The Association between Job Stress and Turnover Intention among Bank Employees: A Meta-Analytical Review

The Association between Job Stress and Turnover Intention among Bank Employees: A Meta-Analytical Review | 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 Systematic Review The Association between Job Stress and Turnover Intention among Bank Employees: A Meta-Analytical Review Md. Reyad Hossen This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9316545/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 Employee turnover represents a critical challenge for banking institutions worldwide, as job stress affects organizational performance. The current study conducted a meta-analysis to estimate the association between job stress and turnover intention. A meta-analytic review comprising 4,177 banking employees from multiple countries. A random-effects meta-analysis using the restricted maximum likelihood (REML) estimator was employed to assess the pooled effect size. The findings showed a moderate positive association between job stress and turnover intention (r = .58, 95% CI [.42, .73], p < .001), indicating that employees experiencing higher levels of job stress who consider leaving their organizations. Psychology Banking employees job stress meta-analysis turnover intention Figures Figure 1 Figure 2 Figure 3 1. Introduction The banking sector plays a vital role in promoting economic growth and financial stability in both developed and developing countries (Beck et al., 2023 ). Efficient banking systems facilitate investment, strengthen financial intermediation, and contribute to overall economic growth. Because banking services are highly knowledge-intensive and customer -oriented, financial institutions based heavily on skilled, stable, and committed employees to maintain operational efficiency and service quality (Beck et al., 2023 ; Tanchi, 2015 ). However, maintaining the workforce has become increasingly challenging because of rising levels of job stress among banking employees. Job stress among banking employees has become a significant global concern. In recent years, this issue has attracted growing attention in organizational behavior due to its significant impact on employee well-being and organizational performance (Giorgi et al., 2017 ). Job stress in the banking sector arises from the demanding and competitive nature of financial institutions. Bank employees frequently encounter strict performance targets, heavy workloads, and customer pressure. Previous studies consistently demonstrate that job stress significantly reduces organizational commitment and increases intention to leave their jobs (Ozkan, 2022 ; Ramdeja & Rungruang, 2026 ). From a motivational perspective, job stress can be understood as a “push factor” that increases employees’ desire to leave, whereas emotional attachment and job satisfaction serve as “pull factors” that encourage employees to remain in the organization (Hom et al., 2025 ). Consequently, it has been widely recognized strongest predictor of turnover intention (Hur & Abner, 2024 ). In the banking sector, empirical evidence increasingly indicates that employees experiencing higher levels of job stress are more likely to consider leaving their organizations (Fuzi & Baki, 2025). A wide range of workplace stressors contribute to job stress among banking employees. These stressors can be categorized into role-related factors, workload-related factors, and organizational issues. Role conflict and role ambiguity have long been recognized as major sources of stress in complex organizational settings (Rizzo et al., 1970 ). When employees receive conflicting instructions or lack clarity regarding their responsibilities, psychological strain increases, and job satisfaction declines (Ojha, 2025 ). Workload-related pressures also represent a significant source of stress in the banking sector. Heavy workload, long working hours, and fast-paced work environments significantly elevate stress levels among bank employees (Rauniyar, 2025 ). In particular, employees working in the private banking sector often face greater pressure due to competitive performance expectations, sales targets, and rapidly evolving organizational demands (Aithal & Iype, 2024 ). Beyond role and workload pressures, several organizational factors further intensify job stress in the banking sector. Poor working conditions, job insecurity, inadequate compensation, inconsistent supervision, and unsupportive organizational culture may significantly increase employees’ psychological strain (Giorgi et al., 2017 ). In addition, work-family conflict and insufficient time for family responsibilities often intensify stress among employees (Suhartini et al., 2025 ). Interpersonal relationship stress, including conflicts with supervisors or colleagues, has also been identified as the strongest predictor of turnover intention (Lin et al., 2024 ). Additionally, limited opportunities for career advancement, insufficient training and development programs, and frequent job transfers may increase dissatisfaction and job stress (Arlinghaus et al., 2013 ; Arti & Kurniawati, 2024 ; Shrestha et al., 2025 ). Collectively, these conditions create a challenging work environment that may gradually weaken employees’ attachment to their organizations. Previous studies have also highlighted several workplace conditions that strengthen turnover intention among bank employees. For instance, employees in private banking institutions often experience excessive workloads, long working hours, and rapid organizational changes that increase the likelihood of turnover intention (Islam et al., 2019 ; Koura et al., 2025 ). Over time, the accumulation of these stressors can result in emotional exhaustion and reduced organizational attachment. Occupational stress has significant consequences at both individual and organizational levels. At the individual level, chronic stress negatively affects employees’ physical and mental health, leading to anxiety, depression, emotional exhaustion, mental fatigue, and long-term burnout(Giorgi et al., 2017 ; Mosharrafa et al., 2025 ). Emotional exhaustion, in particular, has been strongly linked to employees’ motivation to leave their organizations. At the organizational level, occupational stress reduces productivity and increases absenteeism (Giorgi et al., 2017 ). Financial institutions worldwide struggle to retain skilled professionals due to growing workplace pressures (Koura et al., 2025 ). When experienced employees leave their positions, organizations incur substantial costs related to recruitment, training, and productivity losses (Dhungana et al., 2025 ). Consequently, understanding the association between job stress and turnover intention has become a significant factor for both researchers and organizations. Despite the detrimental impact of job stress, several protective factors may reduce intention to leave their organizations. Research suggests that job satisfaction, organizational commitment, and emotional attachment to the organization help retain employees even under stressful conditions (Oh et al., 2014 ; Shrestha, 2019 ). Supportive work environments, clear goals, and effective supervision reduce turnover intentions among employees (Farhan, 2020 ). Moreover, emotional intelligence has been identified as a moderating factor that weakens the relationship between job stress and turnover intention by enabling employees to better cope with workplace challenges (Burki et al., 2020 ). These findings highlight the importance of organizational support systems and employee development initiatives in mitigating stress-related outcomes. Although research on occupational stress in the banking sector has increased considerably, the existing evidence remains fragmented and sometimes inconsistent. Different studies emphasize different stress factors, including workload and role ambiguity (Giorgi et al., 2017 ; Mukherjee et al., 2024 ), interpersonal stress and work-family conflict (Lin et al., 2024 ), and organizational support (Alarcon, 2011 ; Devi & Sharma, 2013 ). Furthermore, the reported strength of the association between job stress and turnover intention varies substantially across studies, possibly because of differences in research designs, sample characteristics, and organizational contexts. These inconsistencies make it difficult to examine the overall magnitude of the association between job stress and turnover intention among bank employees. Therefore, a comprehensive meta-analysis is necessary to synthesize the available empirical evidence and provide a more precise estimate of the association between job stress and turnover intention in the banking sector. By integrating findings from studies conducted across different countries and organizational contexts, a meta-analytic approach can clarify the overall strength of this association and identify potential sources of variation across studies. Accordingly, the current study aims to conduct a meta-analysis to estimate the overall association between job stress and turnover intention among bank employees. The findings are expected to contribute to the literature by giving a clearer comprehension of the magnitude of this association and offering practical insights for banking institutions seeking to develop effective stress management and employee retention strategies. 2. Methods 2.1 Study design and reporting guidelines This study employed a meta- analytical study design to quantitatively synthesize the empirical evidence regarding the association between job stress and turnover intention among bank employees. The review and reporting process followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines to ensure transparency and methodological rigor in the identification, screening, and inclusion of relevant studies. 2.2. Inclusion criteria and Exclusion criteria Studies were considered for this meta-analysis if they met the following criteria. First, the study provided correlation coefficients for understanding the association between job stress and turnover intention. Second, the study design was a cross-sectional and longitudinal design. Third, the study was published between 2010 and 2025. Fourth, the study was a journal article, dissertation, academic thesis, or different kinds of reports published in various journals. Studies that were excluded based on the following criteria. First, the study provided correlation coefficients between factors other than job stress and turnover intention. Second, the study was part of an online post, newspaper, or magazine. In cases where the information was insufficient (e.g., sample characteristics, design), emails were sent to the authors of those studies for clarification. 2.3. Search strategy Various search strategies and database websites were used for a comprehensive understanding of the topic. Some of the databases that were used are as follows: Web of Science (Social Sciences Citation Index), Google Scholar, SocINDEX with full Text, ResearchGate, Scopus Index, and PsycINFO. The search was conducted between October 2025 and December 2026 using combinations of keywords with Boolean operators such as (“job stress”, OR “occupational stress”, OR “workplace stress”, OR “ job related-stress”) AND (“turnover intention”, OR “employee turnover”, OR “quit turnover”, OR “leave turnover”) AND (“bank employees”, OR “commercial bank employees”, OR “bankers”, OR “bank officers”, OR “private bank employees” OR “public bank employees”, OR “banking staff”). 2.4 Study selection process The selection of article which met the inclusion criteria went through several selection steps following the standard PRISMA screening procedure (see Fig. 1 ). First, titles and abstracts were reviewed in order to gather the first set of 146 potential studies. Second, a deeper review of the studies from the first set took place to see whether they met the inclusion criteria or not. The studies that didn’t meet the criteria were considered out of the study. After that, among the studies from the second set, there were 10 studies that contained the proper information (e.g., sample size, design, scales, etc.). This whole process leads to the final set of 10 studies, which we used in the analysis. Although the number of studies included in the final analysis was relatively small (k = 10), this reflects the limited availability of studies determining job stress and turnover intention explicitly among bank employees. 2.5 Data extraction A systematic data extraction procedure was used to collect all desired information from the final set of studies that were included in the meta-analysis. Using Microsoft Excel, a standardized data extraction form was developed to ensure consistency and accuracy. For each study, the following data were independently extracted by two reviewers, with a final consultation with a third reviewer. The extracted information was categorized as follows: 2.5.1 Study Characteristics Authors and Year: Full citation details and year of publication. Country: The nation where the study was conducted. Publication Type: Whether the study was a journal article, dissertation, or academic thesis. Sample Size (N): The total number of participants in the study. 