Leisure’s relationships with hedonic and eudaimonic well-being in daily life: An experience sampling approach
公開日 2022.08.09
A research article co-authored by CTR Visiting Fellow, Dr. Eiji Ito (Chukyo University) has been published in an international journal, Leisure Sciences.
Title
Leisure’s relationships with hedonic and eudaimonic well-being in daily life: An experience sampling approach
Authors
Shintaro Kono, Faculty of Kinesiology, Sport, and Recreation, University of Alberta, Edmonton, Canada
Eiji Ito, School of Health and Sport Sciences, Chukyo University, Toyota, Japan
Jingjing Gui, Department of Psychology, University of Alberta, Edmonton, Canada
Source
Leisure Sciences, 2022
https://doi.org/10.1080/01490400.2022.2102097
* Indexed in Scopus
Journal details: https://www.scopus.com/sourceid/28910
Abstract
Research on leisure and subjective well-being has focused on hedonic well-being (e.g., positive affect). Leisure’s relationships with eudaimonic well-being (e.g., meaning) remains underexplored. The literature also lacks non-Western perspectives. This study examined leisure’s relations with shiawase and ikigai, Japanese concepts that represent hedonic and eudaimonic well-being, respectively. A smartphone-based experience sampling method was used. A total of 2,207 responses were collected from 83 Japanese university students. Multilevel linear modeling showed that free time (e.g., lunch, evenings) predicted higher levels of daily shiawase and ikigai, while ikigai appeared to stay higher during afternoon. Various leisure activities positively predicted shiawase and ikigai levels, with event/trip, eating/drinking, socializing, and hobbies being the best predictors. A few activities (e.g., exercise) differentially predicted the outcomes. Among subjective experiences common during leisure, intrinsic motivation, enjoyment, stimulation, and comfort were positively correlated to shiawase and ikigai, whereas effort predicted only ikigai.
Keywords
Eudaimonic well-being, experience sampling, hedonic well-being, leisure, multilevel linear modeling