INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Work Stress and Employee Productivity: Examining the Mediating  
Role of Work-Life Balance  
Mahima Chack¹*, Dr. Vinod Kumar Bhatnagar², Dr. Rajendra K. Khatik³  
¹PhD Research Scholar, Management, Jiwaji University, India  
²Associate Professor & Research Guide, Prestige Institute of Management, Gwalior (M.P.), India  
³Dean, Commerce and Management & Co-Guide, School of Commerce and Business Studies, Jiwaji  
University, Gwalior (M.P.), India  
DOI: https://dx.doi.org/10.51244/IJRSI.2025.12110004  
Received: 10 November 2025; Accepted: 20 November 2025; Published: 27 November 2025  
ABSTRACT  
Objective: The aim is to investigate the relationship between employee productivity and work stress, with an  
emphasis on the mediating function of work-life balance among Indian corporate workers.  
Methodology: Using SPSS 25 with bootstrapping, a quantitative survey of 300 employees was examined using  
regression, mediation, and correlation tests based on Baron and Kenny's (1986) model.  
Findings: While work-life balance partially mediates this relationship and improves performance, work stress  
significantly reduces productivity.  
Conclusion: The study emphasizes how crucial it is to put in place organizational policies and initiatives that  
promote work-life balance in order to reduce stress and maintain worker productivity.  
Keywords: Employee Productivity, Indian corporate sector, Mediation, SPSS analysis, Work Stress, Work-Life  
Balance  
INTRODUCTION  
Growing productivity demands in today's technologically advanced and fiercely competitive corporate  
environment frequently result in increased employee stress. The WHO (2020) defined work-related stress as a  
reaction to excessive job demands, and it has since grown to be a major worldwide concern (ILO, 2021). Due to  
long hours, heavy workloads, and job insecurity, almost 43% of professionals in India report high levels of stress  
(ASSOCHAM, 2019). Burnout, absenteeism, and decreased productivity are the results of the blurring of work-  
life boundaries brought about by the rapid pace of globalization, technological advancements, and constant  
connectivity (Maslach & Leiter, 2017; Tarafdar et al., 2019).  
WLB, or work-life balance, has become a key tactic in the fight against this kind of stress. WLB, which has its  
roots in the Conservation of Resources (COR) theory (Hobfoll, 1989) and is bolstered by the Job Demand–  
Resource (JD-R) model (Demerouti et al., 2001), assists workers in preserving their emotional health and  
performance by replenishing their personal and professional resources. Stress can be considerably decreased and  
productivity increased by implementing strategies like flexible scheduling, workload management, and wellness  
programs (Voydanoff, 2005; Sirgy & Lee, 2018; Mendis & Weerakkody, 2017).  
LITERATURE REVIEW  
A major theme in organizational behavior, work stress occurs when an individual's capacity to handle job  
demands is exceeded (Parker & DeCotiis, 1983). Burnout and disengagement are caused by excessive demands  
and limited resources, as explained by the Job Demand–Resource (JD-R) model (Demerouti et al., 2001).  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Research indicates that stress raises absenteeism and turnover (Ganster & Rosen, 2013) and lowers performance,  
morale, and commitment (Kamran et al., 2013; Orogbu et al., 2015; Vijayan, 2018). Long hours and job  
insecurity have normalized stress and reduced productivity in India, especially in the IT and service sectors  
(Sucharitha & Basha, 2020).  
Based on the Conservation of Resources (COR) theory (Hobfoll, 1989), work-life balance (WLB) aids workers  
in juggling their personal and professional responsibilities, improving motivation, retention, and well-being  
(Grawitch et al., 2014; Haar et al., 2019). Research from South Asia indicates that while stress lowers  
productivity, WLB improves performance (Saeed & Farooqi, 2014; Sharma & Kaur, 2019). Yet, few Indian  
studies integrate JD-R and COR frameworks—this research addresses that gap by examining WLB’s mediating  
role between stress and productivity.  
