Participants demographics.

With the advance of data technologies, gig platforms have applied data and algorithms to their management and put more stringent requirements on gig workers through algorithmic management. Gig workers might perform destructive deviant behavior when coping with algorithmic management. It is meaningful to examine how the algorithmic management applied to gig platforms could lead to gig workers’ destructive deviant behavior. Based on the challenge–hindrance framework, we developed a research model and validated it with survey data collected from 423 food delivery riders. We employed multi-level linear regression analysis in data analysis and found that perceived algorithmic management was appraised as both a hindrance and a challenge. As a hindrance, it elicits working/family deviant behavior; as a challenge, it helps reduce working/family deviant behavior. Regulatory focus (a prevention focus vs. a promotion focus) moderates the effect of perceived algorithmic management on stress appraisals (hindrance appraisals vs. challenge appraisals). This study explains algorithmic management’s impact on gig workers’ destructive deviant behavior through the appraisal of algorithmic management as both a challenge and a hindrance. It also provides practical advice to gig platforms, gig workers and policymakers on how to balance the challenge and hindrance roles of algorithmic management in gig work.

随着数据技术的演进，零工平台 (gig platforms) 已将数据与算法应用于自身管理，并通过算法管理对零工 (gig workers) 提出了更为严苛的要求。零工在应对算法管理的过程中，可能会产生破坏性越轨行为，因此探究零工平台的算法管理如何引发零工的破坏性越轨行为，具有重要的研究价值。本研究基于挑战-阻碍框架 (challenge–hindrance framework) 构建了研究模型，并依托从423名外卖骑手处收集的调研数据对模型进行了验证。数据分析环节采用多层线性回归分析 (multi-level linear regression analysis) 方法，结果显示，感知到的算法管理同时被评价为阻碍性压力源与挑战性压力源：作为阻碍性压力源，它会引发工作越轨与家庭越轨行为；而作为挑战性压力源，它则有助于抑制此类越轨行为。调节定向 (regulatory focus，包含防御型定向与进取型定向) 会在感知算法管理对压力评价 (阻碍性评价与挑战性评价) 的影响中起到调节作用。本研究通过将算法管理的评价同时划分为阻碍性与挑战性两个维度，阐释了算法管理对零工破坏性越轨行为的作用机制，同时为零工平台、零工群体以及政策制定者提供了实践建议，以帮助其在零工工作中平衡算法管理的阻碍性与挑战性作用。