Balancing bytes and biases: A case study of AI adoption in academic human resource management

The integration of Artificial Intelligence (AI) is rapidly transforming Human Resource Management (HRM), yet its adoption within the unique context of higher education institutions remains underexplored, particularly in developing nations. This study addresses this gap by investigating the application and implications of AI in core HRM functions, specifically recruitment, performance evaluation, and workforce planning, at a public university in Indonesia. Adopting an exploratory qualitative case study methodology, this study sourced data from twelve purposively selected stakeholders, including HR managers, academic leaders, and IT personnel, through semi-structured interviews, document analysis, and non-participant observation. The collected data were systematically analyzed using thematic analysis, revealing a dual impact of AI adoption. While AI integration significantly enhances operational efficiency by automating recruitment screening and supporting data-driven workforce planning, it also introduces critical challenges, including the risk of algorithmic bias, a lack of transparency, and the potential erosion of human judgment in culturally sensitive evaluations. The study concludes that successful AI implementation in academic HRM is contingent upon institutional readiness, a supportive organizational culture, and a robust technological infrastructure. By applying the Technology-Organization-Environment (TOE), Technology Acceptance Model (TAM), and Diffusion of Innovation (DoI) frameworks, this research contributes a nuanced understanding of the factors shaping AI adoption in higher education, underscoring the necessity of a balanced approach that leverages AI's benefits while preserving essential human oversight in university administration.