Prediction of Employee Turnover in Organizations Using Machine Learning Algorithms: A Decision Making Perspective

Digitalization can be defined as the transfer of activities performed in a field to digital environments. The application of digitalization in industry is revolutionary. The digitalization in industry can include applications such as collecting, analyzing, and managing company data with digital technologies, digitally monitoring and controlling the transfer of information between departments, and thus optimizing processes. In human resources management, digitalization can facilitate employee management in a variety of ways, increasing productivity and enabling better decisions. Human resources (HR) departments can develop more effective human resource management strategies by taking into account the amount of time employees are likely to work in the organization while making decisions such as incentives, bonuses, salary increases, and promotions. In this study, a decision support system is proposed to assist HR in determining the most appropriate departments for employees by predicting the potential working hours of current or new/to be hired employees in the organization. To estimate the potential work hours, we have used machine learning techniques that are widely used in the literature. We have adopted an assignment algorithm with work hour prediction to determine of the most suitable departments for employees. An application is carried out on a data set that has been published in the literature, and the results are discussed.