The Mediation of Emotions in Sport Events: A Case Study in Badminton

Abstract

This study examines the relationships between functional quality, outcome quality, satisfaction, and future intentions, influenced by emotions, of spectators who attended the 2018 European Badminton Championships. The population studied was 686 spectators. The mean age was 36.08 ± 14.15 years, 39.1% were females, and 66.1% were local resident spectators. The results allow to affirm that functional quality and outcome quality have an impact on satisfaction, either directly or indirectly through emotions. In the same way, it has been possible to observe the relationship between satisfaction and future intentions of the spectators. These results help understand the factors that predict the loyalty of spectators of sports events of badminton.

Keywords

quality satisfaction future intentions emotions spectators badminton

Introduction

Badminton is one of the most popular sports in the world, with high levels of interest in some of the largest markets such as Asia and with significant growth in the Americas. It attracts 497 million fans, with a mean age of 37.9 years (Repucom, 2016). According to Nielsen (2016), badminton is the sixth most watched sport on television worldwide; 87% of badminton fans get their sports information from television and 62% through mobile devices. Just as the continental and national federations, the Badminton World Federation organizes various competitions attended by thousands of spectators. Spectators become service users when they attend a sports show.

A better knowledge of the evaluation made by the users of sports services is very useful to improve the management of the services, in order to increase their loyalty, by satisfying their needs (García-Fernández et al., 2015; Seetanah et al., 2020). Bringing clients’ interests and needs closer to the offer of sports services is crucial for sports marketing specialists. Sports administrators use different tools to assess service quality and satisfaction as predecessors to user loyalty (Nuviala et al., 2017). This also happens in the context of sports events (Calabuig et al., 2016; Theodorakis et al., 2013), since spectator sports are considered a part of the sports industry oriented toward sports results (Brady et al., 2006).

The management of different service variables influences the future intentions of sport service users, and this is why it is so important for sport managers to be able to know how these variables relate to each other. The central element is satisfaction, since it is the consequence of a quality service (functional quality and quality of results) and a precedent for future intentions (Calabuig et al., 2015). Some studies in the field of sport events have used the emotions of spectators as predictors of their future behavior, studying the direct relationship between the two constructs (Biscaia et al., 2012). Others studies have analyzed the moderating effect of emotions in different groups of spectators grouped by levels of emotions, on the relationship between satisfaction and future intentions in a model that does not include the emotions construct (Calabuig et al., 2015).

The studies mentioned above in the field of sporting events have been carried out with great scientific rigor. The relationships between service quality, satisfaction, and future intentions have been studied, leaving the outcome quality and emotions out of the models or without studying all possible relationships between constructs (Calabuig et al., 2016; Theodorakis et al., 2013). The present study introduces into the same model, in addition to functional quality, satisfaction, and future intentions, the variables of outcome quality and emotions. The purpose of this work is to carry out the study of the relationships between these variables and their ability to predict the future intentions of spectators, depending on whether they are local spectators or tourists, in a sport such as badminton that is spreading in the West and is a reference sport in the east.

Literature Review

Effects of Functional Quality and Satisfaction on Future Intentions

Zeithaml (1988) defined quality as a consumer judgment on the excellence or superiority of a product/service, stating that it is a prerequisite for success. Bitner and Hubbert (1994) conceptualized it as the consumer’s overall impression of the relative superiority or inferiority of an organization and its services. Customer satisfaction is a pleasurable response to a good, service, benefit, or reward (Oliver, 1997) and is a summary of the evaluation of the overall experiences of customers with a service (X. Li & Petrick, 2010). Satisfaction is a different construct from quality of service and has affective elements (Taylor, 1997).

These consumer evaluations, quality and satisfaction, result in post–consumer behavior that determine the intentions of their behavior. These behaviors are varied and diverse. It was Zeithaml et al. (1996) who developed a scale that included a large number of these behaviors. Empirical studies show that satisfaction positively affects intention in various types of services, like tourist services (Han & Hyun, 2013) and equally in the field of sports services. Anderson and Fornell (2000) did so in the banking market. Carlson and O’Cass (2010) concluded that the quality of electronic service influences consumer satisfaction and behavioral intentions on websites. Theodorakis et al. (2014), in a study of sport and fitness centers, found that satisfaction is an antecedent to users’ future intentions.

In the context of sport events, there is empirical evidence of the influence of service quality and satisfaction on behavioral intentions. Bodet and Bernache-Assollant (2011) in a study with French first division ice hockey clubs, with a sample of 395 spectators, revealed that spectator satisfaction is the strongest predictor of intentions to attend sporting events again. In a study conducted in the context of professional basketball in Spain, using a sample of 429 spectators, Calabuig et al. (2016) found a direct relationship between quality and satisfaction with future spectator intentions. On a sample of spectators from the United States and Japan, Yoshida and James (2010) found that quality is a predisposition to both service satisfaction and game satisfaction. Both types of satisfaction are antecedents to the future intentions of spectators in both contexts.

Outcome Quality, Satisfaction, and Future Intentions

The quality of the dimension of output of a service was first proposed by Gronroos (1984). Later, Brady and Cronin (2001) used the term performance quality. This theoretical construct has recently been introduced in studies on sports events (Calabuig et al., 2016; Theodorakis et al., 2013). Studies by Clemes et al. (2011), Theodorakis et al. (2013), and Yoshida and James (2010) used outcome quality in a multidimensional nature in the context of sports spectators. For example, Calabuig et al. (2016) included a single item to assess the influence of the outcome of the match; Theodorakis et al. (2013) defined two dimensions “team performance and game quality” in “Outcome Quality”; Clemes et al. (2011) suggested four dimensions, “game quality, stadium atmosphere, social environment match, and day entertainment,” to measure Outcome Quality. These authors, during their research, did not equally study the relationship between outcome quality, satisfaction, and future intentions of the spectators.