2.5.2 Sample and Participant Characteristics Industry/Sector: The specific industry from which the sample was drawn. For instance, private bank employees and commercial bank employees. Participant Role: Description of the participants' job roles, such as banking officers and frontline staff. 2.5.3 Effect Size Data (Critical for Meta-Analysis) Correlation Coefficient (r): The primary effect size extracted was the Pearson product-moment correlation coefficient (r) between job stress and turnover intention. Other Statistics: In cases where Pearson's r was not directly reported, other statistics such as standardized regression coefficients (β), t-values, and F-values were extracted to allow for conversion to Fisher’s Z and then to r, following established protocols. 2.5.4 Methodological Information Research Design: Confirmation of cross-sectional design, as per the inclusion criteria. Measurement Scales: The specific instruments used to measure job stress, such as the Job Stress Scale, the Perceived Stress Scale, and for turnover intention, the Turnover Intention Scale. This information is important for exploring potential methodological heterogeneity. In cases where a study met the inclusion criteria but had ambiguous or missing data (e.g., unclear sample characteristics, unreported correlation coefficients), the corresponding authors were contacted via email to request the necessary information. Out of the investigations sent, this process gathered responses for five studies, which were then included in the final set of studies. The final dataset for analysis comprised 10 studies, from which all relevant information was successfully extracted and coded for the following statistical analysis. 2.6 Statistical analysis All the analyses were performed in R (version 4.5.2) using the metafor package (version 4.3-0), which is widely used for conducting meta-analysis in the social and behavioral sciences (Viechtbauer, 2010 ). Effect sizes from primary studies, including standardized regression coefficients (β) and Pearson’s r, were converted to Fisher’s Z values to facilitate cross-study comparison and ensure normality for meta-analytic estimation (Borenstein et al., 2009 ). When effect sizes were not directly reported as correlations, established conversion formulas were applied to derive equivalent r values before transformation (Peterson & Brown, 2005 ). Before the main analysis, the standard error and the corresponding sampling variances were computed for each effect size following conventional meta-analytic procedures (Borenstein et al., 2009 ). A random-effects meta-analysis model was employed with the restricted maximum likelihood (REML) estimator, as true effect sizes were expected to vary across studies due to differences in sample characteristics, study designs, and contextual factors (DerSimonian & Laird, 1986 ; Viechtbauer, 2010 ). The pooled effect sizes obtained from the random-effects model were subsequently back-transformed to Pearson’s r to facilitate interpretation. Statistical heterogeneity among studies was evaluated using Cochran’s Q, the I 2 index, and τ 2 , which quantify the extent and magnitude of between-study variability beyond sampling error (Higgins & Thompson, 2002 ; Higgins et al., 2003 ). To evaluate the robustness of the pooled effect size, leave-one-out sensitivity analyses were conducted by sequentially removing each study and re-estimating the overall effect (Viechtbauer, 2010 ). Potential publication bias was explored via visual inspection of funnel plots, which measure symmetry in the distribution of effect sizes, and by applying Egger’s regression test, a widely used statistical method for detecting small-study effects (Egger et al., 1997 ). In addition, forest plots were generated to visually present individual study effect sizes with corresponding 95% confidence intervals alongside the pooled estimate. All statistical tests were two-tailed, with significance set at p < 0.05. 3. Results Figure 2 and Table 1 showed the individuals and pooled effect sizes determining the association between job stress and turnover intention among bank employees. The forest plot visually summarizes the correlation coefficients reported across the included studies. Table 1 Characteristics of included studies . Authors Year Country Industry Employee type Sample Sizes (n) Predictor type Effect Size (r) Dhungana et al. 2025 Nepal Banker both 136 Job stress 0.57 Burki et al. 2020 Pakistan Banker both 153 Job stress 0.52 Ngbea 2019 Nigeria Banker both 348 Job stress 0.36 Chen et al. 2014 Taiwan Banker both 255 Job stress 0.33 Lin et al. 2024 Taiwan Banker both 501 Job stress 0.70 Javed et al. 2014 Pakistan Banker private 150 Job stress 0.80 Shahzad et al. 2020 Pakistan Banker both 312 Job stress 0.30 Sattar & Ahmed 2014 Pakistan Banker both 150 Job stress 0.55 Yukongdi & Shrestha 2020 Nepal Banker private 282 Job stress 0.37 Mohanraj & Ramachandra 2018 India Banker private 540 Job stress 0.53 3.1 Distribution of effect sizes across studies As illustrated in Fig. 2 , all included studies reported a positive association between job stress and turnover intention, indicating that higher levels of job stress are associated with a greater likelihood of employees intending to quit their jobs. This meta-analysis integrated evidence from 10 studies, comprising a total sample of 4177 employees drawn from banking across several countries, to explore the association between job stress and turnover intention (see Table 2 ). Sample sizes range from 136 to 540 for bankers, with effect sizes (r) varying from 0.30 to 0.80 (see Table 1 ), showing variability in the strength of the association across studies. Table 2 Random -Effects Meta-Analysis (REMA) of the association between job stress and turnover intention. Group k N Pooled r 95% CI z p Q(df) I 2 (%) τ 2 Bankers 10 4177 0.58 [0.42, 0.73] 7.29 < .001 145.53(9) 94.09 0.058 Note. k = number of studies; REML = restricted maximum likelihood estimator; Q = Cochran’s heterogeneity statistic; I 2 = proportion of total variability due to between-study heterogeneity; τ 2 = between study variance. The strongest association was reported by Javed et al., with a correlation coefficient of r = .80 (95% CI [.73, .85]), suggesting a very strong association between job stress and turnover intention. In contrast, relatively lower correlations were observed in studies such as Shahzad et al. and Chen et al., although these correlations endured statistically significant. 3.2 Pooled effect size from Random-effects Meta -Analysis Using a random-effects meta- analysis model, the analysis revealed a moderately significant positive association between job stress and turnover intention among bank employees (r = 0.52, 95% CI [0.40, 0.63], p < .001; [see Table 2 ]), confirming that higher levels of stress are associated with a stronger intention to quit. The diamond shape at the bottom of the forest plot represents the pooled estimate from the random-effects model, while the horizontal width of the diamond reflects the 95% confidence interval. The consistent positive direction of the effect sizes across studies suggests that job stress is a strong predictor of turnover intention within the banking sector. 3.3 Publication bias analysis Potential publication bias was assessed using a funnel plot (see Fig. 3 ) and Egger’s regression test. Funnel plots provide a visual assessment of publication bias by examining whether smaller reports large or small effect sizes. As shown in Fig. 3 , the studies are generally distributed around the vertical dashed line representing the pooled effect size from the random-effects model. In the absence of publication bias, effect sizes are expected to be symmetrically distributed around this central estimate, forming a funnel-shaped pattern. Most studies fall within the triangular region representing the 95% confidence limits, suggesting that the distribution of effect sizes largely reflects expected sampling variability. Although a slight asymmetry is observable, such patterns are common in meta-analyses with a relatively small number of studies. Given that the analysis included ten studies, the interpretation of funnel plot asymmetry should be made with caution, as statistical tests for publication bias have limited power when the number of studies is small. Overall, the distribution of studies does not indicate that substantial publication bias is present meta-analysis. 3.4 Heterogeneity and overall effect size The findings of the random-effects meta-analysis are shown in Table 2 . It includes 10 studies, comprising a total sample of 4177 employees drawn from banking across several countries. The pooled effect size indicated a moderately strong positive association between job stress and turnover intention (r = .58, 95% CI [.42, .73], p < .001). This finding recommends that higher levels of job stress are significantly correlated with stronger intentions among bank employees to leave their jobs. Substantial heterogeneity was observed across the included studies. The Cochran’s Q statistic was significant (Q (9) = 145.53), p < .001), indicating that the variability in effect sizes across studies was higher than would be expected by chance alone. Similarly, the I 2 statistic was 94.09%, recommending that a large proportion of the total variability among study findings is attributable to between-study heterogeneity rather than sampling error. The estimated between -study variance (τ 2 = 0.058) further supports the presence of considerable heterogeneity. These findings indicate that the strength of the association between job stress and turnover intention varies across studies, possibly due to differences in study context, sample characteristics, or measurement tools. 3.5 Meta-regression analysis `To further explore potential sources of heterogeneity, a mixed-effects meta-regression analysis was conducted, as presented in Table 3 . The model determined whether the banking sector sample acted as a moderator influencing the association between job stress and turnover intention. The intercept coefficient (β = 0.54, SE = .154, z = 3.51, p < .001) represents the estimated average effect size (in Fisher’s Z units) when the moderator variable is held constant. The significant intercept indicates that the overall association between job stress and turnover intention remains statistically significant. Table 3 Mixed-Effects Meta-regression analysis for evaluating bankers. Moderator β SE z p 95% CI Intercept 0.539 0.154 3.51 < .001 [0.238, 0.841] Note. β = coefficients represent differences in Fisher’s Z values relative to the reference group; CI = confidence interval. 3.6 Moderator analysis of private vs both (public and private) employees The pooled effect size for private bank employees was (r = .58, 95% CI [.53, .63]), indicating a strong positive association between job stress and turnover intention, and both public and private bank employees (r = .48, 95% CI [.44, .52]), recommending a moderate-strong association between job stress and turnover intention. These findings recommend that job stress may have a strong influence on turnover intention in private banking contexts. 3.7 Sensitivity analysis A revealed that leave-one-out sensitivity analysis indicated that the pooled effect size remained stable when each study was sequentially removed (r ranged from .48 to .54) (see Table 4 ). These findings recommend that the overall association between job stress and turnover intention is robust and not influenced by any single study. Table 4 Moderator analysis of private vs both (public and private) employees. Employee type k Pooled effect (r) 95% CI Private 3 .58 [.53, .63] Both (public and private) 7 .48 [.44, .52] Note. k = number of studies; CI = confidence interval. Table 5 Sensitivity analysis (leave-one-out). Study removed Effect size (r) 95% CI Study 1 .52 [.38, .63] Study 2 .52 [.39, .64] Study 3 .54 [.41, .65] Study 4 .54 [.41, .65] Study 5 .50 [.37, 61] Study 6 .48 [.38, .57] Study 7 .54 [.42, .65] Study 8 .52 [.38, .63] Study 9 .54 [.41, .65] Study 10 .52 [.38, .64] Note . CI = confidence interval. 