베이지안 네트워크 모형을 활용한 스포츠 관람객의 친환경 행동 의도 인과구조 추론 = A Bayesian Network Approach to Infer Causality of Sports Spectators' Eco-Friendly Behavioral Intentions

국문 초록 (Abstract)

[목적] 본 연구의 목적은 스포츠 관람 중 친환경 행동 의도에 영향을 미치는 요인을 탐색하고 각 변수들이 친환경 행동 의도로 이어지는 인과구조를 추론하는 것이다.
[방법] 총 364명의 18세 이상 성인 스포츠 팬을 대상으로 설문조사를 수행하여 기후변화에 대한 지식(Knowledge of Climate Change; KCC), 기후변화에 대한 인식(Awareness of Climate Change; ACC), 기후변화에 대한 태도(Attitude of Climate Change; ATT), 주관적 규범(Subjective Norm of Climate Change; SN), 지각된 행동 통제(Perceived Behavior Control of Climate Change), 스포츠 관람 중 일회용품 사용 감축 행동 의도(INT)를 수집한뒤, 확인적 요인분석을 수행하여 측정 결과의 타당성을 검증하였다. 측정 결과의 타당성이 검증된 자료를 바탕으로 잠재변인별 평균 점수를 변수로 재구성하여 인구통계학적 특성과 함께 베이지안 네트워크 학습을 진행하였다.
[결과] 베이지안 네트워크 학습 결과 ACC, ATT, SN, PBC 변수는 모두 스포츠 관람 중 친환경 행동 의도(INT)에 직접적인 영향을 미치는 것으로 나타났으며, 그 과정에서 ATT와 SN은 ACC로부터 영향을 받고 ACC는 KCC와 성별(sex) 에 영향을 받는 것으로 나타났다. 반면, PBC는 INT에 영향을 미치지만 이외의 모든 입력변수들과는 연관성을 나타내지 않았다. 또한 본 연구에서 활용한 변수 중 스포츠 관람 중 친환경 행동 의도에 가장 큰 영향을 미치는 요인은 SN으로 나타났으며, 반대로 PBC의 영향력은 상대적으로 가장 낮은 것으로 나타났다.
[결론] 본 연구의 결과는 그동안 체육·스포츠 분야에서 활발히 논의되지 못하였던 스포츠 팬의 친환경 행동 의도를 설명하기 위한 관계 구조를 자료 주도적 학습에 의해 추론하였다는 점에서 의의를 갖는다. 본 연구의 결과는 지속 가능한스포츠 유관기관들의 전략 수립, 의사결정 과정에서 의미 있는 기초자료로 활용될 수 있을 것이다.

다국어 초록 (Multilingual 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.

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