Transforming public-sector HRM through AI: An integrated TAM–TOE framework

Purpose – This study explores how artificial intelligence (AI) is transforming human resource management (HRM) in public administrations, focusing on both the perceived benefits of AI and the contextual factors that enable or constrain its adoption. Design/methodology/approach – A deductive qualitative design was employed, combining focus groups and semi-structured interviews with 35 public managers across multiple public administrations. Secondary sources, including policy documents and institutional reports, were used to contextualize the evidence. Findings – The findings indicate that AI is perceived as highly useful for improving operational efficiency through recruitment automation, enhanced decision-making, and the streamlining of routine HR tasks. AI is also associated with improved employee engagement and well-being through personalized development tools and real-time feedback mechanisms. However, adoption is shaped by technological readiness (data quality, interoperability, legacy systems), organizational readiness (leadership support, skills development, resistance to change), and environmental constraints (privacy regulation, accountability, transparency pressures). Ethical and governance issues emerge as cross-cutting concerns influencing trust and legitimacy. Practical implications – Effective AI adoption requires investments in interoperable infrastructures, digital upskilling, and governance mechanisms ensuring fairness, transparency, and compliance. Originality/value – This study advances research on AI in public-sector HRM by developing an integrated TAM–TOE framework and proposing theory-driven propositions explaining how individual acceptance mechanisms interact with organizational and environmental conditions in shaping AI adoption.