How gig workers pursue self-leadership under algorithmic management

Abstract

The rapid growth of the gig economy has highlighted the importance of self-leadership for gig workers operating within “hybrid” work-platform relationships, where algorithmic organizations avoid formal employment but apply HRM practices to influence behaviors. Self-leadership enables workers to sustain motivation and manage algorithmic control, yet its development in gig work remains underexplored. Unlike traditional organizations with formal support systems, gig workers under algorithmic management rely on third-party human agents and interpersonal interactions to cultivate self-leadership. This study examines how self-leadership emerges among gig workers through an agential approach facilitated by interpersonal interventions across behavioral, cognitive, and environmental dimensions.
Using a qualitative methodology, grounded theory, and an exploratory case study across two food delivery platforms in China, data were collected via interviews, observations, archival analysis, and informal dialogues. Findings identify three mechanisms—algorithmic interpretation, entrepreneurial cognition, and organizational simulation—that explain how gig workers develop self-leadership under the guidance of leaders such as station managers and team leaders. The study contributes by expanding self-leadership research in algorithmic contexts, positioning it as an actively developed skill rather than an innate trait. It introduces an agential approach that highlights the role of third party leaders in coaching, inspiring entrepreneurial thinking, and fostering supportive environments. By revealing the dynamic interplay among these mechanisms, the study advocates a human-centered perspective on algorithmic management and offers pathways toward a more humanized gig economy.

Date of Award	15 Oct 2025
Original language	English
Awarding Institution	University of Nottingham
Supervisor	Jun Luo (Supervisor) & Martin Liu (Supervisor)