“Good Pressure, Bad Pressure?”The Double-Edged Sword Effect of Algorithmic Regulatory Pressure on Service Performance

Subjects: Psychology >> Management Psychology submitted time 2024-06-24

Gao Xueyuan Zhang Zhipeng Xie Baoguo Long Lirong Yin Kui

Abstract： Gig workers generally face algorithmic regulatory pressure, specifically referring to the pressure exerted ongigworkers through a series of algorithmic management practices by the platform such as automatic task allocation, real-time regulatory guidance, and continuous tracking and evaluation. The platform expects to improve the serviceperformance of gig workers through algorithmic regulatory pressure, but is the outcome really as expectedbytheplatform? To answer this question, based on the Job Demand-Resource (JD-R) model, this paper deeply exploresthe impact and mechanism of algorithmic regulatory pressure on the service performance of gig workers. TheJD-Rtheory divides job characteristics into job demands and job resources, and different types of job demands havedifferent influences on individual behaviors, while the gain of job resources can effectively buffer the negativeeffects brought by job demands. Based on this, we hypothesize that algorithmic regulatory pressure, as a typeof jobdemand in the gig context, has dual attributes and will have both positive and negative impacts on serviceperformance by stimulating different job reconfiguration behaviors of gig workers. Especially when two resources, algorithmic transparency, and online community support, are supplemented, its double-edged sword effect becomesmore obvious. We conducted three studies to test our hypotheses. Considering that the gig works’ regulation pressure is anemerging concept, we first used an exploratory interview (Study 1). Fifteen ride-hailing drivers fromDiDi and12takeaway riders from Meituan were involved in the first phase of the in-depth interview. Then we used anopen-ended questionnaire to collect more data. Study 1 finally got answers from 100 participants, which we usedtoexplore the relationship between the variables. In Study 2, to examine the causal relationship between gigworkers’regulation pressure and job crafting behaviors, a scenario-based experiment was conducted. The participants (481takeaway riders) were recruited from an online survey platform (Credamo) and randomly assigned to one of thetwo scenarios (gig workers’ regulation pressure: high vs. medium). They reported their demographics, readthescenario, and provided responses to manipulation checks and questions regarding job-crafting behaviors. Study3involved a three-wave multi-source survey to test the proposed model and objective statistics of customerperformance were used. Each survey wave was separated by a four-week interval. The participants (450ride-hailing drivers) were recruited from a ride-hailing platform. At Time 1, drivers assessed their demographics, regulation pressure, time pressure, alienation pressure, physical & mental pressure, proactive personality, algorithmic transparency, and online community support. Four weeks later at Time 2, they assessed theirapproaching job crafting and avoidance job crafting. Finally, four weeks later at Time 3, we got the divers’serviceperformance which was automatically calculated by the algorithm. Finally, the data comprised 350 drivers. In study 1, we used Nvivo to code the interviews. In Studies 2 and 3, we used SPSS and Mplus to apply ANOVAanalysis, linear regression, confirmatory factor analysis, and Bayesian estimation to analyze the data. Resultsshowed the double-edged sword effect of gig workers’ regulation pressure on service performance. Specifically, regulation pressure has a positive impact on service performance through approach job crafting behavior andhasanegative impact on service performance through avoidance job crafting behavior. These relationships werestrengthened by algorithmic transparency and online community support. This study has the following theoretical contributions. Firstly, this study deepens the understanding of the effect ofalgorithmic regulatory pressure in the gig context. Algorithmic regulatory pressure combines the dual attributesofhindering job demands and challenging job demands and has a double-edged sword effect on service performance. This finding is conducive to viewing the effect of algorithmic regulatory pressure dialectically and enriches thetheoretical framework of existing research on job stress. Secondly, based on the JD-R model, this paper revealsthe“black box” of the double-edged sword effect of algorithmic regulatory pressure on service performance anddiscloses the mediating role of job crafting between the dualistic and integrated algorithmic regulatory pressureandservice performance, expanding the application context and verification effectiveness of the JD-Rmodel. Thirdly, by deeply exploring the moderating role of algorithmic transparency and online community support, it reveals thegatekeeping role that the two key resources play in exerting the “good” and “bad” effects of algorithmic regulatorypressure. Finally, this paper expands the category of job resources in the JD-R model to “technical resources”and“social resources”, enriching the types of job resources in the JD-R model.

Peer Review Status:Awaiting Review

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