Determinates of Employee Voluntary Turnover and Forecasting in R&D Departments: A Case Study

Xiaojuan Zhu, Rapinder Sawhney, Girish Upreti


employee voluntary turnover factors using logistic regression and forecasts employee tenure using a decision tree for four research and development departments in a large U.S organization. Company job title, gender, ethnicity, age and years of service significantly affect employee voluntary turnover behavior determined by logistic regression. The findings assist managers and human resource departments in specific employee retention strategies to reduce R&D departments’ voluntary turnover rate. The decision tree method built a five-level depth tree model with 17 nodes. This model has the lowest AIC value and the best performance in the validation dataset. Age at hire, jobtitle, division, and race are statistically significant factors to predict employee tenure. The most important variable is age at hire located in the decision tree’s first, third, and fourth nodes. Classification rules assist managers and human resource departments in quickly predicting employee tenure and in making hiring decisions.

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Studies in Engineering and Technology   ISSN 2330-2038 (Print)   ISSN 2330-2046 (Online)

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