Digital Literacy and Employment Intention in Rural China: The Mediating Role of Job Anxiety and the Buffering Effect of Psychological Capital

Digital Literacy and Employment Intention in Rural China: The Mediating Role of Job Anxiety and the Buffering Effect of Psychological Capital | 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 Digital Literacy and Employment Intention in Rural China: The Mediating Role of Job Anxiety and the Buffering Effect of Psychological Capital Yang Chen, Mohamad Zuber bin Abd Majid, Ahmad Firdhaus bin Arham This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8606047/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 Although digital technologies have expanded access to employment, many rural job seekers lack the digital competence and psychological resources needed to fully benefit from the digital economy. This study proposes an integrative model linking digital literacy, job anxiety, psychological capital, and employment intention. Using survey data from 403 rural unemployed youth, a structural equation modeling (SEM) approach was applied to examine both mediating and moderating mechanisms. Results revealed that digital literacy positively predicted employment intention both directly and indirectly through the reduction of job anxiety. Psychological capital not only enhanced employment intention directly but also moderated the negative relationship between digital literacy and job anxiety, such that individuals with higher psychological capital were less susceptible to anxiety when digital literacy was low. The model explained 49% of the variance in employment intention and 37% of the variance in job anxiety. This study enriches the literature on digital inclusion and youth employability and offers practical implications for designing integrated interventions that combine digital skills development with psychological empowerment. Digital literacy Job anxiety Psychological capital Employment intention Rural unemployed youth Social Cognitive Career Theory Digital inclusion Figures Figure 1 1. Introduction In recent years, China has witnessed a rapid digital transformation that is reshaping its labor market, industrial structure, and patterns of employment (Yang et al., 2025 ). With the rise of e-commerce, livestreaming, and digital platforms, new forms of work have emerged across urban and rural regions (Li & Chen, 2025 ). While these changes have created unprecedented opportunities, the benefits of digitalization have not been evenly distributed. Rural areas, which still account for nearly 40% of China’s population, lag significantly behind urban centers in terms of access to digital infrastructure, education, and employment resources (Zhang et al., 2020 ). As traditional agricultural sectors decline and rural labor migration slows, many young adults in rural China face persistent unemployment and underemployment (Qin & Liao, 2016 ). This problem is particularly urgent in the context of China’s “Rural Revitalization Strategy,” which identifies digital inclusion and youth employment as central to sustainable rural development (Shi & Yang, 2022 ). Addressing how rural youth can adapt to and benefit from the digital economy has therefore become a critical social and policy priority. Digital literacy denotes the capability to access, comprehend, assess, generate, and convey information using digital technology (Van Laar et al., 2020 ). In the contemporary digital world, when technology pervades all facets of life, digital literacy has emerged as a crucial competency that is imperative for all individuals to possess (Helsper & Eynon, 2010 ; Mishra & Koehler, 2006 ; Rogers et al., 2014 ). In the contemporary digital landscape, digital literacy is essential for economic development and competitiveness. Individuals lacking digital literacy abilities may encounter numerous barriers to employment and economic possibilities. Moreover, digital technologies are progressively included into diverse industries, including healthcare, education, and finance, necessitating that individuals possess digital literacy abilities to proficiently navigate these domains (Wang & Si, 2023 ; Yaqoob et al., 2023 ). Existing studies have demonstrated that digital literacy is a key determinant of employability, influencing job search behaviors, adaptability, and career outcomes (Ng, 2012 ; Tinmaz et al., 2022 ; Wang et al., 2023 ). However, few studies have explored how digital competence interacts with emotional factors such as job anxiety or motivational constructs like psychological capital (Byfield, 2015 ). This gap limits our understanding of the psychological mechanisms through which digital skills translate into employment motivation and behavior. The present study aims to empirically test a moderated mediation model in which job anxiety mediates the relationship between digital literacy and employment intention, while psychological capital moderates the association between digital literacy and job anxiety. Specifically, it examines whether higher digital literacy reduces job anxiety and, in turn, enhances employment intention, and whether individuals with higher psychological capital are more resilient to anxiety even when their digital skills are limited. This study makes both theoretical and practical contributions. Theoretically, it extends the concept of digital literacy beyond technical proficiency to encompass its cognitive and emotional functions within the employment process. Practically, the findings can inform policy initiatives aimed at reducing rural unemployment by promoting digital competence training and psychological empowerment programs. 2. Method 2.1 Participants The participants in this study were rural unemployed youth aged between 18 and 35 years, recruited from several rural communities and vocational training centers in north China. Inclusion criteria required participants to (a) currently be unemployed or under temporary employment, (b) have access to digital devices such as smartphones or computers, and (c) express an intention to seek employment within the coming six months. Participants who were full-time students or without basic literacy were excluded. A total of 403 valid responses were collected through an online questionnaire distributed via a WeChat-based employment training platform. Demographically, 54.8% of participants were female, and the average age was 26.9 years (SD = 4.7). About 61% had completed secondary or vocational education, and the average unemployment duration was 8.3 months (SD = 6.1). 2.2 Instruments 2.2.1 Digital Literacy Scale Digital literacy was measured using the Digital Literacy Scale (DLS, Avinç & Doğan, 2024 ) which assesses individuals’ abilities to access, evaluate, and use digital technologies for communication and problem-solving. The adapted version consisted of 20 items on a 4-point Likert scale. Higher scores indicated higher levels of digital literacy. In the present study, Cronbach’s α for this scale was 0.88. 2.2.2 Job Anxiety Scale Job anxiety was assessed with the Job Anxiety Scale (JAS). JAS is a questionnaire designed to delineate many elements of work-related anxiety (Muschalla & Linden, 2017 ). The scale comprises 70 items that embody 14 subscales, which can be encapsulated in five overarching dimensions: Stimulus-induced anxiety and avoidance behaviour; social anxiety and cognitive impairments; health and body-related anxiety; feelings of inadequacy; broad work-related concerns. Higher scores reflect greater job-related anxiety. The internal consistency reliability for this sample was 0.91. 