Emotions and Sports Events

When attending a sports event, viewers expect to receive psychological and social benefits, such as emotions, fun, and social interaction (Ko et al., 2011). Emotions are affective states characterized by episodes of intense feelings associated with a specific reference point (e.g., a person, an object, or an event) that instigate a specific response or behavior (Cohen & Areni, 1991). Bagozzi et al. (1999) define emotion as a state of preparedness that arises from cognitive evaluations of events or thoughts and can lead to specific actions to affirm or cope with emotion, depending on its nature and meaning to the person who has it. Emotion is a complex psychological phenomenon that directs us toward a behavior in a consistent manner, and it can influence decision making (Austin, 2002).

The role of emotions in the behavior of consumers has been increasingly recognized in the work on marketing, as illustrated by the constant development of the theory that incorporates concepts related to emotion as a background of commercial behavior (Agarwall & Malhotra, 2005; Morosan, 2017). Few research papers have studied which components or characteristics of consumers’ emotional experience are better determinants of their future behaviors (S. S. Li et al., 2019). There is a growing interest in knowing the emotions of spectators due to the unique form of experiential consumption in sports (Biscaia et al., 2012; Yoshida et al., 2014). Sports events can evoke a wide range of pleasant emotions (joy and excitement) and unpleasant emotions (anger and dejection), which suggests that sports teams have the potential to capitalize on the emotional relationship shared with their followers (Couvelaere & Richelieu, 2005; Koenigstorfer, Groeppel-Klein, & Schmitt, 2010). It is important to note that context analysis is essential for the study of emotions (Lazarus, 2000), and previous studies have reported that unpleasant emotions can negatively influence the satisfaction of spectators (Madrigal, 2003) and behavioral intentions, among which is returning to attend another sporting event (Bougie et al., 2003; Sumino & Harada, 2004; Venkatesh et al., 2003), while pleasant emotions contribute positively to increasing these results (Biscaia et al., 2012), due to the influence of perceptions (Lerner & Keltner, 2000).

After reviewing the literature, it can be observed that the functional quality and the result have an influence on the affective state of the spectators, provoking emotions, and can affect both the satisfaction and the subsequent behavior of the spectators (Figure 1).

Figure 1

Structural Model Predicting Future Intentions of Sports Spectators

Research Method

Hypotheses

In summary, the research model tests the effects of four constructs (functional quality, outcome quality, satisfaction, and emotions) on future intentions and the possibility of differences depending on whether it is local spectators or tourist spectators who are attending a European badminton championship. Therefore, on the basis of the above reasoning and after reviewing the literature, the following hypotheses were established:

Hypothesis 1₀: There is no direct and significant relationship between functional quality and spectator satisfaction at sports events.

Hypothesis 1_a: There is a direct and significant relationship between functional quality and the satisfaction of spectators of sports events.

Hypothesis 2₀: Functional quality has neither direct nor any positive relationship with future intentions of sport event spectators.

Hypothesis 2_a: Functional quality has direct and positive relationship with future intentions of sport event spectators.

Hypothesis 3₀: Satisfaction has neither direct nor any positive relationship with future intentions of sport event spectators.

Hypothesis 3_a: Satisfaction has direct and positive relationship with future intentions of sport event spectators.

Hypothesis 4₀: Quality of the result has neither direct nor positive relationship with satisfaction of sport event spectators.

Hypothesis 4_a: Quality of the result has direct and positive relationship with satisfaction of sport event spectators.

Hypothesis 5₀: Quality of the result has neither direct nor any positive relationship with future intentions of sport event spectators.

Hypothesis 5_a: Quality of the result has direct and positive relationship with future intentions of sport event spectators.

Hypothesis 6₀: Functional quality has no direct relationship with positive emotions.

Hypothesis 6_a: Functional quality has a direct relationship with positive emotions.

Hypothesis 7₀: Functional quality has neither direct nor negative relationship with negative emotions.

Hypothesis 7_a: Functional quality has a direct and negative relationship with negative emotions.

Hypothesis 8₀: The quality of the result has no direct relationship with positive emotions.

Hypothesis 8_a: The quality of the result has a direct relationship with positive emotions.

Hypothesis 9₀: The quality of the result has neither direct nor any negative relationship with negative emotions.

Hypothesis 9_a: The quality of the result has a direct and negative relationship with negative emotions.

Hypothesis 10₀: Positive emotions do not have a direct relationship with satisfaction.

Hypothesis 10_a: Positive emotions have a direct relationship with satisfaction

Hypothesis 11₀: Positive emotions have no direct relationship with future intentions.

Hypothesis 11_a: Positive emotions have a direct relationship with future intentions.

Hypothesis 12₀: Negative emotions do not have a direct and negative relationship with satisfaction.

Hypothesis 12_a: Negative emotions have a direct and negative relationship with satisfaction.

Hypothesis 13₀: Negative emotions do not have a direct and negative relationship with future intentions.

Hypothesis 13_a: Negative emotions have a direct and negative relationship with future intentions.

Participants and Sampling

Given that it was not possible to know the profile of the spectators, the sample for this study was taken by convenience, with a total of 686 spectators attending matches held at the European Badminton Championships in 2018. The ages ranged from 16 to 89 years, with the mean age being 36.08 ± 14.15 years; 39.1% of the spectators were female (see Table 1). The majority of spectators claimed to have a university degree (50.8%), more than half worked (58.3%), and 46.1% were single; 66.1% were local spectators, while 33.9% were not residents of the city.

