Does Algorithmic Control Facilitate Platform Workers’ Deviant Behavior Toward Customers? The Ego Depletion Perspective

Online labor platforms widely implement algorithmic control to ensure that workers consistently deliver quality services. However, extensive evidence suggests that platform workers under the tight monitoring of algorithms still engage in customer-directed deviant behavior, which raises questions about the effectiveness of algorithmic control. Thus, we draw on ego depletion theory to examine the critical issue of why and when algorithmic control fails to reduce workers' undesirable behavior toward customers. This study conducted a three-phase online questionnaire survey with 377 ride-hailing drivers in China. Data were analyzed using the PROCESS macro model. The results show that algorithmic control excessively drains workers' limited self-control resources and drives them into an ego-depleted state with low control ability, which further creates conditions for more deviance. Algorithmic transparency alleviates the influence of algorithmic control on ego depletion, whereas financial dependence on platform work mitigates the impact of ego depletion on customer-directed deviance. The indirect effect of ego depletion is most pronounced when both algorithmic transparency and financial dependence are lower. We shed light on the mediating mechanism and boundary conditions of the unexpected facilitating influence of algorithmic control on workers' customer-directed deviant behavior, providing feasible directions for optimizing algorithmic control system design and reducing customer-directed deviant behavior.