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Abstract

Drawing on a rational choice framework, this study investigates fans’ stated preferences for (more) environmentally-friendly stadium travel (bicycles/e-scooters) on game days. Data from fans of a German Football Bundesliga club were collected using an online survey in 2021 (n = 1,652). Travel preferences were assessed using the contingent behavior method. The results of regression analyses indicate that the likelihood of using environmentally-friendly transportation means increases with perceived benefits, while perceived costs decrease this likelihood. Specifically, perceived environmental benefits and being an environmental role model had a positive effect, while monetary, convenience, and time-related costs had a negative effect.

Keywords

Bundesliga contingent behavior method ecological sustainability rational choice

Introduction

Planet Earth is on the edge of a global climate crisis with a steady temperature rise until today due to increasing anthropogenic greenhouse gases (Intergovernmental Panel on Climate Change [IPCC], 2022). This crisis requires immediate action to avoid irreversible damage to the climate system (Steffen et al., 2018). Especially greenhouse gas (GHG) emissions need to be reduced. The climate challenges and the need to reduce GHG emissions are recognized by policymakers nationally and globally. For example, in 2015, the United Nations (UN) introduced the UN Sustainable Development Goals (SDGs) that include, among others, taking urgent actions to combat climate change (SDG 13) (McCullough et al., 2022). In the same year, 196 nations ratified the Paris Climate Agreement, which is a legally binding treaty on climate change with the goal to limit global warming to below 2°C, but preferably to 1.5°C compared to pre-industrial levels (UN Framework Convention on Climate Change [UNFCCC], 2015). One industry that contributes to climate change through GHG emissions is the tourism industry, including sport tourism.

The tourism sector in general accounts for 8% of total global emissions, with travel emissions contributing the largest portion (23%) to total emissions (Lenzen et al., 2018). Hence, sport tourism in terms of travel to sport vacations, sport events, and league games also yields negative climate externalities (McCullough et al., 2019). Scholars have acknowledged the negative effects of sport tourism and started to measure the negative externalities in different settings with a focus on externalities from travel behavior (Cooper & McCullough, 2021). Previous research has documented the GHG emissions arising from, for example, snow sport tourism (Wicker, 2018), college football game day tourism (Cooper, 2020), college basketball event tourism (Cooper & McCullough, 2021), and professional sport league tourism (Loewen & Wicker, 2021).

A number of professional sport leagues acknowledged their negative climate impacts and started to implement measures to improve their environmental sustainability (e.g., National Basketball Association [NBA], 2022). Most recently, the German Football League (DFL) included (environmental) sustainability in the licensing criteria for first and second division Bundesliga clubs (DFL, 2021). The sustainability-related licensing criteria are grounded in the UN's SDGs and require clubs to implement environmental measures in order to reduce their GHG emissions. Given that spectator travel was found to be the largest contributor to total event or game day emissions (Collins et al., 2009; Cooper, 2020; Cooper & McCullough, 2021), clubs aim at implementing environmental measures to reduce these emissions. However, since these measures can be costly, they need to develop knowledge about their spectators’ travel behavior and their willingness to change their behavior. Changes in travel behavior are connected to the associated benefits and costs of these behavioral changes (Liebe & Preisendörfer, 2010). In turn, benefits and costs of travel behavior might be associated with infrastructural conditions (Ben-Elia & Avineri, 2015).

The purpose of this study is to examine fans’ behavioral intentions of using environmentally-friendly transportation means (i.e., bicycles/e-bicycles, e-scooters)¹ when traveling to game days and to assess how the perceived benefits and costs affect these behavioral intentions. The study proposes the following research question: How do perceived benefits and costs of environmentally-friendly transportation means affect fans’ likelihood of using them when traveling to the stadium? The research question is examined in the context of a German Football Bundesliga club. Fans of the club completed an online survey where their travel intentions were assessed using the contingent behavior method. This approach can inform club officials and policymakers about how environmental measures need to be designed so that they target perceived benefits and costs, ultimately incentivizing spectators to use environmentally-friendly transportation modes more often.

Theoretical Framework and Literature Review

Pro-Environmental Travel Behavior

Pro-environmental travel behavior represents one dimension of pro-environmental behavior, which is referred to as “behavior that consciously seeks to minimize the negative impact of one's actions on the natural and built world” (Kollmuss & Agyeman, 2002, p. 240). In sports, pro-environmental travel behavior has been examined using both stated and revealed preference approaches (e.g., Wicker, 2018; Whitehead & Wicker, 2018). While the latter can be employed to measure existing behavioral patterns and previous travel behavior, the former are typically necessary when such behavior has not yet occurred.

Stated preference approaches present respondents with a hypothetical scenario that targets policy changes, new products, or changes in infrastructural conditions in order to examine how respondents’ behavioral intentions change (Orlowski & Wicker, 2019). One of the most popular methods to elicit stated preferences and behavioral intentions is the contingent behavior method (CBM; Orlowski & Wicker, 2019; Whitehead et al., 2013). CBM assesses behavioral changes based on changing circumstances (i.e., infrastructural enhancement, changing travel distances) (Whitehead & Wicker, 2018). In existing sport travel research, the contingent behavior method has been applied to league game day travel (Whitehead et al., 2013) and participatory sport events like cycling and running events (Whitehead & Wicker, 2018; 2019). However, CBM has not yet been applied to pro-environmental travel behavior.

Turning to revealed preference approaches, previous research has examined past travel behavior using different methods, including the travel cost method (Orlowski & Wicker, 2019) and carbon footprint analysis (e.g., Thormann & Wicker, 2021; Wicker, 2018). The travel cost method has frequently been employed to assess the monetary value of nature sport destinations (for an overview, see Orlowski & Wicker, 2019), but it has not yet been used to examine pro-environmental travel behavior. Such behavior has typically been examined using carbon footprint analysis (e.g., Collins et al., 2009; Wicker, 2019). These studies relied on surveys where participants reported about their travel behavior and specifically travel distances and transportation means. This information was converted into carbon footprint estimates using emission factors of different transportation means, with low carbon emissions indicating high levels of pro-environmental behavior (Wicker, 2019). For example, existing studies examined the carbon footprints of active sport participants (Wicker, 2019), sports club members (e.g., Thormann & Wicker, 2021), snow sport tourists (Wicker, 2018), and spectators of sport events (e.g., Collins et al., 2009) or league games (Cooper & McCullough, 2021).

