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This paper explores the application of predictive analytics in talent management (TM) through a bibliometric analysis of the literature. Although predictive analytics can provide meaningful insight into the future performance of employees, turnover, and recruitment processes, it is not yet widely applied in TM. This study highlights opportunities for further research on the strategic integration of predictive analytics into TM strategies, human resource (HR) decisions, and organizational outcomes by systematically reviewing the existing literature. The analysis examined 547 publications indexed in the Web of Science. Co-occurrence and co-citation techniques were applied using VOSviewer. This approach enabled the identification of the intellectual and conceptual structure of this field, highlighting key themes, influential authors, and emerging research streams. The study identifies two key domains in research on the application of predictive analytics in TM: (1) operational HR functions such as recruitment, talent acquisition, and performance management, where predictive tools are increasingly applied; and (2) strategic and organizational aspects, focusing on dynamic capabilities, leadership, and ethics in artificial intelligence (AI). Recent developments from 2022 to 2024 emphasize AI adoption, generative AI, and ethical concerns, signaling a shift toward more responsible and transparent decision-making frameworks. The study clarifies the conceptual boundaries of the literature on predictive analytics in TM and underscores its evolution toward the study of responsible and strategically oriented analytics. The findings provide a foundation for advancing theoretically grounded and ethically robust applications of predictive analytics in organizational talent strategies.
Al-Naemi et al. (Fri,) studied this question.