How perceived algorithmic control shapes psychological distress among gig workers
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Cite this article:
Ning, Z., Chen, Y., Fan, Y., & He, S.
(2026). How perceived algorithmic control shapes psychological distress among gig workers.
Social Behavior and Personality: An international journal,
54(9),
e16410.
Abstract
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In platform labor, algorithmic management monitors the performance of gig workers, and structures how they receive tasks and understand their role. In this study we examined the psychological consequences of perceived algorithmic control for gig workers’ well-being. We proposed that perceived algorithmic control would increase psychological distress by reducing workers’ sense of work meaning and by weakening their trust in public institutions. Using data obtained in a two-wave survey of 463 gig workers in five Chinese cities, we tested a moderated chain mediation model. The results indicated that work meaninglessness and institutional distrust sequentially mediated the relationship between perceived algorithmic control and psychological distress. Furthermore, regulatory unresponsiveness intensified the effect of institutional distrust on distress. These findings highlight how meaning making and perceived institutional support shape emotional strain in platform work, suggesting that responsive governance and improving transparency are essential to sustaining gig workers’ well-being.
Article Highlights
Perceived algorithmic control was positively associated with psychological distress among gig workers.
Work meaninglessness and institutional distrust sequentially mediated the effect of perceived algorithmic control on gig workers’ psychological distress.
Regulatory unresponsiveness strengthened the positive association between gig workers’ institutional distrust and psychological distress.
The psychological well-being of workers is a central concern for both the functioning of the individual and in broader patterns of social sustainability. Well-being at work is shaped not only by material conditions but also by how individuals interpret the meaning, autonomy, and fairness embedded in their work environment. The recent rapid expansion of the digital platform economy—powered by algorithmic scheduling, mobile tracking, and automated performance analytics—has reshaped how many workers engage with their labor (Kadolkar et al., 2025).
In China, gig workers such as ride-hailing drivers, food-delivery couriers, and same-city couriers increasingly rely on platform-mediated systems to receive and complete tasks (Huang, 2023). Unlike traditional work settings that involve human supervision and interpersonal feedback, platform labor is coordinated through governance, in which algorithms allocate tasks, rate performance, and determine compensation (Möhlmann et al., 2021). Consequently, these workers encounter continuous monitoring and automated behavioral regulation as part of their day-to-day work experience.
From a psychological standpoint, these conditions may influence how workers perceive their autonomy and competence, and the meaningfulness of their labor, which are factors known to be foundational psychological resources for well-being (Hackman & Oldham, 1976; Ryan & Deci, 2000; Steger et al., 2012; Van den Broeck et al., 2016; Zacher & Baumeister, 2025). There is a growing body of research in which scholars have examined the economic, organizational, and labor-process consequences of platform labor and algorithmic management. For example, Cram et al. (2022) examined algorithmic control and technostress among Uber drivers, Lang et al. (2023) found that perceived algorithmic control increased burnout among gig workers, and Kinowska and Sienkiewicz (2023) showed that algorithmic management practices can negatively affect workplace well-being. However, researchers have paid less attention to how gig workers subjectively interpret algorithmic control in everyday practice, and how such interpretations are associated with emotional strain and psychological distress for the workers. In this study we addressed this gap by examining the psychological mechanisms linking perceived algorithmic control to psychological distress among gig workers.
Perceived algorithmic control refers to the degree to which workers subjectively understand their labor process as being monitored, directed, and evaluated by algorithmic systems, whereas perceived algorithmic support refers to the extent to which these systems are perceived as providing guidance, coordination, or task-related assistance (Guo, 2024). Research findings suggest that algorithmic control and support have complex implications for the psychological functioning of gig workers. For some individuals, algorithmic coordination ensures clarity of expectations, enhances efficiency, and provides real-time feedback that supports task engagement (Li et al., 2025; Mousa & Chaouali, 2023). There is also evidence linking perceived algorithmic control to burnout-related outcomes among gig workers. For example, Lang et al. (2023) found that higher perceived algorithmic control was associated with higher levels of burnout among gig workers, whereas algorithmic support could still be associated with relatively high work engagement, suggesting that strain and engagement may coexist under algorithmic management. For others, however, algorithmic control imposed constant monitoring and performance pressure, which increased work intensity, depleted workers’ resources, and contributed to emotional strain (Kinowska & Sienkiewicz, 2023). Moreover, the automation of managerial decision making often leaves limited space for negotiation or explanation, making workers more vulnerable to uncertainty, pressure, and stress (Wiener et al., 2023). Despite the growing scholarly attention, the mechanisms through which perceived algorithmic control influences the mental well-being of gig workers remain insufficiently clarified.