Table 1

Sociodemographic Variables of Spectators Who Attended the European Badminton Championships

Variable	Male	Female	Total
Sex	60.9%	39.1%
Age (years)	35.80 ± 13.91	36.17 ± 14.01	36.08 ± 14.15
Studies
Primary	9.9%	13.9%	11.7%
Secondary	40.8%	33.1%	37.6%
University	49.4%	53.0%	50.8%
Occupation
Work	63.1%	51.7%	58.3%
Retired	4.7%	2.6%	3.9%
Unemployed	2.1%	8.6%	5.2%
Student	29.6%	28.5%	29.0%
Housework/tasks		6.6%	2.6%
Other	0.4%	2.0%	1.0%
Marital status
Single	48.1%	43.0%	46.1%
Married or lives with partner without children	12.0%	15.9%	13.5%
Married or lives with partner with children	32.2%	33.1%	32.4%
Divorced	3.0%	5.3%	3.9%
Widower/widowed	0.4%		.5%
Other	4.3%	2.6%	3.6%
Tourism
Local resident	65.7%	66.2%	66.1%
Tourist	34.3%	33.8%	33.9%

Measurements

The study constructs were measured using multi-item scales. Quality of service was evaluated through a specific scale of 28 items, which measures functional quality and outcome quality. Functional quality was measured, as with Theodorakis and Alexandris (2008) and Theodorakis et al. (2009), with five dimensions: tangibles, with six items (visually appealing, comfortable seats, bars/cafes for refreshments, cleanliness, lighting and air quality, and maintenance of fittings and equipment); responsiveness, with four items (willingness to assist, individual attention, best interests of spectators at heart, and prompt service); four items to measure access (general accessibility of stadium, public transport availability, car parking availability, and ease of entry and exit); security, with four items (surrounds of stadium, inside the stadium, during the game, and general sense of freedom from danger when attending games); and reliability, with four items (delivering services as promised, general trustworthiness, services provided right first time, and response to complaints/problems). The internal consistency of the scale measured with Cronbach’s alpha was .897. To measure the outcome quality, two dimensions related to the results were adapted from the studies by Brady et al. (2006), Koo et al. (2009), and Yoshida and James (2010): the quality of the game, with four items (spectacular games, competitiveness of the games, high level of play, games are usually fast and flowing); and quality of the players, with four items (well-executed plays, plays with intensity, possibility of winning, and great results). The reliability measured with Cronbach’s alpha was .830.

The satisfaction of spectators was measured with five items (happy to attend, satisfied experiences, enjoyed, excited with the experiences, and attending is nice), adapted from Oliver (1997). According to Brady et al. (2006), the last match was the reference to measure satisfaction. Reliability was measured with Cronbach’s alpha, obtaining a value of .946. Three items were used to measure the future intentions of spectators (Zeithaml et al., 1996) providing a good Cronbach’s alpha (.870). Positive emotions were calculated through six items (Cronbach’s α = .961) adapted from Hosany and Gilbert (2010). Three items (Cronbach’s α = .960), adapted from Hosany and Prayag (2013), measured negative emotions.

Respondents were instructed to assess their degree of agreement. The level of agreement was identified in the questionnaire using a 7-point scale ranging from 1 (strongly disagree) to 7 (strongly agree). Several sociodemographic questions about age, gender, studies, place of residence, and marital status and some questions about the physical activity performed were added to the scales.

Procedure

During the European Badminton Championships held in Huelva (Spain) in 2018, research collaborators, previously trained, asked those attending the matches to respond to the questionnaire. The answers were given in the presence of the interviewer, who resolved any doubts that arose during the administration of the questionnaire. Those surveyed agreed to participate voluntarily. Work was carried out on Days 4 and 5 of 6-day championships. The research does have a positive report from the Ethical Commission.

Data Analysis

We have based our analysis on the structural equation model that allows us to test the goodness-of-fit in both local and tourist spectators. The study was carried out with the program AMOS (22). Specifically, the multigroup analysis is useful to explore to what extent the proposed relationships are consistent with the data observed in each of the samples. In addition, it allows to investigate to what extent the relationships are invariable in the different groups through the different samples. The aim of the analysis is to show whether the model that relates functional quality, outcome quality, positive emotions, negative emotions, satisfaction, and future intentions studied is the same for the two groups. For this purpose, the fit of the model in the total population studied was verified. The variation of the model between the two groups of spectators was then explored, which implied specifying a model in which the parameters were restricted to be the same across the groups and then comparing that model with a less restrictive one, in which the parameters were free to take any value. Typically, to measure the overall fit of structural equation models, the following indices are used: chi-square (CMIN), degrees of freedom (df), the CMIN/df ratio, the comparative fit index (CFI), the root mean square error of approximation (RMSEA), and the standardized root mean square residual (RMR). Values less than 3 for CMIN/df, values less than 0.08 for RMSEA and RMR, and values greater than 0.9 for CFI are considered acceptable (Schermelleh-Engel et al., 2003; Schumacker & Lomax, 1996). Finally, regression coefficients were calculated for the relationships in the model.

Results

Table 2 summarizes the mean, standard deviation, and correlation of the variables entered in the model. The means of five of the variables range from 6.17 to 6.29 and the standard deviation between 0.52 and 1.13. Only the mean of negative emotions had a very different value: 1.49 with a standard deviation of 0.91. Significant and positive correlations were observed between most of the variables, with correlation coefficients ranging from .156^** to .611^**. The correlations of emotions with the rest of the variables are negative in all cases.

