Hanen Louati
Khadija Saidi- College of Administrative and Financial Sciences, Saudi Electronic University, Saudi Arabia
In digitally transforming and increasingly complex organizational environments, employee wellbeing has emerged as a strategic imperative for sustainable performance. This study draws on the Job Demands–Resources (JD-R) model to examine how sustainable Human Resource Management (HRM) practices can mitigate workplace stress and promote psychological wellbeing in the context of rapid digitalization. Specifically, it investigates the role of perceived organizational support and transparent advancement pathways as key resources that help employees manage elevated job demands. Through a quantitative study involving 206 employees across government, semi-government, and private organizations in Saudi Arabia—a national context undergoing significant digital reform—the proposed model is empirically tested. Findings reveal that excessive workload and work–family conflict are strongly associated with increased stress, which in turn negatively affects mental health and overall wellbeing. Conversely, supportive organizational climates and clear career development structures serve as protective factors. These results highlight the importance of human-centric HR strategies in digitally evolving workplaces, offering both theoretical insights into the JD-R framework and practical guidance for designing inclusive, wellbeing-oriented systems as a foundation for innovation and long-term success.
1 Introduction
In today's digitally transformed and increasingly complex organizational landscape, human resources are no longer viewed solely as administrative functions but as strategic levers for innovation, resilience, and sustainable growth (Lou et al., 2024). As organizations navigate rapid technological change, shifting work structures, and post-pandemic recovery, employee wellbeing has emerged as a central concern—impacting not only individual performance but also organizational adaptability and long-term viability. This shift reflects broader developments in contemporary organizational theory, which increasingly emphasize human-centricity, psychological safety, and inclusive design as foundational elements of effective management systems (Kurjenniemi and Ryti, 2020; Rishi et al., 2021).
The acceleration of digitalization has redefined how organizations conceptualize their workforce. Traditional performance-driven HR strategies—characterized by extended work hours, limited autonomy, and insufficient attention to work–life balance—have been shown to exacerbate stress, burnout, and disengagement (Herachwati et al., 2024; Maslach and Leiter, 2016; Cooper et al., 2009). These outcomes compromise both employee wellbeing and organizational effectiveness. In contrast, sustainable HR practices that promote supportive environments, transparent career pathways, and opportunities for advancement and growth are increasingly viewed as essential for attracting talent, enhancing engagement, and building resilient workforces (Lou et al., 2024).
As workplaces grow more complex due to globalization, economic instability, and digital disruption, stress levels among employees continue to rise (Rafi and Rafi, 2022). Organizations are responding by adopting HR strategies that view wellbeing not only as an ethical imperative but also as a strategic priority (Prasad et al., 2025). This growing awareness has led to a shift toward holistic HRM approaches that integrate wellbeing into the core of organizational success (Sharma, 2024; Maluegha et al., 2024).
In this context, HR professionals play a pivotal role in identifying workplace stressors and implementing interventions that foster a healthy, inclusive, and productive culture. Despite increased attention to the consequences of poor wellbeing—including low morale, absenteeism, and diminished performance (Amadi, 2024)—practical strategies to support employee wellness, particularly in fast-evolving environments like Saudi Arabia, remain underexplored. In digitally transforming workplaces, stressors and supports are increasingly shaped by technology-mediated work arrangements and constant connectivity (Mazmanian et al., 2013; Rafi and Rafi, 2022). Digitalization intensifies traditional job demands such as workload and work–family conflict, while simultaneously reconfiguring job resources through digitally enabled autonomy, organizational support, and advancement opportunities (Ingusci et al., 2021; Yen, 2024; Bakker et al., 2023). Understanding how these digitally shaped demands and resources interact is therefore critical for explaining employee stress and wellbeing in contemporary organizations.
This study aims to address that gap by examining how HR practices can effectively mitigate stress and improve employee wellbeing. Accordingly, the central research question is: How can Human Resource (HR) practices effectively support employee wellbeing amidst increasing workplace stressors? This question is examined from the perspective of employees.
To investigate this question, the study employs the Job Demands–Resources (JD-R) model as its guiding theoretical framework. Closely aligned with contemporary organizational theory, the JD-R model offers a dynamic lens for understanding how workplace conditions shape employee outcomes. It integrates concepts from job design and job stress literature by distinguishing between job demands—such as workload and work–family conflict—and job resources—such as perceived organizational support and transparent advancement pathways (Bakker and Demerouti, 2007; Demerouti et al., 2001). Maintaining a balance between demands and resources is essential for sustaining employee wellbeing and organizational performance, especially in environments shaped by digital disruption and evolving expectations (Ko, 2022).
This paper is structured as follows: the next section reviews the literature on the Job Demands–Resources (JD-R) model, focusing on how job demands and job resources influence employee stress levels and wellbeing. The subsequent sections outline the methodological approach, including the sampling strategy, survey design, and data analysis procedures. The findings section then presents insights into the relationships between workplace stress, employee wellbeing, and HR practices. Finally, the discussion interprets these results in relation to existing theoretical frameworks and concludes with practical recommendations and directions for future research.
2 Literature review
2.1 Job Demands–Resources (JD-R) model
The Job Demands–Resources (JD-R) model offers a robust framework for understanding how different aspects of the work environment influence employee wellbeing and organizational performance. Originally developed by Demerouti et al. (2001) and later refined by Bakker and Demerouti (2007), the model distinguishes between two core components: job demands and job resources. Job demands refer to the “physical, psychological, social, or organizational elements of a job that require sustained effort and are associated with physiological or psychological costs” (Schaufeli and Bakker, 2004, p. 296). These include excessive workload, time pressure, emotional strain, role ambiguity, and job insecurity. When demands are high and not offset by adequate support, they contribute significantly to stress, exhaustion, and ultimately burnout (Bakker et al., 2023). For example, the findings of Oh et al. (2025) indicate that job demands associated with online teaching exert a significant effect on faculty's emotional exhaustion, underscoring the relevance of the Job Demands–Resources model in digitally mediated academic environments.
In contrast, job resources are the physical, social, and organizational factors that enable employees to meet demands and achieve work goals. These include supportive supervision, autonomy, feedback, learning opportunities, and positive social interactions. Beyond facilitating task completion, resources act as protective factors—buffering the negative effects of demands and promoting engagement, motivation, and resilience (Bakker and Demerouti, 2007; Timms et al., 2020). Blanco-Donoso et al. (2021) emphasize that strong organizational support systems are vital for sustaining wellbeing in demanding contexts. Moreover, job resources encompass a broad range of attributes that not only reduce the impact of stressors but also foster personal growth, learning, and development (Van Ruysseveldt et al., 2011).
