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Brian P. Soebbing
Printed Edition of the Special Issue Published in IJFS
Special Issue Editor Brian P. Soebbing
MDPI • Basel • Beijing • Wuhan • Barcelona • Belgrade
Brian P. Soebbing Louisiana State University USA
Editorial Ofﬁce MDPI St. Alban-Anlage 66 Basel, Switzerland
Special Issue Editor
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Table of Contents
About the Special Issue Editor . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pamela Wicker, Svenja Feiler and Christoph Breuer Organizational Mission and Revenue Diversiﬁcation among Non-proﬁt Sports Clubs doi: 10.3390/ijfs1040119 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Pamela Wicker and Christoph Breuer How the Economic and Financial Situation of the Community Affects Sport Clubs’ Resources: Evidence from Multi-Level Models doi: 10.3390/ijfs3010031 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Kelly M. Hastings and Frank Stephenson The NBA’s Maximum Player Salary and the Distribution of Player Rents doi: 10.3390/ijfs3020075 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31 Ruud Koning, Victor Matheson, Anil Nathan and James Pantano The Long-Term Game: An Analysis of the Life Expectancy of National Football League Players doi: 10.3390/ijfs2010168 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 38
Kenneth Linna, Evan Moore, Rodney Paul and Andrew Weinbach The Effects of the Clock and Kickoff Rule Changes on Actual and Market-Based Expected Scoring in NCAA Football doi: 10.3390/ijfs2020179 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 47 Babatunde Buraimo, David Peel and Rob Simmons Systematic Positive Expected Returns in the UK Fixed Odds Betting Market: An Analysis of the Fink Tank Predictions doi: 10.3390/ijfs1040168 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
Rodney J. Paul and Andrew P. Weinbach Market Efﬁciency and Behavioral Biases in the WNBA Betting Market doi: 10.3390/ijfs2020193 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73
Benjamin Waggoner, Daniel Wines, Brian P. Soebbing, Chad S. Seifried and Jean Michael Martinez “Hot Hand” in the National Basketball Association Point Spread Betting Market: A 34-Year Analysis doi: 10.3390/ijfs2040359 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
About the Special Issue Editor
Brian P. Soebbing is an Assistant Professor in the Faculty of Kinesiology, Sport, and Recreation at the
University of Alberta, Canada. His research focuses on the strategic behavior of sport and recreation
organizations and their constituents. Prior to his current position at the University of Alberta, Dr. Soebbing was a faculty member in the School of Kinesiology at Louisiana State Univeristy and in the School of Sport, Tourism, and Hospitality Management at Temple University.
International Journal of
Organizational Mission and Revenue Diversiﬁcation among Non-proﬁt Sports Clubs Pamela Wicker *, Svenja Feiler and Christoph Breuer
Department of Sport Economics and Sport Management, German Sport University Cologne, Am Sportpark Muengersdorf 6, Cologne 50933, Germany; [email protected] (S.F.); [email protected] (C.B.) * Author to whom correspondence should be addressed; [email protected]; Tel.: +49-221-4982-6107; Fax: +49-221-4982-8144. Received: 2 October 2013; in revised form: 31 October 2013; Accepted: 4 November 2013; Published: 8 November 2013
Abstract: The beneﬁcial effects of diversiﬁed income portfolios are well documented in previous research on non-proﬁt organizations. This study examines how different types of organizational missions affect the level of revenue diversiﬁcation of organizations in one industry, a question that was neglected in previous research. Based on contingency theory, it is assumed that different missions are associated with different funding sources. Since missions can be complementary or conﬂicting, speciﬁc attention needs to be paid to the combination of missions. The sport sector is chosen as an empirical setting because non-proﬁt sports clubs can have various missions while their overall purpose is promoting sport. Panel data from a nationwide survey of non-proﬁt sports clubs in Germany are used for the analysis. The regression results show that revenue diversiﬁcation is signiﬁcantly determined by organizational mission. Historically, typical mission statements like promoting elite sport, tradition, conviviality, non-sport programs, and youth sport have a positive effect on revenue diversiﬁcation, while clubs with a commercial orientation and a focus on leisure and health sport have more concentrated revenues. The ﬁndings have implications for club management in the sense that some missions are associated with higher ﬁnancial risk and that the combination of missions should be chosen carefully.
