The Association between Workload and Turnover Intention: The mediating Role of Job Anxiety of Ready-Made Garment (RMG) Workers in Bangladesh

The Association between Workload and Turnover Intention: The mediating Role of Job Anxiety of Ready-Made Garment (RMG) Workers in Bangladesh | 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 Workload and Turnover Intention: The mediating Role of Job Anxiety of Ready-Made Garment (RMG) Workers in Bangladesh Md. Reyad Hossen. This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9316566/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 Ready-made garment (RMG) workers in Bangladesh face significant challenges, including excessive workload, job anxiety, low wages, long working hours, and poor working conditions. Job anxiety was used as a mediating factor to explore the association between workload and intention to turnover among RMG workers in Bangladesh. Workers experiencing high workload report greater job anxiety with an increasing likelihood of leaving their jobs. A cross-sectional study was conducted among 406 Bangladeshi RMG workers (mean age M = 26.51, SD = 6.21 ). We employed convenience sampling and interviewed them using a structured questionnaire to assess workload, job anxiety, and turnover intention. Results indicate that the workload considerably raised turnover intention both directly and indirectly through job anxiety. There was partial mediation of the association, with job anxiety accounting for 57.58%. Overall, workload predicted higher turnover intention, with job anxiety serving as a partial mediator associating workload with workers’ intention to quit. These findings emphasize the need for an effective workload management program, stress management program, and mental health support in the RMG industry. The study provides that workload is a psychological predictor of turnover intention in the RMG sectors. Psychology Job Anxiety Turnover Intention RMG workers Workload Figures Figure 1 Figure 2 1. Introduction The Ready-Made Garment (RMG) industry is the backbone of Bangladesh’s economy. This sector engages approximately four million workers and contributes the majority of national export earnings (Mondal et al., 2024). Despite the significance of the economy, workers face difficulties, including extended working hours, production pressure, strict delivery deadlines, limited job autonomy, and supervisory abusive behavior. These structural features create demanding working conditions that may demoralize workers’ psychological well-being and organizational attachment. Among the most persistent organizational challenges in this sector is the high turnover intention rate, which interrupts productivity, raises recruitment and training costs, and threatens long-term sustainability (Alzoubi et al., 2024 ; Saeed et al., 2023 ). Recommending the psychological mechanisms through which workplace difficulties contribute to turnover intention is later both theoretically and practically significant. Excessive workload is also found to have a severe impact on sleep disturbance, employee burnout, absenteeism, reduced job satisfaction, job performance, psychological difficulties, and turnover intention (Chen et al., 1991 ; Diehl et al., 2021 ; Khoir et al., 2024; Spector et al., 1998 ; Xiaoming et al., 2014 ). A meta-analysis revealed that Workload has a positive association with anxiety, more stress, depression, frustration, and is more likely to consider leaving their organizations (Spector et al., 1998 ). However, while the direct association between workload and turnover intention is well recognized, less attention has been given to the particular psychological process that explains how workload translates into withdrawal cognitions, particularly in RMG industries. Worker turnover remains a crucial challenge for organizations in the world, particularly in RMG industries where job demands are high, and working conditions are often stressful. High turnover intention can disrupt organizational productivity, increase recruitment and training costs, and negatively affect worker morale. The association between workload and turnover intention can be enlightened using an established theoretical model in organizational behavior. According to the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2007 ), heavy workload entails continued psychological effort and is therefore associated with psychological expenses. When workload exceeds available resources, workers may experience strain and emotional exhaustion, which can eventually lead to turnover intention. Similarly, the Conversion of Resources Theory (Hobfoll, 1989 ) recommends that individuals strive to acquire and maintain valuable resources, including psychological well-being and emotional stability. When workers perceive that their resources are threatened by a heavy workload, they may experience stress and anxiety. Although prior studies have demonstrated a direct association between workload and turnover intention, the psychological mechanisms underlying this relationship remain less explored. Particularly, job anxiety may play a significant mediating role. Workers experiencing heavy workload may feel anxious about meeting performance expectations and maintaining job security. These emotional responses increase workers’ turnover intentions toward the organization. Recent studies have increasingly highlighted the significance of emotional reactions to job stressors in predicting turnover intentions ( Karatepe & Olugbade, 2017 ; McCarthy et al., 2016 ). Despite the significance of workload and worker turnover had several gaps remaining in the literature. First, linking workload to turnover intention has received relatively little attention, particularly regarding the mediating role of job anxiety. Second, prior studies have often examined direct associations between workload and turnover intention without fully investigating the emotional mechanisms that may explain these associations. Addressing these gaps is significant due to identifying the psychological pathways associating workload and turnover intention can provide deeper insights into how workplace stressors influence worker behavior. To address these limitations, the current study determines the mediating role of job anxiety in the association between workload and turnover intention among RMG workers in Bangladesh (see Fig. 1 ). By examining the mechanism, the study purposes to contribute to the literature on job stress and worker turnover in several ways. First, this study contributes to organizational stress literature by identifying job anxiety as a significant psychological mechanism through which heavy workload influences workers’ intention to leave their organization. Second, the study integrates theoretical insights from the JD-R model and COR theory to explain the associations between workload and turnover intention. Finally, the findings may provide practical implications for managers and policymakers seeking to reduce worker turnover and improve psychological well-being in high-demand job environments. 2. Method 2.1 Research Design The current study employed a cross-sectional study design. 2.2 Participants The sociodemographic details of the participants are given in Table 1 . The current study selected garment workers from different industries in Bangladesh as participants. Data were collected from 25 garments in Bangladesh using a convenience sampling method. The final sample comprised 406 garment workers, of whom the majority were female (n = 215; 53%), with ages (M = 26.51 years, SD = 6.21 years) ranging from 18 to 48. Table 1 Socio-demographic characteristics of participants Demographic Variables Categories Number Percentage (%) Age 37 years 27 6.7% Sex Female 215 53% Male 191 47% Tenure 1–5 years 293 72.2% 6–10 years 67 16.5% 11–15 years 33 8.1% 16–20 years 11 2.7% > 20 years 2 .5% Monthly Income 30100 8 2% Marital Status Unmarried 107 26.4% Married 298 73.4% Divorced 1 .2% Although the ready-made garment industry in Bangladesh employs several million workers, a sample size of over 300 is generally considered adequate for behavioral and social sciences research and provides statistical power for mediation analyses (Cohen, 2016 ; Tabachnick & Fidell, 2013 ). Therefore, the sample size of this study’s participants was considered sufficient for the current study. 