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Int. J. Financ. Stud. 2013, 1(4), 119-136; doi:10.3390/ijfs1040119

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
Author to whom correspondence should be addressed; 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


: 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.
revenue diversification; income portfolio; organizational mission; contingency theory; non-profit organization; sports club

1. Introduction

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 [1], Chang and Tuckman [2], and Kingma [3] 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 [1] and to increase their financial viability [2]. Previous research has mainly supported the beneficial effects of revenue diversification on the financial situation of non-profit organizations (e.g., [4]), although a few studies refuted those benefits [5,6]. On the positive side, organizations with diversified revenues were less financially vulnerable (e.g., [7,8,9,10]), had a lower insolvency risk [11], and less volatile revenues [12].

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 [2] 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 [13]. In their comprehensive study, Chang and Tuckman [2] compared organizations operating in 25 different industries and found that revenue concentration was lowest for non-profits concerned with environmental quality and for animal-related organizations, while it was highest in consumer protection and legal aid organizations. The type of activity [2] or mission [13] 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 [2] 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 [2] 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 [14]. 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 [14]. Previous research has supported the notion that sports clubs produce heterogeneous products for heterogeneous stakeholders [15,16,17]. 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 [18]. 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 [19], are less financially vulnerable [20], and have less volatile revenues [21], although not all studies could support a positive relationship [22]. 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 [13], 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 [2] 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 [13] 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 [13] 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 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 [13] based on the findings of the Chang and Tuckman [2] study—the authors themselves have not developed such a theory in their paper. According to Kearns [13], 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 [25] 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 [23]. 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 [13]—also referred to as the institutional perspective [2]—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 [13]. 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 [26]. 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 [31], 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 [32]. These examples show that the mix of mission statements may have an influence on the income portfolio of non-profit sports clubs.

The idea of managing income portfolios originally stems from financial portfolio theory (e.g., [33]), which is one of the economic theories [13]. This theory has already been applied to non-profit organizations in general [3] and in sport [21]. 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 [37]. 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” [37]. 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 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 [2]. The index is calculated with the following formula:

Ijfs 01 00119 i001
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.
Table 1. Overview of variables.
Rev divRevenue 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 Metric
Organizational mission (from 1 = do not agree at all to 5 = totally agree)
Elite Our club promotes competitive sport (elite sport)Ordinal
Leisure Our club promotes leisure and mass sportOrdinal
Health Our club provides health sportOrdinal
Cheap Our club offers a cheap opportunity to play sportOrdinal
Quality Our club cares about the quality of the sport programsOrdinal
Commercial Our club is geared towards the programs of commercial providers Ordinal
TraditionOur club sets value on traditionOrdinal
ConvivialityOur club sets value on companionship and convivialityOrdinal
Non-sportOur club also provides non-sport programsOrdinal
YouthOur club is engaged in the promotion of youthOrdinal
LN Rev/mTotal logged revenues/number of club membersMetric
MembersTotal number of members in the clubMetric
Members²Members squaredMetric
SportsNumber of sports provided by the clubMetric
Sports²Sports squaredMetric
SportType 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)Dummy
YearYear of survey (2005, 2009, or 2011; 1 = yes)Dummy
StateFederal state (Germany has 16 states; from 1 = Bavaria to 16 = Schleswig-HolsteinDummy

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):

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). 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 [38], 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 [38]. 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 [39]. 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 [22]. 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) [40], 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 [40].

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 [35]. 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 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 [41]. 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 [41], 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 [2], the translation of the organizational mission statements is more challenging because the translator needs to pay attention that the statements maintain their original meaning [42]. 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 [13], 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:

Ijfs 01 00119 i002

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 [37]. 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 [43].

The two models are Ordinary Least Squares (OLS) regressions like in the Chang and Tuckman [2] 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 [44].

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 [22]. A slightly higher value of .525 was obtained in another study on sports clubs using the same measure [21]. 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 [19]. 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.
Table 2. Descriptive statistics.
Rev div0.4730.241
Elite 2.801.27
Leisure 4.121.05
Health 3.071.29
Cheap 4.450.88
Quality 4.120.87
Commercial 2.061.01
LN Rev/m0.1210.155
Football (soccer)0.283/
Track and field0.136/
Table tennis0.165/

The German values are higher than the value obtained in the Chang and Tuckman [2] 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). 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 [2] 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. [31], 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 [45], Scotland [46], Belgium [47], and Switzerland [17]. The high standard deviation of 1113.9 indicates that German clubs are heterogeneous in size, a finding that is similar to previous research [48]. 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.

