Anmerkungen:
Nach Informationen von SSRN wurde die ursprüngliche Fassung des Dokuments March 12, 2023 erstellt
Beschreibung:
Gaining highly skilled human capital is one of the key motivations for mergers and acquisitions (M&A), particularly in knowledge-intensive sectors such as the technology industry. However, the inherent cultural differences and organizational misalignments during the integration process can lead to significant tensions and a high rate of talent turnover, which may ultimately result in integration failure. Hence, it is crucial for organizations to proactively anticipate and manage the potential effects of such events on employee turnover. The predominant perspective in existing literature focuses on the dyadic relationship between merging firms while a few other studies recognize the fit between employees and the firm. However, there has been a lack of endeavor to unify these two factors into a coherent framework. In this paper, we propose a novel data-driven neural network approach to predict the talent turnover trend during the post-M&A phase, by considering the compatibility between the merging companies as a key factor. Specifically, drawing on organizational theories, we develop a dual-fit heterogeneous graph neural network with 1) Organization to Organization (O-O) fit, which captures the relatedness and similarity at the firm level, and 2) Person to Organization (P-O) fit, which represents the compatibility and cultural closeness at the employee level. By leveraging this framework, we can effectively integrate multi-sourced, heterogeneous data to gain a more nuanced understanding of the compatibility between firm pairs. Our proposed approach is evaluated on a large-scale real-world dataset and benchmarked against state-of-the-art methods. Experimental results demonstrate the superiority of our approach in predicting talent turnover trends during the post-M&A phase. Our approach also offers interpretable results and provides valuable insights for organizations seeking to manage talent effectively during and after M&A events