2.2 Measures 2.2.1 Quantitative Workload Inventory (QWI ): The QWI, which measures the quantitative workload, was created by Spector et al. ( 1998 ). The QWI is a five-item measure used to evaluate the volume of work required for a job. There are five possible responses: many times a day (coded 5) to less than once a month or never (coded 1). A high workload is indicated by greater scores, which might vary from 5 to 25. In the current investigation, the QWI presented satisfactory internal consistency (Cronbach’s alpha = .821, McDonald’s ω = .834). Furthermore, a three-factor model was found to have satisfactory model fit according to the confirmatory factor analysis results: χ2/df = 1.806, Root Mean Square Error of Approximation (RMSEA) = .045, and Standardized Root Mean Squared Residual (SRMR) = .025, Tucker–Lewis Index (TLI) = .996, Comparative Fit Index (CFI) = .998. The average variance extracted (AVE) was .56, and the composite reliability value was .78. 2.2.2 Job Stress Scale (JSS): Parker et al. (1983) created a 13-item scale that measures two aspects of job stress: “feelings of being under substantial time pressure” (8 items) and “job-related feelings of anxiety” (5 items). In this research, five items relating to anxiety were used. Along with a short form that has reliable psychometric properties, this scale evaluates several job anxiety characteristics. Each item is usually rated by respondents on a scale of 1 (strongly disagree) to 5 (Strongly agree). Greater scores correspond to greater levels of job anxiety. The Job anxiety dimension showed acceptable internal consistency in the current research (Cronbach’s alpha = .743, McDonald’s ω = .766) with factor loading ranging from .549 to .825, indicating acceptable item representation for the intended factor. A satisfactory model fit for the three-factor model was also demonstrated by the confirmatory factor analysis results. Even though the chi-square and degree of freedom ratio(χ²/df = 7.47) is higher than the generally accepted cutoff, the model showed a satisfactory fit according to other indices: Standardized Root Mean Squared Residual (SRMR) = .051, Comparative Fit Index (CFI) = .966, Tucker–Lewis Index (TLI) = .932, show that the data is well-fitted by the model, consistent with recommended cutoffs (Hu et al., 1999 ). The average variance extracted (AVE) was .54, and the composite reliability value was .75. 2.2.3 Turnover Intention Scale (TIS): Dysvik et al. (2008) created the Turnover Intention Scale (TIS), which is widely used for measuring intention to quit. The intention of employees to leave was evaluated utilizing this five-item scale. Typically, respondents assess each topic on a scale of 1 (strongly disagree) to 5 (strongly agree). A greater score specifies a greater level of intention to leave. In the current investigation, the TIS indicated acceptable internal consistency (Cronbach’s alpha = .849, McDonald’s ω = 0.885). For a three-factor model, the confirmatory factor analysis findings also revealed satisfactory model fit: χ2/df = 2.78, Root Mean Square Error of Approximation (RMSEA) = .068, and Standardized Root Mean Squared Residual (SRMR) = .013, Comparative Fit Index (CFI) = .999, Tucker–Lewis Index (TLI) = .998. The average variance extracted (AVE) was .58, and the composite reliability value was .85. 2.3 Ethics We implemented the Declaration of Helsinki to execute the research. We informed the respondents about the nature, privacy, confidentiality, and their freedom to quit the study at any time. Informed consent was attained from each participant, and a signature was obtained before they completed the questionnaire. The ethical clearance was received from the institutions (Ref. No.: AERB-FBSCU-20250527-(1)). 2.4 Statistical analysis The data were cleaned, processed, and analyzed using IBM SPSS version 27.0. Frequencies, means, percentages, and standard deviations were measured. Using z-scores, univariate outliers were filtered [Tabachnick & Fidell, 2013 advocate a cut-off of -3.29 ≤ z ≤ 3.29]. By utilizing skewness and kurtosis values, we evaluated normality; if N > 300, skewness value > 2, and kurtosis value > 7, these values indicate non-normality (Kim, 2013 ). Using Cronbach’s alpha and McDonald’s ω [the suggested cut-off: Cronbach’s alpha > 0.70 (Nunnally, 1978 )], the measures’ dependability was determined. Group differences in workload, job anxiety, and intention to quit were measured using effect size (Cohen’s d) and independent sample t-tests (Cohen, 2016 ). Coefficients of Pearson’s product-moment correlation between the variables under study were assessed the strength and direction of relationships among the study variables. We estimated the mediating role of job anxiety in the association between workload (predictor) and turnover intention (outcome) using Hayes’ PROCESS macro (v4.2) (Model 4) in SPSS (Hayes, 2018 ). Participant age and gender were included as covariates in all models. Furthermore, as outlined by Biesanz et al. ( 2010 ), we used the bootstrapping method with 5000 samples at a 95% confidence interval to analyze the models and the bias-corrected percentile approach to determine confidence intervals, following the recommendations of Biesanz et al. ( 2010 ). All statistical tests were conducted using a significance of p < 0.05. 2.5 Procedure Data were collected through face-to-face structured interviews with garment workers. Researchers visited the residential areas after work hours to conduct the survey. To clarify the study's objectives, informed consent was taken verbally and in writing. We asked questions from different questionnaires. A verbal and written instruction is also given before collecting the data. They answered our questions verbally, and we ticked marks on the tool. The questionnaire claimed to be completely anonymous because it contained no information on the participants' names. 2.2 Common method bias All study variables were collected using self-report measures from a single source. Common method bias (CMB) was evaluated using Harman’s single-factor test. This test was conducted using Principal Component Analysis (PCA) in SPSS version 27. The unrotated factor accounted for less than 50% of the total variance, suggesting that common method bias was not a serious concern in the study (Podsakoff et al., 2012 ). 3. Results 3.1. Descriptives Table 2 displays the means, standard deviations, and Pearson correlations for the three variables that are relevant to the current investigation. For all variables of interest, skewness (ranging from − .304 to .579) and kurtosis (ranging from − 1.390 to − .762) values indicate a normal distribution (Table 2 ). There is a considerable correlation between the research variables, as shown by Pearson product-moment correlations between them in Table 2 . Table 2 Descriptive Statistics of the variables used in the study Variable M SD Skewness Kurtosis 1 2 3 QW 12.49 5.877 .579 − .762 1 JA 13.65 5.744 .257 − .934 .256 ** 1 TI 16.41 6.876 − .304 -1.390 .513 ** .316 ** 1 Note. QW= Quantitative Workload, JA = Job Anxiety, TI= Turnover Intention, M= Mean, SD= Standard deviation Table 3 ’s analysis of mean differences showed that turnover intention (t = 4.369, p < .001, d = 0.42) and workload (t = -3.069, p = .002, d = − 0.30) varied significantly by gender. The CMB results indicated that the first unrotated factor accounted for 32.97% of the total variance, while subsequent components reflect distinct constructs rather than method bias. This suggests that CMB was not a serious concern in this study. Table 3 Gender differences in TI, QW, and JA Variables Gender N M SD t df p Cohen’s d TI Female 215 17.78 6.402 Male 191 14.86 7.077 4.369 404 <.001 .42 QW Female 215 11.65 5.814 Male 191 13.42 5.820 -3.065 404 .002 − .30 JA Female 215 13.25 6.044 Male 191 14.09 5.366 -1.479 404 .140 − .15 Note. N=Sample, M=Mean, SD= Standard deviations, QW= Quantitative Workload, TI= turnover Intention, JA = Job Anxiety 3.2. Mediation analysis The findings of the mediation study (Fig. 2 & Table 4 ) demonstrated a significant positive association between workload and turnover intention (β = .175, p = .001, 