The social and psychological meaning that workers ascribe to their labor may play a crucial role in shaping how they experience algorithmic control. Work meaninglessness, defined as individuals’ perception that their work is worthless and in conflict with their personal values, has been identified as an important psychological resource deficit that enhances stress and undermines resilience (Spreitzer, 1995). Although gig workers who perceive their work as contributing to their personal livelihood or social functioning may view algorithmic direction as structured assistance, this positive interpretation is fragile. When work meaningfulness is diminished, the monitoring and evaluative aspects of algorithmic control are more likely to be experienced as coercive and dehumanizing, reducing workers to mere instruments of the platform and, in turn, intensifying emotional exhaustion and psychological distress.
Furthermore, gig work takes place not only within the immediate context of the platform, but also within a wider institutional and regulatory context, and the extent to which these workers trust governmental institutions may significantly shape their interpretation of platform governance. Institutional distrust reflects individuals’ belief that public authorities are incapable of ensuring fairness, regulating platforms effectively, and safeguarding workers’ rights (Rothstein & Stolle, 2008). Workers with greater institutional trust may perceive algorithmic control as being embedded within a socially legitimate regulatory environment and, thus, will respond with greater tolerance. This buffer quickly weakens when institutional trust is low. Algorithmic constraints are then more likely to be experienced as arbitrary, extractive, and coercive, which may heighten the psychological distress of these workers and further undermine their sense of well-being.
Importantly, the impact of institutional distrust on the worker may depend on the individual’s perception of regulatory unresponsiveness—that is, the belief that relevant authorities do not attend sufficiently to labor concerns, respond to grievances, or adjust governance practices in meaningful ways. When the worker perceives a high level of regulatory unresponsiveness, institutional distrust may be more strongly associated with psychological distress, enhancing the negative emotional consequences of being governed through algorithmic control, including its monitoring, evaluative, and directive aspects. However, when the workers perceive regulators as relatively responsive, institutional distrust may be less strongly associated with psychological distress.
Given these considerations, in this study we responded to the need to develop an integrated framework to examine how perceived algorithmic control influences psychological well-being among gig workers, and to identify the psychological (work meaninglessness), institutional (institutional distrust), and governance-situational (regulatory unresponsiveness) mechanisms that shape this relationship.
Perceived Algorithmic Control and Psychological Distress
From the perspective of the job demands–resources model (Demerouti et al., 2001), the monitoring, evaluative, and constraining aspects of algorithmic control may function as job demands when their intensity exceeds workers’ ability to cope. Under such conditions, gig workers may experience an elevated psychological burden, emotional exhaustion, and stress responses (Kinowska & Sienkiewicz, 2023). Additionally, because algorithmic decision making is often opaque and there is a lack of channels for negotiation, workers may perceive themselves as being without autonomy or recourse, which further heightens emotional strain (Huang, 2023). Thus, perceived algorithmic control is likely to increase psychological distress among gig workers.
Hypothesis 1: Perceived algorithmic control will be positively associated with psychological distress among gig workers.
Mediating Role of Work Meaninglessness
According to conservation of resources theory, meaninglessness can function as an internal psychological resource deficit that produces apathy and stress (Hobfoll, 1989). When workers experience algorithmic control as intrusive or dehumanizing, they may struggle to derive meaning from their tasks, leading to decreased motivation and heightened vulnerability to distress (Cameron, 2020; Mousa & Chaouali, 2023). Therefore, experiencing work meaninglessness may explain how perceived algorithmic control shapes psychological distress.
Hypothesis 2: Work meaninglessness will mediate the relationship between perceived algorithmic control and psychological distress.
Mediating Role of Institutional Distrust
When gig workers feel that their labor is instrumentalized, undervalued, or disconnected from personal purpose, this sense of work meaninglessness can erode their confidence in the broader institutional environment. In this situation, gig workers are more likely to interpret platform governance and regulatory arrangements as extractive rather than protective, thereby increasing their institutional distrust (Schor et al., 2020). A high degree of institutional distrust can, in turn, magnify feelings of uncertainty and vulnerability, making workers more susceptible to psychological distress. Therefore, institutional distrust functions as a second-stage mediator, through which work meaninglessness translates into heightened psychological strain.
