From Stress to Exit: Can Work-Life Balance Pull Employees Back? A JD-R Perspective ()
1. Introduction
Employee turnover intention, the intentional and conscious willingness of an employee to leave their current organization, has emerged as a significant concern in modern workplaces (Omar et al., 2020). High turnover undermines organizational stability, reduces productivity, raises recruitment expenses, and decreases institutional knowledge (Khalil et al., 2020). Despite comprehensive studies on the factors influencing turnover intention, the impact of workplace stress and its psychological mechanisms remains a focal point of academic research, especially in the context of changing work dynamics and post-pandemic restructuring of organizations (Boamah et al., 2022). The post-pandemic reorganization of the Bangladeshi garment industry, characterized by decreased international orders, increased production demands, labor shortages, and more complex compliance requirements, has increased job insecurity and workplace stress, rendering this environment more vulnerable (Kabir et al., 2021).
Job stress arises when job expectations are beyond an employee’s capacity or resources to manage them, resulting in anxiety and reduced well-being (Kerdpitak & Jermsittiparsert, 2020). Numerous studies affirm that excessive burden, role conflict, and time pressure are primary sources of occupational stress, substantially influencing employees’ intentions to resign (Javed et al., 2014; Khalil et al., 2020). Employees enduring chronic stress often suffer from cognitive weariness and emotional distress, which progressively diminish their organizational commitment and increase withdrawal behaviors (Shinde, 2025). In accordance with the Job Demands-Resources (JD-R) theory (Bakker & Demerouti, 2017), when expectations exceed available resources, employees are more prone to experiencing strain-related effects, including burnout, emotional exhaustion, and, finally, intentions to resign from their positions (Sharma & Tiwari, 2023).
Among the elements of burnout, emotional exhaustion is regarded as the primary factor connecting occupational stress to the intention to leave (Boamah et al., 2022). It signifies a condition of emotional exhaustion arising from sustained exposure to challenging work environments. Empirical evidence across diverse contexts, such as hospitality, healthcare, and academia, demonstrates that emotional exhaustion mediates the association between job stress and turnover intentions (Raza et al., 2024; Salama et al., 2022). Since employees experience emotional exhaustion, their capacity to perform efficiently declines, and their tendency to disengage or depart from the organization increases (Shinde, 2025; Maslach & Leiter, 2016). Therefore, analyzing emotional exhaustion as a mediating factor offers a more comprehensive understanding of how stress leads to turnover intention.
Furthermore, an increasing corpus of literature has investigated the moderating role of work-life balance across various contexts, including industries such as healthcare, education, and finance (Chang et al., 2019). Work-life balance represents a person’s capacity to effectively integrate professional and personal roles, acting as a safeguard against the detrimental impacts of occupational stress (Kerdpitak & Jermsittiparsert, 2020; Maharani & Tamara, 2024). Initiatives aimed at work-life balance, such as flexible work schedules, employee wellness programs, and the promotion of supportive organizational cultures, can substantially mitigate workplace stress and reduce the propensity for employee turnover (Chung & Van der Lippe, 2020). Workers who feel that they have a better work-life balance are more inclined to bounce back from work-related stress and maintain emotional stability, which lowers stress-related fatigue (Kamboj & A, 2025; Allen & French, 2020). Conversely, an imbalance between professional and personal life exacerbates emotional stress, thereby elevating the likelihood of employees considering leaving the organization (Boamah et al., 2022). Recent studies have shown that work-life balance impacts stress levels and the emotional elements that influence employees’ decisions to stay or leave (Haar et al., 2021).
Despite a significant amount of research on turnover intention, there is a scarcity of comprehensive models that simultaneously examine the mediating role of emotional exhaustion and the moderating effect of work-life balance (Raza et al., 2024; Sharma & Tiwari, 2023). Most previous research has examined these variables in isolation, without investigating their interactions in influencing turnover decisions. Addressing this research deficiency is essential, especially in developing countries where employees frequently encounter excessive workloads, little organizational assistance, and indistinct work-family boundaries. Therefore, this study seeks to investigate the impact of job stress on employees’ intention to leave, incorporating emotional exhaustion as a mediator and work-life balance as a moderator. By doing so, it provides an in-depth understanding of how stress contributes to turnover intentions and the specific conditions under which this relationship diminishes. The findings of this study will provide valuable insights for organizational leaders, policymakers, and HR professionals aiming to decrease employee turnover through the management of job stress, the enhancement of emotional well-being, and the promotion of effective work-life balance strategies.
