Work Ability and Psychological Well-being of Indian Information Technology Employees: Mediating Role of Job Satisfaction and Moderating Role of Perceived Stress

Abstract

Background

In the face of deadline-bound projects and tight work schedules, Indian Information technology (IT) employees may have to rely on positive psychological resources to maintain and enhance their psychological well-being (PWB). However, the inevitable experience of stress in IT workplaces may pose a significant challenge to their PWB via its negative association with positive psychological resources.

Purpose

Based on the Job Demands-Resources (JD-R) model, the current study attempts to explore the mediating role of job satisfaction (JS) in the relationship between work ability (WA) and PWB of Indian IT employees. Additionally, utilising the Conservation of Resources (COR) theory, the study further explores the moderating role of perceived stress (PS) in the WA-PWB relationship via JS.

Methods

The study used a cross-sectional design and collected data from 312 employees (193 males and 119 females) in the IT sector. Work Ability Index, Job Satisfaction Survey, Perceived Stress Scale and Ryff’s Psychological Well-being scale were used to collect data. Correlation, mediation and moderated mediation analyses were used to analyse the collected data.

Results

Correlation analysis revealed that WA was positively associated with PWB (r = 0.524, p < .01). Mediation analysis revealed that JS partially mediated the relationship between WA and PWB (indirect effect = 0.218, SE = 0.062, 95%CI = [0.105, 0.348]). Moderated mediation analysis revealed that PS moderated the relationship between JS and PWB (β = −0.0085, SE = 0.0026, p < .01, 95%CI = [−0.0135, −0.0034]) as well as the WA-PWB relationship via JS (Index of moderated mediation = −0.0143, SE = 0.0051, 95%CI = [−0.0257, −0.0056]).

Conclusion

The study’s findings reveal how the various pathways shaped by WA, JS and PS are associated with PWB of Indian IT employees. The alignment of the findings with JD-R and COR provides theoretically driven insights for Indian IT organisations to consider while formulating their wellness programmes.

Keywords

Work ability psychological well-being job satisfaction perceived stress Indian IT employees

Introduction

Information technology (IT) employees experience tight work schedules, with their office time structured around meeting organisational goals. Oftentimes, the stress accumulated from the workload affects their personal and professional lives, leading to adverse outcomes such as burnout, depression and physical health complications.^1–3 In the face of rising workplace demands, employees must constantly utilise available resources to function effectively, meet workplace expectations and maintain well-being.⁴ In line with the propositions of the Job Demands-Resources model (JD-R),⁵ which emphasise the role of physical, organisational, psychological and social resources in achieving work goals and personal growth, the current study aims to explore the psychological well-being (PWB) of Indian IT employees in relation to employee-specific resources such as work ability (WA) and job satisfaction (JS). However, as one of the propositions of the Conservation of Resources (COR) theory argues, resources can be lost or depleted in the face of impending or ever-increasing challenges, and such loss occurs more rapidly than the resource gain by the employees.⁶ Based on this proposition, the study further explores the role of perceived stress (PS) in the associations among WA, JS and PWB of Indian IT sector employees. One of the earliest definitions of WA was framed as a question: ‘How long are workers and employees able to work, and to what extent does being able to work depend on the work content and job demands?’.⁷ Current research defines WA as the employees’ occupational competence, health and virtues that allow them to perform reasonable work-related tasks in an acceptable occupational environment.⁸ PWB is a well-documented area in organisational behaviour. According to Carol Ryff (1989), PWB comprises autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance.⁹ The centrality of employees’ health in the conceptualisation of WA makes it a highly relevant area of research for IT employees. Indian IT employees with deteriorating health conditions, such as musculoskeletal problems, diabetes and hypertension, have been found to report higher stress levels, which in turn is associated with lower PWB.¹⁰ However, studies have shown that employee WA is positively associated with desirable outcomes pertinent to increased PWB, such as work-related quality of life, workplace well-being and health-related quality of life.^{11, 12} By integrating the propositions of the JD-R model, it can be argued that the results of these studies designate WA as a significant resource for employees, capable of signalling positive outcomes such as increased PWB. Based on this assumption, the following is the first hypothesis of the current study:

H₁: WA will be positively associated with PWB.