4. Discussion The current meta-analysis aims to synthesize empirical evidence regarding the association between job stress and turnover intention among banking employees. The findings demonstrate a moderately positive association between job stress and turnover intention. This finding indicates that employees experiencing higher levels of job stress are significantly more likely to consider quitting their organizations. The finding aligns with a substantial body of organizational behavior studies recommending that job stress acts as a major antecedent of employee withdrawal behavior, including turnover intention (Hom et al., 2017 ). The findings also showed variability in the strength of the association across the included studies; such differences may be described by variations in organizational environments, cultural contexts, and measurement approaches used across studies. Employees working in highly competitive banking environments often experience strong performance pressure, demanding sales targets, and extended working hours, which may intensify job stress and turnover intentions (Giorgi et al., 2017 ). In contrast, banking sectors that provide supportive leadership, fair reward systems, and opportunities for work-life balance may reduce the negative effects of stress on employees’ attitudes and behaviors. Another significant observation of this study is that the presence of considerable heterogeneity across the included studies is common in meta-analytic studies within the social sciences because organizational structures, management practices, and employee characteristics differ across countries (Hom et al., 2017 ). In the banking sector, factors like job roles, organizational culture, leadership style, and economic conditions may influence how employees perceive and respond to job stress. These contextual differences may explain why the strength of the association between job stress and turnover intention varies across studies. The assessment of publication bias recommends that the overall findings are unlikely to be strongly influenced by the selective publication of statistically significant findings. Although slight asymmetry may appear in funnel plots when the number of studies is limited, a previous methodological study indicates that such patterns are common in meta-analyses with a relatively small sample of studies. It should therefore be interpreted cautiously (Sterne et al., 2011 ). Overall, the available evidence suggests that the findings of the current meta-analysis are reasonably robust. The meta-regression findings indicate that the association between job stress and turnover intention remains consistent across the included studies. This recommends that job stress represents a stable and significant predictor of employees’ intention to leave their organizations within the banking sector. Previous study has demonstrated that job stress is one of the most consistent antecedents of employee turnover intentions across various industries and occupational groups (Hom et al., 2017 ). The leave-one-out sensitivity analysis demonstrated that meta-analytic findings were robust, with pooled estimates remaining consistent in magnitude and direction regardless of which study was excluded. This indicates that the observed association between job stress and turnover intention is stable and not driven by any single study. Persistent heterogeneity across interactions recommends that variability in effect sizes reflects genuine differences in the study contexts, like organizational settings, measurement methods, and employee populations, rather than the influence of outliers. Conducting a sensitivity analysis is suggested to verify the consistency of pooled estimates in a meta-analytic study (Higgins et al., 2003 ). These findings confirm the stability and credibility of the meta-analysis, supporting the conclusion that the association between job stress and turnover intention is consistent across the included studies. From a theoretical framework, these findings can be interpreted through the Job Demands- Resources (JD-R) model, which indicates that excessive job stress can lead to emotional exhaustion and strain, ultimately increasing the likelihood of turnover intentions (Bakker & Demerouti, 2017 ). Aligning with this model, the meta-analytic findings indicate that high job stress in a competitive banking environment depletes employees’ resources, thereby motivating intentions to leave the organization. Similarly, the stress-strain framework recommends that persistent work-related stressors may erode psychological resources, prompting withdrawal from the organization as a coping mechanism (Lazarus & Folkman, 1984 ). Turnover intention represents one such withdrawal-based coping response, whereby employees disengage to protect well-being. The observed association between job stress and turnover intention in this meta-analysis supports this framework, demonstrating that stress-induced strain drives employees’ consideration of leaving their organizations. 4.1 Practical Implications for Banking Management The results of this meta-analysis have a number of significant implications for organizations and banking institutions. Excessive job stress greatly increases employees' intentions to leave their jobs, according to the results, which show a moderately strong positive relationship between job stress and turnover intention. Banking institutions should implement strategies aimed at lowering job stress and enhancing employee well-being top priority given the high costs associated with employee turnover, including productivity losses, recruitment, and employee training (Hom et al., 2017 ). Organizations should concentrate on lowering excessive job demands, such as excessive workloads, unrealistic performance goals, and long working hours. The Job Demands-Resources (JD-R) model states that employee well-being can be improved, and withdrawal behaviors like intention to leave can be reduced through lowering job demands while increasing supportive resources (Bakker & Demerouti, 2017 ). Therefore, putting in place more equitable workload distribution, flexible scheduling, and attainable performance expectations may be beneficial for banking institutions. Enhancing organizational support could reduce the negative impacts of workplace stress. Role ambiguity and psychological strain among employees can be lowered through supportive supervision, clear communication, and fair performance evaluation systems. Banks should fund programs dedicated to stress management and employee well-being. Initiatives such as stress management training, mental health support services, and counseling centers can improve employees’ coping skills and psychological resilience. Furthermore, encouraging work-life balance through flexible work schedules and family-friendly policies may lower stress levels and employee turnover. Finally, organizations should provide their employees with professional development opportunities and pathways. Banking institutions can reduce the risk of employee turnover and increase organizational commitment by funding employee development programs. 4.2 Limitations and future research directions This meta-analysis has a number of limitations that should be taken into account when interpreting the results, even though it offers insightful information about the association between job stress and turnover intention among banking employees. First, the study consisted of a limited number of studies. Methodological research indicates that analyses involving a small number of studies may have limited statistical power for moderating effects or detecting publication bias, even though the meta-analysis included ten studies (Sterne et al., 2011 ). As more empirical data become available, more studies should be included in future research. Second, the included studies showed substantial heterogeneity. The high I² value indicates that variations in organizational contexts, cultural settings, measurement tools, or sample characteristics may have an impact on the variability in effect sizes. Future meta-analyses should explore other moderating factors that could have an impact on the relationship between job stress and turnover intention, such as job satisfaction, organizational culture, emotional intelligence, or leadership style. Third, the studies that were part of this meta-analysis employed cross-sectional research designs, which makes it more difficult to determine whether job stress and turnover intention are causally related. Lastly, the current meta-analysis concentrated particularly on workers in the banking industry. Although this focus increases the findings' contextual relevance, it might reduce the generalizability of the findings to other occupational sectors. To understand whether the strength of this association varies across occupational contexts, future research should compare stress-turnover relationships across various industries. By addressing these issues, we will have a better understanding of the mechanisms underlying the relationship between job stress and intention to leave, which will help us create organizational interventions that are more profitable. 5. Conclusion In order to examine the association between job stress and turnover intention among banking employees, this meta-analysis combined empirical data from ten studies. The results suggest that employees' intentions to leave their jobs have a significant and moderately strong positive relationship with job stress. These findings show that in the banking industry, higher job stress levels lead to higher turnover intentions. By offering a quantitative synthesis of previous research and proving the robustness of the stress-turnover relationship in various banking contexts, the findings add to the literature of organizational behavior. The findings also align with theoretical frameworks that highlight how excessive job demands influence employee attitudes and withdrawal behaviors, such as the stress-strain perspective and the Job Demands–Resources model. From a practical viewpoint, the study emphasizes how crucial supportive organizational environments and workplace stress management are to lowering employee turnover risks. Overall, this meta-analysis highlights the necessity of ongoing study and organizational focus on occupational stress management. Financial institutions can enhance organizational performance and employee well-being by addressing workplace stressors and strengthening employee support systems. References Aithal PS, Iype R (2024) Stress Levels Among Employees: A Study of Selected Banks in Kerala. SSRN Electron J. https://doi.org/10.2139/ssrn.4974294 Alarcon GM (2011) A meta-analysis of burnout with job demands, resources, and attitudes. J Vocat Behav 79:549–562. https://doi.org/10.1016/j.jvb.2011.03.007 Arlinghaus A, Caban-Martinez AJ, Marino M, Reme SE (2013) The role of ergonomic and psychosocial workplace factors in the reporting of back injuries among U.S. home health aides. 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Normative Pressures as a Unifying Mechanism for Relational Antecedents of Employee Turnover. Hum Resour Manag 64:77–98. https://doi.org/10.1002/hrm.22250 Hur H, Abner G (2024) What makes public employees want to leave their job? A meta-analysis of turnover intention predictors among public sector employees. Public Adm Rev 84:115–142. https://doi.org/10.1111/puar.13601 Islam N, Zeesan EA, Chakraborty D, Rahman MN, Ahmed U, Nower SI, N., Nazrul T (2019) Relationship between Job Stress and the Turnover Intention of Private Sector Bank Employees in Bangladesh. Int Bus Res 12:133. https://doi.org/10.5539/ibr.v12n8p133 Koura A, Ghazi S, Boudhar A, Oubrahimi M (2025) The ties that strain: Unraveling the determinants of workplace stress and their impact on turnover intentions in the banking industry. Multidisciplinary Science Journal , 7 . https://doi.org/10.31893/multiscience.2025591 Lazarus RS, Folkman S (1984) Appraisal, Coping, Health Status, and Psychological Symptoms. J Personal Soc Psychol 50:571–579. https://doi.org/10.1037/0022-3514.50.3.571 Lin MH, Yen YH, Chuang TF, Yang P, Sen, Chuang M, Da (2024) The impact of job stress on job satisfaction and turnover intentions among bank employees during the COVID-19 pandemic. Front Psychol 15. https://doi.org/10.3389/fpsyg.2024.1482968 Mohd Fuzi N, Baki NU (2025) Understanding Turnover Intentions in the Financial Services Sector: A Systematic Review of the Quit Mindset. In SAGE Open (Vol. 15). SAGE Publications Inc. https://doi.org/10.1177/21582440251385892 Mosharrafa R, Al, Nazmunnahar B, Khalily MA, Islam MR (2025) Evaluation of mental health problems and psychological burnout among the bankers in Bangladesh: A cross-sectional study. Work 80:1640–1652. https://doi.org/10.1177/10519815241292440 Mukherjee N, Haider SA, Choudhury S, LITERATURE REVIEW ON THE IMPACT OF JOB STRESS ON EMPLOYEE PERFORMANCE WITH SPECIAL REFERENCE TO THE BANKING SECTOR. In (2024) A SYSTEMATIC. Dynamic Relationships Manage J (Vol 13:37–54. https://doi.org/10.17708/DRMJ.2024.v13n02a03 . Slovenian Academy of Management Oh JH, Park J, Rutherford BN (2014) Management of frontline financial sales personnel. J Financial Serv Mark 19:208–220. https://doi.org/10.1057/fsm.2014.19 Ojha G (2025) Impact of Working Hours, Work Overload and Role Ambiguity on Perceived Workplace Stress among Employees in Nepalese Commercial Banks. MVIC J Manage Inform Technol 1:42–62. https://doi.org/10.3126/mvicjmit.v1i1.77319 Ozkan AH (2022) The Effect of Burnout and Work Attitudes on Turnover Intention: A Meta-Analytical Comparison between Asia, Oceania, and Africa. J Asia-Pac Bus 23:113–141. https://doi.org/10.1080/10599231.2022.2065564 Peterson RA, Brown SP (2005) On the use of beta coefficients in meta-analysis. J Appl Psychol 90:175–181. https://doi.org/10.1037/0021-9010.90.1.175 Podsakoff PM, MacKenzie SB, Podsakoff NP (2012) Sources of method bias in social science research and recommendations on how to control it. In Annual Review of Psychology (Vol. 63, pp. 539–569). https://doi.org/10.1146/annurev-psych-120710-100452 Ramdeja K, Rungruang P (2026) Engagement, Citizenship Behavior, Burnout, and Intention to Quit: Mechanisms Fostering Sustainable Well-Being and Driving Retention Among Thai Frontline Bank Employees. Sustainability (Switzerland) , 18 . https://doi.org/10.3390/su18010107 Rauniyar SK (2025) Impact of Stress on Employee Productivity Performance and Turnover in Nepalese Commercial Banks. Nepal J Bus 12:137–149. https://doi.org/10.3126/njb.v12i3.84467 Rizzo JR, House