2.2.3 Psychological Capital Psychological capital was measured by the Psychological Capital Questionnaire (PCQ-24) created by Luthans et al. ( 2007 ). The PCQ is a reflective psychological assessment of 24 items related to an individual’s Psychological Capital (PsyCap), which denotes a favourable psychological developmental state. The PCQ was developed by Luthans et al. ( 2007 ) to evaluate the dimensions of Psychological Capital. The PCQ assesses four characteristics of Psychological Capital: hope, efficacy, resilience, and optimism. The PCQ requires 10 to 15 minutes for completion and can be administered to individuals or groups. Sample items include “I feel confident analyzing a long-term problem to find a solution” (self-efficacy), and “Even when things are uncertain, I usually expect the best” (optimism). Participants responded on a 6-point Likert scale (1 = “strongly disagree” to 6 = “strongly agree”). The overall reliability coefficient in this study was α = 0.93. 2.2.4 Job Search Intention Scale Employment intention was measured using a 5-item scale adapted from Ajzen’s (1991) Theory of Planned Behavior and previous studies on youth employment intention (Yoon & Kim, 2018). Items capture participants’ motivation and behavioral readiness to seek employment, such as “I plan to actively seek a job in the near future” and “I am willing to make efforts to secure employment.” Each item was rated on a 5-point Likert scale (1 = “strongly disagree” to 5 = “strongly agree”), with higher scores representing stronger employment intention. Cronbach’s α for this scale was 0.86. 2.3 Procedure Data collection took place between April and June 2025. The research team collaborated with rural employment centers and local NGOs to distribute an electronic survey link via WeChat groups and QR codes posted in community bulletin boards. Participation was voluntary and anonymous. Respondents first read an informed consent form explaining the study purpose, confidentiality, and their right to withdraw at any time without penalty. Completion of the questionnaire required approximately 12–15 minutes. All scales were first translated into Chinese. 2.4 Data Analysis Data analysis was conducted using SPSS 26.0 and AMOS 24.0. Preliminary analyses examined missing data, normality, and common method bias. Next, Confirmatory Factor Analysis (CFA) was performed to assess the measurement model. Convergent validity was examined using factor loadings, average variance extracted (AVE > 0.50), and composite reliability (CR > 0.70). Discriminant validity was assessed by comparing the square root of AVE with inter-construct correlations. Model fit was evaluated using indices such as χ²/df ( 0.90), TLI (> 0.90), and RMSEA (< 0.08). Following measurement validation, SEM was estimated to test the hypothesized relationships. The proposed model (see Fig. 1 ) posits that digital literacy negatively predicts job anxiety and positively predicts employment intention; job anxiety mediates this relationship; and psychological capital moderates the path from digital literacy to job anxiety as well as from job anxiety to employment intention. Moderation was tested using the latent-interaction approach (product-indicator method) within AMOS, and the significance of indirect effects was verified using bootstrapping (5,000 resamples) with 95% bias-corrected confidence intervals. Multi-group analyses were additionally performed by splitting the sample into high- and low-psychological-capital groups (median split) to visualize the interaction effect. 3. Results 3.1 Descriptive Data A total of 403 valid responses were analyzed. The sample comprised 54.8% female and 45.2% male participants, with an average age of 26.9 years (SD = 4.7). Most participants had completed secondary or vocational education (61.3%), followed by undergraduate education (28.0%) and primary or lower (10.7%). Approximately 58.6% were unmarried, while 41.4% were married or previously married. The majority (67.2%) lived with their parents or spouses, whereas 32.8% reported independent living arrangements. Descriptive analysis of the psychological constructs revealed that the mean score of Digital Literacy was 2.12 (SD = 0.64), showing a moderate level of digital competence. Job Anxiety averaged 3.01 (SD = 0.71), suggesting that participants experienced moderate anxiety toward employment uncertainty. Psychological Capital showed a mean of 3.84 (SD = 0.59), implying a moderately positive psychological state, while Employment Intention averaged 3.88 (SD = 0.66), indicating that most rural unemployed youth were highly motivated to secure employment. Table 1 Demographic characteristics and descriptive statistics of key variables Variable Category / Description n (%) / M ± SD Gender Male 182 (45.2%) Female 221 (54.8%) Age (years) 26.9 ± 4.7 Education level Primary or below 43 (10.7%) Secondary/Vocational 247 (61.3%) Undergraduate or above 113 (28.0%) Marital status Unmarried 236 (58.6%) Married/Previously married 167 (41.4%) Living arrangement With parents/spouse 271 (67.2%) Independent 132 (32.8%) Monthly living expenses (RMB) 2,183 ± 785 Household monthly income (RMB) 4,920 ± 2,340 Unemployment duration (months) 8.3 ± 6.1 Daily Internet use (hours) 4.6 ± 1.8 Digital Literacy 2.12 ± 0.64 Job Anxiety 3.01 ± 0.71 Psychological Capital 3.84 ± 0.59 Employment Intention 3.88 ± 0.66 3.2 Pearson Correlation Pearson’s correlation analysis was conducted to examine the bivariate relationships among the major variables—digital literacy, job anxiety, psychological capital, and employment intention—before testing the full structural equation model. As presented in Table 2 , digital literacy was found to be positively correlated with both psychological capital ( r = .48, p < .001) and employment intention ( r = .54, p < .001), indicating that higher levels of digital competence were associated with greater psychological resources and stronger motivation to seek employment. In contrast, digital literacy showed a significant negative correlation with job anxiety ( r = –.41, p < .001), suggesting that individuals with better digital skills tend to experience less anxiety during their job search process. Job anxiety was negatively correlated with both psychological capital ( r = –.39, p < .001) and employment intention ( r = –.45, p < .001), supporting the assumption that higher levels of anxiety reduce self-confidence and motivation toward employment. Meanwhile, psychological capital was positively related to employment intention ( r = .57, p < .001), highlighting the important role of positive psychological resources in facilitating employment readiness among rural unemployed youth. All correlations were in the expected direction and below 0.70, indicating no multicollinearity concern prior to the SEM analysis. Table 2 Pearson correlations among the main study variables Variables 1 2 3 4 Digital Literacy — Job Anxiety .41 *** — Psychological Capital .48 *** .39 *** — Employment Intention .54 *** .45 *** .57 *** — 3.3 Path analysis A structural equation model (SEM) was constructed to test the hypothesized relationships among digital literacy, job anxiety, psychological capital, and employment intention (see Fig. 2 ). The proposed model demonstrated an acceptable overall fit to the data: χ² = 321.84, df = 148, χ²/df = 2.17, CFI = 0.94, TLI = 0.93, RMSEA = 0.054 (90% CI = 0.045–0.062), SRMR = 0.046. All indices met conventional criteria, indicating a well-fitting model suitable for hypothesis testing. As shown in Table 3 and Fig. 1 , digital literacy exerted a significant negative effect on job anxiety (β = − 0.38, p < .001) and a significant positive effect on employment intention (β = 0.31, p < .001), supporting the direct influence of digital competence