1. Introduction
In recent years, the rapid growth of online shopping has significantly increased the global demand for urban delivery, posing substantial challenges to traditional logistics systems [1]. As a flexible and efficient delivery model, crowdsourced delivery has effectively alleviated logistics pressure and attracted many gig workers into the industry [2,3]. Gig workers are individuals engaged in temporary, flexible, and platform-based task employment. Traditional labor contracts do not bind them; instead they perform fragmented, piece-rate tasks through digital platforms such as food delivery, ride hailing, and domestic services [2]. In the crowdsourced delivery model, gig workers are a critical link in direct consumer interactions, influencing delivery service quality and shaping the platform’s brand image [4]. Their job performance is crucial to the platform’s sustainable development [5]. However, the absence of a formal employment relationship between gig workers and platforms places them in a vulnerable position when dealing with customers, as they lack stable labor protections and frequently experience unfair treatment from customers [3,6]. Customer injustice is a significant factor affecting gig workers’ job experiences. Studies indicate that customer injustice can trigger negative emotions in employees, leading to counterproductive work behavior (CWB) [7]. CWB refers to intentional employee behaviors detrimental to the organization and its stakeholders, such as deliberately violating work rules, providing subpar customer service, or even engaging in confrontational actions [8]. In the crowdsourced delivery industry, the high-frequency interactions between gig workers and customers make customer-directed CWB particularly prominent [9], as such work involves real-time feedback and customer evaluations, where employees’ inappropriate behaviors can rapidly impact customer experience and damage the platform’s reputation [10].
Although scholarly research on gig workers has increased in recent years, it has primarily focused on the effects of platform fairness, skills training, and social security on their positive work behavior [5,11]. With the rapid growth of the gig workforce [12], negative interactions between gig workers and customers have increased significantly [13]. Therefore, scholars’ research focus has expanded beyond gig workers’ positive attitudes and behaviors to include the study of negative behaviors. For example, destructive behaviors exhibited by gig workers in response to adverse treatment by customers [3]. Furthermore, more scholars have focused on CWB directed at external organizational stakeholders, such as customers [14,15,16]. Nevertheless, research on this topic remains relatively scarce, particularly regarding the antecedents of customer-directed CWB among gig workers, which have yet to be addressed. However, as a high-contact service industry heavily relying on human resources to enhance service quality and organizational performance, crowdsourced delivery is highly influenced by employee behavior [15]. In this context, customer-directed CWB may undermine individual service quality and have profound negative consequences for the organization and its stakeholders [8,15]. Therefore, conducting an in-depth investigation into the mechanisms underlying customer-directed CWB is essential to exploring practical strategies for reducing such behaviors and providing theoretical support for intervention measures.
This study delves into the occurrence mechanism of customer-directed CWB from the perspective of customer injustice to fill the research gap and provide theoretical and practical insights for the relevant field. Specifically, this study aims to investigate the following: (1) How are customer injustice, negative emotions, perceived organizational injustice, and customer commitment related to customer-directed CWB? (2) Do negative emotions, perceived organizational injustice, and customer commitment mediate the relationship between customer injustice and customer-directed CWB? (3) Do the gig worker’s perceived job demands moderate the relationship between negative emotions, perceived organizational injustice, customer commitment, customer injustice, and customer-directed CWB?
We developed a more comprehensive and novel research model to achieve the objectives. First, we apply the stressor–emotion model (S-EM) to examine how gig workers’ perceptions of organizational injustice, negative emotions, and customer-directed CWB are triggered by customer injustice. The S-EM is frequently used to explain the formation of CWB among employees [17]. Still, it does not account for the role of the employee–customer relationship, which is often central to service work and crucial for organizational success [18]. This study adopts a social exchange theory (SET) perspective to address this limitation and examine gig workers’ affective commitment [19]. It incorporates customer commitment as a variable to investigate its impact on gig workers’ customer-directed CWB. This study focuses on a phenomenon observed in China and conducts a survey targeting the Chinese gig worker population. The respondents were recruited from major crowdsourced delivery platforms in China, including Meituan, Ele.me, and Dada. These platforms are key players in China’s food delivery and on-demand logistics sectors, providing a representative sample that reflects the working conditions and customer interactions of gig workers in China’s platform economy. After data collection, we employed structural equation modeling to conduct an empirical analysis, examining how customer injustice influences gig workers’ customer-directed CWB.
This study makes significant contributions in several aspects. First, this study is the first to systematically examine customer-directed CWB among gig workers in crowdsourced delivery, filling an important but previously overlooked gap in the existing literature. Second, this study develops a second-order model of negative emotions, which enhances the precision of measuring emotional responses in the context of gig work. Third, we construct an innovative theoretical framework that integrates the S-EM [17] and SET [20]. This integrated approach provides a novel perspective for understanding the mechanisms underlying customer-directed CWB. Expressly, our model confirms the direct impact of customer injustice on gig workers’ CWBs and reveals the chain-mediating effects of negative emotions, perceived organizational injustice, and customer commitment, thereby expanding the theoretical boundaries of CWB research. Fourth, this study uncovers the unique moderating role of job demands. While previous studies have predominantly suggested that high job demands exacerbate negative employee behaviors, our findings challenge this assumption. In the gig economy, we demonstrate that a positive perception of job demands may instead trigger proactive coping strategies, ultimately reducing customer-directed CWB. This discovery offers new directions for future research on the role of job demands in non-standard employment contexts. Finally, our study provides valuable managerial implications at the practical level. The findings offer scientific insights for crowdsourced delivery platforms, enabling operational managers to optimize gig worker management and customer relationship strategies, thereby fostering a more sustainable platform ecosystem.
2. Literature Review
2.1. Customer-Directed CWB Among Gig Workers in Crowdsourced Delivery
Crowdsourced delivery platforms are an emerging logistics service model that leverages the Internet and mobile technology to provide flexible and efficient delivery services [5]. One key characteristic of this logistics service model is that gig workers carry out delivery tasks. However, the relationship between gig workers and platforms is non-binding contractual, lacking the foundation for long-term cooperation [2] and is, thus, characterized by instability [21]. In this context, there is a potential trust issue between customers and gig workers, and customers may engage in unfair behavior, which is one of the most likely precursors to customer-directed CWB among gig workers [8]. Unfair customer behavior refers to actions in which customers violate basic norms of respect and courtesy, engaging in behavior that lacks legitimacy and reasonableness [22]. This includes personal attacks, prejudiced comments, and other low-quality interpersonal interactions [23]. Such disrespect undermines the dignity of employees [24], leading to decreased job performance and increased destructive and impolite behaviors [25,26]. Employee behaviors that violate legitimate organizational interests and target customers can be classified as customer-directed CWB.
CWB is a broad term that refers to employees’ intentional actions to harm the organization and its stakeholders [17]. CWBs may target the employing organization, internal individuals (such as subordinates, supervisors, and coworkers), or external individuals (such as customers) [8]. Studies indicate that antecedents of CWB often include emotional responses to stressful events in the workplace or cognitive evaluations of unfavorable experiences [17]. Customer-directed CWB can be viewed as a retaliatory response to inappropriate customer behavior involving serious violations of commonly accepted norms in the consumption context [15]. Employee behavior is influenced by motivational factors (e.g., compensation and benefits, career development opportunities, work environment, culture, and organizational support) and de-motivational factors (e.g., poor management, lack of advancement, unsupportive environments, and absence of recognition) [27]. In high-contact service settings like crowdsourced delivery, customer injustice (e.g., malicious complaints and unreasonable demands) is a key antecedent of customer-directed CWB [15]. Such unfair behaviors exacerbate gig workers’ stress and deteriorate their work environment, increasing the likelihood of negative coping behaviors [27]. Prior studies have shown that employees who experience customer injustice may retaliate by sabotaging the service, harming the customer’s experience and organizational reputation [3,7]. As a result, more organizations are paying attention to and acting against customer incivility to preserve fairness in the work environment [28]. However, in crowdsourced delivery, gig workers are the sole individuals who interact directly with customers, limiting the organization’s ability to intervene. Due to the lack of real-time supervision during delivery, gig workers often respond independently to customer injustice [21], with organizational influence being applied indirectly through post-event evaluations and platform rules. In frequent customer interactions, most gig workers use emotional labor to suppress emotions and maintain service quality [29]. The instability of temporary employment often leads to heightened perceptions of organizational injustice, prompting workers to adopt personalized, rather than organizationally aligned, coping strategies when facing unfair treatment [30]. Given their weak long-term embeddedness with platforms [2,12], gig workers have greater behavioral autonomy and are more likely to express dissatisfaction directly when encountering customer misconduct [31]. This further increases the likelihood that gig workers will direct CWB toward customers, including withdrawal, neglect, or retaliatory actions [8].
2.2. Theoretical Model
This study integrates the S-EM and SET to understand gig workers’ customer-directed CWBs comprehensively. The S-EM emphasizes how customer injustice, as a stressor, influences individuals’ cognition, emotions, and behavior [17]. In contrast, SET focuses on how customer-induced stress disrupts social exchange relationships, affecting individuals’ perceptions of fairness, psychological contracts, and commitment [18,20]. This integrated model addresses the limitations of single-theory approaches, allowing for a more systematic analysis of both individual contextual factors (e.g., negative emotions) and relational factors (e.g., perceptions of injustice and commitment) associated with customer-directed CWB, thereby enhancing predictive accuracy [2,31]. Previous research has demonstrated that these two theoretical perspectives offer complementary explanations [31]. Integrating these two theories provides a more comprehensive analysis of the impact of customer injustice on gig workers’ CWBs. From an individual-level perspective, the S-EM explains how customer injustice elicits negative emotions in gig workers, leading to CWB. From a social relationship perspective, SET reveals how customer injustice undermines gig workers’ perceptions of fairness and disrupts social exchange relationships, thereby reducing their level of commitment and prompting CWB [18]. Therefore, this study adopts the integrated framework to examine how customer injustice influences gig workers’ CWBs through emotional and social exchange mechanisms, providing theoretical support for the management practices of crowdsourcing platforms.
