Mapping the landscape of algorithmic management in gig economy: A bibliometric analysis

Abstract

Algorithmic management has become a prominent feature of contemporary organizations, particularly in platform-mediated and data-intensive work contexts. While research on algorithmic management has expanded rapidly across management, information systems, and labor studies, existing reviews remain largely narrative in nature and provide limited insight into the field’s underlying intellectual structure. This study addresses this gap by adopting a bibliometric and network-analytic approach to systematically map the evolution and organization of algorithmic management research. Using bibliographic records retrieved from the Web of Science Core Collection, the study applies co-citation analysis, keyword co-occurrence mapping, and citation burst detection to identify dominant research clusters, influential contributions, and emerging themes. All network-based analyses are conducted using the Web of Science Core Collection. The results reveal a rapidly growing yet structurally fragmented research landscape, characterized by a central cluster focused on platform work and algorithmic control alongside multiple loosely connected technical and domain-specific streams. Temporal analyses further indicate a shift from early technical applications toward increasing attention to organizational, labor, and governance-related issues.