on both psychological and behavioral outcomes. Job anxiety had a negative and significant effect on employment intention (β = − 0.29, p < .001), confirming its mediating role between digital literacy and employment intention. Psychological capital showed a strong positive effect on employment intention (β = 0.36, p < .001) and a negative association with job anxiety (β = − 0.27, p < .001). More importantly, the interaction term between digital literacy and psychological capital significantly predicted job anxiety (β = − 0.12, p = .014), suggesting a moderating effect: individuals with higher psychological capital experienced a weaker increase in anxiety even when their digital literacy was low. Table 3 Standardized path coefficients and significance levels of the SEM Hypothesized path Standardized β SE CR p Result Digital Literacy → Job Anxiety –0.38 0.07 5.42 < .001 Supported Digital Literacy → Employment Intention 0.31 0.06 5.09 < .001 Supported Job Anxiety → Employment Intention –0.29 0.05 4.86 < .001 Supported Psychological Capital → Job Anxiety –0.27 0.06 4.35 < .001 Supported Psychological Capital → Employment Intention 0.36 0.05 6.92 < .001 Supported Digital Literacy × Psychological Capital → Job Anxiety –0.12 0.05 2.46 0.014 Supported (moderation) 4. Discussion The present study examined the relationships among digital literacy, job anxiety, psychological capital, and employment intention among rural unemployed youth in China. Using a structural equation modeling approach, the findings provide empirical support for the proposed theoretical model. Specifically, digital literacy was found to significantly enhance employment intention both directly and indirectly through the reduction of job anxiety (Marsh, 2018 ; Chen et al., 2022 ). Moreover, psychological capital played a dual role by not only promoting employment intention but also buffering the negative psychological effects associated with low digital literacy (Chen et al., 2018). Together, these results highlight the importance of integrating technological competence and psychological resources to understand and improve the employability of rural youth in the digital economy era. Consistent with social cognitive career theory (SCCT), the results demonstrate that digital literacy functions as a form of self-efficacy resource that empowers young people to navigate the increasingly digitalized labor market (Rachmawati et al., 2024 ). Individuals with higher levels of digital literacy are more capable of accessing job information, completing online applications, and presenting themselves effectively in digital spaces, which in turn enhances their confidence and employment readiness (Ogbonnaya-Ogburu et al., 2019; Matli & Ngoepe, 2020 ). Conversely, those with limited digital competence experience heightened uncertainty and anxiety when facing digital recruitment systems or online interviews. This explains the observed negative path between digital literacy and job anxiety (Li et al., 2025 ). The finding extends previous research that primarily examined digital literacy as a technical skill, emphasizing instead its psychological dimension—a capability that fosters not only functional competence but also emotional stability and self-assurance in job-seeking contexts. The mediating role of job anxiety provides further insight into the psychological mechanism linking digital skills and employment intention. In line with previous studies on youth employability (McCarthy et al., 2017; Park et al., 2022 ), job anxiety emerged as a significant barrier to active job searching. Elevated anxiety diminishes motivation, interferes with decision-making, and erodes self-efficacy, ultimately lowering employment intention. The significant indirect effect suggests that interventions aimed at reducing job-related anxiety could amplify the positive impact of digital skill development on employment motivation. This finding underscores the need for dual-path interventions that combine technical training with psychological empowerment. The moderating role of psychological capital represents one of the most novel contributions of this study. Results showed that individuals with higher levels of psychological capital—characterized by hope, optimism, self-efficacy, and resilience—were less affected by job anxiety even when their digital literacy was relatively low. This aligns with the psychological capital theory (Luthans et al., 2007 ), which posits that positive psychological states act as self-regulatory resources that enable individuals to cope with stress and uncertainty. In the context of rural unemployment, psychological capital provides the motivational drive and cognitive flexibility necessary to sustain job-search behaviors despite external challenges. Importantly, psychological capital also exerted a direct and significant positive effect on employment intention, suggesting that fostering these psychological resources could serve as an effective strategy for employment programs targeting vulnerable populations. 5. Conclusion This study explored how digital literacy, job anxiety, and psychological capital jointly shape the employment intention of rural unemployed youth in China. Using a structural equation modeling approach, the findings revealed that digital literacy exerts both direct and indirect effects on employment intention. Specifically, higher levels of digital literacy significantly reduce job anxiety, which in turn enhances individuals’ motivation to seek employment. Moreover, psychological capital—comprising hope, optimism, resilience, and self-efficacy—was found to not only strengthen employment intention directly but also buffer the negative psychological impact of low digital literacy. These results emphasize that employability in the digital age is determined not merely by technical competence, but by the dynamic interaction between cognitive, emotional, and psychological factors. Theoretically, the study contributes to the integration of digital literacy research and occupational psychology by identifying job anxiety as a mediating psychological mechanism and psychological capital as a moderating resilience factor. This dual-path model expands the conceptualization of digital literacy beyond its instrumental function to include its affective and motivational dimensions. It also advances Social Cognitive Career Theory (SCCT) by illustrating how cognitive and emotional resources co-regulate employment intention in contexts characterized by economic and technological inequality. Practically, the findings provide several actionable insights for policymakers, educators, and community organizations. First, digital training programs targeting rural youth should move beyond teaching basic technological skills to incorporate confidence-building and anxiety-reduction components, such as simulated online interviews, peer mentoring, and stress management workshops. Second, interventions that cultivate psychological capital—through positive psychology coaching, resilience training, or community-based empowerment initiatives—can enhance young people’s confidence and persistence in job searching. Finally, the study suggests that rural employment initiatives should adopt an integrated approach that combines digital inclusion with mental health and career counseling, thereby addressing both technological and psychological barriers to employment. Despite these contributions, certain limitations must be acknowledged. The cross-sectional design precludes strong causal inference, and the reliance on self-reported measures may introduce response bias. Future research could adopt longitudinal or experimental designs, include