Onward and upward: strategies for overcoming challenges in algorithmic HR implementation

Purpose This study aims to examine the barriers affecting the implementation of algorithmic human resource management (algorithmic HR). Grounded in socio-technical systems (STS) theory, it conceptualizes these barriers as misalignments between social and technical subsystems, aiming to understand their interconnectedness, hierarchical structure and causal dynamics. Design/methodology/approach Sixteen key barriers were identified through literature review and expert consultation. An integrated interpretive structural modeling and decision-making trial and evaluation laboratory approach was used to analyze hierarchical relationships and causal interdependencies among these barriers. Findings Low return on investment emerges as the most critical driving barrier influencing algorithmic human resource (HR) adoption. It is causally linked to resistance to change, fear of job loss and perceived decline in job quality. The findings reveal a structured hierarchy of barriers, demonstrating how foundational socio-technical misalignments shape implementation challenges across HR practices. Research limitations/implications The study is based on expert insights within an emerging economy context, which may limit generalizability. It extends STS theory by explaining how algorithmic HR barriers arise from systemic misalignments between human and technological subsystems. Practical implications The study offers a structured framework to help practitioners prioritize barriers and align organizational capabilities, workforce readiness and technological infrastructure for effective algorithmic HR implementation. Originality/value The study advances algorithmic HR research by developing a hierarchical and causal model grounded in STS theory, providing a system-level explanation of implementation challenges.