The continuous shift to remote and hybrid work in various service sectors has changed how work is organised and experienced in the post-pandemic Information Technology (IT) sector. What started as a response to the COVID-19 pandemic has now become a new normal in the tech industry. Many Indian IT companies have adopted work-from-home and hybrid models to cut costs, widen access to talent and ensure operational efficiency. Technology-driven work arrangements now dominate software development, IT services and many more. This makes Indian IT employees among the most affected workers experiencing prolonged remote work conditions [1-3]. Studies show that information and communication technologies influence job stress and employee well-being [4-6]. ICT increases access, intensifies monitoring, speeds up work and also minimizes the gap between work and personal life [4,7]. For IT employees, whose tasks rely entirely on digital systems, these pressures are increased due to prolonged working from home. Studies also reported a higher level of workload, role ambiguity, work–family conflict, burnout, emotional exhaustion and technostress among IT professionals working from home [8-10]. Recent observations from India confirm that remote IT employees experience mental health issues due to increased workload, unclear job expectations, poor time management, lack of organizational support and social isolation [1-3]. Similarly, some studies also highlighted stressors in remote work, such as unstable internet connectivity, too many online meetings, digital monitoring using artificial intelligence and technology invading personal life [7,8]. Despite this, most studies either examine technology broadly, focus on organizational support, or use general occupational samples. This offers limited insight into how specific work design stressors interact to affect well-being in the present IT work environments.

Although employee well-being has become an important issue for organisations with long-term remote and hybrid work arrangements, there is limited evidence on which job-related factors/stressors in remote work arrangements most harm employee well-being in the Indian IT sector. Prior studies mainly focused on workload, Job autonomy, social support, work–home conflict, organisational climate, or technology use as broad categories [2-4]. However, some studies focused on specific stressors, such as increased workload, permeability of work boundaries, meeting demands, role confusion and job insecurity, but provided limited

evidence for classifying these stressors as job- or personal-life-related and affecting well-being. Additionally, the combined effects of these job factors, along with personal traits such as workaholism and coping styles, have not been systematically examined using hierarchical regression analysis. Thus, the present study aims to fill this research gap by understanding how some employees cope well with such stress and maintain well-being.

Objectives of the Study

The main objective of this study is to examine the extent to which selected job-related stressors, which include Workload Escalation (WE), Digital Monitoring Intensity (DMI), Boundary Permeability (BP), Meeting Load (ML), Role Ambiguity (RA) and Career Insecurity (CI) predict employee well-being working in remote and hybrid mode. Secondly, it aims to assess the role of personal characteristics of the employees, specifically workaholism and coping style, after accounting for demographic factors using hierarchical multiple regression analysis. Moreover, the study aims to quantify the relative explanatory power of job design factors versus personal attributes in determining employee well-being. By isolating these effects using a hierarchical regression model, the study seeks to provide quantitative evidence for revising organisational policies and work design strategies to reduce psychological strain in remote work settings.

Literature Review

The rapid growth of remote and hybrid work cultures in corporations during and after the COVID-19 pandemic has increased research interest in employee well-being. Early research by Gajendran & Harrison [11], on remote work suggested that such arrangements were primarily beneficial, associated with greater autonomy and less work–family conflict. Their meta-analysis of 46 studies showed lower role stress and better job satisfaction among teleworkers. This implies that being away from the workplace can lessen tension and confusion between roles. However, this positive perspective emerged in a pre-pandemic environment where telework was voluntary, digital monitoring was largely absent and collaboration technologies were used sparingly. Today’s remote work is structurally and psychologically different, raising concerns about the validity of earlier conclusions in the present work environment and in organisations with high digital intensity and job uncertainty. Recent evidence from the pandemic challenges the belief that remote work automatically favours employee well-being. A study by Juchnowicz and Kinowska [12], found that working exclusively from home during COVID-19 affected employee well-being by weakening workplace relationships and disrupting work–life balance, even when their self-reported health remained stable. This indicates that the psychological toll of isolation and unclear boundaries between work and family at home might outweigh the previously cited benefits related to greater flexibility in working from home. Similarly, Bilotta et al. [13], using the Job Demands–Resources framework, argued that remote work increases an employee’s time pressure, mental strain and role confusion, which can lead to mental health issues and affect work performance. Moreover, job insecurity and uncertainty about workplace expectations may increase, suggesting that modern remote work environments may create more stress than they relieve.

