Employee Turnover Prediction: A Cross-component Attention Transformer with Consideration of Competitor Influence and Contagious Effect

TL;DR

This paper introduces a novel deep learning model utilizing cross-component attention transformers to predict individual employee turnover across multiple firms, considering competitor influence and contagious effects, with demonstrated superior accuracy and practical cost-saving benefits.

Contribution

The study presents a new deep learning approach based on job embeddedness theory for predicting employee turnover across firms, addressing a gap in existing research.

Findings

The proposed model outperforms existing benchmark methods in accuracy.

It estimates significant cost savings for recruiters.

The model provides interpretable insights into turnover drivers.

Abstract

Employee turnover refers to an individual's termination of employment from the current organization. It is one of the most persistent challenges for firms, especially those ones in Information Technology (IT) industry that confront high turnover rates. Effective prediction of potential employee turnovers benefits multiple stakeholders such as firms and online recruiters. Prior studies have focused on either the turnover prediction within a single firm or the aggregated employee movement among firms. How to predict the individual employees' turnovers among multiple firms has gained little attention in literature, and thus remains a great research challenge. In this study, we propose a novel deep learning approach based on job embeddedness theory to predict the turnovers of individual employees across different firms. Through extensive experimental evaluations using a real-world dataset, our developed method demonstrates superior performance over several state-of-the-art benchmark methods. Additionally, we estimate the cost saving for recruiters by using our turnover prediction solution and interpret the attributions of various driving factors to employee's turnover to showcase its practical business value.