ITM Web Conf.
Volume 12, 2017The 4th Annual International Conference on Information Technology and Applications (ITA 2017)
|Number of page(s)
|Session 4: Information Theory and Information Systems
|05 September 2017
Employee Turnover Prediction Based on State-transition and Semi-Markov- A Case Study of Chinese State-owned Enterprise
College of Information System and Management, National University of Defense Technology, Changsha, China
As a main direction of Human Resource Management, employee turnover can provide decision support for managers. In this paper, we aim at predicting the turnover amount of employee on condition of different variable values. The properties of employee and job position are formulated as two variables, where the value of variable varies according to the the state of properties. Additionally, state-transition model is applied to describing employee’s job-state as well as the turnover type. Subsequently, we proposed a semi-Markov model to calculate the conditional turnover amount of employee. Then, we provide a dataset of employee records to illustrate how these models work in reality. Finally, it is proven that the proposed method in this paper is with great significance for managers to develop recruitment plans, promote rules, and retire regulations
© The Authors, published by EDP Sciences, 2017
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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