The Impact Mechanism of Algorithmic Control on Platform Workers'Customer-Directed Deviant Behavior:An Explanation based on the Job Demands-Resources Model
As the platform economy transitions into a new stage of high-quality development,reducing customer-directed deviant behavior among platform workers and enhancing their overall service quality have become focal points of interest for both theoretical and practical discussions.It is noteworthy that despite the substantial body of research dedicated to exploring management practices that can effectively curb customer-directed deviant behavior,these studies predominantly focus on traditional employment groups.For the emerging cohort of platform workers,whose management models have undergone significant transformation,algorithmic control has become a highly relied-upon management practice for platform enterprises.In this context,it is crucial to understand whether and how algorithmic control influences the customer-directed deviant behavior of platform workers.However,existing research has not directly addressed this issue,making it difficult to reliably predict the relationship between algorithmic control and customer-directed deviant behavior,or to provide effective guidance for management practices.Therefore,clarifying the impact of algorithmic control on customer-directed deviant behavior and exploring the complex relationships and mechanisms involved are of significant theoretical and practical importance.This study posits that the job demands-resources(JD-R)model provides a theoretical basis for deeply explaining the complex relationship between algorithmic control and platform workers'customer-directed deviant behavior.The JD-R model suggests that job demands and job resources influence employee behavior through both depletion and gain pathways.Specific resources can act as"moderators,"buffering the negative effects of job demands and enhancing the positive effects of other job resources.Algorithmic control includes strict monitoring and rigorous assessments as demands but also provides intelligent guidance and real-time feedback as resources,making it a management practice that embodies both job resources and job demands.Based on this,the study introduces emotional exhaustion and goal commitment as parallel mediators and considers servant leadership as the boundary condition.By constructing a moderated competition intermediary model,the study aims to reveal the double-edged sword effect of algorithmic control on platform workers'customer-directed deviant behavior and its underlying mechanisms.To validate the theoretical model of this study,a diary method was employed to track the activities of 112 platform workers over 7 consecutive days,resulting in 720 valid data points.Multi-level confirmatory factor analysis and path analysis were conducted to analyze the data.The results show that algorithmic control,on one hand,increases platform workers'customer-directed deviant behavior by exacerbating their emotional exhaustion.On the other hand,it reduces such behavior by enhancing their goal commitment.Additionally,servant leadership can not only weaken the promoting effect of algorithmic control on platform workers'customer-directed deviant behavior through emotional exhaustion,but also strengthen the inhibitory effect of algorithmic control on such behavior through goal commitment.This study adopts a dialectical perspective to explore the impact mechanism and boundary conditions of algorithmic control on customer-directed deviant behavior among platform workers.It reconciles conflicting viewpoints on the advantages and disadvantages of algorithmic control,thereby uncovering its multifaceted effects.The study also enhances the understanding of the antecedents and formation mechanisms of customer-directed deviant behavior within this context.Furthermore,it examines how the interplay between algorithmic control and human management practices influences platform workers'work psychology and behavioral performance.The findings offer valuable insights for platform companies aiming to effectively develop,implement,and optimize algorithmic management strategies to manage customer-directed deviant behavior among their workforce.
algorithmic controlcustomer-directed deviant behaviorservant leadershipjob demands-resources model
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