2. Literature Review and Hypotheses Development
Job stress refers to the mental and physical strain that emerges when job demands exceed an employee’s capacity or available resources to manage efficiently (Folkman, 2013). It typically arises from task pressure, role conflict, and time constraints, resulting in exhaustion, anxiety, and poor mental health (Bakker & Demerouti, 2017; Sharma & Tiwari, 2023). Prolonged occupational stress frequently leads to emotional exhaustion, characterized by mental and emotional depletion that reduces motivation and engagement (Maslach & Leiter, 2016; Raza et al., 2024). A major symptom of burnout is emotional exhaustion, which is prevalent in high-stress occupations like teaching, healthcare, and manufacturing (Salama et al., 2022). Chronic emotional stress can increase employees’ turnover intention, indicating a conscious and intentional readiness to depart from the organization (Omar et al., 2020; Khalil et al., 2020). Excessive turnover intention undermines organizational stability and performance, driven by both individual and workplace characteristics (Sultana et al., 2024; Shinde, 2025). Work-Life Balance (WLB), the capacity to maintain equilibrium between professional and personal commitments, acts as a protective factor that enhances well-being and mitigates the adverse impacts of job-related stress (Greenhaus & Allen, 2010; Haar et al., 2021). Organizations that support flexible policies and supportive environments frequently experience diminished stress, decreased burnout, and enhanced employee retention (Allen & French, 2020; Maharani & Tamara, 2024; Kamboj & A, 2025).
2.1. Job Demands-Resources (JD-R) Model
The Job Demands-Resources (JD-R) model offers an extensive framework for analyzing the impact of job stress on turnover intention (Bakker & Demerouti, 2007). The JD-R model suggests that job demands, such as excessive workload, time constraints, and emotional pressures, deplete an employee’s resources, resulting in stress, burnout, and a desire to exit the organization. Conversely, job resources like feedback, support from peers, and autonomy can mitigate the adverse impacts of job stress.
When job demands surpass available resources, employees’ physical and emotional resources are exhausted, heightening the likelihood of emotional fatigue, disengagement, and attrition from the organization (Bakker & Demerouti, 2017; Mazzetti et al., 2023). The JD-R model posits that emotional exhaustion mediates the association between job stress and turnover intention, indicating that emotional exhaustion is a vital mechanism through which job stress affects turnover intention. In this research, job stress is identified as a demand; emotional exhaustion, which affects turnover intention, is induced by higher job expectations, including workload and emotional pressure. A balanced work-life dynamic may serve as a mitigating factor against the negative impacts of job demands on emotional exhaustion and turnover intentions (Adil & Baig, 2018; Omar et al., 2020). In summary, work-life balance reduces turnover likelihood and enables employees to manage job-related stress more effectively. Prolonged exposure to elevated job demands, coupled with inadequate job resources, can lead to emotional exhaustion, thereby elevating the intention to leave the organization (Zhang et al., 2025).
2.2. Job Stress and Employee Turnover Intention
Job stress is a recognized factor influencing employee turnover intention. Numerous studies demonstrate that employees subjected to elevated stress levels in the workplace are more inclined to contemplate resigning (Hwang et al., 2014; Rathi & Lee, 2016; Üngüren et al., 2024). Job stress may present as role ambiguity, excessive workload, loss of control, interpersonal conflict, and work-life imbalance (Beehr & Newman, 1978). Research indicated that these pressures affected employees’ overall well-being and their feelings towards the firm, especially their tendency to quit. A direct positive association exists between workplace stress and turnover intention; hence, employees who experience excessive job demands or insufficient resources to manage them are more inclined to seek new employment (Shah et al., 2022). Likewise, workplace stress diminishes job satisfaction and organizational commitment, two essential indicators of employee retention, thereby directly influencing turnover intention (Rathi & Lee, 2016). The correlation between stress and turnover is most pronounced in high-demand fields like healthcare, education, and customer service, where employees frequently indicate elevated levels of job-related stress (Arshadi & Damiri, 2013; Mosadeghrad, 2013; Zhang et al., 2025).
