Analysis of Factors Affecting Organizational Commitment and Job Satisfaction of Young Workers Using Machine Learning: Before and After COVID-19

Abstract

Objectives The purpose of this study is to analyze for factors affecting the organizational commitment and job sat-isfaction of young workers before and after the onset of COVID-19. Methods To do this, Random Forest and SHAP analysis was applied to the 25th year of the Korean Labor and Income Panel Study (KLIPS). Results The results of the results study are as follows. First, young workers' organizational commitment and job satisfaction appeared higher for those employed before the outbreak of COVID-19. Second, regardless of when they were employed, it was the principal variables that current job employment characteristics such as life sat-isfaction, economic status, wages, benefits, and the utility of technical knowledge. Third, economic status of the household was the main variable for the group employed before COVID-19, while variables related to individual job and employment characteristics were predominant for the group employed after COVID-19. Conclusions It is expected to predict the organizational commitment and job satisfaction of young corporate members, which vary according to societal changes or environments, and to provide foundational data for the en-hancement of non-financial performance management.