Table 2

Constructs/Objects of the Study. Mean, Standard Deviation, and Pearson’s Correlations Among Measurement Instruments. Internal Consistency in the Diagonal

Variables	M	SD	1	2	3	4	5	6
1. Functional Quality	6.17	0.61	(.897)	.318**	.401**	−.190**	.477**	.297**
2. Outcome Quality	6.33	0.52		(.830)	.478**	−.270**	.443**	.156**
3. Positive Emotions	6.29	0.87			(.961)	−.299**	.611**	.280**
4. Negative Emotions	1.49	0.91				(.960)	−.337**	−.143**
5. Satisfaction	6.54	0.70					(.946)	.278**
6. Future Intentions	6.33	1.13						(.870)

The validity of the model that relates the functional quality, outcome quality, positive emotions, negative emotions, satisfaction, and future intentions studied was checked. The results (χ²/df = 2.347, goodness-of-fit index [GFI] = .906, CFI = .944, incremental fit index = .944, RMR = .065, RMSEA = .076) show that the goodness-of-fit indices of the model analyzed are correct. The model shows significant relationships between functional quality, positive emotions, and satisfaction. Moreover, outcome quality is related to emotions (positive and negative) and satisfaction. Positive emotions are related to satisfaction. Last, satisfaction is related to future intentions (Table 3).

Table 3

Adjustment Statistics for the Models. Comparison Between Models Using Model 1 as the Correct One

Goodness-of-Fit Indices and Model Comparisons for Tested Models								Comparisons of Conditions Using Measurement Invariance Procedures
Model	CMIN	df	p	CMIN/df	CFI	RMSEA	RMR	Dif. CMIN	Dif. df	p
1	258.578	158	<.001	1.637	.947	.052	.071
2	269.021	171	<.001	1.573	.949	.049	.075	13	10.443	.657
3	274.463	180	<.001	1.525	.951	.047	.074	22	15.885	.822
4	276.051	182	<.001	1.517	.951	.047	.074	24	17.473	.828
5	292.384	184	<.001	1.589	.943	.050	.084	26	33.806	.140
6	342.359	199	<.001	1.720	.925	.055	.087	41	83.781	<.001

Note: Model 1 indicates no parameters constrained to be equal across groups; Model 2, factor loadings constrained to be equal; Model 3, observed variable intercepts and factor loadings constrained to be equal; Model 4, residual variances, factor loadings, and observed variable intercepts constrained to be equal; Model 5, factor variances and covariances, factor loadings, and observed variable intercepts constrained to be equal; Model 6, factor means, factor loadings, observed variable intercepts, factor variances, and covariances constrained to be equal. Dif. CMIN = difference between Model 1 and the other models; Dif. df = difference between Model 1 and the other models; p = significance level between models; df = degrees of freedom; CFI = comparative fit index; RMSEA = root mean square error of approximation; RMR = standardized root mean square residual.

Factor invariance tests were performed to assess whether there were differences in the model depending on the populations under study. First, the basic model (Model 1) was found to have a reasonable fit to the data with a CFI close to .90 and values less than .06 for RMSEA and below .08 in RMR (Table 3). Consequently, the basic model was acceptable in its fit to the data. Later, several models were tested to which some more constraints were added to the previous model (Model 2, factor loadings constrained; Model 3, observed variable intercepts; Model 4, residual variances; Model 5, factor variances and covariances; Model 6, factor means). All models’ fits are acceptable. To check the factor invariance, a differential χ2 test was performed between Model 1 and the rest of the models. Table 2 shows that there are no significant differences between Model 1 and Models 2 to 5. Table 3 shows differences between Model 6 and Model 1 in the χ² difference test (p < .001). When studying the CFI values in Models 1 to 6, with the exception of the value in Model 6, they all have very similar values, with a difference of −.01 between them, suggesting the factor invariance of the model.

The coefficient chi-square divided by the degrees of freedom smallest corresponds to Model 4. The smallest chi-square coefficient divided by the degrees of freedom corresponds to Model 4. When comparing the values of the CFI, RMSEA, and RMR adjustment indices of Models 1, 2, 3, and 5 with Model 4, there are no clear differences in the indices; therefore, this model is considered ideal for comparing the two groups included in the study.

The results of the model, which relates the proposed variables, are shown in Table 4. It has been observed that the variable functional quality has an impact on satisfaction (Hypothesis 1a: β = .366, p < .001), with the standardized values being slightly higher among local viewers (β = .381 vs. β = .335). Satisfaction has a direct relationship with spectators’ intentions to return to sports events (Hypothesis 3a: β = .447, p < .001), with the standardized values being slightly higher among local spectators than tourist spectators (β = .448 vs. β = .380). Hypothesis 4a has also been confirmed by the existence of a relationship between game quality and satisfaction (Hypothesis 4a: β = .289, p < .05), with the standardized value of this relationship being higher in local spectators than in tourist spectators (β = .304 vs. β = .268). Functional quality is an antecedent of positive emotions (Hypothesis 6a: β = .323, p < .001), being the local spectators those who again present a superior beta value (β = .321 vs. β = .300) in this ratio. The quality of the game is antecedent of the positive emotions (Hypothesis 8a: β = .350, p < .05), and as in all other relationships, the beta value is higher in the local spectators (β = .371 vs. β = .322). Similarly, the quality of the game is related to negative emotions (Hypothesis 9a: β = −.605, p < .001) being this association higher in local spectators (β = −.619 vs. β = −.576). Positive emotions are directly related to satisfaction (Hypothesis 10a: β = .265, p < .05). The proposed model explains 53% of satisfaction and 27% of future intentions.