Pro-environmental travel behavior is affected by different factors. From a theoretical perspective, attitudes toward the natural environment represent an important factor (Diekmann & Preisendörfer, 2003), with environmentally conscious individuals being expected to have a higher likelihood of having pro-environmental travel behavior (Wicker, 2018). However, many studies documented that such attitudes do not automatically translate into the respective behavior (Wicker, 2018, 2019), yielding an environmental value-action gap (Blake, 1999). This gap is caused by different barriers which hinder environmentally conscious individuals from having pro-environmental (travel) behavior (Blake, 1999). These barriers can be divided into individuality, responsibility, and practicality and include circumstances like lack of time, money, and effort (Blake, 1999). This explanation is echoed by the low-cost hypothesis, indicating that environmental consciousness only translates into pro-environmental behavior when the costs in terms of money, convenience, and effort are perceived as low (Diekmann & Preisendörfer, 2003).

Existing studies identifying an environmental value-action gap have provided these explanations but did not assess the perceived costs of having the respective behavior (e.g., Wicker, 2018; 2019). Hence, specific costs and also perceived benefits of pro-environmental travel behavior have not been systematically examined. The present study attempts to address this shortcoming by investigating perceived benefits and costs of pro-environmental travel behavior not only empirically but also theoretically by drawing on rational choice theory.

Finally, several socio-economic factors affect pro-environmental travel behavior (Thormann & Wicker, 2021). For example, a higher educational level and being female were found to have a positive influence on pro-environmental travel behavior (Briscoe et al., 2019; Thormann & Wicker, 2021). Moreover, people with higher incomes tend to travel more environmentally-unfriendly (Brand & Preston, 2010). Lastly, older people were found to produce fewer carbon dioxide emissions (Brand & Preston, 2010).

Rational Choice Theory

In environmental research, the rational choice theory is often used to explain perceptions, attitudes, and behavior at the individual level that influence outcomes at the macro level. Hence, environmental consequences are often a result of individual actions (Liebe & Preisendörfer, 2010). This relationship is described using a macro-micro-macro model, which states that a social situation, for example, provision of infrastructure, affects individual decisions. These purposive individual answers to social conditions (i.e., infrastructure) have consequences at a macro-environmental level (Esser, 1999; Liebe & Preisendörfer, 2010).

One example of this macro-micro-macro relationship is individual transportation behavior, where rational choice is often related to attributes of travel alternatives (i.e., cost and time of travel modes, comfort level, environmental friendliness) (Ben-Elia & Avineri, 2015). If infrastructural conditions make it difficult to use environmentally-friendly transportation modes due to high costs or low comfort, individuals choose a less environmentally-friendly alternative. In turn, this individual decision has negative consequences for the climate and environment (Liebe & Preisendörfer, 2010). For individual actions, individuals usually have the possibility to decide between at least two alternatives (Erlinghagen, 2003).

According to the rational choice theory, a decision consists of three propositions: First, preferences or motives determine an individuals’ behavior (perceived benefits). Second, specific constraints (perceived costs) might hinder reaching the goal of the behavior. Third, individuals try to maximize their utility (Opp, 2020). In order to find the best behavioral alternative, individuals can apply decision-making heurism. One possible heurism is to maximize the subjective expected utility derived from perceived benefits and costs of their actions (Erlinghagen, 2003; Opp, 2020). Individuals weigh the perceived associated benefits against the perceived associated costs of their actions and decide on the alternative where the perceived associated benefits exceed the perceived associated costs or the alternative with the highest perceived benefit-cost ratio.

In travel behavior, costs are often divided into monetary costs, costs of inconvenience, and time restrictions (Ben-Elia & Avineri, 2015). Importantly, individual decisions are based on subjective rationality because individuals choose the behavioral alternative which, in their opinion, maximizes their own utility. Hence, rationality does not mean objective rationality but rather goal-oriented subjective rational actions (Erlinghagen, 2003). This decision heurism explains why some individuals choose environmentally-friendly travel means when others do not, even though the macro environment might be similar (Liebe & Preisendörfer, 2010).

Previous research used rational choice theory to explain pro-environmental behavior in different travel contexts (e.g., Li et al., 2019; Whitehead et al., 2013). For example, in public transportation, reducing the fees increased the usage rates and environmentally-friendly travel behavior, respectively (Bamberg & Schmidt, 1998). Also, passengers who decided between multiple railway alternatives (speed or common) tended to choose the alternative with lower ticket prices and shorter travel time (Li et al., 2019). In previous sport research, rational choice theory was used to investigate the demand for hockey game day travel (Whitehead et al., 2013), benefits and costs of participation in a long-distance triathlon competition (Maxcy et al., 2019), and the likelihood of return visitation of sport event participants (Whitehead & Wicker, 2019). The perceived costs and benefits of pro-environmental travel behavior have not yet been studied.

Methods

Data Collection

An online survey of fans from a German Football Bundesliga club was conducted from August 29 to October 31, 2021. The club (Arminia Bielefeld) is located in the biggest German federal state, North Rhine-Westphalia that is home to eight other Football clubs that play in the first or second division. The minimum age of respondents was 18 years. The online survey was programmed on the platform www.soscisurvey.de. A convenience and top-down snowball sampling approach was applied, similar to previous research examining the travel behavior of Football Bundesliga fans (Loewen & Wicker, 2021). This sampling approach was chosen because the characteristics of the full population (i.e., fans of the club) are unknown and there is no directory with fans’ contact details. The approach allows gathering a comparable large sample and can be a useful tool to understand respondents’ views and habits (Jones & Gratton, 2010; Loewen & Wicker, 2021). The weakness of the approach is that respondents self-select into a survey on a topic they are interested in (e.g., sport research). Since the characteristics of the full population are often unknown in sport research, this approach is regularly the only option for researchers to gather appropriate samples (Jones & Gratton, 2010).

The online survey was distributed through various online channels, including university e-mail dictionaries, fan club dictionaries, and social media channels of the club. As an incentive, respondents were informed that they can voluntarily participate in a sweepstake, which included prices like a home jersey, a puzzle of the club's stadium, and a personalized mug. This sampling approach resulted in 3,479 clicks on the survey link. Altogether, 2,460 individuals started the survey and 1,652 respondents completed the whole questionnaire. All answers were checked for plausibility and internal validity. For example, the average time spent answering the questionnaire was checked to identify respondents who speeded through the questionnaire. Since the questionnaire used Likert scales and open questions (e.g., travel distance by transportation mean), it was more difficult for respondents to speed through the questionnaire than with closed questions. Moreover, the environmental consciousness and pro-environmental behavior questions included several reverse-coded questions to control for those respondents, who only clicked left or right. Both issues were not present in the given sample. A final sample of n = 1,652 fans could be used for the empirical analysis.

