Enhancing gig workers’ acceptance and mitigating algorithm aversion: impact of algorithmic transparency from the cognitive appraisal theory perspective

As algorithms increasingly perform human resource management (HRM) functions by assigning tasks, motivating workers, and evaluating performance on online labor platforms (OLPs), algorithmic transparency has become a major concern for gig workers, yet it remains underdeveloped. Drawing on cognitive appraisal theory, this study examines the effects of algorithmic transparency on gig workers’ acceptance of the platform algorithm and aversion to the platform algorithm, with an emphasis on the mediating roles of enjoyment and fear. Data were collected from 297 gig workers using a three-stage survey design. The results show that perceived algorithmic transparency positively affects enjoyment and negatively affects fear. Enjoyment mediates the relationship between perceived algorithmic transparency and acceptance of the platform algorithm, whereas fear mediates the relationship between perceived algorithmic transparency and aversion to the platform algorithm. These findings provide valuable guidance for platform managers seeking to mitigate algorithm aversion.