Table 3. Summary of regression models for the dependent variable Rev div (OLS).
Table 3. Summary of regression models for the dependent variable Rev div (OLS).
Model 1 Model 2
Constant0.38419.30 ***0.38419.76 ***
Elite0.0094.94 ***0.0115.52 ***
Leisure−0.014−6.93 ***−0.013−6.41 ***
Health −0.012−6.56 ***−0.008−4.50 ***
Cheap 0.0031.260.0000.20
Quality −0.014−5.03 ***−0.008−2.87 **
Commercial −0.005−2.22*−0.007−3.26 ***
Tradition0.0041.98 *0.0010.33
Conviviality0.0072.51 *0.0041.45
Non-sport0.0094.66 ***0.0094.75 ***
Youth0.03916.83 ***0.03615.86 ***
LN Rev/m−0.330−10.67 ***−0.311−10.41 ***
Members0.0003.16 **0.0003.73 ***
Members²−0.000−2.48 *−0.000−2.93 **
Sports0.0116.64 ***//
Sports²0.000−4.89 ***//
Badminton//−0.023−3.58 **
Football (soccer)//0.09118.73 ***
Track and field//0.0172.75 **
Shooting//0.0172.60 **
Dancing//−0.030−4.46 ***
Table tennis//0.0040.85
Gymnastics//0.0183.17 **
Volleyball//−0.036−6.28 ***
Year Dummies (Ref: 2005)included included
State Dummies (Ref: Bavaria)included included
0.205 0.229
F100.753 92.297
p<0.001 *** <0.001 ***

Note: Displayed are the unstandardized coefficients; robust standard errors reported [44]; * p < 0.05; ** p < 0.01; *** p < 0.001.

The question is why this is the case. Typically, non-profit organizations having financial difficulties try to increase their commercial activities in order to generate revenues from service fees etc. These revenues from commercial activities are used to finance core areas of the organization through cross-subsidization [49]. This phenomenon is also referred to as the social enterprise movement [26], a phenomenon which also applies to the non-profit sports club sector [30]. Looking at non-profit sports clubs in this research, some clubs also imitate commercial providers in terms of programs (specifically health sport programs) and quality. While such a commercial orientation may increase revenues in some areas (otherwise clubs would not pursue it), it also comes at a price. The results of this study show that those clubs generate a high share of revenues from one source or a few sources, but are not able to attract revenues from a variety of funders. Consequently, clubs pursing a commercial orientation increase their financial risk since organizations with a high level of revenue concentration were found to be more financially vulnerable [4,9,10] and had more volatile revenues [12,21].

The nature of the coefficients of the organizational mission variables in model 1 also indicate that some missions are complementary, while others may be conflicting. Mission statements with the same sign can be considered complementary, i.e., combining those missions seems appropriate since the combination does not irritate possible funders. For example, promoting competitive sport at the elite level and promoting youth are complementary missions which both lead to more diversified revenues. Similarly, the missions of setting value on tradition, setting value on companionship and conviviality, and providing non-sport programs go hand in hand in terms of their effect on the clubs’ revenue portfolio. As introduced earlier, all three missions belong to what clubs historically stand for. The missions of being geared towards the programs of commercial providers and caring about the quality of sport programs are also complementary, but lead to more concentrated revenues.

Some mission statements are conflicting meaning that their influence on the level of revenue diversification is not of the same nature. For instance, the mission of promoting competitive sport at the elite level seems to be conflicting with promoting leisure and mass sport respectively health sport. While the first mission attracts a variety of revenues from different sources, the latter two lead to more concentrated revenues. Thus, clubs promoting leisure and mass sport as well as health sport increase their financial risk more than do clubs promoting competitive sport. The missions of promoting health sport programs and providing cheap opportunities to play sport represent another conflicting combination of missions. While the first leads to more concentrated revenues, the latter increases revenue diversification (although the effect of Cheap is not significant). The nature of effects indicates that those missions are hardly combinable. The same applies to caring about the quality of sport programs and providing a cheap opportunity to play sport. Intuitively, such a combination seems inappropriate since quality programs are typically more cost intensive. Those costs have to be covered somehow. Typically clubs charge service fees for those programs to both members and non-members using the programs. Thus, they increase their commercial revenues, but at the cost of giving up a variety of revenues from other funders. Not surprisingly, the missions of setting value on tradition and being geared towards the programs of commercial providers are conflicting in terms of their influence on the composition of the income portfolio. While the first affects revenue diversification positively, the latter has a negative impact. The findings support that clubs should carefully choose their organizational missions and pay specific attention to the combination of missions.

