Adoption of big data technologies in employee selection: a technology–organization–environment perspective

Big data technologies are increasingly used in human resource management, including employee selection, yet less is known about how their adoption is understood and embedded in organizational practice. Drawing on semi structured interviews with ten practitioners, this qualitative study examines the adoption of big data technologies in employee selection from an interpretive perspective. Grounded theory informed coding was used for analysis, while the technology organization environment framework served as a sensitizing lens. The findings show that adoption is shaped by data and system conditions, organizational readiness and support, and external market and regulatory conditions. Participants associated these technologies with making candidate information more manageable, recruitment processes more structured, responses more timely, and selection records more reviewable. Adoption became embedded in practice when system outputs were reviewed by human actors, applied to selection tasks, coordinated through feedback, and refined during use. The study contributes to human resource analytics research by positioning big data technology adoption in employee selection as a selection specific form of analytics practice and by showing how technology, organizational, and environmental conditions are expressed through everyday selection work involving candidate data, system outputs, human judgment, and feedback.