The JD-R model outlines two psychological mechanisms: an energetic process, where excessive demands lead to strain and health impairment, and a motivational process, where resources enhance engagement and performance (Schaufeli and Taris, 2014). Balancing these dimensions is especially critical in high-demand settings, where sufficient resources can mitigate adverse outcomes. A longitudinal study by Hu et al. (2017) showed that employees with strong perceptions of organizational support experienced lower burnout, even under heavy workloads.
Increasingly, research highlights that digitalization alters the conditions under which the JD-R model operates. Rather than introducing entirely new categories of demands and resources, digital technologies tend to reconfigure their intensity, form, and mode of delivery (Bakker et al., 2023; Demerouti and Bakker, 2023; Schaufeli and Taris, 2021). For instance, digital platforms accelerate work cycles, intensify information flow, and increase constant connectivity, thereby amplifying traditional demands such as workload, role ambiguity, and work–family interference (Mazmanian et al., 2013; Tams et al., 2020; Ingusci et al., 2021; Yen, 2024). At the same time, digital systems can expand access to job resources by enabling flexible scheduling, remote collaboration, and continuous learning opportunities (Timms et al., 2020; Ko, 2022). These developments suggest that the JD-R framework remains highly applicable in digital contexts but requires explicit attention to how technology mediates both the health-impairment process and the motivational process (Bakker et al., 2023). Situating digitalization within the JD-R lens therefore provides a more nuanced understanding of employee stress and wellbeing in contemporary organizations (Rafi and Rafi, 2022).
Beyond digital contexts, a growing body of research continues to validate and expand the JD-R framework across diverse sectors and conditions. For instance, Ho (2025) extended the model by integrating organizational justice dimensions—procedural, distributive, and informational—showing that fair treatment significantly boosts engagement under high-demand conditions. Similarly, Chua et al. (2024) applied the JD-R model to junior doctors in Singapore, revealing how resource constraints and emotional demands affect performance and satisfaction in healthcare settings. In the hospitality sector, Cao et al. (2023) demonstrated that job insecurity mediates the relationship between demands and burnout, with perceived susceptibility to COVID-19 moderating these effects.
The relevance of the JD-R model has become even more pronounced during global crises such as the COVID-19 pandemic, which intensified job demands through remote work, digital overload, and blurred work–life boundaries (Herachwati et al., 2024). Simultaneously, it highlighted the importance of organizational flexibility and mental health support. Giorgi et al. (2020) found that companies adopting empathetic HR measures during the pandemic achieved stronger morale and psychological resilience.
In conclusion, the JD-R model serves as a foundational framework for organizations aiming to enhance employee wellbeing by balancing demands with adequate resources (Ko, 2022; Koroglu and Ozmen, 2021; Bakker and Demerouti, 2018). Its relevance across cultures and industries is supported by extensive research (Brough et al., 2013; Van Den Broeck et al., 2013; Bakker and Demerouti, 2018; Chua et al., 2024). By identifying and addressing both stressors and supports, HR professionals can foster healthier, more adaptive workplaces that promote long-term resilience and success.
2.2 Conceptual model and research hypothesis
2.2.1 Job demands
Job demands encompass a range of physical, emotional, and psychological pressures that employees face within their work environments. They are increasingly recognized as significant stressors that undermine wellbeing, productivity, and overall job satisfaction (Demerouti et al., 2001). Understanding job demands is essential, particularly in contemporary workplaces marked by rapid change, digital acceleration, and heightened performance expectations.
Within the Job Demands–Resources (JD-R) framework, job demands are defined as aspects of work that require sustained physical or psychological effort and are typically associated with adverse outcomes such as stress, emotional exhaustion, and burnout (Bakker and Demerouti, 2007).
In digitally mediated contexts, these demands are increasingly intensified by ICT-related pressures, including constant connectivity, rapid task switching, frequent interruptions, and accelerated work pace (Mazmanian et al., 2013; Tams et al., 2020; Ingusci et al., 2021; Yen, 2024). Such dynamics amplify traditional stressors—workload and work–family conflict—by eroding boundaries between professional and personal life (Abdou et al., 2024). At the same time, ongoing technological change heightens perceptions of job insecurity, as employees confront automation, artificial intelligence, and the risk of skill obsolescence (Cao et al., 2023; Kim et al., 2024). Consistent with the JD-R health-impairment pathway, sustained exposure to these digitally intensified demands elevates cognitive load and emotional strain, thereby increasing stress levels (Demerouti et al., 2001; Bakker and Demerouti, 2007; Salanova et al., 2012). This digital context underscores the importance of examining specific demands that remain central to employee wellbeing.
Accordingly, this study focuses on three key dimensions—job insecurity, work–family conflict, and work overload—given their significant influence on employees' stress responses and workplace experiences (Demerouti and Bakker, 2023). These factors have been consistently identified as critical contributors to diminished mental health and reduced job satisfaction (Galanakis and Tsitouri, 2022).
Job insecurity is a prominent factor contributing to elevated stress levels among employees. It refers to the fear of losing one's job or uncertainty regarding future employment status, which has been consistently linked to increased anxiety, depression, and psychological distress (Sultana et al., 2022; Kim et al., 2024). In the digital era, insecurity is heightened by automation, artificial intelligence, and rapid technological change, which amplify concerns about skill obsolescence, employability and continued employment (Cao et al., 2023; Kim et al., 2024; Kožo et al., 2022). When employees perceive threats to the continuity of their roles, their engagement and productivity decline, and their mental health deteriorates (Bakker et al., 2023). The anxiety stemming from job insecurity fosters a lack of psychological safety, which can hinder employees' willingness to communicate openly, take initiative, or collaborate effectively—further intensifying stress and feelings of vulnerability (Kožo et al., 2022). The pervasive fear of unemployment creates an environment where employees may feel trapped, leading to chronic stress that can spill over into their personal lives and undermine overall wellbeing (Alghamdi and Bahari, 2025).
Work–family conflict is another critical stressor within the JD-R model. It arises when work demands interfere with family responsibilities, leading to emotional exhaustion and reduced life satisfaction (Netemeyer et al., 1996). In digitally intensive workplaces, constant connectivity, remote work, and blurred boundaries between professional and personal domains exacerbate this conflict (Mazmanian et al., 2013; Timms et al., 2020). Employees often struggle to disengage from digital communication, which extends work into family time and heightens role strain (Tams et al., 2020; Abdou et al., 2024). This inter-role conflict contributes to burnout and disengagement by depleting employees' emotional and cognitive resources (Bakker and Demerouti, 2007). Song et al. (2025) found that unresolved work–family conflict significantly predicts absenteeism and psychological distress in healthcare professionals. Similarly, Su and Jiang (2023) demonstrated that educators experiencing high work–family conflict report lower job satisfaction and higher burnout. The ongoing struggle to balance professional obligations with personal life often fosters feelings of inadequacy and frustration, further intensifying stress and undermining wellbeing (Abdou et al., 2024).
