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Exploiting Network Fusion for Organizational Turnover Prediction

Exploiting Network Fusion for Organizational Turnover Prediction 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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Management Information Systems (TMIS) Association for Computing Machinery

Exploiting Network Fusion for Organizational Turnover Prediction

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2021 ACM
ISSN
2158-656X
eISSN
2158-6578
DOI
10.1145/3439770
Publisher site
See Article on Publisher Site

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.

Journal

ACM Transactions on Management Information Systems (TMIS)Association for Computing Machinery

Published: May 20, 2021

Keywords: Talent management

References