The results of model 1 also show that the level of revenue diversification is determined by club size. Total logged revenues per member (LN Rev/m) have a significant negative impact on the dependent variable. This means that clubs with higher per-capita revenues have more concentrated revenues. Thus, the more financial power clubs have, the more concentrated are their revenues, i.e., clubs rely on only a few, but strong income sources. The size variables Members and Sports have a significant positive effect on revenue diversification, while their squared terms (Members², Sports²) negatively impact the dependent variable. The larger clubs get in terms of members and sports, the more diversified are their revenues. Yet, the negative effects of the squared terms show that this relationship is not linear, but quadratic. At some stage, increases in members and sports do not contribute to more diversified revenues anymore—there is a saturation effect.

In model 2 (Table 3), the variables Sports and Sports² are replaced by the 10 sport dummies. The effects of the 10 organizational mission statements under investigation and the size variables are similar supporting the robustness of findings. The coefficients on Tradition and Conviviality still have the same sign, but the effect is not significant anymore. It seems that the sport dummies have overlapped these two effects. Out of the 10 sport dummies in model 2, seven have a significant impact on the dependent variable. Yet, the nature of effects is mixed. While Football, Track and field, Shooting, and Gymnastics have a positive effect, Badminton, Dancing, and Volleyball have a negative impact.

The question is why some sports lead to more diversified revenues than other sports. One explanation could be the potential attractiveness of the sport to funders who support the sport. Evidently, clubs providing football, track and field, shooting, or gymnastics attract revenues from more sources than clubs providing badminton, dancing, or volleyball. Intuitively, the positive coefficient on Football is not surprising since football clubs are able to generate sponsorship income, even when their best team plays in a relatively low division. Another hint comes from the model itself. It appears that the sport dummies have overlapped the positive Tradition and Conviviality effects which are not significant anymore. Thus, there must be some sports, such as football, track and field, shooting, and gymnastics that stand for tradition and conviviality, and some sports, like badminton, dancing, and volleyball that stand less for these missions. For this reason, clubs providing programs in specific sports (i.e., football, track and field, shooting, and gymnastics) may be able to generate more diversified revenues than other clubs. The advanced explanation with tradition seems plausible particularly for gymnastic clubs because they were among the first clubs that were founded in Germany (the German Turnverein).

5. Conclusions

This study looked at the influence of different organizational missions on the level of revenue diversification among non-profit sports clubs in Germany. Panel data from a survey of German sports clubs are used for the empirical analysis. The results show that the level of revenue diversification differs among sports clubs depending on the type of organizational mission. Clubs with mission statements such as promoting competitive sport at the elite level, setting value on tradition, companionship and conviviality, providing non-sport programs, and promoting youth have more diversified revenues than those pursuing missions like promoting leisure and health sport, caring about the quality of sports programs, and being geared towards commercial providers. Thus, clubs pursuing missions in areas clubs historically stand for are able to generate revenues from more sources than clubs with more recent commercial-like missions. Given that previous research has documented a positive relationship between an organization’s level of revenue diversification and financial health (e.g., [12]), pursuing commercial-like missions increases the financial risk of clubs since they make themselves dependent on a few income sources. Consequently, clubs should be aware of the financial consequences of pursuing specific missions.

The findings also indicate that sports clubs should carefully choose their mission portfolio. The nature of effect of mission statements differs supporting the presence of complementary and conflicting missions. While the missions of tradition and non-sport programs go hand in hand in terms of their financial consequences (i.e., they are considered complementary), promoting competitive sport and health sport represent conflicting missions. Thus, similar to the choice of an income portfolio as suggested by financial portfolio theory, sports clubs should carefully select their combination of organizational missions.

This research also showed that sports clubs are a useful research setting for examining the assumptions of contingency theory. In this study, the focus was on internal contingency factors respectively different types of organizational missions. Like in previous research [2,13], the level of revenue diversification was determined by organizational mission. Yet, this study compared organizations within one industry, while mission was equivalent to industry in previous research [2,13]. Thus, this study increases the application area of contingency theory.

This study has some limitations that represent directions for future research. First, this research is limited to three years of panel data. While the sample size is relatively large, the panel is too unbalanced to estimate panel regression models. This may be an avenue that could be pursued in future sports clubs projects. Second, this research is limited to the sports industry and the findings of the current study can only be generalized to the sports sector. They may be applicable to sports clubs in other Western European countries since they were found to have similar financial circumstances despite different policy systems [50]. The generalizability of findings may be extended to comprehensive community sports clubs in Japan that also offer sport and non-sport programs [51] indicating similarities to the Western European sport club system. The results may also inform sport and recreation organizations in Canada that serve a variety of population groups [52]. It would be interesting to examine whether non-profit organizations in other industries also pursue such a variety of missions and how those missions affect revenue diversification.


The authors would like to thank the Federal Institute of Sport Sciences (BISp), the German Olympic Sports Confederation (DOSB), and the 16 state sports confederations for supporting the research into sports clubs in Germany (Sport Development Report).

Conflicts of Interest

The authors declare no conflict of interest.

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