Research on the Mechanism of the Influence of Self-leadership of Gig Workers on Dual Work Behavior under Algorithm Control

Abstract

Abstract: In the context of precise managerial control enabled by platform algorithms, how self-leadership intervenes in work performance has become a critical lens for examining the behavioral multiplicity of gig workers. Drawing on a grounded theory-based case study approach, this research develops a dual-path mechanism through which gig workers’ self-leadership under algorithmic control influences work behaviors. The findings indicate that gig workers’ self-leadership under algorithmic control comprises five core strategies: learning orientation, emotion regulation, behavior adjustment, self-motivation, and thought construction. These strategies can be classified into three developmental stages: cognitive-emotion regulation, behavior motivation, and thought construction. Moreover, self-responsibility and work pressure play mediating roles in the process where self-leadership influences work behavior. This study not only enriches the structural connotation of the self-leadership strategies of gig workers under algorithmic control, but also provides a theoretical reference for platform organizations to strengthen the behavioral management of gig workers in the algorithmic context.

Key words: gig workers, self-leadership, work behavior, algorithmic control, case study

Hou Xuanfang, Liu Yunqi, Chen Wu, Cai Xinyu, Chen Hao. Research on the Mechanism of the Influence of Self-leadership of Gig Workers on Dual Work Behavior under Algorithm Control[J]. Management Review, 2026, 38(7): 263-275.

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