavily to implement their vision and fresh ideas. After these high initial investments, they can not go even higher at a later stage. Then, the decline comes naturally (Shen & Cannella Jr., 2002; Prendergast & Stole, 1996). Nevertheless, the studies by Henderson et al. (2006) and Wang et al. (2016) showed that commitment to the status quo is not always detrimental. Overall, the influence of CEO tenure on firm performance mainly depends on the industry, which is analyzed.

2.4.3. CEO prior career path

Researchers often ignore the CEO's career path before becoming a CEO. However, the UET suggests that this time has a particularly strong influence on the CEOs' strategic decisions. In that time, a CEO can observe and encounter different viewpoints. Thereby, they can watch from an exterior position and hence state strengths and weaknesses in this manager’s way of handling situations. In so doing, they can then conclude how they could act in a comparable situation.

Thus, this time has a considerable impact on how the CEO seeks information and interprets the given information at a later stage (Hambrick & Mason, 1984).

Most research in the UET has focused on the CEOs' functional background; in other words, which functional roles the CEOs had before becoming CEO themselves. For example, Barker and Müller (2002) researched that the CEO's prior professional background strongly influences the CEO's investment strategy. CEOs coming from accounting/finance will invest in increasing efficiency, while CEOs coming from sales will focus more on R&D and innovation. Moreover, Matta and Beamish (2009) found significant evidence that CEOs coming from an output function are more willing to invest in international acquisitions than CEOs coming from a throughput function. According to these researchers, people from output functions worked in marketing, sales, R&D, or engineering, while people from throughput functions worked in finance, accounting, administrative, and production.

However, Wang et al. (2016) introduced a new way of analyzing CEOs’ prior career paths. They stated that the simple accumulation of jobs the CEO prior had would yield interesting and valuable results. They ground this assumption in the fact that every CEO will bring their prior job experience to their position. No matter what position held, they were able to take something with them. For example, they had been acquainted with the industry, started building up social capital to people within the industry, learned how to interpret goals, and saw different ways of reaching these goals. Hence, with every job a person has, they will learn something new and develop knowledge and skills. Furthermore, Wang et al. (2016) also tested their assumptions and found a significant influence of CEO experience on strategic actions and strategic risk-taking. They assumed that with more jobs done, the CEOs had increased their confidence. Thanks to the richer knowledge base and skills they have gained during their previous jobs, they become better at processing information and making decisions more quickly. Moreover, they stated that CEOs with more experience are less in danger of postponing decisions. Interestingly, the researchers further found that the amount of CEOs' prior work experience has a positive influence on firm performance. However, they could not find a significant relationship to firm profitability. According to the study, firm profitability is a short-term oriented measurement, while firm performance is mainly concerned with long-term performance. Thus, in line with other literature, high short-term investments can harm the short-term profitability but lead to long-term success. Crossland et al. (2014), as well as

Geletjanycz and Black (2001), had similar foci in their studies. However, they did not measure the total number of jobs but the number of different positions. They referred to this measurement as CEO career variety. Both studies found similar results: CEOs with a more diverse career path are less committed to the status quo and play a more active role in strategic change. Through effective reallocation of resources, they further try to increase their strategic distinctiveness from other competitors.

Another remarkable subdivision was studied by Wang et al. (2016) and Zhang (2008). Wang et al. (2016) analyzed CEOs’ prior career paths within the four dimensions introduced by Quinones et al. (1995) and Tesluk and Jacobs (1998). Thereby, they focused on the accumulated amount of prior jobs (1) within an industry (prior industry work experience), (2) within this organization (prior organizational work experience), (3) as a CEO (prior job experience), and (4) the number of tasks performed (prior task experience). While they could not find any significant results for prior organizational experience and prior job experience, they stated that CEOs' prior task experience positively influences strategic actions. Moreover, they found that CEO prior industry experience positively influences firm performance. Meaning, if CEOs frequently completed a particular task in a previous job, they will act more actively in the new CEO position. However, this does not mean that firm performance will improve. Thus, this study supports that increasing strategic actions alone does not automatically lead to better firm performance. On the other hand, CEOs' prior industry experience does not increase strategic actions, but it increases firm performance. They based their reasoning on CEOs having a better knowledge of the market and competition. Moreover, in their previous jobs within the industry, the CEOs have built up social capital, meaning relationships with customers and suppliers. Consequently, they have access to a more diverse information pool. Zhang (2008), though, analyzed only CEOs' prior organizational work experience and CEOs' prior job experience and how these two variables influence CEO dismissal. Interestingly, they found no evidence that CEOs with high prior job experience are dismissed earlier or later than CEOs with little job experience. However, they observed that CEOs who have prior organizational experience are granted more freedom to make mistakes before their dismissal than CEOs without any organizational experience.

Finally, Orens and Reheul (2013) analyzed the CEOs’ prior career experience by comparing their same-industry experience and their other-industry experience. Older studies yielded

contradictory results for same-industry experience. For example, Eisenhardt and Schoonhoven (1996) argued that CEOs succeeded in build up social capital to the big players in the industry in previous jobs within the industry. Moreover, they suggested that these CEOs have a more refined knowledge of the market and competition. In contrast, Hambrick et al. (1993) stated that these CEOs often miss opportunities as they are stuck in a rut. However, the new study by Orens and Reheul (2013) did not manage to clarify these results. The study did not find a significant difference in CEOs' cash holdings, meaning no matter whether CEOs have industry-specific work experience or not, they still invest not significantly different. However, Orens and Reheul (2013) could find a significant positive impact of CEOs' other-industry experience on their willingness to invest capital. Therefore, they suggested that CEOs with a substantial amount of other-industry experience are better suited for looking at problems from different perspectives than insiders. Thus, these types of CEOs are more likely to find new ways and revolutionize the industry.

To summarize, over the last decade, many researchers have analyzed CEOs' prior career paths. To this end, many different subgroups were analyzed. Overall, researchers revealed that more prior work experience and a more diverse career path would support the CEO in gaining a more profound understanding of problems. Moreover, diverse experiences can help the CEO contemplate a problem from different viewpoints, possibly leading to new ways of problem-solving.

2.5. Sport industry

As already stated, this study attempts to tap into a, up to now, hardly unexplored industry, namely the sports sector. This industry is recently attracting increasing attention as it has a number of indications which make it exciting and at the same time easy to study. For example, organizational performance is much easier to measure than in other settings, as every sporting event always has a winner. Moreover, a substantial amount of data is available open-source on the internet, which lends itself to data analysis. The following chapter will outline and summarize some relevant earlier research into the sports industry.

Lasalle et al. (2018) studied Champions League soccer teams over a period of twenty years. Thereby, they analyzed how the team performance changes if the club hires a new coach. While 27

comparing successors’ and predecessors' prior experience, measured in years, they found that a high discrepancy between successors’ and predecessors’ prior work experience is negatively correlated to team performance. Furthermore, they controlled to what extent CEOs' nation and CEOs' age influence team performance. However, they did not detect any significant effects.

Eitzen and Yetman (1972) analyzed the performance of college basketball teams. To this end, they analyzed how far performance changes if a new coach is employed. However, they were not able to find a significant immediate change in performance after a CEO exchange. They further studied how the coaches' organizational job tenure influences the performance of the basketball team. Here they found an inverted U-shape, meaning that team performance is constantly improving in the initial years as a coach. However, as soon as the maximum has been reached, team performance starts to drop. Giamatista (2004) found the same inverted U-shape of CEO organizational job tenure on team performance when analyzing the American professional basketball league (NBA).

Cannella Jr. and Rowe (1995) investigated team performance after a succession in the American baseball league (MLB). They found that team performance is significantly better if the newly appointed coach already has had work experience for at least one entire season as coach of a different team. Moreover, they analyzed coaches' prior records, meaning how many games the coach had won at his prior stations in percentage. They concluded that this record positively influences team performance after succession.

Lazear (1999) published a theoretical work in which he describes that people from different nations can offer different skillsets to the organization. However, Lazear (1999) described that when there are too many different cultures, the integration costs are higher than the corporate profit. Later Kahane et al. (2013) studied these assumptions in the National Hockey League (NHL). The results underpinned Lazears' theory. The study indicates that a higher share of international employees improves firm performance. However, as mentioned by Lazear (1999), there seems to be a breakup line. Nevetheless, Kahane et al. (2013) provided a different explanation. According to them, a high degree of multilingualism is more significant a problem than a high degree of multiculturalism.

3. Hypothesis development

The previous chapter has outlined the theoretical background of the study. In the following step, the hypothesis will be developed.

3.1. Amount

Up to now, there has been little research in which CEO experience was measured in terms of the number of jobs a CEO had before being promoted to the current position. However, Wang et al. (2016) argued that with every job held previously, the CEOs developed their expertise. Thus, the more jobs CEOs had before working in the current position, the more experience they managed to gain. This expertise can be seen on different levels. The CEOs gain knowledge about the industry. Further, they develop social capital and build up connections to people inside and outside the organization. All this can help the CEO to perform better. In their study, Wang et al. (2016) could not find significant evidence for these assumptions for three measurement modes (i.e., organization experience, job experience, and task experience). However, the setting of this study is totally different. Consequently, it will be interesting if the results will provide a significant effect within this study. Besides, Wang et al. (2016) showed that CEO industry experience positively influences firm performance. Therefore, the hypotheses are as follows:

H1: Higher CEOs’ industry experience, measured in the number of jobs in the same industry, will lead to higher organizational success.

H2: Higher CEOs’ organizational experience, measured in numbers of jobs within the current organization, will lead to higher organizational success.

H3: Higher CEOs’ job experience, measured as the number of CEO jobs, will lead to higher organizational success.

H4: Higher CEOs’ task experience, measured in the number of tasks performed, will lead to higher organizational success. 29

3.2. Time

Henderson et al. (2006) argued that tenure could have different influences on organizational success, highly depending on the observed industry. They claim that in a stable industry initially, with rising CEO tenure, the organizational performance will rise to a maximum before it starts dropping as the CEO will rely too much on the status quo and tries to secure his legacy. In contrast, in an unstable industry, organizational performance shows a consistent decrease with CEOs’ higher organizational job tenure.

Therefore, it is crucial to understand the soccer industry more in-depth, which is the object of scrutiny in this master thesis. Generally, there are many indicators that the soccer industry is very stable. First, according to a representative study by Statista, soccer is the one sport with the highest number of fans worldwide. With an estimation of four billion soccer fans, it has almost twice as many fans as the second most famous sport, which is cricket, in this ranking (Statista, 2019). Second, two of the top10 most viewed sports events worldwide are found in the soccer industry. On rank one is the soccer world championship with an average of 3 billion viewers. The champions league final is on rank 4 with around 1.7 billion viewers year after year (Magazin, 2020). Moreover, rules hardly ever change, and there has not been a significant reinvention over the past decades. Subsequently, it can be assumed that this industry is very stable. However, within this stable construct, there is a highly volatile transfer market. For example, players and CEOs are constantly switching teams. Nowadays, there are hardly any trainers or players who stay in one organization for their whole career as the pressure is too high. As soon as a CEO does not deliver any longer, they will be dismissed. Statista (2011) compared the legacy of CEOs in a German Bundesliga team with the legacy of CEOs of a German publicly listed company. The juxtaposition of industries showed that the average organizational job tenure in the soccer industry only is 2.1 years, while the average organizational job tenure as CEO of a publicly listed company is 5.1 years. This volatile market within the stable construct makes it hard to predict which characteristics will prevail. However, as Henderson et al. (2006) defined the industry's stability as the critical point concerning profitability, it is assumed that the soccer industry is a stable industry, and thus, an inverted U-shape is expected. Moreover, this is in line with earlier studies in the sports industry (Eitzen & Yetman, 1972; Giambatista, 2004). Thus, this master thesis hypothesizes as follows:

H5a: Medium CEOs’ organizational job tenure will result in higher organizational success.

H5b: High CEOs’ organizational job tenure will result in lower organizational success.

H6a: Medium CEOs’job tenure will result in higher organizational success.

H6b: High CEOs’ job tenure will result in lower organizational success.

3.3. Type

The approach this master thesis follows by analyzing the CEO's prior career path before being promoted into the current CEO position is a combination of the studies by Orens and Reheuls' (2013) and Crossland et al. (2014). On the one hand, Orens & Reheul (2013) studied the CEOs' prior career path concerning same-industry experience and other-industry experience. In addition, Crossland et al. (2014) analyzed CEOs' prior career path in relation to their career variety. This study will follow a mixed approach, meaning that on the one hand, CEOs' prior career path will be analyzed regarding same-organization experience and same-industry experience. On the other hand, career variety will be analyzed within those two subgroups. By integrating two different approaches, new research directions are likely to emerge. However, not only is this mixed approach new to research, but the addition of the same-organization experience adds another level to Oren and Reheuls' (2013) study. In brief, for same-industry experience, each CEO will be analyzed as to whether they have worked in this industry as either an employee or a TMT member, or both. Same-organization experience is measured in the same way but focusing only on the current organization. Therefore, four different variety levels exist for both same-organization experience and same-industry experience, namely (1) no prior work experience, (2) prior work experience as an employee, (3) prior work experience as a TMT member, and (4) prior work experience as an employee and as a TMT member.

Despite research into the influence of same-industry experience being contradictory, this study expects a positive effect of a more diverse prior career path within the same industry on organizational success. It is assumed that experience in various positions in the industry will help the CEO to understand the industry from different viewpoints. Moreover, the CEO can encounter and learn from different ways of acting (Eisenhardt & Schoonhoven, 1996). When

literature suggests that CEOs with high same-industry experience will overcommit to the status quo, this danger is assumed to be exceptionally great for CEOs with a high same-organization experience (Geletkanycz & Black, 2001; Hambrick, Geletkanycz, & Fredrickson, 1993). However, it is presumed that the findings from this literature do not correspond to the analyzed industry within this master thesis for both the same-industry and same-organization experience. As already shown, the soccer industry has a very volatile employee market. Thus, employees and TMT members usually can observe different CEOs in that position and see different approaches. Furthermore, some researchers also found positive effects concerning experience within the organization. For example, Shen and Canella (2002) found that CEOs who are insiders have a higher in-depth knowledge of the organizational structure, organizational resources, and organizational needs. Hence, the researchers stated that this knowledge would help the CEO make the right changes if needed. Moreover, Crossland et al. (2014) indicate that a wider variety of career paths will help the CEO to act more proactively. Therefore, it is hypothesized that:

H7: The more diverse CEOs’ same-industry experience, the better the organizational success will be.

H8: The more diverse CEOs’ same-organization experience, the better the organizational success will be.

4. Empirical Research

Within this section, the data collection and the underlying research design are explained. Therefore, it is clarified which variables were collected, how, and what purpose therefore was. Moreover, the data sample is described and their measurement methods. Finally, the research model is shown, the data is analyzed, and results are summarized.

4.1. Data collection.

This master thesis attempts to test the prior explained hypotheses by analyzing an unexplored industry, namely the soccer industry. Thereby, emphasis is put on the CEOs, their prior work experience, and the influence on their performance. Consequently, it is crucial to understand who the CEO of a soccer team is. Many studies in the sports industry showed that the coaches of a soccer team have a considerable impact on team performance. Moreover, they make weekly strategic decisions when deciding which team member plays and how they tactically play. Thus, in line with other studies, the teams' coach is considered as the CEO of a soccer team in this master thesis (Lassalle, Meschi, & Metais, 2018; Smith & Cushion, 2006).

Now that the fundamentals are settled, the data collection process can be explained. During this process, at first, the most appropriate website is defined. With almost 92 million views in February 2021, www.transfermarkt.de is one of Germany's most known sports web pages. Only www.kicker.de and www.bild.de had more klicks within the sports industry (IVW, 2021). While www.kicker.de not solely concentrates on soccer, as they post topics on other sports like basketball, ice hockey, and many more too, www.bild.de has many more issues besides sports in general. Furthermore, both web pages mainly concentrate on German soccer, whereas www.transfermark.de focuses on all professional soccer leagues worldwide. Therefore, concerning soccer and especially concerning international soccer, www.transfermarkt.de is the number one web page. Consequently, the data for this master thesis was collected from the webpage www.transfermarkt.de. More in detail, the data was gathered by web scraping with the programs “R” and “RStudio”. To understand the principle of web scraping, it is essential to understand that every web page works similarly. Namely, the web page can show the information only by saving it under a unique code. At web scraping, this unique code is used to download the stored data. The programmed web scraping code is attached to this master thesis

appendix. There it can be viewed in detail and, if desired, also executed with the programs “R” and “RStudio”. While reading this code or looking at the observed web page, it is noticeable that www.transfermarkt.de does not entail information on, for example, leadership styles. Moreover, it would be beyond the remit of this thesis to do interviews with all the coaches. Hence, this master thesis only considers the observable characteristics of the teams and the coaches.

Moreover, it was decided to download data from the top10 European soccer leagues. Thereby, the reference is the UEFA five-years ranking at the reference date 31st December of 2020. According to this ranking, the top10 soccer leagues in Europe are (1) England (Premier League), (2) Spain (LaLiga), (3) Italy (Serie A), (4) Germany (German Bundesliga), (5) France (Ligue 1), (6) Portugal (Liga NOS), (7) Netherlands (Eredivisie), (8) Russia (Premier Liga), (9) Belgium (Jupiler Pro League), and (10) Austria (Austrian Bundesliga) (UEFA, 2021). In the second step, data was collected for one decade. Data was generated starting from the 2010/11 season and ending with the 2019/20 season. With these parameters, it was possible to download all the needed data for 2,669 observations. However, for correct regression analysis, the dataset needed to be cleaned. First, as every coach is only allowed to be employed at one team per season, only 2,589 observations remained. Second, all leaders who coached less than ten games in a season were excluded. Hence, it is assumed that such coaches have had too little time to influence strategic decisions. Thus, in the end, the final dataset consists of 2,177 observations. In this dataset, there are 290 different teams and 770 different coaches found.

4.2. Sample description

As already mentioned, the top10 European soccer leagues were analyzed. Table 3 exemplifies the league attributes with some interesting investigations. For example, the number of teams in the division per season varied, which also influenced the number of games each team played per league season. Moreover, several things in the table attract attention. First, not every league has the same number of clubs. As a result, each team plays different numbers of games in the domestic league. For example, in a division with 20 teams (e.g., England), each team plays 38 games, whereas a division with 18 teams (e.g., Germany) only has 34 rounds. Second, there has been an increase in the number of teams within the observed decade in two leagues. Portugal increased its number of clubs in 2014 from 16 to 18 clubs. Additionally, Austria expanded the

Austrian Bundesliga in 2018 from 10 to 12 teams. In Portugal, the adaption led to an increase in games from 30 games to 34 games per team. However, in Austria, the expansion led to a decrease in games from 36 to 32 games per season.

League Number of teams played Number of league games per in this division per season team and season England (Premier League) 20 Teams 10 Seasons 38 Games Spain (LaLiga) 20 Teams 10 Seasons 38 Games Italy (Serie A) 20 Teams 10 Seasons 38 Games Germany (German Bundesliga) 18 Teams 10 Seasons 34 Games France (Ligue 1) 20 Teams 10 Seasons 38 Games Portugal (Liga NOS) 16 Teams 4 Seasons 30 Games 18 Teams 6 Seasons 34 Games Netherlands (Eredivisie) 18 Teams 10 Seasons 34 Games Russia (Premier Liga) 16 Teams 10 Seasons 30 Games Belgium (Jupiler Pro League) 16 Teams 10 Seasons 40 Games Austria (Austrian Bundesliga) 10 Teams 8 Seasons 36 Games 12 Teams 2 Seasons 32 Games Table 3: Summary of league attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de

Moreover, Table 4 investigates the team attributes. As illustrated in Table 4, the widest variety of teams played in England’s Premier League, with 36 different teams. In comparison, only 17 different teams played in the Austrian Bundesliga during the same time period. This difference is mainly due to the unequal number of teams playing in the league, as shown in Table 3. As only a few teams descend per year (e.g., one in Austria up to three in Spain), most teams stay in the highest division for a more extended time period. On average, each team remained in the league for six years. From the 290 different observed teams, 79 teams stayed in the division for the whole ten seasons.

Team attributes

Amount of different Austria 17 teams played per Belgium 22 league within the Netherlands 26 observed time Germany 28 frame Russia 30 Portugal 30 Spain 33 Italy 34 France 34 England 36

Number of seasons One season 34 a team played in the Two seasons 27 highest national Three seasons 27 division within the Four seasons 25 observed seasons Five seasons 20 Six seasons 17 Seven seasons 19 Eight seasons 21 Nine seasons 21 Ten seasons 79

Table 4: Summary of team attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de

Furthermore, Table 5 gives an insight into the CEO attributes of this study. As illuminated in Table 5, from the 770 coaches, most (109) have worked in Spain. As some coaches worked in several leagues (e.g., Carlo Ancelotti worked in England, Spain, Germany, Italy, and France), the numbers from Table 5 sum up to 888. Thereby, most coaches came from the ten countries observed. As exhibited in Table 5, most of them were born in the Netherlands and Spain (86). Interestingly, even if most teams in this data came from England, as seen in Table 4, only a few coaches are English (29). Besides the coaches from England, 15 other coaches came from Great Britain (11 from Scotland, two from Wales, and Ireland). Moreover, Table 5 indicates how many seasons a coach was employed from 2010/11 until 2019/20. Over 40% of the coaches only worked for one year in the analyzed period. On average, every coach worked for 2.8 seasons as a coach. However, it could be that the coaches in that period worked in other or lower leagues.