H1: Job stress is positively related to employee turnover intention.
2.3. Emotional Exhaustion as a Mediator
Emotional exhaustion, defined as the depletion of emotional resources due to continuous stress exposure, is a primary factor contributing to turnover intention (Maslach et al., 2001). When employees experience excessive job expectations, they become emotionally exhausted and incapable of fulfilling their duties. Chronic stress negatively affects an employee’s mental health, job satisfaction, and performance, which may result in their intention to leave the organization (Lee & Ashforth, 1996). Multiple studies suggest that emotional exhaustion plays a crucial role in mediating the relationship between occupational stress and the intention to resign from one’s position (Shah et al., 2022; Zhang et al., 2025). To mitigate the adverse emotional consequences of their employment, individuals might consider leaving due to emotional fatigue induced by occupational stress (Maslach et al., 2001). Emotional exhaustion wholly or partially mediates the relationship between job stress and the intention to leave (Ahuja et al., 2007). Emotionally exhausted individuals are more prone to disengagement from their work and exhibit diminished commitment to their employer, hence increasing their propensity to quit. This research indicates that mitigating workplace stress is crucial for reducing burnout and increasing employee retention (Mazzetti et al., 2023; Üngüren et al., 2024).
H2: Emotional exhaustion mediates the relationship between job stress and employee turnover intention.
2.4. Work-Life Balance as a Moderator
Although emotional exhaustion mediates the association between job stress and turnover intention, work-life balance acts as a crucial moderating factor that can either increase or decrease the impact of job stress on these two variables (Greenhaus & Allen, 2010). Work-life balance functions as a moderating variable in the correlation between emotional exhaustion, job stress, and turnover intention. Employees are less likely to encounter emotional exhaustion and contemplate leaving the organization because they are better equipped to handle the demands of their roles and effectively balance their individual and professional lives (Maharani & Tamara, 2024). Conversely, employees facing a work-life imbalance are more prone to feelings of physical and emotional exhaustion, which increases their propensity to consider quitting their job (Prentice et al., 2025). Employees experiencing a poor work-life balance report higher levels of emotional exhaustion in response to work-related stress, which subsequently increases their intention to leave (Wei & Ye, 2022; Boamah & Laschinger, 2016). This indicates that work-life balance moderates both the relationship between job stress and emotional exhaustion and the association between job stress and turnover intention.
H3: Work-life balance moderates the relationship between job stress and emotional exhaustion.
H4: Work-life balance moderates the relationship between job stress and employee turnover intention.
Figure 1 illustrates the conceptual framework of the study, outlining the hypothesized relationships among job stress, emotional exhaustion, work-life balance, and employee turnover intention.
Figure 1. Conceptual model.
3. Methodology
3.1. Research Design
This research employed a quantitative, longitudinal survey method to investigate the associations between occupational stress, emotional exhaustion, work–life balance, and turnover intention within the Bangladeshi garment sector. A multi-wave strategy was employed to reduce common method bias and enhance the internal validity of the causal inferences.
3.2. Population and Sample
The population consisted of full-time employees engaged in various export-oriented garment and apparel manufacturing firms in Bangladesh. These organizations were a combination of medium- and large-scale factories predominantly in Dhaka, Gazipur, and Narayanganj. For the purposive selection of participating organizations, three criteria were established: the firms’ willingness to engage in a multi-wave data collection process, their status as export-oriented manufacturing enterprises with formalized management and human resources structures, and a minimum workforce of 300 employees to guarantee sufficient sampling coverage. Based on these criteria, a purposive sampling method was utilized to select appropriate firms. Subsequently, simple random sampling was executed within each selected facility to choose employees with diverse employment responsibilities, including sewing machine operators, line supervisors, quality control personnel, machine technicians, and junior administrative staff. After screening for completeness and ensuring respondent retention across data collection waves, a final usable sample of 400 participants was obtained for statistical analysis.
3.3. Measures
Data were obtained through a standardized questionnaire comprising validated measuring items, all assessed on a five-point Likert scale (1 = strongly disagree to 5 = strongly agree).
Job stress: Job stress was assessed using the scale developed by Parker and DeCotiis (1983). Examples include, “My job gets to me more than it should”.