2.2.1. Stressor–Emotion Model
As one of the most prominent frameworks in behavioral ethics, the S-EM [32] posits that stressors in the work environment, such as workload, role conflict, time pressure, and unfair treatment [33], can trigger adverse emotional reactions in employees, including anxiety, anger, depression, and frustration [34]. These reactions, in turn, lead to behavioral outcomes such as decreased job performance, increased conflict with customers, burnout, and resistance to the platform [17]. Existing research suggests that employees may attribute unfair treatment from customers to the organization [35], leading to a perception of organizational injustice. This, in turn, triggers negative emotions and ultimately encourages employees to engage in CWB [9]. Therefore, we can apply the S-EM to examine the response process of gig workers when facing customer injustice, including the resulting perceptions of organizational injustice, negative emotions, and the corresponding customer-directed CWB. This systematic analytical framework helps us better understand the unique behavioral patterns of gig workers.
2.2.2. Social Exchange Theory
Although the S-EM is a critical research perspective, we recognize that frontline workers spend most of their work time interacting with customers, making these social exchange relationships particularly significant for them [18]. Therefore, it is also necessary to examine the impact of these relationships in the gig work environment from the social relations perspective [2].
From an organizational perspective, providing fair conditions for employees enhances gig workers’ organizational commitment [5]. In terms of customer relationships, if customers exhibit unjust behavior, it reduces employees’ commitment to customers and their job performance [18], which can be explained through SET. SET is one of the most influential theories in organizational behavior [20] and provides a crucial foundation for understanding interpersonal relationships in organizational settings. It emphasizes that attitudes and behaviors in the workplace are based on the attitudes and behaviors of others and that only through social exchange can feelings of personal obligation, gratitude, and trust arise. Researchers have widely applied SET to explain the relationships between variables such as affective commitment [19], perceived support [18], and incivility [31]. The reciprocity principle in SET, which involves repaying (positive) or retaliating (negative) actions, is the most well-known social exchange rule [20]. In other words, when employees are treated fairly, they reciprocate by exhibiting favorable attitudes, such as increased commitment to the entity [18]. Conversely, when mistreated, employees are likely to demonstrate a range of negative attitudes and behaviors [31]. Therefore, SET provides a robust theoretical foundation for understanding how gig workers adjust their behavior in response to customer injustice.
2.3. Research Hypothesis
This study takes customer injustice as its starting point. It constructs a research framework based on the S-EM and SET to explore gig workers’ customer-directed CWB formation mechanism systematically. Within the framework of the S-EM, this study examines how customer injustice influences customer-directed CWB through negative emotions and perceived organizational injustice. Additionally, by integrating SET, this study further explores how customer injustice undermines customer commitment, thereby increasing the likelihood of customer-directed CWB. By combining these two theoretical models, this study constructs a structural equation model (as shown in Figure 1) to more comprehensively reveal the pathways through which customer injustice affects gig workers’ customer-directed CWB.
Customer injustice refers to behaviors in which customers violate fundamental standards of respect and etiquette, engaging in unreasonable actions toward employees [23]. This injustice includes various forms of low-quality interpersonal communication and inappropriate behavior between employees and customers, such as verbal attacks, accusations, intentional rule violations, and exploiting company policies for personal gain [22,36]. Customer-directed CWB mainly comes from the employees’ service targets [8]. Previous studies have shown that customer injustice significantly contributes to CWB among employees in traditional service industries [37]. Unlike employees in conventional service industries with stable labor contracts, gig workers on crowdsourced delivery platforms lack fixed employment relationships, enjoy greater autonomy [12], and lack formal organizational norms and regulatory constraints [3]. When confronted with customer injustice, gig workers do not face the same concerns about disciplinary actions as full-time employees [26]. They are more likely to respond accordingly [20]. In the gig economy, customer injustice is one of the most commonly reported work-related issues that gig workers face [6]. Research has shown that customers are the primary trigger of anger and conflict between part-time service workers and clients [38]. Therefore, we propose the following hypothesis:
Customer injustice has a significant positive impact on customer-directed CWB.
Customer injustice has been identified as a stressor [18]. According to the S-EM, such a stressor triggers adverse emotional reactions in employees, such as anger and frustration [17]. As frontline workers on crowdsourced delivery platforms, gig workers serve a diverse clientele from various backgrounds and often experience interpersonal injustice from customers, such as disrespect, rudeness, or incivility [39]. Existing research indicates that customer injustice is associated with negative emotions in employees, such as anger or frustration, and may even provoke retaliatory behavior [40]. Therefore, we propose the following hypothesis:
Customer injustice is positively correlated with negative emotions.
Gig workers use cognitive appraisal when interacting with impolite customers [17], perceiving customer injustice as challenging at work. They may attribute the cause of these challenges to the external organizational work environment [24] and attempt to shift these difficulties onto the organization [35]. For instance, employees may believe that the platform bears greater responsibility in handling customer injustice and should provide necessary support and resources to protect the interests of workers in such cases. They may even believe that compared to the customers’ demands, they deserve better treatment and benefits. Therefore, we propose hypothesis 3:
Customer injustice is positively correlated with perceived organizational injustice.
Gig work possesses a distinct transactional nature [21]. Due to the lack of social and relational ties, customers are the most critical interaction partners in gig work. Workers spend significant time on activities such as confirming orders, delivering goods, and handling customer complaints [5]. We need to consider the impact of unjust customer behaviors from a social and relational perspective [2]. Customer commitment is one of the multiple forms of employee commitment, defined as the psychological bond and level of connection between employees and customers [18]. This study refers to the commitment and attitude of gig workers to provide high-quality service, attend to customer needs, build trust, and be willing to exert extra effort for customers.
SET posits that in addition to exchanges of monetary resources, non-monetary resources, such as love, respect, and service, are also exchanged between relational parties [20]. These relational exchanges are significant for gig workers [18], as they can foster positive attitudes in employees, such as trust, loyalty, and commitment. After perceiving goodwill and benefits, employees are obligated to reciprocate through favorable attitudes, such as commitment. In contrast, when faced with customer injustice, the exchange involves adverse treatment, including but not limited to a decrease in trust, interpersonal conflict, and commitment [41]. Therefore, we propose hypothesis 4:
Customer injustice is negatively correlated with customer commitment.
Stressors can trigger negative emotional responses such as anxiety, frustration, or anger, as reported in numerous studies [36]. Employees may engage in deviant work behaviors to alleviate negative emotions or express dissatisfaction [42]. Moreover, individuals naturally react strongly to potential threat events. Judge and Colquitt (2004) [43] found that interpersonal injustice experienced by employees predicts their self-reported feelings of tension and stress, which, in turn, lead to negative behaviors [44]. Therefore, we propose the following hypotheses:
Negative emotions are positively correlated with customer-directed CWB.
Customer injustice positively influences customer-directed CWB through negative emotions.
In gig work environments, employees also develop subjective perceptions of organizational justice [9]. Employees who perceive fair, respectful treatment and adequate resources from the organization tend to engage in positive, pro-organizational behaviors. Conversely, perceptions of organizational injustice may trigger negative, counterproductive behaviors directed against the organization [45]. Perceived organizational injustice is defined as employees’ belief that they have not received appropriate, fair, and respectful treatment, along with a lack of necessary work resources and recognition [46]. Such negative perceptions can evolve into a range of destructive behavioral responses. Unlike permanent employees, temporary workers are likelier to adopt individualized coping strategies than engage in collective responses when facing injustice [30]. This implies that they are more inclined to express dissatisfaction through individual-level behaviors such as disengagement, shirking responsibilities, or customer-directed CWB. Moreover, perceived organizational injustice essentially constitutes a job stressor [9]. According to the S-EM [17], such stressors may elicit negative emotional responses, such as frustration, anger, and helplessness. These emotions may drive employees to engage in counterproductive behaviors that harm organizational stakeholders or service recipients [39]. Therefore, we propose that perceived organizational injustice affects customer-directed CWB by eliciting negative emotions, with the two variables forming a sequential mediating mechanism. Based on the above theoretical reasoning and empirical findings, we propose the following hypotheses:
Perceived organizational injustice has a significant positive effect on customer-directed CWB.
Perceived organizational injustice has a significant positive effect on negative emotions.
Customer injustice positively affects customer-directed CWB through perceived organizational injustice.
Customer injustice positively affects customer-directed CWB through perceived organizational injustice and negative emotions.
As previously mentioned, customer commitment reflects the psychological bond and level of connection between employees and customers [18]. The level of customer commitment determines employees’ service quality, attentiveness to customer needs, trust establishment, and willingness to make extra efforts for customers. The previous literature has demonstrated that customer commitment positively influences employees’ organizational citizenship behavior; the higher the customer commitment, the more employees are likely to engage in behaviors that exceed expectations to help meet customer needs [18]. Organizational citizenship behavior refers to voluntary actions that benefit the organization, whereas CWB involves intentional actions that harm the organization or its stakeholders [47]. Therefore, we hypothesize that customer commitment is negatively correlated with customer-directed CWB.
According to SET, employees are more inclined to reciprocate by displaying positive work behaviors when treated with respect and fairness [20]. When employees engage in positive social exchanges with clients, they are inclined to exhibit prosocial customer service behaviors and voluntarily perform beyond role requirements to reciprocate fair treatment [48]. However, customer injustice signifies disrespect and malice in interpersonal relationships [25], which may lead employees to deviate from established standards, reduce their commitment to customers, and treat them rudely or indifferently [8]. We anticipate that customer injustice will initially reduce gig workers’ dedication and commitment to clients, increasing CWB. Therefore, we propose the following hypotheses:
Customer commitment has a significant negative impact on customer-directed CWB.
Customer injustice positively influences customer-directed CWB through customer commitment.