CEO attributes

Work location of Austria 70 employed coaches Russia 82 per league within Italy 85 the observed time Belgium 87 frame Netherlands 88 Portugal 89 England 91 Germany 92 France 95 Spain 109

Nationalities of the Spain 86 observed coaches Netherlands 86 France 77 Germany 77 Portugal 77 Italy 74 Russia 57 Austria 45 Belgium 39 England 29 Scotland 11 Serbia 10 others 102 Amount of seasons One season 312 a CEO was Two seasons 146 employed within Three seasons 92 the observed Four seasons 66 leagues and time Five seasons 48 frame Six seasons 35 Seven seasons 24 Eight seasons 19 Nine seasons 17 Ten seasons 11

Table 5: Summary of coach attributes; Source: Own elaboration based on the dataset gathered from transfermarkt.de

4.3. Research model

Having gained a deep understanding of the dataset in the last section, further attention is now paid to the research model, and the variables analyzed. In the part “CEO experience”, Table 1 revealed an integrated work experience framework (Quinones, Ford, & Teachout, 1995; Tesluk

& Jacobs, 1998). Table 6 now uses the framework from Table 1 to explain how the data is used to analyze CEOs' prior work experience in this master thesis. Primarily it is explained how the categories are linked to the observed industry. Like the framework by Quinones et al. (1995) and Tesluk and Jacobs (1998), Table 6 entails two different levels. First, “Level of Specificity” is divided into four subgroups, namely “Industry experience”, “Organizational experience”, “Job experience”, and “Task experience”. Industry experience in the context of this master thesis is related to the soccer industry. Organizational experience only considers the soccer club the CEO is currently employed in. Job experience in this master thesis is concerned with CEO jobs at organizations in the soccer industry. Finally, task experience is related to the soccer games the CEOs have coached their teams. Second, “Measurement mode” is divided into three groups, namely “Amount”, “Time”, and “Type”. They are used in the same way as proposed by Quinones et al. (1995) and Tesluk and Jacobs (1998). Amount is a cumulative measure, while time considers the period aspect the person has held the specific positions. Finally, type is a more qualitative aspect that considers the CEOs' career path before becoming into the current position. Thus, this is a 4x3 framework with up to twelve possible measurements. With the limitation of data availability and through the web scraping process, data was found for eight out of the twelve categories; these eight categories are highlighted in bold type.

Industry Amount of jobs in this Industry tenure Prior industry experience experience industry (0 = no prior industry experience, 1 = prior industry experience as an employee, 2 = prior industry experience as a TMT member, 3 = prior industry experience as an employee and a TMT member)

Organizational Amount of jobs in this Organizational Prior organizational experience organization job tenure experience (0 = no prior organizational experience,

1 = prior organizational Level of Specificity experience as an employee, 2 = prior organizational experience as a TMT- member, 3 = prior organizational experience as an employee and a TMT member) Job experience Amount of CEO jobs Job tenure Job complexity Task experience Amount of games as a Task tenure Task complexity CEO Amount Time Type Measurement Mode Table 6: Work experience framework related to this master thesis; Source: Own elaboration based on the dataset gathered from transfermarkt.de

Additionally, for a better understanding, Figure 6 illustrates the underlying research model. As displayed, the different levels of specificity, namely industry experience, organizational experience, job experience, and task experience (including the respective categories shown in Table 6), are being drawn to influence organizational success directly. Moreover, on the arrows, the prior defined hypotheses are marked.

Figure 6: Research model; Source: Own elaboration

4.4. Measurements

Within this section, each variable shown in Figure 6 is described. For a better understanding, it is explained how the data was gathered. Moreover, short statistical descriptions help to understand the variables more in-depth.

4.4.1. Dependent variable

As illustrated in Figure 6, the dependent variable is organizational success. Following Lassalle et al. (2018), organizational success is defined as the points a soccer coach reached per game per season. Thus, the potential interval of organizational success is from zero points up to three points per game. As a clarification, a team achieves three points with a win, one point with a draw, and zero points if they lose. In the dataset, this variable reaches from zero points per game to a maximum of 2.78 points per game. On average, each coach made 1.36 points per game.

4.4.2. Independent variables

In Table 6, the different independent variables analyzed in this master thesis are already exhibited. Moreover, it also indicates how the variables are measured. However, in the following chapter, a more concrete focus is set on the gathering and measuring processes.

Industry Experience

This master thesis measures CEOs’ prior industry experience by two different categorization schemes. First, “Amount” was operationalized similar to Wang et al. (2016) as the number of jobs the CEO had in this industry before the current position. From employee over TMT member to CEO, every possible position was considered. In line with Lassalle et al. (2018), this master thesis defines a soccer player as an employee of a soccer team. Even if the performance of a soccer player is crucial for organizational success, the player does not have any influence on strategic actions like tactics or team line-up. Further, concerning this master thesis, a TMT member works in any position except the CEO or employee positions. Some job examples would be assistant coach, scout, or sports director. It is assumed that all these positions influence strategic actions (e.g., an assistant coach helps the coach at tactics and team line-up while a scout and the sports director handling investment decisions like transfers and contract extensions). Having a look at the dataset, on average, the observed CEOs had 13 jobs within this industry before becoming in the current position.

Second, as previously mentioned, “Type” is a rather a qualitative approach that indicates the CEO's career variety. Crossland et al. (2014) and Geletjanycz and Black (2001) measured career variety as the number of different positions the CEO has held before. For the best suit to the analyzed sports context, four different levels are developed. Namely (1) no prior work experience, (2) prior work experience as an employee, (3) prior work experience as a TMT member, and (4) prior work experience as an employee and as a TMT member. However, in the sample are only three people (0,14%) with absolutely no industrial experience. Over 80% of the observed CEOs had prior industrial experience as an employee and as a TMT member before becoming into the current CEO position.

Organizational Experience

This master thesis found data for all three levels of organizational experience. First, “Amount” is operationalized in the same way as Wang et al. (2016) proposed and similar as before in the chapter of industry experience, only with a slight but crucial difference. Here the reference is the organization the CEO is currently employed instead of the industry as a whole. Therefore, the number describes how many jobs the CEO had in this organization before becoming its CEO. The average of 1.11 already indicates that only a few had worked in the organization before becoming its CEO. The maximum of jobs within the organization before becoming its CEO is 13.

Additionally, organizational experience is measured in terms of “Time”. CEOs’ organizational job tenure is operationalized as the length of time, measured in days, held the CEO position within this organization. To calculate this period, the last day of a season was taken as a reference. Usually, a season ends on June 30, only the 2019/20 season ended, due to the COVID-pandemic, not before August 23. However, as previously said, literature suggested, a medium tenure is best for success. Consequently, organizational job tenure is divided into three categories, namely “low tenure”, “medium tenure”, and “high tenure” (Henderson, Miller, & Hambrick, 2006). Finkelstein et al. (1996) claimed that the respective cut-offs are below five years for low tenure, from five to nine years for medium tenure, and ten and more years for high tenure. However, as already revealed, soccer coaches' tenure is significantly lower than the tenure of CEOs of publicly traded companies (Statista, 2011). Therefore, a more appropriate categorization scheme is needed. Subsequently, this variable is analyzed in more detail. For calculating the cut-off lines, all the outliers are excluded. The remaining interval, from zero to 1,268 days, is then divided into three same-sized groups. Thus, CEOs with low organizational job tenure held their position for less than 423 days, while CEOs with medium organizational job tenure held their position for 423 to 846 days. Everyone who is longer in the CEO position in the observed organization than 846 days was attributed to high organizational job tenure. This means that CEOs with low organizational job tenure are employed for their first or second full seasons at the same club, while CEOs with medium organizational job tenure are in their third or fourth entire season at this organization. Finally, CEOs with high organizational job tenure are at least in their fifth entire season as CEO at the same club. As a result, over 42% of the observed CEOs have low tenure, while only 23% have medium tenure, and about 35% of CEOs have high organizational job tenure.

The third category for organizational experience is “Type”. Following Crossland et al. (2014) and Geletjanycz and Black (2001), career variety in the current organization is operationalized the same way as in the chapter of industry experience. However, the focus here is on the specific organization instead of the whole industry. Over 65% of observed CEOs had not worked in the organization before, around 10% were an employee, and approximately 11% were part of the TMT. Furthermore, 14% were both employee and TMT member within the organization before becoming the CEO.

Job Experience

Within this master thesis, CEOs’ prior job experience is measured with two categories: “Amount” and “Time”. Both are operationalized, similar as before, but with the only difference that only CEO jobs are counted. Thus, in line with Wang et al. (2016), “Amount” is the number of CEO jobs the leader had before getting the current position. This observation reaches from zero to a maximum of 24 CEO jobs. On average, a CEO had 5.67 CEO jobs before becoming CEO in the current organization.

Similar to before, tenure was measured as the time the CEO held a CEO position within this industry. Therefore, these days were counted, and in a second step, the intervals for job tenure were calculated. In the last step, this variable again was categorized. Due to the specificities in the observed industry, a general categorization scheme did not fit. Therefore, the cut-off lines are calculated the same way as with the variable “Organizational Experience” before. First, all the outliers were excluded. The remaining interval, from zero to 10,539 days, was then divided into three same-sized groups. Consequently, in this master thesis, low job tenure was operationalized as having worked as CEO for less than 3,513 days. This means that CEOs were employed in the CEO position for one to ten full seasons at any soccer club. CEOs with medium job tenure worked as a CEO for 3,513 days to 7,026 days, which means that they hold a CEO position for eleven to twelve full seasons. Everyone who worked as a CEO for more than 7,026 days, or more than 20 full seasons, was classified as CEO with high job tenure. In this dataset, 49% of observed CEOs had a low job tenure, while 34% had a medium job tenure, and 17% had a high job tenure.

Task Experience

Finally, task experience is only measured by one level in this master thesis. Ford et al. (1992) stated that CEOs with the same amount and same time of prior work experience differ in the amount they exceed a specific task. For this reason, this variable focuses on the number of tasks the CEO performed during prior CEO positions. In the specific soccer context of this study, a fulfilled task is considered a game a CEO has coached. Hence, this level is operationalized as the number of games the CEO has worked during his/her prior CEO positions. On average, in the dataset, a CEO worked for 273 games in their prior positions. However, the range is exceptionally high, reaching from zero games up to 1,607 games.

4.4.3. Control variables

Moreover, this study attempts to analyze six different control variables. Namely, (1) CEO age, (2) national differences between CEO and team, (3) linguistic differences between CEO and team, (4) number of employees, (5) average age of employees, and (6) share of legionaries among employees. These six control variables can be divided into two sub-groups. On the one hand, CEO age, national differences, and linguistic differences are related to the CEOs and their observable characteristics. On the other hand, the number of employees, the average age of employees, and the share of legionaries among employees refer to the team characteristics. As a result, both CEO and team characteristics are further analyzed.

Concerning the CEO characteristics, the literature showed that there is a connection between CEO age and prior work experience as CEO (Wang, Holmes Jr., Oh, & Zhu, 2016; Damanpour & Schneider, 2006; Naseem, Lin, Rehman, Ahmad, & Ali, 2019; McClelland, Liang, & Barker, 2010; Barker & Mueller, 2002; Yim, 2013; Orens & Reheul, 2013). However, even if it is also stated that the age solely should not be connected to prior work experience (Quinones, Ford, & Teachout, 1995), this thesis still controls the influence of CEO age on organizational performance. In line with other researchers (Naseem, Lin, Rehman, Ahmad, & Ali, 2019; Yim, 2013), CEO age is measured at the time when the respective season was finished. In this sample, CEO age ranges from 28 years to 73 years, with an average age of approximately 50 years.

Second, the variable “National differences between CEO and team” was introduced because Wang et al. (2012) recommend controlling for cultural differences related to firm performance. Therefore, the nation of the CEO and the nation of the team are compared. By doing so, the program “R” is used to detect differences. If there was a difference, the program coded with “Yes”; otherwise, if the nations corresponded, “R” coded “No”. This way, a new variable was generated with two levels. Interestingly, only in approximately 29% of the investigated cases in this study (627 of 2,177), there was a difference between the nation of the CEO and the team.

Finally, Kahane et al. (2013) argued that cultural differences do not have a significant influence on sports performance. However, they found a considerable impact on linguistic differences. Therefore, another control variable analyzes the influence of linguistic differences on organizational success. For this, the CEO language and the language in the country the team is based were compared. Consequently, a new variable was generated with two levels. If the languages of the CEO and the team differed, the code stated “Yes”, and if the variables corresponded, “No” was the answer. In this study's data sample, the difference rate was relatively low, with only approximately 20% cases (442 of 2,177) where the languages did not match.

Now coming to the team characteristics, which are controlled in this master thesis too. Bercker-Blease et al. (2010) investigated that the number of employees, depending on the industry, affects the firm performance. For this reason, it is analyzed how the number of employees influences firm performance in the sports industry. Thus, “Number of employees” is operationalized as the amount of players employed on the last day of the season. In this data sample, the employee number reached from 22 employees up to 88 employees per team. On average, each team hired about 36 employees.

Second, the impact of the employees' average age is analyzed. In the study of Ng and Feldman (2008), employee's age influenced seven out of ten performance measurements. In this master thesis, the employees' age is taken at the time the respective season was finished. To get the average age of the employees per team, the ages were accumulated and divided by the players' numbers. The dataset shows that the average employee age reaches from 21.1 to 28.4, with an average of 24.2 years.

Finally, coming to the share of legionaries among the employees. Kahane et al. (2013) studied the American ice hockey league (NHL) and found out that a more diverse team, concerning players nations, is more successful. Therefore, “Share of legionaries among employees” is operationalized as the number of foreigners divided by the number of players. Thus, this is a proportional result, which means the results can range from a minimum of 0% to a maximum of 100%. In this data sample, the share of legionaries reached from 3% to 100%, with an average of around 48% of employees are legionaries.

4.4.4. Correlation after Pearson

Table 7 below illustrates the correlation between the numeric variables. Tajeddini and Trueman (2012) set a value above 0.65 as the limit for dangerous multicollinearity between variables. This limit is in one case exceeded, namely the correlation of CEO age and amount of games the CEO led during earlier CEO positions is 0.66. However, it is not surprising that older CEOs also trained more games. Therefore, it is logical that there is a positive correlation between CEO age and amount of games. Moreover, the critical value is only exceeded by 0.01. For these reasons, this correlation value can be accepted and does not need to be further investigated. Furthermore, three other correlations in the existing dataset were found to be significant. First, the correlation between CEO age and the number of CEO jobs was found to be significant, with a value of 0.63. Nevertheless, it is also understandable that older CEOs also had more CEO jobs. Consequently, this is not considered a problem. Second, there was a correlation between the number of jobs in the industry and the number of CEO jobs. Hambrick et al. (1993) found a similar correlation. The reason is that each CEO job is also included in the variable of the jobs within the industry. Nevertheless, industry experience further includes jobs as an employee and jobs as a TMT member, and for that reason is a more all-encompassing variable. For these reasons, this correlation is also not astonishing. Finally, the correlation between the number of games and the number of CEO jobs was found to be significant, as demonstrated in Table 7. However, this, as well, is understandable as in the soccer industry, only a few CEOs stayed at one club for a long time. Therefore, CEOs who had more CEO positions consequently trained for more games too. Thus, this correlation is not considered to be critical for further regression analysis.

Mean SD 1 2 3 4 5 6 7 8 9 1 Organizational success 1.36 0.43 1 2 Industry Experience - Amount of jobs within this industry 13.25 5.20 -0.00 1 3 Organizational Experience - Amount of jobs within this organization 1.11 1.89 0.06 -0.05 1 4 Job Experience - amount of CEO Jobs 5.67 4.22 -0.04 0.58 ** -0.23 1 5 Task Experience - amount of games 272.50 224.46 0.17 0.31 -0.21 † 0.52 * 1 6 CEO age 49.36 7.50 0.01 0.38 † -0.15 0.63 ** 0.66 ** 1 7 Number of employees 36.14 6.33 -0.06 0.22 -0.05 0.20 0.15 0.11 1 8 Average age of employees 24.18 1.16 -0.15 0.03 -0.11 0.11 0.07 0.07 -0.19 1 9 Share of legionaries among employees 48.17 15.78 0.16 0.03 -0.15 0.10 0.18 0.05 0-12 -0.00 1 † Correlation is significant at level 0.10 * Correlation is significant at level 0.05 ** Correlation is significant at level 0.01 *** Correlation is significant at level 0.001 Table 7: Correlation matrix; Source: Own elaboration

5. Data analysis

After analyzing the literature regarding the UET and having an overview of how the study was conducted, the results of this study will be presented in this chapter. Therefore, first, the calculated research models are explained. Furthermore, a Breusch and Pagan Lagrange Multiplier test and a Hausman test are conducted to test which regression model produces the most valid information. Finally, the results from the most appropriate regression model are interpreted.

5.1. Regression analysis

Table 8 below illuminates the performed regression models. As presented there, in total, six different models were calculated. However, as already stated, the data gathered for this master thesis is so-called panel data. This means that soccer teams and soccer coaches were analyzed for ten consecutive seasons. When analyzing panel data, it is essential to test if the analyzed time frame influences the regression results. By doing so, three different types of models are made, namely (1) pooled OLS regression, (2) random-effect regression, and (3) fixed-effect regression, in which the six models can be classified. First, Model 1 and Model 2 are pooled-regression models. Here the explained time frame is not at all considered. Second, Model 3 and Model 4 are regression models with random effects, and finally, Model 5 and Model 6 are regression models with fixed effects. On the one hand, Model 1, Model 3, and Model 5 only measure the influence of the control variables on the dependent variable organizational success. On the other hand, Model 2, Model 4, and Model 6 measure the influence of the control variables and the independent variables on the dependent variable. For better visualization, all independent variables with significant results are highlighted in bold type. However, before analyzing the results, it has to be clarified which of the six models deliver the most valid information.

By doing standard regression analysis, the model with the highest R-square is taken. In this setting, Model 2 delivered the highest R-square with 0.1376. However, as the analyzed data is panel data, to know which of the three test methods is most valid, statistical tests must be conducted. First, a Breusch and Pagan Lagrange Multiplier test is executed. Thereby

Model 2, the pooled-regression model, is compared with Model 4, the regression model with random effects. Figure 7 illustrates the result of this test. As shown there, the p-value is below 0.05. This means that Model 4 delivers a more accurate result than Model 2. Additionally, the Hausman test must be executed to test if the fixed-effect model fits better or the random-effect model. As demonstrated in Figure 8, the p-value is below 0.05, which means that this test is as well significant. Thus, it can be summarized that the fixed-effect model fits best for analyzing this dataset. Subsequently, in the following chapter, only the results of Model 5 and Model 6 are described.

Figure 7: Breusch and Pagagan Lagrange Multiplier Test; Source: Own elaboration

Figure 8: Hausman Test; Source: Own elaboration

Pooled OLS Model Random Effects Model Fixed Effects Model Model 1 Model 2 Model 3 Model 4 Model 5 Model 6

CEO age 0.001 -0.006 *** -0.001 -0.005 * -0.013 *** -0.022

(0.256) (<0.001) (0.634) (0.033) (<0.001) (0.134) National differences 0.053 0.045 0.061 0.067 † 0.009 0.036

(0.105) (0.166) (0.116) (0.080) (0.882) (0.554) Linguistic differences 0.049 0.033 -0.031 -0.036 -0.047 -0.056

(0.183) (0.362) (0.455) (0.383) (0.449) (0.358) Number of employees -0.008 *** -0.009 *** -0.009 *** -0.009 *** -0.008 *** -0.008 ***

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Average age of employees -0.064 *** -0.060 *** -0.053 *** -0.049 *** -0.044 *** -0.045 ***

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001) Share of legionaries among employees 0.004 *** 0.004 *** 0.003 *** 0.003 *** 0.003 *** 0.003 ***

(<0.001) (<0.001) (<0.001) (<0.001) (<0.001) (<0.001)

Amount of jobs in the industry 0.003 0.003 0.023

(0.245) (0.466) (0.137)

Prior employee in the industry 0.003 0.374 X (0.246) (0.129) Prior TMT-member in the industry 0.473 * 0.447 † X (0.044) (0.058)

Industry Experience Industry Prior employee and TMT-member in the 0.453 † 0.460 † X industry (0.055) (0.052)†

Amount of jobs in the organization -0.005 -0.003 -0.010 (0.513) (0.740) (0.478)

Medium organizational job tenure 0.050 * -0.008 -0.043 *

(0.029) (0.668) (0.045) High organizational job tenure 0.047 * -0.021 -0.046 † (0.040) (0.318) (0.065) Prior employee in the organization 0.096 ** 0.094 ** 0.100 * (0.003) (0.003) (0.012)

Prior TMT-member in the organization 0.103 ** 0.121 *** 0.111 * Organizational Experience Organizational (0.002) (<0.001) (0.012) Prior employee and TMT-member in the 0.180 *** 0.190 *** 0.233 *** organization (<0.001) (<0.001) (<0.001)

Amount of CEO jobs in this industry -0.005 -0.007 -0.014

(0.171) (0.153) (0.429) Medium job tenure 0.002 0.019 0.013 (0.949) (0.471) (0.719)

High job tenure -0.081 † -0.026 -0.073 Job Experience Job (0.058) (0.580) (0.261)

Amount of games as CEO 0.001 *** 0.000 *** 0.000

(<0.001) (<0.001) (0.421)

Task Task Experience

Constant 2.922 2.564 2.744 2.274 3.056 3.228 n 2,177 2,177 2,177 2,177 2,177 2,177 Note: † p < 0.10; * p < 0.05; ** p < 0.01; *** p < 0.001 X = dropped because of singularity

Table 8: Results of regression analysis; Source: Own elaboration 50

5.2. Summary of results

At first, the results of the six control variables are explained. As already stated, the control variables are divided into two different groups of CEO characteristics and team characteristics. “CEO age”, “National differences”, and “Linguistic differences” are part of the CEO characteristics, whereas the “Number of employees”, “Average age of employees”, and “Share of legionaries among employees” are rated as team characteristics. Model 5 in Table 8 above indicates that CEO age has a highly significant negative (β=-0.013; p<0.001) influence on organizational success. However, when considering the independent variables too, as in Model 6, it becomes apparent that the effect on organizational success must rather be caused by the general CEO's prior work experience variables than CEO age (β=-0.022; p=0.134). Moreover, the national difference (β=0.036; p=0.554) and the linguistic differences (β=-0.056; p=0.358) between CEO and team were not found to influence organizational performance significantly. Interestingly, all three team characteristics were highly significant in influencing organizational performance in Model 5 and Model 6. A negative correlation (β=-0.008; p<0.001) was found between the number of employees and organizational success. This means that the more employees a soccer club hires, the worse the team performs. Moreover, the average age of the employees negatively influences (β=-0.045; p<0.001) organizational performance, which means that a younger team is more successful than an older soccer team. Finally, the share of legionaries among employees positively influences (β=0.003; p<0.001) organizational performance.