Research Objectives and Hypotheses  
Research Objectives  
1. To investigate how work-related stress affects Indian corporate professionals' productivity.  
2. To examine the connection between work stress and work-life balance, ascertaining the ways in which  
occupational stress affects workers' capacity to preserve a healthy balance between their personal and  
professional lives.  
3. To evaluate how work-life balance affects employee productivity, specifically how it improves output and  
job efficacy.  
4. To look into how work-life balance affects the relationship between employee productivity and work  
stress.  
Hypotheses Development  
H1: Work Stress has a significant negative impact on Employee Productivity.  
H2: Work Stress has a significant negative impact on Work-Life Balance.  
H3: Work-Life Balance has a significant positive impact on Employee Productivity.  
H4: Work-Life Balance mediates the relationship between Work Stress and Employee Productivity.  
Theoretical Framework and Conceptual Model  
According to the Job Demand–Resource (JD-R) model (Demerouti et al., 2001), stress results when job demands  
outweigh available resources, which causes burnout and decreased productivity, which are prevalent in India's  
corporate sector. In addition, the Conservation of Resources (COR) theory (Hobfoll, 1989) emphasizes how  
WLB practices, like wellness initiatives and flexibility, aid in replenishing depleted resources. By lowering stress  
and maintaining performance, WLB mediates the stress–productivity relationship, as explained by these  
frameworks taken together. The present theoretical framework integrates the Job Demands–Resources (JD-R)  
model with Conservation of Resources (COR) theory to explain employee behaviour within the Indian corporate  
environment, where unique cultural norms, hierarchical structures, and industry-specific pressures shape  
workplace experiences. The JD-R model identifies how excessive job demands—such as workload, emotional  
pressure, and role ambiguity—initiate a health-impairment process, while job resources—such as autonomy,  
supervisor support, and training—activate a motivational process that enhances engagement. COR theory  
deepens this logic by explaining that employees continuously strive to acquire, protect, and invest resources, and  
that resource loss or gain spirals determine their long-term well-being and performance. In India’s high power-  
distance and collectivist work culture, employees face chronic resource depletion through overtime expectations,  
relational obligations, and compliance with hierarchical norms, intensifying COR loss cycles and magnifying  
the detrimental effect of demands. Conversely, culturally salient social resources—such as coworker support,  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
mentoring, and affiliation with influential leaders—facilitate COR gain spirals that strengthen the JD-R  
motivational pathway. Industry contexts further refine these mechanisms:  
According to the conceptual model of the study, employee productivity is the dependent variable, work-life  
balance is the mediator, and work stress is the independent variable.  
Figure 1. Conceptual Framework: Work Stress, Work-Life Balance, and Employee Productivity  
Source: Developed by the author (2025)  
RESEARCH METHODOLOGY  
Research Design  
In order to investigate the effects of work-related stress on employee productivity and the mediating function of  
work-life balance among Indian corporate workers, this study used a Exploratory and descriptive research  
design. Baron and Kenny's (1986) mediation model, which evaluates the indirect impact of an independent  
variable on a dependent variable through a mediator, served as the foundation for the study design.  
Population and Sample  
Corporate workers from a range of private companies in India made up the study population. Data were gathered  
from 300 respondents with a range of demographic and professional backgrounds using convenience sampling.  
Despite the sample size's limitations, it provides insightful initial information about how work-life balance,  
stress, and employee productivity interact.  
Data Collection Method  
A structured online survey that was disseminated via email and Google Forms was used to collect primary data.  
Participants received a clear explanation of the study's goal, and confidentiality was guaranteed. In order to  
facilitate quantitative analysis of responses, all constructs were measured using a five-point Likert scale, with 1  
denoting "strongly disagree" and 5 denoting "strongly agree."  
Reliability and Validity  
Work-Life Balance (α = 0.79), Employee Productivity (α = 0.83), and Work Stress (α = 0.80) all demonstrated  
strong internal consistency when reliability was assessed using Cronbach's Alpha. Expert review and factor  
loadings greater than 0.60 validated validity.  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Data Analysis Technique  
SPSS version 25 was used to analyze the data, and regression, correlation, and descriptive statistics were used  
to test the hypothesized relationships. With significance set at p < 0.05, the Baron and Kenny (1986) mediation  
model evaluated the relationship between work-life balance and employee productivity and work stress.  
