칭다오시 미용업 직무만족도의 조직적 효과에 관한 연구: 머신러닝과 몬테카를로 시뮬레이션을 활용한 확률론적 예측 모델

This study proposes a new methodological framework to analyze and predict the complex, nonlinear, and uncertain relationship between job satisfaction and organizational performance. To this end, a two-stage analytical model was developed, combining Random Forest regression with probabilistic simulation using the Monte Carlo method. The analysis was based on survey data collected from 1,000 beauty industry professionals (nail and makeup specialists) in Qingdao, China. In the first stage, Random Forest analysis was used to estimate the relative predictive contributions of five subdimensions of job satisfaction—competence development, self-expression opportunity, quality of customer interaction, work-life balance, and job security—to three organizational outcomes: productivity, customer satisfaction, and creativity. In the second stage, these results were used as core parameters in a Monte Carlo simulation repeated 10,000 times, to predict the distribution of potential changes in organizational outcomes under a job satisfaction improvement scenario. The findings indicate that each organizational outcome is primarily influenced by different satisfaction factors. Moreover, the predictive model demonstrated strong empirical validity, recording a low root mean square error (RMSE) in cross-validation with observed data. This research contributes theoretically by presenting a dynamic, probabilistic approach to understanding the satisfaction- performance linkage, and offers a practical decision-support tool for evidence-based human resource management in uncertain environments.