Coming now more in-depth to the independent variables, it is started with the accumulated variables. In this regard, Hypotheses 1 to 4 state that the number of jobs or completed tasks has a positive influence on the organizational success of the team. Hypothesis 1 focuses on the number of jobs the CEO prior had in this industry, no matter if it is a job as a CEO, job as a TMT member, or job as an employee. Thereby, it states that CEOs with more accumulated jobs within this industry are more successfully leading the current team. However, as exemplified in Table 8, even if the influence is positive (β=0.023; p=0.137), this result is not significant within the analyzed data sample. Consequently, due to a lack of significance, H1 has to be rejected. Second, Hypothesis 2 assumes that the number of jobs the CEO prior had in the current organization positively influences organizational performance. In fact, results reveal a negative effect of CEOs’ organizational experience on organizational success (β=-0.010;

p=0.478). However, no significant evidence for this correlation was found within the analyzed data. Thus, H2 has to be rejected. Third, Hypothesis 3 concentrates on the CEO jobs the CEO prior had in the soccer industry. It was hypothesized that there is a positive relationship between the number of CEO jobs and organizational performance. Similar to Hypothesis 2, results indicate a negative relationship (β=-0.014; p=0.429). However, these results were not significant too. Consequently, H3 has to be rejected. Finally, Hypothesis 4 assumes a positive relationship between the number of tasks performed and organizational success. This is true, findings reveal a slight positive effect (β=0.000; p-value=0.421). Nevertheless, because of the absence of significance, H4 has to be rejected too.

In contrast, Hypotheses 5 and 6 assume that the CEO's tenure is influencing organizational performance. In line with Henderson et al. (2006), it was assumed that a medium tenure would lead to higher organizational success, while a high tenure will lead to lower organizational success. Hypothesis 5 thereby focuses on the CEOs’ organizational job tenure. H5a assumes that a medium organization job tenure of the CEO is leading to higher organizational success. However, as displayed in Table 8, the regression model showed a negative influence (β=-0.043; p=0.045) on organizational success. This correlation is at a threshold of 0.05 significant. This means that CEOs with a medium organizational job tenure perform worse than CEOs with low organizational job tenure. Consequently, H5a has to be rejected. At the same time, H5b assumes that a CEO with a high organizational job tenure performs worse than a CEO with low organizational job tenure. Findings reveal that the performance of CEOs with high organizational job tenure is worse than CEOs with low organizational job tenure (β=-0.046; p=0.065). In fact, performance is 0.003 worse than for CEOs with medium organizational job tenure. However, these results are at a threshold of 0.05, not significant. Nevertheless, at least a tendency (at a threshold of 0.10) could be found. As a result, H5b could be partly supported. Hypothesis 6, in contrast, focuses on the CEOs' job tenure. By doing so, H6a assumes that a medium job tenure will result in higher organizational performance. Results indicate a slightly better organizational performance for organizations that employed a CEO with medium job tenure (β=0.013; p=0.719). However, this result is not significant. Thus, by a missing significance, H6a has to be rejected. Finally, Hypothesis 6b assumes that a high job tenure of the CEO will result in lower organizational performance. This negative correlation was found (β=-0.073; p=0.261); however, this result is, similar to before, not significant. Consequently, H6b has to be rejected.

Hypotheses 7 and 8 are analyzing the diversity of the CEOs' career paths in this industry. Thereby, there were four different levels considered, namely (1) no prior work experience, (2) prior work experience as an employee, (3) prior work experience as a TMT member, and (4) prior work experience as an employee and a TMT member. H7 analyzes these levels on the same-industry level, assuming that a more diverse career path leads to better organizational performance. Unfortunately, due to singularities, the program “R” was not able to calculate this variable. This means that this variable does not change over time, and therefore, it is not suitable for panel data analysis. Moreover, this variable is very poorly distributed. Only 0.14% (3 out of 2,177) of observed coaches have no industry experience at all. Additionally, 1.56% (34 out of 2,177) of the CEOs only have industry experience as an employee, whereas 17.82% (388 out of 2,177) only as a TMT member, and 80.58% (1,752 out of 2,177) of the observed coaches have industry experience as employee and TMT member. Through these problems, no support for H7 was found, and, consequently, H7 has to be rejected. Hypotheses 8, in contrast, analyzes the prior mentioned levels on the same-organizational level. Thereby, H8 assumes that a more diverse CEO experience within the current organization leads to higher organizational performance. In fact, the results from Table 8, are indicating this assumption. CEOs who have prior organizational experience as an employee are significantly better (β=0.100; p=0.012) than a CEO who has no prior organizational experience. Moreover, CEOs who have worked as TMT member in the current organization before becoming into the CEO position are significantly better (β=0.111; p=0.012) than CEOs without any organizational experience. CEOs who have both prior organizational experience as an employee and as a TMT member are better (β=0.233; p<0.001) than CEOs without any organizational experience. All these results are significant. Consequently, the results strongly support H8. Table 9 summarizes all the analyzed Hypotheses and giving an overview of which of them are being supported.

Hypotheses Statistically supported Values H1: Higher CEOs’ industry experience, Not supported due to β=0.023; p=0.137 measured in the number of jobs in the insignificance same industry, will lead to higher organizational success. H2: Higher CEOs’ organizational Not supported due to β=-0.010; p=0.478 experience, measured in numbers of insignificance jobs within the current organization, will lead to higher organizational success. H3: Higher CEOs’ job experience, Not supported due to β=-0.014; p=0.429 measured as the number of CEO jobs, insignificance will lead to higher organizational success. H4: Higher CEOs’ task experience, Not supported due to β=0.000; p=0.421 measured in the number of tasks insignificance performed, will lead to higher organizational success. H5a: Medium organizational job tenure Not supported due to β=-0.043 *; p=0.045 will result in higher organizational the wrong assumption success. H5b: High organizational job tenure Partly supported β=-0.046 †; p=0.065 will result in lower organizational success. H6a: Medium job tenure will result in Not supported due to β=0.013; p=0.719 higher organizational success. insignificance H6b: High job tenure will result in Not supported due to β=-0.073; p=0.261 lower organizational success. insignificance H7: The more diverse CEOs’ same- Not supported due to Dropped because of singularity industry experience, the better the singularity organizational success will be. H8: The more diverse CEOs’ same- Supported Organizational experience as an employee: organization experience, the better the β=0.100 *; p=0.012 organizational success will be. Organizational experience as a TMT member: β=0.111 *; p=0.012 Organizational experience as an employee and TMT member: β=0.233 ***; p<0.001 Table 9: Summary table of Hypotheses and their support; Source: Own elaboration

6. Discussion

As already shown, this work contributes to the UER in several ways. First, it brings light onto a hardly untapped research area, namely the sports industry, specifically the soccer industry. Second, it demonstrates the wide variety of CEO prior work experience and the importance of clarifying the measurement mode when analyzing this broad variable. Therefore, a more in-depth interpretation of the results will follow in the subsequent chapter. The focus lies in particular on what these results mean for theory and practice. Moreover, a comparison with the already existing literature will be made to check if this study confirms or refutes the theory. Finally, the limitations of this study will be listed, and suggestions for future research will be made.

6.1. Implications

Starting with some implications derived from the control variables concerning coach characteristics. First, Model 5 implicated that CEO age negatively influences organizational performance. Even though the majority of existing literature suggests that older CEOs are more risk-averse than younger CEOs (Yim, 2013; Orens & Reheul, 2013; McClelland, Liang, & Barker, 2010; Barker & Mueller, 2002), other researchers stated that CEO age positively influences firm performance as they are making fewer mistakes and more thoughtful investments (Wang, Holmes Jr., Oh, & Zhu, 2016). Thus, this finding would refute the current findings. However, by adding all the independent variables, it could be seen that this significant influence is not coming from the CEO age but rather from the experience measurements. Therefore, it is assumed that Wang et al. (2016) and Naseem et al. (2019) found effects unrelated to CEO age. Consequently, it is suggested for future research to always consider CEO experience measurements additionally to CEO age.

Second, Wang et al. (2012) assumed that cultural differences between the CEO and the organization could reduce firm performance. However, within the analyzed context, this assumption can be refuted. The data showed no significant influence of cultural differences on organizational performance within the soccer industry. Moreover, the data also refute that linguistic differences between CEO and organization could harm firm performance, as Kahane et al. (2013) indicated. It is assumed that this has several reasons. First, almost 50% of the

employees are on average legionaries, meaning employees from different nations. Second, through this high share of foreigners, the coached language is typically English. Third, through competitions like “UEFA Champions League” or “UEFA Europa League” where the best teams from Europe play against each other, internationality is promoted by the UEFA. Finally, over 80% of the observed coaches had a career as a soccer player. Most of the young players want to go abroad to play in the best leagues in the world. Thus, already at an early age, language skills are seen as an essential step to achieve these goals. Consequently, it is concluded that CEO nationality or CEO language does not influence organizational performance in any case. Summarized, this is an exciting finding for management. Only 28.80% of organizations have employed a CEO who is not coming from their home country, and only around 20% a coach who speaks another language. That although the study showed that there is no significant influence on firm performance. Meaning, organizations often limit themselves in their search for a new CEO without any real reason for doing so.

Coming to the implications of the team characteristics. First, the number of employees negatively affects organizational performance. The more employees an organization has, the worse they are performing. This finding is specific to the sports industry. Literature in business cases showed that more prominent companies usually outperform smaller companies due to their ability to scale better (Becker-Blease, Kaen, Etebari, & Baumann, 2010). However, in the soccer industry, teams are, by rules, only allowed to play eleven players plus three substitutions per game. Overall, a maximum of twenty players are allowed to be registered for a game day. Thus, if organizations have more players, they typically spend a massive amount of their budget on players that cannot even play. Therefore, and as the budget is always limited, they lack money for the best players. Managerially speaking, this finding indicates that soccer organizations should bundle their budget to a few outstanding players and have a minimal squad size. However, there is always a risk of injury to players. Due to the high risk involved in this tactic, it is not, or only limited, recommended to implement this finding by the organizations.

Moreover, the findings showed that the average age of employees negatively affects organizational performance. This finding is in line with Ng and Feldman (2008), who stated that younger employees are better at their core task performance. However, they also mentioned that older employees could bring several advantages for the company. For example, older employees are more loyal to the team and have higher social intelligence. It is assumed that for

these reasons, no team only employs young players. No matter how young the team is, there are always some older players integrated.

Finally, results for team characteristics showed that a higher share of legionaries among the employees leads to better performances. This finding was also reported by Kahane et al. (2013). While they studied NHL ice-hockey teams, they found as well a positive influence of legionaries. Kahane et al. (2013) stated that legionaries could offer different skillsets to the organization through different training methods in their youth. Contrary to this literature, no maximum amount of legionaries was found. However, this could also result from minor differences in the measurement mode. For example, this study only analyzed the share of legionaries, while Kahane et al. (2013) looked for the number of different nations. They found a maximum when too many different languages are spoken within the team. Therefore, further research could analyze both the share of legionaries and the number of different nations and see how they interact more accurately. Managerially speaking, the finding of this study implicates that every team should search for as many legionaries as possible. However, it is assumed that a team full of legionaries could harm the relationship with the fans, as they want to identify themselves with the team. Consequently, most teams probably have around 50% legionnaires and 50% homegrown players.

Results concerning CEOs' prior work experience are pretty contradictory. Positive, negative, and as well, no effect could be found. Therefore, in the following section, every variable is watched more in-depth to understand these contradictory results better. Starting with the “Amount” measures. Thereby, four different specificity levels got analyzed, namely, (1) the number of jobs within this industry, (2) the number of jobs within the current organization, (3) the number of CEO jobs within this industry, and (4) the number of tasks performed. All four variables were non-significant. However, this does not mean that these findings are not valuable. While analyzing the first three variables in more detail, an inverted U-shape was found in the relationship from organizational success to the other three variables. However, until now, it has not been analyzed whether this indication would yield significant results. Theoretically speaking, would this be a fascinating topic for further investigation. Furthermore, the findings of this study concerning the influence of CEO prior task experience on firm performance are in line with the findings of Wang et al. (2016). They as well found no influence on firm performance or firm profitability. However, they found that CEOs' prior task

experience positively influences strategic action. Therefore, future research could integrate the variable “strategic actions” as a moderator to get a more all-inclusive view of the impacts of CEOs' prior work experience. In general, it seems like the number of jobs or tasks alone does not deliver many insights on the relationship between CEOs' prior work experience and organizational performance. It is assumed that in the end, every job has to be analyzed separately. Moreover, like Quinones et al. (1995) already stated, not every job is the same. Most apparent are the different positions a person can hold in an organization. Starting as an employee, growing into a TMT member position (e.g., assistant coach, youth coach, translator, and many more), finally becoming a CEO, from each position, the person has a different perspective on decisions so he or she can learn different aspects. Moreover, not every job is the same regarding time aspects. For example, some coaches stayed at one club for over two decades while other coaches were just hired as interim coaches who only worked for a couple of weeks. Thus, the following two measurement modes are exciting.

In the part Hypothesis development/Time, it was discussed what will overwhelm in the soccer setting of this study: the stable character of the observed industry or the unstable players and coaches transfer market within this stable framework. In line with other research in the sports industry (Eitzen & Yetman, 1972; Giambatista, 2004), it was assumed that the stable industry would devastate, and therefore CEOs' prior work experience, measured in time, was categorized and then analyzed on an inverted U-shape. However, the findings now indicate that this assumption was wrong. The findings for organizational job tenure showed a continuous downward curve if the CEO comes from one group into the other. Indicating that the performance of CEOs who stayed in this position for a more extended period at the same organization is getting worse. As previously stated, prior literature is very contradictory within organizational job tenure. While Wang et al. (2016) found a positive impact of organizational job tenure on firm performance, Henderson et al. (2006) found a negative influence of organizational job tenure within an unstable industry. Consequently, this finding refutes Wang et al. (2016) findings and supports Hendersons et al. (2006) findings. Managerially speaking, these results would indicate that an exchange of the CEO after every season or at the latest at the end of the second season would lead to higher organizational performance. However, in line with other researchers, who analyzed the sporting industry (Eitzen & Yetman, 1972; Giambatista, 2004), it is assumed that a longer-tenured CEO can deliver unique advantages to the organization. First, they can establish their system of playing soccer. This system can be

translated to the second team and all the club's youth teams in a further step. As a result, young players learn this system more in-depth already at a very early age. This will give the organization a massive advantage over competitors, i.e., more and more youth players will be playing in the first team. Consequently, the organization can save money for high transfer fees. Moreover, more homegrown players will increase their fan base as fans can better identify themselves with such players. Besides that, longer-tenured CEOs give the organization the advantage that strategic actions, like investments (e.g., transfers or contract extensions), can be planned at an early stage and with the correct person in charge. Therefore, investment costs (e.g., transfer fees) could be lower and give the organization more money to invest in new players or player wages. Overall, it is suggested to deeper analyze this variable. Most appropriately by making more and smaller groups at the categorizations. For example, following Canella Jr. and Rowe (1995), who studied the Major League Baseball (MLB). They subdivided the variable “experience as CEO” by the entire seasons the examined CEO worked in this position before. A similar approach could show if this effect would still be accurate or in which season organizational performance would be at the maximum. Theoretically speaking, the same should be done for CEOs' job tenure. Although the current findings suggest no significant correlation with organizational performance, this may change if more and smaller groups are made.

Finally, the “Type” variables are interpreted, starting with the different levels a CEO worked within the industry. However, as previously stated, this variable was not calculable due to singularities. Still, this variable delivers an interesting managerial finding. Namely, the distribution of this variable indicates that industry experience in this setting is vital to becoming a CEO. Only 0.14% (3 of 2,177 observations) of CEOs do not have any industry experience. Over 80% (1,786 of 2,177 observations) of CEOs have prior work experience as an employee, and over 98% (2,140 of 2,177 observations) of CEOs have prior work experience as a TMT member in this industry. To summarize, this study could not find any significant evidence that CEOs with industry experience are better performers than CEOs without industry experience, but this study showed that CEOs need industry experience to be considered for the job in the first place. Second, coming to the CEOs' organizational career path. The most exciting finding was that a more diverse organizational career path is leading to significantly better performances. This study found out that CEOs who previously worked as an employee within the same organization are as a coach ceteris paribus 0.100 points better per game. If the CEO

has prior organizational work experience as a TMT member, organizational performance is ceteris paribus on average 0.111 points better. Moreover, if the CEO has prior work experience as an employee and a TMT member within this organization, the teams’ performance is ceteris paribus 0.233 points per game better. Theoretically speaking, this finding is in line with Crossland et al. (2014), who stated that a more diverse career path helps the CEO to perform better. Managerially speaking, these findings indicate that CEOs are significantly better when they have priorly worked in several positions of the organization. This diverse career path within the organization helps the CEO to understand the organizational structure, resources, needs, habits, and unwritten rules in more detail. From every position, the CEO can generate a different and unique view of the organization and its problem-solving routines and, as a consequence, results suggest that the first step for an organization hiring a new coach should always be a look at the internal TMT members. Additionally, results show that it is recommendable to promote players whose player career expired into a second career as a TMT member and prepare them for the CEO position in the long run.

Summarized the underlying research question one (RQ1) of this master thesis, which asked: “How does leaders' prior work experiences influence organizational performance within a sport setting?”, can be answered with mixed findings. Like previously showed, all variables which were measured by accumulation did not found any significant influence on organizational performance. The time-measured variables found different results as well. CEOs' job tenure does not influence organizational performance. Though, CEOs' organizational job tenure harms organizational performance, meaning that if a CEO stays at one organization as CEO over a longer period, the teams’ performance is continuously dropping. However, as previously stated, a more detailed look with smaller groups on this variable should deeper analyze whether there is a maximum (e.g., in the second season as CEO of this organization) or if performance constantly drops. Nevertheless, findings suggest that if a CEO had prior work experience in different positions in the current organization, organizational performance is significantly better.

The results are even more interesting when considering the different levels of CEO prior work experience, namely (1) industry experience, (2) organizational experience, (3) job experience, and (4) task experience, as was asked in RQ2. Regarding the first point, “Industry experience”, no significant relationship could be found, and even more, the qualitative approach of CEOs'

prior industry experience could not calculate a result due to singularities. However, as previously mentioned, the distribution of prior industry experience shows that the CEOs need the industry experience to be considered for the CEO job. The second variable, organizational experience, showed every possible result. There was no influence, negative influence, and positive influence found in this work. The simple accumulation of jobs within this organization does not indicate any prove for influencing organizational performance. However, when checking for the different positions the CEO had within this organization prior to getting CEO, the results show that organizational experience helps the CEO perform significantly better. This means that if the CEO had more different jobs within this organization, the better the team performs in the end. Nevertheless, results for organizational job tenure indicate that CEOs who were characterized with a medium or high tenure are significantly worse performers. Therefore, it can be summarized that allowing the CEO to grow into this position by employing him beforehand in different positions at the organization will help him understand the organizational needs and practices. Nevertheless, results as well indicate that when performance starts dropping, there is little hope for improvements. However, it is recommended to make a more detailed analysis to determine precisely in which season the performance is the best. For both CEOs' prior job experience and prior task experience, no significant influence could be found.

6.2. Limitation and further research suggestions

As the last chapter already indicated, this study has limitations. Through these limitations, some exciting future research suggestions were previously discussed. For example, studying CEOs' organizational job tenure with regards to the number of seasons the CEO has worked prior to the current season. This way, researchers can find out how long an organization should keep a coach. Moreover, analyzing the interaction between firm performance and the number of jobs the CEO had prior to the current position for an inverted U-shape effect. This could provide more accurate insights for management.

Another limitation of this study is that too few team characteristics were analyzed. This study's findings indicate that the team seems to be more important than the CEO within the soccer industry. This assumption is based on two main reasons. First, all observed variables concerning team characteristics were highly significant, while only a few CEO characteristics yield

significant impacts on team performance. Second, this finding is in line with Lassalle et al. (2018). In their study, they stated:

As regards the effect of control variables on the dependent variables, the different models in Table 2 show that team performance is significantly higher for clubs listed on the stock market with a strong presence of star players, a large stadium, large budgets, and low UEFA rankings. (Lassalle, Meschi, & Metais, 2018, S. 409)

Therefore, it is further suggested that in future research within the soccer industry, as many team characteristics as possible are included to show an all-encompassing picture.

Besides the team characteristics, the league characteristics could as well influence organizational performance immensely. The gap between Europe's top5 leagues, namely (1) England, (2) Spain, (3) Italy, (4) Germany, and (5) France, and the rest of the world is growing from year to year. The last time a team that does not play in one of the previously listed leagues won the UEFA champions league was Porto from Portugal back in 2004 (Weltfussball, 2021). Unfortunately, this gap is not included in the underlying research design. Thus, future research should attempt to consider league characteristics as well.

Indeed, this leads to another interesting aspect. There is not only a difference in performance from the top5 leagues to the others. Moreover, there is also a cultural difference from nation to nation, i.e., soccer is being lived and played slightly different in Spain than in England or in Germany. Therefore, it is proposed to introduce a fifth level of specificity to the work experience framework by Quinones et al. (1995) and Tesluk and Jacobs (1998), namely "National experience". Table 3, for example, already listed some differences between the leagues. Moreover, Mark van Bommel, a former star player at Bayern Munich, confirmed that soccer is thought differently in each country. During an interview, he indirectly applied for a CEO job in Germany because of his high experience within the league, while saying:

I think a Bundesliga club suits me because I have the experience here. That's not a reason to become a coach here, but I know the way of living soccer in Germany. (van Bommel, 2021)

As previously stated, this study analyzed the top10 European soccer leagues over the last decade. However, as the CEOs are the core foci of this paper, another way would have been to choose (e.g., 100) coaches and analyze their whole careers, from their first CEO job to their last

CEO job. This idea is based on the reasons that simple descriptive statistic measurements would indicate totally different results for the variable "organizational job tenure". By calculating the means (results are exemplified in Table 10) within all three categories, results suggest a positive effect of longer-tenured CEOs at one organization. This would be completely refuting the findings of this study, where a significant adverse effect was found. It is assumed that the main reason for this inconsistency is that, for example, only the last three coaching seasons of Sir Alex Ferguson (coach at Manchester United for 27 years) are included in the dataset. Similarly, only the last eight seasons of Arsene Wenger (coach at Arsenal FC for 22 years) are analyzed. As a result, for all seasons, they were assigned to high organizational job tenure. Consequently, their development is not at all analyzed. Those are only two famous examples; for sure, there are many similar examples. Besides more accurate results at this variable, such an analysis could have other positive advantages. For example, by adding coaches like Edoardo Reja (who has had 26 different CEO jobs in the last 42 years), it can further be analyzed if a longer tenure in one organization or a longer tenure in the industry results in higher organizational performance.

