Exploiting Network Fusion for Organizational Turnover Prediction

Abstract

As an emerging measure of proactive talent management, talent turnover prediction is critically important for companies to attract, engage, and retain talents in order to prevent the loss of intellectual capital. While tremendous efforts have been made in this direction, it is not clear how to model the influence of employees’ turnover within multiple organizational social networks. In this article, we study how to exploit turnover contagion by developing a Turnover Influence-based Neural Network (TINN) for enhancing organizational turnover prediction. Specifically, TINN can construct the turnover similarity network which is then fused with multiple organizational social networks. The fusion is achieved either through learning a hidden turnover influence network or through integrating the turnover influence on multiple networks. Taking advantage of the Graph Convolutional Network and the Long Short-Term Memory network, TINN can dynamically model the impact of social influence on talent turnover. Meanwhile, the utilization of the attention mechanism improves the interpretability, providing insights into the impact of different networks along time on the future turnovers. Finally, we conduct extensive experiments in real-world settings to evaluate TINN. The results validate the effectiveness of our approach to enhancing organizational turnover prediction. Also, our case studies reveal some interpretable findings, such as the importance of each network or hidden state which potentially impacts future organizational turnovers.