Adding to this complexity, work overload represents another pivotal job demand, characterized by an excessive amount of work expected within limited timeframes. This condition leads to feelings of being overwhelmed and has a profound impact on mental health. Recent research shows that heavy workloads are directly correlated with increased stress levels, emotional exhaustion, and burnout symptoms (Rotenstein et al., 2023; Ingusci et al., 2021). When employees are tasked with managing multiple responsibilities simultaneously under tight deadlines, the resulting cognitive load becomes unsustainable. In digital contexts, work overload is magnified by technology-driven acceleration: employees face continuous streams of emails, instant messages, and virtual meetings, which demand rapid responses and multitasking (Mazmanian et al., 2013; Tams et al., 2020; Ingusci et al., 2021; Salanova et al., 2012; Yen, 2024). The rise of “technological overload” has further intensified these pressures, particularly in post-pandemic remote and hybrid work environments (Yen, 2024). Chronic overload not only contributes to physical and emotional strain but also undermines job satisfaction and long-term wellbeing (Wolfe, 2024).
Together, these job demands represent core stressors that challenge employee resilience and organizational sustainability. Based on the above discussion, the following hypotheses are formulated:
H1: Job demands have a significant positive effect on job stress.
H1a: Job insecurity has a significant positive effect on job stress.
H1b: Work–family conflict has a significant positive effect on job stress.
H1c: Work overload has a significant positive effect on job stress.
2.2.2 Job resources
According to the Job Demands–Resources (JD-R) model, job resources are essential for mitigating the adverse effects of job demands on employee stress and wellbeing (Demerouti et al., 2001). While excessive demands can deplete energy and lead to strain, the availability of adequate resources fosters motivation, engagement, and resilience, thereby counteracting these negative outcomes (Bakker and de Vries, 2021; Schaufeli and Taris, 2021). In contemporary workplaces, many job resources are increasingly shaped by digitalization, influencing how employees access support, career development, and autonomy (Bakker et al., 2023; Demerouti and Bakker, 2023; Lou et al., 2024). Building on this context, the present study examines how organizational support, advancement opportunities, and job autonomy function as core resources that promote employee wellbeing and help mitigate stress in high-demand, technology-intensive environments.
Organizational support refers to employees' perceptions that their organization values their contributions, provides guidance and feedback, ensures role clarity, and involves them in work-related decisions, thereby fostering psychological security and supporting their wellbeing (Eisenberger et al., 2002; Cohen and Wills, 1985). A supportive work environment can significantly reduce job stress by fostering a sense of belonging and psychological security among employees. Research has shown that perceived organizational support is associated with lower levels of stress, higher job satisfaction, and stronger commitment (Yu et al., 2021). Such support manifests through effective communication, access to stress-management resources, and a culture that promotes work-life balance. In digitally intensive workplaces, organizational support is frequently mediated through technology, such as online communication platforms, virtual feedback systems, and ICT-enabled supervision (Bentley et al., 2016). These digital channels can enhance perceptions of support by ensuring timely communication and accessible resources, thereby buffering the strain of constant connectivity and high demands (Xu and Yang, 2018; Bakker et al., 2023).
Recent studies further emphasize the importance of organizational support in enhancing employee wellbeing. For instance, a study by Brunetto et al. (2023) shows that a lack of support can intensify job insecurity and disengagement. More recently, Zeshan et al. (2024) demonstrate that HRM systems fostering psychological safety and self-regulation effectively mitigate stress under high job demands.
Advancement opportunities refer to employees' perceptions that their organization provides fair financial progression, adequate remuneration, and access to skill-development opportunities that support future career growth and security. Within the Job Demands–Resources (JD-R) framework, advancement constitutes a critical job resource because it enhances perceived control over future career outcomes and reduces uncertainty related to long-term employment and growth (Demerouti et al., 2001; Bakker and Demerouti, 2007). When employees perceive limited advancement opportunities, they are more likely to experience stagnation, frustration, and heightened job stress, particularly in demanding work environments (Shabbir et al., 2020).
Conversely, clear and transparent advancement pathways alleviate stress by signaling organizational investment in employees' futures and by reducing anxiety related to performance expectations and career uncertainty. Empirical evidence indicates that perceived career progression is associated with lower levels of job stress, emotional exhaustion, and turnover intentions, especially under conditions of high workload and role pressure (Dewi and Nurhayati, 2021; Samsudin et al., 2024).
In the context of digital transformation, advancement opportunities extend beyond financial progression to include learning, development, and enhanced employability, reflecting a shift in which career advancement increasingly integrates material rewards with continuous developmental pathways. In digitally evolving workplaces, such opportunities are shaped by access to digital training, e-learning platforms, and technology-enabled career development systems (Noe et al., 2014; Vrontis et al., 2021). These resources can help buffer employee stress by mitigating concerns related to skill obsolescence and career stagnation (Samsudin et al., 2024; Lou et al., 2024).
Job autonomy—defined as “the degree to which the job provides substantial freedom, independence, and discretion to the individual in scheduling the work and in determining the procedures used in carrying it out” (Hackman and Oldham, 1975, p. 162)—is another critical job resource that helps employees manage stress and digital demands. It buffers the impact of high job demands (Bakker et al., 2005) and is consistently linked to lower burnout and improved wellbeing (Tahar et al., 2022). In digitally intensive environments, autonomy enables employees to regulate their engagement with technology, thereby reducing technostrain and perceived overload (Kraan et al., 2014; Tams et al., 2020; Salanova et al., 2012). Notably, a meta-analysis of 55 studies by Karimikia and Singh (2019) found that autonomy significantly mitigates stress, strain, and exhaustion associated with workplace technology. As Lee and Jo (2023) emphasize, perceived autonomy and psychological wellbeing play a mediating role in the relationship between job resources and work outcomes, reinforcing autonomy's strategic relevance in digitally mediated work environments.
Taken together, job resources in digitally transforming workplaces are increasingly mediated through technology, reshaping how employees experience support, advancement, and autonomy (Bakker et al., 2023; Demerouti and Bakker, 2023; Gerten et al., 2019). Discretion over digital task execution, flexible scheduling, access to online feedback, and digital learning opportunities enhance employees' capacity to cope with intensified job demands (Timms et al., 2020; Bentley et al., 2016; Noe et al., 2014; Lou et al., 2024). In line with the JD-R motivational process, job resources sustain wellbeing and mitigate stress (Schaufeli and Bakker, 2004; Bakker and Demerouti, 2007), a mechanism that remains salient in technology-intensive work environments (Bakker et al., 2023; Demerouti and Bakker, 2023).