Job demands refer to aspects of work or the work environment that potentially cause stress for employees. Job demands are a central construct in the Job Demands–Resources (JD-R) model, referring to the physical or psychological effort required of employees to fulfill their job tasks [49]. In digital platform settings, gig workers face multifaceted and intensive job demands, including a fast work pace, heavy workloads, high physical exertion, psychological strain, and customer-related social stressors [50]. For crowdsourced delivery workers, income is directly tied to the number of orders accepted and the speed of delivery; they often believe that only by investing more time and delivering faster can they increase their earnings [11]. Additionally, customers increasingly demand faster deliveries [51]. The stress induced by these job demands is often subjectively perceived by workers as “high job demands” and may trigger negative emotional and behavioral responses [49]. Although the JD-R model does not directly state that job demands lead to deviant behavior, numerous studies have shown that perceived high job demands often deplete employees’ resource reserves, reduce their positive behaviors [52], and even foster cynical attitudes that increase the likelihood of deviant actions [53]. Furthermore, when employees are already experiencing negative emotions and perceive high job demands, their capacity for emotional regulation may become impaired, increasing the likelihood that they will displace their frustration onto customers, thus leading to customer-directed CWB. Therefore, we propose the following hypothesis:
Job demands significantly amplify the positive impact of negative emotions on customer-directed CWB.
Similarly, when employees perceive organizational injustice—particularly under conditions of high job demands—they are more likely to experience psychological resource depletion and emotional dysregulation. In such “dual-stressor” environments, where organizational injustice and heavy workloads converge, employees’ cognitive control and emotional regulation capacities may be significantly impaired [17]. Job demands encompass not only the task volume and time pressure but also the work pace, physical strain, psychological stress, and customer-induced social challenges [50]. When a lack of organizational support compounds these demands, employees are prone to heightened feelings of anger and helplessness [30], which can increase the likelihood of displacing these emotions through customer-directed CWB [9]. Therefore, we propose the following hypothesis:
Job demands significantly amplify the positive impact of perceived organizational injustice on customer-directed CWB.
Although customer commitment is generally regarded as an essential inhibitory mechanism that reduces employees’ inappropriate behaviors toward customers [18], its protective effect may be weakened under conditions of high job demands. When employees perceive excessive workloads, tight deadlines, or overly demanding customers, they face increased performance pressure and resource conflicts, which may compel them to prioritize task efficiency over maintaining customer relationships [50]. This tendency is particularly pronounced in digital platform-based gig work, where algorithm-driven rating systems, delivery time constraints, and income pressures collectively reinforce an “efficiency-oriented” work logic [2]. As a result, even employees with a high level of customer commitment may place greater emphasis on task completion speed than on emotional responsiveness or service quality, thereby diminishing the inhibitory effect of customer commitment on customer-directed CWB. Based on this reasoning, we propose the following hypothesis:
Job demands significantly weaken the inhibitory effect of customer commitment on customer-directed CWB.
Finally, when employees perceive unfair treatment from customers, they often experience emotional frustration and threats to their identity—such as feeling disrespected, misunderstood, or verbally abused [50]. These perceptions of injustice can trigger strong negative emotional responses, which tend to intensify under conditions of high job demands. Intense task pressure, tightly scheduled deliveries, and platforms’ strict emphasis on time efficiency further reduce employees’ emotional regulation capacity and increase psychological resource depletion [2]. This issue is particularly salient in gig work settings, where employees often lack organizational support or access to emotional coping mechanisms [2]. The combination of frequent customer interactions and real-time performance evaluations makes it more likely that workers will displace their anger onto customers as a form of short-term emotional release. Therefore, under high job demands, the impact of customer injustice is more likely to manifest as customer-directed CWB. Based on this reasoning, we propose the following hypothesis:
Job demands significantly amplify the positive impact of customer injustice on customer-directed CWB.
3. Methodology
3.1. Questionnaire Administration Process
Negative emotions are among the most complex, encompassing feelings such as fear, sadness, guilt, hostility, anger, and shame [34]. Using a single negative emotion scale may make it difficult for respondents to differentiate accurately between the subtle nuances of these emotions, thus affecting the precision of their responses. Therefore, the use of a second-order model has advantages [54]: First, it provides a more concise and interpretable model, separating the variance caused by specific factors from measurement errors, thereby enabling error-free theoretical estimation of particular characteristics. Second, it allows for testing whether the higher-order factor (i.e., negative emotions) explains the relationship patterns among the first-order factors (the different substructures of negative emotions) [55]. Emotions can be categorized into four broad types [56], with low-activation negative emotions such as despondent, dejected, hopeless, and depressed significantly predicting four types of deviant behaviors, including work withdrawal, social avoidance, minor theft, and dissociative silence. These behaviors are relevant in studies of CWB [32]. Therefore, we constructed a second-order model of negative emotions using these four dimensions to improve the model’s accuracy and explanatory power. Confirmatory factor analysis was employed to verify negative emotions’ dimensional structures and examine the relationships between the theoretical constructs.
This study operationalized nine latent constructs using observed measurement items. These measurement items were adapted from existing studies to suit the specific context of crowdsourced logistics platforms, ensuring content validity. Appendix B provides the measurement items and their corresponding source information.
3.2. Survey Administration
This study’s survey was divided into three sections. The first section explains the research background, including the concepts of gig economy delivery workers and customer-oriented CWB, the research objectives, and confidentiality assurances (Appendix A). According to the Privacy Protection Act, this assurance ensures that the data will be used solely for academic purposes, with strict measures to protect participants’ privacy, and encourages gig workers to respond honestly. The second section gathers demographic information. The third section lists the measurement tools for the research variables and asks participants to rate their level of agreement with all items on a seven-point Likert scale, ranging from “1 = Strongly Disagree” to “7 = Strongly Agree”.
We employed a random sampling method to ensure the sample was representative and used the online survey platform Sojump to design and distribute the questionnaire. We posted the survey link in several QQ and WeChat (widely used social applications in China) chat groups of gig economy logistics workers, inviting participants to participate in our study and offering a cash incentive of RMB 5 to all participants who completed the survey. Initially, we conducted a pilot test with 70 questionnaires to identify errors and make necessary adjustments, which were limited to minor formatting issues. The data collection period spanned from 29 April 2024 to 16 May 2024, lasting 18 days, during which 500 questionnaires were collected. To ensure data quality, we established a straight-lining detection criterion. If a respondent selected the same response option for all Likert-scale items or chose identical responses across multiple consecutive items measuring different constructs, the response was considered inattentive or careless. Based on this screening process, 385 valid questionnaires were retained for formal data analysis. The valid response rate was 77%.
3.3. Common Method Bias Test
Using self-report questionnaires for data collection may lead to distortions or overestimating relationships between variables, potentially compromising the validity of the study’s results. We first conducted Harman’s single-factor test on the data to assess this potential bias. The test results revealed that the initial component accounted for 37.429% of the total variance, below the 50% threshold, indicating the absence of common method bias in the study sample [57]. Furthermore, this study also employed the unmeasured latent method construct (ULMC) technique to test for common method bias. The original model was compared with the common method factor model, and the fit indices of both models showed no significant differences, confirming that the common method bias is sufficiently tiny [58]. In summary, this study has no significant common method bias issue.
3.4. Normality Test
The normal distribution function describes the symmetrical distribution of individuals around the mean. Testing for normality is a critical prerequisite for conducting structural equation modeling. In this study, skewness and kurtosis statistics were used to examine the normality of the measurement items. According to the criteria proposed by Kline (2023), the absolute skewness values were all below 3, and the kurtosis values were within 8, indicating that the data met the assumption of approximate normality [59]. All measurement variables in this study met these criteria, and the specific statistics are presented in the same table as the confirmatory factor analysis results, indicating that the variables exhibit good statistical normality. Approximate normality enhances the efficiency of parameter estimation and the robustness of model fit, mainly when using the maximum likelihood estimation method [59]. Therefore, the data demonstrate distributional characteristics suitable for structural equation modeling, providing a solid foundation for subsequent path analysis and hypothesis testing.
3.5. Demographic Characteristics
The demographic breakdown (see Table 1) reveals that male respondents (82.6%) significantly outnumber female respondents (17.4%), which aligns with real-world trends, as workers in crowdsourced logistics platforms are more likely to be male and predominantly from younger age groups [60]. Most respondents are between 21 and 40, representing 67.1% of the sample. Additionally, 68.1% of respondents hold a diploma, either vocational or high school education. Furthermore, 46% of respondents earn between RMB 5001 and 10,000 per month, while 32.5% earn between RMB 10,001 and 20,000. The most significant respondents (34.8%) have worked on crowdsourced logistics platforms for 1 to 2 years. These findings largely align with the characteristics of delivery workers presented in “Riders’ World: A Social Survey of New Occupational Groups” by the Institute of Sociology, Chinese Academy of Social Sciences: 44.3% of delivery workers earn between RMB 4000 and 5999 per month, nearly half have a high school or vocational education, and the majority are from the post-1990 generation (comprising over 50%), followed by those from the post-1980 generation (accounting for nearly 40%).
4. Results and Discussion
4.1. Confirmatory Factor Analysis
Several key indicators, such as factor loadings, composite reliability (CR), average variance extracted (AVE), and Cronbach’s α, were used to assess the validity and reliability of the measurement scales [61]. As shown in Table 2, the factor loadings of all constructs were greater than 0.60, the CR values exceeded 0.70, and the AVE for each construct was above the recommended threshold of 0.50 [62], indicating good convergent validity and composite reliability for the model. Regarding discriminant validity, the square roots of the AVE values for each construct were higher than the squared correlations between the constructs (as shown in Table 3). Therefore, the discriminant validity of the measurement model was also supported [63,64]. We tested the structural model after ensuring the overall measurement model was valid and acceptable. The results showed that all fit indices (CMIN/DF = 2.304, CFI = 0.929, TLI = 0.92, IFI = 0.93, SRMR = 0.047, and RMSEA = 0.058) were within the acceptable range. Furthermore, we used the variance inflation factor (VIF) to assess the collinearity among the formative measurement items. All VIF values were below the critical threshold of 3, indicating no significant collinearity [65,66]. Finally, the R2 value for customer-directed CWB was 0.622, indicating that variables such as customer injustice, negative emotions, perceived organizational injustice, and customer commitment significantly explained its variance. Thus, it can be used for subsequent analyses of related structural models.