JS is an important area of research in organisational behaviour. One of the seminal definitions of JS identifies two perspectives of the concept—an affective perspective and a cognitive perspective.¹³ The affective perspective argues that JS is an overall affective and positive evaluation of the job. In contrast, the cognitive perspective argues that JS is an evaluation of the job’s working conditions viewed through rationality and logic.¹⁴ A comparative study on Indian IT and IT-enabled sector employees revealed that employees from the IT sector experienced lower JS, with men showing higher levels of JS.¹⁵ Another study on the JS of 462 Indian healthcare employees revealed that more than 75% of them were dissatisfied with the working conditions of their workplace.¹⁶ In a first-of-its-kind study on Turkish employees, WA was positively associated with beneficial organisational outcomes, such as task performance, with JS mediating this relationship.¹⁷ Studies on diverse employee populations have revealed positive relationships between WA and JS.^{18, 19} Moreover, research has revealed that certain factors conceptually encompassed within WA, such as job content and quality of work life, are strongly related to JS.^{20, 21} It is worth noting that JS has been shown to share consistent positive associations with PWB in many correlational studies.^22–24 Subsequent studies have used this association to explore other employee-specific outcomes such as mental toughness, organisational commitment and conscientiousness.^{25, 26} Despite being theoretically grounded in the JD-R model, there is a shortage of studies exploring JS’s mediating capacity in the relationship between WA and PWB. Based on the JD-R model and the empirical studies that explored the WA-PWB, WA-JS and JS-PWB relationships, it can be argued that JS has the potential to act as a pathway between WA and PWB. Hence, the following is the second hypothesis of the current study:

H₂: JS will mediate the relationship between WA and PWB.

PS refers to a global stress quotient shaped by individuals’ perceptions about life’s uncontrollable and unpredictable nature, overwhelming daily hassles, unexpected changes in life, and their ability to deal with obstacles and difficulties.^{27, 28} Workplace factors such as duration of work, salary decrements and behavioural shifts between employees and management have been associated with heightened stress levels for employees.²⁹ In the present age, employees often bring home tasks from their workplace, such as projects and reports, and they continue to respond to work-related emails and phone calls from their homes.³⁰ This work pattern blurs the whole work-life balance concept, forcing employees to work boundlessly on time, preventing them from psychologically detaching from their work and office.³¹ This issue is prevalent in the Indian IT sector, where extended work timings damage employees’ work-life balance.³² An integrated literature review on Indian work-life balance policies highlighted that the Indian IT sectors lack policies to promote work-life balance for employees.³³ The pattern of bringing home large or small parts of work from the office may add to the daily hassles of the employee, paving the way for additional stress at the home front, which in turn may increase the likelihood of the subjective experience of PS. The reason for conceptualising PS as a moderating variable in the current study is two-fold. First is the resource loss spiral concept embedded within the COR theory, which states that further investment of resources becomes challenging when individuals lose resources.^{34, 35} In this background, it can be argued that when employees experience heightened PS, their resources, such as WA, JS and PWB, may also be affected, probably leading to resource loss and making further resource gain challenging. Second is the empirical studies that explored the negative associations PS shares with WA, JS and PWB. A study on healthcare employees in Sweden has shown that PS and physiological complexities, such as musculoskeletal pain, have a negative relationship with their WA.³⁶ PS has been shown to have a negative association with JS of employees from diverse fields.^{37, 38} Similarly, research has shown that PS and PWB share a negative relationship.^{39, 40} A recent systematic review on evidence of work stress from India called for more Indian research that explores the varied impacts of stress on employee health.⁴¹ The current study attempts to answer such a call by exploring the nuanced association of PWB of Indian IT employees with their WA, JS and PS using a moderated mediation approach. Hence, the following are the third and fourth hypotheses of the current study:

H₃: PS will moderate the relationship between JS and PWB.

