AI Thrust Seminar | Acqui-hiring or Acqui-quitting: 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 industries. However, the inevitable tensions brought by the divergent cultures and organizational misalignment during the M&A process result in a high talent turnover rate and ultimately integration failure. Hence, it is imperative to understand and prepare for the potential effects of the M&A process on 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 firm-level compatibility and employee-firm fit. This framework can effectively integrate multi-source, heterogeneous data and provide a more nuanced understanding of the compatibility between the firm pairs. Extensive evaluations of large-scale real-world data clearly demonstrate the effectiveness of our approach. We conclude by discussing the theoretical and practical implications of interesting findings.