Keywords: revenue diversiﬁcation; income portfolio; organizational mission; contingency theory; non-proﬁt organization; sports club
The concept of revenue diversiﬁcation and ﬁnancial portfolio theory have received increased academic attention in the non-proﬁt sector during the last two decades with Chabotar , Chang and Tuckman , and Kingma  making signiﬁcant contributions amongst others. The main idea of this theory is that organizations try to diversify their income portfolios to be less susceptible to ﬁnancial crisis  and to increase their ﬁnancial viability . Previous research has mainly supported the beneﬁcial effects of revenue diversiﬁcation on the ﬁnancial situation of non-proﬁt organizations (e.g., ), although a few studies refuted those beneﬁts [5,6]. On the positive side, organizations with diversiﬁed revenues were less ﬁnancially vulnerable (e.g., [7–10]), had a lower insolvency risk , and less volatile revenues . While the beneﬁcial effects of diversiﬁed revenues have been well investigated, only a few studies have examined what types of organizations have more diversiﬁed revenues than others. Chang and Tuckman  were the ﬁrst to show that the level of revenue diversiﬁcation (or concentration in their study) varies depending on the activity of the organization, a ﬁnding that was further supported by Kearns . In their comprehensive study, Chang and Tuckman  compared organizations operating in 25 different industries and found that revenue concentration was lowest for non-proﬁts concerned Int. J. Financial Stud. 2013, 1, 119–136
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with environmental quality and for animal-related organizations, while it was highest in consumer protection and legal aid organizations. The type of activity  or mission  corresponds to the industry or the sector the organization is operating in. Thus, it is only a broad measure of activity or mission, which does not consider that organizations within one industry can have different missions while having the same overall purpose. Having this in mind, Chang and Tuckman  suggested that “future researchers would do well to focus on the speciﬁc activities in which non-proﬁts engage”. The purpose of this study is to examine the relationship between different organizational missions and the level of revenue diversiﬁcation of non-proﬁt organizations within one industry. Building on the Chang and Tuckman  study, this study advances the following main research question: How does the organizational mission affect an organization’s level of revenue diversiﬁcation? The sport industry serves as an empirical setting. Non-proﬁt sports clubs are particularly suited to analyze this research question because they have different types of missions . While every club has the overall mission of promoting sport, several sub-missions exist. One peculiarity is that those sub-missions are not only sport-related such as promoting competitive sport and/or mass sport, but also non-sport related like promoting sociability . Previous research has supported the notion that sports clubs produce heterogeneous products for heterogeneous stakeholders [15–17]. For example, they do not only provide sport programs for their members, they also fulﬁll several social functions such as integrating youths and immigrants, and teaching youths applied democracy . These functions, which contribute to public welfare and social cohesion, are appreciated by the community and by policy makers and represent one reason why sports clubs receive ﬁnancial support from the government. Thus, clubs also produce other products in addition to sport programs. The variety of stakeholders may be one reason why sports organizations were found to have more diversiﬁed revenues than non-proﬁts in other industries [2,19]. Similar to the general non-proﬁt sector, the beneﬁcial effects of revenue diversiﬁcation have also been shown in the sport industry. For example, previous research documented that non-proﬁt sports organizations with a diversiﬁed income portfolio are in a better ﬁnancial condition , are less ﬁnancially vulnerable , and have less volatile revenues , although not all studies could support a positive relationship . However, it has not yet been examined how different types of organizational missions affect the level of revenue diversiﬁcation, i.e., what types of clubs have more diversiﬁed revenues than others. To analyze this question, data from a nationwide panel survey of non-proﬁt sports clubs in Germany are used (n = 45,074). The regression results show that the level of revenue diversiﬁcation is affected by the organizational mission. The ﬁndings have implications for club management. 2. Theoretical Framework and Literature Review
