A bibliometric analysis of algorithmic governance and employee experience in the modern workplace

The diffusion of artificial intelligence in organizations is reshaping how work is allocated, monitored, evaluated, and disciplined through algorithmically mediated management. This article maps the intellectual landscape at the intersection of algorithmic governance, seenn as the rules, oversight mechanisms, and accountability arrangements guiding workplace algorithms, and employee experience, defined as employees’ perceptions of fairness, autonomy, trust, well-being, and recognition. Using a bibliometric analysis of 295 Scopus-indexed peer-reviewed journal articles, the study examines publication trends, influential journals and authors, and the field’s conceptual structure. Analyses were conducted using Biblioshiny and complemented with VOSviewer network visualizations of co-authorship, co-citation, and keyword co-occurrence. Findings show a sharp rise in research output from 2020, with strong concentration since 2022. The field’s conceptual core centers on artificial intelligence, algorithmic management, control, and the gig economy, while employee-experience themes such as autonomy, engagement, job satisfaction, and well-being are becoming more closely linked to governance mechanisms including transparency, contestability, accountability, and human oversight. The literature is evolving from platform-focused studies of control toward broader debates on responsible, human-centered AI, offering useful insights for organizations, HR professionals, and policymakers designing fair and transparent algorithmic workplaces.