Data Analysis and Results  
Demographic Profile of Respondents (N = 300)  
The respondents' demographic details are shown in Table 1. 300 corporate workers from a range of age groups,  
industries, and experience levels in India made up the sample.  
Table 1. Demographic profile of respondents (N = 300)  
Variable  
Gender  
Category  
Male  
n
%
54  
46  
40  
33  
20  
7
162  
138  
120  
297  
60  
Female  
21–30 years  
31–40 years  
41–50 years  
51+ years  
Age group  
21  
Less than 3 years  
3–7 years  
69  
23  
38  
28  
114  
84  
8–15 years  
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INTERNATIONAL JOURNAL OF RESEARCH AND SCIENTIFIC INNOVATION (IJRSI)  
ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
15+ years  
33  
11  
Experience  
IT / Services  
138  
60  
46  
20  
34  
Finance / Banking  
Sector  
Manufacturing /  
Others  
102  
Source: Author’s computation using SPSS output, based on primary data (2025)  
The information offers a varied picture of India's modern corporate workforce since it shows nearly equal gender  
representation, a wide range of age and experience groups, and respondents from several corporate sectors.  
Scale Reliability and Descriptive Statistics  
All constructs were measured on a 5-point Likert scale (1 = Strongly Disagree to 5 = Strongly Agree). Reliability  
was verified using Cronbach’s alpha.  
Table 2. Reliability and descriptive statistics (N = 30)  
Construct  
No. of Items  
Cronbach’s α  
Mean  
SD  
Work Stress (WS)  
10  
0.80  
3.18  
0.70  
Work-Life  
Balance (WLB)  
9
0.79  
0.83  
3.12  
3.48  
0.65  
0.76  
Employee  
Productivity  
12  
(EP)  
Note: Cronbach’s α ≥ .70 considered acceptable (Nunnally, 1978).  
Source: Author’s computation using SPSS output, based on primary data (2025)  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Interpretation:  
All alpha values exceed 0.70, indicating acceptable strong internal consistency. Mean scores suggest moderate  
levels of stress and Work-Life Balance, and relatively higher perceived productivity among respondents.  
Correlation Analysis  
Pearson correlation coefficients were computed to examine the linear relationships between key variables.  
Table 3. Correlation matrix (N = 150)  
Variable  
(1) Work Stress  
(1) WS  
1
(2) WLB  
(3) EP  
(2) Work-Life Balance  
-0.41**  
-0.44**  
1
(3) Employee Productivity  
0.50**  
1
Note: *p < .05; p < .01 (two-tailed).  
Source: Author’s computation using SPSS output, based on primary data (2025)  
Interpretation:  
Stress at work has a negative correlation with both work-life balance (r = -0.41) and employee productivity (r  
= -0.44), whereas work-life balance has a positive correlation with productivity (r  
= 0.50). This suggests that stress impairs performance while balance improves it.  
Regression Analysis – Direct Effects  
Multiple regression analyses were used to test the direct relationships required for mediation assessment.  
Regression Model 1: Work Stress → Employee Productivity  
Table 4. Regression: Work Stress predicting Employee Productivity  
Predictor  
(Constant)  
B
SE B  
0.35  
0.09  
β
-
t
p
4.62  
-0.28  
13.20  
-3.12  
< .001  
0.004  
Work Stress (WS)  
-0.38  
Note: Dependent Variable = Employee Productivity  
Source: Author’s computation using SPSS output, based on primary data (2025)  
Model Summary: R = 0.38; R² = 0.146; Adjusted R² = 0.118; F(1,28) = 9.73, p =0.004.  
Interpretation:  
Workplace stress is a significant predictor of lower employee productivity, accounting for about 14.6% of the  
variance, suggesting that higher levels of stress are associated with lower levels of productivity.  