Emotional exhaustion: Emotional exhaustion was assessed using the subscale of the Maslach Burnout Inventory (Maslach et al., 1997). Examples of items include “I feel burned out from my work”.
Work-life balance: Work-life balance was assessed utilizing items developed by Brough et al. (2014). Examples of items include “I currently have a good balance between the time I spend at work and the time I have available for non-work activities”.
Employee turnover intention: Employee turnover intention was evaluated using items based on the model proposed by Mobley et al. (1978), with additional modifications recommended by Tett and Meyer (1993). Examples of items include “I think a lot about leaving the organization”.
3.4. Data Collection Procedures
Data were gathered using telephone interviews, in-person contacts, and self-administered surveys to enhance accessibility and precision among participants. All participants were apprised of the study’s goal, guaranteed confidentiality, and given informed consent before participation. To mitigate common method bias, the study adhered to the guidelines of Podsakoff et al. (2003) and utilized a three-wave longitudinal survey design, incorporating a two-week break between each wave. At Time 1, 520 employees were contacted, yielding 500 valid responses that included demographic data as well as assessments of job stress and work-life balance. At Time 2, conducted two weeks later, 481 valid replies were obtained from the same respondents, concentrating on emotional exhaustion. At Time 3, which occurred two weeks after Time 2, data on employee turnover intention were collected from an additional 428 individuals who had completed the initial two surveys. Following the processes of screening for completeness, identifying patterned responses, and addressing missing values, a final sample of 400 valid responses was preserved for analysis, resulting in an effective response rate of 77%.
3.5. Control Variables
Multiple control variables were incorporated to eliminate alternative explanations for the correlations among the primary constructs. Demographic variables include gender (1 = male, 2 = female), age (1 = 16 - 24 years, 2 = 25 - 41 years, 3 = above 41 years), and educational qualification (1 = undergraduate, 2 = graduate, 3 = postgraduate, 4 = diploma). Organizational tenure includes the number of years employed in the current organization, categorized as 1 = less than 1 year, 2 = 1 - 3 years, 3 = 4 - 6 years, and 4 = above 6 years. These control variables were incorporated into the structural model to evaluate their possible impact on the dependent variable (turnover intention).
3.6. Analytical Strategy
This study utilized partial least squares structural equation modeling (PLS-SEM) with SmartPLS 4 to assess the measurement and structural models (Hair et al., 2019). Reliability was tested using Cronbach’s alpha and composite reliability, while convergent and discriminant validity were assessed by AVE and the Fornell–Larcker criteria. Following the establishment of a valid and reliable measurement model, the structural model was analyzed to evaluate the proposed relationships among job stress, emotional exhaustion, work–life balance, and turnover intention. Bootstrapping procedures involving 5000 resamples were employed to assess the significance of the path coefficients. Hypothesis testing was performed within the SmartPLS environment to evaluate both direct and indirect relationships.
4. Results, Findings, and Discussion
4.1. Respondent’s Demographics
The demographic details of the 400 study participants are presented in Table 1. Males made up 80.5% of the respondents, and 93.5% of them were in the 25 - 41 age range. With 89.8% of the sample holding a graduate degree, they are highly
Table 1. Demographic information of respondents.
Demographic Information |
Category |
Frequency (N = 400) |
% |
Gender |
Male |
322 |
80.5 |
Female |
78 |
19.5 |
Age |
16 - 24 years |
5 |
1.25 |
25 - 41 years |
374 |
93.5 |
Above 41 years |
21 |
5.25 |
Educational Level |
Undergraduate |
26 |
6.5 |
Graduate |
359 |
89.75 |
Post-graduate |
14 |
3.5 |
Diploma |
1 |
0.25 |
Job tenure |
Less than 1 year |
14 |
3.5 |
1 - 3 years |
255 |
63.75 |
4 - 6 years |
124 |
31 |
Above 6 years |
7 |
1.75 |
educated. A sizable percentage of respondents (63.75%) had been in their current positions for one to three years, while 31% had been there for four to six years. The structural analysis results demonstrated that none of the control variables (gender, age, education, or organizational tenure) exerted a statistically significant influence on turnover intention. Consequently, although incorporated into the model to eliminate alternative explanations, the control variables did not significantly modify the correlations among the key constructs.
