Algorithmic human resource management at work: perceptions of fit, performance and employability among HR professionals

This study examines how algorithmic human resource management assistive technology (AHT) is enacted in the daily work of HR professionals and how its use relates to perceived work performance, skill requirements and employability. Drawing on person–job fit (P–J fit) and task–technology fit (TTF) theories, the study explores how alignment among HR professionals, job tasks and AHT shapes perceptions of fit in technology-mediated HR practice. A qualitative design was employed using semi-structured interviews with 20 experienced HR professionals from diverse organizations in India. Data were analyzed through qualitative content analysis using a combined inductive–deductive approach. Coding was informed by P–J fit and TTF constructs while remaining open to emergent themes concerning everyday HR practices. Participants described AHT as supporting routine, information-intensive HR tasks, especially recruitment, onboarding and administration. Many reported improved efficiency and coordination, alongside new responsibilities such as validating outputs and handling exceptions. Perceived employability was linked to continuous learning, technical skills and adaptability. The findings suggest that perceived performance and employability depend on alignment between AHT, task requirements, skill development and organizational support. This research underscores the importance of incorporating AHT into HR practices, as it can significantly enhance HR professionals' effectiveness and broader organizational outcomes. It also highlights the need for a more comprehensive framework to integrate the supportive role of algorithmic technology in HR. The study advances a practice-oriented integration of P–J fit and TTF, highlighting how AHT is experienced in everyday HR work.