4.2. Structural Model Analysis
After confirming the reliability and validity of the model, a structural equation modeling analysis was conducted. As shown in Figure 2 and Table 4, the model fit was good (CMIN/DF = 2.409, RMSEA = 0.061, SRMR = 0.0497, IFI = 0.934, TLI = 0.924, CFI = 0.933, and NFI = 0.892), with all indices within the acceptable range. Additionally, the p-values for all hypothesized relationships between the variables were significant, supporting the hypotheses. The specific results are as follows: customer injustice had a significant positive effect on perceived organizational injustice (0.471), negative emotions (0.266), and customer-directed CWB (0.280); customer injustice had a significant negative impact on customer commitment (−0.258); perceived organizational injustice had a significant positive effect on negative emotions (0.666) and customer-directed CWB (0.273); negative emotions had a significant positive impact on customer-directed CWB (0.361); and customer commitment had a significant adverse effect on customer-directed CWB (−0.125).
4.3. Analysis of Mediation Effects
We employed structural equation modeling combined with the Bootstrapping method to test for mediation effects. The detailed examination results (see Table 5) indicate that the p-values corresponding to hypotheses H5-1, H6-2, H6-3, and H7-1 were all less than 0.05, thereby confirming the validity of the research hypotheses. The mediation analysis results indicate that customer injustice significantly influenced customer-directed CWB through negative emotions, perceived organizational injustice, and customer commitment. Specifically, negative emotions (mediation effect = 0.098; 95% CI: 0.05–0.21; mediation effect proportion = 26%), perceived organizational injustice (mediation effect = 0.131; 95% CI: 0.025–0.316; mediation effect proportion = 34.7%), and customer commitment (mediation effect = 0.033; 95% CI: 0.003–0.069; mediation effect proportion = 8.7%) each constituted distinct mediating effects. Additionally, perceived organizational injustice and negative emotions formed a serial mediating effect (mediation effect = 0.115; 95% CI: 0.01–0.208; mediation effect proportion = 30.6%).
4.4. Moderation Effect Analysis
This study employed PROCESS Model 1 with the Bootstrap method to test the moderation effect, using a ±1 standard deviation approach and a 95% confidence interval. The analysis results (Table 6) indicate that the regression coefficients of the interaction terms are all significant: the interaction term between negative emotions and job demands (β = −0.227, t = −5.345, and p < 0.001), the interaction term between perceived organizational injustice and job demands (β = −0.314, t = −7.215, and p < 0.001), the interaction term between customer commitment and job demands (β = −0.256, t = −3.952, and p < 0.001), and the interaction term between customer injustice and job demands (β = −0.082, t = −2, and p < 0.05). These findings suggest that job demands significantly moderate the effects of negative emotions, perceived organizational injustice, customer commitment, and customer injustice on customer-directed CWB.
A simple slope analysis was conducted to clarify the direction and trend of job demands’ moderating effects on the relationships among variables (Figure 3). The results indicate that job demands significantly negatively moderate the relationships between negative emotions, perceived organizational injustice, and customer injustice with customer-directed CWB, meaning that under high job demands, the positive impact of these factors on customer-directed CWB is weakened. Moreover, job demands significantly positively moderate the negative relationship between customer commitment and customer-directed CWB, indicating that under high job demands, the inhibitory effect of customer commitment on customer-directed CWB is further strengthened. However, these empirical findings contradict hypotheses H8–H11.
4.5. Discussion of Results
The direct effects indicate that customer injustice significantly impacts perceived organizational injustice, negative emotions, and customer-directed CWB. Additionally, negative emotions significantly positively affect customer-directed CWB, aligning with our predictions. When gig workers experience customer injustice, they are likelier to perceive a lack of organizational support, experience heightened negative emotions, and engage in customer-directed CWB. This result aligns with the theoretical expectations of the S-EM [17], which posits that external stressors, such as customer injustice, trigger negative emotions that further contribute to counterproductive behaviors [7,22,33]. Furthermore, perceived organizational injustice, as another external stressor, induces negative emotions among gig workers and increases their tendency to engage in customer-directed CWB [9] as a response to perceived injustice [20]. Additionally, customer injustice significantly negatively impacts customer commitment, weakening gig workers’ loyalty and commitment to customers [8,18]. Meanwhile, customer commitment has a significant negative effect on customer-directed CWB. When gig workers exhibit a more substantial commitment to their customers, they are more likely to suppress counterproductive behaviors and strive to deliver higher-quality service [18,20].
The mediation analysis results indicate that perceived organizational injustice accounts for the most significant proportion of the total effect (34.7%), meaning that the relationship between customer mistreatment and customer-directed CWB is primarily mediated by gig workers’ perceptions of organizational injustice. When gig workers experience customer injustice, they initially perceive a lack of managerial support and protection, making it difficult to cope with such adversity. Previous studies have also found that employees often attribute customer injustice to workplace conditions and resource allocation, holding the organization accountable [67]. Consequently, employees who experience customer injustice may feel that the organization disregards their contributions and well-being, ultimately leading to customer-directed CWB [24]. Furthermore, negative emotions (despondent, dejected, hopeless, and depressed) are critical in explaining customer-directed CWB [39]. Both customer injustice and perceived organizational injustice, as stressors, positively influence customer-directed CWB through negative emotions. Customer injustice influences gig workers’ customer-directed CWB through a sequential mediation effect involving perceived organizational injustice and negative emotions. Customer commitment partially mediates the relationship between customer injustice and customer-directed CWB. Customer injustice reduces customer commitment, which, in turn, increases customer-directed CWB. This finding aligns with SET, suggesting that when perceiving unfair treatment, gig workers may reduce service engagement or even retaliatory behaviors [18,20].
Moreover, as indicated by the simple slope test (Figure 3), when individuals perceive higher job demands, the facilitative effects of negative emotions, perceived organizational injustice, and customer injustice on customer-directed CWB weaken. In contrast, the inhibitory effect of customer commitment on customer-directed CWB strengthens. In response, we re-evaluated the nature of job demands and recognized that they do not always result in negative consequences [68]. On the contrary, job-related challenges may produce positive effects in certain circumstances, such as enhancing individual motivation or improving adaptability [69]. Previous research has shown that in high job demand environments, individuals may mobilize resources, strengthen their coping abilities, and enhance job performance [70]. This phenomenon is particularly relevant for gig workers. Compared to traditional professionals, gig platforms operate in a more uncertain work environment with more significant consumer pressure [71]. Consumers are highly time-sensitive and may cancel orders due to minor delays. Meanwhile, the increasing demand for crowdsourced services requires gig workers to undertake higher task loads and adapt to more complex technologies [72]. In this context, if gig workers perceive the urgency and importance of their work, they are more likely to mobilize their resources and capabilities to address challenges proactively [73]. In other words, when gig workers accurately perceive job demands, they are more inclined to adopt proactive coping strategies, such as seeking solutions and adjusting their behavior, to manage work-related stress better.
It is worth noting that both direct and mediating pathways are particularly salient within the distinctive context of China’s platform-based economy. Crowdsourcing platforms employ algorithm-driven customer rating and task allocation systems, which convert customer evaluations into performance constraints on gig workers, thereby amplifying the emotional and behavioral impact of customer injustice [2,18]. In the absence of formal employment contracts and labor protections, gig workers are more likely to perceive organizational unsupportiveness, which, in turn, intensifies their adverse emotional reactions and customer-directed CWB [9,30]. This study not only validates the applicability of the S-EM [17] and SET [20] in China’s non-standard employment context but also reveals how dual stressors from customers and platforms jointly influence gig workers’ behavior, thereby enriching the theoretical understanding of the tripartite relationship among workers, customers, and platforms in digital labor environments. Moreover, the moderating role of job demands further indicates that within China’s platform economy, job demands are not merely negative stressors but can also stimulate adaptability and proactive coping behaviors among gig workers [68,69,73]. This finding provides a localized theoretical lens for understanding individual motivation and emotional responses in digital labor environments and offers empirical support for improving platform governance strategies. Although platforms widely rely on algorithms for task assignment, rating, and performance management, such algorithm-driven governance—while efficient—they often overlook the psychological support and cognitive guidance needed by gig workers [71]. The findings suggest that gig workers’ accurate understanding of job demands is crucial to adopting effective coping strategies in response to customer injustice. Therefore, strengthening training mechanisms for gig workers within platform governance should be regarded as a more sustainable and long-term intervention than relying solely on algorithmic control [2,50].
5. Conclusions
5.1. Theoretical Contributions
This study makes several significant theoretical contributions in the following aspects:
Firstly, this study focuses on customer-directed CWB within the context of the gig economy, a topic that has yet to be systematically explored in the existing literature. By integrating the S-EM and SET, this study examines how customer injustice indirectly contributes to customer-directed CWB through a sequential mediation mechanism involving perceived organizational injustice, customer commitment, and negative emotions. This research framework extends the applicability of traditional CWB studies and fills a theoretical gap in understanding customer interaction behaviors within the gig economy, providing new directions and perspectives for future research.
More importantly, this study innovatively developed a second-order structural model of negative emotions, comprising four core dimensions: “despondent”, “dejected”, “hopeless”, and “depressed”. By empirically validating the reliability and validity of this model, the study enhances the quantification of negative emotions, offering a practical, theoretical tool for subsequent research. The construction of this model not only enriches the measurement methods in emotion research but also provides critical insights for capturing and interpreting negative emotions in high-contact service industries. It is a valuable reference for scholars studying negative emotions in other contexts.
Additionally, this study provides a comprehensive and complementary theoretical framework to examine the formation mechanisms of customer-directed CWB from the perspective of customer injustice. This study highlights the unique characteristics of gig work environments, which significantly differ from traditional organizational settings, especially in the relationship between gig workers and customers, which is a core aspect of service work [74]. By integrating the S-EM and SET, this study reveals how customer injustice triggers gig workers to reassess and adjust their social relationships. The S-EM focuses on the direct effects of stressors on individuals and their emotional outcomes [17]. In contrast, the SET emphasizes the indirect impacts of violated social exchange norms on interpersonal relationships [31]. This study validates the causal chain from stressors to negative emotions and subsequently to CWB and captures, for the first time, the complex transfer and interaction processes among stressors. This provides new insights into the dynamic changes in gig workers’ psychological appraisals and cognitions, offering significant theoretical implications.