95% CI [.083, .328]) and with job anxiety (β = .503, p < .001, 95% CI [.409, .578]). Furthermore, job anxiety had a significant positive association with turnover intention (β = .255, p < .001, 95% CI [.172, .428]). Job Anxiety mediated 57.58% of the non-significant association between workload and turnover intention (indirect effect: β = .129, 95% CI [.078, .184]), suggesting a partial mediation effect. Workload on turnover intention was a significant positive association (total effect: β = .304, p < .001, 95% CI [.247, .465]), confirming that workload influences turnover intention through job anxiety. Table 4 Direct, indirect, and total effects of quantitative workload on turnover intention via job anxiety (N = 406) Effect Type B SE β p -value LLCI ULCI Direct Effect (QW→ TI) 0.149 0.064 0.127 0.019 0.024 0.274 Direct Effect (QW → JA) 0.501 0.042 0.513 < .001 0.419 0.582 Direct Effect (JA → TI) 0.300 0.065 0.251 < .001 0.172 0.428 Indirect Effect (QW → JA→TI) 0.150 0.034 0.127 < .001 0.082 0.219 Total Effect (QW→ TI) 0.299 0.056 0.256 < .001 0.189 0.409 Note. QW = Quantitative Workload, JA = Job Anxiety, TI = Turnover Intention; LLCI = lower limit at 95% confidence interval; ULCI = upper limit at 95% confidence interval. 4. Discussion The current study investigated the association between workload and turnover intention among workers, particularly in the high-pressure work environment of the RMG sectors in Bangladesh. In addition, we also examined the mediating role of job anxiety between the relationship of workload and workers’ intention to leave. The findings revealed that workload had a significant positive association with job anxiety and turnover intention, and that job anxiety partially mediates the association between workload and turnover intention. These findings emphasize the major role of job anxiety as a psychological mechanism in comprehending how excessive workload enhances the intention to quit jobs. The findings indicate that workload is positively associated with turnover intention. This finding recommends that workers who experience a greater level of workload are more likely to consider quitting their organizations. This finding is consistent with prior studies showing that excessive workload contributes to worker turnover intentions (Alarcon, 2011 ; Jung et al., 2019 ). The significant positive association between workload and turnover intention can also be explained through the Job Demands -Resources (JD-R) model. According to this model, excessive workload requires sustained psychological effort, which may lead to strain and negative work outcomes when workers lack sufficient resources (Bakker & Demerouti, 2007 ). Furthermore, workload has a significant positive association with job anxiety. Workers who experience excessive workload may feel worried about coping with work-related pressure and meeting deadlines. This finding aligns with prior studies indicating that high job demands can trigger emotional responses like anxiety and psychological distress (McCarthy et al., 2016 ; Scanlan & Still, 2019 ). This finding can also be explained via the Conversion of Resources theory. According to this theory, individuals attempt to maintain and protect their personal resources, including psychological energy and emotional stability (Hobfoll, 1989 ). Additionally, the results show that job anxiety is significantly positively associated with turnover intention. Workers experiencing greater levels of anxiety are more likely to develop intentions to quit their jobs. This finding is consistent with a prior study suggesting that psychological distress and emotional strain significantly predict workers’ intention to leave. When workers perceive that excessive workload threatens these resources, they may experience anxiety. This anxiety can increase turnover intentions (Scanlan & Still, 2019 ). Most importantly, the mediation analysis revealed that job anxiety partially mediates the association between workload and turnover intention. This finding recommends that high workload increases workers’ job anxiety, which subsequently contributes to higher turnover intention. In other words, job anxiety signifies a significant psychological mechanism via which workload impacts workers’ decision to quit their organizations. These findings support the Job Demands -Resources (JD-R) Model (Bakker & Demerouti, 2007 ), which proposes that high workload reduces workers’ mental and emotional resources, resulting in stress, disengagement, and intention to leave (Demerouti et al., 2001 ; Schaufeli et al., 2004). It aligns with prior studies highlighting the role of emotional strain as an intermediary process associating job demands and worker outcomes (Bakker & Demerouti, 2007 ; McCarthy et al., 2016 ). However, unlike many prior studies that focused primarily on emotional exhaustion, the current study specifically identifies job anxiety as a key mediating mechanism. The findings of the current study, therefore, extend previous studies in several ways. First, it contributes to the literature on occupational stress by showing that job anxiety plays a vital role in explaining how workload leads to turnover intention. Second, it provides empirical evidence from the garment industry in Bangladesh, a context that has been relatively underrepresented in organizational behavior studies. Third, the study integrates theoretical perspectives from the JD-R model and COR theory to explain the psychological process underlying the association between workload and turnover intention. 4.1 Practical implications The results have obvious applications in organizations, especially in the work environment of high demand. Workload needs to be understood as a psychological risk factor as well as a productivity concern. Restructuring job allocation, implementing rotational shift arrangements, and lowering overtime demands are all goals of organizational interventions. The strong indirect pathway from Workload to turnover intention via job anxiety highlights the need for workplace mental health monitoring and intervention programs. Well-being surveys and counseling services could be beneficial in detecting and addressing early signs of anxiety (Leka et al., 2010 ). Supervisory conduct is essential for reducing anxiety associated with workload. Since supportive leadership has been demonstrated to mitigate the negative impacts of job demands on stress, the training program should concentrate on cultivating emotionally intelligent leadership and supportive management techniques. 4.2 Limitations and future research directions This study had some limitations. Firstly, we could not establish the causal relationship because of the cross-sectional nature of the study. To establish the causal effect, experimental or longitudinal studies are required to confirm these findings. Secondly, we worked with the garment workers solely, which is also a limitation of our study; the study can be extended to different sectors beyond the RMG industry. Finally, we used a non-probability sampling technique, which decreases the generalizability of the study. Moreover, this study only focused on job anxiety and workload as quantitative indicators of intention to leave. Other significant elements, such as job satisfaction, organizational support, organizational commitment, and leadership style, could be considered further to investigate their role in influencing the relationship between workload and turnover intention. 5. Conclusion This study contributes meaningful insight into psychological mechanisms that associate workload with turnover intention, emphasizing the critical mediation role of job anxiety. The study indicates that excessive workloads affect workers’ intention to leave by making them feel more anxious about their jobs. These insights are essential for organizations looking to reduce employee turnover as well as anxiety. Mental health support and constructive interventions may be useful in lowering job anxiety and turnover intentions. To promote a more stable and healthier workforce, it is essential to acknowledge and manage these psychological processes. References Alarcon GM (2011) A meta-analysis of burnout with job demands, resources, and attitudes. 