A Bayesian Network Approach to Infer Causality of Sports Spectators' Eco-Friendly Behavioral Intentions

Abstract

PURPOSE This study explores the factors influencing eco-friendly behavioral intentions during sports spectating and infers the causal structure linking each variable to eco-friendly behavioral intentions.METHODS A total of 364 sports fans participated in the survey that collected data on Knowledge of Climate Change (KCC), Awareness of Climate Change (ACC), Attitude of Climate Change (ATT), Subjective Norm of Climate Change (SN), Perceived Behavior Control of Climate Change (PBC), and Behavioral intention to Reduce Single-Use Plastic (INT) during sports spectating. The validity of the measurement was examined through confirmatory factor analysis. Based on the validated data, latent variables’ average scores were reconstructed as input variables for the Bayesian Network, along with demographic characteristics.RESULTS The results of Bayesian network learning indicated that ACC, ATT, SN, and PBC variables directly influence INT. ACC affects ATT and SN, while ACC is influenced by KCC and sex. Conversely, PBC influenced INT but showed no association with the other input variables. SN was found to have the greatest impact on INT during sports spectating, while the influence of PBC was relatively low.CONCLUSIONS The causal structure inferred in the current study using Bayesian network learning provides insights into the previously underexplored relationship structure explaining eco-friendly behavioral intentions of sports fans in the field of sports science. The findings of this study can serve as empirical evidence for sports-related organizations to develop strategies and decision-making processes to promote sustainable sports spectatorship.