Work design-related elements have become key factors in the decline in well-being in remote work environments. Vaidya and Bedi [14], observed a significant work overload among remote workers, reporting long work hours, heavy use of various digital tools and frequent work after hours. They further observed that poor boundary control is one of the strongest predictors of work stress among employees, supporting the idea that the blending of work and home life poses a structural risk for coping with the situation. This observation was further confirmed by Kamboj [15], who showed that unclear work–life boundaries significantly lower well-being by disrupting the balance between work and home life. Although that study included mediation and moderation factors, its results still point to workload and boundary erosion as the main reason of stress in remote work situations. The role of digital tools goes beyond just aiding communication; it also reshapes control and performance evaluation. Finstad et al. [16], demonstrated that job insecurity increases work–family conflict both directly and indirectly through technology overload and excessive work habits. Their study offers evidence that digital systems create stronger feelings of workload and encourage overworking as a job-preservation tactic. This finding contradicts earlier telecommuting research that viewed remote work as reducing role stress [11], suggesting that algorithmic monitoring and constant connectivity have cancelled out many of the early benefits of flexible work.

Role clarity is another debated topic in remote work studies. Mihalca et al. [17], found that work overload leads to emotional exhaustion, partly by reducing clarity around roles, increasing confusion about responsibilities and heightening performance standards. Their findings support Bilotta et al.’s [13], view that remote work environments create informational gaps and coordination challenges, thereby increasing role stress. This contradicts pre-pandemic beliefs that telecommuting automatically lessens ambiguity through focused task completion [11]. The inconsistency implies that role confusion depends on the context and is strongly influenced by technology, task interdependence and communication practices within organizations.

Career insecurity adds to psychological stress in remote settings. Finstad et al. [16], described insecurity as a trigger for excessive work effort, which in turn harms family life. Evidence from Wang et al. [18], showed that job security greatly lowers psychological distress and anxiety among workers. Their results suggest that having stable employment acts as a protective factor, regardless of job flexibility, challenging the narrative that remote work serves as an adequate replacement for long-term security. Together, these studies indicate that uncertainty about career advancement and visibility in digital environments undermines well-being, even when flexible work options are available.

Personal behaviour patterns interact with structural stressors to influence individual outcomes. Workaholism, defined as compulsive and excessive working tendencies, has been identified as a key behavioural response to insecurity and technological overload [16]. Employees might justify their overwork as necessary, yet evidence connects this behaviour to increased work–family conflict and emotional exhaustion. Coping strategies also influence outcomes among remote workers. Kamboj [15], showed that workload affects how personal preferences and adjustment strategies help maintain well-being, suggesting that coping tactics operate within the limits of job design. This supports the idea that while individual agency exists, it is constrained by structural demands present in digital work systems.

Evidence from technology-intensive jobs supports the idea that boundary erosion and coordination complexity are key stressors. Sarker et al. [19], studied software developers around the world and found ongoing work-life conflict caused by differences in time zones, task dependence and coordination across locations. They used Border Theory to show that IT work weakens the divide between professional and personal life. The shift to remote work during the pandemic widened these issues for many IT employees, worsening existing problems through constant connectivity and performance monitoring. Remote work has increased employees’ reliance on video communication, making virtual meetings a regular job demand rather than an occasional tool for coordination. Johnson and Mabry [20,21] found a direct link between the frequency and perceived burden of video meetings and emotional exhaustion. Employees reported feeling more strain when meetings disrupt focused work, clash with personal duties, or provide little value. Their research disputes the notion that video meetings are a neutral alternative to in-person interaction. Instead, they act as a psychological burden that depletes emotional resources, especially in fully remote settings.

Mental fatigue and burnout present another line of evidence. Urrejola-Contreras [22], found that work overload, high task dependence and unclear roles predict burnout among remote workers. The review also highlights a strong connection between heavy use of digital platforms and fatigue symptoms, showing that intense digital work is not harmless. The suggestion to improve work-life separation is not just a lifestyle tip; it is a structural essential. Remote work blurs time and space boundaries, which increases cognitive load and accelerates exhaustion when not managed well.

