Research On The "Double-Edged Sword" Effect Of Perceived Algorithmic Control Of Gig Workers On Employee Service Sabotage

The vigorous development of digital technology has given birth to emerging economic forms such as sharing economy and platform economy,and the "gig workers" in the new era have emerged from this.For gig workers in the service industry,customer evaluation is an important factor that affects workers’ compensation and ratings.However,service sabotage occurs frequently.For instance,hitchhiker drivers deliberately detour to harm the interests of passengers and abuse customers in verbal;takeaway riders randomly place meals,resulting in lost,or damage to goods,spit in meals.It is urgent to explore the influencing factors and mechanisms of gig workers’ service sabotage.Previous studies have mainly focused on the influencing factors of employees’ service sabotage in traditional industries such as hotels,medical care and express delivery,and less attention has been paid to the service sabotage phenomenon in the emerging service industry of platform gig-job economy,and few studies have explored the influence of platform organizational factors on gig workers’ service sabotage.In terms of organization,algorithms are seen as virtual supervisors playing a primary management role over workers,and their direct influence on workers’ attitudes and behaviors;therefore,this paper argues that algorithmic control may be an important cause of employee service sabotage.In addition,in the current context where platforms are increasingly focusing on the establishment of base and middle management positions and the training of workers’ professional management knowledge,human supervisors other than algorithms will play an increasingly important role for gig workers.To this end,this study empirically tests the mechanism and boundary conditions of gig workers’ service sabotage influenced by perceived algorithmic control based on the job requirement-resource model.The main innovations of the study are:(1)to expand the study of service sabotage in the emerging service industry and to explore the influence of algorithmic control on gig workers’ service sabotage for the first time based on empirical evidence;(2)to explore the double-edged mechanism of the influence of perceived algorithmic control on service sabotage based on the job requirement-resource model,which provides a new theoretical perspective to explore the relationship between them;(3)Introducing the boundary condition of human leaders outside the platform algorithm,a more comprehensive and practice-guided research model is constructed than previous studies.Based on the classification of platform types by the World Labor Organization,and considering the availability and relevance of the sample,this paper locates the study on take-away riders and online taxi drivers,who perform manual labor on digital platforms based on geographical location.Through a questionnaire survey of 316 riders and drivers,we found that:(1)gig workers’ perceived algorithmic control can inhibit their service sabotage behaviors both by motivating their work engagement and by inducing their self-loss,which in turn can exacerbate their service sabotage behaviors;(2)servant leaders can moderate the positive relationship between perceived algorithmic control and work engagement,self-loss,and the indirect effect of perceived algorithmic control on service sabotage through work engagement and self-loss.This study not only extends the research on the factors influencing service sabotage across industries and organizations,but also advances the empirical research on algorithmic control in the field of organizational behavior,and ultimately provides feasible suggestions for how platforms can optimize algorithms and develop servant leaders.