H₄: PS will moderate the mediating effect of JS in the relationship between WA and PWB.

The conceptual model of the study based on the hypothesised relationships is presented in Figure 1.

Figure 1.

Conceptual Model of the Study.

Methods

Data Collection

The study employed a cross-sectional design. Following ethical approval from the first author’s institution, informed consent was obtained and data were collected, using a purposive sampling technique, from 312 IT employees (193 males and 119 females) through a survey. Data collection took place from January 2024 to January 2025. Detailed demographic information of the employees is provided in Table 1.

Table 1.

Demographic Information of the Employees.

Variables	N (312)	%
Gender
Male	193	61.9
Female	119	38.1
Age
20–30 yrs	184	59
31–40 yrs	67	21.5
41–50 yrs	34	10.9
51–60 yrs	25	8
Above 60 yrs	2	0.6
Marital status
Unmarried	178	57.1
Married	134	42.9
Total work experience
1–5 yrs	176	56.4
6–10 yrs	46	14.7
More than 10 yrs	90	28.8
Current salary
Less than 5 lakhs per annum	122	39.1
5–10 lakhs per annum	101	32.4
More than 10 lakhs per annum	89	28.5
Night shifts
Yes	40	12.8
No	272	87.2
Control over work timings
Yes	164	52.6
No	148	47.4
Work beyond office hours
Yes	197	63.1
No	115	36.9

Note: N = Total number of employees.

Inclusion criteria of the study were employees aged 18 years and above, working in the IT sector with a minimum of 1 year of work experience, proficient in English, and who provided informed consent. Employees with active severe psychiatric conditions were excluded from the study.

Measures

Semi-structured Questionnaire for Demographic Details

A semi-structured questionnaire was prepared by the researchers to collect demographic details of the employees, including gender, age, marital status, work experience, salary, presence of night shifts, control over work timings and work beyond office hours.

The Work Ability Index (WAI)⁴²

WAI was used to measure the WA of the employees. WAI is a questionnaire that consists of seven sections. The items in these sections take into account the employees’ demands of work, their health status, and their resources. The total WA score is obtained by summing the scores of the seven sections. The scores range from 7 to 49. For the current study, the Cronbach’s alpha of WAI was found to be 0.719.

Job Satisfaction Survey (JSS)⁴³

JSS was used to measure the employees’ JS. JSS is a 36-item six-point Likert scale that assesses employees’ attitudes towards their job and aspects of the job. The options to the items range from ‘disagree very much’ to ‘agree very much’. The total job satisfaction score is obtained by summing the scores of the nine facets of the scale: pay, promotion, supervision, fringe benefits, contingent rewards, operating conditions, coworkers, nature of work, and communication. The scores range from 36 to 216. For the current study, the Cronbach’s alpha of JSS was found to be 0.913.

Ryff’s Psychological Well-being Scale⁴⁴

For measuring the PWB of employees, the 18-item version of Ryff’s psychological well-being scale was used. The scale uses a 7-point Likert system to measure six dimensions of PWB: autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance. The options to the items range from ‘strongly agree’ to ‘strongly disagree’. The total PWB score is obtained by summing the scores on all six dimensions. The scores range from 18 to 126. For the current study, the Cronbach’s alpha of Ryff’s psychological well-being scale was found to be 0.759.

Perceived Stress Scale (PSS-10)⁴⁵

PSS-10 was used to measure the PS of the employees. The scale consists of 10 items pertinent to stressful circumstances in life. PSS-10 is a five-point Likert scale with options ranging from ‘never’ to ‘very often’. The total perceived stress score is calculated by summing the scores on all 10 items. The scores range from 0 to 40. For the current study, the Cronbach’s alpha of PSS-10 was found to be 0.784.

Data Analysis

The data were analysed using IBM SPSS version 20. Prior to the main analyses, the dataset was tested for common-method bias using Harman’s one-factor test. H₁ was tested using Spearman’s correlation. H₂ was tested using Model 4, and H₃ and H₄ were tested using Model 14 of Hayes Process Macro.⁴⁶ All demographic variables were controlled in both models.