Digital monitoring adds extra pressure. Ball [23], noted the rapid rise in surveillance tools such as keystroke logging, webcam access and activity tracking during the pandemic. The evidence links heavy monitoring to increased stress, lower job satisfaction and decreased organizational commitment. Monitoring does not just measure productivity; it changes employee behaviour by creating constant pressure for self-regulation. This ongoing scrutiny acts as a chronic stressor, undermining psychological safety and trust—both vital for employee well-being. Prolonged screen exposure and digital overload further worsen these effects. De Lima Nardin et al. [24], found that extended screen time and long working hours strongly predict symptoms of digital burnout, including mental fatigue, sleep disruption and lower quality of life. Managing boundaries is the strongest predictor of fatigue and is a more significant factor than workload alone. Bondanini et al. [26], reached similar conclusions, linking lengthy virtual meetings and constant digital connectivity to cognitive overload, eye strain and increased stress. Their findings confirm that productivity boosts from connectivity come at a real psychological cost.

The notion of “Zoom fatigue” sort of formalises these experiences. Fauville et al. [27], created the Zoom Exhaustion and Fatigue (ZEF) scale, identifying five types: general, social, emotional, visual and motivational fatigue. More frequent meetings, longer meetings and packed schedules significantly increase fatigue. Nesher Shoshan and Wehrt [28], found that meeting size, manager presence and perceived interaction quality affect exhaustion levels. This shows that management decisions directly affect employee strain. Recent studies back these findings. Li and Yee [29], categorised videoconferencing fatigue triggers into psychological, social, technical, temporal and productivity-related areas, noting that constant webcam use and perceived surveillance are significant stressors. Beyea et al. [30], through a meta-analysis of 38 studies, confirmed that information overload and ongoing cognitive effort are the strongest predictors of videoconferencing fatigue, more significant than demographic or personality traits. This challenges simple narratives that emphasise individual resilience and shifts responsibility back to workplace design.

Research consistently demonstrates that Information and Communication Technologies (ICTs) are not neutral tools in remote work settings; they actively contribute to job stress and employee well-being. Day, Scott and Kelloway [4] argue, using the Job Demands–Resources framework, that ICT raises stress levels through constant availability, increased monitoring and work interference in personal life, despite potentially improving flexibility and task coordination. Nixon and Spector [7], support this by showing that technology affects job stress by influencing autonomy and control, either reducing or increasing strain based on its use. For IT employees who rely entirely on technology, ICT’s dual role creates a conflict: the systems that support remote work also raise cognitive load and blur the lines between work and personal life.

Evidence from India and similar IT-heavy environments shows that these pressures have tangible psychological effects. Phadnis and Sengupta [1], found that 67% of IT workers faced increased workload while working from home, along with feelings of loneliness, social disconnection and weak organizational support. Prasad, Mangipudi and others [2,3], identified that role confusion, workplace atmosphere, peer relationships and job satisfaction significantly affect the mental health of IT workers engaged in remote work. A related survey by Prasad et al. [2,3], showed that job stress coupled with remote working negatively impacts psychological well-being, even when employees recognise the benefits of flexibility. These findings challenge the idea that remote work automatically enhances the quality of life for tech professionals.

A recent study by Banerjee and Gupta [8], found that overload from technology and its invasion into personal life increase work-family conflict among remote and hybrid workers, especially in IT and ITeS roles. Costin, Roman and Balica [9], identified higher levels of burnout and emotional exhaustion among remote workers, attributed to greater job demands, long hours, insufficient organizational support and a lack of training in digital work systems. Earlier research by Weinert et al. [10], showed that work overload and work-home conflict are key predictors of burnout among teleworking IT professionals, influencing their intentions to leave telework. Together, these studies reach a clear conclusion: for IT employees, working from home consistently raises psychological strain when workloads, technological demands and organizational support are poorly managed. This makes employee well-being an issue of workplace design rather than personal resilience.

