Abstract
The integration of Artificial Intelligence (AI) is reshaping the global landscape of Human Resource Management (HRM), presenting unique opportunities and challenges, particularly for emerging economies. This study examines the intricate dynamics of AI adoption within Thailand's HRM function, a significant emerging market characterized by a paradox: high public optimism about AI and low corporate adoption of HRM. Employing a sequential exploratory mixed-methods design, the research first utilizes thematic analysis of semi-structured interviews with senior HR professionals to unearth the nuanced socio-technical barriers to AI integration. Subsequently, a second phase develops, validates, and tests an extended technology acceptance model using Partial Least Squares Structural Equation Modeling (PLS-SEM) on data collected from a nationwide survey of 284 HR practitioners. The qualitative results reveal three core themes: ‘Strategic Imperative vs. Operational Paralysis,’ ‘The Trust Deficit,’ and ‘The Human-AI Competency Chasm.’ The quantitative findings from the main effects model validate the proposed framework, demonstrating that while traditional technology adoption drivers are significant, ‘Ethical Governance'—a novel construct rigorously developed and validated in this study—emerges as the most potent predictor of HR professionals' intention to use AI. This intention, in turn, positively influences Perceived HRM Effectiveness. Furthermore, a post-hoc Multi-Group Analysis (MGA) reveals critical contextual moderators: Facilitating Conditions are a more potent driver of adoption intention in large organizations, whereas Ethical Governance is more salient for highly experienced HR professionals. This study contributes to theory by extending the Unified Theory of Acceptance and Use of Technology (UTAUT) with a contextually grounded and methodologically robust construct and by empirically identifying key boundary conditions that highlight the contextual nuances and specific boundary conditions of standard technology acceptance models. It also offers critical practical recommendations for executives and policymakers, underscoring that building trust through transparent governance is a necessary precondition for unlocking AI's transformative potential and that strategies must be tailored to organizational size and practitioner expertise.