Questionnaire and Variables

The questionnaire started with an introduction that informed the respondents about the voluntariness of their participation and the anonymity of data. The questionnaire was divided into different sections assessing fans’ previous game day travel, general environmental consciousness and behavior, environmentally-friendly stadium travel and perceived associated benefits and costs, and several socio-demographic characteristics. Table 1 provides an overview of all variables in this study.

Table 1.

Overview of Variables and Summary Statistics (n = 1,652).

Variable	Description	Mean	SD
Environmentally-friendly travel	(very) likely riding the bicycle (incl. e-bicycles)/e-scooter for stadium travel if free and secure parking is available (1 = yes; 0 = no)	0.308	–
CBM change	Change in behavior from scenario without free parking to CBM scenario with free parking (1 = behavior change; 0 = no change)	0.153	–
Usage	Combination of past behavior, general usage likelihood, and environmentally-friendly travel for the panel model (1 = usage; 0 = no usage) (n = 4,956)	0.185	–
Past behavior	Respondent traveled by bicycles, e-bicycles, or e-scooters to the stadium in the past (1 = yes; 0 = no)	0.078	–
General usage likelihood	(very) likely riding the bicycle (incl. e-bicycle)/e-scooter for stadium travel (1 = yes; 0 = no)	0.168	–
Benefits	Benefits index (range 1-5)	3.00	0.87
Costs	Costs index (range 1–5)	2.53	0.70
Sum benefits	Sum score of benefits by adding up all benefit dummies	2.76	2.01
Sum costs	Sum score of costs by adding up all cost dummies	2.32	1.50
Stated preference	Dummy for stated preference data without free parking scenario (n = 4,956)	0.33	–
Free parking	Dummy for free parking scenario to combat climate change through more environmentally-friendly stadium travel (n = 4,956)	0.33	–
Bicycle	Scenario (0 = e-scooter; 1 = bicycle/e-bicycle)	0.50	–
Environmental consciousness	Environmental consciousness index (mean of nine items; 1 = not environmentally consciousness at all; 5 = highly environmentally consciousness) (items see Table A1)	3.82	0.75
Pro-environmental behavior	Everyday pro-environmental behavior index (mean of 17 items; 1 = never; 5 = always) (items see Table A2)	3.50	0.51
Distance	Distance between residence and stadium (in km)	18.14	12.66
Female	Respondent identifies as female (1 = yes; 0 = no)	0.260	–
Male	Respondent identifies as male (1 = yes; 0 = no)	0.735	–
Diverse	Respondent identifies as diverse (1 = yes; 0 = no)	0.005	–
Age	Age (in years)	32.30	11.94
Low education	Highest degree not more than general secondary school diploma (1 = yes; 0 = no)	0.209	–
A-levels	Highest degree is at least A-levels (1 = yes; 0 = no)	0.419	–
University	Highest degree is from a university (1 = yes; 0 = no)	0.372	–
Income	Monthly net equivalent income per person (in 1,000 €)	1.94	1.146

The survey started with questions about fans’ travel to previous game days. Past behavior was assessed because existing research indicated that the use of environmentally-friendly travel alternatives might be influenced by past behavior (Carrus et al., 2008) as individuals follow their habits (Gärling & Axhausen, 2003). Therefore, respondents were asked what transportation means they usually use to get to the stadium and what distances they cover with each transportation mean. To allow plausibility checks of distances with Google Maps, respondents were asked to provide the starting points and destinations of each transportation. In contrast to previous studies (Dolf & Teehan, 2015; Loewen & Wicker, 2021), the present study allowed reporting up to three transportation means rather than only the main transportation mode. The transportation means provided mirrored those of the Federal Environmental Office (2022), including cars, regional trains, long-distance trains, coaches, busses, and trams. Bicycles, e-bicycles, e-scooters, and walking were added to the list. Based on this information, a dummy variable was created that takes the value of 1 if respondents already used bicycles (incl. e-bicycles) or e-scooters in their typical stadium travel in the past and 0 otherwise (Past behavior). Since the use of environmentally-friendly transportation means like bicycles and e-scooters also depends on fans’ residences, the distance from fans’ residences to the stadium (Distance) is also included in the study. The inclusion of travel distance is important because it might represent an objective cost measure. Even though the maximum distance an individual is willing to travel by a bicycle, an e-bicycle, or an e-scooter might differ depending on subjective perceptions, the distance itself can be considered an objective measure.

Respondents’ level of environmental consciousness was included as a measure of respondents’ attitude toward the environment and was captured with a 9-item scale. This scale consisted of three dimensions (i.e., cognitive, conative, and affective) with three items each (Table A1). It has previously been validated (Diekmann & Preisendörfer, 2003) and has already been applied in existing sport ecology research (Thormann & Wicker, 2021; Wicker, 2019). Cronbach's α measures scale reliability by assessing the consistency of answers by respondents. It takes values between 0 and 1 and the higher the value, the higher is the consistency of items (Hair et al., 2018). The scale showed very good reliability with a Cronbach's α of 0.896. The final index represents the average of all nine items (Environmental consciousness).

The CBM scenario was at the heart of the survey. Before the scenario was provided, respondents answered 14 questions on a five-point scale (from strongly disagree to strongly agree) regarding the perceived benefits and costs (7 questions for benefits; 7 questions for costs) associated with traveling to the stadium by a bicycle (incl. e-bicycle) or an e-scooter. However, one cost question regarding the perceived long distance was highly correlated with the distance variable and hence was not used for further analysis. One index each was calculated that represents the average of the seven (Benefits) and six items (Costs), respectively. The perceived benefits and costs index showed acceptable reliability with Cronbach's α of 0.799 and 0.653, respectively (Hair et al., 2018; Taber, 2018). Since both were created from items measured on a 5-point scale, both indexes and the ratio represent continuous variables (from 1 to 5). Two additional variables were created by summing up the perceived benefit and cost dummies (Sum benefits; Sum costs). Even though the basic idea of the rational choice theory includes weighing perceived benefits against perceived costs, the authors abstained from calculating a benefit-cost ratio. In the present case, the perceived benefits and perceived costs represent nonmonetary benefits and costs. Hence, there is no common unit or currency that could be weighed against each other, which would limit the meaning of a benefit-cost ratio.