Category Average organizational success N Low organizational job tenure 1.305614 912 Medium organizational job tenure 1.386364 495 High organizational job tenure 1.401013 770 Table 10: Descriptive statistics of CEOs' organizational job tenure and organizational success; Source: Own elaboration

Another limitation is presented in Table 6. Unfortunately, due to data limitations, it was impossible to find variables for all the twelve CEOs' prior work experience measurements. Mainly due to limitations in the data gathering process. For example, it was not possible to analyze for how long a CEO was a soccer player. Thus, "Industry tenure" and "Organizational tenure" could not be calculated. Moreover, "Task tenure" could not be analyzed. However, since all soccer games usually last 90 minutes, except knockout games, which go into overtime, this variable will probably correlate very strongly with the variable "Amount of games". Furthermore, "Job complexity" and "Task complexity" could not be analyzed, as the analyzed web page accumulates the number of games instead of splitting them into the type of game. However, those two could deliver exciting results. For example, at "Job complexity", it could be analyzed if performance differs if CEOs have more / or less experience at different levels of

coaching. Therefore, this study already identified five different levels, namely (1) "Share of CEO jobs within the top5 leagues", (2) "Share of CEO jobs within the top10 leagues", (3) "Share of CEO jobs at professional teams not coming from the top10 leagues", (4) "Share of CEO jobs from national teams", (5) "Share of CEO jobs at amateur teams". Moreover, at "Task complexity", the number of games the CEO has coached could be divided into (1) "Share of national competitions", (2) "Share of international competitions", (3) "Share of games with a national team", and (4) "Share of friendly games". This way, it can be analyzed whether the CEO learns more from international or national competition. Unfortunately, this data is not available on www.transfermarkt.de. Thus, searching for further information on different web pages would have been beyond the remit of this master thesis. Nevertheless, Table 11 exemplifies an all-encompassing framework for future research. Moreover, Table 11 already includes the prior mentioned fifth level of specificity, namely "National experience". Consequently, the 5 x 3 matrix sums up to 15 different measurement modes. However, Table 11 demonstrates 17 different variables since for the frames "National experience x Time" and "Organizational experience x Time", two variables could be interesting to research. First, "League tenure" or "Organizational tenure", meaning the years the CEO has spent in this league or organization as an employee, a TMT member, and a CEO. Second, "League job tenure" or "Organizational job tenure", meaning the years the CEO has spent in this league or organization as a CEO. Overall, finding relevant data to all measurement modes listed in Table 11 could give researchers and managers an all-including picture of the interaction between CEOs' prior work experience and organizational performance.

Industry Amount of jobs in this Industry tenure (as Prior industry experience (no experience industry (as an employee, a an employee, a prior industry experience, prior TMT member, and a CEO) TMT member, and industry experience as an a CEO) employee, prior industry experience as a TMT member, prior industry experience as an employee and a TMT member) National Amount of jobs in this League tenure (as Prior league experience (no prior experience league (as an employee, a an employee, a league experience, prior league TMT member, and a CEO) TMT member, and experience as an employee, prior a CEO) league experience as a TMT League job tenure member, prior league experience (as a CEO) as an employee and a TMT member) Organizational Amount of jobs in this Organizational Prior organizational experience

experience organization (as an tenure (as an (no prior organizational

employee, a TMT member, employee, a TMT experience, prior organizational and a CEO) member, and a experience as an employee, prior CEO) organizational experience as a Organizational job TMT-member, prior

Levelof Specificity tenure (as a CEO) organizational experience as an employee and a TMT member) Job experience Amount of CEO jobs Job tenure (as a Job complexity (Share of CEO CEO) jobs within the top5 leagues, Share of CEO jobs within the top10 leagues, Share of CEO jobs at professional teams not coming from the top10 leagues, Share of CEO jobs from national teams, Share of CEO jobs at amateur teams) Task experience Amount of games (as a Task tenure (as a Task complexity (Share of CEO; or as an employee CEO; or as an league games, Share of and as a CEO) employee and as a international games, Share of CEO) games with a national team, Share of friendly games) Amount Time Type Measurement Mode Table 11: Proposal for CEO prior work experience framework for future studies; Source: Own elaboration

Furthermore, it would be particularly interesting to integrate moderators and mediators into analyzing the interaction between CEO prior work experience and organizational performance. For example, this study indicated that longer-tenured CEOs at the same organization negatively influence organizational performance. However, it is assumed that they can improve strategic actions by planning the next season at an earlier stage and with the correct person in charge. Consequently, the costs of strategic actions can be minimized. Strategic actions in the soccer industry could be, for example, transfer fees, net spending (transfer fee minus transfer income), or the number of transfers. By adding such strategic actions in future research, management will gain a more profound knowledge of how the measurements of CEOs’ prior work experience interacts with (1) organizational performance, (2) strategic actions, and (3) over strategic actions on organizational performance.

Last, it should be mentioned that the UET also offers other levels as the one analyzed in this study, as illustrated in Figure 4. First, other variables, besides CEOs' prior work experience, can be studied within the observable facts, for example, formal education, origin, and many more. Moreover, the underlying characteristics are other variables that could deliver valuable results for management. In the UET within this category, the CEOs' personality, CEOs' leadership style, and CEOs' values are analyzed. As such data is not available on the internet, most probably interviews should be held to analyze these underlying characteristics. Figure 4 also implicates a third approach, namely "Interaction with others". Here the focus is on the CEOs' power and how they interact with people inside and outside the organization. Furthermore, as shown in Figure 4, the focus of the UET is the CEO, the whole TMT, and how they interact. However, this thesis only analyzed the CEO. Therefore, adding the TMT or the interaction between CEO and TMT could deliver further in-depth knowledge to management and theory within this industry.

7. Conclusions

In conclusion, this study analyzed an until now untapped industry. Findings indicate mixed effects of CEOs' prior work experience on firm performance. The study found, for example, that a more diverse career path within the same organization helps the CEO to better understand the organizational needs and routines, and as a result, perform better. However, the simple accumulation of jobs within the organization does not lead to significant interactions with firm performance. Therefore, this study showed that analyzing each position separately is crucial for analyzing an accurate picture for future research. In contrast, findings indicate a negative effect of organizational job tenure, meaning the longer the CEOs are employed in this very position within a particular organization, the worse they perform. However, as previously mentioned, analyzing this variable again with more but smaller groups will explain this connection in greater detail and help management make the best decisions.

However, it is worth mentioning that this study did not find considerable support for the UET within this industry. In line with previous research into the soccer industry, the results of this study indicate that team characteristics seem to be more important for organizational performance than the CEOs' characteristics (Lassalle, Meschi, & Metais, 2018). This does not mean that the CEO does not influence firm performance. However, it seems that other variables are more important than the CEO characteristics analyzed in this study, namely CEO prior work experience, CEO age, CEO nationality, and CEO language. Future research, therefore, should expand the field into more directions. First, other observable facts, such as the formal education of the CEO, should be analyzed. Second, while analyzing the entire prior work experience framework explained in Table 11, researchers and managers could win an all-encompassing overview. Third, underlying facts concerning the CEOs’ personality and leadership style need further scrutiny. Successful coaches were almost always said to have a good relationship with the players. Thus, understanding this relationship will give an in-depth insight. Finally, results indicate that the number of team characteristics analyzed should be extended to a maximum.