Accordingly, it is hypothesized that organizational support, advancement opportunities, and job autonomy are crucial job resources that significantly mitigate job stress in digital context. Therefore, the following hypotheses are considered:
H2: Job resources have a significant negative effect on job stress
H2a: Organizational support has a significant negative effect on job stress
H2b: Advancement opportunities have a significant negative effect on job stress
H2c: Job autonomy has a significant negative effect on job stress
2.2.3 Impact of job stress on wellbeing
In digitally transformed and increasingly complex workplaces, job stress has emerged as a critical factor undermining employee wellbeing and organizational sustainability. Defined as the adverse physical and emotional responses that arise when job demands exceed an individual's capacity or resources (Van Vegchel et al., 2005), job stress is no longer confined to isolated incidents—it is a systemic challenge exacerbated by technological acceleration, economic volatility, and evolving work structures. As organizations strive to remain agile and competitive, understanding the implications of job stress on employee wellbeing has become essential for strategic HRM.
The psychological consequences of job stress are particularly pronounced. Employees exposed to sustained stress often experience emotional exhaustion, reduced self-efficacy, and symptoms of burnout—manifesting in depersonalization and diminished personal accomplishment (Maslach and Leiter, 2016). Recent studies have linked high stress levels to increased prevalence of anxiety, depression, and cognitive impairment, which in turn compromise decision-making, concentration, and overall job performance (Chen et al., 2020; Sonnentag et al., 2023). These effects are magnified in high-demand environments where digital tools blur boundaries between work and personal life, contributing to mental fatigue and emotional disengagement (Rahmi et al., 2025).
These psychological disruptions have broader organizational implications. Unresolved stress- related wellbeing issues contribute to tangible declines in workplace functioning—evidenced by reduced productivity, elevated absenteeism, presenteeism, and deteriorating morale (Cooper et al., 2009). In digitally intensive sectors, the erosion of employee wellbeing can compromise adaptability, innovation, and long-term competitiveness (Parent-Lamarche and Marchand, 2022; De Neve et al., 2024).
Beyond psychological strain, job stress has significant physiological repercussions. Chronic exposure to stress is associated with elevated risks of cardiovascular disease, hypertension, metabolic disorders, and immune dysfunction (Kivimäki and Kawachi, 2015). Employees may adopt maladaptive coping behaviors—such as poor nutrition, physical inactivity, or substance use—that further deteriorate their health. A meta-analysis by Kivimäki et al. (2017) underscores the gravity of these outcomes, revealing a significant correlation between work-related stress and increased morbidity and mortality, particularly from heart-related conditions.
Social and relational dynamics are also disrupted by job stress. Heightened irritability, emotional withdrawal, and interpersonal conflict can strain relationships both within and beyond the workplace (Tong and Spitzmueller, 2024). In team-based and collaborative settings, such disruptions undermine cohesion, trust, and psychological safety—factors essential for innovation and resilience in knowledge-driven organizations (Montani et al., 2025).
The organizational implications of job stress are profound. According to the American Psychological Association (2020), stress-related absenteeism alone accounts for billions in lost revenue annually. Moreover, recent evidence suggests that workplace cultures built on trust, transparency, and psychological safety significantly buffer the effects of stress and enhance employee empowerment (Mental Health America, 2024).
Taken together, these findings underscore the significant influence of job stress on employee wellbeing and organizational performance. Addressing this challenge calls for a strategic reorientation of HR practices—toward human-centric, adaptive, and inclusive models that proactively mitigate stress and foster sustainable engagement.
Based on the above discussion, the following hypotheses are formulated:
H3: Work stress has a significant negative effect on employees' wellbeing.
Figure 1 presents the conceptual research model developed in this study. Grounded in the Job Demands–Resources (JD-R) framework, the model illustrates the hypothesized relationships among job demands, job resources, job stress, and employee wellbeing. Specifically, job demands—represented by work overload, work–family conflict, and job insecurity—are expected to increase job stress, while job resources—organizational support, advancement opportunities, and job autonomy—are proposed to mitigate stress. In turn, job stress is hypothesized to have a negative effect on employee wellbeing. The model provides an integrative overview of the theoretical relationships to be examined in the subsequent empirical analysis.
Figure 1. Conceptual research model.
3 Research methodology
To validate the model, a quantitative methodology is adopted. The questionnaires were distributed electronically, allowing for a wider reach and increased accessibility among participants. This approach not only facilitated confidentiality but also promoted honest responses, as participants could complete the survey at their convenience (Leon et al., 2021). The following paragraphs describe relevant details regarding data collection and hypothesis tests using the structural equation model.
3.1 Study sample and data collection
Following Sukamolson's (2007) guidelines for scientific sampling, a structured online questionnaire was designed and distributed to a target population of 250 employees working in digitally intensive industry domains within the Kingdom of Saudi Arabia (KSA), including finance, information technology, education, and healthcare. These domains represent key pillars of the national workforce undergoing rapid digital transformation, with increasing reliance on data-driven systems, virtual collaboration, and technology-mediated service delivery. The survey was administered electronically, ensuring anonymity and voluntary participation while aligning with the digital competencies of the target population. After excluding incomplete and invalid responses, a final sample of 206 valid responses was retained, yielding an effective response rate of 82.4%. Table 1 illustrates the respondents' demographic profiles. The sample consisted of 45.15% public-sector employees, 37.86% private-sector employees, and 16.99% semi-government employees. The gender and age distributions indicate a higher representation of females and younger individuals in the sample, while the information technology sector had the largest number of respondents—reflecting its central role in the digital landscape of contemporary organizations.
Table 1. Sample description.
3.2 Measures
The survey was designed to include questions relating to employee wellbeing, job stress, job demands and resources. It employed a mix of closed-ended questions, Likert scale items, and demographic queries to capture a comprehensive view of the respondents' experiences and perceptions. Such survey designs are prevalent in social science research due to their ability to yield large amounts of data quickly and systematically (Fife-Schaw, 2020).
All constructs were operationalized to reflect employees' perceptions within digitally mediated work contexts, including technology-enabled work demands and digitally delivered job resources.
Several key constructs in this study—job autonomy, advancement opportunities, and organizational support—were assessed using validated scales that treat these domains as unidimensional perceptions. While the items capture diverse facets (e.g., autonomy expressed through scheduling flexibility, methodological discretion, innovation-related initiative, and remote-work independence; advancement encompassing both financial comfort and career development opportunities; organizational support including supervisor guidance, communication clarity, role clarity, and participation in decision making), the original scale developers conceptualized each as a single global resource rather than distinct subdimensions. Consistent with Hackman and Oldham's (1975) definition, autonomy reflects an overall sense of discretion and freedom in one's work. Rothmann et al. (2006) validated advancement as a unified construct that integrates both material and developmental pathways, while Jackson and Rothmann (2005) operationalized organizational support as a holistic perception of clarity, guidance, and involvement. This operationalization aligns with the Job Demands–Resources framework (Bakker and Demerouti, 2007), which emphasizes the functional role of job resources in promoting wellbeing and reducing strain, even when expressed through multiple facets.
