Developing an advanced prediction model for new employee turnover intention utilizing machine learning techniques

In recent years, the turnover phenomenon of new college graduates has been intensifying. The turnover of new employees creates many difficulties for businesses as it is difficult to recover the costs spent on their hiring and training. Therefore, it is necessary to promptly identify and effectively manage new employees who are inclined to change jobs. So far previous studies related to turnover intention have contributed to understanding the turnover phenomenon of new employees by identifying factors influencing turnover intention. However, with these factors, there is a limitation that it has not been able to present how much it is possible to predict employees who are actually willing to change jobs. Therefore, this study proposes a method of developing a machine learning-based turnover intention prediction model to overcome the limitations of previous studies. In this study, data from the Korea Employment Information Service's Job Movement Path Survey for college graduates were used, and OLS regression analysis was performed to confirm the influence of predictors. And model learning and classification were performed using a logistic regression (LR), k-nearest neighbor (KNN), and extreme gradient boosting (XGB) classifier. A novel finding of this research is the diminished or reversed influence of certain traditional factors, such as workload importance and the relevance of one's major field, on turnover intention. Instead, job security emerged as the most significant predictor. The model's accuracy rates, highest with XGB at 78.5%, demonstrate the efficacy of applying machine learning in turnover intention prediction, marking a significant advancement over traditional econometric models. This study breaks new ground by integrating advanced predictive analytics into turnover intention research, offering a more nuanced understanding of the factors influencing the turnover intentions of new college graduates. The insights gained could guide organizations in effectively managing and retaining new talent, highlighting the need for a focus on job security and organizational satisfaction, and the shifting relevance of traditional factors like job preference.