4.2. Measurement Model
The measuring characteristics of the four primary constructs are shown in Table 2. With factor loadings ranging from 0.618 to 0.916, a Cronbach’s alpha of 0.709, composite reliability (rho_a) of 0.757, and an AVE of 0.640, job stress showed acceptable reliability and convergent validity. Emotional exhaustion demonstrated strong reliability and convergent validity, evidenced by factor loadings ranging from 0.737 to 0.895, a Cronbach’s alpha of 0.850, rho_a of 0.857, and an AVE of 0.693. With factor loadings ranging from 0.829 to 0.860, a Cronbach’s alpha of 0.877, a rho_a of 0.920, and an AVE of 0.723, work-life balance also demonstrated strong psychometric properties. Similarly, employee turnover intention exhibited strong reliability and validity, with factor loadings between
Table 2. Construct reliability and validity.
Constructs |
Items |
Factor Loading’s |
Cronbach’s alpha |
Composite reliability (rho_a) |
Composite reliability (rho_c) |
Average Variance Extracted (AVE) |
Job Stress (JS) |
JS1 |
0.618 |
|
|
|
|
JS2 |
0.916 |
|
|
|
|
JS3 |
0.835 |
0.709 |
0.757 |
0.839 |
0.640 |
Emotional Exhaustion (EE) |
EE1 |
0.737 |
|
|
|
|
EE2 |
0.873 |
|
|
|
|
EE3 |
0.895 |
|
|
|
|
EE4 |
0.816 |
0.850 |
0.857 |
0.900 |
0.693 |
Work-Life Balance (WLB) |
WLB1 |
0.855 |
|
|
|
|
WLB2 |
0.857 |
|
|
|
|
WLB3 |
0.829 |
|
|
|
|
WLB4 |
0.860 |
0.877 |
0.920 |
0.913 |
0.723 |
Employee Turnover Intention (ET) |
ET1 |
0.890 |
|
|
|
|
ET2 |
0.872 |
|
|
|
|
ET3 |
0.829 |
|
|
|
|
ET4 |
O.795 |
0.869 |
0.875 |
0.911 |
0.718 |
0.795 and 0.890, a Cronbach’s alpha of 0.869, rho_a of 0.875, and an AVE of 0.718. According to the standards set by Fornell and Larcker (1981), all constructs had AVE values above 0.50 and indicator loadings above 0.70, exceeding the suggested thresholds. These findings affirm the robustness of the measurement model and correspond with current methodological standards, including those outlined by Hair and Alamer (2022), which underscore the significance of high factor loadings and composite reliability in the validation of structural equation models.
Figure 2 presents the measurement model generated using the PLS algorithm, demonstrating the standardized loadings of each construct and the relationships among the latent variables. The figure confirms that all items load significantly on their respective constructs, indicating satisfactory indicator reliability and supporting the validity of the measurement model.
Figure 2. Measurement model from the PLS algorithm.
4.3. Heterotrait-Monotrait Ratio of Correlations (HTMT) Criterion
This research employed the Heterotrait-Monotrait Ratio (HTMT) to evaluate discriminant validity, comparing values against the 0.90 threshold (Henseler et al., 2015). Table 3 shows that all HTMT values fall well below the recommended cut-off, confirming adequate discriminant validity among the constructs. Specifically, the HTMT values between emotional exhaustion (EE) and employee turnover intention (ET) (0.703), EE and job stress (JS) (0.644), and EE and work-life balance (WLB) (0.406) indicate strong discriminant validity. Additionally, the HTMT values between ET and JS (0.574), ET and WLB (0.480), and JS and WLB (0.395) further support the conceptual separation of these constructs. Collectively, these results confirm the study’s discriminant validity.
Table 3. Heterotrait-Monotrait Ratio (HTMT) discriminant validity analysis.
Latent Variable |
EE |
ET |
JS |
WLB |
EE |
|
|
|
|
ET |
0.703 |
|
|
|
JS |
0.644 |
0.574 |
|
|
WLB |
0.406 |
0.480 |
0.395 |
|
4.4. Hypotheses Testing
In order to determine the relevance of path coefficients, the findings of the hypothesis testing in this study were compared with conventional thresholds frequently used in structural equation modeling (SEM), as shown in Table 4. A T-statistic greater than 1.96 and a P-value less than 0.05 are considered statistically significant, per standard recommendations (Hair et al., 2017). Considering these standards:
Table 4. Hypothesis results.