Following Kearns , several theories can be advanced that explain an organization’s revenue composition. The theoretical streams can be assigned to four main areas including organizational behavior, political science, economics, and strategic management. They provide different perspectives on the factors associated with income portfolios of non-proﬁts. For the present research looking at the inﬂuence of organizational mission on revenue diversiﬁcation only streams from organizational behavior, political sciences, and economics are considered relevant. Strategic management theories such as resource dependence approaches look at the relationship between organizations and the external entities that support those  and how those relationships result in external control and power. Their focus is more on the consequences of revenue composition and not on the inﬂuencing factors; therefore, strategic management theories are neglected. This study combines the organizational behavior perspective (contingency theory) with the political science and the ﬁnancial perspective (ﬁnancial portfolio theory) from economics. Kearns  advances one theoretical approach that he calls the contingency theory of income diversiﬁcation that can be assigned to the literature on organizational behavior. When looking at all the theoretical approaches, Kearns  notes that: “the contingency theory seems to be the most promising and intuitively appealing”. Yet, it has not been well developed in the context of revenue diversiﬁcation so
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far. This is different for other organizational contexts such as organizational structure and leadership (e.g., [23,24]). The contingency theory was developed by Kearns  based on the ﬁndings of the Chang and Tuckman  study—the authors themselves have not developed such a theory in their paper. According to Kearns , the main idea of this theory is that an organization’s mission determines the concentration (or diversiﬁcation) of its income sources, an assumption that intuitively ﬁts with the present study. To provide some context, contingency theory is based on the seminal work of Woodward  who argued at the time that several contingencies such as technology and external stakeholders (e.g., government, consumers) inﬂuence organizational behavior. Generally speaking, contingency theories have the underlying assumption that there is no optimal way of managing organizations that can be applied to all organizations. In fact, the management of each organization is contingent on internal factors (e.g., organizational culture) and external factors (e.g., environment, regulations) that vary among organizations . Consequently, those factors that are potentially variable are called contingency factors. In this study, the focus is on internal contingency factors relating to organizational mission. The theory supports the notion that organizations within one industry cannot be treated equally because they are likely to have different missions that are contingent on various internal and external factors. Different missions may in turn attract different funding sources thus inﬂuencing an organization’s income portfolio and its level of revenue diversiﬁcation. The present study seeks to analyze the relationship between organizational mission (as one contingency factor) and revenue diversiﬁcation. This study tries to enhance the understanding of contingency theory in the context of revenue diversiﬁcation by applying it to the sports club context. Following more established theories from political sciences —also referred to as the institutional perspective —an organization is mainly concerned with its legitimacy and acceptance in the community. Legitimacy is also created by the origin of its funding sources. Thus, not only the overall amount of money available to an organization is considered important, but also where the money comes from . This means that organizations pursue funding from recognized sources that increase their social acceptance. Moreover, it is likely that organizations generating funds from recognized institutions will increase their revenues from other institutions because they are considered worth of being funded. This is what has been referred to as the crowd-in effect in previous research, while the opposite effect, i.e., crowd-out effect, must also be considered . Crowd-out and crowd-in effects have been examined both in general non-proﬁt research [27,28] and in sport [29,30]. This theoretical stream has implications for portfolio management in the sense that both the origin of ﬁnancial resources and the interactions among income sources have to be taken into account. This information is also critical to the present research. Given that an organization’s revenue composition is a result of the services it provides , organizations should carefully choose their missions (and associated services) and pay attention to the relationships between different types of missions. Organizations’ missions may have a complementary or conﬂicting character—content wise and consequently also ﬁnancial wise. Missions can be complementary in the sense that funding institutions are likely to support both missions. In the sports club context, for example, missions relating to the promotion of competitive sport and the promotion of youth would be complementary because typically young people take part in competitive sport at the elite level. Thus, potential funding organizations would not see a discrepancy between the two missions. On the contrary, some mission statements could be regarded as conﬂicting. For example, the promotion of health sport and competitive sport at the same time may not be intuitively appealing to potential resource providers since both mission statements target different groups of people. While younger people are more likely to participate in competitive sport at the elite level, older people are more likely to demand health sport programs . These examples show that the mix of mission statements may have an inﬂuence on the income portfolio of non-proﬁt sports clubs.