RJ, Lirtzman SI (1970) Role Conflict and Ambiguity in Complex Organizations. Adm Sci Q 15:150. https://doi.org/10.2307/2391486 Shrestha P (2019) The influence of affective organizational commitment, job satisfaction and job stress on turnover intention: a study of the employees of bank of Kathmandu Lumbini LTD. In http://203.159.5.9/ait-thesis/detail.php?q=B05894 Shrestha P, Chhetri GR, Panthi BR (2025) Employee Turnover Behavior in Nepalese Commercial Banks. J Balkumari Coll 14:10–19. https://doi.org/10.3126/jbkc.v14i1.80753 Sterne JAC, Sutton AJ, Ioannidis JPA, Terrin N, Jones DR, Lau J, Carpenter J, Rücker G, Harbord RM, Schmid CH, Tetzlaff J, Deeks JJ, Peters J, Macaskill P, Schwarzer G, Duval S, Altman DG, Moher D, Higgins JPT (2011) Recommendations for examining and interpreting funnel plot asymmetry in meta-analyses of randomised controlled trials. BMJ (Online) , 343 . https://doi.org/10.1136/bmj.d4002 Suhartini E, Kankaew K, Syariati A (2025) She Works, Therefore She Worries: The Hidden Costs of Balance for Women in Indonesian Banking. Jurnal Manajemen Bisnis 16:442–459. https://doi.org/10.18196/mb.v16i2.27224 Tanchi KR (2015), June Analyzing the Factors Influencing Employee Turnover in Private Commercial Banks in Bangladesh . Http://Dspace.Daffodilvarsity.Edu.Bd:8080/Bitstream/Handle/20.500.11948/1395/Paper-09.Pdf?IsAllowed = y&sequence = 1 Viechtbauer W (2010) Conducting Meta-Analyses in R with the metafor Package. Journal of Statistical Software , 36 . https://doi.org/10.18637/jss.v036.i03 Appendix A No. of studies Title Author (s) No. of Sampling Scales used in studies Findings 1 Occupational Stress and Turnover Intentions of Commercial Banks Employees in Pokhara, Nepal Bharat Ram Dhungana, Kush Adhikar et al. (2025) 136 OS (α = 0.923) JT (α = 0.761) A significant positive correlation between work overload and turnover intention (r = 0.571, p = 0.05) 2 The Impact of Job Stress on Turnover Intentions–The Moderating Role of Emotional Intelligence Farah Naz Burki, Naimat U. Khan, and Imran Saeed (2020) 153 JS (Parker and Decotiis, 1983) (α = 0.75) TI (Lance, 1988) (α = 0.71) EI (Wong and Law, 2002) (α = 0.88) Job stress has a strong direct significant relationship with employee turnover intentions (β = 0.516) 3 Perceived Job Insecurity, Work Overload and Work-Family Conflict as Predictors of Turnover Intention among Bank Employees in Benue State (Nigeria) Kwasedoo Martha Ngbea (2019) 348 JIS (De Witte, 2000) WOS (Schlots et al., 2004) WFC (Netemeyer et al., 1996) TI (Seashore et al., 1982) (α = .92) Job insecurity has a significant positive relationship with Turnover Intension (r = 0.66, P < 0.01) Significant positive relationship between Work overload and turnover intention (r = 0.36, P < 0.05) work-family conflict and turnover intention (r = 0.64, P < 0.01) 4 Modelling job stress as a mediating role in predicting turnover intention (Taiwan) Mei-Fang Chen, Chieh-Peng Lin, and Gin-Yen Lien (2014) 255 SS (Caplan et al., 1975) (α = 0.90) JS (Lait and Wallace, 2002) (α = 0.71) TI (Turner et al., 1987) (α = 0.89) Job stress and turnover intention (β = 0.33, p < 0.01) 5 The impact of job stress on job satisfaction and turnover intentions among bank employees during the COVID-19 pandemic (Taiwan) Mei-Hui Lin, Ya-Hui Yen et al. (2024) 501 JSQ, Taiwan’s Ministry of Labor (2020) (α = 0.973) MSQ (University of Minnesota, 2020) (α = 0.816) ILS (Scott et al., 1999) Turnover intention is positively correlated with the overall average level of job stress (r = 0.704) and negatively correlated with both intrinsic (r = − 0.441) and extrinsic job satisfaction (r = − 0.511) All at p < 0.001 level 6 Effect of Role Conflict, Work Life Balance and Job Stress on Turnover Intention: Evidence from Pakistan (Pakistan) Muhammad Javed, Muhammad Arsalan Khan, Muhammad Yasir et al., (2014) 150 JS (α = 0.698) RC (α = 0.824) WLB (α = 0.760) TI (α = 0.847) Job stress is positively and significantly related with turnover intention (r = 0.800) Role conflict is also positively related (r = 0.103) Work life balance and turnover intention (r = 0.203) 7 An Empirical Analysis of Work Overload, Organizational Commitment and Turnover Intentions among Employees of Banking Sector (Pakistan) Qasim Shahzad, Bahadar Shah et al., (2020) 296 WO (Reilly, 1982) OC (Mowday et al., 1979) TI (Eisenverger et al., 2002) Work Overload has a significant positive correlation with turnover intention (r = 0.297) 8 Factors Effecting Employee Turnover in Banking Sector (Pakistan) Saba Sattar, Shehzad Ahmed (2014) 150 Abdulla et al., (2011) and Zuber (2009) 11(α = 0.853) All relationships with turnover intention are significant, such as work environment (r = 0.553), compensation (r = 0.287, weak), job stress (r = 0.548), career growth (r = 0.453) 9 The Influence of Affective Commitment, Job Satisfaction and Job Stress on Turnover Intention: A Study of Nepalese Bank Employees (Nepal) Vimolwan Yukongdi, Pooja Shrestha (2020) 282 A12C (Meyer and Alle13n, 1991) (α = 0.87) JS (Ramalho Luz et al., 2018) (α = 0.73) JS, stress (Firth et al., 2004) (α = 0.73) TI (Alniaçik et al., 2013) (α = 0.81) job satisfaction (β = -0.460, p < 0.001) had a greater impact on employees’ intention to leave followed by job stress (β = 0.369, p < 0.001) and affective commitment (β = -0.306, p < 0.001) 10 Impact of Job Stress on Turnover Intentions Among Employees of Private Sector Banks, Coimbatore (India) Mr. S. Mohanraj, Dr. K. K. Ramachandran (2018) 540 JS TI Strong positive significant correlation between Job stress and turnover intentions (r = 0.531, sig.0.000) 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-9316545","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":617350940,"identity":"ef299a54-52ba-4284-b7b4-4820c7a7af5a","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 23:51:02","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-9316545/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-9316545/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":106356119,"identity":"24c8e674-2cf2-4b2b-b652-554875b87220","added_by":"auto","created_at":"2026-04-07 18:42:09","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":151118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003ePRISMA Flow diagram of study selection process.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-9316545/v1/cf102bd7e20db7dd981a10c8.jpeg"},{"id":106356120,"identity":"bc076e31-4640-498d-a9df-26049745c578","added_by":"auto","created_at":"2026-04-07 18:42:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":14948,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eForest Plot of the correlation between job stress and turnover intention among bankers.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote.\u003c/em\u003e Effect sizes are revealed as correlations (\u003cem\u003er\u003c/em\u003e). The diamond represents the overall effect size, and its width reflects the 95% confidence interval.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-9316545/v1/d3998183a449368fc09f56ff.png"},{"id":106356121,"identity":"00e49625-b8bc-4d9f-809e-cdde42159c40","added_by":"auto","created_at":"2026-04-07 18:42:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":7906,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eFunnel Plot for assessment of publication bias among bankers.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eNote. \u003c/em\u003eThe vertical dashed line represents the pooled effect size, and the triangular region indicates the expected 95% confidence limits.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-9316545/v1/edc252147421605549303b40.png"},{"id":106404416,"identity":"d9baafc3-64bd-4e90-8735-f4879a5d5efa","added_by":"auto","created_at":"2026-04-08 09:15:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1261339,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-9316545/v1/3509bbfe-2a43-4e3b-ae36-30e8e278f16d.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eThe Association between Job Stress and Turnover Intention among Bank Employees: A Meta-Analytical Review\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eThe banking sector plays a vital role in promoting economic growth and financial stability in both developed and developing countries (Beck et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Efficient banking systems facilitate investment, strengthen financial intermediation, and contribute to overall economic growth. Because banking services are highly knowledge-intensive and customer -oriented, financial institutions based heavily on skilled, stable, and committed employees to maintain operational efficiency and service quality (Beck et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Tanchi, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). However, maintaining the workforce has become increasingly challenging because of rising levels of job stress among banking employees. Job stress among banking employees has become a significant global concern. In recent years, this issue has attracted growing attention in organizational behavior due to its significant impact on employee well-being and organizational performance (Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eJob stress in the banking sector arises from the demanding and competitive nature of financial institutions. Bank employees frequently encounter strict performance targets, heavy workloads, and customer pressure. Previous studies consistently demonstrate that job stress significantly reduces organizational commitment and increases intention to leave their jobs (Ozkan, \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Ramdeja \u0026amp; Rungruang, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2026\u003c/span\u003e). From a motivational perspective, job stress can be understood as a \u0026ldquo;push factor\u0026rdquo; that increases employees\u0026rsquo; desire to leave, whereas emotional attachment and job satisfaction serve as \u0026ldquo;pull factors\u0026rdquo; that encourage employees to remain in the organization (Hom et al., \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, it has been widely recognized strongest predictor of turnover intention (Hur \u0026amp; Abner, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). In the banking sector, empirical evidence increasingly indicates that employees experiencing higher levels of job stress are more likely to consider leaving their organizations (Fuzi \u0026amp; Baki, 2025).\u003c/p\u003e \u003cp\u003eA wide range of workplace stressors contribute to job stress among banking employees. These stressors can be categorized into role-related factors, workload-related factors, and organizational issues. Role conflict and role ambiguity have long been recognized as major sources of stress in complex organizational settings (Rizzo et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e1970\u003c/span\u003e). When employees receive conflicting instructions or lack clarity regarding their responsibilities, psychological strain increases, and job satisfaction declines (Ojha, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Workload-related pressures also represent a significant source of stress in the banking sector. Heavy workload, long working hours, and fast-paced work environments significantly elevate stress levels among bank employees (Rauniyar, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). In particular, employees working in the private banking sector often face greater pressure due to competitive performance expectations, sales targets, and rapidly evolving organizational demands (Aithal \u0026amp; Iype, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBeyond role and workload pressures, several organizational factors further intensify job stress in the banking sector. Poor working conditions, job insecurity, inadequate compensation, inconsistent supervision, and unsupportive organizational culture may significantly increase employees\u0026rsquo; psychological strain (Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In addition, work-family conflict and insufficient time for family responsibilities often intensify stress among employees (Suhartini et al., \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Interpersonal relationship stress, including conflicts with supervisors or colleagues, has also been identified as the strongest predictor of turnover intention (Lin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Additionally, limited opportunities for career advancement, insufficient training and development programs, and frequent job transfers may increase dissatisfaction and job stress (Arlinghaus et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Arti \u0026amp; Kurniawati, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Shrestha et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Collectively, these conditions create a challenging work environment that may gradually weaken employees\u0026rsquo; attachment to their organizations.\u003c/p\u003e \u003cp\u003ePrevious studies have also highlighted several workplace conditions that strengthen turnover intention among bank employees. For instance, employees in private banking institutions often experience excessive workloads, long working hours, and rapid organizational changes that increase the likelihood of turnover intention (Islam et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Koura et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eOver time, the accumulation of these stressors can result in emotional exhaustion and reduced organizational attachment.