The Association between Loneliness and Perceived Stress among Delivery Gig Workers in Urban China: The Moderating Effect of Job Identity

The Association between Loneliness and Perceived Stress among Delivery Gig Workers in Urban China: The Moderating Effect of Job Identity | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Association between Loneliness and Perceived Stress among Delivery Gig Workers in Urban China: The Moderating Effect of Job Identity Hao Wen, Zhan Yu, Anao Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6798152/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract This study examined the association between loneliness and perceived stress among delivery gig workers in urban China and explored the moderating effect of job identity. Based on survey data from 264 full-time delivery workers in Shanghai and Xi’an, findings showed that loneliness was positively associated with perceived stress, while job identity significantly moderated this relationship. Workers with higher job identity experienced a weaker association between loneliness and stress. Regression analyses and subgroup comparisons confirmed the buffering role of job identity. These results underscore the importance of enhancing social identity and job-related belongingness to alleviate stress among gig workers. Implications for digital platform companies and mental health interventions are discussed. gig workers perceived stress loneliness mental health job identity Figures Figure 1 Figure 2 Introduction The gig economy, also known as the digital platform economy, can be defined as paid tasks carried out by independent contractors mediated by online platforms (Koutsimpogiorgos et al., 2020). Over the past two decades, there has been a dramatic increase in the number of digital platform workers, or gig workers, especially after the globalization of the gig economy in 2012 (Tina, 2023). According to the market report from Business Research Insight (2022), the global gig economy size will reach $1.86 trillion in 2031 with an average annual growth rate of 16.88%. After the revolution of the gig economy in 2012, China has since grown into one of the world’s largest gig economy markets. According to the market report from Haitou Academy in 2023, the gig economy in China has approximately $350 billion in market share, which constitutes 53.53% of the total global gig market and almost the entire GDP of Malaysia, followed by the U.S. (27.68 % of the global total) and Indonesia (8.01% of the global total). On the other hand, the Chinese gig economy has a huge global impact in that it enables small businesses worldwide to access affordable goods. As a major consumer of raw materials and energy in the world, the disruption in the Chinese gig economy can not only destroy its own logistic and production networks, but it can also lead to market inefficiency in the global economy (Blagrave & Vesperoni). The size of China’s gig economy market warrants heightened attention to its health and stability, including the wellness of gig workers. Gig work is a type of short-term contract work mediated by virtual platforms (Kuhn, 2016; Tran & Sokas, 2017; Keith et al., 2020), and laborers involved in gig work are generally referred to as gig workers. Based on the report from the National Bureau of Statistics of China (2021), delivery gig workers are the largest gig workforce in China, with an estimated 13 million delivery workers. Delivery gig work in China is considered a precarious type of occupation (Fu, 2023) because workers have unfixed working hours and fluctuating hourly income rates while receiving limited social benefits and statutory protections (Kalleberg 2000; Vosko 2010). The precarious nature of delivery gig work in China might be explained by the dualism of the labor force structure in urban China, the urban vs. rural household type (hukou). Nowadays, migrant workers from rural households dominate the delivery gig workforce. The National Bureau of Statistics of China (2021) showed that 75% of the delivery gig workers in China have a rural household type. Compared to workers from urban households, migrant workers from rural households are disproportionately subject to employment insecurity, low pay, alienation, discrimination, and even violence (Swider 2015). For example, migrant workers of rural household types are often not qualified for urban medical insurance, which makes them highly reluctant to go to hospitals and pay for medical services out of pocket. Therefore, the identity of migrant workers of rural household type further strengthened the vulnerability and precariousness among delivery gig workers. This dualism of labor structure is important because it explains the systematic vulnerability experienced by the majority of delivery gig workers in China, with the intersectionality of precarious work and rural household type. As a consequence, the population of delivery gig workers in China experiences substantially higher stress than other employees. Despite the rapid growth of delivery gig work in China, attention to the stress of delivery gig workers has been surprisingly insufficient (Wei & Van, 2023). Attention to the stress of delivery gig workers is particularly important because studies showed that prolonged driving could cause stress, which can affect an individual’s mood, mental state, and cognitive abilities, making them more susceptible to traffic accidents (e.g., Taylor & Dorn, 2016; Fuller, 2005; Lal & Craig, 2001). For example, Glavin & Schieman (2022) found that gig workers reported higher levels of psychological distress than other self-employed, which was predicted by the financial strain of the nature of gig work. In addition, Wang & Coutts (2022) found that gig workers reported their mental health and life satisfaction worse than those employed full-time but better than the unemployed. Moreover, Mbare (2023) investigated the psychosocial work environment of gig drivers and how it relates to gig drivers’ psychological well-being in Helsinki, Finland. The result showed that the precarious nature of the working environment substantially contributed to psychological stress among gig drivers. Previous studies also revealed that the high level of stress among delivery gig workers might also come from social challenges. For example, in China and most countries in the world, the legal labor rights of gig workers have not been treated equally. When other employees in the private sector were under the protection of modern labor laws, gig workers were excluded from the laws because “gig workers” are not legally considered as “employees” but instead, private contractors. As a result, gig workers cannot receive the benefits such as minimum wage, overtime payment, or health insurance provided by their employers. This legal challenge was intimately connected with their stress and other vulnerabilities in their mental health (Bajwa et al., 2018). The intense stress experienced by delivery gig workers in China has been a growing social challenge. Previous studies have been looking for the risk factors that predict the stress level of delivery gig workers. Loneliness was found to be one of the most salient predictors of the stress level of delivery gig workers (Wang & Coutts, 2022; Glavin et al., 2021). Delivery gig workers are at significantly higher risk of loneliness, as reports from Tsinghua University estimated that 93.8% of gig drivers in major Chinese cities spend more than 11 hours per day alone in their vehicles. According to the social support theory (Hupcey, 1998), social connections and relationships are essential for individuals' well-being, particularly during times of stress or challenge. Delivery gig workers might be subject to higher levels of stress because of their limited social connections with groups, leading to severe feelings of loneliness and further stress-related symptoms. Wang and Coutts’s (2022) study showed that gig workers’ worse mental health and life satisfaction was due to their higher levels of loneliness, given the nature of gig workers driving long hours for work. On the other hand, the impact of loneliness on stress among delivery gig workers might be amplified by the difficulties in forming a job identity. Based on social identity theory (Huddy, 2001), people draw a sense of self from their affiliation with others through communicative processes, and the lack of a sense of self can negatively impact mental health. During delivery gig work, it is hard to maintain sustainable communication with the organizational level in the gig company and with other peer gig workers as most delivery gig workers work alone, which might lead to difficulty in forming a high level of job identity