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Development of four self-report measures of job stressors and strain: interpersonal conflict at work scale, organizational constraints scale, quantitative workload. Psycnet.Apa.OrgPE Spector, SM JexJournal of Occupational Health Psychology, 1998•psycnet.Apa.Org , 3 (4), 356–367. https://doi.org/10.1037/1076-8998.3.4.356 Tabachnick BG, Fidell LS (2013) Using multivariate statistics (6. Baskı). MA: Pearson Xiaoming Y, Ma BJ, Chang CL, Shieh CJ (2014) Effects of workload on burnout and turnover intention of medical staff: A study. Stud ethno-medicine 8(3):229–237. https://doi.org/10.31901/24566772.2014/08.03.040 Additional Declarations The authors declare no competing interests. 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-9316566","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":617352526,"identity":"89ccdc28-e6a8-4eb0-be8b-fabd932fc2f6","order_by":0,"name":"Md. Reyad Hossen.","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA50lEQVRIiWNgGAWjYBACAzBiYGDc39588AGQwcNHtJYNPMeSQSweNuK1SOSoSYBYBLWYix3e9uFjm53sdoYctsqvOXYybAzMDx/dwKPFcnZa8cyZbcnGOxvOHrstuy0Z6DA2Y+McfA67nWPMzNvGnNhwsC/ttuQ2ZqAWHjZpglr+ttUnNhzmMSuW3FZPpBbGtsOJG47xmDF+3HaYGC1pxYw9544bz+xhS5Zm3Hach42ZoF+SNzP8KKuW7Zd/fPDjz23V9vzszQ8f49MCBozQuGDmAZOElIPBH6jWH0SpHgWjYBSMgpEGAMFeSuEHMFoqAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0009-0007-4681-1932","institution":"University of Chittagong","correspondingAuthor":true,"prefix":"","firstName":"Md.","middleName":"Reyad","lastName":"Hossen.","suffix":""}],"badges":[],"createdAt":"2026-04-03 23:58:12","currentVersionCode":1,"declarations":{"humanSubjects":true,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":true,"humanSubjectConsent":true,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-9316566/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9316566/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106356153,"identity":"6b8b68a0-0945-4ff5-9971-9f0af3432f01","added_by":"auto","created_at":"2026-04-07 18:45:33","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":24083,"visible":true,"origin":"","legend":"\u003cp\u003eHypothesized Model.\u003c/p\u003e","description":"","filename":"Picture1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9316566/v1/42e270ad74c2dc71d25affbe.jpg"},{"id":106356154,"identity":"5a69a180-1687-4df9-856c-96ee562adf36","added_by":"auto","created_at":"2026-04-07 18:45:33","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":32162,"visible":true,"origin":"","legend":"\u003cp\u003eJob anxiety as a mediator in the association between workload and turnover intention.\u003c/p\u003e","description":"","filename":"Picture2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-9316566/v1/6e3c5dfdf726fbed445093df.jpg"},{"id":106404437,"identity":"aa5c3b59-2fa2-43e8-aa01-80b8009e7991","added_by":"auto","created_at":"2026-04-08 09:16:00","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":720313,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9316566/v1/89f16587-3943-4965-8ae0-c5222e20bd0c.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Association between Workload and Turnover Intention: The mediating Role of Job Anxiety of Ready-Made Garment (RMG) Workers in Bangladesh\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe Ready-Made Garment (RMG) industry is the backbone of Bangladesh\u0026rsquo;s economy. This sector engages approximately four million workers and contributes the majority of national export earnings (Mondal et al., 2024). Despite the significance of the economy, workers face difficulties, including extended working hours, production pressure, strict delivery deadlines, limited job autonomy, and supervisory abusive behavior. These structural features create demanding working conditions that may demoralize workers\u0026rsquo; psychological well-being and organizational attachment. Among the most persistent organizational challenges in this sector is the high turnover intention rate, which interrupts productivity, raises recruitment and training costs, and threatens long-term sustainability (Alzoubi et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Saeed et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Recommending the psychological mechanisms through which workplace difficulties contribute to turnover intention is later both theoretically and practically significant.\u003c/p\u003e \u003cp\u003eExcessive workload is also found to have a severe impact on sleep disturbance, employee burnout, absenteeism, reduced job satisfaction, job performance, psychological difficulties, and turnover intention (Chen et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e1991\u003c/span\u003e; Diehl et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Khoir et al., 2024; Spector et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e; Xiaoming et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). A meta-analysis revealed that Workload has a positive association with anxiety, more stress, depression, frustration, and is more likely to consider leaving their organizations (Spector et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). However, while the direct association between workload and turnover intention is well recognized, less attention has been given to the particular psychological process that explains how workload translates into withdrawal cognitions, particularly in RMG industries.\u003c/p\u003e \u003cp\u003eWorker turnover remains a crucial challenge for organizations in the world, particularly in RMG industries where job demands are high, and working conditions are often stressful. High turnover intention can disrupt organizational productivity, increase recruitment and training costs, and negatively affect worker morale. The association between workload and turnover intention can be enlightened using an established theoretical model in organizational behavior. According to the Job Demands-Resources (JD-R) model (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), heavy workload entails continued psychological effort and is therefore associated with psychological expenses. When workload exceeds available resources, workers may experience strain and emotional exhaustion, which can eventually lead to turnover intention. Similarly, the Conversion of Resources Theory (Hobfoll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e) recommends that individuals strive to acquire and maintain valuable resources, including psychological well-being and emotional stability. When workers perceive that their resources are threatened by a heavy workload, they may experience stress and anxiety.\u003c/p\u003e \u003cp\u003eAlthough prior studies have demonstrated a direct association between workload and turnover intention, the psychological mechanisms underlying this relationship remain less explored. Particularly, job anxiety may play a significant mediating role. Workers experiencing heavy workload may feel anxious about meeting performance expectations and maintaining job security. These emotional responses increase workers\u0026rsquo; turnover intentions toward the organization. Recent studies have increasingly highlighted the significance of emotional reactions to job stressors in predicting turnover intentions ( Karatepe \u0026amp; Olugbade, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; McCarthy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite the significance of workload and worker turnover had several gaps remaining in the literature. First, linking workload to turnover intention has received relatively little attention, particularly regarding the mediating role of job anxiety. Second, prior studies have often examined direct associations between workload and turnover intention without fully investigating the emotional mechanisms that may explain these associations. Addressing these gaps is significant due to identifying the psychological pathways associating workload and turnover intention can provide deeper insights into how workplace stressors influence worker behavior.