Hypotheses |
Relation |
β |
T-statistics |
P-value |
Result |
H1 |
JS → ET |
0.145 |
2.774 |
0.006 |
Supported |
H2 |
JS → EE → ET |
0.173 |
5.532 |
0.000 |
Supported |
H3 |
WLB × JS → EE |
0.092 |
2.314 |
0.021 |
Supported |
H4 |
WLB × JS → ET |
1.138 |
3.092 |
0.002 |
Supported |
H1: JS → ET (β = 0.145, T-statistic = 2.774, P-value = 0.006) is the first hypothesis. With the T-statistic above the 1.96 cutoff and the P-value below 0.05, this hypothesis is validated, and there is a strong positive direct relationship between job stress and employee turnover intention.
H2: JS → EE → ET (β = 0.173, T-statistic = 5.576, P-value = 0.000): This indirect path has substantial support, as the P-value is significantly below 0.05 and the T-statistic is well above the 1.96 threshold. According to normal expectations for significant mediation effects, this supports the mediating role of emotional exhaustion in the link between job stress and employee turnover intention.
H3: WLB × JS → EE (β = 0.092, T-statistic = 2.314, P-value = 0.021): The T-statistic is above 1.96 and the P-value is below 0.05, which meet the standard criteria for a significant moderation effect, further supporting the moderating effect of work-life balance on the relationship between job stress and emotional exhaustion.
H4: WLB × JS → ET (T-statistic = 3.092, P-value = 0.002, β = 0.138): T-statistic above 1.96 and a P-value below 0.05 further support this hypothesis by demonstrating that work-life balance has a moderating influence on the association between job stress and employee turnover intention.
All hypotheses have significant results (P-values < 0.05, T-statistics > 1.96), which are in line with the accepted cutoff points for assessing significance in SEM. These results highlight the significance of work-life balance as a moderator and emotional exhaustion as a mediator in the link between job stress and employee turnover intention.
Figure 3 presents the bootstrapped structural model, showing the p-values for all hypothesized paths. The results indicate that all p-values fall below the acceptable significance threshold, confirming that each proposed relationship in the model is statistically supported. Thus, all hypotheses are validated based on the bootstrapping results.
Figure 3. Measurement model from bootstrapping.
4.5. Moderation Graph
The findings of moderation can be further described through moderation graphs. Figure 4 indicates that there is significant moderation of work-life balance (WLB) on the relationship between job stress (JS) and emotional exhaustion (EE) because, under low WLB, the relationship is stronger, as indicated by the steeper slope of the solid line, suggesting that increases in JS significantly enhance EE when WLB is low. Conversely, when WLB is high, the relationship weakens, as shown by the flatter slope of the dashed line, indicating that high WLB buffers the effect of JS on EE.
Figure 5 indicates that there is a significant moderation of work-life balance (WLB) on the relationship between job stress (JS) and employee turnover intention (ET) because, under low WLB, the relationship is stronger, as indicated by the steeper slope of the solid line, suggesting that increases in JS significantly enhance ET when WLB is low. Conversely, when WLB is high, the relationship
Figure 4. Moderation effect of WLB on JS and EE.
Figure 5. Moderation effect of WLB on JS and ET.
weakens, as shown by the flatter slope of the dashed line, indicating that high WLB buffers the effect of JS on ET.