and further impact the perceived stress among delivery gig workers. According to Walsh & Gordon (2008), job identity is one dimension of work identity, which is a work-based self-concept that affects the roles people adopt and the corresponding ways they behave when performing their work. Previous studies showed that job identity is significantly associated with job stress among educators, social workers, and employees in other sectors (e.g. Oh & Kwon, 2010; Levin et al., 2022). The high level of stress among gig drivers in urban China is a growing and urgent social issue that greatly impacts the gig economy, digital platform companies, and a huge number of laborers' mental health. To reduce stress among delivery gig workers, there is an urgent need for research to understand the mechanism of the relationship between loneliness and stress among delivery gig workers. Based on our knowledge, previous studies have not explored the potential moderators between loneliness and stress among delivery gig workers. The current study fills this gap by examining the moderating role of job identity. Specifically, we test the following research hypotheses: Loneliness positively correlates with perceived stress among delivery gig workers in China. Job identity has a significant moderating effect on the association between loneliness and perceived stress. Methods Participants We recruited 279 delivery gig workers in the metropolis areas of Shanghai and Xi’an, which are two big cities in East and West China. After reaching out to the two largest food delivery gig companies (“Meituan” and “E’leme”) and receiving their permissions, we employed a convenience sampling method by approaching delivery gig workers in rest hubs, where delivery gig workers gather and rest during non-rush hours. To be eligible for the study, a participant must engage in gig work as their full-time job. Notably, 49% of gig drivers work part-time, and gig work is not their full-time job (Tina, 2023 ). We decided to include only full-time delivery gig workers because, according to Keith et al. ( 2020 ), the effect of working as a gig driver would differ significantly between full-time and part-time gig drivers, which warrants separate investigations. Second, a participant needs to plan to continue working as a gig driver for at least 6 months. This is to rule out the effects of career plans on participants’ stress. After the participants gave their consent to engage in the study, they were presented with a QR code that directed them to the online survey. The survey took five to ten minutes to complete. After participants completed the surveys, they were provided with 15 RMB as compensation upon finishing the surveys through e-payment immediately. We also included a quality assurance process by excluding surveys that were finished within 180 seconds or providing extreme answers with patterns which indicates a clear signal of randomly choosing the answers. Measures Dependent variable: Perceived stress We used the Perceived Stress Scale (PSS) (Cohen et al., 1994 ) to measure perceived stress. PSS is the most widely used psychological instrument for measuring the perception of stress, which can capture how unpredictable, uncontrollable, and overloaded respondents find out about their lives. PSS is a 5-point Likert scale that has 10 items (e.g. “In the past month, how often have you felt unable to control important things in your life?”). The answer set is 1 = “never”, 2 = “rarely”, 3 = “sometimes”, 4 = “often”, and 5 = “always”. Primary Independent variable: Loneliness The level of loneliness was measured by a short 5-point Likert scale. This scale was used in Hughes et al.’s ( 2004 ) study and was revised and validated based on the widely used 20-item UCLA Loneliness Scale (Russell et al., 1980 ) to fit the measurement of loneliness in larger studies. The current scale includes three items (e.g. “How often do you feel that you lack companionship?”). The participants responded by choosing from a 5-point answer set: 1 = “never”, 2 = “rarely”, 3 = “sometimes”, 4 = “often”, and 5 = “always”. Moderating variable: Job identity The tool for measuring job identity was a 5-point Likert scale that has 2 items. The current scale was extracted from the Role-based Identity Scale (RBIS) (Welbourne & Paterson, 2017 ). The job identity measurement included 2 items (e.g. “Being able to talk about my job with friends.”). The answer set is 1 = “never”, 2 = “rarely”, 3 = “sometimes”, 4 = “often”, and 5 = “always”. Covariates This study included 5 covariates: biological sex (0 = “female”, 1 = “male”), age, marital status (0 = “not currently married”, 1 = “currently married”), education attainment (0 = “primary school or below”, 1 = “middle school”, 2 = “technical secondary school/technical school/vocational high school”, 3 = “general high school”, 4 = “junior college”, 5 = “undergraduate”, 6 = “graduate”), working hours, whether having kid(s) (0 = “no”, 1 = “yes”). Statistical analysis All data analyses were completed in STATA 18. We first conducted descriptive statistics of means and standard deviations. We also evaluated the Pearson correlation among all variables. We also calculated the variable inflation factors to detect the level of multicollinearity. For regression analysis, we first conducted a bivariate ordinary least square (OLS) linear regression to examine the effect of loneliness on perceived stress. In the second model, we evaluated the association between loneliness and perceived stress after controlling for the covariates. In the third model, we evaluated the moderating effect of job identity on the loneliness and perceived stress relationship by adding an interaction term to the previous model. Lastly, as a post-doc follow-up analysis, we conducted Fisher's r-to-z transformation of the bivariate correlations between loneliness and perceived stress by subgroups of “high” and “low” job-identity determined by sample mean. Then, we examine whether the associations between loneliness and perceived stress in these 2 subsamples are significantly different to further illustrate the moderation effect of job-identity. Results Descriptive analyses After the quality assurance process, we excluded 15 participants from further data analysis, 11 of them did not give consent to participate in the study, 4 of them finished the surveys within 180 seconds, and 1 of them provided extreme answers. We finally included 264 surveys in our data analysis with a survey respondent rate of 94.62%. Table 1 shows the descriptive statistics of all variables in the current study, including sample size, means, standard deviations, and the range of the variables. The average scores of perceived stress and loneliness were 2.769 and 2.626, indicating a moderate level of perceived stress and loneliness among participants. Also, the mean value of job identity was 3.299, which showed a moderately high level of job identity on average. Notably, 92.8% of the gig drivers who participated in the study were male. The average age of the participants was 29.13 years old. 36.4% of participants were married and 38.6% had a kid(s). Only 18.9% of participants had an urban household type. In addition, the average working hours per day was 10.88 hours among the participants. Table 1 Descriptive Statistics Variables n Mean SD Min Max Perceived stress 264 2.769 .655 1 5 Loneliness 264 2.626 .961 1 5 Job-based identity 264 2.700 .914 1 5 Age 264 29.134 7.826 18 59 Marital status (married = 1) 264 .364 .482 0 1 Sex (male = 1) 264 .928 .259 0 1 Hukou (household location) (urban = 1) 264 .189 .393 0 1 Education attainment 264 3.580 1.42 1 7 Having kid(s) (yes = 1) 264 .386 .488 0 1 Working hours 264 10.880 2.498 3 24 Correlation analysis Table 2 shows the Pearson correlations among all variables. The correlation analysis showed that the main predictor variable, loneliness, was positively correlated with the outcome variable, perceived stress ( r = 0.569, p < 0.05). Job identity was negatively correlated with age ( r = -0.122, p < 0.05) and household type ( r = -0.185, p < 0.05). Table 3 shows the variable inflation factors (VIF) for all variables. The mean VIF was 1.708, indicating a low level of multicollinearity. Table 2 Pairwise correlations Variables Perceived stress Loneliness Job-based identity Age Marital status Sex Hukou Education attainment Having kid(s) Working hours Perceived stress 1.000 Loneliness .569*** 1.000 Job-based identity − .060 .086 1.000 Age .043 .046 .122* 1.000 Marital status .086 − .017 − .015 .551*** 1.000 Sex .056 − .052 − .091 .029 − .003 1.000 Hukou .034 − .037 .185** .086 − .064 − .090 1.000 Education attainment .084 .087 − .112 − .227*** − .137* .031 .062 1.000 Having