| With the deep integration of technologies such as artificial intelligence,big data,cloud computing,and different fields such as transportation and logistics services,the gig economy relying on online platforms is growing rapidly and rising.The rise of the gig economy has not only spawned a series of new industries,but also disrupted the operating models of some traditional industries,thereby creating a large number of employment opportunities.The gig economy has become a catalyst for China’s new economic growth and employment model transformation,attracting high attention from academia and practitioners.Especially in recent years,due to the impact of the COVID-19 pandemic,physical service workers represented by food delivery riders and ride-hailing drivers have played an important role in ensuring the normal operation of cities and maintaining social stability.However,due to the service nature of gig work,gig workers are inevitably subject to inappropriate behavior from customers,because no matter what measures individuals take,irrational and inappropriate customers always exist.Especially in the context of gig work,the platform transfers the performance evaluation right of gig workers to customers through an algorithm-based customer rating system,giving customers full supervisory and evaluation rights over gig workers.As customers directly hold the performance evaluation power of gig workers,some customers may abuse their power and cause losses to gig workers.Therefore,in the context of gig work,the negative impact of inappropriate customer behavior on gig workers is becoming increasingly prominent.However,the impact of customer behavior on gig workers has not been fully researched in the case where customers are given the right to directly manage and supervise employees.Previous research on customer misbehavior has mostly been based on traditional service organizations,while the gig work context presents significant differences in terms of employment relationships,work methods,performance evaluation,and management.Therefore,in the context of gig work,the connotation and mechanism of customer misbehavior in traditional service industries need to be re-examined.At the same time,existing research mainly regards employee responses to customer misbehavior from a negative perspective.However,customer misbehavior as a negative feedback of service failure not only brings financial losses and negative emotions to gig workers,but also provides them with an opportunity for reflection and learning from failure.This also means that the impact mechanism of customer misbehavior is more complex and needs to be explained from multiple perspectives.Therefore,this study focuses on customer misbehavior in the context of gig work,aiming to answer questions such as: What are the nature and manifestations of customer misbehavior in gig work contexts? Are the existing research findings and theoretical paradigms on customer misbehavior applicable to gig work situations?Do gig workers employ proactive coping strategies when faced with customer misbehavior,and what are the specific mechanisms involved?To answer the research questions mentioned above,this study has developed a research framework based on a thorough understanding of existing literature.Specifically,this study first conducted topic analysis on a large amount of textual data left by gig workers on social media to clarify the topic structure of the data,and then further cleaned the data as a source for case studies.Secondly,through case-rooted analysis,this study examined the types of customer misbehavior in the new context and summarized the mechanisms of customer misbehavior on gig workers.Finally,based on the case analysis,this study further constructed the overall model of the research through theoretical deduction using experiential learning theory,resource conservation theory,and attribution theory.The results of the study show that:(1)the interaction between customers and gig workers is a hot topic that gig workers discuss through online communities;(2)the types of customer misbehavior encountered by gig workers can be divided into three categories: rude behavior,violation of transaction norms,and malicious negative feedback;(3)customer misbehavior affects service disruption through a chain-mediated effect of financial loss and emotional exhaustion;(4)customer misbehavior can also affect learning from failure through a chain-mediated effect of financial loss and selfreflection;(5)the perceived fairness of the platform can effectively regulate the chain mediating relationship between economic loss and emotional exhaustion in customer misbehavior and service disruption;(6)locus of control can effectively regulate the relationship between financial loss and self-reflection.This study explores the mechanism by which customer misbehavior affects service sabotage by gig workers through a combination of text-based case analysis and quantitative research.The main contributions are as follows: First,from the perspective of customers,this study investigates the impact of gig work changes on the emotional experience and behavior of gig workers,which helps scholars gain a more comprehensive understanding of the impact of customer factors on individuals in gig work contexts and enriches organizational behavior research in gig work contexts.Second,this study summarizes the types of customer misbehavior in gig worker contexts and proposes that such misbehavior can facilitate Learning from failure from an experiential learning perspective,which is a useful addition to existing research on customer misbehavior.Third,based on attribution theory and fairness theory,this study explores the boundary conditions of the impact of customer misbehavior on service sabotage by gig workers,which not only enriches the application scope of relevant theories but also provides insights into how to reduce the negative impact of customer misbehavior from a theoretical perspective.Finally,this study innovatively uses a large amount of text data retained by gig workers on online communities for text-based topic and case analysis,which helps improve the reliability of research results. |