Table 4

Comparison Between the Standardized and Nonstandardized. Regression of the Two Groups of Spectators: Critical Ratios of Differences Between the Two Groups of Spectators: Hypothesis Testing Results

Relation	Total Spectators		Local Residents		Tourists		Local Residents Versus Tourists	Hypothesis
Relation	β	p	β	p	β	p	Critical Ratio	Hypothesis
Hyp. 1_a: SAT ← FQ	.366	***	.381	***	.335	***	4.209	Accepted
Hyp. 2_a: FI ← FQ	.130	.155	.171	.083	.128	.083	1.733	Not accepted
Hyp. 3_a: FI ← SAT	.447	***	.448	***	.380	***	4.300	Accepted
Hyp. 4_a: SAT ← OQ	.289	.049	.304	.046	.268	.046	1.991	Accepted
Hyp. 5_a: FI ← OQ	.014	.923	.031	.840	.023	.840	.202	Not accepted
Hyp. 6_a: PE ← FQ	.323	***	.321	***	.300	***	3.910	Accepted
Hyp. 7_a: NE ← FQ	−.133	.082	−.142	.076	−.124	.076	−1.772	Not accepted
Hyp. 8_a: PE ← OQ	.350	.001	.371	.001	.322	.001	3.243	Accepted
Hyp. 9_a: NE ← OQ	−.605	***	−.619	***	−.576	***	−3.986	Accepted
Hyp. 10_a: SAT ← PE	.265	.013	.282	.008	.267	.008	2.632	Accepted
Hyp. 11_a: FI ← PE	−.061	.542	−.061	.562	−.049	.562	−.579	Not accepted
Hyp. 12_a: SAT ← NE	−.093	.128	−.082	.179	−.083	.179	−1.343	Not accepted
Hyp. 13_a: FI ← NE	−.025	.704	−.041	.552	−.035	.552	−.595	Not accepted
Satisfaction variance explained	53		54		43
Future Intentions variance explained	27		30		20

Note: Hyp. = Hypothesis; FQ = Functional Quality; OQ = Outcome Quality; PE = Positive Emotions; NE = Negative Emotions; SAT = Satisfaction; FI = Future Intentions.

***

p < .001.

Discussion

This study, by using a case study of a badminton international event that took place in Spain, explores a model that relates the functional quality, the quality of the results, the emotions, the satisfaction, and the future intentions of the spectators, distinguishing between local spectators and tourist spectators. It is necessary to highlight the existing correlation between all the constructs integrated in the model, which adds to the evidence of the validity. In the same way, it is important to mention that there is a negative correlation of the construct titled negative emotions with the rest of the variables. This is due to the fact that a low level of negative emotions implies the lack or inexistence of them, so positive values of other constructs imply low values in negative emotions. The results show the importance that functional quality and outcome quality have for satisfaction, either directly or indirectly through emotions. Likewise, it has been possible to observe the relationship between satisfaction and future intentions of the spectators.

The first results of this study are those that relate functional quality with satisfaction, that is to say, Hypothesis 1_a. The hypothesis is confirmed following the line of results exposed by Clemes et al. (2011), who linked quality with satisfaction in spectator sports. It is important to add that beta values are higher in functional quality in local spectators. This result is important when it comes to those responsible for the sport event managing quality. Satisfying local spectators means ensuring their loyalty, with the consequent effect that this has on future sports events, with it being more complicated when it comes to one-off events, since there is a lack of continuity over time or identification with a sports team for fans (Clemes et al., 2011). The results of the study, a direct relationship of functional quality with future intentions, have not been proven, which is Hypothesis 2_a. There are a few studies in the context of sports events where the direct influence of quality of service on behavioral intentions of spectators has been revealed, as concluded by Calabuig et al. (2016). Unfortunately, despite the immediate relevance of those responsible for managing sports events in functional quality, there is no direct relationship between quality and future intentions of spectators.

The satisfaction does have a direct and significant relationship with future intentions of spectators, a result that corroborates previous studies (Calabuig et al., 2016; Clemes et al., 2011; Theodorakis et al., 2013; Yoshida & James, 2010) and confirms Hypothesis 3_a. It is the local spectators who have higher values in the satisfaction and future intentions relationship. This result is in line with the conclusion offered by Bodet and Bernache-Assollant (2011), although they did it for team sports, in which home loyalty became an important driver of attitudinal loyalty. Charleston (2009) similarly, for team sports, recognized that home loyalty can be an important driver of attitudinal loyalty toward a specific team, arguing that research in environmental psychology has validated a link between sports fans and the ground of their team, which becomes a symbolic home, and which could explain why the beta values of local spectators were higher than those of tourist spectators.

The outcome quality is an antecedent of spectator satisfaction, which confirms Hypothesis 4_a. Result is similar to that exposed by Clemes et al. (2011), who linked quality with satisfaction in spectator sports. The division of quality into functional quality and outcome quality allows us to observe which of them has the highest impact on satisfaction. Results are similar to those of Greenwell et al. (2002) who reported that functional quality has a greater influence on satisfaction, in contrast to the findings of other authors who highlighted that outcome quality had a higher impact than functional quality (Brady et al., 2006; Theodorakis et al., 2013; Tsuji et al., 2007). It is important to highlight that beta values are higher, both in functional quality and in outcome quality, in local spectators.

A direct relationship outcome quality with future intentions has not been proven, which does not confirm Hypothesis 5_a. There are few studies in the context of sports events where the direct influence of quality of service on behavioral intentions of spectators has been revealed, as concluded by Calabuig et al. (2016). This result does not support the efforts of sports event organizers to improve outcome quality as a strategy for improving the future intentions of spectators.