Finally, this study reveals the unique moderating effect of job demands within the gig economy, challenging the traditional assumption about the negative impact of high job demands. The study finds that employees’ customer-directed CWB may be suppressed under high perceived job demands. This finding overturns the traditional Job Demands–Resources (JD-R) model’s singular assumption of the adverse effects of high job demands, offering a new perspective to reassess the role of job demands in various occupational contexts. The study finds that gig workers’ positive perception of job demands facilitates their proactive responses to stressors and adaptation to high-intensity work requirements, thereby reducing the occurrence of CWB. This may be attributed to the short-term nature of gig work, which allows workers to exercise greater autonomy in adjusting their work pace and time allocation [47,75]. Under high-job-demand scenarios, gig workers are more likely to activate their enthusiasm and motivation, efficiently complete tasks, and effectively regulate their emotions [52]. This finding provides theoretical support for exploring the effects of job demands across different occupational types and broadens the research boundary of job demands within the gig economy.
5.2. Policy/Management Implications
Our study contributes to a deeper understanding of crowdsourced delivery workers’ psychological and behavioral characteristics, providing theoretical support and practical guidance for platform operations and management.
Firstly, based on the finding that customer injustice is a key antecedent of customer-directed CWB among gig workers, companies should establish clear customer behavior guidelines and complaint mechanisms, emphasizing proactive management to reduce the occurrence of unjust customer behaviors. Through platform campaigns, customer interaction guidelines, and managing customer behavior [22], companies can enhance customers’ recognition of and respect for the value of gig workers’ labor. For instance, platforms can utilize public accounts or apps to share information about the working environment and pressures crowdsourced delivery workers face, fostering customer empathy and enhancing mutual understanding between customers and gig workers [76]. Establishing smooth complaint channels can encourage customers to provide specific feedback and evaluations of gig workers rather than resorting to simplistic complaints [77]. This can protect gig workers from subjective bias or unfair evaluations, ensuring healthier and more transparent customer–gig worker relationships while fostering positive interactions [78].
More importantly, in response to the impact of negative emotions on work behaviors highlighted in the study, platforms can develop mental health support systems, such as emotion management courses, online counseling services, or regular emotional regulation training [22], to help gig workers better cope with stressors. Platforms can enhance workers’ problem-solving capabilities by training gig workers to consider issues from the customer’s perspective [23] and equipping them with knowledge on preventing and handling likely occurrences of customer injustice. For instance, crowdsourced delivery companies can break training content into concise video or audio clips, allowing gig workers to learn and review anytime, anywhere flexibly. Remote teaching via technologies such as video conferencing can enable gig workers to participate in interactive online discussions [79,80]. Utilizing social platforms can encourage gig workers to exchange and share experiences, fostering the dissemination and sharing of learning insights [81].
Additionally, organizational managers should acknowledge the existence of unfair customer behaviors [22] and ensure the fair treatment of gig workers. Excessively favoring or indulging customers further encourages unfair customer behaviors [28] and exacerbates the negative consequences of such injustice [39]. Organizational managers should provide timely psychological counseling to gig workers who have experienced unfair treatment from customers. For instance, they can encourage gig workers to report such incidents promptly to the management team and establish dedicated internal communication channels [82], such as hotlines, internal forums, or regular discussion meetings. They could also implement mechanisms for emotional management, such as assigning professional counselors [83] or utilizing AI algorithms to monitor gig workers’ workloads and emotional states in real time, automatically identifying potential high-stress situations and providing timely adjustment suggestions or emotional support services. These measures can help gig workers alleviate negative emotions, mitigate the adverse effects of unfair customer behaviors, and ultimately reduce customer-directed CWB.
Finally, the study indicates that perceiving high job demands can stimulate gig workers’ motivation and mitigate the occurrence of customer-directed CWB. Therefore, assessing gig workers’ understanding of job content before onboarding [84] is crucial for improving service quality and reducing CWB. Specifically, Q&A sessions, simulation exercises, role playing, and emergency scenario setups can be employed to comprehensively evaluate gig workers’ knowledge of job procedures, safety protocols, customer service awareness, and emergency response skills. On the one hand, this helps ensure the safety and efficiency of delivery services. On the other hand, it facilitates gig workers’ more profound understanding of job content and objectives. It enables them to anticipate potential challenges in delivery tasks and better manage their emotions and stress [85].
5.3. Limitations
Although our study makes a significant contribution, it also has some limitations, and we encourage future research to address them. First, due to our study’s cross-sectional nature, we cannot accurately determine causal relationships between the variables. For instance, gig workers experiencing burnout may be more likely to perceive customer injustice, or customers may become more critical due to poor service quality. Future research could consider conducting field experiments and collecting data in waves to more accurately examine the causal sequence between the constructions.
Second, our study did not account for the different types of gig workers. Based on their work, gig workers can be classified as searchers, lifers, short-term workers, long-term workers, and dabblers [86]. Their employment status, financial situation, total platform work hours, and other factors may vary, leading to significant differences in their perceptions of job demands, participation motivation, and career strategies [86]. Therefore, gig workers from different categories will likely exhibit varying emotional and behavioral responses when confronted with customer injustice.
Third, this study’s data were collected from gig workers in a specific region of China, which may limit the generalizability of the findings. Different factors across various countries and cultural contexts may influence customer-directed CWB among gig workers. For instance, variations in labor laws, platform governance models, and customer behavior norms across countries may significantly affect gig workers’ perceptions of customer injustice and their behavioral responses. Therefore, the findings of this study are primarily applicable to crowdsourced delivery platforms in China, and their applicability to other countries or regions requires further validation. Future research could expand the sample to include gig workers from diverse national and cultural backgrounds to examine the influence of cultural factors on customer-directed CWB.
Conceptualization, Y.L.; Data curation, L.C. and X.W.; Formal analysis, Y.L. and X.T.; Investigation, L.C. and X.W.; Methodology, Y.L. and X.T.; Project administration, X.T.; Resources, X.T.; Software, Y.L.; Supervision, X.T.; Validation, Y.L.; Visualization, X.T.; Writing—original draft, Y.L., L.C., and X.T.; Writing—review and editing, X.T. All authors have read and agreed to the published version of the manuscript.
The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.
The authors declare they have no conflicts of interest.
Footnotes
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.
Figure 1 The theoretical framework.
Figure 2 Hypothesis test results. Note: * p < 0.05, ** p < 0.01, and *** p < 0.001.
Figure 3 Adjustment under different levels of job demand perception.
Profile of the respondents.
| Characteristics | Items | Frequency (385) | Percentage (%) |
|---|---|---|---|
| Gender | Male | 318 | 82.6 |
| Female | 67 | 17.4 | |
| Age (Year) | <20 | 31 | 8.1 |
| 21–30 | 93 | 24.2 | |
| 31–40 | 165 | 42.9 | |
| 41–50 | 83 | 21.6 | |
| >51 | 13 | 3.4 | |
| Education | Junior high school and below | 79 | 20.5 |
| Technical secondary school or high school | 162 | 42.1 | |
| Junior college | 100 | 26 | |
| Undergraduate | 41 | 10.6 | |
| Master’s degree or above | 3 | 0.8 | |
| Monthly income (RMB) | <5000 | 76 | 19.7 |
| 5001–10,000 | 177 | 46.0 | |
| 10,001–20,000 | 125 | 32.5 | |
| >20,001 | 7 | 1.8 | |
| Working seniority | Less than half a year | 63 | 16.4 |
| Half a year to 1 year | 113 | 29.4 | |
| 1–2 years | 134 | 34.8 | |
| Over two years | 75 | 19.5 |
Results of confirmatory factor analysis.
| Construct | Item | Mean | SD | Skewness | Kurtosis | λ | Cronbach’s α | AVE | CR | |
|---|---|---|---|---|---|---|---|---|---|---|
| Negative emotions | Despondent | DP1 | 4.223 | 1.156 | −0.76 | 0.782 | 0.719 | 0.76 | 0.514 | 0.76 |
| DP2 | 4.192 | 1.179 | −0.732 | 0.889 | 0.72 | |||||
| DP3 | 4.442 | 1.23 | −0.688 | 0.454 | 0.711 | |||||
| Dejected | DJ1 | 4.166 | 1.179 | −0.595 | 0.704 | 0.729 | 0.765 | 0.522 | 0.766 | |
| DJ2 | 4.138 | 1.243 | −0.516 | 0.36 | 0.741 | |||||
| DJ3 | 4.491 | 1.275 | −0.624 | 0.49 | 0.697 | |||||
| Hopeless | HP1 | 4.138 | 1.148 | −0.511 | 0.536 | 0.716 | 0.785 | 0.552 | 0.787 | |
| HP2 | 4.358 | 1.182 | −0.439 | 0.47 | 0.726 | |||||
| HP3 | 4.403 | 1.238 | −0.826 | 0.72 | 0.785 | |||||
| Depressed | DS1 | 4.091 | 1.175 | −0.759 | 0.419 | 0.769 | 0.784 | 0.547 | 0.784 | |
| DS2 | 4.26 | 1.173 | −0.556 | 0.633 | 0.71 | |||||
| DS3 | 4.345 | 1.257 | −0.738 | 0.409 | 0.739 | 0.825 | 0.616 | 0.827 | ||
| Job demands | JD1 | 4.93 | 0.994 | 0.221 | −0.035 | 0.766 | ||||
| JD2 | 4.782 | 1.101 | 0.229 | 0.121 | 0.86 | |||||
| JD3 | 4.795 | 1.064 | 0.039 | 0.309 | 0.722 | |||||
| Customer injustice | CI1 | 4.166 | 1.322 | −0.546 | 0.333 | 0.869 | 0.922 | 0.75 | 0.923 | |
| CI2 | 4.096 | 1.33 | −0.457 | −0.013 | 0.886 | |||||
| CI3 | 4.099 | 1.356 | −0.583 | 0.092 | 0.859 | |||||
| CI4 | 4.192 | 1.395 | −0.537 | −0.225 | 0.849 | |||||
| Customer commitment | CC1 | 4.543 | 0.999 | −0.119 | 1.631 | 0.727 | 0.764 | 0.524 | 0.767 | |
| CC2 | 4.582 | 1.035 | 0.027 | 1.245 | 0.729 | |||||
| CC3 | 4.665 | 1.143 | 0.21 | 0.122 | 0.715 | |||||
| Perceived organizational injustice | POI1 | 4.244 | 1.122 | −0.726 | 0.838 | 0.708 | 0.771 | 0.53 | 0.772 | |
| POI2 | 4.306 | 1.159 | −0.759 | 0.875 | 0.729 | |||||
| POI3 | 4.488 | 1.193 | −0.699 | 0.553 | 0.746 | |||||
| Customer-directed CWB | CDC1 | 4.073 | 1.365 | −0.99 | 0.083 | 0.879 | 0.915 | 0.747 | 0.922 | |
| CDC2 | 4.158 | 1.351 | −1.041 | 0.234 | 0.895 | |||||
| CDC3 | 4.197 | 1.473 | −0.885 | 0.008 | 0.891 | |||||
| CDC4 | 4.306 | 1.682 | −0.781 | −0.518 | 0.788 | |||||
Model fit indices: CMIN/DF = 2.304, CFI =0.929, TLI = 0.92, IFI = 0.93, SRMR = 0.047, and RMSEA = 0.058.