\u003c/p\u003e \u003cp\u003eTo address these limitations, the current study determines the mediating role of job anxiety in the association between workload and turnover intention among RMG workers in Bangladesh (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). By examining the mechanism, the study purposes to contribute to the literature on job stress and worker turnover in several ways. First, this study contributes to organizational stress literature by identifying job anxiety as a significant psychological mechanism through which heavy workload influences workers\u0026rsquo; intention to leave their organization. Second, the study integrates theoretical insights from the JD-R model and COR theory to explain the associations between workload and turnover intention. Finally, the findings may provide practical implications for managers and policymakers seeking to reduce worker turnover and improve psychological well-being in high-demand job environments.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"2. Method","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Research Design\u003c/h2\u003e \u003cp\u003eThe current study employed a cross-sectional study design.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Participants\u003c/h2\u003e \u003cp\u003eThe sociodemographic details of the participants are given in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The current study selected garment workers from different industries in Bangladesh as participants. Data were collected from 25 garments in Bangladesh using a convenience sampling method. The final sample comprised 406 garment workers, of whom the majority were female (n\u0026thinsp;=\u0026thinsp;215; 53%), with ages (M\u0026thinsp;=\u0026thinsp;26.51 years, SD\u0026thinsp;=\u0026thinsp;6.21 years) ranging from 18 to 48.\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\u003e\u003cem\u003eSocio-demographic characteristics of participants\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDemographic Variables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCategories\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNumber\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003ePercentage (%)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\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\u0026lt;\u0026thinsp;28 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e61.6%\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\u003e28\u0026ndash;37 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31.8%\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\u003e\u0026gt;\u0026thinsp;37 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e27\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.7%\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 \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e53%\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\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTenure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u0026ndash;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e293\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.2%\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\u003e6\u0026ndash;10 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16.5%\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\u003e11\u0026ndash;15 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e33\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e8.1%\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\u003e16\u0026ndash;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2.7%\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\u003e\u0026gt;\u0026thinsp;20 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.5%\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMonthly Income\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;15100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e129\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34.2%\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\u003e15100\u0026ndash;30000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e259\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e63.8%\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\u003e\u0026gt;\u0026thinsp;30100\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2%\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\u003e107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26.4%\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\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e298\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e73.4%\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\u003eDivorced\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.2%\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\u003eAlthough the ready-made garment industry in Bangladesh employs several million workers, a sample size of over 300 is generally considered adequate for behavioral and social sciences research and provides statistical power for mediation analyses (Cohen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Therefore, the sample size of this study\u0026rsquo;s participants was considered sufficient for the current study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Measures\u003c/h2\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e\u003cb\u003e2.2.1 Quantitative Workload Inventory (QWI\u003c/b\u003e):\u003c/h2\u003e \u003cp\u003eThe QWI, which measures the quantitative workload, was created by Spector et al. (\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1998\u003c/span\u003e). The QWI is a five-item measure used to evaluate the volume of work required for a job. There are five possible responses: many times a day (coded 5) to less than once a month or never (coded 1). A high workload is indicated by greater scores, which might vary from 5 to 25. In the current investigation, the QWI presented satisfactory internal consistency (Cronbach\u0026rsquo;s alpha = .821, McDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;.834). Furthermore, a three-factor model was found to have satisfactory model fit according to the confirmatory factor analysis results: χ2/df\u0026thinsp;=\u0026thinsp;1.806, Root Mean Square Error of Approximation (RMSEA) = .045, and Standardized Root Mean Squared Residual (SRMR) = .025, Tucker\u0026ndash;Lewis Index (TLI) = .996, Comparative Fit Index (CFI) = .998. The average variance extracted (AVE) was .56, and the composite reliability value was .78.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.2.2 Job Stress Scale (JSS):\u003c/h2\u003e \u003cp\u003eParker et al. (1983) created a 13-item scale that measures two aspects of job stress: \u0026ldquo;feelings of being under substantial time pressure\u0026rdquo; (8 items) and \u0026ldquo;job-related feelings of anxiety\u0026rdquo; (5 items). In this research, five items relating to anxiety were used. Along with a short form that has reliable psychometric properties, this scale evaluates several job anxiety characteristics. Each item is usually rated by respondents on a scale of 1 (strongly disagree) to 5 (Strongly agree). Greater scores correspond to greater levels of job anxiety. The Job anxiety dimension showed acceptable internal consistency in the current research (Cronbach\u0026rsquo;s alpha = .743, McDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;.766) with factor loading ranging from .549 to .825, indicating acceptable item representation for the intended factor. A satisfactory model fit for the three-factor model was also demonstrated by the confirmatory factor analysis results. Even though the chi-square and degree of freedom ratio(χ\u0026sup2;/df\u0026thinsp;=\u0026thinsp;7.47) is higher than the generally accepted cutoff, the model showed a satisfactory fit according to other indices: Standardized Root Mean Squared Residual (SRMR) = .051, Comparative Fit Index (CFI) = .966, Tucker\u0026ndash;Lewis Index (TLI) = .932, show that the data is well-fitted by the model, consistent with recommended cutoffs (Hu et al., \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e1999\u003c/span\u003e). The average variance extracted (AVE) was .54, and the composite reliability value was .75.