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The idea of managing income portfolios originally stems from ﬁnancial portfolio theory (e.g., ), which is one of the economic theories . This theory has already been applied to non-proﬁt organizations in general  and in sport . Originally, portfolio management relates to the composition of the income portfolio in the sense of ﬁnancial risk and volatility. As stated earlier in this paper, the idea is that organizations diversify their revenues in order to be more ﬁnancially viable and experience lower revenue volatility. Yet, this study focuses on organizational missions and not directly on ﬁnancial risk (although it will be shown later in the paper that some missions may be indirectly associated with higher ﬁnancial risk than others). Therefore, this study is more concerned with different types of missions than with income sources of different risk levels. Nevertheless, attention needs to be paid to the combination of different missions since they may have ﬁnancial consequences. 3. Method 3.1. Data Source
This research is based on data from the Sport Development Report, a project looking at the situation of sports clubs in Germany. Germany is home to over 91,000 sports clubs that are well spread throughout the country and that provide sporting opportunities to the German population. Out of the approximately 80 million German citizens, 27.7 million are members of sports clubs [34,35]. Within this project, sports clubs are surveyed online every two years. Thus, the project has a panel character. The ﬁrst wave was conducted in 2005 with another three waves following in 2007, 2009, and 2011. The email addresses for the online survey are provided by the 16 state sports confederations before the start of each wave. From the ﬁrst to the fourth wave, the number of provided email addresses has increased considerably documenting that more and more clubs are online. In 2005, 18,085 valid email addresses were provided, 37,206 in 2007, 58,069 in 2009, and 67,708 in 2011 [15,16,18,36]. The sports clubs receive an invitation email including some information about the purpose of the project, anonymity and privacy of data, and a personalized link to the online questionnaire. This means that respondents can log in and out and that several people can complete the survey, which may be useful given its length and variety of questions. The survey usually starts in fall (with the exception of the ﬁrst wave where the survey started in spring). The survey period is approximately three months and one or two reminders are sent to the clubs which have not yet responded. Similar to the number of provided email addresses, the response rates have increased during the years (2005: n = 3,731; 2007: n = 13,068; 2009: n = 19,345; and 2011: n = 21,998). Each survey questionnaire consists of a standard set of questions that are similar in every wave (e.g., member statistics, sports offerings, volunteers, ﬁnances, organizational problems) and a set of questions addressing speciﬁc and current topics in sports club management (e.g., demographic change, doping, changes in the German school system, need of support). For the current study, only data from the ﬁrst (2005), third (2009), and fourth wave (2011) can be used for the analysis since questions about the organizational mission of clubs were omitted in the second wave in 2007. Consequently, the ﬁnal sample amounts to n = 45,074 sports clubs. Since the sub-samples of each wave are different in size and do not consist of the same clubs (although some clubs participated in more than one wave), the dataset is considered an unbalanced panel consisting of independently pooled cross sections . Pooled samples drawn from the same population are considered favorable for the analysis since “we can get more precise estimators and test statistics with more power” . Thus, this unbalanced panel is preferred over a normal cross-sectional dataset covering only one wave. Generally speaking, panel data are relatively rare in sports club research. To the knowledge of the authors, the data from the German Sport Development Report represent the largest panel data in quantitative sports club research. 3.2. Measures and Variables
