Between Proactive and Reactive Coping: How Food Delivery Workers Cope With Algorithmic Management Threats
本研究用Q方法识别出四类外卖骑手应对算法管理威胁的策略，提出主动应对促进良性循环、被动应对导致恶性循环的动态模型，对理解零工工人能动性有参考价值。
This study explores how gig workers in the food delivery sector cope with algorithmic management threats. Algorithmic management involves using learning algorithms to manage and control workers in online labour platforms. Although it offers opportunities for platforms, algorithmic management may present threats for workers including anxiety, burnout, and isolation. While focusing on resistance in the context of algoactivism, limited attention has been paid to how workers perceive algorithmic management threats and the emotion-focused and problem-focused coping behaviours they employ to cope with them. Using Q-methodology, the research identified four types of workers displaying unique coping strategies: the Empowered Collectivist fosters resilience through collective meaning-making and emotional support; the Savvy Opportunist leverages technical literacy; the Isolated Denier struggles with opacity and isolation; and the Anxious Conspiracist is marked by over-adapting and conspiracy theorizing. Building on this typology, the study proposes a dynamic coping model in which proactive coping strategies can support a virtuous cycle of positive reappraisal, increased agency, and resilience.In contrast, reactive coping strategies may contribute to a vicious cycle of negative reappraisal, emotional exhaustion, and disengagement. The study refines coping theory in technology mediated work, contributing to a nuanced understanding of algoactivism and redefining worker agency under algorithmic management.