\u003c/p\u003e \u003cp\u003eOccupational stress has significant consequences at both individual and organizational levels. At the individual level, chronic stress negatively affects employees\u0026rsquo; physical and mental health, leading to anxiety, depression, emotional exhaustion, mental fatigue, and long-term burnout(Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mosharrafa et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Emotional exhaustion, in particular, has been strongly linked to employees\u0026rsquo; motivation to leave their organizations. At the organizational level, occupational stress reduces productivity and increases absenteeism (Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Financial institutions worldwide struggle to retain skilled professionals due to growing workplace pressures (Koura et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). When experienced employees leave their positions, organizations incur substantial costs related to recruitment, training, and productivity losses (Dhungana et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Consequently, understanding the association between job stress and turnover intention has become a significant factor for both researchers and organizations.\u003c/p\u003e \u003cp\u003eDespite the detrimental impact of job stress, several protective factors may reduce intention to leave their organizations. Research suggests that job satisfaction, organizational commitment, and emotional attachment to the organization help retain employees even under stressful conditions (Oh et al., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Shrestha, \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). Supportive work environments, clear goals, and effective supervision reduce turnover intentions among employees (Farhan, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Moreover, emotional intelligence has been identified as a moderating factor that weakens the relationship between job stress and turnover intention by enabling employees to better cope with workplace challenges (Burki et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These findings highlight the importance of organizational support systems and employee development initiatives in mitigating stress-related outcomes.\u003c/p\u003e \u003cp\u003eAlthough research on occupational stress in the banking sector has increased considerably, the existing evidence remains fragmented and sometimes inconsistent. Different studies emphasize different stress factors, including workload and role ambiguity (Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Mukherjee et al., \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), interpersonal stress and work-family conflict (Lin et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2024\u003c/span\u003e), and organizational support (Alarcon, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Devi \u0026amp; Sharma, \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Furthermore, the reported strength of the association between job stress and turnover intention varies substantially across studies, possibly because of differences in research designs, sample characteristics, and organizational contexts. These inconsistencies make it difficult to examine the overall magnitude of the association between job stress and turnover intention among bank employees.\u003c/p\u003e \u003cp\u003eTherefore, a comprehensive meta-analysis is necessary to synthesize the available empirical evidence and provide a more precise estimate of the association between job stress and turnover intention in the banking sector. By integrating findings from studies conducted across different countries and organizational contexts, a meta-analytic approach can clarify the overall strength of this association and identify potential sources of variation across studies. Accordingly, the current study aims to conduct a meta-analysis to estimate the overall association between job stress and turnover intention among bank employees. The findings are expected to contribute to the literature by giving a clearer comprehension of the magnitude of this association and offering practical insights for banking institutions seeking to develop effective stress management and employee retention strategies.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and reporting guidelines\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"BlockQuote\"\u003e \u003cp\u003eThis study employed a meta- analytical study design to quantitatively synthesize the empirical evidence regarding the association between job stress and turnover intention among bank employees. The review and reporting process followed the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) guidelines to ensure transparency and methodological rigor in the identification, screening, and inclusion of relevant studies.\u003c/p\u003e \u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Inclusion criteria and Exclusion criteria\u003c/h2\u003e \u003cp\u003eStudies were considered for this meta-analysis if they met the following criteria. First, the study provided correlation coefficients for understanding the association between job stress and turnover intention. Second, the study design was a cross-sectional and longitudinal design. Third, the study was published between 2010 and 2025. Fourth, the study was a journal article, dissertation, academic thesis, or different kinds of reports published in various journals.\u003c/p\u003e \u003cp\u003eStudies that were excluded based on the following criteria. First, the study provided correlation coefficients between factors other than job stress and turnover intention. Second, the study was part of an online post, newspaper, or magazine. In cases where the information was insufficient (e.g., sample characteristics, design), emails were sent to the authors of those studies for clarification.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Search strategy\u003c/h2\u003e \u003cp\u003eVarious search strategies and database websites were used for a comprehensive understanding of the topic. Some of the databases that were used are as follows: Web of Science (Social Sciences Citation Index), Google Scholar, SocINDEX with full Text, ResearchGate, Scopus Index, and PsycINFO. The search was conducted between October 2025 and December 2026 using combinations of keywords with Boolean operators such as (\u0026ldquo;job stress\u0026rdquo;, OR \u0026ldquo;occupational stress\u0026rdquo;, OR \u0026ldquo;workplace stress\u0026rdquo;, OR \u0026ldquo; job related-stress\u0026rdquo;) AND (\u0026ldquo;turnover intention\u0026rdquo;, OR \u0026ldquo;employee turnover\u0026rdquo;, OR \u0026ldquo;quit turnover\u0026rdquo;, OR \u0026ldquo;leave turnover\u0026rdquo;) AND (\u0026ldquo;bank employees\u0026rdquo;, OR \u0026ldquo;commercial bank employees\u0026rdquo;, OR \u0026ldquo;bankers\u0026rdquo;, OR \u0026ldquo;bank officers\u0026rdquo;, OR \u0026ldquo;private bank employees\u0026rdquo; OR \u0026ldquo;public bank employees\u0026rdquo;, OR \u0026ldquo;banking staff\u0026rdquo;).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Study selection process\u003c/h2\u003e \u003cp\u003eThe selection of article which met the inclusion criteria went through several selection steps following the standard PRISMA screening procedure (see Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). First, titles and abstracts were reviewed in order to gather the first set of 146 potential studies. Second, a deeper review of the studies from the first set took place to see whether they met the inclusion criteria or not. The studies that didn\u0026rsquo;t meet the criteria were considered out of the study. After that, among the studies from the second set, there were 10 studies that contained the proper information (e.g., sample size, design, scales, etc.). This whole process leads to the final set of 10 studies, which we used in the analysis. Although the number of studies included in the final analysis was relatively small (k\u0026thinsp;=\u0026thinsp;10), this reflects the limited availability of studies determining job stress and turnover intention explicitly among bank employees.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Data extraction\u003c/h2\u003e \u003cp\u003eA systematic data extraction procedure was used to collect all desired information from the final set of studies that were included in the meta-analysis. Using Microsoft Excel, a standardized data extraction form was developed to ensure consistency and accuracy. For each study, the following data were independently extracted by two reviewers, with a final consultation with a third reviewer.\u003c/p\u003e \u003cp\u003eThe extracted information was categorized as follows:\u003c/p\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Study Characteristics\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eAuthors and Year: Full citation details and year of publication.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCountry: The nation where the study was conducted.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003ePublication Type: Whether the study was a journal article, dissertation, or academic thesis.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSample Size (N): The total number of participants in the study.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Sample and Participant Characteristics\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eIndustry/Sector: The specific industry from which the sample was drawn. For instance, private bank employees and commercial bank employees.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eParticipant Role: Description of the participants' job roles, such as banking officers and frontline staff.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Effect Size Data (Critical for Meta-Analysis)\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eCorrelation Coefficient (r): The primary effect size extracted was the Pearson product-moment correlation coefficient (r) between job stress and turnover intention.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eOther Statistics: In cases where Pearson's r was not directly reported, other statistics such as standardized regression coefficients (β), t-values, and F-values were extracted to allow for conversion to Fisher\u0026rsquo;s Z and then to r, following established protocols.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Methodological Information\u003c/h2\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eResearch Design: Confirmation of cross-sectional design, as per the inclusion criteria.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMeasurement Scales: The specific instruments used to measure job stress, such as the Job Stress Scale, the Perceived Stress Scale, and for turnover intention, the Turnover Intention Scale. This information is important for exploring potential methodological heterogeneity.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eIn cases where a study met the inclusion criteria but had ambiguous or missing data (e.g., unclear sample characteristics, unreported correlation coefficients), the corresponding authors were contacted via email to request the necessary information. Out of the investigations sent, this process gathered responses for five studies, which were then included in the final set of studies. The final dataset for analysis comprised 10 studies, from which all relevant information was successfully extracted and coded for the following statistical analysis.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Statistical analysis\u003c/h2\u003e \u003cp\u003eAll the analyses were performed in R (version 4.5.2) using the metafor package (version 4.3-0), which is widely used for conducting meta-analysis in the social and behavioral sciences (Viechtbauer, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). Effect sizes from primary studies, including standardized regression coefficients (β) and Pearson\u0026rsquo;s r, were converted to Fisher\u0026rsquo;s Z values to facilitate cross-study comparison and ensure normality for meta-analytic estimation (Borenstein et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). When effect sizes were not directly reported as correlations, established conversion formulas were applied to derive equivalent r values before transformation (Peterson \u0026amp; Brown, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2005\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eBefore the main analysis, the standard error and the corresponding sampling variances were computed for each effect size following conventional meta-analytic procedures (Borenstein et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). A random-effects meta-analysis model was employed with the restricted maximum likelihood (REML) estimator, as true effect sizes were expected to vary across studies due to differences in sample characteristics, study designs, and contextual factors (DerSimonian \u0026amp; Laird, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e1986\u003c/span\u003e; Viechtbauer, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e). The pooled effect sizes obtained from the random-effects model were subsequently back-transformed to Pearson\u0026rsquo;s r to facilitate interpretation.