Strategic drivers of AI-based recruitment system adoption in organizations

Abstract

This study explores the fundamental strategic drivers of organizations adopting AI-based recruitment systems. It introduces novel insights into the factors leading to AI adoption and use in HRM as AI technology continues to evolve. Employing an integrated framework consisting of the Technology-Organization-Environment (TOE) model and the Technology Acceptance Model (TAM), the research investigation outlines critical technological, organizational, and environmental factors influencing the intent to adopt. This quantitative research design used data obtained through a survey of HR and IT professionals from various industries. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) to test the proposed model, we examined the strength and significance of the hypothesized relationships. The results show that technological readiness, top management support, perceived usefulness, and external pressure explain adoption intent. The takeaway from these findings is the strategic importance of collaborating innovation with organizational capacity and environmental factors. The study adds to the emerging knowledge on digital transformation within HRM. It gives practitioners and policymakers practical insights into using AI technologies to improve recruitment processes. The research emphasizes essential adoption enablers and enables informed decision-making and strategic planning regarding AI integration.