Additionally, for a more detailed assessment of the perceived benefits and costs, 13 dummy variables were created based on the five-point scale variables, representing the value 1 if respondents selected the “agree” or “strongly agree” answer and 0 otherwise (Table 2). These items reflect the notion that transportation behavior might be a subjectively rational decision by individuals because they may be affected by social norms and symbolic values that should be included in benefits and costs (Higham et al., 2013). For example, some individuals might value being in the fresh air and perceive it as a benefit, while others do not. Moreover, the extent to which traveling by a bicycle (incl. e-bicycle) or an e-scooter is considered (too) physically demanding for an individual depends on the physical condition of the individual and his/her subjective assessment.

Table 2.

Overview of Perceived Benefits and Costs (n = 1,652; Sorted in Descending Order).

Item	Description	% of Respondents
Benefits
B_Fresh air	I appreciate being out in the fresh air while biking/driving with the scooter.	64.59
B_Role model	If I come by bicycle/e-scooter myself, I can be a role model for other spectators.	44.43
B_Environment	I can basically imagine coming by bicycle/e-scooter to do something for the environment.	42.62
B_Train	The train is always crowded and the crowd makes me uncomfortable.	42.49
B_Cheaper	I appreciate that it is less expensive to travel by bicycle/ e-scooter than by train/car.	33.78
B_Faster	I am faster by bicycle/e-scooter than by car or train (e.g., no waiting at a train station, no traffic jams, no searching for a parking space).	26.82
B_Reputation	My friends would appreciate it if I rode my bicycle/ e-scooter to the stadium.	21.73
Costs
C_Long journey	It takes longer for me to get there by bicycle/e-scooter.	61.86
C_Availability	I am concerned that my bicycle will be stolen/demolished at the stadium./I am concerned that my e-scooter will not be available after the game.	46.73
C_Parking spaces	There are not enough parking spaces at the stadium where you can plug in bicycles/e-scooters.	38.32
C_Comfort	Traveling by bicycle/e-scooter is too inconvenient for me.	33.54
C_Expensive	A bicycle/an e-scooter is too expensive for me.	30.21
C_Effort	Traveling by bicycle/e-scooter is too physically demanding for me.	22.09

Note: 1 = (strongly) agree; 0 = (strongly) disagree, neither agree nor disagree.

Afterward, the CBM scenario was presented. Although transportation behavior is, among others, influenced by habits and preferences, they are not fixed in time and might change based on the experience derived from them (Van Acker et al., 2010). One way of breaking transportation habits is to challenge them and let individuals reflect on their decision. A change of circumstances, for example, free public transport or secure spaces to lock the bicycle, can introduce the process of deliberation (Fuji & Kitamura, 2003; Garvill et al., 2003). The present scenario considers these aspects:

Suppose that Arminia Bielefeld wants to contribute to reducing the CO₂-emissions generated on Bundesliga match days. This can be achieved, among other things, by reducing the number of spectators traveling by car and instead using transportation means that produce fewer direct CO₂-emissions. Specifically, the increased usage of bicycles (incl. e-bicycles) and e-scooters is considered.

Suppose a free, guarded parking area for bicycles (incl. e-bicycles)/e-scooters would be set up at the Schüco Arena. Under these conditions, how likely would you travel to one or more Bundesliga home games in the 2021/2022 season by bicycle (incl. e-bicycle)/e-scooter?

Respondents were randomly allocated to one alternative, either traveling by a bicycle (incl. e-bicycle) or an e-scooter (Bicycle) to reduce the length and complexity of the questionnaire. The likelihood of using an environmentally-friendly transportation mean was assessed with a five-point scale from very unlikely to very likely. In line with previous research (Whitehead & Wicker, 2018), a dummy variable was created taking the value of 1 when respondents answered likely or very likely, and 0 otherwise (Environmentally-friendly travel). The hypothetical scenario did not cover the intensive margin (i.e., the number of game fans would travel with a specific transportation mode) as previous research indicated transportation habits do not significantly change without a change in external circumstances (e.g., change in infrastructure or price for transportation mode) (Fujii & Kitamura, 2003). Hence, it is expected that fans usually take the same transportation mode to travel to the stadium. To identify how realistic the scenario is for respondents, they were asked for their general likelihood of traveling to the stadium from downtown Bielefeld (e.g., main station, car park of the university) with a bicycle (incl. e-bicycle)/an e-scooter. The 5-point scale was also converted into a dummy variable (General usage likelihood).

Next, everyday pro-environmental behavior was assessed with 17 items assessing behavior in five categories, including food, consumption, recycling, energy, and transportation (Table A2). The original scale of Diekmann and Preisendörfer (2003) was slightly adapted and extended by items from recent studies looking at environmentally-friendly behavior (Corral-Verdugo et al., 2011; Kaida & Kaida, 2016; Vassallo et al., 2016). With a Cronbach's α of 0.825, the scale showed good reliability (Hair et al., 2018). The calculated pro-environmental behavior index represents the average of the 17 items (Pro-environmental behavior).

The survey finished with socio-demographic characteristics of respondents, including gender (Female; Male, Diverse), age (Age), educational level (Low education; A-levels; University), and income (Income). Especially, income might be important because it represents an objective cost measure. Since the pandemic, the costs for bicycles have increased as bicycle prices have nearly doubled in Germany since 2017 (Ilg, 2021). Renting an e-scooter is comparably expensive with a minimum price of €2 for a usage time of 5 min. Since purchasing bicycles/e-bicycles and renting e-scooters is rather expensive, low-income individuals might be less likely to use these environmentally-friendly transportation modes.

Empirical Analysis Strategy

The empirical analysis consisted of four steps, starting with summary statistics and progressing from a baseline logistic regression (using a cross-section) to the exploration of intention changes to a pseudo-vertical panel providing more detailed results step by step. In a first step, descriptive statistics are provided to give an overview of the sample structure and the perceived benefit and cost items. In a second step, three logistic regression models were estimated with the environmentally-friendly travel dummy as a dependent variable similar to the previously stated preference analysis (e.g., Whitehead & Wicker, 2018). To allow a systematic analysis of perceived benefits and costs as per the rational choice framework, the first model included the benefits index and costs index as main independent variables, while the second model included the sum of benefits and costs. The third model provides a detailed analysis as it includes all 13 perceived benefit and cost items.