\u003c/p\u003e \u003cp\u003eStatistical heterogeneity among studies was evaluated using Cochran\u0026rsquo;s Q, the I\u003csup\u003e2\u003c/sup\u003e index, and τ\u003csup\u003e2\u003c/sup\u003e, which quantify the extent and magnitude of between-study variability beyond sampling error (Higgins \u0026amp; Thompson, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2002\u003c/span\u003e; Higgins et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). To evaluate the robustness of the pooled effect size, leave-one-out sensitivity analyses were conducted by sequentially removing each study and re-estimating the overall effect (Viechtbauer, \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2010\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePotential publication bias was explored via visual inspection of funnel plots, which measure symmetry in the distribution of effect sizes, and by applying Egger\u0026rsquo;s regression test, a widely used statistical method for detecting small-study effects (Egger et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, forest plots were generated to visually present individual study effect sizes with corresponding 95% confidence intervals alongside the pooled estimate. All statistical tests were two-tailed, with significance set at \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results","content":"\u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e showed the individuals and pooled effect sizes determining the association between job stress and turnover intention among bank employees. The forest plot visually summarizes the correlation coefficients reported across the included studies.\u003c/p\u003e \u003cp\u003e \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\u003eCharacteristics of included studies\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eAuthors\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYear\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eCountry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIndustry\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eEmployee type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eSample Sizes (n)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePredictor type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEffect Size (r)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDhungana et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNepal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.57\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBurki et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.52\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNgbea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNigeria\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.36\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChen et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.33\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLin et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2024\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTaiwan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.70\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eJaved et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.80\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eShahzad et al.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e312\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.30\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSattar \u0026amp; Ahmed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePakistan\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eboth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.55\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYukongdi \u0026amp; Shrestha\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eNepal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.37\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMohanraj \u0026amp; Ramachandra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBanker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eprivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eJob stress\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e0.53\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Distribution of effect sizes across studies\u003c/h2\u003e \u003cp\u003eAs illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, all included studies reported a positive association between job stress and turnover intention, indicating that higher levels of job stress are associated with a greater likelihood of employees intending to quit their jobs. This meta-analysis integrated evidence from 10 studies, comprising a total sample of 4177 employees drawn from banking across several countries, to explore the association between job stress and turnover intention (see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Sample sizes range from 136 to 540 for bankers, with effect sizes (r) varying from 0.30 to 0.80 (see Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), showing variability in the strength of the association across studies.\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\u003eRandom -Effects Meta-Analysis (REMA) of the association between job stress and turnover intention.\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"10\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c10\" colnum=\"10\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGroup\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ek\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\u003ePooled r\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eQ(df)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003eI\u003csup\u003e2\u003c/sup\u003e(%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c10\"\u003e \u003cp\u003eτ\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBankers\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4177\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e[0.42, 0.73]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e7.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003e145.53(9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e \u003cp\u003e94.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c10\"\u003e \u003cp\u003e0.058\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"10\"\u003e\u003cem\u003eNote.\u003c/em\u003e k\u0026thinsp;=\u0026thinsp;number of studies; REML\u0026thinsp;=\u0026thinsp;restricted maximum likelihood estimator; Q\u0026thinsp;=\u0026thinsp;Cochran\u0026rsquo;s heterogeneity statistic; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;proportion of total variability due to between-study heterogeneity; τ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;between study variance.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe strongest association was reported by Javed et al., with a correlation coefficient of r = .80 (95% CI [.73, .85]), suggesting a very strong association between job stress and turnover intention. In contrast, relatively lower correlations were observed in studies such as Shahzad et al. and Chen et al., although these correlations endured statistically significant.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Pooled effect size from Random-effects Meta -Analysis\u003c/h2\u003e \u003cp\u003eUsing a random-effects meta- analysis model, the analysis revealed a moderately significant positive association between job stress and turnover intention among bank employees (r\u0026thinsp;=\u0026thinsp;0.52, 95% CI [0.40, 0.63], p \u0026lt; .001; [see Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e]), confirming that higher levels of stress are associated with a stronger intention to quit. The diamond shape at the bottom of the forest plot represents the pooled estimate from the random-effects model, while the horizontal width of the diamond reflects the 95% confidence interval. The consistent positive direction of the effect sizes across studies suggests that job stress is a strong predictor of turnover intention within the banking sector.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Publication bias analysis\u003c/h2\u003e \u003cp\u003ePotential publication bias was assessed using a funnel plot (see Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and Egger\u0026rsquo;s regression test. Funnel plots provide a visual assessment of publication bias by examining whether smaller reports large or small effect sizes. As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, the studies are generally distributed around the vertical dashed line representing the pooled effect size from the random-effects model. In the absence of publication bias, effect sizes are expected to be symmetrically distributed around this central estimate, forming a funnel-shaped pattern.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eMost studies fall within the triangular region representing the 95% confidence limits, suggesting that the distribution of effect sizes largely reflects expected sampling variability. Although a slight asymmetry is observable, such patterns are common in meta-analyses with a relatively small number of studies. Given that the analysis included ten studies, the interpretation of funnel plot asymmetry should be made with caution, as statistical tests for publication bias have limited power when the number of studies is small. Overall, the distribution of studies does not indicate that substantial publication bias is present meta-analysis.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Heterogeneity and overall effect size\u003c/h2\u003e \u003cp\u003eThe findings of the random-effects meta-analysis are shown in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. It includes 10 studies, comprising a total sample of 4177 employees drawn from banking across several countries. The pooled effect size indicated a moderately strong positive association between job stress and turnover intention (r = .58, 95% CI [.42, .73], p \u0026lt; .001). This finding recommends that higher levels of job stress are significantly correlated with stronger intentions among bank employees to leave their jobs.\u003c/p\u003e \u003cp\u003eSubstantial heterogeneity was observed across the included studies. The Cochran\u0026rsquo;s Q statistic was significant (Q (9)\u0026thinsp;=\u0026thinsp;145.53), p \u0026lt; .001), indicating that the variability in effect sizes across studies was higher than would be expected by chance alone. Similarly, the I\u003csup\u003e2\u003c/sup\u003e statistic was 94.09%, recommending that a large proportion of the total variability among study findings is attributable to between-study heterogeneity rather than sampling error. The estimated between -study variance (τ\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;0.058) further supports the presence of considerable heterogeneity. These findings indicate that the strength of the association between job stress and turnover intention varies across studies, possibly due to differences in study context, sample characteristics, or measurement tools.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Meta-regression analysis\u003c/h2\u003e \u003cp\u003e`To further explore potential sources of heterogeneity, a mixed-effects meta-regression analysis was conducted, as presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e. The model determined whether the banking sector sample acted as a moderator influencing the association between job stress and turnover intention. The intercept coefficient (β\u0026thinsp;=\u0026thinsp;0.54, SE = .154, z\u0026thinsp;=\u0026thinsp;3.51, p \u0026lt; .001) represents the estimated average effect size (in Fisher\u0026rsquo;s Z units) when the moderator variable is held constant. The significant intercept indicates that the overall association between job stress and turnover intention remains statistically significant.\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\u003e\u003cem\u003eMixed-Effects Meta-regression analysis for evaluating bankers.\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eModerator\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ\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\u003ez\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003ep\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIntercept\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.154\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.51\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;.001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003e[0.238, 0.841]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003e\u003cem\u003eNote.