Hypothesis 3: Institutional distrust will mediate the relationship between work meaninglessness and psychological distress.
Moderating Role of Regulatory Unresponsiveness
Results of research on social support and stress buffering suggest that when individuals perceive social or regulatory responsiveness as high, they are more likely to feel supported and protected, which can buffer psychological distress (Carlson & Perrewé, 1999). In the context of platform labor, regulatory unresponsiveness signals that relevant authorities are unwilling or unable to address workers’ grievances, enforce fair rules, or provide effective recourse when work-related harm occurs, such as unfair penalties, income loss, excessive workload, or opaque account restrictions (Fieseler et al., 2019; Liu & Wei, 2025; Yang & Holzer, 2006). Under such conditions, institutional distrust becomes more psychologically consequential, because workers not only doubt institutional fairness but also perceive that there are few viable channels for protection or correction, which can intensify feelings of vulnerability and distress (Beck et al., 2024; Carlson & Perrewé, 1999). By contrast, when workers perceive the regulatory body as responsive, they may believe that there are institutional mechanisms available to respond to and mitigate risks, weakening the extent to which institutional distrust translates into psychological distress. Therefore, we expected that regulatory unresponsiveness would strengthen the link between institutional distrust and psychological distress.
Hypothesis 4: Regulatory unresponsiveness will moderate the relationship between institutional distrust and psychological distress, such that the association is stronger when regulatory unresponsiveness is high.
The theoretical framework that this study seeks to validate is illustrated in Figure 1.
Figure 1. Conceptual Model
Method
Participants and Procedure
We utilized an online anonymous survey in this study. We collected data in two independent waves (April 2025 and July 2025) from gig workers in five major Chinese cities: Beijing, Hangzhou, Changsha, Chengdu, and Nanning. Participants responded to the same survey items in both waves, and the second wave was conducted to increase the sample size after the initial wave yielded a relatively small number of valid responses. Specifically, in Wave 1 (April 2025) we distributed surveys to 613 workers and received 173 valid responses, whereas in Wave 2 (July 2025) we distributed surveys to 637 workers and received 398 valid responses. Thus, we distributed surveys to 1,250 workers across the two waves, yielding 571 valid responses (response rate = 45.68%). After removing incomplete or patterned responses, the final sample consisted of 463 gig workers. Among them, 392 (84.67%) were men, 71 (15.33%) were women, the average age was 31.3 years (SD = 6.94), ranging from 18 to 57 years, and 69.11% reported platform work as their primary form of paid employment and were classified as full-time workers. The remaining respondents engaged in platform work on a part-time basis, often alongside other paid work. Participants reported an average of 8.52 working hours per day on the platform (SD = 3.21), based on self-reported daily work time rather than a fixed 7-day weekly schedule.
We recruited participants from large platform service companies engaged primarily in food delivery, ride hailing, and same-city courier services. Because gig workers typically operate across dispersed urban areas, trained field researchers approached potential participants at delivery stations, rest hubs, and in commercial zones and public transit areas.
In accordance with the ethical guidelines of Zhejiang University for minimal-risk social research, ethical approval was not required for this study. The study involved anonymous and noninterventional survey data. We did not collect personally identifiable information, the survey items did not include sensitive topics, and we did not recruit vulnerable populations. All participants were adults (18 years or older) and provided informed consent prior to completing the survey.
Measures
All measures used in this study were originally developed in the English language. To minimize potential measurement bias, we utilized well-established scales from prior scholarly research instead of constructing new measurement items. The original English items were translated into Chinese by two bilingual researchers with experience in social science research, then reviewed and reconciled for conceptual equivalence. A separate bilingual researcher back-translated the Chinese version into English, and discrepancies were resolved through team discussion. Unless otherwise specified, variables were assessed using a 5-point Likert scale (1 = strongly disagree, 5 = strongly agree).
