Perceived Control Boosts Gig Worker Identification Under Dehumanizing Algorithmic Management

Gig workers who perceive higher control over their labor report stronger identification with their work roles through enhanced work meaningfulness and decreased anxiety, according to a three-wave study of platform laborers in China. The investigation examines how individuals navigate platform algorithmic management, assessing the mechanisms that allow workers to develop professional identification despite the precarious conditions common across platform-mediated labor environments.

The precarious nature of gig arrangements frequently leaves workers without a clear sense of identification with their professional roles, an absence previously associated with reduced job satisfaction and elevated career instability. Drawing on Job Demands-Resources theory, the study investigates how identification with gig work can be fostered under platform algorithmic management and the boundary conditions under which this process unfolds.

The theoretical framework posits that accessible job resources prove exceptionally salient for maintaining workplace motivation and psychological well-being specifically when environmental job demands become elevated or taxing.

The empirical investigation utilized a three-wave dataset of 225 platform workers in China.

Statistical evaluation confirmed that greater perceived control over gig tasks was associated with higher levels of experienced work meaningfulness alongside marked reductions in work-related anxiety. In turn, both heightened meaningfulness and diminished anxiety demonstrated statistically positive relationships with stronger overall identification with gig work among surveyed participants.

The study also identified critical boundary conditions that shape these psychological pathways, revealing that the indirect associations linking perceived control to role identification proved significantly stronger when workers perceived higher levels of dehumanizing algorithmic management.

These observations extend current organizational literature by illuminating specific antecedents and operational boundary conditions governing gig worker identification under automated management systems, providing quantitative evidence that complements existing qualitative and career-outcome research. Furthermore, the findings indicate that platform design frameworks could benefit from directly prioritizing enhanced worker control while minimizing dehumanizing supervisory interactions to better support worker identification.

Published in Current Psychology, the research was conducted by Xue Lei, Lian Zhou, and Xinran Huang.

Subject of Research: Gig worker identification and perceived control under platform algorithmic management

Article Title: Identification with gig work under algorithmic management: The roles of gig work control and dehumanization

Article References: Lei, X., Zhou, L., & Huang, X. (2026). Identification with gig work under algorithmic management: The roles of gig work control and dehumanization. Current Psychology, 45(22), Article 1755. https://doi.org/10.1007/s12144-026-10310-6

Image Credits: AI Generated

DOI: 10.1007/s12144-026-10310-6

Keywords: algorithmic management, gig work, platform workers, work identification, perceived control, dehumanization, job demands-resources theory, work meaningfulness, workplace anxiety, job resources, platform design, precarious work, organizational psychology, job satisfaction, career instability, China platform economy