\u003c/em\u003e β\u0026thinsp;=\u0026thinsp;coefficients represent differences in Fisher\u0026rsquo;s Z values relative to the reference group; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec19\" class=\"Section2\"\u003e \u003ch2\u003e3.6 Moderator analysis of private vs both (public and private) employees\u003c/h2\u003e \u003cp\u003eThe pooled effect size for private bank employees was (r = .58, 95% CI [.53, .63]), indicating a strong positive association between job stress and turnover intention, and both public and private bank employees (r = .48, 95% CI [.44, .52]), recommending a moderate-strong association between job stress and turnover intention. These findings recommend that job stress may have a strong influence on turnover intention in private banking contexts.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec20\" class=\"Section2\"\u003e \u003ch2\u003e3.7 Sensitivity analysis\u003c/h2\u003e \u003cp\u003eA revealed that leave-one-out sensitivity analysis indicated that the pooled effect size remained stable when each study was sequentially removed (r ranged from .48 to .54) (see Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These findings recommend that the overall association between job stress and turnover intention is robust and not influenced by any single study.\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\u003eModerator analysis of private vs both (public and private) employees.\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=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEmployee type\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ek\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePooled effect (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrivate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.58\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.53, .63]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBoth (public and private)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e.48\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e[.44, .52]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003e\u003cem\u003eNote.\u003c/em\u003e k\u0026thinsp;=\u0026thinsp;number of studies; CI\u0026thinsp;=\u0026thinsp;confidence interval.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003e\u003cem\u003eSensitivity analysis (leave-one-out).\u003c/em\u003e\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=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy removed\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect size (r)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e95% CI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.38, .63]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.39, .64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 3\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[.41, .65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 4\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[.41, .65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.37, 61]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 6\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[.38, .57]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 7\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[.42, .65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.38, .63]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 9\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[.41, .65]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eStudy 10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e[.38, .64]\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003e\u003cem\u003eNote\u003c/em\u003e. CI\u0026thinsp;=\u0026thinsp;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 meta-analysis aims to synthesize empirical evidence regarding the association between job stress and turnover intention among banking employees. The findings demonstrate a moderately positive association between job stress and turnover intention. This finding indicates that employees experiencing higher levels of job stress are significantly more likely to consider quitting their organizations. The finding aligns with a substantial body of organizational behavior studies recommending that job stress acts as a major antecedent of employee withdrawal behavior, including turnover intention (Hom et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe findings also showed variability in the strength of the association across the included studies; such differences may be described by variations in organizational environments, cultural contexts, and measurement approaches used across studies. Employees working in highly competitive banking environments often experience strong performance pressure, demanding sales targets, and extended working hours, which may intensify job stress and turnover intentions (Giorgi et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In contrast, banking sectors that provide supportive leadership, fair reward systems, and opportunities for work-life balance may reduce the negative effects of stress on employees\u0026rsquo; attitudes and behaviors.\u003c/p\u003e \u003cp\u003eAnother significant observation of this study is that the presence of considerable heterogeneity across the included studies is common in meta-analytic studies within the social sciences because organizational structures, management practices, and employee characteristics differ across countries (Hom et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). In the banking sector, factors like job roles, organizational culture, leadership style, and economic conditions may influence how employees perceive and respond to job stress. These contextual differences may explain why the strength of the association between job stress and turnover intention varies across studies.\u003c/p\u003e \u003cp\u003eThe assessment of publication bias recommends that the overall findings are unlikely to be strongly influenced by the selective publication of statistically significant findings. Although slight asymmetry may appear in funnel plots when the number of studies is limited, a previous methodological study indicates that such patterns are common in meta-analyses with a relatively small sample of studies. It should therefore be interpreted cautiously (Sterne et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Overall, the available evidence suggests that the findings of the current meta-analysis are reasonably robust.\u003c/p\u003e \u003cp\u003eThe meta-regression findings indicate that the association between job stress and turnover intention remains consistent across the included studies. This recommends that job stress represents a stable and significant predictor of employees\u0026rsquo; intention to leave their organizations within the banking sector. Previous study has demonstrated that job stress is one of the most consistent antecedents of employee turnover intentions across various industries and occupational groups (Hom et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe leave-one-out sensitivity analysis demonstrated that meta-analytic findings were robust, with pooled estimates remaining consistent in magnitude and direction regardless of which study was excluded. This indicates that the observed association between job stress and turnover intention is stable and not driven by any single study. Persistent heterogeneity across interactions recommends that variability in effect sizes reflects genuine differences in the study contexts, like organizational settings, measurement methods, and employee populations, rather than the influence of outliers. Conducting a sensitivity analysis is suggested to verify the consistency of pooled estimates in a meta-analytic study (Higgins et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2003\u003c/span\u003e). These findings confirm the stability and credibility of the meta-analysis, supporting the conclusion that the association between job stress and turnover intention is consistent across the included studies.\u003c/p\u003e \u003cp\u003eFrom a theoretical framework, these findings can be interpreted through the Job Demands- Resources (JD-R) model, which indicates that excessive job stress can lead to emotional exhaustion and strain, ultimately increasing the likelihood of turnover intentions (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Aligning with this model, the meta-analytic findings indicate that high job stress in a competitive banking environment depletes employees\u0026rsquo; resources, thereby motivating intentions to leave the organization. Similarly, the stress-strain framework recommends that persistent work-related stressors may erode psychological resources, prompting withdrawal from the organization as a coping mechanism (Lazarus \u0026amp; Folkman, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e1984\u003c/span\u003e). Turnover intention represents one such withdrawal-based coping response, whereby employees disengage to protect well-being. The observed association between job stress and turnover intention in this meta-analysis supports this framework, demonstrating that stress-induced strain drives employees\u0026rsquo; consideration of leaving their organizations.\u003c/p\u003e \u003cdiv id=\"Sec22\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Practical Implications for Banking Management\u003c/h2\u003e \u003cp\u003eThe results of this meta-analysis have a number of significant implications for organizations and banking institutions. Excessive job stress greatly increases employees' intentions to leave their jobs, according to the results, which show a moderately strong positive relationship between job stress and turnover intention. Banking institutions should implement strategies aimed at lowering job stress and enhancing employee well-being top priority given the high costs associated with employee turnover, including productivity losses, recruitment, and employee training (Hom et al., \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Organizations should concentrate on lowering excessive job demands, such as excessive workloads, unrealistic performance goals, and long working hours. The Job Demands-Resources (JD-R) model states that employee well-being can be improved, and withdrawal behaviors like intention to leave can be reduced through lowering job demands while increasing supportive resources (Bakker \u0026amp; Demerouti, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Therefore, putting in place more equitable workload distribution, flexible scheduling, and attainable performance expectations may be beneficial for banking institutions. Enhancing organizational support could reduce the negative impacts of workplace stress. Role ambiguity and psychological strain among employees can be lowered through supportive supervision, clear communication, and fair performance evaluation systems. Banks should fund programs dedicated to stress management and employee well-being. Initiatives such as stress management training, mental health support services, and counseling centers can improve employees\u0026rsquo; coping skills and psychological resilience. Furthermore, encouraging work-life balance through flexible work schedules and family-friendly policies may lower stress levels and employee turnover. Finally, organizations should provide their employees with professional development opportunities and pathways. Banking institutions can reduce the risk of employee turnover and increase organizational commitment by funding employee development programs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec23\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Limitations and future research directions\u003c/h2\u003e \u003cp\u003eThis meta-analysis has a number of limitations that should be taken into account when interpreting the results, even though it offers insightful information about the association between job stress and turnover intention among banking employees. First, the study consisted of a limited number of studies. Methodological research indicates that analyses involving a small number of studies may have limited statistical power for moderating effects or detecting publication bias, even though the meta-analysis included ten studies (Sterne et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). As more empirical data become available, more studies should be included in future research. Second, the included studies showed substantial heterogeneity. The high I\u0026sup2; value indicates that variations in organizational contexts, cultural settings, measurement tools, or sample characteristics may have an impact on the variability in effect sizes. Future meta-analyses should explore other moderating factors that could have an impact on the relationship between job stress and turnover intention, such as job satisfaction, organizational culture, emotional intelligence, or leadership style. Third, the studies that were part of this meta-analysis employed cross-sectional research designs, which makes it more difficult to determine whether job stress and turnover intention are causally related. Lastly, the current meta-analysis concentrated particularly on workers in the banking industry. Although this focus increases the findings' contextual relevance, it might reduce the generalizability of the findings to other occupational sectors. To understand whether the strength of this association varies across occupational contexts, future research should compare stress-turnover relationships across various industries.