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II. Appendix

II.I. Web scraping code used in R

```{r, results='hide', warning=FALSE, eval = FALSE} library(rvest) library(stringr) library(tidyverse) library(lubridate, warn.conflicts = FALSE) library(AER) library(corrplot) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} links <- read_csv2("Ligalinks.csv") link <- links %>% pull('Liga') %>% as.character()

Teams <- data.frame(Team_Name=character(), Link=character(), Number_of_employees=character(), Average_age_of_employees=character(), Amount_of_Legionaries_among_employees=character(), Liga=character()) for (i in link){ link1 <- read_html(i) Team_Name = link1 %>% html_nodes("#yw1 .no-border-rechts") %>% html_nodes("a") %>% html_nodes("img") %>% html_attr("alt") Link = link1 %>% html_nodes("#yw1 .no-border-rechts") %>% html_nodes("a") %>% html_attr("href") Number_of_employees = link1 %>% html_nodes(".show-for-pad+ .zentriert") %>% html_text() %>% as.numeric() Average_age_of_employees = link1 %>% html_nodes("tbody .hide-for-pad:nth-child(5)") %>% html_text() %>% str_trim() Amount_of_Legionaries_among_employees = link1 %>% html_nodes("tbody .hide-for-pad+ .zentriert") %>% html_text() %>% as.numeric() Liga = link1 %>% html_nodes(".tab-print .table-header") %>% html_text() %>% str_trim() temp <- data.frame(Team_Name, Link, Number_of_employees, Average_age_of_employees, Amount_of_Legionaries_among_employees, Liga) Teams <- rbind(Teams, temp) cat("*") } rm(link1, temp, Average_age_of_employees, i, Number_of_employees, Amount_of_Legionaries_among_employees, link, Link, Team_Name, Liga, links) link <- Teams %>% pull(Link) %>% as.character()

Austria <- read.csv2("Österreich.csv")

Teams <- Teams %>% mutate(Liga = case_when(Liga == "Tabelle Premier Liga 10/11" ~ "Tabelle Premier Liga 11/12", Liga == "Tabelle Premier Liga 2010" ~ "Tabelle Premier Liga 10/11", TRUE ~ as.character(Liga))) %>% separate(col = "Liga", XII

into = c("entfernen", "Liga", "Saison"), sep = " ") %>% mutate(Team_Nation = case_when(Liga == "Premier League" ~ "England", Liga == "LaLiga" ~ "Spanien", Liga == "Serie A" ~ "Italien", Liga == "Bundesliga" ~ "Deutschland", Liga == "Ligue 1" ~ "Frankreich", Liga == "Premier Liga" ~ "Russland", Liga == "Liga NOS" ~ "Portugal", Liga == "Eredivisie" ~ "Niederlande", Liga == "Jupiler Pro League" ~ "Belgien")) %>% mutate(Team_Language = case_when(Liga == "Premier League" ~ "Englisch", Liga == "LaLiga" ~ "Spanisch", Liga == "Serie A" ~ "Italienisch", Liga == "1. Bundesliga" ~ "Deutsch", Liga == "Ligue 1" ~ "Französisch", Liga == "Bundesliga" ~ "Deutsch", Liga == "Premier Liga" ~ "Russisch", Liga == "Liga NOS" ~ "Portugiesisch", Liga == "Eredivisie" ~ "Niederländisch", Liga == "Jupiler Pro League" ~ "Niederländisch")) %>% mutate(Team_Nation = case_when(Team_Name %in% Austria$Team ~ "Österreich", TRUE ~ as.character(Team_Nation))) %>% mutate(Team_Name = case_when(Team_Name == "Dynamo Moskau" ~ "Dinamo Moskau", Team_Name == "Terek Grozny" ~ "Akhmat Grozny", Team_Name == "AC Cesena" ~ "Cesena FC", Team_Name == "FC Évian Thonon Gaillard" ~ "Thonon Évian Grand Genève FC", Team_Name == "Royal Mouscron-Péruwelz" ~ "Royal Excel Mouscron", Team_Name == "Parma FC" ~ "Parma Calcio 1913", Team_Name == "FC Internazionale" ~ "Inter Mailand", Team_Name == "Robur Siena" ~ "ACN Siena 1904", Team_Name == "AS Bari" ~ "SSC Bari", Team_Name == "Naval 1º de Maio" ~ "Associação Naval 1893", Team_Name == "Zenit St. Petersburg" ~ "Zenit St. Petersburg", Team_Name == "FK Tambov" ~ "PFK Tambov", Team_Name == "Germinal Beerschot Antwerpen" ~ "Beerschot AC", Team_Name == "SC Magna Wiener Neustadt" ~ "SC Wiener Neustadt", Team_Name == "SPAL 2013" ~ "SPAL", TRUE ~ as.character(Team_Name))) %>% select(!c("Link", "entfernen")) link <- paste0("https://www.transfermarkt.at", link)

Transfers <- data.frame(Team_Name = character(), Trainer_Name = character(), Trainer_Nation = character(), TE_amount_of_games_this_season = character(), Organizational_Success = character(), Link = character(), Jahr = character()) for (i in link){ link1 <- read_html(i) Team_Name = link1 %>% html_nodes(".dataName span") %>% html_text() %>% str_trim() Trainer_Name = link1 %>% html_nodes(".container-hauptinfo") %>% html_text() %>% str_trim() Trainer_Nation = link1 %>% html_nodes(".container-zusatzinfo .flaggenrahmen") %>% html_attr("alt") TE_amount_of_games_this_season = link1 %>% html_nodes("tr~ tr+ tr .zentriert.hauptlink") %>% html_text() %>% as.numeric() Organizational_Success = link1 %>% html_nodes(".table-border tr~ tr+ tr .zentriert:nth-child(5)") %>% html_text() %>% str_trim() Link = link1 %>% html_nodes(".container-hauptinfo") %>% html_nodes("a") %>% html_attr("href") Jahr = link1 %>%

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html_nodes(".mitarbeiterVereinSlider-prev+ .text span") %>% html_text() %>% str_trim() temp <- data.frame(Team_Name, Trainer_Name, Trainer_Nation, TE_amount_of_games_this_season, Organizational_Success, Link, Jahr) Transfers <- rbind(Transfers, temp) cat("*") } rm(link1, temp, i, link, Link, Organizational_Success, TE_amount_of_games_this_season, Team_Name, Trainer_Nation, Trainer_Name, Jahr)

Russland <- read.csv("Russland.csv")

Transfers <- Transfers %>% mutate(Jahr = case_when(Jahr == "Trainer in der Saison 10/11" & Team_Name %in% Russland$ï..Team ~ "Trainer in der Saison 11/12", Jahr == "Trainer in der Saison 09/10" ~ "Trainer in der Saison 10/11", TRUE ~ as.character(Jahr))) %>% mutate(Trainer_Language = case_when(Trainer_Nation == "England" ~ "Englisch", Trainer_Nation == "Spanien" ~ "Spanisch", Trainer_Nation == "Italien" ~ "Italienisch", Trainer_Nation == "Deutschland" ~ "Deutsch", Trainer_Nation == "Frankreich" ~ "Französisch", Trainer_Nation == "Argentinien" ~ "Spanisch", Trainer_Nation == "Österreich" ~ "Deutsch", Trainer_Nation == "Brasilien" ~ "Portugiesisch", Trainer_Nation == "Portugal" ~ "Portugiesisch", Trainer_Nation == "Schottland" ~ "Englisch", Trainer_Nation == "Schweiz" ~ "Deutsch", Trainer_Nation == "Armenien" ~ "Armenisch", Trainer_Nation == "Bosnien-Herzegowina" ~ "Bosnisch", Trainer_Nation == "Chile" ~ "Spanisch", Trainer_Nation == "Kroatien" ~ "Kroatisch", Trainer_Nation == "Mexiko" ~ "Spanisch", Trainer_Nation == "Niederlande" ~ "Niederländisch", Trainer_Nation == "Nordirland" ~ "Englisch", Trainer_Nation == "Norwegen" ~ "Norwegisch", Trainer_Nation == "Schweden" ~ "Schwedisch", Trainer_Nation == "Serbien" ~ "Serbisch", Trainer_Nation == "Slowenien" ~ "Slowenisch", Trainer_Nation == "Uruguay" ~ "Spanisch", Trainer_Nation == "Vereinigte Staaten" ~ "Englisch", Trainer_Nation == "Angola" ~ "Portugiesisch", Trainer_Nation == "Belarus" ~ "Weissrussisch", Trainer_Nation == "Belgien" ~ "Niederländisch", Trainer_Nation == "Dänemark" ~ "Dänisch", Trainer_Nation == "Kamerun" ~ "Französisch", Trainer_Nation == "Kap Verde" ~ "Portugiesisch", Trainer_Nation == "Montenegro" ~ "Montegrinisch", Trainer_Nation == "Nordirland" ~ "Englsich", Trainer_Nation == "Rumänien" ~ "Rumänisch", Trainer_Nation == "Russland" ~ "Russisch", Trainer_Nation == "Wales" ~ "Englisch", Trainer_Nation == "Irland" ~ "Englisch", Trainer_Nation == "Türkei" ~ "Türkisch", Trainer_Nation == "Ungarn" ~ "Ungarisch", Trainer_Nation == "Jamaika" ~ "Englisch", Trainer_Nation == "Ukraine" ~ "Ukrainisch", Trainer_Nation == "Griechenland" ~ "Griechisch", Trainer_Nation == "Tschechien" ~ "Tschechisch", Trainer_Nation == "Moldawien" ~ "Rumänisch", Trainer_Nation == "Bolivien" ~ "Spanisch", Trainer_Nation == "Island" ~ "Isländisch", Trainer_Nation == "Nordmazedonien" ~ "Mazedonisch", Trainer_Nation == "Algerien" ~ "Arabisch", Trainer_Nation == "Albanien" ~ "Albanisch", Trainer_Nation == "Israel" ~ "Hebräisch", Trainer_Nation == "Marokko" ~ "Arabisch", Trainer_Nation == "Finnland" ~ "Finnisch", Trainer_Nation == "Bulgarien" ~ "Bulgarisch", Trainer_Nation == "Mosambik" ~ "Portugiesisch")) %>% separate(col = "Jahr", into = c("entfernen", "entfernen1", "entfernen2", "entfernen3", "Saison"), sep = " ") %>% select(!c("entfernen",

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"entfernen1", "entfernen2", "entfernen3")) %>% filter(TE_amount_of_games_this_season > 0) %>% mutate(Team_Name = case_when(Team_Name == "Palermo FC" ~ "US Palermo", Team_Name == "1. Wiener Neustädter SC" ~ "SC Wiener Neustadt", TRUE ~ as.character(Team_Name)))

Transfers <- distinct(Transfers) data <- Teams %>% inner_join(Transfers, by = c("Team_Name", "Saison")) %>% select("Liga", "Team_Name", "Saison", "Number_of_employees", "Average_age_of_employees", "Amount_of_Legionaries_among_employees", "Trainer_Name", "TE_amount_of_games_this_season", "Organizational_Success", "Trainer_Nation", "Trainer_Language", "Link") rm(Teams, Transfers, Russland, Austria, Trainer)

Spieler_link <- data.frame(Name = data$Trainer_Name, Link = data$Link)

Spieler_link <- distinct(Spieler_link)

Spieler_link$Link <- paste0("https://www.transfermarkt.at", Spieler_link$Link) link <- Spieler_link %>% pull(Link) data <- data %>% select(!c("Link"))

Trainer <- data.frame(Team_Name = character(), Amtsantritt = character(), Amtsende = character(), Funktion = character(), Spiele = character(), Trainer_Name = character(), Geburtstag = character()) for(i in link){ tr <- read_html(i) Team_Name = tr %>% html_nodes("#yw1 .no-border-rechts") %>% html_nodes("img") %>% html_attr("alt") Amtsantritt = tr %>% html_nodes("#yw1 .no-border-links+ .zentriert") %>% html_text()%>% str_trim() Amtsende = tr %>% html_nodes("#yw1 .zentriert+ td.zentriert") %>% html_text() %>% str_trim() Funktion = tr %>% html_nodes("#yw1 td:nth-child(5)") %>% html_text() %>% str_trim() Spiele = tr %>% html_nodes("#yw1 .rechts+ td") %>% html_text() %>% str_trim() Trainer_Name = tr %>% html_nodes("h1") %>% html_text() %>% str_trim() Geburtstag = tr %>%

html_nodes(".dataDaten:nth-child(1) p:nth-child(1) .dataValue") %>% html_text() %>% str_trim() temp <- data.frame(Team_Name, Amtsantritt, Amtsende, Funktion, Spiele, Trainer_Name, Geburtstag) Trainer <- rbind(Trainer, temp) cat("*") } rm(Amtsantritt, Amtsende, Funktion, Spiele, Geburtstag, i, Team_Name, Trainer_Name, tr, temp, link)

Trainer <- Trainer %>% mutate(Team_Name = case_when(Team_Name == "SPAL 2013" ~ "SPAL", Team_Name == "SD Huesca " ~ "SD Huesca", Team_Name == "AC Arles-Avignon" ~ "Athlétic Club Arlésien", Team_Name == "WSG Wattens" ~ "WSG Tirol", Team_Name == "Dynamo Moskau" ~ "Dinamo Moskau", Team_Name == "Deportivo La Coruna " ~ "Deportivo La Coruna", Team_Name == "Gazovik Orenburg" ~ "FK Orenburg", Team_Name == "Terek Grozny" ~ "Akhmat Grozny", Team_Name == "1. Wiener Neustädter Sportclub" ~ "SC Wiener Neustadt", Team_Name == "AC Cesena" ~ "Cesena FC", Team_Name == "FC Évian Thonon Gaillard" ~ "Thonon Évian Grand Genève FC", Team_Name == "Royal Mouscron-Péruwelz" ~ "Royal Excel Mouscron", Team_Name == "FC Internazionale" ~ "Inter Mailand", Team_Name == "WAC - St. Andrä" ~ "Wolfsberger AC", Team_Name == "FC Zwolle" ~ "PEC Zwolle", Team_Name == "Parma FC" ~ "Parma Calcio 1913", Team_Name == "Robur Siena" ~ "ACN Siena 1904", Team_Name == "Real Saragossa " ~ "Real Saragossa", Team_Name == "AS Bari" ~ "SSC Bari", Team_Name == "SC Magna Wiener Neustadt" ~ "SC Wiener Neustadt", Team_Name == "Naval 1º de Maio" ~ "Associação Naval 1893", Team_Name == "FK Tambov" ~ "PFK Tambov", Team_Name == "Milan AC" ~ "AC Mailand", TRUE ~ as.character(Team_Name))) %>% mutate(Team = case_when(Team_Name %in% data$Team_Name ~ "Ja", TRUE ~ "Nein")) %>% separate(col = "Amtsantritt", into = c("Jahr_Antritt", "Amtsantritt"), sep = " ") %>% separate(col = "Amtsende", into = c("Jahr_Ende", "Amtsende"), sep = " ") %>% separate(col = "Geburtstag", into = c("Geburtstag", "entfernen3"), sep = " ", convert = TRUE) %>% select(!c("entfernen3")) write.csv2(Trainer, "Trainer.csv")

Trainer <- read.csv2("Trainer.csv") %>% select(!c("Jahr_Antritt", "Jahr_Ende", "X"))

Trainer$Geburtstag <- stringr::str_replace_all(Trainer$Geburtstag, pattern = "\t", replacement = "")

Trainer$Amtsende <- str_replace_all(Trainer$Amtsende, "[(]", "") Trainer$Amtsende <- str_replace_all(Trainer$Amtsende, "[)]", "") Trainer$Amtsantritt <- str_replace_all(Trainer$Amtsantritt, "[(]", "") Trainer$Amtsantritt <- str_replace_all(Trainer$Amtsantritt, "[)]", "")

Trainer$Amtsantritt <- dmy(Trainer$Amtsantritt) Trainer$Amtsende <- dmy(Trainer$Amtsende) Trainer$Geburtstag <- dmy(Trainer$Geburtstag) ind <- read.csv2("Spieler.csv")

Spieler_link <- Spieler_link %>% mutate(spieler = case_when(Link %in% ind$ï..Link ~ "Nein", TRUE ~ "Ja")) %>% filter(spieler == "Ja")

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link <- Spieler_link %>% pull(Link)

Spieler <- data.frame(Team_Name = character(), Trainer_Name = character()) for(i in link){ sp <- read_html(i) Team_Name = sp %>% html_nodes(".zentriert+ .no-border-rechts.zentriert") %>% html_nodes("img") %>% html_attr("alt") Trainer_Name = sp %>% html_nodes("h1") %>% html_text() %>% str_trim() temp <- data.frame(Team_Name, Trainer_Name) Spieler <- rbind(Spieler, temp) cat("*") } rm(Team_Name, sp, i, temp, Spieler_link, ind, Trainer_Name, link, x)

Spieler$Funktion <- "Spieler" Spieler$Team <- "Ja" Spieler$Amtsantritt <- dmy("01.01.1900")

Jobs_trainer <- Trainer %>% select(Trainer_Name, Team_Name, Funktion, Team, Amtsantritt)

Jobs <- bind_rows(Jobs_trainer, Spieler)

Jobs <- Jobs %>% mutate(Team_Name = case_when(Team_Name == "1.FC Kaiserslautern U19" ~ "1.FC Kaiserslautern", Team_Name == "1.FC Köln II" ~ "1.FC Köln", Team_Name == "1.FC Köln Jugend" ~ "1.FC Köln", Team_Name == "1.FC Köln U17" ~ "1.FC Köln", Team_Name == "1.FC Köln U19" ~ "1.FC Köln", Team_Name == "1.FC Nürnberg II" ~ "1.FC Nürnberg", Team_Name == "1.FC Nürnberg Jugend" ~ "1.FC Nürnberg", Team_Name == "1.FC Nürnberg U19" ~ "1.FC Nürnberg", Team_Name == "1.FSV Mainz 05 II" ~ "1.FSV Mainz 05", Team_Name == "1.FSV Mainz 05 U19" ~ "1.FSV Mainz 05", Team_Name == "AC Arles-Avignon" ~ "Athlétic Club Arlésien", Team_Name == "AC Florenz U17" ~ "AC Florenz", Team_Name == "AC Florenz U19" ~ "AC Florenz", Team_Name == "AC Hellas Verona" ~ "Hellas Verona", Team_Name == "AC Mailand Jugend" ~ "AC Mailand", Team_Name == "AC Mailand UEFA U19" ~ "AC Mailand", Team_Name == "AC Mailand U19" ~ "AC Mailand", Team_Name == "AC Brescia" ~ "Brescia Calcio", Team_Name == "AC Carpi" ~ "Carpi FC 1909", Team_Name == "AC Cesena" ~ "Cesena FC", Team_Name == "AC Parma" ~ "Parma Calcio 1913", Team_Name == "Académica Coimbra Jugen" ~ "Académica Coimbra", Team_Name == "Académica Coimbra B" ~ "Académica Coimbra", Team_Name == "Académica Coimbra U15" ~ "Académica Coimbra", Team_Name == "Académica Coimbra U17" ~ "Académica Coimbra", Team_Name == "ADO Den Haag Amateure" ~ "ADO Den Haag", Team_Name == "ADO Den Haag Jugend" ~ "ADO Den Haag", Team_Name == "ADO Den Haag U17" ~ "ADO Den Haag", Team_Name == "ADO Den Haag U19" ~ "ADO Den Haag", Team_Name == "ADO Den Haag U21" ~ "ADO Den Haag", Team_Name == "AFC Bournemouth U18" ~ "AFC Bournemouth", Team_Name == "Ajax Amsterdam II" ~ "Ajax Amsterdam", Team_Name == "Ajax Amsterdam Jugend" ~ "Ajax Amsterdam", Team_Name == "Ajax Amsterdam U17" ~ "Ajax Amsterdam", Team_Name == "Ajax Amsterdam U19" ~ "Ajax Amsterdam", Team_Name == "AKA Admira Wacker Mödling U18" ~ "FC Admira Wacker Mödling", Team_Name == "AKA Admira Wacker Mödling U19" ~ "FC Admira Wacker Mödling", Team_Name == "AKA Austria Wien U16" ~ "FK Austria Wien", Team_Name == "AKA Austria Wien U18" ~ "FK Austria Wien",

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Team_Name == "AKA Austria Wien U19" ~ "FK Austria Wien", Team_Name == "AKA Rapid Wien U15" ~ "SK Rapid Wien", Team_Name == "AKA Rapid Wien U18" ~ "SK Rapid Wien", Team_Name == "AKA Rapid Wien U19" ~ "SK Rapid Wien", Team_Name == "AKA Red Bull Salzburg U15" ~ "Red Bull Salzburg", Team_Name == "AKA Red Bull Salzburg U19" ~ "Red Bull Salzburg", Team_Name == "AKA Red Bull Salzburg U16" ~ "Red Bull Salzburg", Team_Name == "AKA Red Bull Salzburg U17" ~ "Red Bull Salzburg", Team_Name == "AKA Red Bull Salzburg U18" ~ "Red Bull Salzburg", Team_Name == "AKA Sturm Graz U18" ~ "SK Sturm Graz", Team_Name == "AKA Sturm Graz U17" ~ "SK Sturm Graz", Team_Name == "AKA Sturm Graz U19" ~ "SK Sturm Graz", Team_Name == "AKA St. Pölten U19" ~ "SKN St. Pölten", Team_Name == "AKA SV Ried U18" ~ "SV Ried", Team_Name == "Akademia FK Ufa" ~ "FK Ufa", Team_Name == "Akademia Krylya Sovetov Samara" ~ "Krylya Sovetov Samara", Team_Name == "Akademia Spartak Moskau" ~ "Spartak Moskau", Team_Name == "Akademia Dinamo Moskau" ~ "Dinamo Moskau", Team_Name == "Akademia Lokomotiv Moskau" ~ "Lokomotiv Moskau", Team_Name == "Akademia Rubin Kazan " ~ "Rubin Kazan", Team_Name == "Akademia Zenit St. Petersburg" ~ "Zenit St. Petersburg", Team_Name == "Akademia ZSKA Moskau" ~ "ZSKA Moskau", Team_Name == "Akhmat 2 Grozny" ~ "Akhmat Grozny", Team_Name == "Akhmat Grozny II" ~ "Akhmat Grozny", Team_Name == "Amkar Perm II" ~ "Amkar Perm", Team_Name == "Anzhi Makhachkala II" ~ "Anzhi Makhachkala", Team_Name == "Arminia Bielefeld II" ~ "Arminia Bielefeld", Team_Name == "Arminia Bielefeld U19" ~ "Arminia Bielefeld", Team_Name == "Arsenal Tula U19" ~ "Arsenal Tula", Team_Name == "Anzhi 2 Makhachkala" ~ "Anzhi Makhachkala", Team_Name == "AS Bari" ~ "SSC Bari", Team_Name == "AS Cannes U19" ~ "AS Cannes", Team_Name == "AS Nancy-Lorraine B" ~ "AS Nancy-Lorraine", Team_Name == "AS Nancy-Lorraine U19" ~ "AS Nancy-Lorraine", Team_Name == "AS Monaco B" ~ "AS Monaco", Team_Name == "AS Monaco U19" ~ "AS Monaco", Team_Name == "AS Rom Jugend" ~ "AS Rom", Team_Name == "AS Rom U17" ~ "AS Rom", Team_Name == "AS Rom U19" ~ "AS Rom", Team_Name == "AS Saint-Étienne B" ~ "AS Saint-Étienne", Team_Name == "AS Saint-Étienne U19" ~ "AS Saint-Étienne", Team_Name == "Ascoli Calcio U19" ~ "Ascoli Calcio", Team_Name == "Ascoli Calcio 1898" ~ "Ascoli Calcio", Team_Name == "Aston Villa U23" ~ "Aston Villa", Team_Name == "Atalanta BC" ~ "Atalanta Bergamo", Team_Name == "Atalanta Bergamo U19" ~ "Atalanta Bergamo", Team_Name == "Atalanta Bergamo U17" ~ "Atalanta Bergamo", Team_Name == "Athletic Bilbao B" ~ "Athletic Bilbao", Team_Name == "Athletic Bilbao U19" ~ "Athletic Bilbao", Team_Name == "Athletic Bilbao Jugend" ~ "Athletic Bilbao", Team_Name == "Atlético CP U19" ~ "Atlético CP", Team_Name == "Atlético Madrileño CF" ~ "Atlético Madrid", Team_Name == "Athlétic Club Arlésien B" ~ "Athlétic Club Arlésien", Team_Name == "AZ '67 Alkmaar" ~ "AZ Alkmaar", Team_Name == "AZ Alkmaar II" ~ "AZ Alkmaar", Team_Name == "AZ Alkmaar U17" ~ "AZ Alkmaar", Team_Name == "AZ Alkmaar U19" ~ "AZ Alkmaar", Team_Name == "AZ Alkmaar Jugend" ~ "AZ Alkmaar", Team_Name == "Bayer 04 Leverkusen U17" ~ "Bayer 04 Leverkusen", Team_Name == "Bayer 04 Leverkusen U19" ~ "Bayer 04 Leverkusen", Team_Name == "Bayer 04 Leverkusen UEFA U19" ~ "Bayer 04 Leverkusen", Team_Name == "Belenenses SAD U23" ~ "Belenenses SAD", Team_Name == "Benfica Lissabon B" ~ "Benfica Lissabon", Team_Name == "Benfica Lissabon U15" ~ "Benfica Lissabon", Team_Name == "Benfica Lissabon U17" ~ "Benfica Lissabon", Team_Name == "Benfica Lissabon U19" ~ "Benfica Lissabon", Team_Name == "Benfica Lissabon Jugend" ~ "Benfica Lissabon", Team_Name == "Birmingham City U18" ~ "Birmingham City", Team_Name == "Blackburn Rovers U21" ~ "Blackburn Rovers", Team_Name == "BNZ Rapid Wien U19" ~ "SK Rapid Wien", Team_Name == "BNZ Red Bull Salzburg U15" ~ "Red Bull Salzburg", Team_Name == "BNZ Red Bull Salzburg U19" ~ "Red Bull Salzburg", Team_Name == "BNZ SV Casino Salzburg U15" ~ "Red Bull Salzburg", Team_Name == "BNZ SV Casino Salzburg U17" ~ "Red Bull Salzburg", Team_Name == "BNZ SV Casino Salzburg U19" ~ "Red Bull Salzburg", Team_Name == "Brescia Calcio U19" ~ "Brescia Calcio", Team_Name == "Brescia Calcio Jugend" ~ "Brescia Calcio",

XVIII

Team_Name == "Boavista FC Jugend" ~ "Boavista Porto FC", Team_Name == "Boavista FC U15" ~ "Boavista Porto FC", Team_Name == "Boavista FC U17" ~ "Boavista Porto FC", Team_Name == "Boavista FC U19" ~ "Boavista Porto FC", Team_Name == "Bolton Wanderers U18" ~ "Bolton Wanderers", Team_Name == "Borussia Dortmund II" ~ "Borussia Dortmund", Team_Name == "Borussia Dortmund U17" ~ "Borussia Dortmund", Team_Name == "Borussia Dortmund U19" ~ "Borussia Dortmund", Team_Name == "Borussia Dortmund Jugend" ~ "Borussia Dortmund", Team_Name == "Borussia Dortmund UEFA U19" ~ "Borussia Dortmund", Team_Name == "Borussia Mönchengladbach II" ~ "Borussia Mönchengladbach", Team_Name == "Borussia Mönchengladbach U19" ~ "Borussia Mönchengladbach", Team_Name == "Brest Armorique FC" ~ "Stade Brest 29", Team_Name == "AS Brest" ~ "Stade Brest 29", Team_Name == "CA Osasuna B" ~ "CA Osasuna", Team_Name == "CA Osasuna U19" ~ "CA Osasuna", Team_Name == "Cagliari Calcio U19" ~ "Cagliari Calcio", Team_Name == "Carpi Berretti" ~ "Carpi FC 1909", Team_Name == "Cesena FC Jugend" ~ "Cesena FC", Team_Name == "Cesena Primavera" ~ "Cesena FC", Team_Name == "CD Leganés B" ~ "CD Leganés", Team_Name == "CD Feirense U19" ~ "CD Feirense", Team_Name == "CD Málaga" ~ "FC Málaga", Team_Name == "CD Nacional U19" ~ "CD Nacional", Team_Name == "CD Numancia B" ~ "CD Numancia", Team_Name == "CF Belenenses U19" ~ "Belenenses SAD", Team_Name == "CF União da Madeira B" ~ "CF União da Madeira", Team_Name == "Chievo Verona U19" ~ "Chievo Verona", Team_Name == "CS Marítimo B" ~ "CS Marítimo", Team_Name == "Crystal Palace U18" ~ "Crystal Palace", Team_Name == "De Graafschap Doetinchem II" ~ "De Graafschap Doetinchem", Team_Name == "De Graafschap Doetinchem U18" ~ "De Graafschap Doetinchem", Team_Name == "De Graafschap Doetinchem U19" ~ "De Graafschap Doetinchem", Team_Name == "Delfino Pescara 1936 U19" ~ "Delfino Pescara 1936", Team_Name == "Deportivo Alavés U19" ~ "Deportivo Alavés", Team_Name == "Deportivo Alavés B" ~ "Deportivo Alavés", Team_Name == "Deportivo Alavés Jugend" ~ "Deportivo Alavés", Team_Name == "Desportivo Aves U23" ~ "Desportivo Aves", Team_Name == "Deportivo de La Coruña B" ~ "Deportivo La Coruna", Team_Name == "Dinamo 93 Minsk" ~ "Dinamo Minsk", Team_Name == "Dinamo Minsk II" ~ "Dinamo Minsk", Team_Name == "Dinamo 2 Moskau" ~ "Dinamo Moskau", Team_Name == "Dinamo Moskau II" ~ "Dinamo Moskau", Team_Name == "Dinamo Moskau UEFA U19" ~ "Dinamo Moskau", Team_Name == "Dinamo 2 St. Petersburg" ~ "Dinamo St. Petersburg", Team_Name == "Dynamo Moskau" ~ "Dinamo Moskau", Team_Name == "DOS Kampen U19" ~ "DOS Kampen", Team_Name == "EA Guingamp B" ~ "EA Guingamp", Team_Name == "Espanyol Barcelona B" ~ "Espanyol Barcelona", Team_Name == "Espanyol Barcelona U19" ~ "Espanyol Barcelona", Team_Name == "Eintracht Braunschweig U19" ~ "Eintracht Braunschweig", Team_Name == "Eintracht Braunschweig II" ~ "Eintracht Braunschweig", Team_Name == "Eintracht Frankfurt II" ~ "Eintracht Frankfurt", Team_Name == "Eintracht Frankfurt U19" ~ "Eintracht Frankfurt", Team_Name == "Eintracht Frankfurt U17" ~ "Eintracht Frankfurt", Team_Name == "Eintracht Frankfurt Jugend" ~ "Eintracht Frankfurt", Team_Name == "Enisey Krasnoyarsk II" ~ "Enisey Krasnoyarsk", Team_Name == "ES Troyes AC B" ~ "ES Troyes AC", Team_Name == "ES Troyes AC U19" ~ "ES Troyes AC", Team_Name == "Espanyol Barcelona Jugend" ~ "Espanyol Barcelona", Team_Name == "Estoril Praia U19" ~ "GD Estoril Praia", Team_Name == "Estoril Praia U23" ~ "GD Estoril Praia", Team_Name == "Excelsior Rotterdam U19" ~ "SBV Excelsior Rotterdam", Team_Name == "Excelsior Rotterdam II" ~ "SBV Excelsior Rotterdam", Team_Name == "FC Admira Wacker Mödling II" ~ "FC Admira Wacker Mödling", Team_Name == "FC Admira/Wacker" ~ "FC Admira Wacker Mödling", Team_Name == "FC Alverca U19" ~ "FC Alverca", Team_Name == "FC Arsenal Jugend" ~ "FC Arsenal", Team_Name == "FC Arsenal UEFA U19" ~ "FC Arsenal", Team_Name == "FC Arsenal U18" ~ "FC Arsenal", Team_Name == "FC Arsenal U23" ~ "FC Arsenal", Team_Name == "FC Arsenal Reserves" ~ "FC Arsenal", Team_Name == "FC Augsburg II" ~ "FC Augsburg", Team_Name == "FC Augsburg Jugend" ~ "FC Augsburg", Team_Name == "FC Augsburg U19" ~ "FC Augsburg", Team_Name == "FC Augsburg U17" ~ "FC Augsburg", Team_Name == "FC Barcelona B" ~ "FC Barcelona",

XIX

Team_Name == "FC Barcelona U16" ~ "FC Barcelona", Team_Name == "FC Barcelona U18" ~ "FC Barcelona", Team_Name == "FC Barcelona U19" ~ "FC Barcelona", Team_Name == "FC Barcelona C (aufgel.)" ~ "FC Barcelona", Team_Name == "FC Barcelona Jugend" ~ "FC Barcelona", Team_Name == "FC Barcelona Juvenil" ~ "FC Barcelona", Team_Name == "FC Barcelona Promesas" ~ "FC Barcelona", Team_Name == "FC Bari 1908" ~ "SSC Bari", Team_Name == "FC Barreirense U19" ~ "FC Barreirense", Team_Name == "FC Bayern München II" ~ "FC Bayern München", Team_Name == "FC Bayern München Jugend" ~ "FC Bayern München", Team_Name == "FC Bayern München U17" ~ "FC Bayern München", Team_Name == "FC Bayern München U19" ~ "FC Bayern München", Team_Name == "FC Bologna Jugend" ~ "FC Bologna", Team_Name == "FC Bologna U17" ~ "FC Bologna", Team_Name == "FC Bologna U19" ~ "FC Bologna", Team_Name == "FC Brügge Jugend" ~ "FC Brügge", Team_Name == "FC Brügge U17" ~ "FC Brügge", Team_Name == "FC Brügge U19" ~ "FC Brügge", Team_Name == "FC Brügge UEFA U19" ~ "FC Brügge", Team_Name == "FC Burnley U18" ~ "FC Burnley", Team_Name == "FC Cádiz B" ~ "FC Cádiz", Team_Name == "FC Chelsea Reserves" ~ "FC Chelsea", Team_Name == "FC Chelsea U18" ~ "FC Chelsea", Team_Name == "FC Córdoba B" ~ "FC Córdoba", Team_Name == "FC Den Haag U19" ~ "ADO Den Haag", Team_Name == "FC Den Haag" ~ "ADO Den Haag", Team_Name == "FC Dordrecht II" ~ "FC Dordrecht", Team_Name == "Dordrecht'90" ~ "FC Dordrecht", Team_Name == " SVV/Dordrecht'90" ~ "FC Dordrecht", Team_Name == "FC Elche Jugend" ~ "FC Elche", Team_Name == "FC Empoli Jugend" ~ "FC Empoli", Team_Name == "FC Empoli U19" ~ "FC Empoli", Team_Name == "FC Emmen U19" ~ "FC Emmen", Team_Name == "FC Everton U18" ~ "FC Everton", Team_Name == "FC Everton U21" ~ "FC Everton", Team_Name == "FC Famalicão U17" ~ "FC Famalicão", Team_Name == "FC Famalicão U19" ~ "FC Famalicão", Team_Name == "FC Fulham U18" ~ "FC Fulham", Team_Name == "FC Fulham U23" ~ "FC Fulham", Team_Name == "FC Getafe B" ~ "FC Getafe", Team_Name == "FC Getafe Jugend" ~ "FC Getafe", Team_Name == "FC Granada B (Recreativo)" ~ "FC Granada", Team_Name == "FC Groningen II" ~ "FC Groningen", Team_Name == "FC Groningen U19" ~ "FC Groningen", Team_Name == "FC Groningen Jugend" ~ "FC Groningen", Team_Name == "FC Girondins Bordeaux B" ~ "FC Girondins Bordeaux", Team_Name == "FC Girondins Bordeaux U19" ~ "FC Girondins Bordeaux", Team_Name == "FC Ingolstadt 04 II" ~ "FC Ingolstadt 04", Team_Name == "FC Internazionale" ~ "Inter Mailand", Team_Name == "FC Liefering" ~ "Red Bull Salzburg", Team_Name == "FC Lorient U19" ~ "FC Lorient", Team_Name == "FC Lorient B" ~ "FC Lorient", Team_Name == "FC Magna Wiener Neustadt" ~ "SC Wiener Neustadt", Team_Name == "FC Málaga Jugend" ~ "FC Málaga", Team_Name == "Málaga CF B" ~ "FC Málaga", Team_Name == "FC Metz B" ~ "FC Metz", Team_Name == "FC Metz U19" ~ "FC Metz", Team_Name == "FC Metz Jugend" ~ "FC Metz", Team_Name == "FC Middlesbrough U18" ~ "FC Middlesbrough", Team_Name == "FC Middlesbrough U21" ~ "FC Middlesbrough", Team_Name == "FC Nantes B" ~ "FC Nantes", Team_Name == "FC Nantes U19" ~ "FC Nantes", Team_Name == "FC Paços de Ferreira U19" ~ "FC Paços de Ferreira", Team_Name == "FC Paris Saint-Germain B" ~ "FC Paris Saint-Germain", Team_Name == "FC Paris Saint-Germain U19" ~ "FC Paris Saint-Germain", Team_Name == "FC Penafiel Jugend" ~ "FC Penafiel", Team_Name == "FC Penafiel U15" ~ "FC Penafiel", Team_Name == "FC Penafiel U17" ~ "FC Penafiel", Team_Name == "FC Penafiel U19" ~ "FC Penafiel", Team_Name == "FC Porto Jugend" ~ "FC Porto", Team_Name == "FC Porto B" ~ "FC Porto", Team_Name == "FC Porto U19" ~ "FC Porto", Team_Name == "FC Porto U17" ~ "FC Porto", Team_Name == "FC Porto UEFA U19" ~ "FC Porto", Team_Name == "FC Reading U23" ~ "FC Reading", Team_Name == "FC Salzburg" ~ "Red Bull Salzburg",