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.2.3 Turnover Intention Scale (TIS):\u003c/h2\u003e \u003cp\u003eDysvik et al. (2008) created the Turnover Intention Scale (TIS), which is widely used for measuring intention to quit. The intention of employees to leave was evaluated utilizing this five-item scale. Typically, respondents assess each topic on a scale of 1 (strongly disagree) to 5 (strongly agree). A greater score specifies a greater level of intention to leave. In the current investigation, the TIS indicated acceptable internal consistency (Cronbach\u0026rsquo;s alpha = .849, McDonald\u0026rsquo;s ω\u0026thinsp;=\u0026thinsp;0.885). For a three-factor model, the confirmatory factor analysis findings also revealed satisfactory model fit: χ2/df\u0026thinsp;=\u0026thinsp;2.78, Root Mean Square Error of Approximation (RMSEA) = .068, and Standardized Root Mean Squared Residual (SRMR) = .013, Comparative Fit Index (CFI) = .999, Tucker\u0026ndash;Lewis Index (TLI) = .998. The average variance extracted (AVE) was .58, and the composite reliability value was .85.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Ethics\u003c/h2\u003e \u003cp\u003eWe implemented the Declaration of Helsinki to execute the research. We informed the respondents about the nature, privacy, confidentiality, and their freedom to quit the study at any time. Informed consent was attained from each participant, and a signature was obtained before they completed the questionnaire. The ethical clearance was received from the institutions (Ref. No.: AERB-FBSCU-20250527-(1)).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Statistical analysis\u003c/h2\u003e \u003cp\u003eThe data were cleaned, processed, and analyzed using IBM SPSS version 27.0. Frequencies, means, percentages, and standard deviations were measured. Using z-scores, univariate outliers were filtered [Tabachnick \u0026amp; Fidell, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e advocate a cut-off of -3.29\u0026thinsp;\u0026le;\u0026thinsp;z\u0026thinsp;\u0026le;\u0026thinsp;3.29]. By utilizing skewness and kurtosis values, we evaluated normality; if N\u0026thinsp;\u0026gt;\u0026thinsp;300, skewness value\u0026thinsp;\u0026gt;\u0026thinsp;2, and kurtosis value\u0026thinsp;\u0026gt;\u0026thinsp;7, these values indicate non-normality (Kim, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Using Cronbach\u0026rsquo;s alpha and McDonald\u0026rsquo;s ω [the suggested cut-off: Cronbach\u0026rsquo;s alpha\u0026thinsp;\u0026gt;\u0026thinsp;0.70 (Nunnally, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e1978\u003c/span\u003e)], the measures\u0026rsquo; dependability was determined.\u003c/p\u003e \u003cp\u003eGroup differences in workload, job anxiety, and intention to quit were measured using effect size (Cohen\u0026rsquo;s d) and independent sample t-tests (Cohen, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Coefficients of Pearson\u0026rsquo;s product-moment correlation between the variables under study were assessed the strength and direction of relationships among the study variables.\u003c/p\u003e \u003cp\u003eWe estimated the mediating role of job anxiety in the association between workload (predictor) and turnover intention (outcome) using Hayes\u0026rsquo; PROCESS macro (v4.2) (Model 4) in SPSS (Hayes, 2018 ). Participant age and gender were included as covariates in all models. Furthermore, as outlined by Biesanz et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e), we used the bootstrapping method with 5000 samples at a 95% confidence interval to analyze the models and the bias-corrected percentile approach to determine confidence intervals, following the recommendations of Biesanz et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). All statistical tests were conducted using a significance of p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Procedure\u003c/h2\u003e \u003cp\u003eData were collected through face-to-face structured interviews with garment workers. Researchers visited the residential areas after work hours to conduct the survey. To clarify the study's objectives, informed consent was taken verbally and in writing. We asked questions from different questionnaires. A verbal and written instruction is also given before collecting the data. They answered our questions verbally, and we ticked marks on the tool. The questionnaire claimed to be completely anonymous because it contained no information on the participants' names.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Common method bias\u003c/h2\u003e \u003cp\u003eAll study variables were collected using self-report measures from a single source. Common method bias (CMB) was evaluated using Harman\u0026rsquo;s single-factor test. This test was conducted using Principal Component Analysis (PCA) in SPSS version 27. The unrotated factor accounted for less than 50% of the total variance, suggesting that common method bias was not a serious concern in the study (Podsakoff et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptives\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e displays the means, standard deviations, and Pearson correlations for the three variables that are relevant to the current investigation. For all variables of interest, skewness (ranging from \u0026minus;\u0026thinsp;.304 to .579) and kurtosis (ranging from \u0026minus;\u0026thinsp;1.390 to \u0026minus;\u0026thinsp;.762) values indicate a normal distribution (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There is a considerable correlation between the research variables, as shown by Pearson product-moment correlations between them in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\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\u003e\u003cem\u003eDescriptive Statistics of the variables used in the study\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\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=\"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 \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\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\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSkewness\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eKurtosis\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQW\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e12.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.877\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.579\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJA\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e13.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e5.744\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e.257\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.934\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.256\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e16.41\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.876\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.390\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e.513\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e.316\u003csup\u003e**\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"8\"\u003eNote. QW= Quantitative Workload, JA\u0026thinsp;=\u0026thinsp;Job Anxiety, TI= Turnover Intention, M= Mean, SD= Standard deviation\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u0026rsquo;s analysis of mean differences showed that turnover intention (t\u0026thinsp;=\u0026thinsp;4.369, p \u0026lt;\u0026thinsp;.001, d\u0026thinsp;=\u0026thinsp;0.42) and workload (t = -3.069, p = .002, d\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.30) varied significantly by gender. The CMB results indicated that the first unrotated factor accounted for 32.97% of the total variance, while subsequent components reflect distinct constructs rather than method bias. This suggests that CMB was not a serious concern in this study.