Athletic goal achievement: A critical antecedent of event satisfaction, re-participation intention, and future exercise intention in participant sport events

Highlights

•	Athletic performance is a predictor of participant sport events satisfaction.
•	Service quality and perceived value are important predictors of event satisfaction.
•	Achieving athletic performance goals increases re-participation intention.
•	Achieving athletic performance goals increases future exercise intention.
•	Performance goal importance does not moderate the effect of athletic performance.

Abstract

The purpose of this study is to develop a comprehensive understanding of consumer experiences in participant sport events. The authors explored two traditionally prominent components (i.e., service quality and perceived economic value) and one emerging factor (i.e., athletic goal achievement) of consumer experiences at participant sport events. The authors examined the effects of these three factors on re-participation intention and the effect of athletic goal achievement on future exercise intention. Results of moderated serial mediation analyses based on data from a sample of marathon runners (N = 3186), all three factors exerted significant effects on re-participation intention. In particular, athletic goal achievement was positively associated with re-participation intention and future exercise intention through the serial mediation of performance satisfaction and overall event satisfaction. However, the moderating effects of athletic goal importance on the serial indirect relationship between athletic goal achievement and two outcome variables (i.e., re-participation intention and future exercise intention) were not significant. The findings of this study offer participant sport service providers with insights to manage participants’ athletic performance, service quality, and perceived economic value for better consumer experiences. Such efforts should result in higher participant satisfaction, better retention rates, and a higher post-event physical activity level.

Keywords:

• Participant sport events
• Consumer experiences
• Athletic goal achievement
• Performance satisfaction
• Event satisfaction
• Future behavioral intention

Notes

1 Although some researchers have voiced concerns regarding the use of gap scores (e.g., performance minus expectations) because of the potentially low reliability of gap scores (Peter, Churchill, & Brown, 1993), the use of gap scores in this study was necessary and preferable to other measures for two reasons. First, athletic performance standards in participant sport events are highly subjective, depending on runners’ athletic abilities. For example, whereas high scores in consumer satisfaction always indicate greater satisfaction, high scores in actual time performance do not necessarily reflect better performance. A runner’s athletic ability must be considered by taking her time goal into account. Second, the reliability problem with gap scores is derived from multi-item constructs for measuring latent variables, such as satisfaction (Peter, Churchill, & Brown, 1993). Peter et al. (1993) found that the reliability of the gap score between two multi-item constructs can be minimal. In the current study, however, goal time and actual time performance were each taken as indicators with assumed reliabilities of 1. Mathematically, this assumption leads to a reliability of 1 for the gap score as well, thereby alleviating concerns regarding the use of a gap score.

2 The unstandardized factor loadings and error variances of single-item latent variables were fixed at 1 and (1 – scale reliability estimate)$\times$sample variance, respectively (Kline, 2015). As per MacKenzie (2001), the scale reliability of re-participation intention was set to 0.71 because the reliability value of the multi-item measure of re-participation intention was obtained from a previous study (e.g., Du et al., 2015). Reliability values of goal importance and future exercise intention were set to 0.85. In the event that either no multi-item measure or reliability value was available, Petrescu (2013) and Jöreskog and Sörbom (1982) suggested using 0.85 as a scale reliability estimate.

3 To estimate the interaction effects between latent variables, Ping (1996) recommended a two-step approach of creating a latent variable interaction term. First, factor loadings and error variances of two latent variables (i.e., X and Z) are estimated through the measurement model. Next, indicators of an interaction term (i.e., XZ) are created by generating all possible combinations of products of items from X and Z. For example, if X has n items and Z has m items, then the number of items for XZ will be n$\times$m. Moreover, the factor loadings and error variances of the new set of items for the interaction term are calculated and fixed as constants. The formulas for calculating factor loadings and error variances are as follows: ${\lambda }_{x_n{z}_m}={\lambda }_{x_n}\times {\lambda }_{z_m}$ $Var\left({\epsilon }_{x_n{z}_m}\right)={\lambda }_{x_n}^2\times Var\left(X\right)\times Var\left({\epsilon }_{z_m}\right)+{\lambda }_{z_m}^2\times Var\left(Z\right)\times Var\left({\epsilon }_{x_n}\right)+Var({\epsilon }_{x_n})\times Var({\epsilon }_{z_m})$ where ${\lambda }_{x_n{z}_m}$ represents the factor loading of the n$\times$m^th item of XZ, ${\lambda }_{x_n}$ represents the factor loading of the n^th item of X, ${\lambda }_{z_m}$ represents the factor loading of the m^th item of Z, $Var\left({\epsilon }_{x_n{z}_m}\right)$ represents the error variance of the n$\times$m^th item of XZ, $Var({\epsilon }_{x_n})$ represents the error variance of the n^th item of X, and $Var({\epsilon }_{z_m})$ represents the error variance of the m^th item of Z.

4 An additional analysis was conducted for a robustness check of the insignificant moderating effect of goal importance. In particular, it was examined if the insignificant results were due to “the Simpson Paradox”, which refers to a phenomenon that a certain relationship observed in a subgroup of a population disappears or reversed at the population level (Wagner, 1982). Specifically, the same structural model was tested with runners who only participated in the 5K. Results were virtually identical to those involving the entire sample. First, the moderating effect of goal importance on the relationship between goal achievement and performance satisfaction was not significant (β = .721, p > .05). Second, the moderating effect of goal importance on the serial indirect effect of goal achievement on future exercise intention was not significant (Δserial indirect effect = -.002, LLCI = -.151, ULCI = .351). Third, the moderating effect of goal importance on the serial indirect effects of goal achievement on re-participation intention was also not significant (Δserial indirect effect = .006, LLCI = -.159, ULCI = .160). Therefore, it was shown that there were little concerns regarding “the Simpson Paradox” in the current study.

5 Consistent with the entire sample, runners who participated in the 5K had high levels of goal importance; 90.32% of 5K participants scored above the mid-point (i.e., 4 on a 7-point scale) with a median of 6.