Early Turnover Prediction of New Restaurant Employees from Their Attendance Records and Attributes

Abstract

It is widely known that the turnover rate of new employees is high. Several studies have been conducted on filtering candidates during the recruitment process to avoid hiring employees that are likely to leave early. However, studies on the prediction of early turnover of new employees, which might enable appropriate interventions, are scarce. In the restaurant industry, which suffers from labor shortages, filtering candidates is unrealistic, and it is important to maintain newly hired employees. In this study, we propose a new model, based on recurrent neural networks, that predicts the early turnover of new restaurant employees by using their attendance records and attributes. We have evaluated the effectiveness of the proposed model by using anonymized data from a restaurant chain in Japan, and we confirmed that the proposed model performs better than baseline models. Furthermore, our analysis revealed that gender and hiring channel had little influence on early turnover and decreased prediction performance. We believe that these results will help in designing efficient interventions to prevent new restaurant employees from leaving early.