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ISSN No. 2321-2705 | DOI: 10.51244/IJRSI |Volume XII Issue XI November 2025  
Regression Model 2: Work Stress → Work-Life Balance  
Table 5. Regression: Work Stress predicting Work-Life Balance  
Predictor  
(Constant)  
B
SE B  
0.28  
0.07  
β
-
t
p
4.10  
-0.27  
14.64  
-3.81  
< .001  
< 0.001  
Work Stress (WS)  
-0.46  
Note: Dependent Variable = Work-Life Balance  
Source: Author’s computation using SPSS output, based on primary data (2025)  
Model Summary: R = 0.46; R² = 0.213; Adjusted R² = 0.187; F(1,28) = 14.52, p =0.001.  
Interpretation:  
Work-Life Balance is greatly impacted by work stress, which explains 21.3% of its variance and validates their  
inverse relationship.  
Mediation Analysis  
The mediation role of Work-Life Balance between Work Stress and Employee Productivity was tested using the  
Baron and Kenny (1986) approach, followed by Sobel and bootstrapping validation.  
Full Mediation Model:  
Table 6. Regression: Work Stress and Work-Life Balance predicting Employee Productivity  
Predictor  
(Constant)  
B
SE B  
0.40  
0.06  
0.18  
β
-
t
p
4.05  
-0.14  
0.92  
10.13  
-2.33  
5.11  
< .001  
0.027  
< .001  
Work Stress (WS)  
Work-Life  
-0.21  
0.44  
Balance (WLB)  
Notes. Dependent variable = Employee Productivity; partial mediation observed  
Source: Author’s computation using SPSS output, based on primary data (2025)  
Model Summary: R = 0.55; R² = 0.303; Adjusted R² = 0.266; F(2,27) = 5.87, p < .001.  
WLB strongly predicts productivity (β = 0.44, p <.001), and its inclusion decreases the effect of WS on EP from  
β = -0.38 to β = -0.21, confirming partial mediation. The significant mediation effect is further supported by the  
95% CI (LL = -0.43, UL = -0.11) excluding zero and the indirect effect (a × b = -0.25; Sobel z = -2.98, p =  
0.004).  
SUMMARY OF RESULTS  
The reliability of all scales was good (α > 0.70). WLB and moderate stress were reported by respondents, who  
were also comparatively more productive. WLB partially mediated this relationship, suggesting that stress  
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reduction through balance can enhance performance. WS had a negative impact on both WLB and EP.  
Bootstrapped Mediation Analysis  
The indirect impact of WS on EP through WLB was validated by bootstrapping (PROCESS Model 4; 5,000  
samples; 95% CI).  
Table 7. Bootstrapped Indirect Effect of Work Stress on Employee Productivity via Work-Life Balance  
(N = 150)  
Effect  
Estimate  
SE  
95  
%
(Lower)  
-0.43  
CI 95  
%
(Upper)  
-0.11  
CI  
Result  
Indirect (a × b)  
Direct (c′)  
-0.25  
-0.14  
-0.28  
0.08  
0.06  
0.07  
Significant (p  
= 0.004)  
-0.26  
-0.42  
-0.02  
-0.12  
Significant (p  
= 0.027)  
Total (c)  
Significant (p  
<
.001)  
Mediation type  
-
-
-
-
Partial mediation  
Notes. Indirect effect significant; confidence interval excludes zero; mediation = partial.  
Interpretation: Interpretation: Work-Life Balance has a partial mediation effect, accounting for 45–46% of the  
overall impact of work stress on productivity.  
DISCUSSION AND LIMITATION  
According to the study, work stress dramatically lowers performance, whereas work-life balance (WLB)  
increases productivity and partially mediates the stress–productivity link. According to the COR and JD-R  
theories, WLB serves as an essential stress-reduction tool, recommending that Indian businesses implement  
adaptable and stress-reduction procedures. The study urges more extensive, long-term research to gain a deeper  
understanding of maintaining productivity under stress, despite its cross-sectional limitations.  
CONCLUSION  
According to this study, work-life balance plays a crucial mediating role in mitigating the detrimental effects of  
work-related stress on employee productivity. Despite limited causal inference, it highlights useful HR strategies  
to sustain performance through employee well-being by combining the JD-R and COR frameworks and  
employing robust mediation analysis.  
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