Common-method Bias Testing

Harman’s one-factor test revealed that a single factor explained a total variance of 18.06%, which is less than the commonly accepted threshold of 40%.⁴⁷ Therefore, the study is unlikely to be affected by common-method bias.

Results

Correlation Analysis

To test H₁ of the study, Spearman’s correlation analysis was performed using all the variables of the study. The mean, standard deviation and intercorrelations between all the variables are shown in Table 2.

Table 2.

Mean, Standard Deviation and Intercorrelations Between all the Variables of the Study.

Variable	M	SD	1	2	3	4
1. Work ability	39.54	5.84	–
2. Psychological well-being	88.42	13.73	.524**	–
3. Job satisfaction	136.29	25.24	.383**	.479**	–
4. Perceived stress	18.23	6.35	−.478**	−.534**	−.441**	–

Notes: M = Mean, SD = Standard deviation.

**p < .01.

The results revealed that WA had a significant and positive correlation with PWB (r = 0.524, p < .01), thereby proving that WA is positively associated with PWB.

Mediation Analysis

To test H₂ of the study, we used Model 4 of the Hayes process macro. The model was computed with WA as the predictor, JS as the mediator and PWB as the outcome variable. The results of the mediation analysis are summarised in Figure 2.

Figure 2.

Results of the Mediation Analysis with Work Ability as the Predictor, Psychological Well-being as the Outcome and Job Satisfaction as the Mediator.

The results revealed that this model was significant, explaining a variance of 39 % of PWB [R² = 0.39, F (8, 303) = 24.243, p < .001]. The effect of WA on JS was found to be positive and significant (β = 1.36, SE = 0.224, p < .001, 95% CI = [0.919, 1.801]). The effect of JS on PWB was found to be positive and significant (β = 0.16, SE = 0.028, p < .001, 95% CI = [0.104, 0.216]). The total effect of WA on PWB was found to be positive and significant (total effect = 1.054, SE = 0.116, p < .001, 95% CI = [0.825, 1.282]). After controlling for the effect of JS, the direct effect of WA on PWB was found to be positive and significant (direct effect = 0.836, SE = 0.117, p < .001, 95% CI = [0.606, 1.066]). Finally, the indirect effect of JS in the relationship between WA and PWB was found to be significant (indirect effect = 0.218, SE = 0.062, 95% CI = [0.105, 0.348]), thereby confirming that JS partially mediated the relationship between WA and PWB.

Moderated Mediation Analysis

To test H₃ and H₄ of the study, we used Model 14 of Hayes Process Macro. The model was computed with WA as the predictor, JS as the mediator, PWB as the outcome, and PS as the moderator in the path between JS and PWB. The results of the moderated mediation are given in Table 3.

Table 3.

Results of the Moderated Mediation Analysis.

Predictor	Outcome	β	SE		95% CI
WA	PWB	0.5815***	0.1171		[0.3511, 0.812]
WA	JS	1.687***	0.225		[1.2433, 2.1311]
JS	PWB	0.119***	0.0277		[0.0652, 0.1741]
JS × PS (interaction)	PWB	−0.0085**	0.0026		[−0.0135, −0.0034]
Conditional effects of JS on PWB at different levels of PS			β	SE	95% CI
Low PS (−1SD)			0.173***	0.0329	[0.1087, 0.238]
Medium PS (mean)			0.119***	0.0277	[0.0652, 0.1741]
High PS (+1SD)			0.066*	0.0314	[0.0042, 0.1277]
Conditional effects of WA on PWB through JS at different levels of PS			β	SE	95% CI
Low PS (−1SD)			0.2925	0.0771	[0.1544, 0.4576]
Medium PS (mean)			0.2019	0.0653	[0.0841, 0.3375]
High PS (+1SD)			0.1113	0.0685	[−0.0227, 0.2449]
Index of moderated mediation			−0.0143	0.0051	[−0.0257, −0.0056]

Notes: WA = Work ability, JS = Job satisfaction, PWB = Psychological well-being, PS = Perceived stress, SD = Standard deviation, β = Coefficient, SE = Standard error, CI = confidence interval, SD = Standard deviation.

