关键词: 零工工作者, 顾客不当行为, 算法管理, 压力应对, 人工智能干预

Abstract: With the increasing use of algorithmic management in the gig economy, customers are no longer merely service recipients but are increasingly empowered by algorithms. Through rating systems, complaint mechanisms, and platform-mediated feedback, customers can influence gig workers’ task opportunities, income, reputation, and occupational sustainability. In this context, customer mistreatment becomes embedded in platform rules and amplified by algorithm-enabled customer empowerment. Existing research has examined how platforms control gig workers through algorithms, but has paid insufficient attention to how algorithms empower customers and reshape gig workers’ coping with customer mistreatment. Drawing on cognitive appraisal theory, this study develops an integrative framework to examine the dynamic mechanisms and interventions in gig workers’ coping with customer mistreatment. It addresses three questions: the forms that customer mistreatment takes when customers are empowered by algorithms and how it can be measured; how customer mistreatment affects long-term occupational outcomes through cognitive appraisal, coping responses, and emotional labor; and whether AI-mediated responsive forgiveness intervention can support post-event adjustment and recovery.
First, this study reconceptualizes customer mistreatment in the algorithmic management context and develops a measurement instrument tailored to gig work. Prior research has focused mainly on traditional service settings, where customer mistreatment typically appears as direct verbal abuse, threats, disrespect, or harassment. However, when customers are empowered by algorithms, mistreatment may become more indirect, concealed, and instrumental. Customers may exploit gig workers’ dependence on ratings, threaten negative reviews, manipulate evaluation systems, abuse complaint channels, or impose additional service demands through platform rules. Such mistreatment may occur before, during, and after task completion, and its effects may be intensified when customer feedback is converted by algorithms into future opportunities, income, and platform status. Existing measures may therefore fail to capture the distinctive forms of mistreatment produced by algorithm-enabled customer empowerment. To address this limitation, this study identifies the dimensions of customer mistreatment through in-depth interviews, textual analysis, and topic modeling, and develops a gig-specific measurement instrument. This effort clarifies the conceptual boundaries of customer mistreatment in the gig economy and provides a foundation for research on gig workers’ coping processes.
Second, this study constructs a dynamic, cross-temporal mechanism model to explain how gig workers cope with customer mistreatment and how such coping shapes occupational outcomes. Existing studies often portray gig workers as passive objects of algorithmic control, while overlooking their active cognitive appraisal and coping responses. Based on cognitive appraisal theory, this study argues that coping depends on how gig workers evaluate the mistreatment event. When customer mistreatment is appraised as a challenge, gig workers may adopt problem-focused coping responses, such as adjusting communication strategies or managing customer expectations. When it is appraised as a hindrance, they may rely more on emotion-focused coping, avoidance, defensive responses, or withdrawal-oriented strategies. These coping responses may accumulate over time and develop into stable emotional labor patterns. In particular, this study proposes deep acting and surface acting as key mediating mechanisms linking customer mistreatment to occupational burnout, perceived employability, and exit intention. The model moves beyond short-term stress reactions and highlights how repeated exposure to customer mistreatment may influence occupational sustainability. It further examines personal, platform, and social resources as moderators, explaining why some gig workers can sustain adaptive coping whereas others become vulnerable to exhaustion and withdrawal.
Third, this study proposes AI-mediated responsive forgiveness intervention as an innovative approach for supporting gig workers’ coping with customer mistreatment. Platform technologies are often discussed as instruments of surveillance, control, and performance discipline. In contrast, this study emphasizes that AI can also empower workers and support psychological recovery. Because gig workers often lack formal organizational support, they may have limited access to psychological resources after customer mistreatment. By integrating forgiveness intervention theory with AI-mediated conversational support, this study proposes that AI can guide gig workers to reinterpret mistreatment events, reduce rumination, regulate negative emotions, and restore adaptive coping. The intervention is “responsive” because it responds to gig workers’ specific mistreatment experiences and post-event adjustment needs rather than offering generic psychological advice. This framework extends forgiveness intervention theory to the gig economy and provides a new direction for AI-enabled occupational mental health support.
In summary, this study extends customer mistreatment theory by revealing how algorithm-enabled customer empowerment generates new forms of customer mistreatment in gig work. It advances cognitive appraisal and coping research by linking customer mistreatment, cognitive appraisal, coping responses, emotional labor, and occupational outcomes in a dynamic framework. It also introduces AI-mediated responsive forgiveness intervention as a worker-centered approach, shifting the role of technology from labor control to worker empowerment. These contributions provide foundations for understanding gig workers’ coping with customer mistreatment and for building more human-centered platform governance.

Key words: gig workers, customer mistreatment, algorithmic management, stress coping, AI intervention