The impact of emotions on the behavior of customers is increasingly recognized (Agarwall & Malhotra, 2005; Morosan, 2017). Therefore, it is important to know whether or not quality is directly related to emotions, positive and/or negative, as precedents to the behavior of spectators of sports events. Our findings show that functional quality and positive emotions are strongly interrelated, which confirms Hypothesis 6_a. However, the results do not allow confirmation of Hypothesis 7_a, since there is no relationship between functional quality and negative emotions. Outcome quality is related to positive and negative emotions, which confirms Hypotheses 8_a and 9_a. The relationship between outcome quality and negative emotions is inverse, that is, an increase in outcome quality means a decrease in negative emotions. In all cases, the relationships have higher values in local spectators. Koo et al. (2009) obtained similar results and endorsed that sport events stimulate emotional reactions and can have an impact on spectator satisfaction.

A relationship between positive emotions and satisfaction of spectators has also been found, which is Hypothesis 10_a. Wong (2004) highlighted that the contact of a customer with frontline employees may determine positive behavioral responses. Another study has highlighted that the environment and ambiance on a stadium plays a major role in making the sport event more attractive (Koenigstorfer, Groeppel-Klein, & Kunkel, 2010). Overall, our results are in line with the findings of other studies that highlight that by improving the functional quality of a sports event, satisfaction levels can be affected (Biscaia et al., 2012) and this in turn can affect the behavioral intentions of the spectators, as suggested by Yoshida and James (2010). There is no relationship between positive emotions and future intentions, which is Hypothesis 11_a. Nor could Hypotheses 12_a and 13_a, which sought to test whether negative emotions are related to satisfaction and future intentions, be confirmed.

Limitations

Like any other type of cross-sectional study, this research work is not without its limitations. First, the nonprobability convenience sample that was used could limit the generalizability of the results of this study. The presence of tourist spectators, who were not residents of the city, was less than that of local spectators who were very loyal to one of the players in the tournament. And this may have influenced the results, especially those results that refer to the local spectators. The study was carried out in Spain, specifically in the South of Spain, which could mean a limited applicability of the results due to the connotations that the sociotourist environment may have. However, these limitations provide a potential for future research that could be replicated in other environments. This study investigated the differences in the incidence of quality, functional and outcome, in the emotions and subsequent behavioral intentions of spectators at an international sports event that is held on a one-off basis. Furthermore, only differences in relation to tourism were investigated, without studying the incidence of gender, socioeconomic status, or spectator sport practice. The study has tried to find out if the quality of the organization affects emotions.

Concluding Summary and Managerial Implications

The overarching aim of this study was to contribute to the exploration of the hypothesis that quality management of a sport event can have an impact on emotions. The findings supported that functional quality has a significant and positive impact on positive emotions. Similarly, outcome quality has a significant and positive relationship with positive emotions. Also, outcome quality shows a significant and negative relationship with negative emotions. It should be highlighted that the positive emotions are an antecedent of the viewers’ satisfaction. The data also show how important emotions are, especially for local viewers. Increasing them could mean greater fidelity. Sport managers should deepen their understanding of these interests and explore further and identify the specific emotional aspects that increase the satisfaction component of their activities. This study has shown the importance of quality management, functional and outcome, in the emotions of the spectators. In the same way, quality has a direct influence on satisfaction and an indirect influence on future intentions. Sports managers in charge of sports events must strive to ensure a high indicator of perceived quality in order to increase the overall experience of the event and therefore the level of “excitement” of the spectators. By doing so, they will increase the satisfaction of attending sports events. Therefore, new strategies should be sought to increase the excitement in the experience of attending the sports event.

The direct impact of emotions on the future intentions of spectators could be studied, as has happened in other research that did not have spectators of specific sporting events as its target population. In this case, the emotions would not be the result of the quality of the sports event.

Footnotes

ORCID iD

David Cabello-Manrique

David Cabello-Manrique, PhD (e-mail: dcabello@ugr.es), is an assistant professor at the Department of Physical Education and Sport, Faculty of Sports Sciences, University of Granada, Granada, Spain. Román Nuviala, PhD (e-mail: roman.nuviala@uca.es), is an assistant professor the Department of Didactics of Musical, Plastic, and Corporal Expression, Faculty of Education Sciences, University of Cádiz, Cádiz, Spain. Athanasios (Sakis) Pappous, PhD (e-mail: a.pappous@kent.ac.uk), is an assistant professor the School of Sport and Exercise Sciences, University of Kent, Canterbury, United Kingdom. Esther Puga-González, PhD (e-mail: estherpu@yahoo.com), is an assistant professor the Department of Physical Education and Sport, Faculty of Sports Sciences, University of Granada, Granada, Spain. Alberto Nuviala, PhD (e-mail: anuvnuv@upo.es), is an assistant professor the Department of Physical Education and Sport, Faculty of Sports Sciences, University of Pablo de Olavide, Spain.

References

Agarwall

Malhotra

N. K.

(2005). An integrated model of attitude and affect: Theoretical foundations and an empirical investigation. Journal of Business Research, 58(4), 483-493. https://doi.org/10.1016/S0148-2963(03)00138-3

Anderson

E. W.

Fornell

(2000). Foundations of the American Customer Satisfaction Index. Total Quality Management, 11(7), 869-882. https://doi.org/10.1080/09544120050135425

Austin

(2002). Managing heritage attractions: Marketing challenges at sensitive historical sites. International Journal of Tourism Research, 4(6), 447-457. https://doi.org/10.1002/jtr.403

Bagozzi

R. P.

Gopinath

Nyer

P. U.

(1999). The role of emotions in marketing. Journal of the Academy of Marketing Science, 27(2), 184-206. https://doi.org/10.1177/0092070399272005

