Algorithmic Competency of On-Demand Labor Platform Workers: Scale Development, Antecedents, and Consequences

Abstract

Despite the highly controlled algorithmic work environment, on-demand labor platform workers may devise strategies to enhance their algorithmic competency (AC), facilitating better adaptation to the system. Yet, the proactive engagement of platform workers with algorithms remains underexplored in empirical research. This study endeavors to fill this gap by developing and validating the first scale of AC, while also shedding light on its antecedents and consequences. Analysis of data from five samples of Chinese on-demand labor platform workers reveals that AC encompasses four dimensions: understanding, embracing, leveraging, and remediating algorithmic management. It is found that AC is positively influenced by social support from peers and cognitive job crafting. Furthermore, AC is shown to account for additional variance in customer-oriented service behavior and identification with gig work, beyond that explained by related constructs. The paper concludes with a discussion on the implications of China's distinctive on-demand economy context for the generalizability of the findings.