kid(s) .056 − .048 .030 .602*** .823*** − .050 − .026 − .154* 1.000 Working hours − .013 − .048 − .037 .156* .230** .170** − .079 .007 .233** 1.000 *p < .05, **p < .01, ***p < .001. Table 3 Variable Inflation Factor VIF 1/VIF Having kid(s) 3.497 .286 Marital Status 3.162 .316 Age 1.685 .594 Working hours 1.098 .911 Education attainment 1.079 .927 Sex 1.062 .942 Job-based identity 1.049 .953 Loneliness 1.035 .966 Mean VIF 1.708 Main and moderation effect results Table 4 shows the results of OLS regression models. Model 1 was a bivariate regression showing that loneliness was significantly associated with perceived stress ( β = 0.387, p < 0.001). Model 2 further included job identity and the covariates. The results showed that controlling for all covariates, both loneliness and job identity were significantly associated with perceived stress, β = 0.398, p < 0.001 and β = -0.07, p < 0.05, respectively. These results indicated that higher levels of loneliness and lower levels of job-identity were associated with higher levels of perceived stress respectively. In model 3, we added the interaction term between loneliness and job identity into model 2. The result revealed that the interaction term was negatively and significantly associated with perceived stress ( β = -0.388, p < 0.001), indicating a significant moderating effect of job identity on the loneliness and perceived stress relationship. This means that higher levels of job identity decreased the intensity of the effect of loneliness on perceived stress. Figure 2 visualizes the moderating effect that for different values of job identity, the intensities of the effects of loneliness on perceived stress are different. Table 4 Regression models on perceived stress Model 1 Model 2 Model 3 β SE β SE β SE Loneliness .387*** .035 .398*** .035 .787*** .105 Job-based identity − .07* .037 − .388*** .089 Age − .004 .006 − .003 .005 Marital status .1 .123 .105 .12 Sex .214 .132 .202 .128 Education attainment .016 .025 .011 .024 Having kid(s) .094 .128 .083 .124 Working hours − .004 .015 − .004 .014 Loneliness* job-based identity − .122*** .031 F statistics 125.01*** 17.38*** 18.04*** Adjusted R 2 32.04% 33.43% 37.01% Notes: Model 1: Univariate OLS regression, not adjusted for covariates. Model 2: Adjusted for job-based identity, age, sex, education attainment, whether having kid(s), and working hours. Model 3: Adjusted for the covariates in Model 2 plus the interaction term of loneliness and job-based identity. *p < .05, **p < .01, ***p < .001. Post-hoc analysis of the moderating effect We used Fisher’s r-to-z transformation to conduct the post-hoc analysis of the moderating effect. We divided the sample into 2 subgroups, high job identity (N = 129) and low job identity (N = 135), and directly compared the group-specific r-to-z transformed correlation between loneliness and perceived stress. The result indicated the loneliness and perceived stress association was significantly lower in the high job identity subgroup versus their peers in the low job identity group, Z = 2.45, p < 0.05. Discussion Over the past decade, China has witnessed a blossoming of the gig economy and the population of the delivery gig workforce. While the high levels of stress among delivery gig workers have grown into a social issue, the current study critically examined this issue by examining the association between loneliness and stress among delivery gig workers in urban China. To our knowledge, the current study is the first study that has exclusively collected survey data from delivery gig workers to examine the buffer effect of job identity on perceived stress. The findings of this study showed that a higher level of loneliness is positively associated with stress, indicating that delivery gig workers with higher levels of loneliness were more susceptible to having higher levels of stress. Notably, such a positive relationship remained statistically significant after we accounted for demographic covariates, i.e., age, sex, and marital status. Moreover, the current study also revealed a significant moderate effect of job identity on the relationship between loneliness and stress. Consistent with our hypothesis, the positive association between loneliness and stress was weaker among delivery gig workers with higher levels of job identity. The significant positive main effect of loneliness on stress among delivery gig workers in urban China is consistent with our hypothesis and the existing literature. For instance, Wang & Coutts (2022) found that gig workers with high levels of loneliness tended to have worse mental health and lower life satisfaction. Also, Glavin et al. ( 2021 ) found that digital platform workers were more likely to develop feelings of loneliness and powerlessness, leading to psychological distress. Glavin et al. ( 2021 ) also pointed out that gig drivers, i.e., rideshare drivers or delivery gig workers, had stronger feelings of loneliness and powerlessness because of the algorithmic control and distancing strategies that undermine gig workers' job autonomy and social connections. However, most existing investigations, including the current study, are cross-sectional in nature, limiting the establishment of causality. Therefore, little was known about the mechanism of the association between loneliness and stress among delivery gig workers, which is a topic requiring further research. Given the strong association between loneliness and stress revealed in this study, related intervention programs should be developed to reduce the stress for delivery gig workers, for example, strength-based coping skill interventions and establishing social support groups. Furthermore, we found that job identity had a significant and moderating effect on the relationship between loneliness and stress among delivery gig workers in urban China. In other words, while delivery gig workers with higher levels of loneliness tended to have higher levels of stress, this relationship between loneliness and stress was weaker for those who had higher levels of job identity. The moderating role of job identity could be explained by the social identity theory. Based on the social identity theory, people can form a sense of self-identity, i.e., professional self, through communication and connections with others. This self-identity can protect group members from adverse reactions to strain because it provides a basis for group members to receive and benefit from social support (Haslam et al., 2005 ). In the context of delivery gig workers in urban China, their identities as migrant workers holding a rural household type inhibit their social connections with urban residents, making it difficult for them to form a self-identity in the urban area. In addition, they can also hardly develop a high level of job identity during gig work because the nature of online platform work prevents them from communicating with colleagues and clients. Therefore, this dilemma of forming a high level of social identity might reduce the potential social support that gig workers receive in urban areas, leading to higher levels of stress. We recommend that gig companies notice the importance of job identity among gig workers because the overall job identity among Chinese gig workers might be substantially low. Although the current population of delivery gig workers is huge, the turnover rate is also distinctively high compared to other workers in China, indicating that delivery gig work is more like a temporary job (Fu, 2023 ). The high turnover rate makes it more difficult to develop stable social connections during gig work and to develop a strong job identity. With higher levels of job identity, gig companies can benefit from lower turnover rates for gig workers, and gig workers may have lower levels of stress and increased well-being. Gig companies should consider launching more initiatives that increase gig workers’ job identity, such as team building or reinforcing organizational culture. Limitations Notably, the current study suffers from several limitations. First, the current study has a limited sample size and a cross-sectional design, making the results less generalizable and hard to infer a causal relationship. Second, the quality assurance process in the survey might not be enough. Since delivery gig workers are usually extremely busy, it is more likely that they provided invalid responses in the surveys. Third, the association between loneliness and stress might be confounded by other factors, such as financial precarity. Future studies can use randomized sampling and a longitudinal design to investigate the causal relationship between loneliness and stress among delivery gig workers. In addition, if applicable, future studies could include measurements on more potential predictors of stress. Furthermore, studies on other types of gig workers or delivery gig workers from other areas or cultures are highly encouraged to help better understand the stress of gig workers. References Abate, J., Schaefer, T., & Pavone, T. (2018). Understanding generational identity, job burnout, job satisfaction, job tenure and turnover intention. 