Talent analytics: a systematic review and topic modeling approach

Purpose Talent analytics has emerged as a vital tool for enhancing organizational effectiveness by informing strategies for attracting, developing and retaining talent, as well as predicting employee turnover. In response to growing interest in data-driven decision-making, this study aims to explore recent developments in talent analytics, synthesizing key themes from the literature and proposing directions for future research. Design/methodology/approach This paper presents a systematic literature review of 117 scholarly publications retrieved from the Scopus database. To uncover underlying thematic structures, this study uses Latent Dirichlet Allocation, a topic modeling technique that enables the identification of prominent research clusters within the field. Findings The analysis identifies three themes: attraction in talent management, development and retention in talent management and applications of talent analytics in sports. Their configuration indicates that talent analytics is progressing from discrete analytical applications toward a broader organizational capability spanning the talent lifecycle. The findings of this study further show that the value of this capability depends on managerial translation, contextual applicability and the legitimacy and governance of data-driven talent decisions. Originality/value This study moves beyond descriptive mapping of talent analytics by combining topic modeling with qualitative interpretation to examine relationships among research streams across the talent lifecycle. The synthesis positions talent analytics as an emerging organizational capability whose value depends on strategic alignment, complementary organizational capabilities, contextual applicability and responsible governance.