Data-driven Post-M&A Turnover Prediction via a Dual-fit Model

Gaining highly skilled human capital is one of the primary reasons for corporate mergers and acquisitions (M&A), especially for knowledge intensive industry. However, the inevitable tensions brought by the divergent cultures and organizational misalignment during the M&A process result in high talent turnover rate and ultimately the integration failure. Hence, it is imperative to understand and prepare for the potential effects of M&A process on the employee turnover. To this end, we propose a novel dual-fit model induced heterogeneous Graph Neural Network (GNN) model to predict the talent turnover trend in the post-M&A process, by taking into account the complex relationship among the acquirer firm, the acquiree firm, and the acquired employees. Specifically, we creatively design a dual-fit model comprised of both the firm-level compatibility and employee-firm fit. Extensive evaluations on large-scale real-world data clearly demonstrate the effectiveness of our approach.

Hao (Howard) ZHONG
Hao (Howard) ZHONG
Assistant Professor | Scientific Co-director of MSc in Big Data and Business Analytics