Psychological Distress (Dependent Variable)
We assessed psychological distress using the PROMIS Psychological Stress Experiences Short Form 8a, an eight-item measure originally developed and psychometrically evaluated with pediatric populations (Bevans et al., 2018). Given that the items capture general stress reactions that are conceptually not age specific, we retained this measure for our adult gig worker sample. PROMIS measures have been extensively developed and used with adult populations (Cella et al., 2019). In the present study, the scale demonstrated good internal consistency (Cronbach’s α = .87) and was treated as a continuous indicator of stress experiences without reliance on pediatric normative interpretations. A sample item is “I felt that work was consuming me in a way that I could not resist.”
Perceived Algorithmic Control (Independent Variable)
We measured algorithmic control using a three-item scale developed by Cram et al. (2022) and later adapted/applied by Guo (2024). A sample item is “I have little control over how my work tasks are assigned by the algorithm.”
Work Meaninglessness (Mediator 1)
We measured work meaninglessness using a five-item scale based on Steger et al. (2012). A sample item is “My work feels empty and lacks personal significance.”
Institutional Distrust (Mediator 2)
We assessed institutional distrust using a three-item scale adapted from Rothstein and Stolle (2008) to fit the context of platform labor. Specifically, the item wording was adjusted to refer more directly to the ability and willingness of public authorities to protect workers, ensure fairness, and respond to labor-related concerns. A sample item is “I do not believe public authorities will protect workers like me.”
Regulatory Unresponsiveness (Moderator)
Regulatory unresponsiveness was operationalized as perceived governmental regulatory inaction toward platform labor issues. We based this measure on a three-item scale that was adapted from governance responsiveness literature (Yang & Holzer, 2006). A sample item is “Even when issues are raised, nothing meaningful changes.”
Control Variables
We controlled for participants’ gender, age, education level, daily working hours, and employment type (full-time vs. part-time), consistent with prior platform labor research (Guo, 2024; Mousa & Chaouali, 2023; Zhao et al., 2025).
Data Analysis
We performed data analyses using Stata (Version 17). Descriptive statistics and bivariate correlations were first computed for all study variables. To test the hypothesized sequential mediation model, we estimated the indirect effects using regression-based mediation procedures with bootstrapped confidence intervals. To examine the moderating effect of regulatory unresponsiveness, we included the interaction term between institutional distrust and regulatory unresponsiveness in the regression model and assessed moderation based on the statistical significance of the interaction effect. Statistical significance was evaluated at the .05 level.
Results
As shown in Table 1, all measures demonstrated good reliability and acceptable model fit.
Table 1. Model Reliability and Validity
Note. Cronbach’s α values above .70 indicate acceptable internal consistency reliability; χ²/df ratios below 3 indicate adequate comparative fit; RMSEA = root-mean-square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root-mean-square residual. Model fit indices suggested a good model fit (RMSEA < .05, CFI and TLI > .95, SRMR < .08).
Descriptive Statistics and Correlation Analysis of Main Variables
The descriptive statistics and correlations among the five key variables are presented in Table 2.
Table 2. Correlations Among Study Variables
Note. N = 463.
*** p < .001.
Common Method Bias Test
To assess potential common method bias, we conducted Harman’s single-factor test. The first factor accounted for approximately 30.0% of the total variance, which is below the conventional 50% threshold, suggesting that common method bias is unlikely to be a serious concern.
Main Analysis
The results supported all proposed hypotheses, as shown in Table 3. Perceived algorithmic control was positively associated with psychological distress. Hypothesis 1 was supported.
Moreover, perceived algorithmic control exerted a significant indirect effect on psychological distress through work meaninglessness, supporting Hypothesis 2. Given that the total effect of perceived algorithmic control on psychological distress was 0.249, the indirect effect through work meaninglessness accounted for approximately 21.3% of the total effect, indicating a substantively meaningful mediation.
In addition, work meaninglessness was positively associated with institutional distrust, which, in turn, was linked to greater psychological distress. This supported Hypothesis 3. Moreover, the indirect effect of work meaninglessness via institutional distrust accounted for 35.6% of the total effect of work meaninglessness on psychological distress (0.331), suggesting that institutional distrust played a substantial mediating role in this relationship.
Importantly, we further tested the sequential (chain) mediation pathway from perceived algorithmic control to psychological distress through work meaninglessness and institutional distrust. The sequential indirect effect was significant, accounting for approximately 16.9% of the total effect of perceived algorithmic control on psychological distress.
