Exposing factors influencing Korean leisure life satisfaction through machine learning techniques

Abstract

Abstract This study examines factors influencing leisure life satisfaction (LLS) through machine learning techniques based on the data from the 2019 National Leisure Activity Survey in Korea. The results show that using machine learning techniques in identifying LLS influencing factors improves predictive power and helps detect effective leisure interventions. We also provide insights into the factors influencing LLS by standardizing activity measures based on leisure type and examining differences in resource accessibility and experiences across groups. The findings suggest that a diverse and balanced leisure repertoire is associated with greater levels of LLS, particularly in active leisure and social activities. However, the relationship between the repertoire of passive leisure activities and LLS is negative, suggesting that the optimal point for leisure activities lies found between various leisure experiences and limited resources. Leisure resource availability, such as expenditure, time, facilities, and interpersonal factors, may affect LLS, but varies by age. The results provide new insights and more accurate models of the factors influencing LLS and their complex relationships.

Keywords

• Leisure repertoire
• Resource availability
• Leisure experience
• Leisure life satisfaction
• Machine learning techniques