Summary
Accurate employee turnover prediction models are critical in the early detection of unanticipated turnover, giving managers sufficient time to deal with turnover related management issues. The logit and probit models have been successfully applied to solve nonlinear classifying and regression problems. However, the feasibility of applying them to voluntary turnover prediction still has not been sufficiently explored. Consequently, a numerical example involving voluntary turnover data from a motor marteting enterprise in central Taiwan is employed, and the empirical results reveal that the proposed models have high prediction capabilities. Accordingly, these two models also provide a promising alternative for predicting employee turnover in human resource management.
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A Comparative Test of Two Employee Turnover Prediction Models
1 Introduction
1.1 Employee turnoverAn increasing number of models that accurately predict employee turnover provide early detection of unanticipated turnover, particularly from high performance employees. Meanwhile, accurate prediction models also leave managers with adequate time to deal with turnover related management issues (Morrow, McElroy, & Laczniak, 1999). Generally, two forms of employee turnover exist, involuntary turnover and voluntary turnover. Involuntary turnover is frequently defined as movements across organizational boundaries, over which the employee is only slightly affected (Price, 1977), the representation forms of withdrawal from an organization often categorized with strategy (e.g. downsizing), dismissal (e.g. firing), or policy (e.g. compulsory retirement) (Campion, 1991). In contrast, voluntary turnover is defined as movements across organizational boundaries, over which the employee is heavily affected (Price, 1977), the representation forms of withdrawal from an organization often categorized with absenteeism and lateness (Campion, 1991).In accordance with the above definitions, involuntary turnover is most often initiated by organizational changes and ins...See the full content of this document
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