Platform algorithmic stress and career commitment of Chinese gig workers: a double-edged sword effect study

Area of Science:

• Management Science
• Organizational Behavior
• Sociology of Work

Background:

• The platform economy offers gig employment but introduces algorithmic stress.
• Existing research often overlooks gig workers' high career commitment despite this stress.
• Self-Determination Theory provides a framework to explore worker motivation and commitment.

Purpose of the Study:

• To investigate the paradoxical relationship between algorithmic stress and gig worker career commitment.
• To propose and test a dual-path mediation model explaining this paradox.
• To extend Self-Determination Theory to the context of algorithmic management.

Main Methods:

• Survey of 354 full-time gig workers in China.
• Development and validation of a dual-path mediation model.
• Statistical analysis of the indirect effects of algorithmic stress on career commitment.

Main Results:

• Algorithmic stress positively influences job crafting, which enhances career commitment.
• Algorithmic stress negatively impacts the sense of relative deprivation, reducing career commitment.
• Both facilitative and inhibitive pathways of algorithmic stress on career commitment were identified.

Conclusions:

• Algorithmic stress has complex, dual effects on gig worker career commitment.
• Job crafting and relative deprivation are key mediators in the algorithmic stress-commitment relationship.
• Platform algorithm design should consider these pathways to foster high-quality gig employment.