Consistent with these validated frameworks, the study employed existing items from prior research and utilized a 5-point Likert-type scale, ranging from “strongly disagree” to “strongly agree”. The questionnaire is detailed in Appendix A, which outlines all constructs, their corresponding sources, and the number of items associated with each construct. All survey items were translated into Arabic to ensure accessibility for respondents. The study employed a backward translation technique to maintain consistency between the original English version and the Arabic version, aligning with best practices in cross-linguistic research (Mullen, 1995; Singh, 1995). This approach not only enhances the reliability of the data but also ensures that the nuances of the constructs are preserved across languages, facilitating a more accurate analysis of employee experiences (Behr, 2016). Recent studies emphasize the importance of such methodologies in fostering inclusive research practices, particularly in diverse linguistic contexts (Ganassin and Georgiou, 2022).
3.3 Common method bias assessment
Because all variables were collected using a single self-report instrument, the potential influence of common method variance (CMV) was assessed. First, Harman's single-factor test was conducted using principal component analysis in SPSS (Harman, 1976; Podsakoff et al., 2003). The unrotated solution showed that the first factor accounted for 24.22% of the variance—well below the 50% threshold—indicating that CMV is not a major concern.
Second, a single-factor confirmatory factor analysis (CFA) model was estimated in AMOS with all items loading onto one latent factor. The model demonstrated poor fit (χ2/df = 5.286, RMSEA = 0.145, CFI = 0.311, GFI = 0.308, AGFI = 0.251), confirming that no single factor could explain the covariance among the indicators.
Taken together, these tests provide strong evidence that CMV is unlikely to have biased the study's findings. Full diagnostics and model comparisons are reported in Appendix D.
3.4 Data analysis and results
In this study, two software programs were utilized for data analysis: the Statistical Package for the Social Sciences (SPSS-26) and Analysis of Moment Structures (AMOS-24). The analysis employed a structural equation modeling (SEM) approach, which is recognized for its efficacy in exploring and delineating the relationships among various variables (Smith and Hoyle, 1996). This methodological framework allows for a comprehensive understanding of how different factors interact within the dataset, thereby facilitating deeper insights into the underlying patterns and connections.
3.4.1 Construct validity and scale reliability
The assessment of scale reliability and construct validity was based on three key criteria:
• Reliability Testing—Composite reliability (CR) and Cronbach's Alpha (α) must exceed 0.7.
• Convergent Validity—Communalities should be above 0.4, and the average variance extracted (AVE) must exceed 0.5.
• Discriminant Validity—The square root of AVE should be greater than the inter-construct correlations (Fornell and Larcker, 1981).
To ensure robust measurement, a two-stage validation procedure was employed, consisting of exploratory factor analysis (EFA) followed by confirmatory factor analysis (CFA), in line with Hair J. F. (2010). As an initial screening step, Principal Component Analysis (PCA) with Varimax rotation was conducted to examine item communalities and shared variance. Most items demonstrated satisfactory communalities above the recommended threshold, indicating adequate representation of their underlying constructs. However, three items—JI1 (Job Insecurity), A6 (Advancement opportunities), and JS9 (Job Stress)—exhibited comparatively weak communalities, with JS9 showing particularly low shared variance. Item-level communalities from this preliminary EFA are reported in Appendix B.
Reliability analysis was subsequently performed following Churchill (1979) and Parasuraman et al. (1988). Consistent with the EFA results, items JI1, A6, and JS9 displayed weak psychometric properties. Specifically, JS9 showed a corrected item-total correlation of 0.113 and increased Cronbach's alpha from 0.878 to 0.907 if deleted. Item JI1 exhibited a lower corrected item-total correlation (0.536) relative to the remaining Job Insecurity items (0.761–0.836), and its removal improved reliability from 0.874 to 0.909. Similarly, A6 showed the lowest corrected item-total correlation within the Advancement opportunities scale (0.530), and its removal increased Cronbach's alpha from 0.897 to 0.906. Detailed item-level reliability diagnostics for these analyses are reported in Appendix C.
Based on convergent evidence from communalities and reliability diagnostics, these items were removed prior to CFA to strengthen measurement quality. Because items JI1, A6, and JS9 failed preliminary reliability and validity thresholds, including them in CFA would have compromised model fit and interpretability. Consequently, CFA was conducted only on the retained items. All retained indicators demonstrated standardized CFA loadings above recommended thresholds, and the final measurement model achieved acceptable fit indices. The full wording of all retained and removed measurement items, along with their original sources, is provided in Appendix A.
Table 2 presents the reliability and validity results, showing that Cronbach's Alpha ranged from 0.906 to 0.934, while composite reliability (CR) values ranged between 0.834 and 0.950, confirming high internal consistency. Additionally, AVE values exceeded 0.5, validating strong convergent validity (Fornell and Larcker, 1981). Table 3 further confirms sufficient discriminant validity, as the square root of AVE for each construct (diagonal values) is consistently greater than the off-diagonal inter-construct correlations (Fornell and Larcker, 1981), meaning each construct is distinct from others and indicating a strong discriminant validity.
Table 2. Factor loadings and reliability statistics.
Table 3. Discriminant validity.
3.4.2 Structural model
This study employed structural equation modeling (SEM) using AMOS V.24 to conduct inferential analysis and test the proposed hypotheses. Following the recommendations of Byrne (2016) and Hair et al. (2016), the model fit was evaluated using a combination of absolute, incremental, and parsimony indices. The p-value criterion was set at a 95% confidence level (p ≤ 0.05) to determine statistical significance across the structural paths.
Although the sample size (N = 206) is modest for a structural equation model with multiple latent variables, it remains within acceptable ranges reported in prior SEM research. Methodological guidelines indicate that models of comparable complexity can be reliably estimated with samples slightly above 200 when factor loadings are strong and overall model fit is satisfactory (Hair et al., 2019).
As shown in Table 4, the model demonstrates an acceptable fit to the data. The Chi-square/df ratio (χ2/df = 1.411 < 5) indicates a good level of parsimony and suggests that the model is not overfitted.
Table 4. Model fit indices (AMOS v24).
The absolute fit indices reveal moderate results, with GFI = 0.840 and AGFI = 0.810, both exceeding the minimum acceptable threshold of 0.80 but falling short of the more stringent 0.90 benchmark.
Although the GFI and AGFI values fall below the conservative 0.90 threshold, values exceeding 0.80 are generally considered acceptable in complex SEM models that incorporate multiple latent constructs and indicators. Prior research highlights that these indices are highly sensitive to sample size and model complexity, often attenuating in large or multifactorial models due to the greater number of estimated parameters (Field, 2013; Byrne, 2016; Hair et al., 2019). In such cases, moderately lower GFI and AGFI values should not be interpreted as evidence of poor fit, particularly when other absolute and incremental indices demonstrate strong performance.
