Expression or suppression: the double-edged sword effect of algorithmic management systems on service provider performance on digital gig platforms

Algorithmic management systems (AMSs) sustain the triadic business model of gig platforms by combining continuous algorithmic monitoring with customer-based rating systems, thereby impacting service provider performance. Given that service provider performance is crucial to the growth of the gig economy, this study examines the mechanisms by which AMSs impact it. Drawing on social cognitive theory, we collected survey data from 209 service providers across major gig platforms in China (e.g. Meituan, Eleme, Didi, Zhubajie, Credamo, Zhihu) and analyzed the data using partial least squares structural equation modelling. The results indicate that algorithmic monitoring facilitates expressive emotional labour, positively impacting service performance; whereas negative algorithmic ratings reduce expressive emotional labour and increase suppressive emotional labour, thereby undermining service performance. These findings offer novel insights into the double-edged sword effect of AMSs on service provider performance, viewed through the lens of emotional labour as a cognitive self-regulation mechanism. This perspective provides key practical implications for digital gig platforms to optimise the effective design and implementation of AMSs.