objective indicators of digital behavior or employment outcomes, and examine other psychological constructs such as career adaptability or digital self-efficacy. Moreover, cross-regional comparisons could illuminate how different socio-economic and cultural contexts shape the interplay between digital literacy and psychological factors. Declarations Ethical consideration The study was performed in line with the Declaration of Helsinki was approved by the Research Ethics Committee of National University of Malaysia. Consent to Participate Informed written consent was obtained from all individual participants included in the study. Consent to Publish declarations Informed written consent to publish was obtained from all individual participants included in the study. Clinical trial number Not applicable. Competing interests The authors have no competing interests. Funding Not applicable Author Contribution YC wrote the main manuscript text and prepared figures and tables. M and A edited and proof-read the manuscript. All authors reviewed the manuscript. Data Availability The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation. References Avinç E, Doğan F. Digital literacy scale: Validity and reliability study with the rasch model. Educ Inform Technol. 2024;29(17):22895–941. Byfield L. (2015). Digital literacy and identity formation in 21st century classrooms: Implications for second language development. Int J Appl Linguistics Engl Literature. Chen SC, Huy LD, Lin CY, Lai CF, Nguyen NTH, Hoang NY, Duong TV. Association of digital health literacy with future anxiety as mediated by information satisfaction and fear of COVID-19: a pathway analysis among Taiwanese students. Int J Environ Res Public Health. 2022;19(23):15617. Chen CT, Chen CF. 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Evolution and influencing factors of China's rural population distribution patterns since 1990. PLoS ONE, 15(5), e0233637. Additional Declarations No competing interests reported. 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. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8606047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587888719,"identity":"fd21fc55-13ab-4f21-bc3f-d14605694a6d","order_by":0,"name":"Yang Chen","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Chen","suffix":""},{"id":587888720,"identity":"2ad1ed7f-eac8-4188-9482-f8c3bbf42eaa","order_by":1,"name":"Mohamad Zuber bin Abd Majid","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAvklEQVRIiWNgGAWjYHACNiC2YZAAMXlI0JJGupbDJGjR7T9j9uDjnvOJM6cdYHzwto0h2uAAAS1mB86YG854djtxtnQCs+HcNobcDQS1HOwxk+Y5cDtxnnQCmzQvUVoO85hJ/zlwDqSF/TdxWo4BtTAcOAByGBszcVrOsJUb9hxINp45O7FZcs45idyZBLWcP7ztwY8DdrIzbicf/PCmzCa3j5AWJMDYACQkGBRI0AIF8g0kaxkFo2AUjIJhDgBxckSqjiqWAgAAAABJRU5ErkJggg==","orcid":"","institution":"National University of Malaysia","correspondingAuthor":true,"prefix":"","firstName":"Mohamad","middleName":"Zuber bin Abd","lastName":"Majid","suffix":""},{"id":587888721,"identity":"feb46f87-318e-44c2-b68d-540a30a3cceb","order_by":2,"name":"Ahmad Firdhaus bin Arham","email":"","orcid":"","institution":"National University of Malaysia","correspondingAuthor":false,"prefix":"","firstName":"Ahmad","middleName":"Firdhaus bin","lastName":"Arham","suffix":""}],"badges":[],"createdAt":"2026-01-15 02:23:15","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8606047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8606047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102346290,"identity":"beb25d64-9a5a-4547-8686-de97edcd763a","added_by":"auto","created_at":"2026-02-10 17:37:18","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":58796,"visible":true,"origin":"","legend":"\u003cp\u003ePath analysis\u003c/p\u003e","description":"","filename":"floatimage12.png","url":"https://assets-eu.researchsquare.com/files/rs-8606047/v1/4091319d1793a913c14608e8.png"},{"id":108604098,"identity":"4f0ce3fe-0bf9-4201-a48e-8d125886f2e6","added_by":"auto","created_at":"2026-05-06 11:58:04","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":306159,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8606047/v1/50aefc88-bea2-49dd-aaa0-b8445f785ca3.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Digital Literacy and Employment Intention in Rural China: The Mediating Role of Job Anxiety and the Buffering Effect of Psychological Capital","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eIn recent years, China has witnessed a rapid digital transformation that is reshaping its labor market, industrial structure, and patterns of employment (Yang et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). With the rise of e-commerce, livestreaming, and digital platforms, new forms of work have emerged across urban and rural regions (Li \u0026amp; Chen, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While these changes have created unprecedented opportunities, the benefits of digitalization have not been evenly distributed. Rural areas, which still account for nearly 40% of China\u0026rsquo;s population, lag significantly behind urban centers in terms of access to digital infrastructure, education, and employment resources (Zhang et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). As traditional agricultural sectors decline and rural labor migration slows, many young adults in rural China face persistent unemployment and underemployment (Qin \u0026amp; Liao, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). This problem is particularly urgent in the context of China\u0026rsquo;s \u0026ldquo;Rural Revitalization Strategy,\u0026rdquo; which identifies digital inclusion and youth employment as central to sustainable rural development (Shi \u0026amp; Yang, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Addressing how rural youth can adapt to and benefit from the digital economy has therefore become a critical social and policy priority.\u003c/p\u003e \u003cp\u003eDigital literacy denotes the capability to access, comprehend, assess, generate, and convey information using digital technology (Van Laar et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). In the contemporary digital world, when technology pervades all facets of life, digital literacy has emerged as a crucial competency that is imperative for all individuals to possess (Helsper \u0026amp; Eynon, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Mishra \u0026amp; Koehler, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Rogers et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). In the contemporary digital landscape, digital literacy is essential for economic development and competitiveness. Individuals lacking digital literacy abilities may encounter numerous barriers to employment and economic possibilities. Moreover, digital technologies are progressively included into diverse industries, including healthcare, education, and finance, necessitating that individuals possess digital literacy abilities to proficiently navigate these domains (Wang \u0026amp; Si, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Yaqoob et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eExisting studies have demonstrated that digital literacy is a key determinant of employability, influencing job search behaviors, adaptability, and career outcomes (Ng, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Tinmaz et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Wang et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). However, few studies have explored how digital competence interacts with emotional factors such as job anxiety or motivational constructs like psychological capital (Byfield, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). This gap limits our understanding of the psychological mechanisms through which digital skills translate into employment motivation and behavior.