The RMSEA value of 0.045 falls within the excellent fit range (<0.05), indicating a strong approximation of the model to the population covariance matrix and minimal residual error.
Incremental fit indices such as NFI = 0.872, CFI = 0.958, and IFI = 0.959 demonstrate robust comparative fit, with CFI and IFI exceeding the recommended threshold of 0.90. Although NFI is slightly below the conventional cutoff, it remains within an acceptable range and is supported by the strength of the other indices. Collectively, these results suggest that the proposed model adequately fits the data and provides a reliable basis for hypothesis testing.
Additional SEM diagnostics were conducted to assess model specification and robustness. Modification indices were examined; although the largest value (MI = 18.35) exceeded the heuristic cutoff of 15, it suggested only cross-loadings or error covariances without theoretical justification, so no post-hoc modifications were introduced. Standardized residuals were inspected, with the largest observed value at 1.94, well below the 2.58 threshold, indicating no localized areas of misfit. Inter-construct correlations ranged from −0.125 to 0.553, comfortably below the 0.85 criterion, and all variance inflation factors (VIFs) were below 3.0, with the maximum VIF at 2.2, confirming construct distinctiveness and the absence of multicollinearity. Finally, model comparison tests supported the superiority of the multi-factor specification over a single-factor alternative, further validating the robustness of the structural model specification. A summary of these diagnostics is provided in Appendix E.
As shown in Table 5, both work–family conflict (β = 0.749, t = 9.278, p < 0.001) and work overload (β = 0.247, t = 2.491, p < 0.05) significantly increase job stress. These findings confirm that excessive demands are major contributors to psychological strain, with work–family conflict having the most pronounced impact. Conversely, advancement opportunities (β = −0.193, t = −3.591, p < 0.001) and organizational support (β = −0.140, t = −1.974, p < 0.05) were found to significantly reduce job stress, highlighting the protective role of job resources.
Table 5. Structural model results and hypothesis testing.
The model also reinforces the adverse impact of stress on employee wellbeing, as evidenced by the significant negative relationship between job stress and wellbeing (β = −0.192, t = −2.926, p < 0.01).
Two hypotheses were not supported: job insecurity (β = 0.097, p = 0.085) and job autonomy (β = −0.120, p = 0.093) were found to have no significant impact on job stress.
To further examine whether sectoral differences influenced the non-significant relationship between job insecurity and job stress, a supplementary regression analysis was conducted using SPSS, with sector (public, private, semi-government) entered as a control variable. The results indicated that job insecurity remained non-significant (β = 0.075, p = 0.236), while sector was also non-significant (β = −0.108, p = 0.232). The model explained a small proportion of variance in job stress (R2 = 0.014). These findings suggest that sectoral composition does not account for the non-significant effect of job insecurity observed in the SEM model. The full results of this supplementary regression analysis are reported in Appendix F.
Overall, the model validates five of the hypotheses and contributes to a deeper understanding of stress-buffering mechanisms and employee wellbeing. These findings are consistent with prior research (e.g., Bakker and Demerouti, 2007; Eisenberger et al., 2002; Greenhaus and Beutell, 1985).
4 Discussion and implications
This study contributes to the organizational behavior and HRM literature by empirically examining the antecedents and consequences of job stress, with a particular focus on the interplay between job demands and resources. The findings demonstrate that not all job demands and resources exert equivalent effects on stress. Specifically, workload and work–family conflict were found to be significant stressors, while organizational support and career advancement opportunities functioned as protective resources that alleviate stress. These findings reinforce the JD-R model's core proposition that employee wellbeing depends on balancing demands with adequate resources (Bakker and Demerouti, 2007; Demerouti et al., 2001). By situating these dynamics within the Saudi organizational context, the study advances understanding of how job demands and resources shape employee stress and wellbeing in digitally evolving environments. Specifically, it contributes to the relatively understudied domain of sustainable HRM by highlighting the psychological costs of excessive demands and the protective role of supportive organizational conditions. The findings also complement emerging literature on human-centric management and digital transformation, which increasingly recognize employee wellbeing as a strategic priority in designing resilient and health-promoting workplaces (Ko, 2022; Chrusciak et al., 2025).
This study reveals that all the hypotheses are supported except for two: job insecurity, which was found to have no significant effect on job stress (H1a not supported), and job autonomy, which showed no significant relationship with job stress (H2c not supported). These results suggest that the assumed impact of these two HRM dimensions on employee stress is not fully supported in the Saudi context. In fact, the findings indicate that job demands and resources exert differentiated effects on employee stress levels, with work–family conflict emerging as a prominent stressor within the studied sample. Prior research conducted in collectivist settings, including Saudi Arabia, has highlighted the close interconnection between professional and familial roles, where individuals often navigate overlapping responsibilities across both domains (Almutairi, 2017; Alotaibi and Campbell, 2022). This dynamic is compounded by familial obligations—such as financial support and caregiving—which are identified as key contributors to elevated stress and intensified work–family conflict in similar contexts (Bakar and Salleh, 2015; Al-Alawi et al., 2021). Within digitally transforming environments, these pressures are further amplified by increased connectivity and blurred temporal boundaries, reinforcing work–family conflict as a central stress mechanism. Additionally, ongoing national transformation efforts, such as those associated with Vision 2030, are introducing shifts in workplace expectations that emphasize individual performance, and clearer role boundaries. This creates a complex environment where modern organizational norms coexist with enduring social expectations, positioning work–family conflict as a critical challenge to employee wellbeing in digitally reforming environments.
This study reveals also that work overload has a significant effect on job stress. In fact, the pressure to meet continuous demands, particularly in digitally connected workplaces, can lead to persistent strain and disengagement. Employees perceive that when tasks accumulate beyond manageable levels, their psychological resources are depleted, making it difficult to maintain performance and wellbeing (Maslach and Leiter, 2016). In other words, when workload exceeds capacity, it directly undermines employees' ability to cope, resulting in elevated stress levels. This finding aligns with previous studies that identify unmanaged workload as a key predictor of burnout and psychological strain in modern workplaces (Lei et al., 2025).
Furthermore, this study highlights the significant role of perceived organizational support in reducing job stress. When employees believe that their organization values their contributions and invests in their wellbeing, they are more likely to experience psychological safety and resilience, confirming the findings of several researchers (Eisenberger et al., 2002; Xu and Yang, 2018; Bonaiuto et al., 2022). In digitally evolving Saudi workplaces, organizational support appears particularly critical in offsetting the intensified demands associated with technological change.