An overview of the variables used in this study is presented in Table 1. In order to obtain revenue diversiﬁcation, a concentration measure was calculated ﬁrst. Revenue concentration is measured with
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an index (Herf ) similar to the Herﬁndahl-Hirschmann Index, a measure which has already been used in previous research [2,7,10,12]. Importantly, the index covers two aspects of revenue concentration, i.e., the number of different income sources and the extent to which revenues are distributed equally or unequally across sources . The index is calculated with the following formula: (1)
where N represents the total number of income sources (25 in this study); ri the revenue generated from source i; and Rev the total revenues a club generates in one year. To put it short, Herf is obtained by adding the squared proportions of all income sources. Table 1. Overview of variables. Variable Rev div
Elite Leisure Health Cheap Quality Commercial Tradition Conviviality Non-sport Youth LN Rev/m Members Members2 Sports Sports2 Sport Year State
Revenue diversiﬁcation = 1 − Herf ; 0 = perfect concentration, i.e., club has only one income source; 1 = perfect diversiﬁcation; Herf = sum of the squared proportions of all 25 income sources of sports clubs Organizational mission (from 1 = do not agree at all to 5 = totally agree) Our club promotes competitive sport (elite sport) Our club promotes leisure and mass sport Our club provides health sport Our club offers a cheap opportunity to play sport Our club cares about the quality of the sport programs Our club is geared towards the programs of commercial providers Our club sets value on tradition Our club sets value on companionship and conviviality Our club also provides non-sport programs Our club is engaged in the promotion of youth Total logged revenues/number of club members Total number of members in the club Members squared Number of sports provided by the club Sports squared Type of sport provided by the club (ten most frequent sports: badminton, football, track and ﬁeld, shooting, swimming, dancing, tennis, table tennis, gymnastics, volleyball; 1 = yes) Year of survey (2005, 2009, or 2011; 1 = yes) Federal state (Germany has 16 states; from 1 = Bavaria to 16 = Schleswig-Holstein
Ordinal Ordinal Ordinal Ordinal Ordinal Ordinal Ordinal Ordinal Ordinal Ordinal Metric Metric Metric Metric Metric
In the survey, sports clubs were asked to state their revenues in the following 25 different categories: revenues from (1) membership fees; (2) admission fees; (3) donations; (4) subsidies from sport organizations; (5) subsidies from the state; (6) subsidies from the district/community; (7) subsidies from the European Union; (8) subsidies from the friends’ association; (9) subsidies from other programs (e.g., employment ofﬁce); (10) fund management (e.g., interests); (11) self-operated restaurant; (12) sport events (e.g., gate revenues); (13) service fees from members (e.g., facility fees); (14) convivial gatherings (e.g., club parties and festivities); (15) sponsorship: jerseys, equipment; (16) sponsorship: boards; (17) sponsorship: broadcasting rights; (18) sponsorship: advertisements; (19) own business company; (20) course fees; (21) service fees from non-members (e.g., facility fees); (22) service fees from collaborating institutions; (23) rent/lease of own facilities; (24) credits; and (25) other (i.e., sum of all other miscellaneous revenues that could not be assigned to one of the 24 categories). All 25 income sources are used to calculate Herf. Since the index (Herf ) represents a measure for revenue concentration, the ﬁnal value was subtracted from 1 to capture revenue diversiﬁcation (Rev div): (2) Rev div = 1 − Herf
Organizational mission was assessed with a closed question. Respondents were asked to state the extent to which the club’s board agreed to a list of mission statements using ﬁve-point Likert scales (from 1 = do not agree at all to 5 = totally agree). As noted previously, organizational mission was assessed in wave 1, 3, and 4. Out of the list of 19 statements that were assessed in all three waves, 10 statements are selected for the current analysis. Using more items was not considered useful given the redundancy of some items (e.g., several items capture competitive sport or a commercial orientation).