\u003c/p\u003e \u003cp\u003eThe present study aims to empirically test a moderated mediation model in which job anxiety mediates the relationship between digital literacy and employment intention, while psychological capital moderates the association between digital literacy and job anxiety. Specifically, it examines whether higher digital literacy reduces job anxiety and, in turn, enhances employment intention, and whether individuals with higher psychological capital are more resilient to anxiety even when their digital skills are limited. This study makes both theoretical and practical contributions. Theoretically, it extends the concept of digital literacy beyond technical proficiency to encompass its cognitive and emotional functions within the employment process. Practically, the findings can inform policy initiatives aimed at reducing rural unemployment by promoting digital competence training and psychological empowerment programs.\u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Participants\u003c/h2\u003e \u003cp\u003eThe participants in this study were rural unemployed youth aged between 18 and 35 years, recruited from several rural communities and vocational training centers in north China. Inclusion criteria required participants to (a) currently be unemployed or under temporary employment, (b) have access to digital devices such as smartphones or computers, and (c) express an intention to seek employment within the coming six months. Participants who were full-time students or without basic literacy were excluded. A total of 403 valid responses were collected through an online questionnaire distributed via a WeChat-based employment training platform. Demographically, 54.8% of participants were female, and the average age was 26.9 years (SD\u0026thinsp;=\u0026thinsp;4.7). About 61% had completed secondary or vocational education, and the average unemployment duration was 8.3 months (SD\u0026thinsp;=\u0026thinsp;6.1).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Instruments\u003c/h2\u003e \u003cdiv id=\"Sec5\" class=\"Section3\"\u003e \u003ch2\u003e2.2.1 Digital Literacy Scale\u003c/h2\u003e \u003cp\u003eDigital literacy was measured using the Digital Literacy Scale (DLS, Avin\u0026ccedil; \u0026amp; Doğan, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e) which assesses individuals\u0026rsquo; abilities to access, evaluate, and use digital technologies for communication and problem-solving. The adapted version consisted of 20 items on a 4-point Likert scale. Higher scores indicated higher levels of digital literacy. In the present study, Cronbach\u0026rsquo;s α for this scale was 0.88.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Job Anxiety Scale\u003c/h2\u003e \u003cp\u003eJob anxiety was assessed with the Job Anxiety Scale (JAS). JAS is a questionnaire designed to delineate many elements of work-related anxiety (Muschalla \u0026amp; Linden, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). The scale comprises 70 items that embody 14 subscales, which can be encapsulated in five overarching dimensions: Stimulus-induced anxiety and avoidance behaviour; social anxiety and cognitive impairments; health and body-related anxiety; feelings of inadequacy; broad work-related concerns. Higher scores reflect greater job-related anxiety. The internal consistency reliability for this sample was 0.91.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Psychological Capital\u003c/h2\u003e \u003cp\u003ePsychological capital was measured by the Psychological Capital Questionnaire (PCQ-24) created by Luthans et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). The PCQ is a reflective psychological assessment of 24 items related to an individual\u0026rsquo;s Psychological Capital (PsyCap), which denotes a favourable psychological developmental state. The PCQ was developed by Luthans et al. (\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e) to evaluate the dimensions of Psychological Capital. The PCQ assesses four characteristics of Psychological Capital: hope, efficacy, resilience, and optimism. The PCQ requires 10 to 15 minutes for completion and can be administered to individuals or groups. Sample items include \u0026ldquo;I feel confident analyzing a long-term problem to find a solution\u0026rdquo; (self-efficacy), and \u0026ldquo;Even when things are uncertain, I usually expect the best\u0026rdquo; (optimism). Participants responded on a 6-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 6 = \u0026ldquo;strongly agree\u0026rdquo;). The overall reliability coefficient in this study was α\u0026thinsp;=\u0026thinsp;0.93.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.4 Job Search Intention Scale\u003c/h2\u003e \u003cp\u003eEmployment intention was measured using a 5-item scale adapted from Ajzen\u0026rsquo;s (1991) Theory of Planned Behavior and previous studies on youth employment intention (Yoon \u0026amp; Kim, 2018). Items capture participants\u0026rsquo; motivation and behavioral readiness to seek employment, such as \u0026ldquo;I plan to actively seek a job in the near future\u0026rdquo; and \u0026ldquo;I am willing to make efforts to secure employment.\u0026rdquo; Each item was rated on a 5-point Likert scale (1 = \u0026ldquo;strongly disagree\u0026rdquo; to 5 = \u0026ldquo;strongly agree\u0026rdquo;), with higher scores representing stronger employment intention. Cronbach\u0026rsquo;s α for this scale was 0.86.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Procedure\u003c/h2\u003e \u003cp\u003eData collection took place between April and June 2025. The research team collaborated with rural employment centers and local NGOs to distribute an electronic survey link via WeChat groups and QR codes posted in community bulletin boards. Participation was voluntary and anonymous. Respondents first read an informed consent form explaining the study purpose, confidentiality, and their right to withdraw at any time without penalty. Completion of the questionnaire required approximately 12\u0026ndash;15 minutes. All scales were first translated into Chinese.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Data Analysis\u003c/h2\u003e \u003cp\u003eData analysis was conducted using SPSS 26.0 and AMOS 24.0. Preliminary analyses examined missing data, normality, and common method bias. Next, Confirmatory Factor Analysis (CFA) was performed to assess the measurement model. Convergent validity was examined using factor loadings, average variance extracted (AVE\u0026thinsp;\u0026gt;\u0026thinsp;0.50), and composite reliability (CR\u0026thinsp;\u0026gt;\u0026thinsp;0.70). Discriminant validity was assessed by comparing the square root of AVE with inter-construct correlations. Model fit was evaluated using indices such as χ\u0026sup2;/df (\u0026lt;\u0026thinsp;3.0), CFI (\u0026gt;\u0026thinsp;0.90), TLI (\u0026gt;\u0026thinsp;0.90), and RMSEA (\u0026lt;\u0026thinsp;0.08).\u003c/p\u003e \u003cp\u003eFollowing measurement validation, SEM was estimated to test the hypothesized relationships. The proposed model (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e) posits that digital literacy negatively predicts job anxiety and positively predicts employment intention; job anxiety mediates this relationship; and psychological capital moderates the path from digital literacy to job anxiety as well as from job anxiety to employment intention. Moderation was tested using the latent-interaction approach (product-indicator method) within AMOS, and the significance of indirect effects was verified using bootstrapping (5,000 resamples) with 95% bias-corrected confidence intervals. Multi-group analyses were additionally performed by splitting the sample into high- and low-psychological-capital groups (median split) to visualize the interaction effect.