4.6. Findings and Discussion
Job stress significantly influences employee turnover intentions, with emotional exhaustion acting as a critical mediator and work-life balance serving as a moderator. Prolonged job stress depletes employees’ emotional resources, leading to emotional exhaustion, which, in turn, increases the likelihood of turnover (Shah et al., 2022; Xue et al., 2022). However, work-life balance mitigates these effects by reducing emotional exhaustion and weakening the relationship between job stress and turnover intention. Employees with a higher work-life balance experience better stress management and reduced tendencies toward turnover, while those with a lower balance are more susceptible to stress-induced turnover (Aman-Ullah et al., 2024; Ahmad Saufi et al., 2023). These findings underscore the importance of implementing organizational policies to mitigate stress, alleviate emotional exhaustion, and foster a work-life balance, thereby effectively retaining talent. The results of this study advance the knowledge of the relationship between workplace stress and employee turnover intention, with work-life balance serving as a moderator and emotional exhaustion as a key mediating factor. It has been discovered that emotional exhaustion, which in turn raises turnover intention, is strongly influenced by job stress. This is consistent with earlier studies that found burnout to be a strong predictor of employee turnover (Taris et al., 2017) and that extended job stress can result in burnout (Maslach & Leiter, 2016). Additionally, it has been demonstrated that work-life balance lessens the detrimental effects of workplace stress on emotional exhaustion, bolstering the notion that workers who have more control over their personal and professional lives are less stressed and less prone to turnover (Greenhaus & Allen, 2010). These results highlight how crucial work-life balance is for improving organizational retention tactics as well as for fostering well-being. This study further emphasizes how important emotional exhaustion is in mediating the relationship between job stress and turnover intention. Employees are more likely to think about quitting the company when burnout from job stress rises, supporting the Job Demands-Resources (JD-R) model (Bakker & Demerouti, 2017). According to the model, excessive job demands, such as stress, lead to emotional exhaustion, which in turn results in unfavorable outcomes, including employee turnover.
4.7. Theoretical Implications
This study provides substantial theoretical insights that improve the understanding of the influence of job stress on employee turnover intention within the framework of the Job Demands-Resources (JD-R) model.
Firstly, this study enhances the JD-R model by empirically establishing that job stress functions as a significant job demand that induces emotional exhaustion, therefore elevating turnover intention (Bakker & Demerouti, 2007). By establishing emotional exhaustion as a mediator within this relationship, the study enhances the theoretical comprehension of the psychological mechanisms that connect workplace stressors to withdrawal behaviors, providing helpful information regarding how stress influences employees’ intentions to leave (Schaufeli & Bakker, 2004).
Secondly, the findings emphasize the moderating influence of work-life balance, demonstrating how personal and organizational resources can lessen the negative impact of job stress on emotional exhaustion. Within the JD-R framework, work-life balance is categorized as a hybrid resource, incorporating both personal resources (the employees’ capacity to navigate conflicting life domains) and organizational resources (policies, support systems, and flexible arrangements that facilitate balance). This contribution advances the JD-R literature by defining the boundary conditions under which job stress results in burnout-related outcomes (Beauregard & Henry, 2009). By conceptualizing work-life balance as a hybrid resource, the study reinforces the argument that both individual capacities and organizational structures jointly buffer the detrimental effects of excessive job demands. The study emphasizes the significance of aligning job requirements with available resources to reduce negative psychological impacts.
Thirdly, the study offers contextual insight by concentrating on the garments sector in Bangladesh, illustrating the relevance of the JD-R model within labor-intensive, high-pressure settings characteristic of developing economies (Sonnentag & Fritz, 2015). This application improves the cross-cultural and sector-specific generalizability of the model, offering helpful details about the functioning of the JD-R framework within diverse contexts characterized by unique challenges.
Lastly, by incorporating the constructs of stress, emotional exhaustion, and turnover intention into a unified model, this study provides a more comprehensive theoretical framework for understanding the progression of human resource challenges in demanding work environments (Maslach et al., 1997). This holistic approach facilitates bridging gaps in understanding the influence of various workplace stressors on employee behaviors over time.
4.8. Practical Implications
Beyond theoretical contributions, the findings also offer practical insights for managers and policymakers, particularly in Bangladesh’s garment industry.
Firstly, the findings indicate that organizations within the garments sector should focus on implementing strategies to mitigate job stress, including labor regulation, enhanced supervision, and the setting of attainable production goals. These strategies can mitigate emotional exhaustion and the subsequent rise in turnover intention, which is essential for sustaining a stable workforce (Kabat-Zinn & Hanh, 2009).
Secondly, the mediating function of emotional exhaustion demonstrates the importance of interventions aimed at fostering psychological well-being. Garment industry employers should contemplate the implementation of stress management training, the provision of counseling services, and the establishment of employee assistance programs to support workers in managing emotional exhaustion. Such initiatives can assist in alleviating the effects of stress and enhancing overall job satisfaction (Rothmann, 2008).
Thirdly, the moderating influence of work-life balance indicates that implementing supportive human resource policies, such as flexible scheduling, sufficient rest breaks, and rigorous compliance with labor regulations, can substantially mitigate the adverse impacts of occupational stress. This is particularly crucial in the labor-intensive garment sector, where long working hours are prevalent, as such policies can help foster a healthier work environment (Greenhaus & Allen, 2011).