Except for past travel behavior and the general usage likelihood, the remaining variables from Table 1 were included as control variables, since previous studies supported that gender, educational level, age, and income might influence individuals’ pro-environmental travel behavior (Brand & Preston, 2010; Thormann & Wicker, 2021). Past travel behavior and the general usage likelihood were used in the fourth step for the calculation of pseudo-vertical panel models that check for a potential hypothetical bias and the specific effect of the free parking scenario. The models are based on the following equations:

\begin{aligned} l o g i t (Pr (E n v i r o n m e n t a l l y - f r i e n d l y t r a v e l_{i} = 1)) = β_{0} + β_{1} B e n e f i t s_{i} + β_{2} C o s t s_{i} \\ + β_{3} B i c y c l e_{i} + β_{4} E n v i r o n m e n t a l c o n s c i o u s n e s s_{i} + β_{5} E n v i r o n m e n t a l b e h a v i o r_{i} \\ + β_{6} D i s t a n c e_{i} + \sum_{j = 1}^{8} β_{j} S o c i o d e m o g r a p h i c_{i} \end{aligned}

(1)

\begin{aligned} l o g i t (Pr (E n v i r o n m e n t a l l y - f r i e n d l y t r a v e l_{i} = 1)) = β_{0} + β_{1} B i c y c l e_{i} \\ + β_{2} E n v i r o n m e n t a l c o n s c i o u s n e s s_{i} + β_{3} E n v i r o n m e n t a l b e h a v i o r_{i} \\ + β_{4} D i s t a n c e_{i} + \sum_{j = 1}^{7} β_{j} B e n e f i t s_{i} + \sum_{j = 1}^{6} β_{j} C o s t s_{i} + \sum_{j = 1}^{8} β_{j} S o c i o d e m o g r a p h i c_{i} \end{aligned}

(2)where Benefits _i represents the perceived benefits index in the first model and the sum of benefits in the second model, Costs _i represents the costs index in the first model and the sum of costs in the second model, the vector Benefits _i in the third model contains the seven benefit items, the vector Costs _i in the third model contains the six cost items, and the vector Sociodemographic _i contains the socio-demographic variables. Individuals are indexed i = 1,…,n.

All independent variables were checked for multicollinearity through correlation analyses and variance inflation factors. All correlation coefficients were below 0.8 and variance inflation factors were below 10 (Hair et al., 2018). Hence, there is no indication of multicollinearity. Since our dependent variable in the logit model is binary and equal variance is not an assumption for using logistic regression, heteroscedasticity is not assumed to be an issue in these models and, hence, the models were calculated without robust standard errors (Williams, 2009). The panel models were calculated with standard errors clustered by individuals to account for having the same individuals three times in the data set.

For the third step, a dummy was created that identifies those individuals whose stated intention changes between the scenario without and with the free and secured parking (CBM change). This variable was used as a dependent variable and two models were calculated with only two cost items (C_Availability and C_Parking spaces) as main independent variables (Model 4) and all perceived benefit and cost items (Model 5).

Finally, a three-period pseudo-vertical panel was created and two dummy variables were calculated, which represent the stated preference data without the free parking scenario (Stated preference) and with the free parking scenario (Free parking) (n = 4,956, t = 3). This empirical approach follows previous CBM studies (Whitehead & Wicker, 2018; Wicker et al., 2017) and uses a panel logit model with a cross-section of the same individuals over three time periods. Moreover, a usage variable (Usage) was calculated that combines past travel behavior, general usage likelihood, and environmentally-friendly travel and has the value of 1 if respondents used or would use a bicycle or an e-scooter to travel to the stadium and 0 otherwise. The following logistic regression model was estimated for Usage:

\begin{aligned} l o g i t (\Pr (U s a g e_{i t} = 1)) = β_{0} + β_{1} D i s t a n c e_{i t} + β_{2} S t a t e d p r e f e r e n c e_{i t} \\ + β_{3} F r e e p a r k i n g_{i t} + β_{4} B i c y c l e_{i t} + β_{5} E n v i r o n m e n t a l c o n s c i o u s n e s s_{i t} \\ + β_{6} E n v i r o n m e n t a l b e h a v i o r_{i t} + \sum_{j = 1}^{7} β_{j} B e n e f i t s_{i t} + \sum_{j = 1}^{6} β_{j} C o s t s_{i t} \\ + \sum_{j = 1}^{8} β_{j} S o c i o d e m o g r a p h i c_{i t}, t = 1, 2, 3 \end{aligned}

where Stated preference_it is a dummy for stated preference data without the free parking scenario, Free parking_it is a dummy for the free parking scenario that aims to increase spectators’ environmentally-friendly travel to mitigate climate change, the vector Benefits_it represents all perceived benefit items, the vector Costs_it represents all perceived cost items, and the vector Sociodemographic_it contains the socio-demographic variables. Individuals are indexed i = 1,…,n, and the scenarios are index t = 1, 2, 3. Thereby, the parameter β₂ captures the potential for hypothetical bias (difference between Usage_i1 and Usage_i2) and the parameter β₃ the effect of free and secured parking on environmentally-friendly travel behavior while holding the hypothetical bias constant. The first logistic regression model only includes the distance, stated preference, free parking, and bicycle (Model 6), the perceived benefit and cost items (Model 7) are added in the second model, while the final model includes all parameters (Model 8).

Results

Table 1 summarizes the descriptive statistics. On average, respondents are 32.3 years old and 26.0% are female. In terms of highest educational level, 37.2% of respondents have some form of a university degree, 41.9% of respondents have a university entry degree, and 20.9% have no degree higher than a general secondary diploma. The average monthly net income is €1,944. On average, respondents score 3.82 on the environmental consciousness scale and 3.50 on the everyday pro-environmental behavior scale, with both scales ranging from one to five. From all observations, only 7.8% traveled to games by bicycles, e-bicycles, or e-scooters. However, with the current status quo in terms of parking spaces and security for bicycles, e-bicycles, and e-scooters, 16.8% of respondents would be (very) likely to travel to the stadium with these transportation alternatives. This share increases to 30.8% after the CBM scenario with free and secured parking spaces.

Turning to the perceived benefit and cost items, the perceived benefits index has a mean value of 3.00 and it is higher than the perceived cost index with a mean value of 2.53. Table 2 summarizes the mean values of all perceived benefit and cost items. On average, the highest perceived benefits come from enjoying fresh air and being a role model for other spectators. In contrast, respondents scored lower regarding the time investment for traveling by bicycles (incl. e-bicycles)/e-scooters. Hence, the highest perceived costs were the longer journey to get to the stadium by a bicycle (incl. e-bicycles)/an e-scooter and concerns that the bicycle (incl. e-bicycles)/e-scooter might be stolen or demolished after the game. The lowest perceived costs were the physical effort of traveling by bicycles (incl. e-bicycles)/e-scooters.

