Utilizing Big Data Technologies in Employee Selection: A Systematic Literature Review

Abstract

Research on big data technologies in employee selection has expanded rapidly but remains fragmented. This study presents a systematic literature review of empirical applications of big data technologies in employee selection. The study analyzed 50 publications obtained from the Scopus and Web of Science databases. Specifically, this review synthesizes prior research on the theoretical foundations of big data technologies in employee selection, the data sources used, the analytic approaches applied, and their contributions to employee selection research and practice at theoretical, methodological, and practical levels. Results show limited theoretical anchoring, with only minor links to established theories such as person‐environment fit theory, trait theory, needs theory, trait activation theory, and Brunswik's lens model. Most empirical applications are based on field data collected from real selection or human resource management processes, with fewer studies relying on lab data, such as mock interviews or simulated selection tasks, or on mixed data sources. Analytically, researchers have employed a broad range of big data technologies, including supervised machine learning, unsupervised machine learning, natural language processing embeddings, n‐gram text mining, large language models, and others, with increasingly multimodal and stage‐specific applications across the selection process. Overall, the empirical application of big data technologies in employee selection is promising, but remains contingent on theory and method alignment, transparent targets and labels, and ongoing auditing.