This study uses a quantitative, exploratory research design to examine how certain job-related factors and personal traits predict employee well-being among employees in the Indian information technology sector. The target population for the study includes full-time IT employees in India with recent experience working from home, either regularly or on a rotation/requirement basis. We have collected data from employees in various software companies in India. We randomly distributed survey invitations through professional networks, organisational contacts and online platforms. To maintain relevance, we screened respondents for inclusion in the survey based on criteria such as remote work experience for at least 3 months. We intentionally excluded responses from employees who have not had remote work exposure for the last 3 consecutive months. After removing incomplete and ineligible surveys, we had a final sample of 391 respondents.

Data was collected via a structured, self-administered questionnaire on Google Forms. The survey included sections on demographic details, job-related stressors, personal traits and employee well-being. Participation in the survey was voluntary and we informed respondents about the purpose of the study and the confidentiality of their responses. The study variables were measured using a standardised psychological well-being scale that included emotional functioning, positive feelings and life satisfaction [31]. Similarly, Job-related predictors included workload, digital monitoring, boundary permeability, meeting frequency, role confusion and career insecurity, which were measured using multiple statements rated on a 5-point Likert scale. Personal traits, including workaholism and coping style, have been measured through items that identify compulsive and excessive working behaviours, while coping style is measured using selected dimensions from the Brief COPE inventory [32,33]. We created two composite variables for problem-focused coping and avoidance-based coping. All multi-item constructs were evaluated using five-point Likert-type scales, ranging from strongly disagree to agree strongly.

We included age, gender, total work experience, marital status and the presence of dependent children or elderly members at home as control variables, since they are known to affect employee well-being in remote work scenarios. Before analysis, we checked the dataset for missing values, outliers and patterned responses. We removed cases with significant missing data and assessed the internal consistency of all multi-item scales using Cronbach’s alpha coefficients, with values above 0.70 considered acceptable [34].

We have used SPSS version 26 for the analysis and computed descriptive statistics and Pearson correlation coefficients to examine the distribution and relationships among variables. We assessed multicollinearity using variance inflation factor and tolerance statistics. Then, we conducted hierarchical multiple linear regression analysis in three stages. In the first stage, control variables are regressed on employee well-being, then job-related stressors and finally personal traits. Evaluated changes in explained variance using incremental R² values and used standardised regression coefficients to determine the strength of predictors. The model assumptions of normality, homoscedasticity and residual independence are also confirmed through diagnostic tests. The study followed ethical standards for social science research, participants gave informed consent before taking part and the primary data were stored securely and used only for academic purposes.

Analysis

The demographic profile presented in Table 1 displays that 64.45% of the IT employees were male and 35.55% were female. The sample comprises mid-career IT professionals deeply embedded in sustained remote and hybrid work arrangements. Most respondents fall in the 29–38 age group, with a smaller but substantial share in the 39–50 bracket, suggesting a workforce balancing career growth with family responsibilities. A clear majority are married and hold at least a bachelor’s or master’s degree, reflecting the typical educational and life-stage structure of India’s IT sector. Over half of the employees work entirely remotely and the rest operate in hybrid mode, confirming that WFH is no longer a temporary arrangement but a dominant work model. Family size is concentrated between four and six members, which implies significant household interaction and potential role interference during working hours. Weekly WFH hours cluster around full-time levels, indicating prolonged exposure to remote-work conditions rather than occasional telework. Additionally, the high proportion of employees lacking a dedicated home workspace and the noticeable variation in internet reliability highlight structural constraints specific to the Indian context and directly relevant to work efficiency and psychological well-being. Overall, the sample is well distributed across demographic categories to examine how sustained remote work interacts with job demands and home-environment conditions to influence employee well-being in the Indian IT sector.

Table 1: Demographic and Work-Context Profile of Respondents (N = 391)

The descriptive statistics presented in Table 2 show that respondents report moderately high levels of remote-work demands and strain-related conditions, with mean values above the scale midpoint for workload escalation (M = 3.612), meeting load (M = 3.534), boundary permeability (M = 3.794), workaholism (M = 3.626) and digital monitoring intensity (M = 3.369), indicating that intensified work pace, frequent virtual meetings, blurred work–home boundaries and continuous electronic oversight are common experiences among IT employees. Role ambiguity (M = 3.319) and career insecurity (M = 3.219) are also non-trivial, suggesting persistent uncertainty about job expectations and future stability under work-from-home arrangements. Coping style records a moderate average level (M = 3.361), implying that employees possess only limited psychological resources to counterbalance these pressures.