*p < .05, **p < .01, ***p < .001.

The results revealed that this model was significant, explaining a variance of 43.71% of PWB [R² = 0.4371, F (4, 307) = 59.58, p < .001]. The direct effect of WA on PWB was found to be significant and positive (direct effect = 0.5815, SE = 0.1171, p < .001, 95%CI = [0.3511, 0.812]). WA was shown to have a significant and positive effect on JS (β = 1.687, SE = 0.225, p < .001, 95% CI = [1.2433, 2.1311]). JS was shown to have a significant and positive effect on PWB (β = 0.119, SE = 0.0277, p < .001, 95% CI = [0.0652, 0.1741]) and this path was moderated by PS (β = −0.0085, SE = 0.0026, p < .01, 95%CI = [−0.0135, −0.0034]). Conditional process analysis revealed the effect of JS on PWB at three levels of PS-low (−1SD), medium (mean) and high (+1SD). Figure 3 demonstrates the simple slope showing the effect of JS on PWB at three levels of PS.

Figure 3.

Simple Slope Demonstrating the Effect of Job Satisfaction on Psychological Well-being at Three Levels (Low, Medium and High) of Perceived Stress.

At low PS, the effect of JS on PWB was found to be significant (β = 0.173, SE = 0.0329, p < .001, 95% CI = [0.1087, 0.238]). At medium PS, JS had a significant but reduced effect on PWB (β = 0.119, SE = 0.0277, p < .001, 95% CI = [0.0652, 0.1741]). Finally, at high PS, the effect of JS on PWB was significant but lesser compared to medium and low levels of the moderator (β = 0.066, SE = 0.0314, p < .05, 95% CI = [0.0042, 0.1277]). Similarly, the model also revealed the moderating effect of three levels of PS in the indirect effect of WA on PWB through JS. However, this moderation was only significant at low (−1SD) and medium (mean) levels of PS and not at the high (+1SD) level. At low PS, the effect of WA on PWB through JS was found to be significant (β = 0.2925, SE = 0.0771, 95% CI = [0.1544, 0.4576]). At medium PS, the effect of WA on PWB through JS was found to be significant (β = 0.2019, SE = 0.0653, 95% CI = [0.0841, 0.3375]). Finally, at high PS, the effect of WA on PWB through JS was found to be non-significant (β = 0.1113, SE = 0.0685, 95%CI = [−0.0227, 0.2449]). Furthermore, the model revealed a significant index of moderated mediation (β = −0.0143, SE = 0.0051, 95%CI = [−0.0257, −0.0056]), thereby confirming that PS moderated the relationship between WA and PWB through JS.

Discussion

The current study explored how WA, JS and PS are associated with PWB of Indian IT employees. Consistent with H₁, the correlation analysis revealed that WA is positively associated with PWB. This finding aligns with a recent study that revealed a positive association between WA and PWB.⁴⁸ It can be argued that the factors within WA, such as perceived health, competence, skills and resources of IT employees, may share a meaningful association with their PWB. This finding emphasises that PWB of IT employees is not only associated with organisational and relational factors but also with performance-related personal resources such as WA. Human resources of IT organisations can utilise this finding to formulate holistic wellness programmes that emphasise intrapersonal performance-centric resources of employees, like WA. Furthermore, in the context of the Indian IT sector, where employee health deterioration is rampant,^{49, 50} the health-specific dimensionality of WA and its association with PWB is pivotal, as it underscores that the health of the employees should be safeguarded and enhanced in ways that may contribute beneficially to their PWB. Aligning with H₂, the mediation analysis revealed that JS partially mediated the relationship between WA and PWB. The WA-PWB relationship was positively mediated by JS, which shared positive associations with both WA and PWB. It is important to contextualise this finding in the motivational process principle of the JD-R model, which states that job resources act as external motivating factors, launching a motivational pathway wherein employees strive to be fully engaged in work and expedite the process of goal attainment while simultaneously reducing job demands.⁵¹ This motivational pathway, activated by the job resource, leads to positive outcomes for the employee.⁵² Applying this principle to the mediation findings, we can argue that WA acts as a job resource, positively associating with JS as part of a motivational pathway, which further positively associates itself with a positive outcome, that is, PWB. Even though this particular mediating pathway has not been explored empirically, studies have reported the mediating capacity of JS in the relationships among WA, task performance and ergonomic factors.^{17, 53}