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BMJ open , 12(12), e066389. https://doi.org/10.1136/bmjopen-2022-066389 Wei, H., & van Tongeren, M. (2023). Gig work and health. In N. Sultan-Taïeb, A. Garde, & C. S. Degryse (Eds.), Handbook of life course occupational health (pp. 343–357). Springer. https://doi.org/10.1007/978-3-030-98153-0_25 Welbourne, T. M., & Paterson, T. A. (2017). Advancing a richer view of identity at work: The role‐based identity scale. Personnel Psychology , 70 (2), 315-356. https://doi-org.ezproxy.cul.columbia.edu/10.1111/peps.12150 Zhu, Q. (2021). 我国快递企业快递员离职倾向研究. [Research on Turnover Intention of Couriers Based on Job Embeddedness]. Frontiers of Engineering Management , 7(7), 1. (Paper in Chinese). http://www.chinaqking.com/yc/2021/3082114.html Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6798152","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":466621490,"identity":"cc125746-cfb7-4c3b-ad35-fe72b3aca02e","order_by":0,"name":"Hao Wen","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIie3PIQvCQBTA8QeDrRysnsV9hSfDiSDzq9w4MBkuWQyGgRY/wPwWgmBWFizDVeGCriwZZl/wBJeUbTbD/duD9+PxAHS6PwzBOGYFjgAsAEONKtZETO5SMXnttybEo6SI3wRakAFlLlJMHdsg/Z0QPtjWFGvJMGLBTaDsbULiyQg5dNb3eoKSxeqKZBgrQlC9c2m4gjJYUoLniixg3Ey4qcihIjEgbSLX3HApcvWLOVO/nAhNclFPkvSRFaXv2Fa4l6Kcd+0V39aSj8hv6zqdTqf72hNZJELmloMuIwAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0002-3030-7447","institution":"Columbia University School of Social Work","correspondingAuthor":true,"prefix":"","firstName":"Hao","middleName":"","lastName":"Wen","suffix":""},{"id":466621491,"identity":"68c627dd-8665-43c0-b2f7-c3456f91902f","order_by":1,"name":"Zhan Yu","email":"","orcid":"","institution":"East China Normal University","correspondingAuthor":false,"prefix":"","firstName":"Zhan","middleName":"","lastName":"Yu","suffix":""},{"id":466621492,"identity":"9631fbf6-481a-40a4-a241-2634c3f91ec3","order_by":2,"name":"Anao Zhang","email":"","orcid":"","institution":"University of Michigan School of Social Work","correspondingAuthor":false,"prefix":"","firstName":"Anao","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2025-06-02 01:53:20","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6798152/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6798152/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":84162864,"identity":"1296c3d5-b42e-4b58-8f67-50bf9110803a","added_by":"auto","created_at":"2025-06-08 14:40:59","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":15798,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model of the moderating effect\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6798152/v1/6b3af0cd272a6eb76ed53974.png"},{"id":84162310,"identity":"80831e30-4af1-4919-876d-ed9046636159","added_by":"auto","created_at":"2025-06-08 14:32:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":35872,"visible":true,"origin":"","legend":"\u003cp\u003eThe moderating effect of job-based analysis between loneliness and perceived stress\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6798152/v1/1e167433d676e2c8b60155e2.png"},{"id":84258130,"identity":"6b9806d3-944e-41ce-9f43-4a496f7e0c5a","added_by":"auto","created_at":"2025-06-09 21:59:47","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":725223,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6798152/v1/442a0760-ce8a-4e7b-9cdc-558ea3e504be.pdf"}],"financialInterests":"","formattedTitle":"The Association between Loneliness and Perceived Stress among Delivery Gig Workers in Urban China: The Moderating Effect of Job Identity","fulltext":[{"header":"Introduction","content":"\u003cp\u003eThe gig economy, also known as the digital platform economy, can be defined as paid tasks carried out by independent contractors mediated by online platforms (Koutsimpogiorgos et al., 2020). Over the past two decades, there has been a dramatic increase in the number of digital platform workers, or gig workers, especially after the globalization of the gig economy in 2012 (Tina, 2023). According to the market report from Business Research Insight (2022), the global gig economy size will reach $1.86 trillion in 2031 with an average annual growth rate of 16.88%. After the revolution of the gig economy in 2012, China has since grown into one of the world\u0026rsquo;s largest gig economy markets. According to the market report from Haitou Academy in 2023, the gig economy in China has approximately $350 billion in market share, which constitutes 53.53% of the total global gig market and almost the entire GDP of Malaysia, followed by the U.S. (27.68 % of the global total) and Indonesia (8.01% of the global total). On the other hand, the Chinese gig economy has a huge global impact in that it enables small businesses worldwide to access affordable goods. As a major consumer of raw materials and energy in the world, the disruption in the Chinese gig economy can not only destroy its own logistic and production networks, but it can also lead to market inefficiency in the global economy (Blagrave \u0026amp; Vesperoni). The size of China\u0026rsquo;s gig economy market warrants heightened attention to its health and stability, including the wellness of gig workers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eGig work is a type of short-term contract work mediated by virtual platforms (Kuhn, 2016; Tran \u0026amp; Sokas, 2017; Keith et al., 2020), and laborers involved in gig work are generally referred to as gig workers. Based on the report from the National Bureau of Statistics of China (2021), delivery gig workers are the largest gig workforce in China, with an estimated 13 million delivery workers. Delivery gig work in China is considered a precarious type of occupation (Fu, 2023) because workers have unfixed working hours and fluctuating hourly income rates while receiving limited social benefits and statutory protections (Kalleberg 2000; Vosko 2010). The precarious nature of delivery gig work in China might be explained by the dualism of the labor force structure in urban China, the urban vs. rural household type (hukou). Nowadays, migrant workers from rural households dominate the delivery gig workforce. The National Bureau of Statistics of China (2021) showed that 75% of the delivery gig workers in China have a rural household type. Compared to workers from urban households, migrant workers from rural households are disproportionately subject to employment insecurity, low pay, alienation, discrimination, and even violence (Swider 2015). For example, migrant workers of rural household types are often not qualified for urban medical insurance, which makes them highly reluctant to go to hospitals and pay for medical services out of pocket. Therefore, the identity of migrant workers of rural household type further strengthened the vulnerability and precariousness among delivery gig workers. This dualism of labor structure is important because it explains the systematic vulnerability experienced by the majority of delivery gig workers in China, with the intersectionality of precarious work and rural household type. As a consequence, the population of delivery gig workers in China experiences substantially higher stress than other employees. \u0026nbsp; \u0026nbsp; \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eDespite the rapid growth of delivery gig work in China, attention to the stress of delivery gig workers has\u0026nbsp;been surprisingly insufficient (Wei \u0026amp; Van, 2023). Attention to the stress of delivery gig workers is particularly important because studies showed that prolonged driving could cause stress, which can affect an individual\u0026rsquo;s mood, mental state, and cognitive abilities, making them more susceptible to traffic accidents (e.g., Taylor \u0026amp; Dorn, 2016; Fuller, 2005; Lal \u0026amp; Craig, 2001). For example, Glavin \u0026amp; Schieman (2022) found that gig workers reported higher levels of psychological distress than other self-employed, which was predicted by the financial strain of the nature of gig work. In addition, Wang \u0026amp; Coutts (2022) found that gig workers reported their mental health and life satisfaction worse than those employed full-time but better than the unemployed. Moreover, Mbare (2023) investigated the psychosocial work environment of gig drivers and how it relates to gig drivers\u0026rsquo; psychological well-being in Helsinki, Finland. The result showed that the precarious nature of the working environment substantially contributed to psychological stress among gig drivers. Previous studies also revealed that the high level of stress among delivery gig workers might also come from social\u0026nbsp;challenges. For example, in China and most countries in the world, the legal labor rights of gig workers have not been treated equally. When other employees in the private sector were under the protection of modern labor laws, gig workers were excluded from the laws because \u0026ldquo;gig workers\u0026rdquo; are not legally considered as \u0026ldquo;employees\u0026rdquo; but\u0026nbsp;instead, private contractors. As a result, gig workers cannot receive the benefits such as minimum wage, overtime payment, or health insurance provided by their employers. This legal challenge was intimately connected with their stress and other vulnerabilities in their mental health (Bajwa et al., 2018).