Table 2: Descriptive Statistics and reliability test of Study Variables (N = 391)

Employee well-being exhibits a substantially negative mean score (M = −6.243), confirming that the cumulative effect of these conditions is a low level of perceived well-being rather than a neutral or positive state. The standard deviations indicate reasonable variability across respondents, supporting the presence of meaningful individual differences. Importantly, all constructs demonstrate strong internal consistency, with Cronbach’s alpha values ranging from 0.810 to 0.915, which confirms that the measurement scales are statistically reliable and suitable for subsequent correlation and regression analyses.

The correlation results presented in Table 3 show that employee well-being among IT professionals is strongly and consistently associated with both job-related stressors and personal factors in the work-from-home context. All job stress variables—workload escalation (r = −0.412), digital monitoring intensity (r = −0.353), boundary permeability (r = −0.367), meeting load (r = −0.301), role ambiguity (r = −0.342), career insecurity (r = −0.298) and workaholism (r = −0.315) exhibit moderate negative correlations with well-being, indicating that higher remote-work demands and blurred work structures are linked with poorer psychological outcomes. In contrast, coping style shows a substantial positive relationship with well-being (r = 0.405), suggesting that employees who use adaptive coping strategies are significantly better protected against remote-work strain. Among the control variables, family size (r = −0.149) and the presence of dependents (r = −0.197) display weak but meaningful negative associations with well-being, reflecting added domestic pressure in Indian households, whereas having a dedicated workspace (r = 0.211) and reliable internet connectivity (r = 0.173) are positively related to well-being, emphasizing the importance of home infrastructure for effective remote work. The pattern of correlations is theoretically coherent and supports the regression findings, confirming that remote-work job demands erode well-being, whereas personal coping capacity and supportive home conditions act as protective factors.

Table 3: Correlation Matrix

Table 4 shows that Model 1, which includes basic demographics and work-home context, already explains a substantial amount of variation in employee well-being (R² = 0.226). it can be noted that gender, age and work experience are statistically irrelevant, so any claim that older, younger, male, or more experienced IT employees systematically cope better with WFH is not supported by the data. In contrast, family structure and home conditions matter. Being married has a small but negative effect on well-being (β = −0.051, p<0.05) and the effect becomes stronger for household size (β = −0.101, p<0.01) and the presence of dependents (β = −0.146, p<0.01), indicating that domestic responsibilities materially erode well-being in remote work settings. On the positive side, having a dedicated workspace (β = 0.173, p<0.001) and reliable internet access (β = 0.118, p<0.001) significantly improve well-being, confirming that WFH efficiency in the Indian IT context is constrained by physical and digital infrastructure rather than by personal demographics. The strong overall model fit (F = 14.02, p<0.001) makes it clear that ignoring these “non-work” conditions would be methodologically sloppy, because they form a substantial baseline against which the effects of workload, monitoring, meetings and role stress must be judged.

Table 4: Hierarchical Regression – Model 1 (Control Variables)

Model Fit: R ² = 0.226, Adjusted R² = 0.212, F (8,382) = 14.02, p<0.001, Note: * p<0.01, **p<0.05

Model 2 presented in Table 5 shows a good improvement in explanatory power when the job-related stressors are added. The value of R² rises from 0.226 to 0.538, indicating that these variables alone account for an additional 31% of the variance in well-being. That is a massive effect in behavioral research and it demolishes any argument that WFH well-being is mainly a demographic or home-setup issue. Every job-related variable is negative and highly significant (p<0.01), which means the pattern is not random noise but structurally consistent. Workload escalation is the strongest predictor (β = −0.398), clearly the main driver of declining well-being, followed by boundary permeability (β = −0.297) and digital monitoring intensity (β = −0.282), showing that constant availability expectations and surveillance culture are almost as damaging as sheer workload. Role ambiguity (β = −0.259) and career insecurity (β = −0.243) further erode well-being by injecting uncertainty into daily work and future prospects, while meeting load (β = −0.201) confirms that excessive virtual meetings are not just annoying but psychologically costly. Even after controlling for family structure, workspace and internet quality, these effects remain strong, indicating they are not artefacts of poor home conditions. The overall model fit is very strong (F = 31.11, p<0.001), making it clear that if IT firms want sustainable WFH productivity, they must redesign workload allocation, monitoring practices, role clarity and meeting culture tweaking demographic composition or offering token wellness programs will not fix the core problem.