Furthermore, the interaction between institutional distrust and regulatory unresponsiveness was significant, supporting Hypothesis 4. This indicates that the effect of institutional distrust on psychological distress becomes substantially stronger when regulatory unresponsiveness is high. In other words, when authorities are perceived as unresponsive, institutional distrust actively amplifies psychological distress, supporting a moderated chain mediation mechanism.
Based on the simple slope estimates (Figure 2 and Table 4), institutional distrust was positively associated with psychological distress across levels of regulatory unresponsiveness, and this association became progressively stronger as regulatory unresponsiveness increased (with regulatory unresponsiveness = 3 as the neutral midpoint).
Table 3. Hypotheses and Mechanism Results
Note. CI = confidence interval; LL = lower limit; UL = upper limit; PAC = perceived algorithmic control; PD = psychological distress; WM = work meaninglessness; ID = institutional distrust; RU = regulatory unresponsiveness. The estimate of WM → PD was .331, p < .001. All analyses included control variables, and robust standard errors were applied in all models.
Figure 2. Interaction Effect Between Institutional Distrust and Regulatory Unresponsiveness
Note. Dependent variable is psychological distress. Marginal effects (dy/dx) of institutional distrust on psychological distress are plotted across regulatory unresponsiveness (1–5). Error bars represent 95% confidence intervals (CI); the dashed line indicates a zero effect.
Table 4. Simple Slope Estimates for Institutional Distrust by Degree of Regulatory Unresponsiveness
Note. Regulatory unresponsiveness is the moderator. Because the midpoint (3) represents a neutral response, values 1 and 2 are treated as low regulatory unresponsiveness and values 4 and 5 as high regulatory unresponsiveness. Estimate = the marginal effect (β) of institutional distrust on psychological distress at each level of regulatory unresponsiveness; CI = confidence interval; LL = lower limit; UL = upper limit. The sample sizes for each level of regulatory unresponsiveness (1–5) were 23, 70, 128, 191, and 51, respectively.
Discussion
This study enhances understanding of gig workers’ psychological well-being by revealing how perceived algorithmic control is related to the workers’ psychological distress through work meaninglessness and institutional distrust, and by identifying regulatory unresponsiveness as a boundary condition in this process. These findings move beyond viewing algorithmic management as a purely technical or organizational system and instead highlight the psychological consequences of algorithmic management for workers embedded in a platform-based labor environment.
Theoretical Implications
First, the results support and extend the job demands–resources framework (Demerouti et al., 2001) by providing a psychological perspective. Although algorithmic systems are often promoted as efficient and objective, gig workers in our study experienced them as psychological demands—characterized by surveillance, performance pressure, and limited negotiation space. This suggests that distress under algorithmic governance arises not only from workload intensity, but from a perception of reduced autonomy and diminished control, both of which are known predictors of stress and burnout (Bakker & Demerouti, 2017).
Second, the results identified work meaninglessness as a core psychological pathway linking algorithmic control to psychological distress. When workers feel their labor lacks personal value or significance, key psychological resources are depleted, reducing coping and resilience (Hobfoll, 1989). This is consistent with recent evidence, including findings of longitudinal studies and studies conducted with large samples, that greater meaning in work is associated with less emotional exhaustion and greater well-being (e.g., Alacovska et al., 2024). Building on this literature, we extended the mechanism to platform settings by showing that algorithmic control can undermine meaning, which then feeds into institutional distrust—a second-stage mediator that further elevates distress. This sequential route complements work with general occupational samples linking meaningfulness to increased engagement (Allan et al., 2019; Blustein et al., 2023; Kahn, 1990; May et al., 2004), while specifying how, in gig work, algorithmic governance erodes meaning and cascades into distrust and distress.
Third, we demonstrated institutional distrust to be a second-stage mediator. When workers feel empty or coerced, they are more likely to interpret organizational and societal systems as unresponsive or unfair. This is consistent with theories linking perceived injustice to trust erosion (Rothstein & Stolle, 2008). Our findings also show that this distrust becomes especially consequential when the workers perceive a high level of regulatory unresponsiveness, which amplifies their psychological distress. This suggests that the workers’ broader psychological perception of the institutional context, rather than the work conditions alone, shapes the emotional impact of platform labor (Schor et al., 2020).