Biscaia

Correia

Rosado

Marôco

Ross

(2012). The effects of emotions on football spectators’ satisfaction and behavioural intentions. European Sport Management Quarterly, 12(3), 227-242. https://doi.org/10.1080/16184742.2012.679949

Bitner

M. J.

Hubbert

A. R.

(1994). Encounter satisfaction versus overall satisfaction versus quality: The consumer’s voice. In Rust

R. T.

Oliver

R. L.

(Eds.), Service quality: New directions in theory and practice (pp. 72-94). Sage. https://doi.org/10.4135/9781452229102.n3

Bodet

Bernache-Assollant

(2011). Consumer loyalty in sport spectatorship services: The relationships with consumer satisfaction and team identification. Psychology and Marketing, 28(8), 781-802. https://doi.org/10.1002/mar.20412

Bougie

Pieters

Zeelenberg

(2003). Angry customers don’t come back, they get back: The experience and behavioral implications of anger and dissatisfaction in services. Journal of the Academy of Marketing Science, 31(4), 377-393. https://doi.org/10.1177/0092070303254412

Brady

M. K.

Cronin

J. J.

(2001). Some new thoughts on conceptualizing perceived service quality: A hierarchical approach. Journal of Marketing, 65(3), 34-49. https://doi.org/10.1509/jmkg.65.3.34.18334

10.

Brady

M. K.

Voorhees

J. J.

Cronin

Jr. Bourdeau

B. L.

(2006). The good guys don’t always win: The effect of valence on service perceptions and consequences. Journal of Services Marketing, 20(2), 83-91. https://doi.org/10.1108/08876040610657011

11.

Calabuig

Prado-Gasco

Crespo

Nunez-Pomar

Año

(2015). Spectator emotions: Effects on quality, satisfaction, value, and future intentions. Journal of Business Research, 68(7), 1445-1449. https://doi.org/10.1016/j.jbusres.2015.01.031

12.

Calabuig

Prado-Gasco

Crespo

Nuñez-Pomar

Año

(2016). Predicting future intentions of basketball spectators using SEM and fsQCA. Journal of Business Research, 6(4), 1396-1400. https://doi.org/10.1016/j.jbusres.2015.10.114

13.

Carlson

O’Cass

(2010). Exploring the relationships between e-service quality, satisfaction, attitudes and behaviours in content-driven e-service web sites. Journal of Services Marketing, 24(2), 112-127. https://doi.org/10.1108/08876041011031091

14.

Charleston

(2009). The English football ground as a representation of home. Journal of Environmental Psychology, 29(1), 144-150. https://doi.org/10.1016/j.jenvp.2008.06.002

15.

Clemes

M. D.

Brush

Collins

M. J.

(2011). Analysing the professional sport experience: A hierarchical approach. Sport Management Review, 14(4), 370-388. https://doi.org/10.1016/j.smr.2010.12.004

16.

Cohen

J. B.

Areni

(1991). Affect and consumer behavior. In Robertson

S. T.

Kassarjian

H. H.

(Eds.), Handbook of consumer behavior (pp. 188-240). Prentice Hall.

17.

Couvelaere

Richelieu

(2005). Brand strategy in professional sports: The case of French soccer teams. European Sport Management Quarterly, 5(1), 23-46. https://doi.org/10.1080/16184740500089524

18.

García-Fernández

Fernández-Gavira

Velez-Colón

(2015). The activity on social networks: A case study in the fitness industry. Retos-Nuevas tendencias en Educación Física Deporte y Recreación, 28, 44-49.

19.

Greenwell

C. T.

Fink

J. S.

Pastore

Y. D. L.

(2002). Assessing the influence of the physical sports facility on customer satisfaction within the context of the service experience. Sport Management Review, 5(2), 129-148. https://doi.org/10.1016/S1441-3523(02)70064-8

20.

Gronroos

(1984). A service quality model and its marketing implications. European Journal of Marketing, 18(4), 36-44. https://doi.org/10.1108/EUM0000000004784

21.

Han

Hyun

S. S.

(2013). Image congruence and relationship quality in predicting switching intention conspicuousness of product use as a moderator variable. Journal of Hospitality & Tourism Research, 37(3), 303-329. https://doi.org/10.1177/1096348012436381

22.

Hosany

Gilbert

(2010). Measuring tourists’ emotional experiences toward hedonic holiday destinations. Journal of Travel Research, 49(4), 513-526. https://doi.org/10.1177/0047287509349267

23.

Hosany

Prayag

(2013). Patterns of tourists’ emotional responses, satisfaction, and intention to recommend. Journal of Business Research, 66(6), 730-737. https://doi.org/10.1016/j.jbusres.2011.09.011

24.

Y. J.

Zhang

Catani

Pastore

(2011). Assessment of event quality in major spectator sports. Managing Service Quality, 21(3), 304-332. https://doi.org/10.1108/09604521111127983

25.

Koenigstorfer

Groeppel-Klein

Kunkel

(2010). The attractiveness of national and international football leagues: Perspectives of fans of “star clubs” and “underdogs.” European Sport Management Quarterly, 10(2), 127-163. https://doi.org/10.1080/16184740903563406

26.

Koenigstorfer

Groeppel-Klein

Schmitt

(2010). You’ll never walk alone: How loyal are soccer fans to their clubs when they are struggling to relegation? Journal of Sport Management, 24(6), 649-675. https://doi.org/10.1123/jsm.24.6.649

27.

Koo

G.-Y.

Hardin

McClung

Jung

Cronin

Vorhees

Bourdeau

(2009). Examination of the causal effects between the dimensions of service quality and spectator satisfaction in minor league baseball. International Journal of Sports Marketing and Sponsorship, 11(1), 46-59. https://doi.org/10.1108/IJSMS-11-01-2009-B004

28.