Table 3 displays the results of the logistic regression models for environmentally-friendly travel. Average marginal effects are provided. In Model 1, perceived benefits have a significant and positive effect on the usage likelihood of environmentally-friendly transportation means, while the effect of perceived costs is significantly negative. Average marginal effects show that the effect of perceived benefits was higher than the effect of perceived costs. The results of Model 2 are similar to those of Model 1. The sum of perceived benefits has a significant positive effect on the usage likelihood of environmentally-friendly travel means, while the sum of perceived costs is significantly negative. Again, the average marginal effect of perceived benefits is higher than that of the perceived costs.

Table 3.

Logistic Regression Results for Environmentally-Friendly Travel (n = 1,652).

	Model 1: Environmentally-Friendly Travel	Model 2: Environmentally-Friendly Travel	Model 3: Environmentally-Friendly Travel
Benefits	0.241***	–	–
Costs	−0.068***	–	–
Sum benefits	–	0.092***	–
Sum costs	–	−0.033***	–
B_Fresh air	–	–	0.096***
B_Role model	–	–	0.086***
B_Environment	–	–	0.179***
B_Train	–	–	0.047*
B_Cheaper	–	–	0.009
B_Faster	–	–	0.035
B_Reputation	–	–	0.062**
C_Long journey	–	–	−0.053*
C_Availability	–	–	0.052*
C_Parking spaces	–	–	0.072***
C_Comfort	–	–	−0.101***
C_Expensive	–	–	−0.141***
C_Effort	–	–	−0.064
Bicycle	−0.091***	−0.090***	−0.107***
Environmental consciousness	−0.024	−0.011	−0.011
Pro-environmental behavior	0.005	0.003	−0.004
Distance	−0.002*	−0.003***	−0.002*
Female	0.012	0.020	0.017
Male	Ref.	Ref.	Ref.
Diverse	0.091	0.076	0.085
Age	−0.001	−0.001	−0.001
Low education	Ref.	Ref.	Ref.
A-levels	0.018	0.004	−0.000
University	−0.016	−0.030	−0.025
Income	0.009	0.010	0.003
n	1,652	1,652	1,652
Pseudo R²	0.251	0.226	0.293
Wald χ²	329.01***	325.70***	370.99***

Note: *p < 0.05; **p < 0.01; ***p < 0.001; displayed are average marginal effects; Ref. = reference category.

In Model 3 including the 13 perceived benefit and cost dummies, the perceived benefits of doing something good for the environment, being a role model, being out in the fresh air, avoiding the crowded train, and appreciation by friends all have a positive and significant association with the likelihood to travel by bicycles (incl. e-bicycles) or e-scooters. In contrast, if monetary costs for a bicycle (incl. e-bicycle) or an e-scooter or high inconvenience increase, the likelihood of using the two travel modes significantly decreases. Finally, two cost items measuring the availability of bicycles (incl. e-bicycles)/e-scooters after the game and limited parking spaces for these transportation means have a positive and significant effect on their usage likelihood with free, secured parking.

Some control variables show also significant effects on the likelihood of using an environmentally-friendly transportation mean. The distance between fans’ residence to the stadium has a significant negative effect in all three models, meaning the further away fans live from the stadium, the less likely they come by bicycles (incl. e-bicycles) or e-scooters. Moreover, respondents who were assigned the CBM scenario for traveling by bicycles (incl. e-bicycles) have a significantly lower probability of using the environmentally-friendly travel mode than respondents with the e-scooter scenario. Collectively, all environmental variables and socio-demographic characteristics were insignificant.

Table 4 provides the results for the change in stated intentions and for the usage of environmentally-friendly travel that serve as additional models to provide more details in addition to the previous results. The dependent variable in Models 4 and 5 identifies those individuals whose stated intentions changed between the general usage likelihood and the CBM scenario. Overall, 15.3% of all respondents stated a change in intention due to the CBM scenario. Results of both models show that concern about the availability after the game (i.e., the bicycle is demolished or stolen) and the feeling of not having enough parking spaces lead to a change in intention when free and secured parking spaces are provided. Moreover, higher distances between fans’ residences and the stadium are negatively associated with a change in intention, while higher environmental consciousness has a significant positive effect. In Model 5, doing something good for the environment has a significant and positive effect on the change in intention, while the monetary costs are negatively associated.

Table 4.

Logistic Regression Results for CBM Change and Usage.

	Model 4: CBM Change	Model 5: CBM Change	Model 6: Usage	Model 7: Usage	Model 8: Usage
Distance	−0.003***	−0.001	−0.006***	−0.002***	−0.002***
Stated preference	–	–	0.121***	0.114***	0.114***
Free parking	–	–	0.233***	0.228***	0.228***
B_Fresh air	–	0.047*	–	0.054**	0.054**
B_Role model	–	0.039	–	0.040**	0.042**
B_Environment	–	0.127***	–	0.101***	0.101***
B_Train	–	0.027	–	0.025*	0.026*
B_Cheaper	–	−0.026	–	0.021	0.022
B_Faster	–	−0.012	–	0.044**	0.042**
B_Reputation	–	−0.023	–	0.055***	0.057***
C_Long journey	–	−0.014	–	−0.033*	−0.032*
C_Availability	0.061**	0.069***	–	0.009	0.009
C_Parking spaces	0.076***	0.063***	–	0.028*	0.027*
C_Comfort	–	−0.058*	–	−0.069***	−0.070***
C_Expensive	–	−0.042*	–	−0.074***	−0.073***
C_Effort	–	−0.022	–	−0.052*	−0.054**
Bicycle	0.000	−0.031	0.003	−0.073***	−0.073***
Environmental consciousness	0.037*	0.006	–	–	−0.002
Pro-environmental behavior	0.029	0.006	–	–	−0.008
Socio-demographics	Included	Included	–	–	Included
Individuals	1,652	1,652	1,652	1,652	1,652
Time periods	1	1	3	3	3
Pseudo R²	0.053	0.137	0.105	0.269	0.270
Wald χ²	70.38***	157.31***	557.86***	671.94***	677.33***

Note: *p < 0.05; **p < 0.01; ***p < 0.001; displayed are average marginal effects; Models 6–8 are estimated with standard errors clustered by individuals.