\u003c/p\u003e\n\u003cp\u003eThe intense stress experienced by delivery gig workers in China has been a growing social challenge. Previous studies have been looking for the risk factors that predict the stress level of delivery gig workers. Loneliness was found to be one of the most salient predictors of the stress level of delivery gig workers (Wang \u0026amp; Coutts, 2022; Glavin et al., 2021). Delivery gig workers are at significantly higher risk of loneliness, as reports from Tsinghua University estimated that 93.8% of gig drivers in major Chinese cities spend more than 11 hours per day alone in their vehicles. According to the social support theory (Hupcey, 1998), social connections and relationships are essential for individuals\u0026apos; well-being, particularly during times of stress or challenge. Delivery gig workers might be subject to higher levels of stress because of their limited social connections with groups, leading to severe feelings of loneliness and further stress-related symptoms. Wang and Coutts\u0026rsquo;s (2022) study showed that gig workers\u0026rsquo; worse mental health and life satisfaction was due to their higher levels of loneliness, given the nature of gig workers driving long hours for work.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOn the other hand, the impact of loneliness on stress among delivery gig workers might be amplified by the difficulties in forming a job identity. Based on social identity theory (Huddy, 2001), people draw a sense of self from their affiliation with others through communicative processes, and the lack of a sense of self can negatively impact mental health. During delivery gig work, it is hard to maintain sustainable communication with the organizational level in the gig company and with other peer gig workers as most delivery gig workers work alone, which might lead to difficulty in forming a high level of job identity and further impact the perceived stress among delivery gig workers. According to Walsh \u0026amp; Gordon (2008), job identity is one dimension of work identity, which is a work-based self-concept that affects the roles people adopt and the corresponding ways they behave when performing their work. Previous studies showed that job identity is significantly associated with job stress among educators, social workers, and employees in other sectors (e.g. Oh \u0026amp; Kwon, 2010; Levin et al., 2022). The high level of stress among gig drivers in urban China is a growing and urgent social issue that greatly impacts the gig economy, digital platform companies, and a huge number of laborers\u0026apos; mental health. To reduce stress among delivery gig workers, there is an urgent need for research to understand the mechanism of the relationship between loneliness and stress among delivery gig workers. Based on our knowledge, previous studies have not explored the potential moderators between loneliness and stress among delivery gig workers. The current study fills this gap by examining the moderating role of job identity. Specifically, we test the following research hypotheses:\u003c/p\u003e\n\u003col\u003e\n \u003cli\u003eLoneliness positively correlates with perceived stress among delivery gig workers in China.\u003c/li\u003e\n \u003cli\u003eJob identity has a significant moderating effect on the association between loneliness and perceived stress.\u0026nbsp;\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eWe recruited 279 delivery gig workers in the metropolis areas of Shanghai and Xi\u0026rsquo;an, which are two big cities in East and West China. After reaching out to the two largest food delivery gig companies (\u0026ldquo;Meituan\u0026rdquo; and \u0026ldquo;E\u0026rsquo;leme\u0026rdquo;) and receiving their permissions, we employed a convenience sampling method by approaching delivery gig workers in rest hubs, where delivery gig workers gather and rest during non-rush hours. To be eligible for the study, a participant must engage in gig work as their full-time job. Notably, 49% of gig drivers work part-time, and gig work is not their full-time job (Tina, \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). We decided to include only full-time delivery gig workers because, according to Keith et al. (\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), the effect of working as a gig driver would differ significantly between full-time and part-time gig drivers, which warrants separate investigations. Second, a participant needs to plan to continue working as a gig driver for at least 6 months. This is to rule out the effects of career plans on participants\u0026rsquo; stress. After the participants gave their consent to engage in the study, they were presented with a QR code that directed them to the online survey. The survey took five to ten minutes to complete. After participants completed the surveys, they were provided with 15 RMB as compensation upon finishing the surveys through e-payment immediately. We also included a quality assurance process by excluding surveys that were finished within 180 seconds or providing extreme answers with patterns which indicates a clear signal of randomly choosing the answers.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eMeasures\u003c/h2\u003e \u003cdiv id=\"Sec4\" class=\"Section3\"\u003e \u003ch2\u003eDependent variable: Perceived stress\u003c/h2\u003e \u003cp\u003eWe used the Perceived Stress Scale (PSS) (Cohen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1994\u003c/span\u003e) to measure perceived stress. PSS is the most widely used psychological instrument for measuring the perception of stress, which can capture how unpredictable, uncontrollable, and overloaded respondents find out about their lives. PSS is a 5-point Likert scale that has 10 items (e.g. \u0026ldquo;In the past month, how often have you felt unable to control important things in your life?\u0026rdquo;). The answer set is 1 = \u0026ldquo;never\u0026rdquo;, 2 = \u0026ldquo;rarely\u0026rdquo;, 3 = \u0026ldquo;sometimes\u0026rdquo;, 4 = \u0026ldquo;often\u0026rdquo;, and 5 = \u0026ldquo;always\u0026rdquo;.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e\n\u003ch3\u003ePrimary Independent variable: Loneliness\u003c/h3\u003e\n\u003cp\u003eThe level of loneliness was measured by a short 5-point Likert scale. This scale was used in Hughes et al.\u0026rsquo;s (\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e) study and was revised and validated based on the widely used 20-item UCLA Loneliness Scale (Russell et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e1980\u003c/span\u003e) to fit the measurement of loneliness in larger studies. The current scale includes three items (e.g. \u0026ldquo;How often do you feel that you lack companionship?\u0026rdquo;). The participants responded by choosing from a 5-point answer set: 1 = \u0026ldquo;never\u0026rdquo;, 2 = \u0026ldquo;rarely\u0026rdquo;, 3 = \u0026ldquo;sometimes\u0026rdquo;, 4 = \u0026ldquo;often\u0026rdquo;, and 5 = \u0026ldquo;always\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eModerating variable: Job identity\u003c/h3\u003e\n\u003cp\u003eThe tool for measuring job identity was a 5-point Likert scale that has 2 items. The current scale was extracted from the Role-based Identity Scale (RBIS) (Welbourne \u0026amp; Paterson, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The job identity measurement included 2 items (e.g. \u0026ldquo;Being able to talk about my job with friends.\u0026rdquo;). The answer set is 1 = \u0026ldquo;never\u0026rdquo;, 2 = \u0026ldquo;rarely\u0026rdquo;, 3 = \u0026ldquo;sometimes\u0026rdquo;, 4 = \u0026ldquo;often\u0026rdquo;, and 5 = \u0026ldquo;always\u0026rdquo;.