Fourthly, reducing turnover intention directly improves operational stability, productivity, and cost efficiency. By implementing strategies that enhance employee retention, readymade garment companies can boost performance while simultaneously lowering the financial and operational costs associated with high turnover (Beauregard & Henry, 2009).
Lastly, the findings of this study can assist governments, labor organizations, and factory owners in formulating sector-specific policies and standards that foster healthier work environments and mitigate workforce instability. This may result in more sustained growth in sectors like garments, which are essential to developing countries (Sharma & Tiwari, 2023).
5. Limitations, Future Research Directions, and Conclusion
5.1. Limitations and Future Research Directions
This study provides significant insights into the impact of job stress and emotional exhaustion on turnover intention in the garments sector in Bangladesh; however, several limitations must be acknowledged that could inform future research directions.
Firstly, this research was carried out within a particular industry, the garments sector in Bangladesh, which may restrict the applicability of the findings to other industries or countries with differing socio-economic and cultural environments. The effects of job stress and emotional exhaustion may vary across diverse cultural contexts owing to variations in perceptions of work-life balance, societal values, and organizational standards. For instance, collectivist cultures may experience work-related stress and intentions to leave at higher rates than individualistic societies. Future research may examine the applicability of the JD-R model, incorporating cross-cultural comparisons to investigate how these factors affect employee well-being and turnover intentions across various cultural and industrial settings, thereby enhancing the generalizability of the findings (Hobfoll et al., 2018; Bakker & Demerouti, 2007).
Secondly, as a longitudinal study, the data were obtained in three distinct phases over a period of time, which might introduce temporal biases. Although this approach made it easier to analyze the progression of job stress and turnover intention, it also renders the study vulnerable to external environmental changes that could influence these variables, such as fluctuations in the economic climate or modifications in labor legislation. Future investigations may explore real-time data collection or adopt a more dynamic methodology to effectively capture the influence of external variables (Spector & Jex, 1998).
Thirdly, this study relied on self-reported measures to evaluate job stress, emotional exhaustion, work-life balance, and turnover intention, which may be affected by social desirability bias or individual perceptions. In addition to self-report bias, employing a single-source survey design introduces concerns related to Common Method Variance (CMV), which could potentially overstate the observed associations among variables. Although the multi-wave longitudinal design mitigates CMV by temporally distinguishing the measurement of predictors and outcomes, it does not completely eliminate this issue. Future studies may integrate more objective metrics, such as physiological assessments of stress or behavioral indicators, to minimize potential bias and offer a more precise understanding of the effect of job stress on employee performance (Podsakoff et al., 2012; Khamisa et al., 2015).
Finally, this study examined emotional exhaustion as a mediator and work-life balance as a moderator; however, other possible mediators and moderators were not explored. Future research may explore other psychological or organizational factors, such as job autonomy, job resources, or organizational support, that could further clarify the relationship between job stress and turnover intention. Furthermore, investigating the influence of individual differences, including personality traits and coping mechanisms, may offer additional insights into how job stress impacts employees across different demographics (Bakker & Demerouti, 2007; Judge & Bono, 2001; Tett & Meyer, 1993).
By acknowledging these constraints and exploring the proposed pathways for future research, organizations can attain a more comprehensive understanding of the factors affecting employee turnover intention, thereby enabling the development of more effective strategies and sustainable workforce management.
5.2. Conclusion
This research investigated the associations between job stress, emotional exhaustion, and turnover intention, emphasizing the moderating influence of work-life balance. The findings indicate that work-life balance mitigates the influence of job stress on emotional exhaustion, thereby decreasing the probability of turnover intention even under conditions of excessive stress. Emotional exhaustion functioned as a principal mediator, linking heightened job stress to elevated turnover intention. The findings highlight the importance of organizations addressing job-related stress and promoting a healthy work-life balance to improve employee retention. Theoretically, the study advances comprehension of how stress and emotional exhaustion affect employees’ intentions to resign from their positions, while practically, it promotes the implementation of work-life balance initiatives and stress reduction measures. Future research ought to investigate additional influencing factors and evaluate the efficacy of such programs within diverse organizational contexts.