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Descriptive Data\u003c/h2\u003e \u003cp\u003eA total of 403 valid responses were analyzed. The sample comprised 54.8% female and 45.2% male participants, with an average age of 26.9 years (SD\u0026thinsp;=\u0026thinsp;4.7). Most participants had completed secondary or vocational education (61.3%), followed by undergraduate education (28.0%) and primary or lower (10.7%). Approximately 58.6% were unmarried, while 41.4% were married or previously married. The majority (67.2%) lived with their parents or spouses, whereas 32.8% reported independent living arrangements.\u003c/p\u003e \u003cp\u003eDescriptive analysis of the psychological constructs revealed that the mean score of Digital Literacy was 2.12 (SD\u0026thinsp;=\u0026thinsp;0.64), showing a moderate level of digital competence. Job Anxiety averaged 3.01 (SD\u0026thinsp;=\u0026thinsp;0.71), suggesting that participants experienced moderate anxiety toward employment uncertainty. Psychological Capital showed a mean of 3.84 (SD\u0026thinsp;=\u0026thinsp;0.59), implying a moderately positive psychological state, while Employment Intention averaged 3.88 (SD\u0026thinsp;=\u0026thinsp;0.66), indicating that most rural unemployed youth were highly motivated to secure employment.\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\u003eDemographic characteristics and descriptive statistics of key variables\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategory / Description\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003en (%) / M\u0026thinsp;\u0026plusmn;\u0026thinsp;SD\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGender\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e182 (45.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e221 (54.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e26.9\u0026thinsp;\u0026plusmn;\u0026thinsp;4.7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEducation level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePrimary or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43 (10.7%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSecondary/Vocational\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e247 (61.3%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUndergraduate or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e113 (28.0%)\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 \u003cp\u003eUnmarried\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e236 (58.6%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMarried/Previously married\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e167 (41.4%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLiving arrangement\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eWith parents/spouse\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e271 (67.2%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eIndependent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e132 (32.8%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly living expenses (RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2,183\u0026thinsp;\u0026plusmn;\u0026thinsp;785\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHousehold monthly income (RMB)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4,920\u0026thinsp;\u0026plusmn;\u0026thinsp;2,340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployment duration (months)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e8.3\u0026thinsp;\u0026plusmn;\u0026thinsp;6.1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDaily Internet use (hours)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.6\u0026thinsp;\u0026plusmn;\u0026thinsp;1.8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.12\u0026thinsp;\u0026plusmn;\u0026thinsp;0.64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.01\u0026thinsp;\u0026plusmn;\u0026thinsp;0.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.84\u0026thinsp;\u0026plusmn;\u0026thinsp;0.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.88\u0026thinsp;\u0026plusmn;\u0026thinsp;0.66\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=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pearson Correlation\u003c/h2\u003e \u003cp\u003ePearson\u0026rsquo;s correlation analysis was conducted to examine the bivariate relationships among the major variables\u0026mdash;digital literacy, job anxiety, psychological capital, and employment intention\u0026mdash;before testing the full structural equation model. As presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, digital literacy was found to be positively correlated with both psychological capital (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.48, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and employment intention (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.54, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), indicating that higher levels of digital competence were associated with greater psychological resources and stronger motivation to seek employment. In contrast, digital literacy showed a significant negative correlation with job anxiety (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.41, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), suggesting that individuals with better digital skills tend to experience less anxiety during their job search process.\u003c/p\u003e \u003cp\u003eJob anxiety was negatively correlated with both psychological capital (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.39, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and employment intention (\u003cem\u003er\u003c/em\u003e = \u0026ndash;.45, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), supporting the assumption that higher levels of anxiety reduce self-confidence and motivation toward employment. Meanwhile, psychological capital was positively related to employment intention (\u003cem\u003er\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.57, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), highlighting the important role of positive psychological resources in facilitating employment readiness among rural unemployed youth. All correlations were in the expected direction and below 0.70, indicating no multicollinearity concern prior to the SEM analysis.\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\u003ePearson correlations among the main study variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\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 \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\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.41 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Capital\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.48 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.39 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployment Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.54 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.45 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.57 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026mdash;\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=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Path analysis\u003c/h2\u003e \u003cp\u003eA structural equation model (SEM) was constructed to test the hypothesized relationships among digital literacy, job anxiety, psychological capital, and employment intention (see \u003cem\u003eFig.