Finally, the moderated mediation analysis revealed that PS moderated the JS-PWB relationship and the mediation effect of JS in the relationship between WA and PWB, thereby supporting H₃ and H₄. According to the conditional process analysis, the strength of the positive relationship between JS and PWB decreased as PS increased. Similarly, the strength of the mediating effect of JS in the WA-PWB relationship decreased from low to moderate PS, eventually becoming statistically insignificant at high PS. This finding echoes the resource loss spiral concept of the COR theory, which states that in the face of stress, the resource loss in employees subsequently leads to more loss, and the entire process of loss is more noticeable than their resource gain.⁵⁴ In this context, it can be argued that the PS of the IT employees functioned as a negative moderator by associating negatively with the strength of both the direct JS-PWB relationship and the indirect WA-PWB relationship via JS, which became statistically insignificant under high levels of PS. Connecting this finding to the mediation results, we can argue that PS is negatively associated with the strength of the motivational pathway shaped by the WA-JS relationship, which was associated with higher PWB. Indian research that explores relationships between PS and internal resources of IT employees seems scarce, with one niche study reporting a negative association between gratitude and PS of Indian IT employees.⁵⁵ The moderated mediation result of the current study contributes to a specialised domain of PS-related Indian research by offering a nuanced understanding of how PS is related negatively to internal positive resources of employees, such as WA and JS, as well as how the motivational pathway, which might have been initiated by these positive resources and was associated with higher PWB, became insignificant under high levels of PS. The findings of the study offer a broader perspective for Indian organisations to understand how PS is associated with the erosion of positive psychological resources and pathways within IT employees. An Indian study conducted during COVID-19 emphasised that PS stems from employees’ hardships in balancing personal and professional lives.⁵⁶ Taking into account the lack of work-life balance policies in the Indian IT sector,³³ it is crucial for organisations to adopt positive management practices that foster effective work-life balance for IT employees. Indian IT organisations can adopt management approaches such as a health-promoting management style, which has been shown to buffer the negative association between perceived stress and job performance in the workplace.⁵⁷ The current study’s findings are insightful for IT organisations for two reasons. First, utilising a moderated mediation approach, the study outlines how PWB of Indian IT employees is associated with intricate pathways shaped by their WA, JS and PS. Second, the study results align with established psychological theories such as JD-R and COR. These findings provide a substantial theory-driven empirical backing for organisations to formulate their wellness programmes by emphasising the positive associations among WA, JS and PWB and the detrimental associations each shares with PS. Moreover, with culturally significant practices like Yoga shown to improve well-being and reduce perceived stress of Indian IT employees,^{58, 59} organisations can expand the scope of such practices by assessing their utility in enhancing WA and JS, both of which were shown to share significant associations with PWB and PS in the current study. Although certain multinational corporations provide counselling, health check-ups and screening tests in India, employees rarely use them because of time limitations and a lack of motivation and awareness.⁶⁰ In this context, it is important to effectively communicate the current study’s findings to the IT employees so that they stay aware of how PWB is associated with WA, JS and PS. Finally, the results of the current study contribute to Goal 8 of the Sustainable Development Goals (SDGs) of the United Nations, which reads as ‘Promote sustained, inclusive and sustainable economic growth, full and productive employment and decent work for all’.⁶¹

The study’s primary strength lies in its theoretically grounded exploration of the association of PWB of Indian IT employees with their WA, JS and PS using a moderated mediation approach. Despite the strength, the limitations of the current study should be noted. The study adopted a cross-sectional methodology to test its hypotheses. As such, though statistically significant, the relationships and pathways found in the study are correlational and do not imply causation. Rigorous longitudinal and intervention studies are required to validate the study’s proposed conceptual model. Moreover, the study strictly sought to explore the relationships between the four primary variables: WA, JS, PWB and PS, and hence did not explore the influence of demographic factors in those relationships. Future studies can employ advanced statistical tests such as structural equation modelling and network analysis on a larger sample to further explore the relationships among variables and their demographic associations.