\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\u003eGender differences in TI, QW, and JA\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\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 \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=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\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\u003eGender\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eM\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSD\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003et\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003edf\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eCohen\u0026rsquo;s d\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTI\u003c/p\u003e \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\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e17.78\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.402\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.86\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e7.077\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4.369\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e\u0026lt;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e.42\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQW\u003c/p\u003e \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\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e11.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.814\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.820\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-3.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJA\u003c/p\u003e \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\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e13.25\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e6.044\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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\u003e191\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e14.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e-1.479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e.140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e\u0026minus;\u0026thinsp;.15\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003eNote. N=Sample, M=Mean, SD= Standard deviations, QW= Quantitative Workload, TI= turnover Intention, JA\u0026thinsp;=\u0026thinsp;Job Anxiety\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Mediation analysis\u003c/h2\u003e \u003cp\u003eThe findings of the mediation study (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e \u0026amp; Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e) demonstrated a significant positive association between workload and turnover intention (β\u0026thinsp;=\u0026thinsp;.175, p = .001, 95% CI [.083, .328]) and with job anxiety (β\u0026thinsp;=\u0026thinsp;.503, p \u0026lt; .001, 95% CI [.409, .578]). Furthermore, job anxiety had a significant positive association with turnover intention (β\u0026thinsp;=\u0026thinsp;.255, p \u0026lt; .001, 95% CI [.172, .428]). Job Anxiety mediated 57.58% of the non-significant association between workload and turnover intention (indirect effect: β\u0026thinsp;=\u0026thinsp;.129, 95% CI [.078, .184]), suggesting a partial mediation effect. Workload on turnover intention was a significant positive association (total effect: β\u0026thinsp;=\u0026thinsp;.304, p \u0026lt; .001, 95% CI [.247, .465]), confirming that workload influences turnover intention through job anxiety.\u003c/p\u003e \u003cp\u003e \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\u003e\u003cem\u003eDirect, indirect, and total effects of quantitative workload on turnover intention via job anxiety (N\u0026thinsp;=\u0026thinsp;406)\u003c/em\u003e\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=\"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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEffect Type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eB\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\u003eβ\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cem\u003ep\u003c/em\u003e-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLLCI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eULCI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect (QW\u0026rarr; TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.149\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.064\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.274\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect (QW\u003cb\u003e\u0026rarr;\u003c/b\u003eJA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.042\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.513\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.419\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.582\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDirect Effect (JA \u003cb\u003e\u0026rarr;\u003c/b\u003eTI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.300\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.172\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.428\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIndirect Effect (QW\u003cb\u003e\u0026rarr;\u003c/b\u003e JA\u0026rarr;TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.127\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.082\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.219\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTotal Effect (QW\u0026rarr; TI)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e0.299\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.056\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.189\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e0.409\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote. QW\u0026thinsp;=\u0026thinsp;Quantitative Workload, JA\u0026thinsp;=\u0026thinsp;Job Anxiety, TI\u0026thinsp;=\u0026thinsp;Turnover Intention; LLCI\u0026thinsp;=\u0026thinsp;lower limit at 95% confidence interval; ULCI\u0026thinsp;=\u0026thinsp;upper limit at 95% confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe current study investigated the association between workload and turnover intention among workers, particularly in the high-pressure work environment of the RMG sectors in Bangladesh. In addition, we also examined the mediating role of job anxiety between the relationship of workload and workers\u0026rsquo; intention to leave. The findings revealed that workload had a significant positive association with job anxiety and turnover intention, and that job anxiety partially mediates the association between workload and turnover intention. These findings emphasize the major role of job anxiety as a psychological mechanism in comprehending how excessive workload enhances the intention to quit jobs.\u003c/p\u003e \u003cp\u003eThe findings indicate that workload is positively associated with turnover intention. This finding recommends that workers who experience a greater level of workload are more likely to consider quitting their organizations. This finding is consistent with prior studies showing that excessive workload contributes to worker turnover intentions (Alarcon, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Jung et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). The significant positive association between workload and turnover intention can also be explained through the Job Demands -Resources (JD-R) model. According to this model, excessive workload requires sustained psychological effort, which may lead to strain and negative work outcomes when workers lack sufficient resources (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFurthermore, workload has a significant positive association with job anxiety. Workers who experience excessive workload may feel worried about coping with work-related pressure and meeting deadlines. This finding aligns with prior studies indicating that high job demands can trigger emotional responses like anxiety and psychological distress (McCarthy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Scanlan \u0026amp; Still, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). This finding can also be explained via the Conversion of Resources theory. According to this theory, individuals attempt to maintain and protect their personal resources, including psychological energy and emotional stability (Hobfoll, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1989\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eAdditionally, the results show that job anxiety is significantly positively associated with turnover intention. Workers experiencing greater levels of anxiety are more likely to develop intentions to quit their jobs. This finding is consistent with a prior study suggesting that psychological distress and emotional strain significantly predict workers\u0026rsquo; intention