Table 5: Hierarchical Regression–Model 2 (Job-Related Variables Added)

Model Fit: R² = 0.538, Adjusted R² = 0.524, F (14,376) = 31.11, p<0.001, Note: * p<0.01

The final model presented in Table 6 provides the full picture, including personal variables. The explained variance in well-being climbs to 64.7%, which is exceptionally high and leaves little doubt about what actually drives WFH outcomes in the Indian IT sector. Job stressors remain strongly negative and significant even after accounting for individual differences, confirming they are structurally harmful rather than psychologically “imagined.” Workload escalation still dominates (β = −0.341), followed closely by workaholism (β = −0.297), showing that both externally imposed pressure and self-driven overwork independently damage well-being. Digital monitoring, boundary permeability, role ambiguity, meeting load and career insecurity all retain sizable negative effects, proving that surveillance, constant availability, unclear roles, meeting saturation and job uncertainty systematically erode mental health. The standout positive factor is coping style (β = 0.401), the strongest coefficient in the model, indicating that employees with effective coping strategies can partially buffer but not neutralize the damage caused by poor job design. A very strong model fit (F = 42.08, p<0.001) indicates that WFH well-being is not a vague or unpredictable phenomenon: it is largely the result of measurable organizational practices and how employees manage stress. The practical implication is blunt—companies cannot rely on “resilient personalities” to compensate for dysfunctional remote-work systems; reducing workload escalation, monitoring intensity and boundary violations is non-negotiable if they want sustainable productivity and retention.

Table 6: Hierarchical Regression – Model 3 (Personal Variables Added)

Model Fit: R² = 0.647, Adjusted R² = 0.632, F (17,373) = 42.08, p<0.001 Note: * p<0.01

As shown in Table 7, the hierarchical models exhibit a clear and substantial improvement in explanatory power at each step. Demographic and home-context variables alone account for 22.6% of the variance in well-being (Model 1), but the inclusion of job-related factors more than doubles this to 53.8% (Model 2), indicating that work design under WFH is the dominant driver of employee well-being. Adding personal factors further raises the explained variance to 64.7% (Model 3), confirming that individual coping and work orientation provide additional but secondary predictive value. The steadily increasing F-statistics (all p<0.01) confirm that each model significantly improves upon the previous one, validating the hierarchical structure of the analysis.

Table 7: Model Summary and Explained Variance

* p<0.01

This study provides strong evidence that employee well-being in Work-From-Home (WFH) settings in the Indian IT sector is primarily driven by job design rather than demographic factors. The significant increase in explained variance from Model 1 to Model 2 shows that rising workloads, digital monitoring, blurred boundaries, heavy meeting loads, unclear roles and career insecurity are harmful conditions. This aligns with previous research on technostress and telework stressors. It identifies work overload, role ambiguity, work-home conflict and constant connectivity as key predictors of psychological strain and exhaustion among IT professionals [3,8,10]. The final model highlights the impact of workload escalation, supporting findings from Phadnis and Sengupta [1] and Costin et al. [9]. They noted that longer working hours and increased job demands under WFH arrangements significantly harm mental health and work sustainability.

The intensity of digital monitoring and the burden of meetings also appear to be strong negative predictors of well-being. This reinforces recent findings on “Zoom fatigue,” emotional exhaustion and stress from surveillance in remote work settings [20,21,28,30]. These results build on Ball’s [23], argument that digital surveillance causes psychosocial harm. This study shows its effect even after considering family structure and home environment. Likewise, the ongoing negative effect of boundary permeability supports research indicating that unclear time and space boundaries lead to fatigue, burnout and mental overload [24-26]. Together, these findings challenge the common view that technology-based monitoring and frequent meetings are neutral productivity tools. In reality, they act as ongoing stressors that take a toll on employee well-being.

