Digital gig platforms widely rely on algorithmic rating systems that aggregate customer evaluations to assess gig worker performance, with unfairly negative customer algorithmic rating experiences (UNCAREs) emerging as an inherent drawback of these algorithmic rating systems. Yet, research has ignored that when gig workers cannot directly defend against UNCAREs, the impact of UNCAREs can be displaced to safer, more accessible non-work domains, such as online communities. Following displaced aggression theory, we examine how and when gig workers' UNCAREs affect their online community knowledge sharing.
To enhance the generalizability of our findings, we collected a heterogeneous survey sample of 209 gig workers across various gig platforms (e.g. Meituan, Eleme, Didi, and Zhubajie) in China. Using the partial least squares structural equation modeling approach, we tested the moderated mediation model.
UNCAREs increase perceived job insecurity and reduce perceived rating system fairness, which in turn decreases gig workers' online community knowledge sharing. Interactional algorithmic monitoring weakens these two indirect negative effects.
We extend prior studies mainly focusing on negative customer ratings arising from objective service problems by theorizing UNCAREs, revealing how and when their detrimental effects are displaced to non-work domains (e.g. online community knowledge sharing), and highlighting interactional algorithmic monitoring as a key upward information transfer mechanism against unfair service problems (i.e. UNCAREs).
