동기 및 위생요인 간 상호작용을 통한 직무 만족의 예측과 설명 : 설명가능한 인공지능(eXplainable AI)을 활용한 탐색적 연구

초록

Job satisfaction is defined as a phenomenon that reflects complex emotional responses to work, and the motivation-hygiene theory suggests that it is caused by a combination of various motivation and hygiene factors(Herzberg, 1959). Despite the importance of job satisfaction, there have been no researches to date that compared the relative influence of various factors and empirically explored their combined effects. This study was conducted to predict and explain the job satisfaction based on motivation-hygiene theory, and to do so, Automated Machine Learning and eXplainable Artificial Intelligence techniques were utilized. The results showed that various motivational and hygiene factors have a complex effect, and among them, recognition and personal life factors have a particularly strong effect. We also found that there are various types of interaction effects on job satisfaction through feature interaction detection, showing that the interaction between recognition and personal life factors is particularly strong and the pattern of interaction varies depending on the specific combination of variables. These results suggest that various motivation and hygiene factors should be considered holistically in interventions to improve workers' job satisfaction and the fulfillment of recognition and personal life factors should be prioritized.

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