\u003c/p\u003e \u003cp\u003eBy addressing these issues, we will have a better understanding of the mechanisms underlying the relationship between job stress and intention to leave, which will help us create organizational interventions that are more profitable.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eIn order to examine the association between job stress and turnover intention among banking employees, this meta-analysis combined empirical data from ten studies. The results suggest that employees' intentions to leave their jobs have a significant and moderately strong positive relationship with job stress. These findings show that in the banking industry, higher job stress levels lead to higher turnover intentions. By offering a quantitative synthesis of previous research and proving the robustness of the stress-turnover relationship in various banking contexts, the findings add to the literature of organizational behavior. The findings also align with theoretical frameworks that highlight how excessive job demands influence employee attitudes and withdrawal behaviors, such as the stress-strain perspective and the Job Demands\u0026ndash;Resources model. From a practical viewpoint, the study emphasizes how crucial supportive organizational environments and workplace stress management are to lowering employee turnover risks. Overall, this meta-analysis highlights the necessity of ongoing study and organizational focus on occupational stress management. Financial institutions can enhance organizational performance and employee well-being by addressing workplace stressors and strengthening employee support systems.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAithal PS, Iype R (2024) Stress Levels Among Employees: A Study of Selected Banks in Kerala. SSRN Electron J. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.2139/ssrn.4974294\u003c/span\u003e\u003cspan address=\"10.2139/ssrn.4974294\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAlarcon GM (2011) A meta-analysis of burnout with job demands, resources, and attitudes. 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Http://Dspace.Daffodilvarsity.Edu.Bd:8080/Bitstream/Handle/20.500.11948/1395/Paper-09.Pdf?IsAllowed\u0026thinsp;=\u0026thinsp;y\u0026amp;sequence\u0026thinsp;=\u0026thinsp;1\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eViechtbauer W (2010) Conducting Meta-Analyses in \u003cem\u003eR\u003c/em\u003e with the metafor Package. \u003cem\u003eJournal of Statistical Software\u003c/em\u003e, \u003cem\u003e36\u003c/em\u003e. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.18637/jss.v036.i03\u003c/span\u003e\u003cspan address=\"10.18637/jss.v036.i03\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"},{"header":"Appendix A","content":"\u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"No\" id=\"Taba\" border=\"1\"\u003e \u003ccolgroup cols=\"6\"\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=\"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\u003eNo. of studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTitle\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAuthor (s)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNo. of Sampling\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eScales used in studies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eFindings\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOccupational Stress and Turnover Intentions of Commercial Banks Employees in Pokhara, Nepal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eBharat Ram Dhungana, Kush Adhikar et al. (2025)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eOS (α\u0026thinsp;=\u0026thinsp;0.923)\u003c/p\u003e \u003cp\u003eJT (α\u0026thinsp;=\u0026thinsp;0.761)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eA significant positive correlation between work overload and turnover intention (r\u0026thinsp;=\u0026thinsp;0.571, p\u0026thinsp;=\u0026thinsp;0.05)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Impact of Job Stress on Turnover Intentions\u0026ndash;The Moderating Role of Emotional Intelligence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFarah Naz Burki, Naimat U. Khan, and Imran Saeed (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJS (Parker and Decotiis, 1983) (α\u0026thinsp;=\u0026thinsp;0.75)\u003c/p\u003e \u003cp\u003eTI (Lance, 1988) (α\u0026thinsp;=\u0026thinsp;0.71)\u003c/p\u003e \u003cp\u003eEI (Wong and Law, 2002) (α\u0026thinsp;=\u0026thinsp;0.88)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJob stress has a strong direct significant relationship with employee turnover intentions (β\u0026thinsp;=\u0026thinsp;0.516)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePerceived Job Insecurity, Work Overload and Work-Family Conflict as Predictors of Turnover Intention among Bank Employees in Benue State (Nigeria)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKwasedoo Martha Ngbea (2019)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e348\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJIS (De Witte, 2000)\u003c/p\u003e \u003cp\u003eWOS (Schlots et al., 2004)\u003c/p\u003e \u003cp\u003eWFC (Netemeyer et al., 1996)\u003c/p\u003e \u003cp\u003eTI (Seashore et al., 1982) (α\u0026thinsp;=\u0026thinsp;.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJob insecurity has a significant positive relationship with Turnover Intension (r\u0026thinsp;=\u0026thinsp;0.66, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003cp\u003eSignificant positive relationship between Work overload and turnover intention (r\u0026thinsp;=\u0026thinsp;0.36, P\u0026thinsp;\u0026lt;\u0026thinsp;0.05)\u003c/p\u003e \u003cp\u003ework-family conflict and turnover intention (r\u0026thinsp;=\u0026thinsp;0.64, P\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eModelling job stress as a mediating role in predicting turnover intention (Taiwan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMei-Fang Chen, Chieh-Peng Lin, and Gin-Yen Lien (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eSS (Caplan et al., 1975) (α\u0026thinsp;=\u0026thinsp;0.90)\u003c/p\u003e \u003cp\u003eJS (Lait and Wallace, 2002) (α\u0026thinsp;=\u0026thinsp;0.71)\u003c/p\u003e \u003cp\u003eTI (Turner et al., 1987) (α\u0026thinsp;=\u0026thinsp;0.89)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJob stress and turnover intention (β\u0026thinsp;=\u0026thinsp;0.33, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe impact of job stress on job satisfaction and turnover intentions among bank employees during the COVID-19 pandemic (Taiwan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMei-Hui\u0026nbsp;Lin, Ya-Hui\u0026nbsp;Yen et al. (2024)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e501\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJSQ, Taiwan\u0026rsquo;s Ministry of Labor (2020) (α\u0026thinsp;=\u0026thinsp;0.973)\u003c/p\u003e \u003cp\u003eMSQ (University of Minnesota, 2020) (α\u0026thinsp;=\u0026thinsp;0.816)\u003c/p\u003e \u003cp\u003eILS (Scott et al., 1999)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eTurnover intention is positively correlated with the overall average level of job stress (r\u0026thinsp;=\u0026thinsp;0.704) and negatively correlated with both intrinsic (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.441) and extrinsic job satisfaction (r\u0026thinsp;=\u0026thinsp;\u0026minus;\u0026thinsp;0.511)\u003c/p\u003e \u003cp\u003eAll at p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 level\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eEffect of Role Conflict, Work Life Balance and Job Stress on Turnover Intention: Evidence from Pakistan\u003c/p\u003e \u003cp\u003e(Pakistan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMuhammad Javed, Muhammad Arsalan Khan, Muhammad Yasir et al., (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJS (α\u0026thinsp;=\u0026thinsp;0.698)\u003c/p\u003e \u003cp\u003eRC (α\u0026thinsp;=\u0026thinsp;0.824)\u003c/p\u003e \u003cp\u003eWLB (α\u0026thinsp;=\u0026thinsp;0.760)\u003c/p\u003e \u003cp\u003eTI (α\u0026thinsp;=\u0026thinsp;0.847)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eJob stress is positively and significantly related with turnover intention (r\u0026thinsp;=\u0026thinsp;0.800)\u003c/p\u003e \u003cp\u003eRole conflict is also positively related (r\u0026thinsp;=\u0026thinsp;0.103)\u003c/p\u003e \u003cp\u003eWork life balance and turnover intention (r\u0026thinsp;=\u0026thinsp;0.203)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eAn Empirical Analysis of Work Overload, Organizational Commitment and Turnover Intentions among Employees of Banking Sector (Pakistan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eQasim Shahzad, Bahadar Shah et al., (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e296\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWO (Reilly, 1982)\u003c/p\u003e \u003cp\u003eOC (Mowday et al., 1979)\u003c/p\u003e \u003cp\u003eTI (Eisenverger et al., 2002)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eWork Overload has a significant positive correlation with turnover intention (r\u0026thinsp;=\u0026thinsp;0.297)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e8\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFactors Effecting Employee Turnover in Banking Sector (Pakistan)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSaba Sattar, Shehzad Ahmed (2014)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAbdulla et al., (2011) and Zuber (2009)\u003c/p\u003e \u003cp\u003e11(α\u0026thinsp;=\u0026thinsp;0.853)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eAll relationships with turnover intention are significant, such as work environment (r\u0026thinsp;=\u0026thinsp;0.553), compensation (r\u0026thinsp;=\u0026thinsp;0.287, weak), job stress (r\u0026thinsp;=\u0026thinsp;0.548), career growth (r\u0026thinsp;=\u0026thinsp;0.453)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e9\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eThe Influence of Affective Commitment, Job Satisfaction and Job Stress on Turnover Intention: A Study of Nepalese Bank Employees (Nepal)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVimolwan Yukongdi, Pooja Shrestha (2020)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e282\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eA12C (Meyer and Alle13n, 1991) (α\u0026thinsp;=\u0026thinsp;0.87)\u003c/p\u003e \u003cp\u003eJS (Ramalho Luz et al., 2018) (α\u0026thinsp;=\u0026thinsp;0.73)\u003c/p\u003e \u003cp\u003eJS, stress (Firth et al., 2004) (α\u0026thinsp;=\u0026thinsp;0.73)\u003c/p\u003e \u003cp\u003eTI (Alnia\u0026ccedil;ik et al., 2013) (α\u0026thinsp;=\u0026thinsp;0.81)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ejob satisfaction (β = -0.460, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) had a greater impact on\u003c/p\u003e \u003cp\u003eemployees\u0026rsquo; intention to leave followed by job stress (β\u0026thinsp;=\u0026thinsp;0.369, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) and affective\u003c/p\u003e \u003cp\u003ecommitment (β = -0.306, p\u0026thinsp;\u0026lt;\u0026thinsp;0.001)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eImpact of Job Stress on Turnover Intentions Among Employees of Private Sector Banks, Coimbatore (India)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMr. S. Mohanraj, Dr. K. K. Ramachandran (2018)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e540\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eJS\u003c/p\u003e \u003cp\u003eTI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eStrong positive significant correlation between Job stress and turnover intentions (r\u0026thinsp;=\u0026thinsp;0.531, sig.0.000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"University 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":"Banking employees, job stress, meta-analysis, turnover intention","lastPublishedDoi":"10.21203/rs.3.rs-9316545/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9316545/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eEmployee turnover represents a critical challenge for banking institutions worldwide, as job stress affects organizational performance. The current study conducted a meta-analysis to estimate the association between job stress and turnover intention. A meta-analytic review comprising 4,177 banking employees from multiple countries. A random-effects meta-analysis using the restricted maximum likelihood (REML) estimator was employed to assess the pooled effect size. The findings showed a moderate positive association between job stress and turnover intention (r = .58, 95% CI [.42, .73], p \u0026lt; .001), indicating that employees experiencing higher levels of job stress who consider leaving their organizations.\u003c/p\u003e","manuscriptTitle":"The Association between Job Stress and Turnover Intention among Bank Employees: A Meta-Analytical Review","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-04-07 18:42:05","doi":"10.21203/rs.3.rs-9316545/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":65700478,"name":"Psychology"}],"tags":[],"updatedAt":"2026-04-07T18:42:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-04-07 18:42:05","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9316545","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9316545","identity":"rs-9316545","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}