Algorithmic management in the gig economy: A systematic review and research integration

Rapid growth in the gig economy has been facilitated by the increased use of algorithmic management (AM) in online platforms (OPs) coordinating gig work. There has been a concomitant increase in scholarship related to AM across scientific domains (e.g., computer science, engineering, operations management, management, sociology, and law). However, this literature is fragmented with scholars disagreeing on the conceptualization and measurement of AM, as well as a lack of consensus on the dimensions of AM influencing various gig worker-related outcomes, the mechanisms through which these influences are exerted, and the relevant boundary conditions. To address these issues, we systematically reviewed the academic literature across scientific disciplines related to the AM of gig workers using natural language processing (NLP)-based topic modeling. Our analysis yielded 12 topics, which we integrate using an input-process-output (IPO) framework to illustrate differing effects of AM on worker-related outcomes. Based on our findings, we provide a comprehensive definition of AM, including its key dimensions, and highlight main mediating pathways through which the individual dimensions of AM impact various gig worker-related outcomes. Finally, we provide a roadmap for future research on AM in the gig economy (GE) using an organizational behavior lens.