Practical Implications
For platform organizations, the findings imply that worker well-being cannot be improved through efficiency optimization alone. Instituting management practices that acknowledge workers’ need for purpose, autonomy, and recognition may help reduce psychological distress. Transparency of platforms around task allocation could be improved, with clearer communication channels provided and an avoidance of system designs that reduce workers to algorithmic inputs.
For policymakers, the results emphasize the importance of visible and trusted regulatory responsiveness. When workers perceive that those in authority recognize and address platform labor issues, the emotional strain associated with algorithmic control is mitigated. Conversely, perceived indifference or inaction can intensify distrust and psychological harm. Thus, promoting gig worker well-being is inseparable from credible, transparent, and responsive governance in the platform economy.
Limitations and Future Research
This study has several limitations. First, because the two waves of our research involved different participants rather than tracking the same individuals over time, causal inference remains limited; in future work researchers could employ longitudinal or quasi-experimental approaches. Second, we focused on psychological outcomes rather than behavioral responses such as turnover, resistance, or coping strategies; linking distress to behavioral outcomes would offer a fuller understanding of platform labor dynamics. Finally, regulatory environments and platform practices may vary across regions, platform types, and in broader geographic contexts, suggesting the need for comparative research across different institutional and national settings.
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Figure 1. Conceptual Model
Table 1. Model Reliability and Validity
Note. Cronbach’s α values above .70 indicate acceptable internal consistency reliability; χ²/df ratios below 3 indicate adequate comparative fit; RMSEA = root-mean-square error of approximation; CFI = comparative fit index; TLI = Tucker–Lewis index; SRMR = standardized root-mean-square residual. Model fit indices suggested a good model fit (RMSEA < .05, CFI and TLI > .95, SRMR < .08).
Table 2. Correlations Among Study Variables
Note. N = 463.
*** p < .001.
Table 3. Hypotheses and Mechanism Results
Note. CI = confidence interval; LL = lower limit; UL = upper limit; PAC = perceived algorithmic control; PD = psychological distress; WM = work meaninglessness; ID = institutional distrust; RU = regulatory unresponsiveness. The estimate of WM → PD was .331, p < .001. All analyses included control variables, and robust standard errors were applied in all models.
Figure 2. Interaction Effect Between Institutional Distrust and Regulatory Unresponsiveness
Note. Dependent variable is psychological distress. Marginal effects (dy/dx) of institutional distrust on psychological distress are plotted across regulatory unresponsiveness (1–5). Error bars represent 95% confidence intervals (CI); the dashed line indicates a zero effect.
Table 4. Simple Slope Estimates for Institutional Distrust by Degree of Regulatory Unresponsiveness
Note. Regulatory unresponsiveness is the moderator. Because the midpoint (3) represents a neutral response, values 1 and 2 are treated as low regulatory unresponsiveness and values 4 and 5 as high regulatory unresponsiveness. Estimate = the marginal effect (β) of institutional distrust on psychological distress at each level of regulatory unresponsiveness; CI = confidence interval; LL = lower limit; UL = upper limit. The sample sizes for each level of regulatory unresponsiveness (1–5) were 23, 70, 128, 191, and 51, respectively.
This research was supported by the Zhejiang University Double First-Class Graduate Excellence Program for Global Competitiveness (188310*194212301).
Zibing Ning, Yuxing Chen, and Yang Fan are co-first authors.
All authors have reviewed and agreed to the revised authorship arrangement, author order, and final version of the manuscript. Zibing Ning, Shengyu He, and Yuxing Chen contributed to conceptualization, study design, and methodology; Zibing Ning, Shengyu He, and Yang Fan performed data collection and analysis; Shengyu He and Yang Fan were responsible for writing the original draft; Zibing Ning and Yang Fan reviewed and edited the manuscript; Zibing Ning and Yuxing Chen provided project supervision; and Zibing Ning, Shengyu He, and Yuxing Chen were responsible for funding acquisition.
The authors are deeply grateful to all participants and to the anonymous reviewers for their thoughtful comments and professional guidance, which strengthened the clarity and rigor of the manuscript.
The anonymized survey dataset and analysis code are available from the corresponding author upon reasonable request. The data cannot be publicly released owing to privacy and fieldwork access agreements.
Shengyu He, School of Public Affairs, Zhejiang University, 866 Yuhangtang Road, Xihu District, Hangzhou 310058, People’s Republic of China. Email: [email protected]
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