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The 10 statements cover the main areas of sports clubs’ missions. Their concrete wording in the questionnaire can be seen in Table 1. The 10 mission statements under investigation can be divided into six sport-related and four non-sport statements. With regard to sport-related statements, promoting competitive sport at the elite level (Elite) is one of the core missions of sports clubs historically. Sports clubs have the monopoly for competitive sport in Germany. This means that people who want to take part in league competitions or championships at the district, state, or national level have to be a member of a sports club. Thus, promoting competitive sport is one of the clubs’ original missions. Also, clubs promote sport for the masses and ensure the provision of sport programs all over the country. Yet, leisure and mass sport programs (Leisure) have less of a competitive character. More recently, some clubs also provide health sport programs (Health) as a result of changes in individual demand. Many people are less interested in sport competitions; they want to play sport in order to become or remain ﬁt and healthy. Thus, providing health sport programs can be considered a relatively new mission of clubs. Following Heinemann , providing relatively cheap programs (Cheap) compared with other providers is one of the core strengths of clubs. One of the reasons for the low membership fees lies in the fact that many clubs receive public subsidies . Given the increasing number of ﬁtness centers with some chains also offering relatively cheap prices, more and more sports clubs are faced with increasing competition from commercial sport providers. One of the strength of commercial providers is the focus on quality, both in terms of facilities and in terms of the qualiﬁcation of coaches. As a result of increasing competition, some clubs have started copying the programs of commercial providers (Commercial) and pay more attention to the quality of their sport programs (Quality). Regarding non-sport missions, sports clubs are organizations with a ﬁne tradition and thus set value on tradition (Tradition). Since many sports clubs were founded in the late 1890s or at the beginning of the 20th century, they are known for being traditional organizations. Notwithstanding tradition is not only associated with positive aspects since it may also lead to resistance to change . Tradition can be fostered through non-sport programs (Non-sport) such as all sorts of social events and festivities where values and social cohesion are fostered. Social events are an integral part of many clubs, particularly of those setting value on companionship and conviviality (Conviviality). Previous research has documented the beneﬁcial effects of social events for the functioning of sports clubs . Finally, the promotion of youth (Youth) is one of the core areas of sports clubs. Historically, sports clubs are particularly concerned with getting youths off the street and provide them with a location to play sport and to learn values. Since revenue diversiﬁcation is not only inﬂuenced by organizational mission, this study also controls for other potential inﬂuencing factors. Since previous research has shown that organizational size has an impact on the functioning of sports clubs (speciﬁcally on production costs and organizational problems) , organizational size should be controlled for in the present research. The size measures are LN Rev/m which is obtained by dividing total logged revenues by club members, Members representing the total number of club members and its squared term (Members2 ), and Sports representing the total number of different sports provided by the club and its squared term (Sports2 ). The squared terms are included to capture quadratic effects of size in terms of members and sports. These size measures have already been used in previous research on non-proﬁt sports clubs . In addition to organizational size, this study also controls for type of sport, year of the data, and state. Sports clubs in Germany provide more than 60 different sports . For this research, the 10 most frequently stated sports in the survey are selected to see whether there are sports that lead to more concentrated or diversiﬁed revenues. Since approximately 40% of the sports clubs in Germany are multi-sports clubs (i.e., they provide more than one type of sport), one dummy is calculated for each sport. The types of sport variables are dummy variables, where 1 indicates that the sport is provided by the club, and 0 otherwise. Since the dataset contains observations from three waves, the year dummies control for the year of the survey. It could be that changes in the revenue composition result from