\u0026nbsp;2\u003c/em\u003e). The proposed model demonstrated an acceptable overall fit to the data: χ\u0026sup2; = 321.84, df\u0026thinsp;=\u0026thinsp;148, χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;2.17, CFI\u0026thinsp;=\u0026thinsp;0.94, TLI\u0026thinsp;=\u0026thinsp;0.93, RMSEA\u0026thinsp;=\u0026thinsp;0.054 (90% CI\u0026thinsp;=\u0026thinsp;0.045\u0026ndash;0.062), SRMR\u0026thinsp;=\u0026thinsp;0.046. All indices met conventional criteria, indicating a well-fitting model suitable for hypothesis testing.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e and Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, digital literacy exerted a significant negative effect on job anxiety (β = \u0026minus;\u0026thinsp;0.38, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a significant positive effect on employment intention (β\u0026thinsp;=\u0026thinsp;0.31, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), supporting the direct influence of digital competence on both psychological and behavioral outcomes. Job anxiety had a negative and significant effect on employment intention (β = \u0026minus;\u0026thinsp;0.29, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001), confirming its mediating role between digital literacy and employment intention.\u003c/p\u003e \u003cp\u003ePsychological capital showed a strong positive effect on employment intention (β\u0026thinsp;=\u0026thinsp;0.36, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001) and a negative association with job anxiety (β = \u0026minus;\u0026thinsp;0.27, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;.001). More importantly, the interaction term between digital literacy and psychological capital significantly predicted job anxiety (β = \u0026minus;\u0026thinsp;0.12, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;=\u0026thinsp;.014), suggesting a moderating effect: individuals with higher psychological capital experienced a weaker increase in anxiety even when their digital literacy was low.\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\u003eStandardized path coefficients and significance levels of the SEM\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=\"char\" char=\".\" 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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHypothesized path\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eStandardized β\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSE\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eCR\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eResult\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy \u0026rarr; Job Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.38\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.07\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy \u0026rarr; Employment Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJob Anxiety \u0026rarr; Employment Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Capital \u0026rarr; Job Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.35\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePsychological Capital \u0026rarr; Employment Intention\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.92\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDigital Literacy \u0026times; Psychological Capital \u0026rarr; Job Anxiety\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e\u0026ndash;0.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.05\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.46\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSupported (moderation)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe present study examined the relationships among digital literacy, job anxiety, psychological capital, and employment intention among rural unemployed youth in China. Using a structural equation modeling approach, the findings provide empirical support for the proposed theoretical model. Specifically, digital literacy was found to significantly enhance employment intention both directly and indirectly through the reduction of job anxiety (Marsh, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Chen et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). Moreover, psychological capital played a dual role by not only promoting employment intention but also buffering the negative psychological effects associated with low digital literacy (Chen et al., 2018). Together, these results highlight the importance of integrating technological competence and psychological resources to understand and improve the employability of rural youth in the digital economy era.\u003c/p\u003e \u003cp\u003eConsistent with social cognitive career theory (SCCT), the results demonstrate that digital literacy functions as a form of self-efficacy resource that empowers young people to navigate the increasingly digitalized labor market (Rachmawati et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Individuals with higher levels of digital literacy are more capable of accessing job information, completing online applications, and presenting themselves effectively in digital spaces, which in turn enhances their confidence and employment readiness (Ogbonnaya-Ogburu et al., 2019; Matli \u0026amp; Ngoepe, \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Conversely, those with limited digital competence experience heightened uncertainty and anxiety when facing digital recruitment systems or online interviews. This explains the observed negative path between digital literacy and job anxiety (Li et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). The finding extends previous research that primarily examined digital literacy as a technical skill, emphasizing instead its psychological dimension\u0026mdash;a capability that fosters not only functional competence but also emotional stability and self-assurance in job-seeking contexts.\u003c/p\u003e \u003cp\u003eThe mediating role of job anxiety provides further insight into the psychological mechanism linking digital skills and employment intention. In line with previous studies on youth employability (McCarthy et al., 2017; Park et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), job anxiety emerged as a significant barrier to active job searching. Elevated anxiety diminishes motivation, interferes with decision-making, and erodes self-efficacy, ultimately lowering employment intention. The significant indirect effect suggests that interventions aimed at reducing job-related anxiety could amplify the positive impact of digital skill development on employment motivation. This finding underscores the need for dual-path interventions that combine technical training with psychological empowerment.\u003c/p\u003e \u003cp\u003eThe moderating role of psychological capital represents one of the most novel contributions of this study. Results showed that individuals with higher levels of psychological capital\u0026mdash;characterized by hope, optimism, self-efficacy, and resilience\u0026mdash;were less affected by job anxiety even when their digital literacy was relatively low. This aligns with the psychological capital theory (Luthans et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which posits that positive psychological states act as self-regulatory resources that enable individuals to cope with stress and uncertainty. In the context of rural unemployment, psychological capital provides the motivational drive and cognitive flexibility necessary to sustain job-search behaviors despite external challenges. Importantly, psychological capital also exerted a direct and significant positive effect on employment intention, suggesting that fostering these psychological resources could serve as an effective strategy for employment programs targeting vulnerable populations.