This study further underscores the significant role of advancement opportunities in alleviating job stress, aligning with recent research that identifies a strong negative relationship between the two constructs (Walsh et al., 2023). When employees perceive career progression as clear, attainable, and equitable, their psychological strain is notably reduced. In the context of Saudi Arabia's Vision 2030 and ongoing digital transformation, structured development opportunities may function as a stabilizing resource, reinforcing employability perceptions and buffering stress associated with rapid change (Alshurtan et al., 2024).
This study reveals a weak relationship between job insecurity and job stress within the examined Saudi sample. Supplementary analyses controlling for sectoral differences indicate that this null effect is not attributable to the predominance of public-sector employment in the sample, where employment is typically characterized by relatively stable contractual arrangements, strong employment protection, and lower exposure to short-term job loss. Rather than being driven solely by sectoral employment structures, a theoretically grounded explanation is that insecurity associated with digital transformation and technological change may be appraised as a gradual and manageable concern, rather than as an acute or uncontrollable threat to job continuity.
From a stress appraisal and JD-R perspective, job insecurity is more likely to elicit strain when it is perceived as imminent and uncontrollable, whereas insecurity that is perceived as manageable tends to remain salient without translating into heightened stress (Lazarus and Folkman, 1984). In the Saudi context, national workforce initiatives and organizational practices emphasizing reskilling, employment continuity, and career development may further shape these appraisals, reducing the immediacy with which technology-related insecurity is experienced.
Consequently, while employees may be aware of ongoing digital change, such concerns may not directly interfere with day-to-day work functioning, thereby weakening their direct association with job stress. This pattern contrasts with findings from more competitive labor markets (De Witte, 1999; Cheng and Chan, 2007; Alfadhel, 2024) and suggests that the stress effects of job insecurity depend not only on its presence, but also on how it is appraised within specific institutional and national contexts.
Interestingly, this study found no significant relationship between job autonomy and job stress, diverging from prior research that often identifies autonomy as a consistent stress-buffering resource (Rattrie et al., 2020; Zeuge et al., 2023). A plausible theoretical explanation is that autonomy may not exert a uniform effect in digitally intensive work settings. While autonomy traditionally provides employees with discretion over task execution and scheduling (Hackman and Oldham, 1975), digital environments can reshape this experience. Autonomy can reduce strain by offering flexibility, yet it may also heighten self-regulation demands, as employees must manage their own availability, prioritize tasks, and navigate performance expectations (Karimikia et al., 2021). This form of digitally mediated autonomy can foster “always-on” pressures and continuous decision-making, which may blur boundaries between work and personal life and partially weaken autonomy's protective role (Mazmanian et al., 2013). At the same time, digital systems may constrain experienced autonomy through standardized workflows and monitoring mechanisms, limiting the extent to which discretion translates into reduced stress (Gerten et al., 2019; Karimikia et al., 2021). Taken together, these insights suggest that autonomy functions as a conditional job resource within the JD-R framework, whose potential benefits for stress reduction depend on clear role expectations, effective boundary management, and supportive organizational structures.
Finally, the study confirms the adverse effect of job stress on employee wellbeing, a relationship consistently observed across diverse organizational contexts. Within the Saudi setting, this outcome reflects the impact of ongoing digital transformation and structural reforms that are reshaping work environments. Employees are increasingly confronted with intensified workloads, evolving performance metrics, and shifting role expectations—factors that, while intended to enhance organizational efficiency, may inadvertently contribute to psychological strain (Almutairi, 2017; Alotaibi and Campbell, 2022). These pressures are further compounded by socio-cultural obligations and normative expectations surrounding professional achievement, which can amplify stress responses. In light of these dynamics, the findings underscore the importance of adopting integrated HRM approaches that explicitly address employee wellbeing as a strategic priority, particularly in sectors undergoing rapid technological change where adaptability and workforce sustainability are essential (Bhoir and Sinha, 2024; Elufioye et al., 2024).
Overall, the findings provide strong empirical support for the JD-R framework in digitally transforming work environments. Consistent with the health-impairment process, digitally intensified job demands—particularly work–family conflict and work overload—were significantly associated with higher job stress. At the same time, organizational support and advancement opportunities emerged as effective stress-buffering resources, confirming the JD-R motivational pathway. These results demonstrate that digitalization reshapes the intensity and expression of traditional job demands and resources rather than replacing them, thereby amplifying their effects on employee stress and wellbeing in context-specific ways.
4.1 Theoretical contributions
This study offers meaningful theoretical contributions to the literature on occupational stress and employee wellbeing, particularly within emerging economies and culturally embedded organizational environments such as Saudi Arabia. Grounded in the Job Demands–Resources (JD-R) model (Bakker and Demerouti, 2007), the research develops and validates a framework that links key job demands—work overload, job insecurity, and work–family conflict—and job resources—organizational support, advancement opportunities, and job autonomy—to job stress and its impact on employee wellbeing.
By applying this framework in a context undergoing rapid socio-economic transformation, the study extends existing theory by demonstrating how cultural norms, institutional reforms, and evolving organizational expectations shape employees' stress experiences. This contextualization contributes to a more nuanced understanding of the JD-R model, particularly in settings where traditional workplace values intersect with modern performance and technological imperatives.
A notable theoretical insight emerges from the differentiated impact of job resources. While organizational support and advancement opportunities significantly reduce job stress, job autonomy and job insecurity did not show statistically significant effects in this study. This outcome suggests that not all job resources exert equal influence in every context, and challenges assumptions commonly found in Western-centric applications of the JD-R model, where autonomy and security are often emphasized as universal buffers against stress. In the Saudi organizational context, employees may place greater value on tangible relational support and visible career progression than on abstract notions of autonomy or perceived job stability. These findings underscore the importance of contextualizing resource constructs to reflect localized interpretations of support, and workplace expectations.
Finally, the research contributes to broader discussions on human-centric management by illustrating how context-sensitive HRM strategies can enhance employee wellbeing. These insights offer a foundation for future comparative studies and theoretical refinements that integrate cultural, technological, and institutional dimensions into models of organizational behavior.
4.2 Managerial implications
From a practical perspective, the findings of this study yield actionable insights for human resource professionals, organizational leaders, and policymakers committed to cultivating healthier and more sustainable work environments. The demonstrated impact of work overload and work–family conflict on elevated job stress underscores the pressing need for strategic workload management and the adoption of flexible work arrangements. Organizations—particularly those operating in high-demand sectors—should consider implementing policies that facilitate work–life integration, including remote work options, family leave provisions, and time management training.
