Artificial intelligence as manager: confronting implementation barriers and shaping future research
Research on algorithmic management is expanding rapidly but remains highly fragmented, lacking a systematized understanding of how cross-disciplinary debates address its real-world implementation barriers. To address this gap, this study conducts a comprehensive bibliometric analysis of 340 peer-reviewed journal articles (2015–2025), explicitly adopting a blended review approach that integrates phenomenon- and field-focused strands. Moving beyond descriptive science mapping, we elevate thematic trajectories into causal narratives, demonstrating how the academic discourse has fundamentally matured from isolated technical inquiry into a systemic socio-ethical critique driven by macro-forces such as generative AI and modern labor regulations. By unpacking the field’s most influential literature, we conceptualize the status quo across four core socio-technical tensions: coordination efficiency versus psychological well-being, algorithmic control versus worker resistance, algorithmic opacity versus structural governance, and the transition from platform labor to mainstream corporate human resource management. Based on this synthesis, we propose an actionable, multi-stakeholder future research agenda and outline practical governance interventions, offering an essential roadmap for researchers, practitioners, and policymakers navigating the algorithmic workplace.