Discriminant validity test results.
| Customer Injustice | Job Demands | Customer Commitment | Perceived Organizational Injustice | Customer-Directed CWB | Negative Emotions | |
|---|---|---|---|---|---|---|
| Customer injustice | 0.866 | |||||
| Job demands | −0.129 | 0.785 | ||||
| Customer Commitment | −0.225 | 0.319 | 0.724 | |||
| Perceived organizational injustice | 0.402 | −0.001 | −0.094 | 0.728 | ||
| Customer-directed CWB | 0.606 | −0.206 | −0.273 | 0.604 | 0.864 | |
| Negative emotions | 0.530 | −0.033 | −0.199 | 0.660 | 0.628 b | 0.928 a |
a. Average variance extracted is along the main diagonal. b. Squared correlations between constructs are below the main diagonal.
Path relationships of the hypothesis test results.
| Hypothesis | Path | Coefficients | S.E. | C.R. | p | Test Results |
|---|---|---|---|---|---|---|
| H1 | Customer injustice → customer-directed CWB | 0.28 | 0.049 | 5.861 | *** | Supported |
| H2 | Customer injustice → negative emotions | 0.266 | 0.033 | 5.187 | *** | Supported |
| H3 | Customer injustice → perceived organizational injustice | 0.471 | 0.041 | 7.667 | *** | Supported |
| H4 | Customer injustice → customer commitment | −0.258 | 0.038 | −4.239 | *** | Supported |
| H5 | Negative emotions → customer-directed CWB | 0.361 | 0.133 | 4.256 | *** | Supported |
| H6 | Perceived organizational injustice → customer-directed CWB | 0.273 | 0.122 | 3.379 | *** | Supported |
| H6-1 | Perceived organizational injustice → negative emotions | 0.666 | 0.072 | 8.93 | *** | Supported |
| H7 | Customer commitment → customer-directed CWB | −0.125 | 0.067 | −3.046 | ** | Supported |
| Second-order constructs | Negative emotions → despondent | 0.919 | 0.086 | 11.681 | *** | Supported |
| Negative emotions → dejected | 0.945 | 0.089 | 12.043 | *** | Supported | |
| Negative emotions → depressed | 0.927 | 0.089 | 12.413 | *** | Supported | |
| Negative emotions → hopeless | 0.922 | 0.078 | 12.043 | *** | Supported |
Model fit indices: CMIN/DF = 2.409, RMSEA = 0.061, SRMR = 0.0497, IFI = 0.934, TLI = 0.924, CFI = 0.933, NFI = 0.892. Note: ** p < 0.01, and *** p < 0.001.
Results of mediation analysis.
| Hypothesis | Path | Coefficients | Lower | Upper | p | Test Results | Percentage of |
|---|---|---|---|---|---|---|---|
| H5-1 | Customer injustice → negative emotions → customer-directed CWB | 0.098 | 0.005 | 0.21 | * | Supported | 26 |
| H6-2 | Customer injustice → perceived organizational injustice → customer-directed CWB | 0.131 | 0.025 | 0.316 | * | Supported | 34.7 |
| H6-3 | Customer injustice → perceived organizational injustice → negative emotions → customer-directed CWB | 0.115 | 0.01 | 0.208 | * | Supported | 30.6 |
| H7-1 | Customer injustice → customer commitment → customer-directed CWB | 0.033 | 0.003 | 0.069 | * | Supported | 8.7 |
Note: * p < 0.05.
Moderation effect analysis results.
| Hypotheses | Path | Coeff | se | t | p | LLCI | ULCI |
|---|---|---|---|---|---|---|---|
| Moderation effects of job demands | |||||||
| H8 | Negative emotions → customer-directed CWB | −0.227 | 0.042 | −5.345 | *** | −0.31 | −0.143 |
| H9 | Perceived organizational injustice → customer-directed CWB | −0.314 | 0.044 | −7.215 | *** | −0.399 | −0.228 |
| H10 | Customer commitment → customer-directed CWB | −0.256 | 0.065 | −3.952 | *** | −0.383 | −0.129 |
| H11 | Customer injustice → customer-directed CWB | −0.082 | 0.041 | −2 | * | −0.163 | −0.001 |
Note: * p < 0.05 and *** p < 0.001.
Appendix A. Definitions and Privacy Commitment
| This survey aims to gather more information about “customer-directed counterproductive work behavior among gig workers in crowdsourced delivery”. We appreciate your time and participation in this survey. There are no right or wrong answers; we encourage you to respond honestly and authentically. According to statistics law, this survey is intended for statistical purposes. |
| Crowdsourced delivery gig workers provide delivery services through crowdsourced logistics platforms, using their vehicles to deliver goods from stores or warehouses to consumers in exchange for payment [ |
| Customer-directed counterproductive work behavior refers to actions taken by employees that harm or negatively affect customers, service quality, or customer relationships [ |
Appendix B. Measurement Items and Source
| Construct | ID | Measurement | Source |
| Customer injustice | CI1 | The customer is not polite to me. | [ |
| CI2 | Customers underestimate my work. | ||
| CI3 | The customer does not respect me. | ||
| CI4 | Customers have inappropriate comments or comments about me. | ||
| Job demands | JD1 | My work requires much effort. | [ |
| JD2 | My job requires a breakneck work speed. | ||
| JD3 | My job requires a long period of highly focused attention. | ||
| Despondent | DP1 | I think my work pressure has caused me sleep problems. | [ |
| DP2 | My work experience makes me feel like a failure. | ||
| DP3 | Due to work pressure, I feel uneasy and unable to stay still. | ||
| Dejected | DJ1 | Everything related to my work is terrible. | [ |
| DJ2 | Most work-related activities make me feel sad and useless. | ||
| DJ3 | I don’t see any future for me. | ||
| Hopeless | HP1 | This job has brought me a lot of unpleasantness. | [ |
| HP2 | Work will not develop as I hope. | ||
| HP3 | To me, the future seems vague and uncertain. | ||
| Depressed | DS1 | I find everything at work very annoying. | [ |
| DS2 | Customer behavior sometimes makes me very angry. | ||
| DS3 | Sometimes I feel furious. | ||
| Customer | CC1 | How much do you care about customers? | [ |
| CC2 | What is your level of dedication to customers? | ||
| CC3 | To what extent have you chosen to be accountable to customers? | ||
| Perceived | POI1 | Considering the pressure and tension of my work, the company’s treatment is unfair. | [ |
| POI2 | Considering the education and training I have received, I believe the company’s treatment is unfair. | ||
| POI3 | If I consider the work I am doing, the company is not treating me fairly. | ||
| Customer-directed | CDC1 | I sometimes cheat customers. | [ |
| CDC2 | I sometimes have conflicts with customers. | ||
| CDC3 | I sometimes deliberately damage or contaminate customers’ goods or express parcels. | ||
| CDC4 | I sometimes refuse reasonable requests from customers. |
1. Zhen, L.; Wu, Y.; Wang, S.; Yi, W. Crowdsourcing mode evaluation for parcel delivery service platforms. Int. J. Prod. Econ.; 2021; 235, 108067.
2. Wu, D.; Huang, J.L. Gig work and gig workers: An integrative review and agenda for future research. J. Organ. Behav.; 2024; 45, pp. 183-208. [DOI: https://dx.doi.org/10.1002/job.2775]
3. He, X.; Lu, W.; Luo, H.; Liu, S. How gig worker responds to negative customer treatment: The effects of work meaningfulness and traits of psychological resilience. Front. Psychol.; 2021; 12, 783372.
4. Masorgo, N.; Mir, S.; Hofer, A.R. You’re driving me crazy! How emotions elicited by negative driver behaviors impact customer outcomes in last mile delivery. J. Bus. Logist.; 2023; 44, pp. 666-692.
5. Li, X.; Tan, A.J.H.; Wang, X.; Yuen, K.F. Investigating gig workers’ commitment to crowdsourced logistics platforms: Fair employment and social exchange perspectives. Technol. Soc.; 2023; 74, 102311.
6. Puram, P.; Gurumurthy, A.; Narmetta, M.; Mor, R.S. Last-mile challenges in on-demand food delivery during COVID-19: Understanding the riders’ perspective using a grounded theory approach. Int. J. Logist. Manag.; 2021; 33, pp. 901-925.
7. Walker, D.D.; Van Jaarsveld, D.D.; Skarlicki, D.P. Sticks and stones can break my bones but words can also hurt me: The relationship between customer verbal aggression and employee incivility. J. Appl. Psychol.; 2017; 102, 163.
8. Hunter, E.M.; Penney, L.M. The waiter spit in my soup! Antecedents of customer-directed counterproductive work behavior. Hum. Perform.; 2014; 27, pp. 262-281.
9. Aisha, R.; Channa, N.A.; Mirani, M.A.; Qureshi, N.A. Investigating the influence of perceived organizational justice on counterproductive work behaviours: Mediating role of negative emotions. Int. J. Emerg. Mark.; 2024; 19, pp. 2264-2292.
10. Ta, H.; Esper, T.L.; Hofer, A.R. Designing crowdsourced delivery systems: The effect of driver disclosure and ethnic similarity. J. Oper. Manag.; 2018; 60, pp. 19-33.
