Unraveling the Most Important Predictors of Eudaimonic and Hedonic Well-Being in Korean Adults: A Machine Learning Approach

Abstract

AbstractThe quest to unravel what contributes to happiness continues to captivate interest in both everyday experiences and academic discourse. Nonetheless, empirical research on the relative importance of possible candidates and their associations with two key aspects of well-being—eudaimonia (the good life) and hedonia (pleasure)—is limited. This study addresses this gap by exploring the relative strength of 32 predictors from multiple domains on psychological well-being (PWB) and subjective well-being (SWB). Using a machine learning approach on a dataset of 559 Korean adults, we identified distinct primary determinants for each well-being aspect. For PWB, meaning in life, self-esteem, and essentialist beliefs about happiness emerged as the strongest predictors requiring careful consideration. For SWB, depressive symptoms, subjective socioeconomic status, and emotional stability were salient predictors. Our findings highlight potential cultural nuances in the prioritization of happiness and offer valuable insights for policymakers and decision-makers in tailoring interventions and strategies to optimize individual well-being.