to leave. When workers perceive that excessive workload threatens these resources, they may experience anxiety. This anxiety can increase turnover intentions (Scanlan \u0026amp; Still, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eMost importantly, the mediation analysis revealed that job anxiety partially mediates the association between workload and turnover intention. This finding recommends that high workload increases workers\u0026rsquo; job anxiety, which subsequently contributes to higher turnover intention. In other words, job anxiety signifies a significant psychological mechanism via which workload impacts workers\u0026rsquo; decision to quit their organizations. These findings support the Job Demands -Resources (JD-R) Model (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e), which proposes that high workload reduces workers\u0026rsquo; mental and emotional resources, resulting in stress, disengagement, and intention to leave (Demerouti et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Schaufeli et al., 2004). It aligns with prior studies highlighting the role of emotional strain as an intermediary process associating job demands and worker outcomes (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2007\u003c/span\u003e; McCarthy et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). However, unlike many prior studies that focused primarily on emotional exhaustion, the current study specifically identifies job anxiety as a key mediating mechanism.\u003c/p\u003e \u003cp\u003eThe findings of the current study, therefore, extend previous studies in several ways. First, it contributes to the literature on occupational stress by showing that job anxiety plays a vital role in explaining how workload leads to turnover intention. Second, it provides empirical evidence from the garment industry in Bangladesh, a context that has been relatively underrepresented in organizational behavior studies. Third, the study integrates theoretical perspectives from the JD-R model and COR theory to explain the psychological process underlying the association between workload and turnover intention.\u003c/p\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Practical implications\u003c/h2\u003e \u003cp\u003eThe results have obvious applications in organizations, especially in the work environment of high demand. Workload needs to be understood as a psychological risk factor as well as a productivity concern. Restructuring job allocation, implementing rotational shift arrangements, and lowering overtime demands are all goals of organizational interventions. The strong indirect pathway from Workload to turnover intention via job anxiety highlights the need for workplace mental health monitoring and intervention programs. Well-being surveys and counseling services could be beneficial in detecting and addressing early signs of anxiety (Leka et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Supervisory conduct is essential for reducing anxiety associated with workload. Since supportive leadership has been demonstrated to mitigate the negative impacts of job demands on stress, the training program should concentrate on cultivating emotionally intelligent leadership and supportive management techniques.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations and future research directions\u003c/h2\u003e \u003cp\u003eThis study had some limitations. Firstly, we could not establish the causal relationship because of the cross-sectional nature of the study. To establish the causal effect, experimental or longitudinal studies are required to confirm these findings. Secondly, we worked with the garment workers solely, which is also a limitation of our study; the study can be extended to different sectors beyond the RMG industry. Finally, we used a non-probability sampling technique, which decreases the generalizability of the study. Moreover, this study only focused on job anxiety and workload as quantitative indicators of intention to leave. Other significant elements, such as job satisfaction, organizational support, organizational commitment, and leadership style, could be considered further to investigate their role in influencing the relationship between workload and turnover intention.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study contributes meaningful insight into psychological mechanisms that associate workload with turnover intention, emphasizing the critical mediation role of job anxiety. The study indicates that excessive workloads affect workers\u0026rsquo; intention to leave by making them feel more anxious about their jobs. These insights are essential for organizations looking to reduce employee turnover as well as anxiety. Mental health support and constructive interventions may be useful in lowering job anxiety and turnover intentions. To promote a more stable and healthier workforce, it is essential to acknowledge and manage these psychological processes.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAlarcon GM (2011) A meta-analysis of burnout with job demands, resources, and attitudes. J Vocat Behav 79(2):549\u0026ndash;562\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlzoubi MM, Al-Mugheed K, Oweidat I, Alrahbeni T, Alnaeem MM, Alabdullah AAS, Abdelaliem SMF, Hendy A (2024) Moderating role of relationships between workloads, job burnout, turnover intention, and healthcare quality among nurses. 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Stud ethno-medicine 8(3):229\u0026ndash;237. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.31901/24566772.2014/08.03.040\u003c/span\u003e\u003cspan address=\"10.31901/24566772.2014/08.03.040\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"univesity of chittagong","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":"Job Anxiety, Turnover Intention, RMG workers, Workload","lastPublishedDoi":"10.21203/rs.3.rs-9316566/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9316566/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eReady-made garment (RMG) workers in Bangladesh face significant challenges, including excessive workload, job anxiety, low wages, long working hours, and poor working conditions.\u003c/p\u003e \u003cp\u003eJob anxiety was used as a mediating factor to explore the association between workload and intention to turnover among RMG workers in Bangladesh. Workers experiencing high workload report greater job anxiety with an increasing likelihood of leaving their jobs. A cross-sectional study was conducted among 406 Bangladeshi RMG workers (mean age \u003cem\u003eM\u0026thinsp;=\u0026thinsp;26.51, SD\u0026thinsp;=\u0026thinsp;6.21\u003c/em\u003e). We employed convenience sampling and interviewed them using a structured questionnaire to assess workload, job anxiety, and turnover intention. Results indicate that the workload considerably raised turnover intention both directly and indirectly through job anxiety. There was partial mediation of the association, with job anxiety accounting for 57.58%. Overall, workload predicted higher turnover intention, with job anxiety serving as a partial mediator associating workload with workers\u0026rsquo; intention to quit. These findings emphasize the need for an effective workload management program, stress management program, and mental health support in the RMG industry. The study provides that workload is a psychological predictor of turnover intention in the RMG sectors.\u003c/p\u003e","manuscriptTitle":"The Association between Workload and Turnover Intention: The mediating Role of Job Anxiety of Ready-Made Garment (RMG) Workers in Bangladesh","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 18:45:29","doi":"10.21203/rs.3.rs-9316566/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":"dad7d676-869e-43bb-9561-b5159fa6bd1b","owner":[],"postedDate":"April 7th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":65700479,"name":"Psychology"}],"tags":[],"updatedAt":"2026-04-07T18:45:29+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 18:45:29","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9316566","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9316566","identity":"rs-9316566","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}