\u003c/p\u003e\n\u003ch3\u003eCovariates\u003c/h3\u003e\n\u003cp\u003eThis study included 5 covariates: biological sex (0 = \u0026ldquo;female\u0026rdquo;, 1 = \u0026ldquo;male\u0026rdquo;), age, marital status (0 = \u0026ldquo;not currently married\u0026rdquo;, 1 = \u0026ldquo;currently married\u0026rdquo;), education attainment (0 = \u0026ldquo;primary school or below\u0026rdquo;, 1 = \u0026ldquo;middle school\u0026rdquo;, 2 = \u0026ldquo;technical secondary school/technical school/vocational high school\u0026rdquo;, 3 = \u0026ldquo;general high school\u0026rdquo;, 4 = \u0026ldquo;junior college\u0026rdquo;, 5 = \u0026ldquo;undergraduate\u0026rdquo;, 6 = \u0026ldquo;graduate\u0026rdquo;), working hours, whether having kid(s) (0 = \u0026ldquo;no\u0026rdquo;, 1 = \u0026ldquo;yes\u0026rdquo;).\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eAll data analyses were completed in STATA 18. We first conducted descriptive statistics of means and standard deviations. We also evaluated the Pearson correlation among all variables. We also calculated the variable inflation factors to detect the level of multicollinearity. For regression analysis, we first conducted a bivariate ordinary least square (OLS) linear regression to examine the effect of loneliness on perceived stress. In the second model, we evaluated the association between loneliness and perceived stress after controlling for the covariates. In the third model, we evaluated the moderating effect of job identity on the loneliness and perceived stress relationship by adding an interaction term to the previous model. Lastly, as a post-doc follow-up analysis, we conducted Fisher's r-to-z transformation of the bivariate correlations between loneliness and perceived stress by subgroups of \u0026ldquo;high\u0026rdquo; and \u0026ldquo;low\u0026rdquo; job-identity determined by sample mean. Then, we examine whether the associations between loneliness and perceived stress in these 2 subsamples are significantly different to further illustrate the moderation effect of job-identity.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eDescriptive analyses\u003c/h2\u003e \u003cp\u003eAfter the quality assurance process, we excluded 15 participants from further data analysis, 11 of them did not give consent to participate in the study, 4 of them finished the surveys within 180 seconds, and 1 of them provided extreme answers. We finally included 264 surveys in our data analysis with a survey respondent rate of 94.62%. Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the descriptive statistics of all variables in the current study, including sample size, means, standard deviations, and the range of the variables. The average scores of perceived stress and loneliness were 2.769 and 2.626, indicating a moderate level of perceived stress and loneliness among participants. Also, the mean value of job identity was 3.299, which showed a moderately high level of job identity on average. Notably, 92.8% of the gig drivers who participated in the study were male. The average age of the participants was 29.13 years old. 36.4% of participants were married and 38.6% had a kid(s). Only 18.9% of participants had an urban household type. In addition, the average working hours per day was 10.88 hours among the participants.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive Statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.655\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.961\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob-based identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.700\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.914\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e29.134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.826\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e59\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status (married\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.364\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.482\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (male\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHukou (household location) (urban\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e3.580\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving kid(s) (yes\u0026thinsp;=\u0026thinsp;1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.386\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.488\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e10.880\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e24\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eCorrelation analysis\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e shows the Pearson correlations among all variables. The correlation analysis showed that the main predictor variable, loneliness, was positively correlated with the outcome variable, perceived stress (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.569, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Job identity was negatively correlated with age (\u003cem\u003er\u003c/em\u003e = -0.122, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05) and household type (\u003cem\u003er\u003c/em\u003e = -0.185, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05). Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e shows the variable inflation factors (VIF) for all variables. The mean VIF was 1.708, indicating a low level of multicollinearity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePairwise correlations\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"11\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c11\" colnum=\"11\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eJob-based identity\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eHukou\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eHaving kid(s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c11\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePerceived stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.569***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob-based identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.060\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.043\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.046\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.122*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.017\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.551***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.052\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.091\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHukou\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.185**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.086\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.090\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.084\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.087\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.112\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.227***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.137*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving kid(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.030\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.602***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.823***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.050\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.026\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.154*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c10\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c11\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.048\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.156*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.230**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.170**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e.233**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c11\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"11\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVariable Inflation Factor\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1/VIF\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving kid(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.497\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.286\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital Status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.316\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.685\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.594\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.098\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.911\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.079\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.927\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.062\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.942\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob-based identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.953\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.966\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMean VIF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.708\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003eMain and moderation effect results\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e shows the results of OLS regression models. Model 1 was a bivariate regression showing that loneliness was significantly associated with perceived stress (\u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.387, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001). Model 2 further included job identity and the covariates. The results showed that controlling for all covariates, both loneliness and job identity were significantly associated with perceived stress, \u003cem\u003eβ\u003c/em\u003e\u0026thinsp;=\u0026thinsp;0.398, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 and \u003cem\u003eβ\u003c/em\u003e = -0.07, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05, respectively. These results indicated that higher levels of loneliness and lower levels of job-identity were associated with higher levels of perceived stress respectively. In model 3, we added the interaction term between loneliness and job identity into model 2. The result revealed that the interaction term was negatively and significantly associated with perceived stress (\u003cem\u003eβ\u003c/em\u003e = -0.388, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001), indicating a significant moderating effect of job identity on the loneliness and perceived stress relationship. This means that higher levels of job identity decreased the intensity of the effect of loneliness on perceived stress. Figure\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e visualizes the moderating effect that for different values of job identity, the intensities of the effects of loneliness on perceived stress are different.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression models on perceived stress\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003eModel 1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003eModel 2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003eModel 3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eβ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.387***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.398***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.787***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob-based identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.07*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.388***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMarital status\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.123\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.105\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.12\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.214\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.132\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.128\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation attainment\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHaving kid(s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.094\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.128\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.083\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.124\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWorking hours\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e.015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.004\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.014\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLoneliness* job-based identity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.122***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.031\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF statistics\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e125.01***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e17.38***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e18.04***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAdjusted R\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e32.04%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e33.43%\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e37.01%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNotes: Model 1: Univariate OLS regression, not adjusted for covariates. Model 2: Adjusted for job-based identity, age, sex, education attainment, whether having kid(s), and working hours. Model 3: Adjusted for the covariates in Model 2 plus the interaction term of loneliness and job-based identity.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e*p\u0026thinsp;\u0026lt;\u0026thinsp;.05, **p\u0026thinsp;\u0026lt;\u0026thinsp;.01, ***p\u0026thinsp;\u0026lt;\u0026thinsp;.001.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003ePost-hoc analysis of the moderating effect\u003c/h2\u003e \u003cp\u003eWe used Fisher\u0026rsquo;s r-to-z transformation to conduct the post-hoc analysis of the moderating effect. We divided the sample into 2 subgroups, high job identity (N\u0026thinsp;=\u0026thinsp;129) and low job identity (N\u0026thinsp;=\u0026thinsp;135), and directly compared the group-specific r-to-z transformed correlation between loneliness and perceived stress. The result indicated the loneliness and perceived stress association was significantly lower in the high job identity subgroup versus their peers in the low job identity group, Z\u0026thinsp;=\u0026thinsp;2.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOver the past decade, China has witnessed a blossoming of the gig economy and the population of the delivery gig workforce. While the high levels of stress among delivery gig workers have grown into a social issue, the current study critically examined this issue by examining the association between loneliness and stress among delivery gig workers in urban China. To our knowledge, the current study is the first study that has exclusively collected survey data from delivery gig workers to examine the buffer effect of job identity on perceived stress. The findings of this study showed that a higher level of loneliness is positively associated with stress, indicating that delivery gig workers with higher levels of loneliness were more susceptible to having higher levels of stress. Notably, such a positive relationship remained statistically significant after we accounted for demographic covariates, i.e., age, sex, and marital status. Moreover, the current study also revealed a significant moderate effect of job identity on the relationship between loneliness and stress. Consistent with our hypothesis, the positive association between loneliness and stress was weaker among delivery gig workers with higher levels of job identity.\u003c/p\u003e \u003cp\u003eThe significant positive main effect of loneliness on stress among delivery gig workers in urban China is consistent with our hypothesis and the existing literature. For instance, Wang \u0026amp; Coutts (2022) found that gig workers with high levels of loneliness tended to have worse mental health and lower life satisfaction. Also, Glavin et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) found that digital platform workers were more likely to develop feelings of loneliness and powerlessness, leading to psychological distress. Glavin et al. (\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) also pointed out that gig drivers, i.e., rideshare drivers or delivery gig workers, had stronger feelings of loneliness and powerlessness because of the algorithmic control and distancing strategies that undermine gig workers' job autonomy and social connections. However, most existing investigations, including the current study, are cross-sectional in nature, limiting the establishment of causality. Therefore, little was known about the mechanism of the association between loneliness and stress among delivery gig workers, which is a topic requiring further research. Given the strong association between loneliness and stress revealed in this study, related intervention programs should be developed to reduce the stress for delivery gig workers, for example, strength-based coping skill interventions and establishing social support groups.