Models 6 to 8 provide further insights into a potential hypothetical bias and the effect of the free and secured parking scenario while holding the hypothetical bias constant. In all three models, the stated preference dummy is significant and positive, meaning that the stated preferences overstate the actual intentions to use environmentally-friendly travel means. The dummy for the free and secured parking scenario is also positive and statistically significant in all three models, indicating that free and secured parking spaces increase the likelihood of using environmentally-friendly travel means, while holding the hypothetical bias constant. Similar to the other models, the distance variable is significantly and negatively associated with the likelihood of using environmentally-friendly travel means. Similar to the results of Model 3, all perceived benefit items, except for the appreciation that it is cheaper to travel with environmentally-friendly travel means, have a significant positive association with bicycle (incl. e-bicycles) and e-scooter usage. In contrast to Model 3, perceived costs regarding concerns about availability after the game are not significantly associated with the usage, while the average marginal effect of limited parking spaces is smaller and less significant compared to Model 3. All other cost items have a significant negative association with the bicycle (incl. e-bicycle) and e-scooter usage.

Discussion

The purpose of this study was to examine the likelihood of environmentally-friendly stadium travel of spectators from a German Football Bundesliga club and which perceived benefits and costs affect this likelihood. The analysis was based on a comprehensive sample of football fans, which was larger than in previous research (Loewen & Wicker, 2021). The sample structure was skewed toward male, higher educated, and younger spectators, which is typical for football spectator surveys (Loewen & Wicker, 2021).

Turning to perceived benefits and costs, the associated benefits of traveling by bicycles (incl. e-bicycles) or e-scooters exceeded the associated costs, which is a promising sign for clubs that want to encourage spectators to use more environmentally-friendly transportation means for stadium travel. Compared to the general usage likelihood, the percentage of spectators who are (very) likely to travel by bicycles (incl. e-bicycles) or e-scooters to the stadium if free, secured parking is introduced nearly doubled, implying that infrastructural changes might have a positive effect for more sustainable mobility (Higham et al., 2013). Respondents’ level of general environmental consciousness was relatively high compared to previous applications of the scale in sport research (e.g., Thormann & Wicker, 2021).

The regression results showed that environmental consciousness measuring individuals’ general attitudes toward the natural environment had no significant association with travel intentions. This result is contrary to the literature assuming that attitudes drive travel decisions (Van Acker et al., 2010). In the present study, an environmental value-action gap is evident (Blake, 1999), meaning that environmental attitudes do not automatically translate into pro-environmental behavior like using environmentally-friendly transportation means. Hence, different barriers seem to be at work and explain why environmental consciousness does not transform into pro-environmental behavior (Blake, 1999). Such a gap has already been found in previous sport-related travel research (Loewen & Wicker, 2021; Wicker, 2018, 2019), indicating that sport-related travel is perceived as a high-cost situation (Diekmann & Preisendörfer, 2003). These previous studies assumed that the costs in terms of time or inconvenience were perceived as too high, resulting in environmentally conscious individuals not showcasing pro-environmental behavior. However, previous studies did not measure these costs. The contribution of this study lies in the assessment of both perceived benefits and costs of using environmentally-friendly transportation means and the provision of a theoretical underpinning by drawing on rational choice theory.

The regression models supported the relevance of perceived benefits and perceived costs as the higher the perceived absolute benefits, the higher the likelihood of using environmentally-friendly transportation means. In turn, the higher the perceived absolute costs, the lower the likelihood of using environmentally-friendly transportation means. Following rational choice theory, individuals weigh perceived benefits against perceived costs when making a subjective rational decision (Erlinghagen, 2003; Opp, 2020). Based on average marginal effects, the models indicated that perceived benefits are considered more important than perceived costs in the subjective rational decision about using environmentally-friendly transportation means. The results were robust when applying the sum of perceived benefits and costs, again with higher average marginal effects for perceived benefits than perceived costs, which indicates that fans express rational behavioral intentions. When confronted with a hypothetical scenario, fans tend to increase their subjective utility by comparing benefits with costs (Erlinghagen, 2003; Opp, 2020). Thus, rational choice theory can be considered an adequate theoretical underpinning for behavioral travel intentions, even though its suitability to explain travel behavior has been criticized (Opp, 2020). Considering the macro-micro-macro scheme, the findings suggest that infrastructural enhancement at the macro level, in this case, free and secured parking spaces, affects deliberations and behavioral intentions at the individual level about how to travel to the stadium (Liebe & Preisendörfer, 2010).

Another contribution of the present study is the detailed assessment of perceived benefits and costs which are included in Model 3. Starting with perceived benefits, doing something good for the environment, being out in the fresh air, and avoiding crowded trains significantly increased the likelihood of using environmentally-friendly travel modes. Hence, the connection to the natural environment was perceived as a strong benefit by fans, suggesting that they are aware of the macro-level effect of their individual choices (Esser, 1999; Liebe & Preisendörfer, 2010). This finding echoes previous research showing a positive relationship between pro-environmental orientations and environmentally-friendly travel behavior (Eriksson et al., 2008; Lange et al., 2018). Additionally, being out in the fresh air and avoiding the crowded train might also result in health benefits, especially during the pandemic where the Covid-19 virus is more likely to spread in closed rooms. Moreover, being an environmental role model for others not only ranked highest among all perceived benefit items but also positively predicted travel intentions. Hence, not only professional sport teams have an environmental role model function and can promote more sustainable behavior among their spectators (Inoue & Kent, 2012) but fans also value the possibility of acting as role models. Finally, appreciation of friends increased the likelihood of using environmentally-friendly travel modes, which is in line with previous studies that highlighted the importance of the opinion of significant others in individuals’ decision to act environmentally-friendly (McCullough & Cunningham, 2011; Thormann & Wicker, 2021).

Turning to perceived costs, only three cost items had a significant negative effect on the likelihood of using environmentally-friendly transportation means. These were monetary costs, convenience costs, and time costs. Especially with insecure parking spaces and potential damages during the game, driving one's own bicycle to the stadium seems too costly for some fans. This finding mirrors existing research showing that travelers prefer lower-cost alternatives when making mobility decisions (Li et al., 2019). It confirms that travel intentions are rational choices shaped by attributes of travel alternatives like money, time, and comfort level (Ben-Elia & Avineri, 2015). These three negative effects of monetary, convenience, and time costs also confirm the tenets of the low-cost hypothesis (Diekmann & Preisendörfer, 2003): When specific costs are perceived as too high, environmental consciousness does not translate into pro-environmental behavior, as evident in the present study.