Team_Name == "FC Schalke 04 II" ~ "FC Schalke 04", Team_Name == "FC Schalke 04 Jugend" ~ "FC Schalke 04", Team_Name == "FC Schalke 04 U16" ~ "FC Schalke 04", Team_Name == "FC Schalke 04 U17" ~ "FC Schalke 04", Team_Name == "FC Schalke 04 U19" ~ "FC Schalke 04", Team_Name == "FC Sochaux-Montbéliard U19" ~ "FC Sochaux-Montbéliard", Team_Name == "FC Sochaux-Montbéliard B" ~ "FC Sochaux-Montbéliard", Team_Name == "FC St. Pauli U19" ~ "FC St. Pauli", Team_Name == "FC Stade Rennes B" ~ "FC Stade Rennes", Team_Name == "FC Stade Rennes U19" ~ "FC Stade Rennes", Team_Name == "FC Sevilla B (Atlético)" ~ "FC Sevilla", Team_Name == "FC Sevilla B (Atlético) " ~ "FC Sevilla", Team_Name == "FC Sevilla U19" ~ "FC Sevilla", Team_Name == "FC Southampton U18" ~ "FC Southampton", Team_Name == "FC Tirol Innsbruck" ~ "FC Wacker Innsbruck", Team_Name == "FC Tirol Innsbruck II" ~ "FC Wacker Innsbruck", Team_Name == "FC Tirol Innsbruck Jugend" ~ "FC Wacker Innsbruck", Team_Name == "FC Turin Jugend" ~ "FC Turin", Team_Name == "FC Turin U19" ~ "FC Turin", Team_Name == "FC Toulouse B" ~ "FC Toulouse", Team_Name == "FC Tours B" ~ "FC Tours", Team_Name == "FC Tours U19" ~ "FC Tours", Team_Name == "FC Twente Enschede II" ~ "FC Twente Enschede", Team_Name == "FC Twente Enschede U17" ~ "FC Twente Enschede", Team_Name == "FC Twente Enschede U19" ~ "FC Twente Enschede", Team_Name == "FC Utrecht II" ~ "FC Utrecht", Team_Name == "FC Utrecht U19" ~ "FC Utrecht", Team_Name == "FC Utrecht U17" ~ "FC Utrecht", Team_Name == "FC Valenciennes B" ~ "FC Valenciennes", Team_Name == "FC Valenciennes U19" ~ "FC Valenciennes", Team_Name == "FC Valencia B (Mestalla)" ~ "FC Valencia", Team_Name == "FC Valencia U19" ~ "FC Valencia", Team_Name == "FC Valencia Jugend" ~ "FC Valencia", Team_Name == "FC Villarreal B" ~ "FC Villarreal", Team_Name == "FC Villarreal Jugend" ~ "FC Villarreal", Team_Name == "FC Villarreal U19" ~ "FC Villarreal", Team_Name == "FC Villarreal C" ~ "FC Villarreal", Team_Name == "FC Volendam U19" ~ "FC Volendam", Team_Name == "FC VVV" ~ "VVV-Venlo", Team_Name == "FC Wacker Innsbruck II" ~ "FC Wacker Innsbruck", Team_Name == "FC Wacker Tirol" ~ "FC Wacker Innsbruck", Team_Name == "FC Watford U18" ~ "FC Watford", Team_Name == "FC Watford U23" ~ "FC Watford", Team_Name == "FC Zwolle" ~ "PEC Zwolle", Team_Name == "FCO Dijon B" ~ "Dijon FCO", Team_Name == "FCO Dijon U19" ~ "Dijon FCO", Team_Name == "Feyenoord II" ~ "Feyenoord Rotterdam", Team_Name == "Feyenoord U19" ~ "Feyenoord Rotterdam", Team_Name == "Feyenoord Rotterdam Jugend" ~ "Feyenoord Rotterdam", Team_Name == "Feyenoord Rotterdam U17" ~ "Feyenoord Rotterdam", Team_Name == "FK Austria Wien II" ~ "FK Austria Wien", Team_Name == "FK Austria Wien Jugend" ~ "FK Austria Wien", Team_Name == "FK Austria Wien UEFA U19" ~ "FK Austria Wien", Team_Name == "FK Krasnodar II" ~ "FK Krasnodar", Team_Name == "FK Krasnodar 2" ~ "FK Krasnodar", Team_Name == "FK Krasnodar UEFA U19" ~ "FK Krasnodar", Team_Name == "FK Khimki 2" ~ "FK Khimki", Team_Name == "FK Moskau II" ~ "FK Moskau", Team_Name == "FK Rostov II" ~ "FK Rostov", Team_Name == "FK Rostov UEFA U19" ~ "FK Rostov", Team_Name == "FK Sochi (bis 2017)" ~ "FK Sochi", Team_Name == "FK Tambov" ~ "PFK Tambov", Team_Name == "FK Tambov II" ~ "PFK Tambov", Team_Name == "FK Ufa II" ~ "FK Ufa", Team_Name == "Florentia Viola" ~ "AC Florenz", Team_Name == "FSA Austria Wien U17" ~ "FK Austria Wien", Team_Name == "FSA Austria Wien U19" ~ "FK Austria Wien", Team_Name == "Fortuna Düsseldorf U19" ~ "Fortuna Düsseldorf", Team_Name == "Fortuna Sittard II" ~ "Fortuna Sittard", Team_Name == "Fortuna Sittard Jugend" ~ "Fortuna Sittard", Team_Name == "Fortuna Sittard U17" ~ "Fortuna Sittard", Team_Name == "Fortuna Sittard U19" ~ "Fortuna Sittard", Team_Name == "Frosinone Calcio U19" ~ "Frosinone Calcio", Team_Name == "Gazélec Football Club Olympique Ajaccio" ~ "GFC Ajccio", Team_Name == "Genoa 1893" ~ "Genua CFC", Team_Name == "Genua CFC U19" ~ "Genua CFC", Team_Name == "Genua CFC Jugend" ~ "Genua CFC",

XXI

Team_Name == "Germinal Beerschot Antwerpen" ~ "Beerschot AC", Team_Name == "Go Ahead Eagles Deventer U19" ~ "Go Ahead Eagles Deventer", Team_Name == "Gil Vicente FC U19" ~ "Gil Vicente FC", Team_Name == "Hamburger SV II" ~ "Hamburger SV", Team_Name == "Hamburger SV U17" ~ "Hamburger SV", Team_Name == "Hamburger SV U19" ~ "Hamburger SV", Team_Name == "Hamburger SV Jugend" ~ "Hamburger SV", Team_Name == "Hamburger SV U16" ~ "Hamburger SV", Team_Name == "Hannover 96 Jugend" ~ "Hannover 96", Team_Name == "Hannover 96 U17" ~ "Hannover 96", Team_Name == "Hannover 96 U19" ~ "Hannover 96", Team_Name == "Hannover 96 II" ~ "Hannover 96", Team_Name == "Heracles Almelo II" ~ "Heracles Almelo", Team_Name == "Hertha BSC Jugend" ~ "Hertha BSC", Team_Name == "Hertha BSC U16" ~ "Hertha BSC", Team_Name == "Hertha BSC U17" ~ "Hertha BSC", Team_Name == "Hertha BSC U19" ~ "Hertha BSC", Team_Name == "Hertha BSC II" ~ "Hertha BSC", Team_Name == "Hellas Verona U19" ~ "Hellas Verona", Team_Name == "Helmond Sport U19" ~ "Helmond Sport", Team_Name == "Huddersfield Town U23" ~ "Huddersfield Town", Team_Name == "Hull City U18" ~ "Hull City", Team_Name == "HSC Montpellier B" ~ "Montpellier HSC", Team_Name == "HSC Montpellier U19" ~ "Montpellier HSC", Team_Name == "Inter Mailand Jugend" ~ "Inter Mailand", Team_Name == "Inter Mailand U19" ~ "Inter Mailand", Team_Name == "Inter Mailand UEFA U19" ~ "Inter Mailand", Team_Name == "Juventus Turin Jugend" ~ "Juventus Turin", Team_Name == "Juventus Turin U19" ~ "Juventus Turin", Team_Name == "Juventus Turin U23" ~ "Juventus Turin", Team_Name == "Juventus Turin UEFA U19" ~ "Juventus Turin", Team_Name == "KAA Gent Reserve" ~ "KAA Gent", Team_Name == "KAA Gent U21" ~ "KAA Gent", Team_Name == "Karlsruher SC II" ~ "Karlsruher SC", Team_Name == "KFCO Beerschot Wilrijk" ~ "Beerschot AC", Team_Name == "KRC Genk U19" ~ "KRC Genk", Team_Name == "Krylya Sovetov 2 Samara" ~ "Krylya Sovetov Samara", Team_Name == "Krylya Sovetov Samara II" ~ "Krylya Sovetov Samara", Team_Name == "Kuban Krasnodar U19" ~ "Kuban Krasnodar", Team_Name == "Kuban Krasnodar II" ~ "Kuban Krasnodar", Team_Name == "KSC Lokeren Reserve" ~ "KSC Lokeren", Team_Name == "KSC Lokeren U19" ~ "KSC Lokeren", Team_Name == "KSC Lokeren-Temse" ~ "KSC Lokeren", Team_Name == "KV Kortrijk U19" ~ "KV Kortrijk", Team_Name == "KV Mechelen Reserve" ~ "KV Mechelen", Team_Name == "KV Mechelen Jugend" ~ "KV Mechelen", Team_Name == "KV Mechelen U21" ~ "KV Mechelen", Team_Name == "KV Mechelen U19" ~ "KV Mechelen", Team_Name == "Las Palmas B" ~ "UD Las Palmas", Team_Name == "LASK Linz II" ~ "LASK", Team_Name == "Lazio Rom Jugend" ~ "Lazio Rom", Team_Name == "Lazio Rom U17" ~ "Lazio Rom", Team_Name == "Lazio Rom U19" ~ "Lazio Rom", Team_Name == "Leicester City U23" ~ "Leicester City", Team_Name == "Leixões SC U17" ~ "Leixões SC", Team_Name == "Leixões SC U19" ~ "Leixões SC", Team_Name == "Levante UD B" ~ "UD Levante", Team_Name == "Lierse SK U19" ~ "Lierse SK", Team_Name == "LOSC Lille U19" ~ "LOSC Lille", Team_Name == "LOSC Lille B" ~ "LOSC Lille", Team_Name == "Lokomotiv Moskau II" ~ "Lokomotiv Moskau", Team_Name == "Lokomotiv 2 Moskau" ~ "Lokomotiv Moskau", Team_Name == "Lokomotiv-D Moskau" ~ "Lokomotiv Moskau", Team_Name == "Manchester City UEFA U19" ~ "Manchester City", Team_Name == "Manchester City U21" ~ "Manchester City", Team_Name == "Manchester City U23" ~ "Manchester City", Team_Name == "Manchester City Jugend" ~ "Manchester City", Team_Name == "Manchester United U23" ~ "Manchester United", Team_Name == "Manchester United U18" ~ "Manchester United", Team_Name == "Manchester United Jugend" ~ "Manchester United", Team_Name == "Milan AC" ~ "AC Mailand", Team_Name == "Mordovia Saransk II" ~ "Mordovia Saransk", Team_Name == "Nachwuchsmodell Kapfenberg" ~ "SV Kapfenberg", Team_Name == "NAC" ~ "NAC Breda", Team_Name == "NAC Breda U19" ~ "NAC Breda", Team_Name == "NAC Breda U21" ~ "NAC Breda", Team_Name == "Naval 1º de Maio" ~ "Associação Naval 1893",

XXII

Team_Name == "NEC Nijmegen U19" ~ "NEC Nijmegen", Team_Name == "NEC Nijmegen U21" ~ "NEC Nijmegen", Team_Name == "NEC Nijmegen Jugend" ~ "NEC Nijmegen", Team_Name == "NEC NijmegenFC Oss II" ~ "NEC Nijmegen", Team_Name == "Nottingham Forest U18" ~ "Nottingham Forest", Team_Name == "Norwich City U18" ~ "Norwich City", Team_Name == "Norwich City U23" ~ "Norwich City", Team_Name == "OGC Nizza B" ~ "OGC Nizza", Team_Name == "OGC Nizza U19" ~ "OGC Nizza", Team_Name == "Olympia Volgograd U19" ~ "Olympia Volgograd", Team_Name == "Olympique Alès U19" ~ "Olympique Alès", Team_Name == "Olympique Lyon UEFA U19" ~ "Olympique Lyon", Team_Name == "Olympique Lyon U19" ~ "Olympique Lyon", Team_Name == "Olympique Lyon B" ~ "Olympique Lyon", Team_Name == "Olympique Nîmes B" ~ "Nîmes Olympique", Team_Name == "Olympique Marseille B" ~ "Olympique Marseille", Team_Name == "Olympique Marseille U19" ~ "Olympique Marseille", Team_Name == "Parma FC" ~ "Parma Calcio 1913", Team_Name == "Parma Primavera" ~ "Parma Calcio 1913", Team_Name == "Parma Calcio 1913 Jugend" ~ "Parma Calcio 1913", Team_Name == "PEC Zwolle '82" ~ "PEC Zwolle", Team_Name == "PEC Zwolle II" ~ "PEC Zwolle", Team_Name == "PEC Zwolle U19" ~ "PEC Zwolle", Team_Name == "PEC Zwolle Jugend" ~ "PEC Zwolle", Team_Name == "PSV Eindhoven II" ~ "PSV Eindhoven", Team_Name == "PSV Eindhoven U17" ~ "PSV Eindhoven", Team_Name == "PSV Eindhoven U19" ~ "PSV Eindhoven", Team_Name == "Portimonense SC U23" ~ "Portimonense Futebol SAD", Team_Name == "Queen's Park FC" ~ "Queens Park Rangers", Team_Name == "Queen's Park FC U20" ~ "Queens Park Rangers", Team_Name == "Queens Park Rangers U18" ~ "Queens Park Rangers", Team_Name == "RasenBallsport Leipzig U17" ~ "RasenBallsport Leipzig", Team_Name == "RasenBallsport Leipzig U19" ~ "RasenBallsport Leipzig", Team_Name == "Racing Straßburg U19" ~ "RC Straßburg Alsace", Team_Name == "Racing Straßburg" ~ "RC Straßburg Alsace", Team_Name == "Racing Straßburg B" ~ "RC Straßburg Alsace", Team_Name == "Racing de Santander B" ~ "Racing Santander", Team_Name == "Rayo Vallecano B" ~ "Rayo Vallecano", Team_Name == "RC Lens B" ~ "RC Lens", Team_Name == "RCD Mallorca B" ~ "RCD Mallorca", Team_Name == "Real Betis B" ~ "Real Betis Sevilla", Team_Name == "Real Betis U19" ~ "Real Betis Sevilla", Team_Name == "Real Betis Sevilla U19" ~ "Real Betis Sevilla", Team_Name == "Real Madrid C (aufgel.)" ~ "Real Madrid", Team_Name == "Real Madrid B" ~ "Real Madrid", Team_Name == "Real Madrid U17" ~ "Real Madrid", Team_Name == "Real Madrid U18" ~ "Real Madrid", Team_Name == "Real Madrid U19" ~ "Real Madrid", Team_Name == "Real Madrid UEFA U19" ~ "Real Madrid", Team_Name == "Real Madrid B (Castilla)" ~ "Real Madrid", Team_Name == "Real Madrid Deportivo" ~ "Real Madrid", Team_Name == "Real Saragossa B" ~ "Real Saragossa", Team_Name == "Real Saragossa U19" ~ "Real Saragossa", Team_Name == "Real SC U17" ~ "Real SC", Team_Name == "Real SC U19" ~ "Real SC", Team_Name == "Real Sociedad B" ~ "Real Sociedad San Sebastián", Team_Name == "Real Sociedad Jugend" ~ "Real Sociedad San Sebastián", Team_Name == "Real Sociedad U19" ~ "Real Sociedad San Sebastián", Team_Name == "Real Valladolid B" ~ "Real Valladolid", Team_Name == "Real Valladolid U19" ~ "Real Valladolid", Team_Name == "Red Bull Juniors Salzburg" ~ "Red Bull Salzburg", Team_Name == "Red Bull Salzburg UEFA U19" ~ "Red Bull Salzburg", Team_Name == "Red Bull Salzburg Jugend" ~ "Red Bull Salzburg", Team_Name == "RSC Anderlecht Jugend" ~ "RSC Anderlecht", Team_Name == "RSC Anderlecht Reserve" ~ "RSC Anderlecht", Team_Name == "RSC Anderlecht U17" ~ "RSC Anderlecht", Team_Name == "RSC Anderlecht U19" ~ "RSC Anderlecht", Team_Name == "RSC Anderlecht U21" ~ "RSC Anderlecht", Team_Name == "RSC Anderlecht UEFA U19" ~ "RSC Anderlecht", Team_Name == "RKC Waalwijk II" ~ "RKC Waalwijk", Team_Name == "RKC Waalwijk U19" ~ "RKC Waalwijk", Team_Name == "Rubin-TAN Kazan" ~ "Rubin Kazan", Team_Name == "Roda JC Kerkrade U19" ~ "Roda JC Kerkrade", Team_Name == "RSC Charleroi U19" ~ "RSC Charleroi", Team_Name == "Robur Siena U19" ~ "ACN Siena 1904", Team_Name == "Robur Siena" ~ "ACN Siena 1904", Team_Name == "Royal Antwerpen FC U19" ~ "Royal Antwerpen FC",

XXIII

Team_Name == "Royal Antwerpen FC U21" ~ "Royal Antwerpen FC", Team_Name == "Royal Antwerpen FC Reserve" ~ "Royal Antwerpen FC", Team_Name == "Royal Excel Mouscron Reserve" ~ "Royal Excel Mouscron", Team_Name == "Rubin Kazan II" ~ "Rubin Kazan", Team_Name == "Rubin 2 Kazan" ~ "Rubin Kazan", Team_Name == "San Sebastián" ~ "Real Sociedad San Sebastián", Team_Name == "Saturn Ramenskoe II" ~ "Saturn Ramenskoe", Team_Name == "Saturn REN-TV Ramenskoe" ~ "Saturn Ramenskoe", Team_Name == "SC Bastia B" ~ "SC Bastia", Team_Name == "SC Bastia U19" ~ "SC Bastia", Team_Name == "SC Braga B" ~ "SC Braga", Team_Name == "SC Braga U15" ~ "SC Braga", Team_Name == "SC Braga U17" ~ "SC Braga", Team_Name == "SC Braga U19" ~ "SC Braga", Team_Name == "SC Braga U23" ~ "SC Braga", Team_Name == "SC Benevento" ~ "Benevento Calcio", Team_Name == "SC Cambuur-Leeuwarden Jugend" ~ "SC Cambuur-Leeuwarden", Team_Name == "Cambuur-Leeuwarden bvo" ~ "SC Cambuur-Leeuwarden", Team_Name == "SC Freiburg Jugend" ~ "SC Freiburg", Team_Name == "SC Freiburg U17" ~ "SC Freiburg", Team_Name == "SC Freiburg U19" ~ "SC Freiburg", Team_Name == "SC Freiburg II" ~ "SC Freiburg", Team_Name == "SC Heracles '74" ~ "Heracles Almelo", Team_Name == "SC Heerenveen U19" ~ "SC Heerenveen", Team_Name == "SC Heerenveen U21" ~ "SC Heerenveen", Team_Name == "SC Cambuur-Leeuwarden U19" ~ "SC Cambuur-Leeuwarden", Team_Name == "SC Cambuur" ~ "SC Cambuur-Leeuwarden", Team_Name == "SC Magna Wiener Neustadt" ~ "SC Wiener Neustadt", Team_Name == "SC Paderborn 07 II" ~ "SC Paderborn 07", Team_Name == "SC Paderborn 07 U19" ~ "SC Paderborn 07", Team_Name == "SC Salgueiros U19" ~ "SC Salgueiros", Team_Name == "SCN Admira/Wacker" ~ "FC Admira Wacker Mödling", Team_Name == "SCO Angers B" ~ "SCO Angers", Team_Name == "SCO Angers U19" ~ "SCO Angers", Team_Name == "SCR Altach Juniors" ~ "SCR Altach", Team_Name == "SCR Altach Jugend" ~ "SCR Altach", Team_Name == "SD Eibar B" ~ "SD Eibar", Team_Name == "SD Huesca " ~ "SD Huesca", Team_Name == "Sevilla FC B" ~ "FC Sevilla", Team_Name == "SKA Khabarovsk II" ~ "SKA Khabarovsk", Team_Name == "SKN St. Pölten Juniors" ~ "SKN St. Pölten", Team_Name == "SK Sturm Graz II" ~ "SK Sturm Graz", Team_Name == "SK Sturm Graz Jugend" ~ "SK Sturm Graz", Team_Name == "SK Rapid Wien Jugend" ~ "SK Rapid Wien", Team_Name == "SK Rapid Wien II" ~ "SK Rapid Wien", Team_Name == "SKA-Energia Khabarovsk" ~ "SKA Khabarovsk", Team_Name == "SKA Khabarovsk Jugend" ~ "SKA Khabarovsk", Team_Name == "SM Caen B" ~ "SM Caen", Team_Name == "SPAL U19" ~ "SPAL", Team_Name == "SPAL 1907" ~ "SPAL", Team_Name == "SPAL 2013" ~ "SPAL", Team_Name == "Sparta Rotterdam Jugend" ~ "Sparta Rotterdam", Team_Name == "Sparta Rotterdam U17" ~ "Sparta Rotterdam", Team_Name == "Sparta Rotterdam U19" ~ "Sparta Rotterdam", Team_Name == "Sparta Rotterdam 2" ~ "Sparta Rotterdam", Team_Name == "Spartak Moskau II" ~ "Spartak Moskau", Team_Name == "Spartak-D Moscow" ~ "Spartak Moskau", Team_Name == "Spartak 2 Moskau" ~ "Spartak Moskau", Team_Name == "Spartak Orel U19" ~ "Spartak Orel", Team_Name == "Spartak Vladikavkaz II" ~ "Spartak Vladikavkaz", Team_Name == "Spartak 2 Vladikavkaz" ~ "Spartak Vladikavkaz", Team_Name == "DYuSSh Spartak Vladikavkaz" ~ "Spartak Vladikavkaz", Team_Name == "Sporting Étoile Club Bastia " ~ "SC Bastia", Team_Name == "Sporting Gijón B" ~ "Sporting Gijón", Team_Name == "Sporting Gijón U19" ~ "Sporting Gijón", Team_Name == "Sporting Gijón Jugend" ~ "Sporting Gijón", Team_Name == "Sporting Lissabon B" ~ "Sporting Lissabon", Team_Name == "Sporting Lissabon U15" ~ "Sporting Lissabon", Team_Name == "Sporting Lissabon U17" ~ "Sporting Lissabon", Team_Name == "Sporting Lissabon Jugend" ~ "Sporting Lissabon", Team_Name == "Sporting Lissabon U19" ~ "Sporting Lissabon", Team_Name == "Sporting Lissabon U23" ~ "Sporting Lissabon", Team_Name == "SSC Napoli" ~ "SSC Neapel", Team_Name == "SSC Neapel U19" ~ "SSC Neapel", Team_Name == "SSC Neapel Jugend" ~ "SSC Neapel", Team_Name == "SSC Palermo" ~ "US Palermo", Team_Name == "SSD Palermo" ~ "US Palermo",

XXIV

Team_Name == "SSD Palermo Jugend" ~ "US Palermo", Team_Name == "SSD Palermo U19" ~ "US Palermo", Team_Name == "SpVgg Greuther Fürth II" ~ "SpVgg Greuther Fürth", Team_Name == "SpVgg Greuther Fürth U17" ~ "SpVgg Greuther Fürth", Team_Name == "SpVgg Greuther Fürth U19" ~ "SpVgg Greuther Fürth", Team_Name == "Stade Brest 29 B" ~ "Stade Brest 29", Team_Name == "Stade Reims B" ~ "Stade Reims", Team_Name == "Stade Reims U19" ~ "Stade Reims", Team_Name == "Stade Laval B" ~ "Stade Laval", Team_Name == "Stade Laval U19" ~ "Stade Laval", Team_Name == "Standard Lüttich U19" ~ "Standard Lüttich", Team_Name == "Standard Lüttich Jugend" ~ "Standard Lüttich", Team_Name == "Standard Lüttich Reserve" ~ "Standard Lüttich", Team_Name == "SV Darmstadt 98 U19" ~ "SV Darmstadt 98", Team_Name == "SV Kapfenberg II" ~ "SV Kapfenberg", Team_Name == "SV Grödig II" ~ "SV Grödig", Team_Name == "SV Mattersburg Jugend" ~ "SV Mattersburg", Team_Name == "SV Mattersburg II" ~ "SV Mattersburg", Team_Name == "SV Ried Jugend" ~ "SV Ried", Team_Name == "SV Neuhofen/SV Ried II" ~ "SV Ried", Team_Name == "SV Casino Salzburg" ~ "Red Bull Salzburg", Team_Name == "SV Wüstenrot Salzburg" ~ "Red Bull Salzburg", Team_Name == "SV Salzburg Amateure" ~ "Red Bull Salzburg", Team_Name == "SV Sandhausen U19" ~ "SV Sandhausen", Team_Name == "SV Werder Bremen II" ~ "SV Werder Bremen", Team_Name == "SV Werder Bremen III" ~ "SV Werder Bremen", Team_Name == "SV Werder Bremen Jugend" ~ "SV Werder Bremen", Team_Name == "SV Werder Bremen U17" ~ "SV Werder Bremen", Team_Name == "SV Werder Bremen U18" ~ "SV Werder Bremen", Team_Name == "SV Werder Bremen U19" ~ "SV Werder Bremen", Team_Name == "Swansea City U21" ~ "Swansea City", Team_Name == "Terek Grozny" ~ "Akhmat Grozny", Team_Name == "Terek Grozny II" ~ "Akhmat Grozny", Team_Name == "Torpedo Moskau II" ~ "Torpedo Moskau", Team_Name == "Torpedo Moskau U19" ~ "Torpedo Moskau", Team_Name == "Torpedo-ZiL Moskau II" ~ "Torpedo Moskau", Team_Name == "Torpedo-ZiL Moskau U19" ~ "Torpedo Moskau", Team_Name == "Torpedo-Metallurg Moskau" ~ "Torpedo Moskau", Team_Name == "Tom Tomsk U19" ~ "Tom Tomsk", Team_Name == "Tottenham Hotspur U18" ~ "Tottenham Hotspur", Team_Name == "Tottenham Hotspur U21" ~ "Tottenham Hotspur", Team_Name == "TSG 1899 Hoffenheim Jugend" ~ "TSG 1899 Hoffenheim", Team_Name == "TSG 1899 Hoffenheim U17" ~ "TSG 1899 Hoffenheim", Team_Name == "TSG 1899 Hoffenheim U19" ~ "TSG 1899 Hoffenheim", Team_Name == "TSG 1899 Hoffenheim UEFA U19" ~ "TSG 1899 Hoffenheim", Team_Name == "TSG 1899 Hoffenheim II" ~ "TSG 1899 Hoffenheim", Team_Name == "TSV 1860 München II" ~ "TSV 1860 München", Team_Name == "TSV 1860 München U17" ~ "TSV 1860 München", Team_Name == "TSV 1860 München U19" ~ "TSV 1860 München", Team_Name == "Ural 2 Ekaterinburg" ~ "Ural Ekaterinburg", Team_Name == "Uralmash Ekaterinburg" ~ "Ural Ekaterinburg", Team_Name == "Ural Ekaterinburg II" ~ "Ural Ekaterinburg", Team_Name == "União Leiria U17" ~ "União Leiria", Team_Name == "União Leiria U19" ~ "União Leiria", Team_Name == "US Livorno" ~ "AS Livorno", Team_Name == "UC Livorno" ~ "AS Livorno", Team_Name == "Pro Livorno Calcio" ~ "AS Livorno", Team_Name == "UD Almería B" ~ "UD Almería", Team_Name == "UD Almería B " ~ "UD Almería", Team_Name == "UD Levante B" ~ "UD Levante", Team_Name == "Udinese Calcio Jugend" ~ "Udinese Calcio", Team_Name == "Udinese Calcio U19" ~ "Udinese Calcio", Team_Name == "Valencia CF B" ~ "FC Valencia", Team_Name == "VAC Beerschot" ~ "Beerschot AC", Team_Name == "Verona FC" ~ "Hellas Verona", Team_Name == "VfB Admira Wacker Mödling" ~ "FC Admira Wacker Mödling", Team_Name == "VfB Stuttgart II" ~ "VfB Stuttgart", Team_Name == "VfB Stuttgart Jugend" ~ "VfB Stuttgart", Team_Name == "VfB Stuttgart U16" ~ "VfB Stuttgart", Team_Name == "VfB Stuttgart U17" ~ "VfB Stuttgart", Team_Name == "VfB Stuttgart U19" ~ "VfB Stuttgart", Team_Name == "VfL Bochum II" ~ "VfL Bochum", Team_Name == "VfL Bochum U17" ~ "VfL Bochum", Team_Name == "VfL Bochum U19" ~ "VfL Bochum", Team_Name == "VfL Wolfsburg II" ~ "VfL Wolfsburg", Team_Name == "VfL Wolfsburg Jugend" ~ "VfL Wolfsburg", Team_Name == "VfL Wolfsburg U17" ~ "VfL Wolfsburg",

XXV

Team_Name == "VfL Wolfsburg U19" ~ "VfL Wolfsburg", Team_Name == "Videoton FC II" ~ "Videoton FC", Team_Name == "Vitesse Arnheim II" ~ "Vitesse Arnheim", Team_Name == "Vitesse Arnheim Jugend" ~ "Vitesse Arnheim", Team_Name == "AVC Vitesse Arnheim" ~ "Vitesse Arnheim", Team_Name == "Vitesse/AGOVV U21" ~ "Vitesse Arnheim", Team_Name == "Vitesse/AGOVV U19" ~ "Vitesse Arnheim", Team_Name == "Vitesse/AGOVV Jugend" ~ "Vitesse Arnheim", Team_Name == "Vitesse Arnheim U19" ~ "Vitesse Arnheim", Team_Name == "Vitesse Arnheim U21" ~ "Vitesse Arnheim", Team_Name == "Vitesse 1892 Arnheim" ~ "Vitesse Arnheim", Team_Name == "Vitória Guimarães SC B" ~ "Vitória Guimarães SC", Team_Name == "Vitória Guimarães SC U15" ~ "Vitória Guimarães SC", Team_Name == "Vitória Guimarães SC U17" ~ "Vitória Guimarães SC", Team_Name == "Vitória Guimarães SC U19" ~ "Vitória Guimarães SC", Team_Name == "Vitória de Guimarães SC Jugend" ~ "Vitória Guimarães SC", Team_Name == "Vitória Setúbal FC U15" ~ "Vitória Setúbal FC", Team_Name == "Vitória Setúbal FC U17" ~ "Vitória Setúbal FC", Team_Name == "Vitória Setúbal FC U19" ~ "Vitória Setúbal FC", Team_Name == "Volga Nizhniy Novgorod II" ~ "Volga Nizhniy Novgorod", Team_Name == "VV St. Truiden Reserve" ~ "VV St. Truiden", Team_Name == "VV St. Truiden U18" ~ "VV St. Truiden", Team_Name == "VV St. Truiden U19" ~ "VV St. Truiden", Team_Name == "VV St. Truiden U21" ~ "VV St. Truiden", Team_Name == "WAC - St. Andrä" ~ "Wolfsberger AC", Team_Name == "West Bromwich Albion U18" ~ "West Bromwich Albion", Team_Name == "West Bromwich Albion U23" ~ "West Bromwich Albion", Team_Name == "West Ham United U18" ~ "West Ham United", Team_Name == "West Ham United U21" ~ "West Ham United", Team_Name == "Willem II Tilburg II" ~ "Willem II Tilburg", Team_Name == "Wolverhampton Wanderers U21" ~ "Wolverhampton Wanderers", Team_Name == "WSG Wattens" ~ "WSG Tirol", Team_Name == "Zenit St. Petersburg II" ~ "Zenit St. Petersburg", Team_Name == "Zenit St. Petersburg UEFA U19" ~ "Zenit St. Petersburg", Team_Name == "Zenit 2 St. Petersburg" ~ "Zenit St. Petersburg", Team_Name == "Zvezda Perm U19" ~ "Zvezda Perm", Team_Name == "ZSKA Moskau II" ~ "ZSKA Moskau", TRUE ~ as.character(Team_Name)))

TMT <- Jobs %>% filter(Funktion != "Spieler", Team == "Nein" | Funktion != "Trainer") %>% select(Trainer_Name, Team_Name, Funktion, Amtsantritt) %>% unite("TMT", "Trainer_Name":"Team_Name", sep = "_")

Spieler <- Jobs %>% filter(Funktion == "Spieler") %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs <- Jobs %>% select(Trainer_Name, Team_Name, Funktion, Amtsantritt) rm(Jobs_trainer)

Trainer_Industry <- Trainer %>% select(!c("Geburtstag", "Amtsantritt", "Team")) write.csv2(data, "WICHTIG_data.csv") write.csv2(Jobs, "WICHTIG_jobs.csv") write.csv2(Spieler, "WICHTIG_spieler.csv") write.csv2(TMT, "WICHTIG_tmt.csv") write.csv2(Trainer, "WICHTIG_trainer.csv") write.csv2(Trainer_Industry, "WICHTIG_trainer_industry.csv")

XXVI

Jobs <- read.csv2("WICHTIG_jobs.csv") %>% select(!c("X")) data <- read.csv2("WICHTIG_data.csv") %>% select(!c("X")) Spieler <- read.csv2("WICHTIG_spieler.csv") %>% select(!c("X")) TMT <- read.csv2("WICHTIG_tmt.csv") %>% select(!c("X")) Trainer <- read.csv2("WICHTIG_trainer.csv") %>% select(!c("X")) Trainer_Industry <- read.csv2("WICHTIG_trainer_industry.csv") %>% select(!c("X")) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} data2020 <- data %>% filter(Saison == "19/20") data2019 <- data %>% filter(Saison == "18/19") data2018 <- data %>% filter(Saison == "17/18") data2017 <- data %>% filter(Saison == "16/17") data2016 <- data %>% filter(Saison == "15/16") data2015 <- data %>% filter(Saison == "14/15") data2014 <- data %>% filter(Saison == "13/14") data2013 <- data %>% filter(Saison == "12/13") data2012 <- data %>% filter(Saison == "11/12") data2011 <- data %>% filter(Saison == "10/11") ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("23.08.2020") Trainer_Industry$Jahr <- dmy("01.07.2019") TMT$Jahr <- dmy("01.07.2019") Jobs$Jahr <- dmy("23.08.2020")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2020-08-23", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5)

XXVII

sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2020 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2020, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer_2 <- Trainer_2 %>% mutate(prior_as_spieler = case_when(Trainer_Team %in% Spieler$Spieler ~ "Ja", TRUE ~ "Nein")) %>% mutate(prior_as_TMT = case_when(Trainer_Team %in% TMT$TMT ~ "Ja", TRUE ~ "Nein")) %>% inner_join(Jobs_per_club, by = "Trainer_Team")

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

XXVIII

TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2020 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2020$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% filter(x == "Nein")

# !! Name ändern write.csv2(final_data2020, "finaldata2020.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2019") Trainer_Industry$Jahr <- dmy("01.07.2018") TMT$Jahr <- dmy("01.07.2018") Jobs$Jahr <- dmy("30.06.2019")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2019-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2019 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2019, sum_Trainer)

Trainer_2 <- data2020_1 %>%

XXIX

select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2019 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2019$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>%

XXX

filter(x == "Nein")

# !! Name ändern write.csv2(final_data2019, "finaldata2019.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2018") Trainer_Industry$Jahr <- dmy("01.07.2017") TMT$Jahr <- dmy("01.07.2017") Jobs$Jahr <- dmy("30.06.2018")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2018-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2018 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2018, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

XXXI

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2018 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2018$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% filter(x == "Nein")

# !! Name ändern write.csv2(final_data2018, "finaldata2018.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2017") Trainer_Industry$Jahr <- dmy("01.07.2016") TMT$Jahr <- dmy("01.07.2016") Jobs$Jahr <- dmy("30.06.2017")

XXXII

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2017-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs_Trainer <- Jobs %>% filter(Funktion == "Trainer")

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2017 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2017, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

XXXIII

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2017 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2017$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% filter(x == "Nein")

# !! Name ändern write.csv2(final_data2017, "finaldata2017.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2016") Trainer_Industry$Jahr <- dmy("01.07.2015") TMT$Jahr <- dmy("01.07.2015") Jobs$Jahr <- dmy("30.06.2016")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2016-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

XXXIV

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2016 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2016, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name",

XXXV

sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2016 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2016$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% filter(x == "Nein")

# !! Name ändern write.csv2(final_data2016, "finaldata2016.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2015") Trainer_Industry$Jahr <- dmy("01.07.2014") TMT$Jahr <- dmy("01.07.2014") Jobs$Jahr <- dmy("30.06.2015")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2015-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0)

XXXVI

options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2015 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2015, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

XXXVII

sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2015 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name") d <- sum_Trainer_moment %>% mutate(x = case_when(Trainer_Name %in% final_data2015$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% filter(x == "Nein")

# !! Name ändern write.csv2(final_data2015, "finaldata2015.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2014") Trainer_Industry$Jahr <- dmy("01.07.2013") TMT$Jahr <- dmy("01.07.2013") Jobs$Jahr <- dmy("30.06.2014")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2014-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2014 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2014, sum_Trainer)

XXXVIII

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2014 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name")

# !! Name ändern

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write.csv2(final_data2014, "finaldata2014.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2013") Trainer_Industry$Jahr <- dmy("01.07.2012") TMT$Jahr <- dmy("01.07.2012") Jobs$Jahr <- dmy("30.06.2013")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2013-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2013 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2013, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2013 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name")

# !! Name ändern write.csv2(final_data2013, "finaldata2013.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2012") Trainer_Industry$Jahr <- dmy("01.07.2011") TMT$Jahr <- dmy("01.07.2011") Jobs$Jahr <- dmy("30.06.2012")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2012-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

XLI

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>% select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2012 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2012, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

XLII

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Trainer_moment <- Trainer_2 %>% inner_join(Trainer3, by = "Trainer_Team") sum_Trainer_moment <- Trainer_moment %>% group_by(Trainer_Team, prior_as_spieler, prior_as_TMT, jobs_per_club) %>% summarise(days_as_trainer_at_this_club = sum(Days)) %>% separate(col = "Trainer_Team", into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2012 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name")

# !! Name ändern write.csv2(final_data2012, "finaldata2012.csv") rm(d, data2020_1) ```