The partial support observed for job insecurity and limited job autonomy indicates that while these factors may not universally trigger stress, they remain influential in shaping employees' perceptions of control, stability, and engagement. Managers should foster a supportive climate by clearly communicating job stability and offering structured career development. While autonomy alone may not reduce stress, it becomes effective when paired with clear expectations and resources. Moreover, the strong buffering effect of organizational support and advancement opportunities underscores the importance of cultivating a climate where employees feel valued and supported. Human resource departments are encouraged to implement mentorship programs, performance-based promotion systems, and recognition initiatives that visibly affirm employees' contributions and growth potential. These efforts not only enhance psychological wellbeing but also foster sustained engagement and organizational commitment.
Further, the adverse consequences of job stress on employee wellbeing reinforce the strategic importance of integrated stress management interventions. Organizations should embed wellness programs, psychological support services, and routine stress audits within their HR frameworks.
Aligned with Saudi Arabia's national transformation agenda, organizations are uniquely positioned to champion employee wellbeing as a foundational element of sustainable development. By addressing both the structural and psychological dimensions of job stress, managers can cultivate resilient, engaged, and high-performing teams capable of navigating the evolving complexities of contemporary work environments.
This study's contribution lies in its contextual specificity: it illustrates how collectivist cultural norms—especially within the Saudi context—intensify the effects of work–family conflict, rendering wellbeing interventions not only strategically beneficial but culturally essential. By exploring the intersection of digital reform, cultural expectations, and human resource management, this research offers practical guidance for organizations seeking to harmonize modernization efforts with support systems that are specifically designed for and resonate with local cultural values.
5 Conclusion, limitations and future research
In recent years, organizational modernization has accelerated across sectors, driven by digital transformation, global competitiveness, and evolving workforce expectations. While these shifts have introduced innovative management practices, they have also raised concerns about employee wellbeing and cultural alignment in rapidly changing workplaces. Building on these dynamics, this study contributes to the literature by demonstrating how specific job demands and resources relate to employee stress within a culturally embedded organizational context.
The findings suggest that organizations operating in culturally rich environments may benefit from balancing global Human Resource Management (HRM) trends with context-sensitive practices to foster employee wellbeing, reduce stress levels, and enhance psychological safety. In this regard, culturally responsive support systems can be viewed as potential strategic levers for harmonizing modernization with employee-centric values. This implication offers practical insights for HR practitioners and leaders seeking to implement modernization strategies without compromising cultural coherence. The study also advances theory by extending the JD-R framework through the integration of cultural and digital work considerations in a non-Western context.
Despite its contributions, this study has several limitations that should be acknowledged. Although data were drawn from employees across multiple sectors, the findings remain contextually grounded and should be interpreted with caution when applied to other national or institutional settings. In addition, while the study relied on well-established and validated measurement scales, several constructs were operationalized as global, unidimensional perceptions that may encompass multiple underlying facets. This approach is consistent with the original scale validations and with the JD-R model's functional treatment of job resources; however, it may obscure potential variability in the effects of specific subdimensions. Accordingly, future research may benefit from disaggregating these constructs to examine whether different facets exert distinct influences on employee stress and wellbeing.
A specific limitation concerns the operationalization of the autonomy construct. The job autonomy scale used in this study captures general task and scheduling discretion, consistent with the resource-based conceptualization of autonomy within the JD-R framework. As such, the measure does not capture digitally mediated autonomy or connectivity-related pressures, such as expectations of constant availability or technology-enabled self-regulation. This distinction is important for interpreting the non-significant relationship observed between job autonomy and job stress, as the scale may not fully reflect the forms of autonomy most salient in technology-intensive work environments. Future research should therefore employ measures that explicitly capture digital autonomy, including both its beneficial flexibility and its potentially demanding aspects.
From a methodological perspective, although the sample size (N = 206) is adequate for the present structural equation model, it is modest relative to model complexity and may limit statistical power for detecting small-to-moderate effects. In addition, goodness-of-fit indices such as GFI and AGFI, which fell below the conventional 0.90 threshold, may improve with larger samples in future research.
Furthermore, the cross-sectional design limits causal inference, and reliance on self-reported measures may introduce common method bias. While structural equation modeling (SEM) provided robust analytical insights, future studies could adopt longitudinal or mixed-method designs to capture dynamic processes and deepen understanding of employee experiences. Expanding sample size and diversity across organizational and national contexts would further strengthen the robustness and generalizability of the findings.
Future research may also investigate how culturally responsive support mechanisms operate across different institutional environments using comparative designs. Finally, while this study incorporates digitalization within the JD-R framework, future work could examine more fine-grained digital demands and resources to better delineate technology-specific stress processes and their boundary conditions
Data availability statement
The datasets presented in this article are not readily available because the dataset generated and analyzed during this study is not publicly available due to participant confidentiality and the anonymous nature of the responses. Sharing the full dataset may compromise the privacy assurances provided to participants. Aggregated data or summary statistics may be made available upon reasonable request to the corresponding author. Requests to access the datasets should be directed to Hanen Louati, aC5sb3VhdGlAc2V1LmVkdQ==.
Ethics statement
Ethical approval was not required for the studies involving humans because it did not involve any experimental intervention, clinical procedures, or manipulation of participants and was conducted as an anonymous survey in accordance with institutional and national guidelines for minimal-risk social science research. The studies were conducted in accordance with the local legislation and institutional requirements. Written informed consent for participation was not required from the participants or the participants' legal guardians/next of kin in accordance with the national legislation and institutional requirements because participation was voluntary, and informed consent was implied through completion of the questionnaire. No sensitive or personally identifiable data were collected.
Author contributions
HL: Formal analysis, Methodology, Software, Writing – original draft, Writing – review & editing. KS: Formal analysis, Writing – review & editing. JB: Validation, Writing – review & editing. SS: Validation, Writing – review & editing. AA: Investigation, Writing – review & editing.
Funding
The author(s) declared that financial support was not received for this work and/or its publication.
Conflict of interest
The author(s) declared that this work was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Generative AI statement
The author(s) declared that generative AI was not used in the creation of this manuscript.
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Supplementary material
The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/frsus.2026.1696502/full#supplementary-material
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Keywords: digital transformation, employee wellbeing, Job Demands–Resources (JD-R) model, structural equation modeling (SEM), workplace stress
Citation: Louati H, Saidi K, Bouslimi J, Sayari S and Aljohaini A (2026) The critical role of HR in managing stress and enhancing wellbeing in the age of digitalization. Front. Sustain. 7:1696502. doi: 10.3389/frsus.2026.1696502
Received: 31 August 2025; Revised: 07 January 2026;
Accepted: 12 January 2026; Published: 03 February 2026.
Edited by:
Mei-I Cheng, De Montfort University, United KingdomReviewed by:
Sudhanshu Joshi, Doon University, IndiaSylcien Chang, National Taipei University of Business, Taiwan
Copyright © 2026 Louati, Saidi, Bouslimi, Sayari and Aljohaini. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
*Correspondence: Hanen Louati, bG91YXRpaGFuZW4yMDIyQGdtYWlsLmNvbQ==