Lazarus

R. S.

(2000). How emotions influence performance in competitive sports. The Sport Psychologist, 14(3), 229-252. https://doi.org/10.1123/tsp.14.3.229

29.

Lerner

J. S.

Keltner

(2000). Beyond valence: Toward a model of emotion-specific influences on judgement and choice. Cognition and Emotion, 14(4), 473-493. https://doi.org/10.1080/026999300402763

30.

S. S.

Walters

Packer

Scott

(2019). Using facial electromyography to test the peak–end rule in tourism advertising. Journal of Hospitality & Tourism Research. Advance online publication. https://doi.org/10.1177%2F1096348019890047

31.

Petrick

J. F.

(2010). Towards an integrative model of loyalty formation: The role of quality and value. Leisure Sciences, 32(3), 201-221. https://doi.org/10.1080/01490401003709123

32.

Madrigal

(2003). Investigating an evolving leisure experience: Antecedents and consequences of spectator affect during a live sporting event. Journal of Leisure Research, 35(1), 23-48. https://doi.org/10.18666/jlr-2003-v35-i1-609

33.

Morosan

(2017). Information disclosure to biometric e-gates: The roles of perceived security, benefits, and emotions. Journal of Travel Research, 57(5), 644-657. https://doi.org/10.1177/0047287517711256

34.

Nielsen. (2016). Badminton overview [Unpublished manuscript]. Badminton World Federation.

35.

Nuviala

Tamayo-Fajardo

J. A.

Ruiz-Alejos

Nuviala

Dalmau-Torres

J. M.

(2017). Adaptation and validation of the Epod 2.1 questionnaire in free customers of sport centers. Retos, 31, 40-45. http://recyt.fecyt.es/index.php/retos/article/view/41928/31129

36.

Oliver

R. L.

(1997). Satisfaction: A behavioral perspective on the customer. McGraw-Hill.

37.

Repucom. (2016). Global report about sport and badminton [Unpublished manuscript]. Badminton World Federation.

38.

Schermelleh-Engel

Moosbrugger

Müller

(2003). Evaluating the fit of structural equation models: Tests of significance and descriptive goodness-of-fit measures. Methods of Psychological Research, 8(2), 23-74.

39.

Schumacker

E. R.

Lomax

G. R.

(1996). A beginner’s guide to structural equation modeling. Lawrence Erlbaum.

40.

Seetanah

Teeroovengadum

Nunkoo

(2020). Destination satisfaction and revisit intention of tourists: does the quality of airport services matter? Journal of Hospitality & Tourism Research, 44(1), 134-148. https://doi.org/10.1177/1096348018798446

41.

Sumino

Harada

(2004). Affective experience of J. League fans: The relationship between affective experience, team loyalty and intention to attend. Managing Leisure, 9(4), 181-192. https://doi.org/10.1080/1360671042000273855

42.

Taylor

S. A.

(1997). Assessing regression-based importance weights for quality perceptions and satisfaction judgments in the presence of higher order and/or interaction effects. Journal of Retailing, 73(1), 135-159. https://doi.org/10.1016/S0022-4359(97)90018-X

43.

Theodorakis

N. D.

Alexandris

(2008). Can service quality predict spectators’ behavioral intentions in professional soccer? Managing Leisure, 13(3), 162-178. https://doi.org/10.1080/13606710802200852

44.

Theodorakis

N. D.

Alexandris

Tsigilis

Karvounis

(2013). Predicting spectators’ behavioural intentions in professional football: The role of satisfaction and service quality. Sport Management Review, 16(1), 85-96. https://doi.org/10.1016/j.smr.2012.05.004

45.

Theodorakis

N. D.

Howat

Y. J.

Avourdiadou

(2014). A comparison of service evaluation models in the context of sport and fitness in Greece. Managing Leisure, 19(1), 18-35. https://doi.org/10.1080/13606719.2013.849505

46.

Theodorakis

N. D.

Koustelios

Robinson

Barlas

(2009). Moderating role of team identification on the relationship between service quality and repurchase intentions among spectators of professional sports. Managing Service Quality, 19(4), 456-473. https://doi.org/10.1108/09604520910971557

47.

Tsuji

Bennett

Zhang

(2007). Consumer satisfaction with an action sports event. Sport Marketing Quarterly, 16(4), 199-208. https://search.proquest.com/docview/228090708?accountid=14695

48.

Venkatesh

Morris

M. G.

Davis

G. B.

Davis

F. D.

(2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(4), 425-478. https://doi.org/10.2307/30036540

49.

Wong

(2004). The role of emotional satisfaction in service encounters. Managing Service Quality, 14(5), 365-376. https://doi.org/10.1108/09604520410557976

50.

Yoshida

Gordon

Nakazawa

Biscaia

(2014). Conceptualization and measurement of fan engagement: Empirical evidence from a professional sport context. Journal of Sport Management, 28(4), 399-417. https://doi.org/10.1123/jsm.2013-0199

51.

Yoshida

James

(2010). Customer satisfaction with game and service experience: Antecedents and consequences. Journal of Sport Management, 24(3), 338-361. https://doi.org/10.1123/jsm.24.3.338

52.

Zeithaml

V. A.

(1988). Consumer perceptions of price, quality, and value: A means-end model and synthesis of evidence. Journal of Marketing, 52(3), 2-22. https://doi.org/10.2307/1251446

53.

Zeithaml

V. A.

Berry

L. L.

Parasuraman

(1996). The behavioral consequences of service quality. Journal of Marketing, 60(2), 31-46. https://doi.org/10.1177/002224299606000203