```{r, warning = FALSE, message = FALSE, results='hide', eval = FALSE} # !! Datum ändern Trainer$Jahr <- dmy("30.06.2011") Trainer_Industry$Jahr <- dmy("01.07.2010") TMT$Jahr <- dmy("01.07.2010") Jobs$Jahr <- dmy("30.06.2011")

# !! Datum ändern Trainer <- Trainer %>% filter(Amtsantritt < Jahr) %>% mutate(Amtsende = case_when(Amtsende > Jahr ~ "2011-06-30", TRUE ~ as.character(Amtsende)))

Trainer_Industry <- Trainer_Industry %>% filter(Amtsende < Jahr)

TMT <- TMT %>% filter(Amtsantritt < Jahr)

Jobs <- Jobs %>% filter(Amtsantritt < Jahr)

Trainer$Amtsende2 <- Trainer$Amtsende

Trainer$Geburtstag <- ymd(Trainer$Geburtstag) Trainer$Amtsantritt <- ymd(Trainer$Amtsantritt) Trainer$Amtsende <- ymd(Trainer$Amtsende2)

Trainer$Days <- Trainer$Amtsende - Trainer$Amtsantritt

Trainer$Trainer_Age <- floor((Trainer$Jahr - Trainer$Geburtstag)/365.25) write.csv2(Trainer, file = "Trainer2020.csv")

Trainer2 <- read.csv2("Trainer2020.csv") %>%

XLIII

select(!c("X", "Jahr", "Geburtstag", "Amtsende2", "Amtsende", "Amtsantritt")) %>% filter(Days > 0) options(digits = 5) sum_Trainer <- Trainer2 %>% filter(Funktion == "Trainer") %>% group_by(Trainer_Name, Trainer_Age) %>% summarise(days_as_trainer = sum(Days), amount_of_trainer_jobs = length(Team_Name)) data2020_1 <- data2011 %>% inner_join(sum_Trainer, by = "Trainer_Name") rm(data2011, sum_Trainer)

Trainer_2 <- data2020_1 %>% select("Trainer_Name", "Team_Name") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_TMT <- Trainer_Industry %>% mutate(Trainer = Trainer_Name) %>% select(Trainer, Team_Name, Trainer_Name, Funktion) %>% unite("Spieler", "Trainer":"Team_Name", sep = "_")

Jobs_Industry <- bind_rows(Spieler, Jobs_TMT)

Jobs_per_club <- Jobs %>% group_by(Trainer_Name, Team_Name) %>% summarise(jobs_per_club = length(Funktion)) %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

Jobs_per_industry <- Jobs_Industry %>% group_by(Trainer_Name) %>% summarise(jobs_per_industry = length(Funktion))

Trainer3 <- Trainer2 %>% select("Trainer_Name", "Team_Name", "Days") %>% unite("Trainer_Team", "Trainer_Name":"Team_Name", sep = "_")

XLIV

into = c("Trainer_Name", "Team_Name"), sep = "_") %>% mutate(Industry_TMT = case_when(Trainer_Name %in% Jobs_TMT$Trainer_Name ~ "Ja", TRUE ~ "Nein")) %>% mutate(Industry_spieler = case_when(Trainer_Name %in% Spieler$Trainer ~ "Ja", TRUE ~ "Nein")) final_data2011 <- data2020_1 %>% inner_join(sum_Trainer_moment, by = c("Team_Name", "Trainer_Name")) %>% filter(days_as_trainer_at_this_club > 10) %>% left_join(Jobs_per_industry, by = "Trainer_Name")

# !! Name ändern write.csv2(final_data2011, "finaldata2011.csv") rm(d, data2020_1) ```

```{r, eval = FALSE} rm(data, Jobs, Jobs_Industry, Jobs_per_club, Jobs_per_industry, Jobs_TMT, Spieler, sum_Trainer_moment, TMT, Trainer, Trainer_2, Trainer_Industry, Trainer_moment, Trainer2, Trainer3, final_data2011, final_data2012, final_data2013, final_data2014, final_data2015, final_data2016, final_data2017, final_data2018, final_data2019, final_data2020) ```

```{r, warning = FALSE, message = FALSE, eval = FALSE} data2020 <- read.csv2("finaldata2020.csv") %>% select(!c("X")) data2019 <- read.csv2("finaldata2019.csv") %>% select(!c("X")) data2018 <- read.csv2("finaldata2018.csv") %>% select(!c("X")) data2017 <- read.csv2("finaldata2017.csv") %>% select(!c("X")) data2016 <- read.csv2("finaldata2016.csv") %>% select(!c("X")) data2015 <- read.csv2("finaldata2015.csv") %>% select(!c("X")) data2014 <- read.csv2("finaldata2014.csv") %>% select(!c("X")) data2013 <- read.csv2("finaldata2013.csv") %>% select(!c("X")) data2012 <- read.csv2("finaldata2012.csv") %>% select(!c("X")) data2011 <- read.csv2("finaldata2011.csv") %>% select(!c("X")) finaldata <- bind_rows(data2020, data2019, data2018, data2017, data2016, data2015, data2014, data2013, data2012, data2011) rm(data2020, data2019, data2018, data2017, data2016, data2015, data2014, data2013, data2012, data2011) write.csv2(finaldata, "finaldata_neu.csv") finaldata <- read.csv2("finaldata_neu.csv") %>% select(!c("X")) ```