Adding personal variables in Model 3 clarifies that individual traits are important, but they do not replace structural factors. Workaholism significantly reduces well-being, consistent with earlier studies showing that excessive work increases burnout and emotional exhaustion in remote workers [9,35]. On the other hand, coping style shows the most positive effect in the model. This supports psychological research indicating that effective coping strategies can reduce stress when job demands are high [36]. However, the continued existence of large negative coefficients for workload, monitoring and boundary permeability shows a crucial boundary condition. Coping skills may lessen harm, but they cannot fix broken work systems. This aligns with the Job Demands-Resources framework presented by Day et al. [4] and Nixon and Spector [7]. They argue that too many demands can overwhelm personal resources, even among resilient employees.

Lastly, the control model indicates that family responsibilities, dependents and poor home conditions significantly lower well-being. This confirms that WFH experiences in India are influenced by social and material realities, not just individual preferences. It supports earlier Indian findings on household interference, isolation and insufficient organizational support during remote work [1-3]. Overall, these results suggest that employee well-being in WFH contexts is more of an organizational design issue, not just a personal adjustment challenge. Companies that try to improve declining well-being through webinars or resilience training without tackling workload increases, monitoring intensity, role clarity and meeting overload are only addressing symptoms, not the underlying causes. For sustainable remote and hybrid work in the IT sector, firms must reform performance management, communication practices and digital governance practices rather than implement superficial well-being initiatives.

Implications

This study highlights important and uncomfortable issues for IT firms considering long-term remote or hybrid work models. The findings indicate that employee well-being is influenced much more by work system design than by demographics or personal strength. This means that organizations cannot simply rely on “coping ability” or resilience training to address mental health. Managers need to view issues like increasing workloads, constant digital monitoring, too many meetings, unclear roles and boundary violations as operational risks rather than minor HR concerns. Specifically, firms should limit working hours, change performance metrics to avoid rewarding constant availability, restrict the use of surveillance tools to essential tasks, enforce strict meeting guidelines and clearly define role expectations in remote teams. Additionally, providing reliable internet and promoting designated home workspaces will lead to noticeable improvements in well-being and productivity. If these aspects are overlooked, firms can expect burnout, disengagement and turnover. These outcomes will directly hinder the productivity improvements that organizations believe working from home can achieve.

This study explains why some IT employees in India handle working from home arrangements better than others. Employees cope better mainly when their jobs are designed to prevent excessive workloads, intrusive digital monitoring, constant boundary management, meeting overload, role confusion and career uncertainty. These job conditions account for most of the differences in well-being, overshadowing the impact of demographic factors such as age, gender and experience. Moreover, employees with effective coping abilities tend to experience significantly higher well-being. On the other hand, workaholic habits typically lead to worse results, even with the same job conditions prevail. In conclusion, it can be said that better coping under work-from-home arrangements results from the combination of manageable job demands and effective psychological responses, which is not possible from remote work-related factors itself. Long-term success of remote and hybrid models therefore depends less on expanding flexibility and more on controlling the structural pressures embedded in digitally mediated work itself.

Limitations and Scope

This study has certain limitations; first, the findings reflect psychological exposure to control and overload, not the technical intensity of these practices, which may vary significantly between companies based on their specific environments. Second, the model treats hybrid and fully remote employees as a single group, even though their exposure to meetings, monitoring and boundary violations is often different, given their work options. Future research can examine these subgroups better to understand remote work-related stressors and their impact on well-being. Third, the study defines coping style broadly but does not differentiate among problem-focused, emotion-focused, avoidance, or technology-specific coping behaviours. So, we believe these elements likely operate differently in a remote work environment. Fourth, the strong negative impact of workaholism may depend on cultural factors in India’s IT sector, where overwork is often valued and accepted. The study cannot determine whether workaholism is a personal trait or a learned survival strategy within organizations. Finally, the model explains well-being at the individual level but overlooks team-level design, such as scrum practices, leadership influence, client time zones, peer pressures, etc. These factors may drive overload and monitoring intensity more than company policies alone. These limitations highlight the possible scope for future research.

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