The distance as an objective cost measure had a negative effect on the likelihood of using environmentally-friendly transportation means, supporting the corresponding findings for perceived costs. Traveling long distances by bicycles or e-scooters is associated with more effort, is more time-consuming, and might be even restricted by the range of the e-scooter, which can explain the consistent negative effect throughout the models.

Notably, two cost items had a positive effect on environmentally-friendly travel intentions, even though cost items are usually expected to have a negative sign on their coefficient. These are concerns about the availability of bicycles (incl. e-bicycles)/e-scooters after the game and potential damage. Fans scoring high on these concerns were significantly more likely to use these transportation means as these concerns were addressed in the hypothetical scenario encompassing free, secured parking. Hence, the proposed infrastructural changes in the scenario already targeted these perceived costs, ultimately turning a constraint into a contributor of behavioral travel intentions. The additional models confirm these results as both cost items were significantly and positively associated with the outcome variable in Models 4 and 5 that identified those individuals whose behavioral intentions changed due to the free and secured parking scenario. Moreover, when controlling for hypothetical bias and the effect of the free and secured parking scenario while holding the hypothetical bias constant, concerns about demolishment or availability turn insignificant and the effect that there are not enough parking spaces available diminishes as the effect of the hypothetical scenario addresses these perceived costs. Again, this finding supports the subjective rationality of individuals: Once perceived costs are addressed, they decide rationally and are more likely to intend a certain behavior.

The results of Models 6–8 indicated that the stated preference data overstate the actual intention to use environmentally-friendly travel means, which was also evident in a CBM study examining return intentions in participatory sport events (Whitehead & Wicker, 2019). Even though there is a hypothetical bias, the hypothetical scenario seemed to be valid, because the effect of the free and secured parking scenario still increases the intention to use environmentally-friendly travel modes while holding the hypothetical bias constant as the effect is significant in all three models.

The results have implications for club managers and policymakers. Starting with the beneficial role model function for others, not only clubs should become environmental role models, but they should also actively support their fans who want to serve as role models. One way could be to publicly reward those spectators, with public recognition making them more visible to others so that other spectators might follow their example. A second implication for both club managers and policymakers can be derived from the perceived monetary costs related to travel by bicycles (incl. e-bicycles) or e-scooters. Many German cities have city bicycles included in a rental system. Next to infrastructural enhancements like free, secured parking spaces, clubs and policymakers could cooperate to make these city bicycles free of use during game days. This cooperation would benefit all stakeholders: fans, because monetary costs decrease; the natural environment, because the travel-related carbon footprint decreases, which in turn helps clubs meeting licensing criteria in the area of environmental sustainability; and policymakers, because the rental offers become more popular among spectators. Given that monetary costs and prices of bicycles (incl. e-bicycles)/e-scooters are considered high, policymakers might consider subsidizing the purchase of bicycles (incl. e-bicycles)/e-scooters. This measure could increase their usage and the likelihood of using environmentally-friendly travel alternatives not only when traveling to the stadium, but also in everyday life.

Conclusion

This study applied the contingent behavior method to examine behavioral travel intentions of football fans through the lens of a rational choice framework. Fans were presented with a hypothetical scenario assuming that free and secured parking would be available for bicycles (incl. e-bicycles)/e-scooters at the stadium and they were asked for their likelihood of using these transportation means for stadium travel under these conditions. Consulting the average marginal effects, the findings suggest that perceived benefits are more important than perceived costs for shaping environmentally-friendly travel intentions. A positive benefit-cost ratio increases the likelihood of employing environmentally-friendly transportation means, suggesting that fans’ deliberations follow the tenets of subjective rationality. Since the reduction of GHG emissions is a major challenge for all stakeholders of the sport industry where travel is involved, the present study provided evidence that infrastructural enhancements can make sport-related travel and the sport industry more environmentally sustainable.

This study contributes to the existing literature on environmental sustainability in sport and sport-related travel in several ways. It is among the first that applied CBM to elicit the stated preferences of fans in relation to their stadium travel. CBM offers the opportunity to examine behavioral intentions contingent on specific changes in circumstances, for example, infrastructural changes, when the respective changes or policies have not yet been established. Second, this study explicitly examined the perceived benefits and costs of using environmentally-friendly transportation means. This is an enhancement of previous research which only speculated about perceived costs and the reasons for an environmental value-action gap. The present study did not only examine them empirically but also provided a rational choice framework as a theoretical foundation. In doing so, the study further developed existing theoretical explanations like the environmental value-action gap and the low-cost hypothesis.

This study is not without limitations that can guide future research. CBM included a change in infrastructure that targeted only two travel modes, namely, bicycles (incl. e-bicycles) and e-scooters. However, other transportation modes that can contribute to more sustainable travel, like public transportation, were neglected. Future studies could apply CBM to other modes of transportation. Additionally, the hypothetical scenario only proposed infrastructural changes. Future studies could target changes in supply or include a provision of free public transport in their hypothetical scenarios. The CBM scenario did not include the intensive margin (i.e., the number of games fans would travel to with a specific transportation mode), which could be added in future research. The present study only included a few objective cost measures (i.e., travel distance, income). Importantly, the environmental behavior literature stresses that these travel decisions result from subjective perceptions of costs and benefits, future research might explore more objective cost measures. Future investigations should also include the club's environmental initiatives and image in the analysis of fans’ decisions toward more environmentally-sustainable stadium travel.

Supplemental Material

sj-docx-1-jse-10.1177_15270025231200889 - Supplemental material for Environmentally-Friendly Stadium Travel of Football Fans: A Stated Preferences Study

Supplemental material, sj-docx-1-jse-10.1177_15270025231200889 for Environmentally-Friendly Stadium Travel of Football Fans: A Stated Preferences Study by Tim F. Thormann and Pamela Wicker in Journal of Sports Economics

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iDs

Tim F. Thormann

Pamela Wicker

Supplemental Material

Supplemental material for this article is available online.

Notes

Author Biographies

Tim F. Thormann is a PhD student in the Department of Sports Science at Bielefeld University. His research addresses sport participation, wellbeing, and public health; societal responsibility of sport; and environmental sustainability of sport.

Pamela Wicker is a professor of Sport Management and Sport Sociology in the Department of Sports Science at Bielefeld University. Her research interests include sport organizations; societal relevance of sport; sport participations, wellbeing and public health; and environmental sustainability of sport.

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