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events that happened in the year of the survey. For example, ﬁnancial crisis or other external events could inﬂuence a club’s revenues. The study also controls for the state the club is located since there are differences among German states in terms of e.g., ﬁnancial realities of state government, funding, and regulations that may inﬂuence a club’s revenue composition. Since this English article is based on German survey data, possible translation issues need to be considered . While the questionnaire was designed by native German speakers in the German language and the survey was also conducted in the German language, the questions and resulting variables were translated into English for the purpose of this article. Thus, translation issues were not present for the design and conduction of the survey, but may be present for the writing of the article. Following Temple and Young , the researcher can serve as the translator or the translation can be performed by an external (professional) translator. While the term revenue diversiﬁcation is a common term that has already been used in previous research , the translation of the organizational mission statements is more challenging because the translator needs to pay attention that the statements maintain their original meaning . Therefore, the translation by the researcher was preferred in this article since the researcher is more experienced regarding the meaning of (mission) statements. The translation of the statement Our club is geared towards the programs of commercial providers was the most challenging because it could not be translated directly from the German language. The statement should express that clubs are aware of the types of programs commercial providers offer and tend to imitate or copy the programs of those providers. The challenge was to ﬁnd one verb for the long explanation provided in the earlier sentence. If a word by word translation had been performed, part of the meaning would have been lost. The translation of the control variables was not considered problematic since these terms are used throughout the literature (e.g., [14,20]). 3.3. Statistical Analysis
Following Kearns , an organization’s income portfolio is adapted “to its changing mission and activities”. Therefore, the use of panel data seems appropriate because they capture changes in organizational missions over time. To obtain panel data, the three datasets from each wave are matched and integrated into one vertical panel dataset. Speciﬁc attention was paid to ensuring that all variables used for the analysis were assessed similarly in all waves, and are thus comparable. A similar data cleaning procedure had been undertaken in each wave to ensure the comparability of data. During this procedure, speciﬁcally the answers to any open-ended questions were checked for plausibility and content validity. Implausible values were set to missing values. Descriptive statistics are provided to give an overview of the sample structure. In a second step, regression analyses are performed to answer the main research question of this study (i.e., how does organizational mission affect an organization’s level of revenue diversiﬁcation?). The regression models are of the following general form:
Altogether, two regression models are estimated. In model 2 the variables Sports and Sports2 are replaced by the type of sport variables to avoid collinearity issues. Importantly, there is no reference category for type of sport since it is not a nominal variable—the 10 dummy variables are included the analysis. When T is small relative to N (which is the case for this study where T = 3 and N = 45,074), time dummies should be included in the models . Therefore, two year dummies (2009, 2011) are included; the reference category for Year is 2005. The study also controls for state inﬂuences with Bavaria being the reference category for State. There should be no collinearity problems in the models since all variance inﬂation factors (including those of Members2 and Sports2 ) are below the suggested threshold of 10 . 7
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The two models are Ordinary Least Squares (OLS) regressions like in the Chang and Tuckman  study. In addition to the OLS estimator, several speciﬁcations were tried. Yet, typical panel regression models like random-effects or ﬁxed-effects models could not be estimated because of the unbalanced nature of the panel. There are too many clubs which have only participated in one or two of the three waves. Thus, ﬁxed-effects models cannot be estimated without losing observations. It was also not possible to use clustered standard errors to control for unobserved club heterogeneity. Regression models with robust standard errors are estimated to control for heteroskedasticity . 4. Results and Discussion 4.1. Descriptive Statistics
The descriptive statistics are summarized in Table 2. They show that the average level of revenue diversiﬁcation among German sports clubs is .473. This value is similar to previous research on sports clubs where revenue concentration based on the Herﬁndahl Index was .518 leading to a diversiﬁcation value of .482 . A slightly higher value of .525 was obtained in another study on sports clubs using the same measure . Revenue diversiﬁcation has also been examined for sports governing bodies which represent the sports organizations at the middle layers (e.g., at the community level, district level, state level, and national level) of the pyramid of the German sports system. A similar value of .46 was obtained for sports governing bodies in Germany . The average revenue diversiﬁcation values from this study and from previous research indicate that non-proﬁt sports organizations in Germany have a medium level of revenue diversiﬁcation. Table 2. Descriptive statistics.