XLV

```{r, eval = FALSE} write.csv2(finaldata, "XYZ.csv")

Österreich <- read.csv("Österreich.csv") finaldata <- read.csv2("XYZ.csv") %>% mutate(Liga = case_when(Team_Name %in% Österreich$Team ~ "Ö. Bundesliga", Liga == "Liga Sagres" ~ "Liga NOS", TRUE ~ as.character(Liga)), Team_Nation = case_when(Liga == "Premier League" ~ "England", Liga == "LaLiga" ~ "Spanien", Liga == "Serie A" ~ "Italien", Liga == "Bundesliga" ~ "Deutschland", Liga == "Ligue 1" ~ "Frankreich", Liga == "Ö. Bundesliga" ~ "Österreich", Liga == "Premier Liga" ~ "Russland", Liga == "Liga NOS" ~ "Portugal", Liga == "Eredivisie" ~ "Niederlande", Liga == "Jupiler Pro League" ~ "Belgien"), Team_Language = case_when(Liga == "Premier League" ~ "Englisch", Liga == "LaLiga" ~ "Spanisch", Liga == "Serie A" ~ "Italienisch", Liga == "Bundesliga" ~ "Deutsch", Liga == "Ligue 1" ~ "Französisch", Liga == "Ö. Bundesliga" ~ "Deutsch", Liga == "Premier Liga" ~ "Russisch", Liga == "Liga NOS" ~ "Portugiesisch", Liga == "Eredivisie" ~ "Niederländisch", Liga == "Jupiler Pro League" ~ "Niederländisch"), National_differences = case_when(Team_Nation == Trainer_Nation ~ "No", Team_Nation != Trainer_Nation ~ "Yes"), Linguistic_differences = case_when(Team_Language == Trainer_Language ~ "No", Team_Language != Trainer_Language ~ "Yes"), Season = case_when(Saison == "19/20" ~ "2019/20", Saison == "18/19" ~ "2018/19", Saison == "17/18" ~ "2017/18", Saison == "16/17" ~ "2016/17", Saison == "15/16" ~ "2015/16", Saison == "14/15" ~ "2014/15", Saison == "13/14" ~ "2013/14", Saison == "12/13" ~ "2012/13", Saison == "11/12" ~ "2011/12", Saison == "10/11" ~ "2010/11"), Share_of_Legionaries_among_employees = Amount_of_Legionaries_among_employees / Number_of_employees * 100, OE_Type = case_when(prior_as_spieler == "Ja" & prior_as_TMT == "Ja" ~ "Prior_Employee_and_TMT", prior_as_spieler == "Ja" & prior_as_TMT == "Nein" ~ "Prior_Employee", prior_as_spieler == "Nein" & prior_as_TMT == "Ja" ~ "Prior_TMT", TRUE ~ "A")) %>% mutate(IE_Type = case_when(Industry_spieler == "Ja" & Industry_TMT == "Ja" ~ "Prior_Employee_and_TMT", Industry_spieler == "Ja" & Industry_TMT == "Nein" ~ "Prior_Employee", Industry_spieler == "Nein" & Industry_TMT == "Ja" ~ "Prior_TMT", TRUE ~ "A"), IE_Amount_of_Jobs = jobs_per_industry, OE_Amount_of_Jobs = jobs_per_club, JE_Amount_of_Jobs = amount_of_trainer_jobs, OE_Tenure = days_as_trainer_at_this_club, JE_Tenure = days_as_trainer, CEO_Age = Trainer_Age) %>% select(Team_Name, Season, Organizational_Success, IE_Amount_of_Jobs, IE_Type, OE_Amount_of_Jobs, OE_Tenure, OE_Type, JE_Amount_of_Jobs, JE_Tenure, TE_amount_of_games_this_season, CEO_Age, National_differences, Trainer_Nation, Linguistic_differences, Trainer_Language, Number_of_employees, Average_age_of_employees,

XLVI

Share_of_Legionaries_among_employees, Trainer_Name, Liga) write.csv2(finaldata, "Datensatz_gross.csv") rm(finaldata, Österreich) ```

```{r, eval = FALSE} finaldata <- read.csv2("Datensatz_gross.csv") %>% select(!c("X"))

XYZ <- finaldata %>% group_by(Season, Trainer_Name) %>% summarise(TE_amount_of_games_this_season = max(TE_amount_of_games_this_season))

Trainerdata <- XYZ %>% inner_join(finaldata, by= c("Trainer_Name", "Season", "TE_amount_of_games_this_season")) %>% select(Team_Name, Season, Organizational_Success, IE_Amount_of_Jobs, IE_Type, OE_Amount_of_Jobs, OE_Tenure, OE_Type, JE_Amount_of_Jobs, JE_Tenure, TE_amount_of_games_this_season, CEO_Age, National_differences, Trainer_Nation, Linguistic_differences, Trainer_Language, Number_of_employees, Average_age_of_employees, Share_of_Legionaries_among_employees, Trainer_Name, Liga)

Trainerdata <- Trainerdata %>% group_by(Season, Trainer_Name) %>% mutate(x = length(Trainer_Name)) %>% filter(x == 1) write.csv2(Trainerdata, "Datensatz_mittel.csv")

Trainerdata2 <- Trainerdata %>% filter(TE_amount_of_games_this_season > 9) write.csv2(Trainerdata2, "AAAAAA.csv") data <- read.csv2("AAAAAA.csv") data <- Trainerdata2 %>% select(!c(x, Trainer_Nation, Trainer_Language, Liga, Team_Name)) data %>% ggplot(aes(y = JE_Tenure)) + geom_boxplot() summary(data$OE_Tenure) data %>% ggplot(aes(y = OE_Tenure)) + geom_boxplot() data <- data %>% mutate(OE_Tenure_Categories = case_when(OE_Tenure < 423 ~ "low Tenure", OE_Tenure < 846 ~ "medium Tenure", TRUE ~ "high Tenure"), JE_Tenure_Categories = case_when(JE_Tenure < 3513 ~ "low Tenure", JE_Tenure < 6526 ~ "medium Tenure", TRUE ~ "high Tenure"))

data$IE_Amount_of_Jobs <- stringr::str_replace_na(data$IE_Amount_of_Jobs,

XLVII

replacement = 0) data$OE_Amount_of_Jobs <- data$OE_Amount_of_Jobs - 1 data$JE_Amount_of_Jobs <- data$JE_Amount_of_Jobs - 1 write.csv2(data, "AAAAAA_neu.csv") data <- read.csv2("AAAAAA_neu_test.csv") %>% select(Trainer_Name, Season, Organizational_Success, IE_Amount_of_Jobs, IE_Type, OE_Amount_of_Jobs, OE_Tenure, OE_Tenure_Categories, OE_Type, JE_Amount_of_Jobs, JE_Tenure, JE_Tenure_Categories, TE_Amount_of_games, CEO_Age, National_differences, Linguistic_differences, Number_of_employees, Average_age_of_employees, Share_of_Legionaries_among_employees)

XLVIII

II.II. List of all observed coaches

Abel Ferreira André Schubert Bert van Marwijk Cláudio Braga Abel Resino André Villas-Boas Besnik Hasi Claudio Ranieri Abelardo Andrea Mandorlini Bob Bradley Co Adriaanse Achim Beierlorzer Andrea Stramaccioni Bob Peeters Constantin Galca Adi Hütter Andreas Ogris Boris Schommers Cosmin Contra Adnan Custovic Andrey Gordeev Bortolo Mutti Costinha Adrie Koster Andrey Kobelev Brendan Rodgers Craig Shakespeare Adrie Poldervaart Andrey Tikhonov Brian McDermott Cristian Bucchi Aimé Anthuenis Andries Jonker Bruno Génésio Cristiano Bergodi Aitor Karanka Andries Ulderink Bruno Labbadia Cristóbal Parralo Alain Casanova Anquela Bruno Lage Csaba László Alan Irvine Ante Covic Bruno Ribeiro Cuco Ziganda Alan Pardew Antoine Kombouaré Carlo Ancelotti Damir Buric Albert Cartier Anton Janssen Carlos Azenha Damir Canadi Albert Celades Antonio Álvarez Carlos Brito Dan Petrescu Albert Emon Antonio Conte Carlos Carvalhal Daniel Farke Albert Ferrer António Folha Carlos Mozer Daniel Ramos Albert Stuivenberg Antonio Mohamed Carlos Pinto Daniel Sanchez Alberto Cavasin Ariel Jacobs Casquilha Daniele Arrigoni Alberto Malesani Armin Veh Cedomir Janevski Danny Buijs Aleksandar Jankovic Arnar Vidarsson Cesare Prandelli Danny Ost Aleksandr Grigoryan Arnauld Mercier Chris Hughton Darije Kalezic Aleksandr Tarkhanov Arne Slot Chris Janssens Darko Milanic Aleksandr Tochilin Arsène Wenger Chris O'Loughlin Daúto Faquirá Aleksandr Tsygankov Art Langeler Chris Ramsey David Gallego Aleksey Poddubskiy Asier Garitano Chris Wilder David Guion Alex Dupont Attilio Tesser Christian Benbennek David Moyes Alex McLeish Augusto Inácio Christian Gourcuff David Wagner Alex Neil Aurelio Andreazzoli Christian Gross Davide Ballardini Alex Pastoor Avram Grant Christian Ilzer Davide Nicola Alexander Nouri Bart De Roover Christian Streich Dean Smith Alexander Schmidt Bart Van Lancker Christoph Daum Delio Rossi Alexander Zorniger Beñat San José Christophe Galtier Dennis van Wijk Alfons Groenendijk Bernard Blaquart Christophe Pélissier Devis Mangia Alfred Schreuder Bernard Casoni Ciro Ferrara Dick Advocaat Álvaro Cervera Bernd Hollerbach Clarence Seedorf Dick Lukkien Anatoliy Baydachnyi Bernd Schuster Claude Makélélé Didier Deschamps André Breitenreiter Bernd Storck Claude Puel Diego López

XLIX

Diego Martínez Evgeni Perevertaylo Fred Rutten Guy Luzon Diego Simeone Fabiano Soares Frédéric Antonetti Hannes Wolf Dieter Hecking Fabien Mercadal Frédéric Hantz Hans de Koning Dietmar Kühbauer Fabio Liverani Frédéric Vanderbiest Hans-Dieter Flick Dirk Geeraerd Fabio Pecchia Friedhelm Funkel Harm van Veldhoven Dirk Schuster Fabri Gabriel Calderón Harry Redknapp Dmitri Alenichev Fabrizio Castori Gadzhi Gadzhiev Héctor Cúper Dmitri Cheryshev Fabrizio Ravanelli Gaizka Garitano Heiko Herrlich Dmitri Khokhlov Faruk Hadzibegic Garry Monk Heiko Vogel Dmitri Parfenov Fedor Shcherbachenko Gary Neville Heimo Pfeifenberger Domenico Di Carlo Felice Mazzu Gennaro Gattuso Hein Vanhaezebrouck Domenico Tedesco Felix Magath Georges Leekens Heinz Fuchsbichler Domingos Paciência Ferdinand Feldhofer Gerald Baumgartner Helgi Kolvidsson Dominique Arribagé Fernando Da Cruz Gérard Houllier Helmut Kraft Dominique D'Onofrio Fernando Vázquez Gerardo Martino Henk de Jong Dorinel Munteanu Filipe Gouveia Gerhard Schweitzer Henk Fraser Dwight Lodeweges Filipe Martins Gerhard Struber Henrique Calisto Eddie Howe Filippo Inzaghi Gert Heerkes Herbert Gager Edoardo Reja Florian Kohfeldt Gert Verheyen Hervé Renard Eduardo Berizzo Foeke Booy Gertjan Verbeek Holger Stanislawski Edward Sturing Foppe de Haan Ghislain Printant Hubert Fournier Élie Baup Fran Escribá Gian Piero Gasperini Hugo Broos Emilio Ferrera Francesco Guidolin Gian Piero Ventura Huub Stevens Enrique Martín Francis Gillot Gianfranco Zola Ian Holloway Enzo Scifo Francisco Rodríguez Giannis Anastasiou Igor Cherevchenko Eric Hellemons Franck Passi Giovanni Martusciello Igor Kolyvanov Eric Hély Francky Dury Giovanni Stroppa Igor Kriushenko Éric Roy Franco Colomba Giovanni van Bronckhorst Igor Shalimov Erik ten Hag Franco Foda Giuseppe Galderisi Igor Tudor Ernest Faber François Ciccolini Giuseppe Iachini Imanol Alguacil Ernesto Valverde Frank de Boer Giuseppe Sannino Isa Baytiev Ernie Brandts Frank Defays Glen De Boeck Ivan Daniliant Ernst Baumeister Frank Lampard Gonzalo García Ivan Juric Erwin Koeman Frank Schaefer Goran Djuricin Ivan Leko Erwin Sánchez Frank Vercauteren Graham Potter Ivan Vukomanovic Erwin van de Looi Frank Wormuth Gregorio Manzano Ivica Vastic Esteban Vigo Franky Van der Elst Guido Brepoels Ivo Pulga Eugenio Corini Franz Lederer Gustavo Poyet Ivo Vieira Eusebio Franz Ponweiser Guus Hiddink Jaap Stam Eusebio Di Francesco Fred Grim Guy Lacombe Jacky Mathijssen

Jagoba Arrasate Jordi Condom Jürgen Klopp Luka Elsner Jan Ceulemans Jordi Roura Jürgen Kramny Malky Mackay Jan de Jonge Jorge Costa Jurgen Streppel Manolo Jiménez Jan Olde Riekerink Jorge Jesus Kare Ingebrigtsen Manolo Preciado † Jan Siewert Jorge Paixão Karim Belhocine Manuel Baum Jan van Dijk Jorge Sampaoli Karl Daxbacher Manuel Cajuda Jan Vreman Jorge Simão Kasper Hjulmand Manuel Fernandes Jan Wouters Jos Luhukay Kirill Novikov Manuel Machado Javi Gracia José Anigo Klaus Schmidt Manuel Pellegrini Javier Aguirre José Antonio Camacho Kurban Berdyev Marc Brys Javier Calleja José Antonio Romero Landry Chauvin Marcel Keizer Javier Clemente José Aurelio Gay Lassaad Chabbi Marcelino Jean Fernandez José Couceiro László Bölöni Marcelo Bielsa Jean Tigana José Francisco Molina Laurent Banide Marcelo Romero Jean-Guy Wallemme José Gomes Laurent Blanc Marco Baroni Jean-Louis Garcia José González Laurent Fournier Marco Giampaolo Jean-Louis Gasset José Luis Mendilibar Laurent Guyot Marco Kurz Jean-Luc Vasseur José Luis Oltra Leonardo Marco Paulo Jean-Marc Furlan José Mota Leonardo Jardim Marco Pezzaiuoli Jean-Paul de Jong José Mourinho Leonardo Semplici Marco Rose Jens Keller José Peseiro Leonel Pontes Marco Silva Jess Thorup José Ramón Sandoval Leonid Kuchuk Marco van Basten Jesse Marsch José Riga Leonid Slutski Marcus Sorg Jesualdo Ferreira José Viterbo Lito Vidigal Marinus Dijkhuizen João de Deus Josef Zinnbauer Litos Mario Been João Henriques Juan Antonio Pizzi Lorenz-Günther Köstner Mario Beretta João Pedro Sousa Juan Carlos Garrido Lorenzo Staelens Mark Hughes Joaquín Caparrós Juan Carlos Unzué Louis van Gaal Mark van Bommel Jocelyn Gourvennec Juan Ignacio Martínez Luca Gotti Markus Babbel Jochen Fallmann Juan Merino Lucas Alcaraz Markus Gisdol Johan Walem Juan Muñiz Luciano Spalletti Markus Kauczinski John Carver Juande Ramos Lucien Favre Markus Schopp John Karelse Juanjo González Luigi De Canio Markus Weinzierl John Lammers Juanma Lillo Luigi Delneri Martin Jol John Stegeman Julen Lopetegui Luigi Di Biagio Martín Lasarte John van den Brom Julian Nagelsmann Luís Castro Martin O'Neill John van 't Schip Julien Stéphan Luis Enrique Martin Scherb Johnny Jansen Julio Velázquez Luis García Martin Schmidt Jon Dahl Tomasson Jupp Heynckes Luís Miguel Massimiliano Allegri Jonas De Roeck Jürgen Klinsmann Luís Norton de Matos Massimo Carrera

Massimo Ficcadenti Nebojsa Gudelj Paul Clement Ranko Popovic Massimo Oddo Neil Warnock Paul Gludovatz Rashid Rakhimov Massimo Rastelli Nelo Vingada Paul Lambert Reiner Geyer Maurice Steijn Nenad Bjelica Paulo Alves Rémi Garde Mauricio Pellegrino Nestor El Maestro Paulo Fonseca René Eijer Mauricio Pochettino Nigel Adkins Paulo Sérgio René Girard Maurizio Sarri Nigel Pearson Paulo Sousa René Hake Mehmed Bazdarevic Niko Kovac Pavel Vrba René Marsiglia Michael Angerschmid Nikolay Savichev Pedro Caixinha René Meulensteen Michael Baur Norbert Meier Pedro Emanuel René Weiler Michael Frontzeck Nuno Capucho Pedro Gómez Carmona Ricardo Moniz Michael Köllner Nuno Espírito Santo Pedro Martins Ricardo Sá Pinto Michael Laudrup Nuno Manta Santos Pedro Ribeiro Ricardo Soares Michael Skibbe Ole Gunnar Solskjaer Pep Guardiola Rinat Bilyaletdinov Michael Streiter Oleg Kononov Pêpa Rob Alflen Michael Wiesinger Oleg Protasov Pepe Bordalás Rob Maas Míchel Oleg Vasilenko Pepe Mel Robert Evdokimov Michel Der Zakarian Oliver Glasner Petar Vasiljevic Robert Ibertsberger Michel Jansen Oliver Lederer Peter Bosz Robert Maaskant Michel Preud'homme Olivier Dall'Oglio Peter Hyballa Robert Molenaar Mick McCarthy Olivier Guégan Peter Maes Robert Moreno Mickaël Debève Olivier Pantaloni Peter Pacult Roberto D'Aversa Miguel Ángel Lotina Omari Tetradze Peter Schöttel Roberto De Zerbi Miguel Ángel Portugal Óscar García Peter Stöger Roberto Di Matteo Miguel Cardoso Otto Rehhagel Peter Zeidler Roberto Donadoni Miguel Leal Owen Coyle Petit Roberto Mancini Mike Büskens Pablo Correa Philippe Clement Roberto Martínez Mike Phelan Pablo Franco Philippe Hinschberger Roberto Stellone Mikel Arteta Pablo Machín Philippe Montanier Robin Dutt Miodrag Bozovic Paco Herrera Phillip Cocu Roger Schmidt Mircea Lucescu Paco Jémez Pierpaolo Bisoli Roland Kirchler Mircea Rednic Paco López Pieter Huistra Rolando Maran Mirko Slomka Pál Dárdai Predrag Jokanovic Rolland Courbis Miroslav Djukic Pascal Dupraz Quim Machado Roman Mählich Mitchell van der Gaag Pasquale Marino Quique Sánchez Flores Roman Sharonov Mohamed Bradja Patrice Carteron Quique Setién Ron Jans Moreno Longo Patrice Garande Rachid Chihab Ronald Koeman Murad Musaev Patrick Collot Rafa Benítez Ronny Van Geneugden Murat Yakin Patrick Gabriel Ralf Rangnick Roy Hodgson Natxo González Patrick Vieira Ralph Hasenhüttl Rúben Amorim

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Rubi Sir Alex Ferguson Thomas Schaaf Viktor Skrypnyk Rudi Garcia Sir Kenny Dalglish Thomas Schneider Vincent Hognon Rui Almeida Sjors Ultee Thomas Silberberger Vincenzo Montella Rui Bento Slaven Bilic Thomas Tuchel Vítor Campelos Rui Quinta Slavisa Jokanovic Thomas von Heesen Vítor Oliveira Rui Vitória Slavisa Stojanovic Thorsten Fink Vítor Pereira Rúnar Kristinsson Slavoljub Muslin Tiago Fernandes Vladimir Eshtrekov Ruslan Agalarov Stale Solbakken Tim Sherwood Vladimir Fedotov Rustem Khuzin Stanislav Cherchesov Tito Vilanova Vladimir Gazzaev Ruud Brood Stanley Menzo Ton du Chatinier Vladimir Petkovic Ruud Gullit Stefan Ruthenbeck Ton Lokhoff Vladimir Shevchuk Sabri Lamouchi Stefano Colantuono Tony Pulis Voro Sam Allardyce Stefano Pioli Torsten Frings Walter Knaller Sami Hyypiä Steffen Baumgart Torsten Lieberknecht Walter Kogler Sandro Mendes Stéphane Jobard Trond Sollied Walter Mazzarri Sandro Schwarz Stéphane Moulin Ulisses Morais Walter Schachner Santiago Solari Steve Agnew Unai Emery Walter Zenga Sascha Lewandowski Steve Bruce Urs Fischer Werner Grabherr Scott Parker Steve Clarke Uwe Rösler Werner Gregoritsch Sean Dyche Steve Kean Vadim Evseev Wil Boessen Sergey Kiryakov Steve McClaren Vadim Skripchenko Wiljan Vloet Sergey Pavlov Stijn Vreven Vahid Halilhodzic Willy Sagnol Sergey Pervushin Sven Vermant Valeri Chalyi Wouter Vrancken Sergey Semak Sylvain Ripoll Valeri Gazzaev Yannick Ferrera Sergey Silkin Sylvinho Valeri Karpin Yuri Gazzaev Sergey Tashuev Tayfun Korkut Valeri Nepomnyashchiy Yuri Krasnozhan Sergey Tomarov Terry Connor Valeri Petrakov Yuri Semin Sergio Theo Bos Valérien Ismaël Yuriy Kalitvintsev Sérgio Conceição Thiago Motta Vasco Seabra Yves Vanderhaeghe Sérgio Vieira Thierry Henry Vasili Baskakov Zdenek Zeman Serse Cosmi Thierry Laurey Vicente Moreno Zeljko Petrovic Silas Thomas Doll Víctor Fernández Zinédine Zidane Simone Inzaghi Thomas Grumser Víctor Sánchez Zoran Barisic Sinisa Mihajlovic Thomas Letsch Viktor Goncharenko Zvonimir Soldo Table 12: List of all observed coaches; Source: Own elaboration

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III. Affidavit

I hereby declare that this Master’s thesis has been written only by the undersigned and without any assistance from third parties. I confirm that no sources have been used in the preparation of this thesis other than those indicated in the thesis itself.

This Master’s thesis has heretofore not been submitted or published elsewhere, neither in its present form, nor in a similar version.

Innsbruck, May 25, 2021

Place, Date Signature

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