The Nexus Between Talent Management Attention and Artificial Intelligence: Evidence from Companies Operating Within the AI Domain

This study examines the relationship between talent management (TM) attention and the performance of leading artificial intelligence (AI) companies. Using Google Trends data, TM attention is quantified through search queries related to talent acquisition, employee development, and workforce planning, while additional corporate metrics, such as HR performance reports and employee retention rates, are incorporated to increase the robustness of the analysis. AI company performance is measured via the stock returns of Microsoft, Google, Amazon, and NVIDIA, which represent key players in the AI sector. A nonparametric causality-in-quantiles test is applied to capture the asymmetric and heterogeneous effects of TM attention on stock returns across different market conditions, ranging from bearish to bullish scenarios. The results reveal significant causality from TM attention to AI stock performance under bearish and normal market conditions, emphasizing the importance of TM strategies during periods of market stress or stability. In contrast, TM attention exerts limited influence during bullish conditions, where performance is likely driven by other factors, such as market sentiment and technological advancements. A facet-specific analysis highlights that talent acquisition consistently influences stock performance across all market conditions, whereas employee development has a significant effect only during bearish and normal conditions. Workforce planning has limited causal influence, suggesting that its market impact depends on company-specific factors and contextual dynamics. This study makes important contributions to theory and practice by offering a nuanced understanding of TM's role in shaping organisational performance within the dynamic AI landscape. For companies, prioritizing effective TM strategies, particularly talent acquisition and employee development, can enhance resilience and competitiveness. Investors can leverage TM insights to refine portfolio strategies, whereas policymakers are encouraged to implement initiatives such as grants for workforce training or public‒private partnerships to foster talent pipelines in the AI sector. These findings underscore the critical interplay between TM practices and market performance, providing actionable insights for navigating the complexities of the rapidly evolving AI industry.