11. Zhang, Y.; Shi, X.; Abdul-Hamid, Z.; Li, D.; Zhang, X.; Shen, Z. Factors influencing crowdsourcing riders’ satisfaction based on online comments on real-time logistics platform. Transp. Lett.; 2023; 15, pp. 363-374. [DOI: https://dx.doi.org/10.1080/19427867.2022.2052643]
12. Caza, B.B.; Reid, E.M.; Ashford, S.J.; Granger, S. Working on my own: Measuring the challenges of gig work. Hum. Relat.; 2022; 75, pp. 2122-2159. [DOI: https://dx.doi.org/10.1177/00187267211030098]
13. Maffie, M.D. The perils of laundering control through customers: A study of control and resistance in the ride-hail industry. ILR Rev.; 2022; 75, pp. 348-372. [DOI: https://dx.doi.org/10.1177/0019793920972679]
14. Song, Y.H.; Skarlicki, D.P.; Shao, R.; Park, J. Reducing customer-directed deviant behavior: The roles of psychological detachment and supervisory unfairness. J. Manag.; 2021; 47, pp. 2008-2036. [DOI: https://dx.doi.org/10.1177/0149206320925877]
15. Bednarska, M.A.; Łuka, P. Customer-directed counterproductive work behaviour in a high-contact service environment–selected antecedent and consequent variables. Forum Sci. Oeconomia; 2022; 10, pp. 47-66.
16. Gong, T.; Wang, C.Y. The effects of a psychological contract breach on customer-directed deviance. J. Bus. Res.; 2022; 138, pp. 374-386. [DOI: https://dx.doi.org/10.1016/j.jbusres.2021.09.040]
17. Spector, P.E.; Fox, S. The stressor-emotion model of counterproductive work behavior. Counterproductive Work Behavior: Investigations of Actors and Targets; American Psychological Association: Washington, DC, USA, 2005.
18. Lavelle, J.J.; Rupp, D.E.; Herda, D.N.; Lee, J. Customer injustice and service employees’ customer-oriented citizenship behavior: A social exchange perspective. J. Organ. Behav.; 2023; 44, pp. 421-440. [DOI: https://dx.doi.org/10.1002/job.2679]
19. Nazir, S.; Qun, W.; Hui, L.; Shafi, A. Influence of social exchange relationships on affective commitment and innovative behavior: Role of perceived organizational support. Sustainability; 2018; 10, 4418. [DOI: https://dx.doi.org/10.3390/su10124418]
20. Cropanzano, R.; Mitchell, M.S. Social exchange theory: An interdisciplinary review. J. Manag.; 2005; 31, pp. 874-900. [DOI: https://dx.doi.org/10.1177/0149206305279602]
21. Chen, J.; Luo, J.; Hu, W.; Ma, J. Fit into work! From formalizing governance of gig platform ecosystems to helping gig workers craft their platform work. Decis. Support Syst.; 2023; 174, 114016.
22. Berry, L.L.; Seiders, K. Serving unfair customers. Bus. Horiz.; 2008; 51, pp. 29-37.
23. Ho, V.T.; Gupta, N. Retaliating against Customer Interpersonal Injustice in a S ingaporean Context: Moderating Roles of Self-Efficacy and Social Support. Appl. Psychol.; 2014; 63, pp. 383-410.
24. Wen, J.; Li, Y.; Hou, P. Customer mistreatment behavior and hotel employee organizational citizenship behavior: The mediating role of perceived organizational support. Nankai Bus. Rev. Int.; 2016; 7, pp. 322-344.
25. Colquitt, J.A. On the dimensionality of organizational justice: A construct validation of a measure. J. Appl. Psychol.; 2001; 86, 386. [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/11419799]
26. Cameron, L.D. “Making out” while driving: Relational and efficiency games in the gig economy. Organ. Sci.; 2022; 33, pp. 231-252.
27. Nguyen, P.H. A fully completed spherical fuzzy data-driven model for analyzing employee satisfaction in the logistics service industry. Mathematics; 2023; 11, 2235. [DOI: https://dx.doi.org/10.3390/math11102235]
28. Kamran-Disfani, O.; Bagherzadeh, R.; Bhattarai, A.; Farhang, M.; Scheer, L.K. Constructive resistance in the frontlines: How frontline employees’ resistance to customer incivility affects customer observers. J. Serv. Res.; 2023; 26, pp. 560-577.
29. Jung, H.; Koh, D.H.; Choi, S.; Park, J.H.; Kim, H.C.; Lee, S.G.; Park, D. Estimates of the prevalence, intensity and the number of workers exposed to cigarette smoking across occupations and industries in Korea. J. Korean Med. Sci.; 2019; 34, 1130351.
30. Lheureux, F.; Parmentier, C. Work motivation and reactions to injustice of temporary workers: Roles of social identities, autonomy, and compensations. Eur. J. Psychol.; 2022; 18, 403.
31. Han, S.; Harold, C.M.; Oh, I.S.; Kim, J.K.; Agolli, A. A meta-analysis integrating 20 years of workplace incivility research: Antecedents, consequences, and boundary conditions. J. Organ. Behav.; 2022; 43, pp. 497-523.
32. Ng, K.; Yang, W.N. Feeling stuck and feeling bad: Career plateaus, negative emotions, and counterproductive work behaviors. Hum. Resour. Manag. J.; 2023; 34, pp. 921-941.
33. Matta, F.K.; Erol-Korkmaz, H.T.; Johnson, R.E.; Biçaksiz, P. Significant work events and counterproductive work behavior: The role of fairness, emotions, and emotion regulation. J. Organ. Behav.; 2014; 35, pp. 920-944.
34. Guo, L.; Luo, J.; Cheng, K. Exploitative leadership and counterproductive work behavior: A discrete emotions approach. Pers. Rev.; 2024; 53, pp. 353-374.
35. Zagenczyk, T.J.; Powell, E.E.; Scott, K.L. How exhausting!? Emotion crossover in organizational social networks. J. Manag. Stud.; 2020; 57, pp. 1589-1609.
36. Lavelle, J.J.; Rupp, D.E.; Herda, D.N.; Pandey, A.; Lauck, J.R. Customer injustice and employee performance: Roles of emotional exhaustion, surface acting, and emotional demands–abilities fit. J. Manag.; 2021; 47, pp. 654-682.
37. Cheng, B.; Guo, G.; Tian, J.; Shaalan, A. Customer incivility and service sabotage in the hotel industry. Int. J. Contemp. Hosp. Manag.; 2020; 32, pp. 1737-1754.
38. Villarreal, A.T. The bus owner, the bus driver, and his lover: Gendered class struggle in the service work triangle. Work. Occup.; 2010; 37, pp. 272-294.
39. Van Jaarsveld, D.D.; Walker, D.D.; Restubog, S.L.D.; Skarlicki, D.; Chen, Y.; Frické, P.H. Unpacking the relationship between customer (in) justice and employee turnover outcomes: Can fair supervisor treatment reduce employees’ emotional turmoil?. J. Serv. Res.; 2021; 24, pp. 301-319.
40. McCance, A.S. Emotional Labor in Intercultural Service Encounters: An Experience Sampling Study; University of Illinois at Urbana-Champaign: Champaign, IL, USA, 2010.
41. Bunk, J.A.; Magley, V.J. The role of appraisals and emotions in understanding experiences of workplace incivility. J. Occup. Health Psychol.; 2013; 18, 87.
42. Kelloway, E.K.; Francis, L.; Prosser, M.; Cameron, J.E. Counterproductive work behavior as protest. Hum. Resour. Manag. Rev.; 2010; 20, pp. 18-25.
43. Judge, T.A.; Colquitt, J.A. Organizational justice and stress: The mediating role of work-family conflict. J. Appl. Psychol.; 2004; 89, 395. [DOI: https://dx.doi.org/10.1037/0021-9010.89.3.395] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/15161400]
44. Van Jaarsveld, D.D.; Walker, D.D.; Skarlicki, D.P. The role of job demands and emotional exhaustion in the relationship between customer and employee incivility. J. Manag.; 2010; 36, pp. 1486-1504. [DOI: https://dx.doi.org/10.1177/0149206310368998]
45. Dongrey, R.; Rokade, V. Linkage between hiring and retention of diverse workforce, affective commitment and counterproductive work behavior. Kybernetes; 2024; 53, pp. 2624-2637. [DOI: https://dx.doi.org/10.1108/K-05-2022-0742]
46. Fein, E.C.; Tziner, A.; Vasiliu, C. Perceptions of ethical climate and organizational justice as antecedents to employee performance: The mediating role of employees’ attributions of leader effectiveness. Eur. Manag. J.; 2023; 41, pp. 114-124. [DOI: https://dx.doi.org/10.1016/j.emj.2021.11.003]
47. Elliethey, N.S.; Aly Abou Hashish, E.; Ahmed Mohamed Elbassal, N. Work ethics and its relationship with workplace ostracism and counterproductive work behaviours among nurses: A structural equation model. BMC Nurs.; 2024; 23, 126. [DOI: https://dx.doi.org/10.1186/s12912-024-01785-4]
48. Chen, M.; Lyu, Y.; Li, Y.; Zhou, X.; Li, W. The impact of high-commitment HR practices on hotel employees’ proactive customer service performance. Cornell Hosp. Q.; 2017; 58, pp. 94-107. [DOI: https://dx.doi.org/10.1177/1938965516649053]
49. Bakker, A.B.; Demerouti, E. Job demands–resources theory: Taking stock and looking forward. J. Occup. Health Psychol.; 2017; 22, 273. [DOI: https://dx.doi.org/10.1037/ocp0000056]
50. Zhao, S.; Liu, Y. Job demands-resources on digital gig platforms and counterproductive work behavior. Front. Psychol.; 2024; 15, 1378247. [DOI: https://dx.doi.org/10.3389/fpsyg.2024.1378247]
51. Fatehi, S.; Wagner, M.R. Crowdsourcing last-mile deliveries. Manuf. Serv. Oper. Manag.; 2022; 24, pp. 791-809. [DOI: https://dx.doi.org/10.1287/msom.2021.0973]
52. Van den Broeck, A.; De Cuyper, N.; De Witte, H.; Vansteenkiste, M. Not all job demands are equal: Differentiating job hindrances and job challenges in the Job Demands–Resources model. Eur. J. Work Organ. Psychol.; 2010; 19, pp. 735-759. [DOI: https://dx.doi.org/10.1080/13594320903223839]
53. Balducci, C.; Schaufeli, W.B.; Fraccaroli, F. The job demands–resources model and counterproductive work behaviour: The role of job-related affect. Eur. J. Work Organ. Psychol.; 2011; 20, pp. 467-496.