Rev div Elite Leisure Health Cheap Quality Commercial Tradition Conviviality Non-sport Youth LN Rev/m Members Members2 Sports Sports2 Badminton Football (soccer) Track and ﬁeld Shooting Swimming Dancing Tennis Table tennis Gymnastics Volleyball
0.473 2.80 4.12 3.07 4.45 4.12 2.06 3.60 4.29 3.04 4.06 0.121 373.9 1,380,493.6 3.32 26.61 0.102 0.283 0.136 0.104 0.078 0.094 0.137 0.165 0.307 0.167
0.241 1.27 1.05 1.29 0.88 0.87 1.01 1.08 0.83 1.11 1.15 0.155 1113.9 85,754,551.0 3.95 73.27 / / / / / / / / / /
The German values are higher than the value obtained in the Chang and Tuckman  study for non-proﬁts in the area of recreation, leisure, or sports in the United States. In their study, they had an average level of revenue concentration of .64 (which is equivalent to a diversiﬁcation level of .36). 8
Int. J. Financial Stud. 2013, 1, 119–136
Yet, the values are hardly comparable since there are no organizations in the United States that are equivalent to the European sport club concept. When comparing the average level of revenue diversiﬁcation of sports clubs with non-proﬁt organizations in other industries (e.g., [2,12]), it stands out that non-proﬁts in sport tend to have more diversiﬁed revenues. One reason could be the measurement of revenues which is relatively detailed in this study using 25 different income sources. This relatively high number of income sources could ultimately lead to higher levels of diversiﬁcation since Herf considers the number of income sources. Yet, this explanation is speculative since details about the number of income sources assessed in the Chang and Tuckman  study are not provided. Another explanation could relate to the variety of income sources of sports clubs being a result of heterogeneous stakeholders. As mentioned earlier in this article, sports clubs produce a variety of products, not only sport programs, but also non-sport programs like social events. Moreover, they produce other products such as applied democracy and integration of multiple population groups that may attract funding from different stakeholders. Following Fischer et al. , an organization’s revenue composition is a result of the products it provides and therefore, the variety of products may lead to a variety of income sources among sports clubs which may in turn lead to more diversiﬁed revenues. Looking at the organizational mission of sports clubs, Table 2 shows that the provision of a cheap opportunity to play sport is most important to clubs on average (M = 4.46), followed by setting value on companionship and conviviality (M = 4.29), promoting leisure and mass sport, and caring about the quality of sport programs (both M = 4.12). The mean values show that both historical and more recent missions are important which may not be compatible with each other. For example, the mission of providing high quality programs is cost-intensive and may be conﬂicting with providing cheap programs. At the bottom of the mission ranking are promoting competitive sport at the elite level (M = 2.80) and being geared towards the programs of commercial providers (M = 2.06; Table 2). The clubs in this sample have on average 374 members and provide 3.3 different sports. German clubs are thus larger in terms of members and sports than clubs in other countries such as the UK , Scotland , Belgium , and Switzerland . The high standard deviation of 1113.9 indicates that German clubs are heterogeneous in size, a ﬁnding that is similar to previous research . The most frequently stated sport (30.7%) is gymnastics, which includes all disciplines that are covered by the German Gymnastics Association, the national governing body for gymnastics. These are, for example, apparatus gymnastics, ﬂoor exercise, trampoline, and gym wheel. The second most frequently stated sport is football (soccer; 28.3%), followed by volleyball (16.7%), table tennis (16.5%), and tennis (13.7%; Table 2). 4.2. Regression Models
The regression models are presented in Table 3. The results in model 1 show that all organizational missions (with the exception of Cheap) have a signiﬁcant inﬂuence on the dependent variable. While the variables Elite, Tradition, Conviviality, Non-sport, and Youth have a positive effect, Leisure, Health, Quality, and Commercial have a negative impact on Rev div. Thus, sports clubs pursuing those missions they historically stand for have more diversiﬁed revenues than clubs having more recent and commercial missions.
Int. J. Financial Stud. 2013, 1, 119–136
Table 3. Summary of regression models for the dependent variable Rev div (OLS). Model 1 Constant Elite Leisure Health Cheap Quality Commercial Tradition Conviviality Non-sport Youth LN Rev/m Members Members2 Sports Sports2 Badminton Football (soccer) Track and ﬁeld Shooting Swimming Dancing Tennis Table tennis Gymnastics Volleyball Year Dummies (Ref: 2005) State Dummies (Ref: Bavaria) R2 F p
Coeff. 0.384 0.009 −0.014 −0.012 0.003 −0.014 −0.005 0.004 0.007 0.009 0.039 −0.330 0.000 −0.000 0.011 0.000 / / / / / / / / / /
Model 2 t 19.30 *** 4.94 *** −6.93 *** −6.56 *** 1.26 −5.03 *** −2.22* 1.98 * 2.51 * 4.66 *** 16.83 *** −10.67 *** 3.16 ** −2.48 * 6.64 *** −4.89 *** / / / / / / / / / /
Coeff. 0.384 0.011 −0.013 −0.008 0.000 −0.008 −0.007 0.001 0.004 0.009 0.036 −0.311 0.000 −0.000 / / −0.023 0.091 0.017 0.017 0.009 −0.030 −0.004 0.004 0.018 −0.036