Is algorithm control a double-edged sword? Uber drivers’ positive and negative techno-stressors

Thursday, August 10, 2023

by Jessie Ge, PhD Candidate

Gig economy platforms continue to struggle with high worker attrition. A plausible reason is that workers’ well-being suffers because of an inability to cope with technology demands. Nonetheless, automation is trending, especially for online gig-economy platforms. For example, Uber drive implements algorithm control to screen qualifying drivers (gatekeeping algorithm control) and to monitor and advise on driver behavior (guiding algorithm control). It is important to understand whether and how algorithm control may be a double-edged sword for employees’ well-being in terms of technostress, affecting their intention to continue working on the platform and/or to pursue system workarounds.

Specifically, this study conceptualizes that Uber drivers may feel motivated by challenges raised by algorithm control, increasing work satisfaction (challenge techno-stressors), or they may feel overburdened with the overload and uncertainty of algorithm control, causing work attrition (threat techno-stressors). Using a survey instrument with 621 valid responses from U.S. Uber drivers, this study examines how algorithmic control imposes the two techno-stressors on Uber drivers, affecting their intention to continue working on the platform and to pursue system workarounds. This study also examines whether disclosing information about algorithm control to Uber drivers could enhance (weaken) their positive (negative) reactions to algorithm control.


The findings suggest that Uber drivers do not broadly view algorithmic control as uniformly “good” or “bad,” but instead form a type of love-hate relationship that allows a simultaneous recognition of both their benefits and drawbacks. This study finds that algorithmic control transparency does not necessarily mitigate the potential dark-side influences of algorithmic control on techno-stressors. The findings of this study fresh the view in practice on workers’ perception of technostress and its effect on work performance, in the context of the Uber platform.


This research makes a key theoretical contribution by conceptualizing algorithm control as an antecedent of techno-stressors and by integrating the concept of algorithmic control with both positive and negative forms of technostress. This study also contributes to the literature since there is little discussion on the positive effects of algorithm control on workers’ well-being, despite the benefits of operational efficiency.

The study, Examining the Impact of Algorithmic Control on Uber Drivers' Technostress, authored by Cram, Wiener, Tarafdar, and Benlian was recently published in the Journal of Management Information Systems.