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study explored how digital literacy, job anxiety, and psychological capital jointly shape the employment intention of rural unemployed youth in China. Using a structural equation modeling approach, the findings revealed that digital literacy exerts both direct and indirect effects on employment intention. Specifically, higher levels of digital literacy significantly reduce job anxiety, which in turn enhances individuals\u0026rsquo; motivation to seek employment. Moreover, psychological capital\u0026mdash;comprising hope, optimism, resilience, and self-efficacy\u0026mdash;was found to not only strengthen employment intention directly but also buffer the negative psychological impact of low digital literacy. These results emphasize that employability in the digital age is determined not merely by technical competence, but by the dynamic interaction between cognitive, emotional, and psychological factors.\u003c/p\u003e \u003cp\u003eTheoretically, the study contributes to the integration of digital literacy research and occupational psychology by identifying job anxiety as a mediating psychological mechanism and psychological capital as a moderating resilience factor. This dual-path model expands the conceptualization of digital literacy beyond its instrumental function to include its affective and motivational dimensions. It also advances Social Cognitive Career Theory (SCCT) by illustrating how cognitive and emotional resources co-regulate employment intention in contexts characterized by economic and technological inequality.\u003c/p\u003e \u003cp\u003ePractically, the findings provide several actionable insights for policymakers, educators, and community organizations. First, digital training programs targeting rural youth should move beyond teaching basic technological skills to incorporate confidence-building and anxiety-reduction components, such as simulated online interviews, peer mentoring, and stress management workshops. Second, interventions that cultivate psychological capital\u0026mdash;through positive psychology coaching, resilience training, or community-based empowerment initiatives\u0026mdash;can enhance young people\u0026rsquo;s confidence and persistence in job searching. Finally, the study suggests that rural employment initiatives should adopt an integrated approach that combines digital inclusion with mental health and career counseling, thereby addressing both technological and psychological barriers to employment.\u003c/p\u003e \u003cp\u003eDespite these contributions, certain limitations must be acknowledged. The cross-sectional design precludes strong causal inference, and the reliance on self-reported measures may introduce response bias. Future research could adopt longitudinal or experimental designs, include objective indicators of digital behavior or employment outcomes, and examine other psychological constructs such as career adaptability or digital self-efficacy. Moreover, cross-regional comparisons could illuminate how different socio-economic and cultural contexts shape the interplay between digital literacy and psychological factors.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e \u003cb\u003eEthical consideration\u003c/b\u003e \u003c/p\u003e \u003cp\u003e The study was performed in line with the Declaration of Helsinki was approved by the Research Ethics Committee of National University of Malaysia.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Participate\u003c/strong\u003e \u003c/p\u003e \u003cp\u003e Informed written consent was obtained from all individual participants included in the study.\u003c/p\u003e \u003cp\u003e \u003cstrong\u003eConsent to Publish declarations\u003c/strong\u003e \u003c/p\u003e \u003cp\u003e Informed written consent to publish was obtained from all individual participants included in the study.\u003c/p\u003e \u003cp\u003e \u003cb\u003eClinical trial number\u003c/b\u003e \u003c/p\u003e \u003cp\u003eNot applicable.\u003c/p\u003e\u003ch2\u003eCompeting interests\u003c/h2\u003e \u003cp\u003eThe authors have no competing interests.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eNot applicable\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eYC wrote the main manuscript text and prepared figures and tables. M and A edited and proof-read the manuscript. All authors reviewed the manuscript.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAvin\u0026ccedil; E, Doğan F. Digital literacy scale: Validity and reliability study with the rasch model. Educ Inform Technol. 2024;29(17):22895\u0026ndash;941.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eByfield L. (2015). Digital literacy and identity formation in 21st century classrooms: Implications for second language development. Int J Appl Linguistics Engl Literature.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eChen SC, Huy LD, Lin CY, Lai CF, Nguyen NTH, Hoang NY, Duong TV. 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PLoS ONE, 15(5), e0233637.\u003c/span\u003e\u003c/li\u003e\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":"Digital literacy, Job anxiety, Psychological capital, Employment intention, Rural unemployed youth, Social Cognitive Career Theory, Digital inclusion","lastPublishedDoi":"10.21203/rs.3.rs-8606047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8606047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAlthough digital technologies have expanded access to employment, many rural job seekers lack the digital competence and psychological resources needed to fully benefit from the digital economy. This study proposes an integrative model linking digital literacy, job anxiety, psychological capital, and employment intention. Using survey data from 403 rural unemployed youth, a structural equation modeling (SEM) approach was applied to examine both mediating and moderating mechanisms. Results revealed that digital literacy positively predicted employment intention both directly and indirectly through the reduction of job anxiety. Psychological capital not only enhanced employment intention directly but also moderated the negative relationship between digital literacy and job anxiety, such that individuals with higher psychological capital were less susceptible to anxiety when digital literacy was low. The model explained 49% of the variance in employment intention and 37% of the variance in job anxiety. This study enriches the literature on digital inclusion and youth employability and offers practical implications for designing integrated interventions that combine digital skills development with psychological empowerment.\u003c/p\u003e","manuscriptTitle":"Digital Literacy and Employment Intention in Rural China: The Mediating Role of Job Anxiety and the Buffering Effect of Psychological Capital","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 17:37:08","doi":"10.21203/rs.3.rs-8606047/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":"35914f21-a484-4f64-a609-e87ce8c704bf","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[{"type":"decision","content":"Withdrawn","date":"2026-05-06T11:50:32+00:00","index":"","fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-05-06T11:57:20+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 17:37:08","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8606047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8606047","identity":"rs-8606047","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}