54. Chen, F.F.; Sousa, K.H.; West, S.G. Teacher’s corner: Testing measurement invariance of second-order factor models. Struct. Equ. Model.; 2005; 12, pp. 471-492.
55. Rindskopf, D.; Rose, T. Some theory and applications of confirmatory second-order factor analysis. Multivar. Behav. Res.; 1988; 23, pp. 51-67. [DOI: https://dx.doi.org/10.1207/s15327906mbr2301_3] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/26782257]
56. Warr, P.; Bindl, U.K.; Parker, S.K.; Inceoglu, I. Four-quadrant investigation of job-related affects and behaviours. Eur. J. Work Organ. Psychol.; 2014; 23, pp. 342-363.
57. Podsakoff, P.M.; MacKenzie, S.B.; Lee, J.Y.; Podsakoff, N.P. Common method biases in behavioral research: A critical review of the literature and recommended remedies. J. Appl. Psychol.; 2003; 88, 879.
58. Baron, R.M.; Kenny, D.A. The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. J. Personal. Soc. Psychol.; 1986; 51, 1173. [DOI: https://dx.doi.org/10.1037/0022-3514.51.6.1173] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/3806354]
59. Kline, R.B. Principles and Practice of Structural Equation Modeling; Guilford Publications: New York, NY, USA, 2023.
60. Huws, U.; Spencer, N.; Joyce, S. Crowd Work in Europe: Preliminary Results from a Survey in the UK, Sweden, Germany, Austria and The Netherlands; Foundation for European Progressive Studies: Bruxelles, Belgium, 2016.
61. Liu, Y.; Li, X.; Yuen, K.F. Revenge buying: The role of negative emotions caused by lockdowns. J. Retail. Consum. Serv.; 2023; 75, 103523. [DOI: https://dx.doi.org/10.1016/j.jretconser.2023.103523]
62. Fornell, C.; Larcker, D.F. Evaluating structural equation models with unobservable variables and measurement error. J. Mark. Res.; 1981; 18, pp. 39-50.
63. Henseler, J.; Ringle, C.M.; Sinkovics, R.R. The use of partial least squares path modeling in international marketing. New Challenges to International Marketing; Emerald Group Publishing Limited: Bingley, UK, 2009; pp. 277-319.
64. Ramkissoon, H.; Smith, L.D.G.; Weiler, B. Testing the dimensionality of place attachment and its relationships with place satisfaction and pro-environmental behaviours: A structural equation modelling approach. Tour. Manag.; 2013; 36, pp. 552-566.
65. Hair, J.F.; Risher, J.J.; Sarstedt, M.; Ringle, C.M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev.; 2019; 31, pp. 2-24.
66. Liu, Y.; Cai, L.; Ma, F.; Wang, X. Revenge buying after the lockdown: Based on the SOR framework and TPB model. J. Retail. Consum. Serv.; 2023; 72, 103263.
67. Babakus, E.; Bienstock, C.C.; Van Scotter, J.R. Linking perceived quality and customer satisfaction to store traffic and revenue growth. Decis. Sci.; 2004; 35, pp. 713-737.
68. Hentrich, S.; Zimber, A.; Garbade, S.F.; Gregersen, S.; Nienhaus, A.; Petermann, F. Relationships between transformational leadership and health: The mediating role of perceived job demands and occupational self-efficacy. Int. J. Stress Manag.; 2017; 24, 34.
69. Skaalvik, C. Self-efficacy for instructional leadership: Relations with perceived job demands and job resources, emotional exhaustion, job satisfaction, and motivation to quit. Soc. Psychol. Educ.; 2020; 23, pp. 1343-1366.
70. Tu, B.; Luo, X.; Sitar, S.; Huang, C. Job demands, resources, and task performance in Chinese social workers: Roles of burnout and work engagement. Front. Public Health; 2022; 10, 908921.
71. Friedland, J.; Balkin, D.B. When gig workers become essential: Leveraging customer moral self-awareness beyond COVID-19. Bus. Horiz.; 2023; 66, pp. 181-190.
72. Lee, S.; Chang, H.; Cho, M. Applying the sociotechnical systems theory to crowdsourcing food delivery platforms: The perspective of crowdsourced workers. Int. J. Contemp. Hosp. Manag.; 2022; 34, pp. 2450-2471.
73. Bakker, A.B.; Tims, M.; Derks, D. Proactive personality and job performance: The role of job crafting and work engagement. Hum. Relat.; 2012; 65, pp. 1359-1378.
74. Barley, S.R.; Bechky, B.A.; Milliken, F.J. The changing nature of work: Careers, identities, and work lives in the 21st century. Acad. Manag. Discov.; 2017; 3, pp. 111-115.
75. Rosenblat, A.; Hwang, T. Regional diversity in autonomy and work: A case study from Uber and Lyft drivers. Intell. Auton.; 2016; 1, 15.
76. Ngo, L.V.; Nguyen, T.N.Q.; Tran, N.T.; Paramita, W. It takes two to tango: The role of customer empathy and resources to improve the efficacy of frontline employee empathy. J. Retail. Consum. Serv.; 2020; 56, 102141. [DOI: https://dx.doi.org/10.1016/j.jretconser.2020.102141]
77. Phabmixay, C.S.; Rodríguez-Escudero, A.I.; Rodríguez-Pinto, J. Benefits from the standardisation of the complaint management system. Total Qual. Manag. Bus. Excell.; 2021; 32, pp. 737-757. [DOI: https://dx.doi.org/10.1080/14783363.2019.1633295]
78. Lin, C.Y.; Lin, J.S.C. The influence of service employees’ nonverbal communication on customer-employee rapport in the service encounter. J. Serv. Manag.; 2017; 28, pp. 107-132. [DOI: https://dx.doi.org/10.1108/JOSM-08-2015-0251]
79. Cascio, W.F. Training trends: Macro, micro, and policy issues. Hum. Resour. Manag. Rev.; 2019; 29, pp. 284-297. [DOI: https://dx.doi.org/10.1016/j.hrmr.2017.11.001]
80. Joaquim, I.M.; Morolong, M.P.; Tundumula, E.T.; Cheng, W. Preferences and insights of learning process in pandemic era: A case study of South Africa. High. Educ. Ski. Work.-Based Learn.; 2023; 13, pp. 800-812. [DOI: https://dx.doi.org/10.1108/HESWBL-01-2022-0010]
81. Tsegay, S.M.; Ashraf, M.A.; Perveen, S.; Zegergish, M.Z. Online teaching during COVID-19 pandemic: Teachers’ experiences from a Chinese university. Sustainability; 2022; 14, 568. [DOI: https://dx.doi.org/10.3390/su14010568]
82. Mishra, K.; Boynton, L.; Mishra, A. Driving employee engagement: The expanded role of internal communications. Int. J. Bus. Commun.; 2014; 51, pp. 183-202. [DOI: https://dx.doi.org/10.1177/2329488414525399]
83. Kim, H.; Leach, R.B. Mitigating burnout through organizational justice: Customer support workers’ experiences of customer injustice and emotional labor. Manag. Commun. Q.; 2021; 35, pp. 497-517. [DOI: https://dx.doi.org/10.1177/08933189211012040]
84. Yang, J.; Diefendorff, J.M. The relations of daily counterproductive workplace behavior with emotions, situational antecedents, and personality moderators: A diary study in Hong Kong. Pers. Psychol.; 2009; 62, pp. 259-295. [DOI: https://dx.doi.org/10.1111/j.1744-6570.2009.01138.x]
85. De Jonge, J.; Dollard, M.F.; Dormann, C.; Le Blanc, P.M.; Houtman, I.L. The demand-control model: Specific demands, specific control, and well-defined groups. Int. J. Stress Manag.; 2000; 7, pp. 269-287. [DOI: https://dx.doi.org/10.1023/A:1009541929536]
86. Dunn, M. Making gigs work: Digital platforms, job quality and worker motivations. New Technol. Work. Employ.; 2020; 35, pp. 232-249.
87. Gonzalez-Mulé, E.; Cockburn, B. Worked to death: The relationships of job demands and job control with mortality. Pers. Psychol.; 2017; 70, pp. 73-112.
88. Bianchi, R.; Fiorilli, C.; Angelini, G.; Dozio, N.; Palazzi, C.; Palazzi, G.; Vitiello, B.; Schonfeld, I.S. Italian version of the Occupational Depression Inventory: Validity, reliability, and associations with health, economic, and work-life characteristics. Front. Psychiatry; 2022; 13, 1061293. [DOI: https://dx.doi.org/10.3389/fpsyt.2022.1061293] [PubMed: https://www.ncbi.nlm.nih.gov/pubmed/36620692]
89. Jaworek, M.A.; Marek, T.; Karwowski, W. The scale of work-related affective feelings (WORAF). Appl. Ergon.; 2020; 82, 102945.
90. Beck, A.T.; Weissman, A.; Lester, D.; Trexler, L. The measurement of pessimism: The hopelessness scale. J. Consult. Clin. Psychol.; 1974; 42, 861. [DOI: https://dx.doi.org/10.1037/h0037562]
91. Klein, H.J.; Cooper, J.T.; Molloy, J.C.; Swanson, J.A. The assessment of commitment: Advantages of a unidimensional, target-free approach. J. Appl. Psychol.; 2014; 99, 222.
92. Tang, T.L.P.; Sarsfield-Baldwin, L.J. Distributive and Procedural Justice as Related to Satisfaction and Commitment; Education Resources Information Center: Houston, TX, USA, 1996.
© 2025 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.
Details
; Cai Lanhui 2 ; Wang, Xueqin 2
; Tan Xueli 1 1 Graduate School of Management of Technology, Pukyong National University, Busan 48547, Republic of Korea
2 Department of International Logistics, Chung-Ang University, Seoul 06874, Republic of Korea