\u003c/p\u003e \u003cp\u003eFurthermore, we found that job identity had a significant and moderating effect on the relationship between loneliness and stress among delivery gig workers in urban China. In other words, while delivery gig workers with higher levels of loneliness tended to have higher levels of stress, this relationship between loneliness and stress was weaker for those who had higher levels of job identity. The moderating role of job identity could be explained by the social identity theory. Based on the social identity theory, people can form a sense of self-identity, i.e., professional self, through communication and connections with others. This self-identity can protect group members from adverse reactions to strain because it provides a basis for group members to receive and benefit from social support (Haslam et al., \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). In the context of delivery gig workers in urban China, their identities as migrant workers holding a rural household type inhibit their social connections with urban residents, making it difficult for them to form a self-identity in the urban area. In addition, they can also hardly develop a high level of job identity during gig work because the nature of online platform work prevents them from communicating with colleagues and clients. Therefore, this dilemma of forming a high level of social identity might reduce the potential social support that gig workers receive in urban areas, leading to higher levels of stress. We recommend that gig companies notice the importance of job identity among gig workers because the overall job identity among Chinese gig workers might be substantially low. Although the current population of delivery gig workers is huge, the turnover rate is also distinctively high compared to other workers in China, indicating that delivery gig work is more like a temporary job (Fu, \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). The high turnover rate makes it more difficult to develop stable social connections during gig work and to develop a strong job identity. With higher levels of job identity, gig companies can benefit from lower turnover rates for gig workers, and gig workers may have lower levels of stress and increased well-being. Gig companies should consider launching more initiatives that increase gig workers\u0026rsquo; job identity, such as team building or reinforcing organizational culture.\u003c/p\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003eLimitations\u003c/h2\u003e \u003cp\u003eNotably, the current study suffers from several limitations. First, the current study has a limited sample size and a cross-sectional design, making the results less generalizable and hard to infer a causal relationship. Second, the quality assurance process in the survey might not be enough. Since delivery gig workers are usually extremely busy, it is more likely that they provided invalid responses in the surveys. Third, the association between loneliness and stress might be confounded by other factors, such as financial precarity. Future studies can use randomized sampling and a longitudinal design to investigate the causal relationship between loneliness and stress among delivery gig workers. In addition, if applicable, future studies could include measurements on more potential predictors of stress. Furthermore, studies on other types of gig workers or delivery gig workers from other areas or cultures are highly encouraged to help better understand the stress of gig workers.\u003c/p\u003e \u003c/div\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAbate, J., Schaefer, T., \u0026amp; Pavone, T. (2018). Understanding generational identity, job burnout, job satisfaction, job tenure and turnover intention. \u003cem\u003eJournal of Organizational Culture, Communications and Conflict\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 1-12. http://ezproxy.cul.columbia.edu/login?url=https://www.proquest.com/scholarly-journals/personal-value-versus-cultural-competency-towards/docview/2046081505/se-2?accountid=10226 \u003c/li\u003e\n\u003cli\u003eBajwa, U., Gastaldo, D., Di Ruggiero, E., \u0026amp; Knorr, L. (2018). 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A., O\u0026apos;Brien, A., Jetten, J., Vormedal, K., \u0026amp; Penna, S. (2005). Taking the strain: Social identity, social support, and the experience of stress. \u003cem\u003eBritish journal of social psychology\u003c/em\u003e, \u003cem\u003e44\u003c/em\u003e(3), 355-370. https://doi-org.ezproxy.cul.columbia.edu/10.1348/014466605X37468 \u003c/li\u003e\n\u003cli\u003eHuddy, L. (2001). From social to political identity: A critical examination of social identity theory. \u003cem\u003ePolitical psychology\u003c/em\u003e, \u003cem\u003e22\u003c/em\u003e(1), 127-156. https://www.jstor.org/stable/3791909 \u003c/li\u003e\n\u003cli\u003eHughes, M. E., Waite, L. J., Hawkley, L. C., \u0026amp; Cacioppo, J. T. (2004). A short scale for measuring loneliness in large surveys: Results from two population-based studies. Research on aging, 26(6), 655-672. https://doi-org.ezproxy.cul.columbia.edu/10.1177/01640275042685 \u003c/li\u003e\n\u003cli\u003eHupcey, J. E. (1998). Clarifying the social support theory‐research linkage. \u003cem\u003eJournal of advanced nursing\u003c/em\u003e, \u003cem\u003e27\u003c/em\u003e(6), 1231-1241. https://doi-org.ezproxy.cul.columbia.edu/10.1046/j.1365-2648.1998.01231.x \u003c/li\u003e\n\u003cli\u003eKalleberg, A. L. (2000). Nonstandard employment relations: Part-time, temporary and contract work. \u003cem\u003eAnnual review of sociology\u003c/em\u003e, \u003cem\u003e26\u003c/em\u003e(1), 341-365. https://www.jstor.org/stable/223448 \u003c/li\u003e\n\u003cli\u003eKeith, M. G., Long, A. C., \u0026amp; Harms, P. D. (2020). Worker health and well-being in the gig economy: A proposed framework and research agenda. In L. T. Eby \u0026amp; T. D. Allen (Eds.), \u003cem\u003eEntrepreneurial and small business stressors, experienced stress, and well-being\u003c/em\u003e (Vol. 18, pp. 1\u0026ndash;33). Emerald Publishing. https://doi.org/10.1108/S1479-355520200000018001 \u003c/li\u003e\n\u003cli\u003eKoutsimpogiorgos, N., Van Slageren, J., Herrmann, A. M., \u0026amp; Frenken, K. (2020). Conceptualizing the gig economy and its regulatory problems. \u003cem\u003ePolicy \u0026amp; Internet\u003c/em\u003e, \u003cem\u003e12\u003c/em\u003e(4), 525-545. https://doi-org.ezproxy.cul.columbia.edu/10.1002/poi3.237 \u003c/li\u003e\n\u003cli\u003eKuhn, K. M. (2016). The rise of the \u0026ldquo;gig economy\u0026rdquo; and implications for understanding work and workers. \u003cem\u003eIndustrial and Organizational Psychology\u003c/em\u003e, 9(1), 157\u0026ndash;162. https://doi.org/10.1017/iop.2015.129 \u003c/li\u003e\n\u003cli\u003eLal, S. K., \u0026amp; Craig, A. (2001). 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(Paper in Chinese). http://www.chinaqking.com/yc/2021/3082114.html \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"gig workers, perceived stress, loneliness, mental health, job identity","lastPublishedDoi":"10.21203/rs.3.rs-6798152/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6798152/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study examined the association between loneliness and perceived stress among delivery gig workers in urban China and explored the moderating effect of job identity. Based on survey data from 264 full-time delivery workers in Shanghai and Xi\u0026rsquo;an, findings showed that loneliness was positively associated with perceived stress, while job identity significantly moderated this relationship. Workers with higher job identity experienced a weaker association between loneliness and stress. Regression analyses and subgroup comparisons confirmed the buffering role of job identity. These results underscore the importance of enhancing social identity and job-related belongingness to alleviate stress among gig workers. Implications for digital platform companies and mental health interventions are discussed.\u003c/p\u003e","manuscriptTitle":"The Association between Loneliness and Perceived Stress among Delivery Gig Workers in Urban China: The Moderating Effect of Job Identity","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-08 14:32:55","doi":"10.21203/rs.3.rs-6798152/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"ab9468ff-df58-4b59-bc79-b622c4386218","owner":[],"postedDate":"June 8th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-06-09T21:51:40+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-08 14:32:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6798152","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6798152","identity":"rs-6798152","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}