Conclusion

The current study demonstrates that JS mediates the relationship between WA and PWB, and PS moderates this mediating pathway and the JS-PWB relationship. With the mediation results aligning with the motivational pathway principle of the JD-R model and the moderation results aligning with the resource loss spiral concept of COR, the current study elucidates the intricate ways in which PWB of Indian IT employees associates with positive psychological resources such as WA and JS, as well as with challenges to well-being such as PS. Indian IT organisations can utilise the study’s findings to formulate wellness programmes and capacity-building initiatives to promote and enhance employees’ WA, JS and PWB and reduce their vulnerability to stress.

Footnotes

Acknowledgement

The authors convey their gratitude to all the study participants and to Bhagyalakshmi J S for her assistance with the post–peer-review corrections of the article.

Authors’ Contribution

Anandapadmanabhan G conceptualised the study, collected and analysed the data, and wrote the manuscript. Priyanka Kanake contributed to writing the manuscript and assisted in data collection. Padmaja Gadiraju supervised and contributed to the study’s conceptualisation and edited the manuscript. Harish Chiluka assisted in data collection, proofreading, and ensuring the technical correctness of the manuscript.

Consent to Participate

Written informed consent was obtained from all participants prior to the administration of the survey.

Consent for Publication

Not applicable.

Data Availability

Data used in the study will be made available upon reasonable request.

Declaration of Conflicting Interests

The authors declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.

Funding

The authors received no financial support for the research, authorship and/or publication of this article.

Informed Consent

Written informed consent was obtained from all participants prior to the administration of the survey.

ICMJE Statement

The authors declared that the article has been prepared in accordance with the ICMJE guidelines.

Statement of Ethics

The study has been approved by the Institutional Ethics Committee of the University of Hyderabad (UH/IEC/2022/339).

ORCID iD

Anandapadmanabhan G.

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Work Ability and Psychological Well-being of Indian Information Technology Employees: Mediating Role of Job Satisfaction and Moderating Role of Perceived Stress

Abstract

Background

Purpose

Methods

Results

Conclusion

Keywords

Introduction

Conceptual Model of the Study.

Methods

Data Collection

Demographic Information of the Employees.

Measures

Semi-structured Questionnaire for Demographic Details

The Work Ability Index (WAI) 42

Job Satisfaction Survey (JSS) 43

Ryff’s Psychological Well-being Scale 44

Perceived Stress Scale (PSS-10) 45

Data Analysis

Common-method Bias Testing

Results

Correlation Analysis

Mean, Standard Deviation and Intercorrelations Between all the Variables of the Study.

Mediation Analysis

Results of the Mediation Analysis with Work Ability as the Predictor, Psychological Well-being as the Outcome and Job Satisfaction as the Mediator.

Moderated Mediation Analysis

Results of the Moderated Mediation Analysis.

Simple Slope Demonstrating the Effect of Job Satisfaction on Psychological Well-being at Three Levels (Low, Medium and High) of Perceived Stress.

Discussion

Conclusion

Footnotes

Acknowledgement

Authors’ Contribution

Consent to Participate

Consent for Publication

Data Availability

Declaration of Conflicting Interests

Funding

Informed Consent

ICMJE Statement

Statement of Ethics

ORCID iD

References

The Work Ability Index (WAI)⁴²

Job Satisfaction Survey (JSS)⁴³

Ryff’s Psychological Well-being Scale⁴⁴

Perceived Stress Scale (PSS-10)⁴⁵