Blessing or curse? The two-sided effects of algorithmic control on ego-depletion and safety performance of gig workers

Abstract

The emergence of gig economy has facilitated the adoption of algorithm-based management systems, creating favorable conditions for gig workers. Nevertheless, it also brings various challenges and uncertainties, especially gig workers' safety performance, having become a prominent concern. Based on ego-depletion theory, we investigate the underlying mechanism through which platform algorithmic control influences on safety performance of gig workers. Based on two points of data from 314 gig workers in China, we found that the three dimensions of algorithmic control (viz., standardized guidance, tracking evaluation and behavioral constraint) have differential effects on safety performance. On one hand, algorithmic standardized guidance is negatively related to ego-depletion, which in turn improves safety performance. On the other hand, algorithmic tracking evaluation and behavioral constraint are positively related to ego-depletion, and consequently reducing safety performance. Furthermore, algorithmic transparency moderates the relationship between algorithmic standardized guidance, algorithmic tracking evaluation, and ego-depletion; self-efficacy moderates the relationship between algorithmic standardized guidance and ego-depletion; both trait mindfulness and leadership safety commitment moderate the relationship between ego-depletion and safety performance. This study offers valuable insights for platform enterprises to optimize the effectiveness of algorithmic control.