Navigating Digital Demands: A Reassessment of Resources for Healthcare Workers’ Workplace Wellbeing

Navigating Digital Demands: A Reassessment of Resources for Healthcare Workers’ Workplace Wellbeing | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Navigating Digital Demands: A Reassessment of Resources for Healthcare Workers’ Workplace Wellbeing Parisa Afshin, Barbara Rebeca Mutonyi, Erlend Nybakk This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6448853/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background Digital innovations (DIs) are constantly reshaping healthcare, affecting healthcare workers’ practices and wellbeing both positively and negatively. To balance this dual impact, it is essential to understand the specific demands introduced by DIs and assess whether existing personal and organizational resources are remain effective in addressing them. The aim of this study is to contribute to healthcare services research by examining the relevance and effectiveness of key psychological and organizational resources in buffering digital job demands (DJDs) in today’s evolving healthcare context. Methods Data were collected from N = 292 healthcare workers in the UK using an online quantitative survey platform by adopting items from established measurements. Covariance-Based Structural Equation Modelling (CB-SEM) was used to test the hypothesized relationships amnong the vriables, with the help of R 4.4.3 software. Results The direct relationship of digital system overload, a DJDs, was found to have a negative and significant relation to employee job satisfaction (Beta = -0.141). In addition, the direct relationship of digital innovation support on job satisfaction (Beta = 0.250) and thriving at work (Beta = 0.336) was supported. The direct relationship of resilience on job satisfaction (Beta = 0.208) was supported. As was the direct relationship between autonomy and job satisfaction (Beta = 0.160) and thriving at work (Beta = 0.266). The remaining direct relationship found no support. Finnaly, the results shows that digital innovation support mediates the relationship between digital system overload and job satisfaction (Beta = -0.085), and the relationship between digital system overload and thriving at work (Beta = -0.144). The remaining proposed mediating relationship found no support. Conclusions The results confirm the positive impact of psychological and organizational resources on healthcare workers’ positive well-being outcomes, with the exception of psychological resilience. In addition, the direct effects of identified DJDs on well-being were not supported, possibly due to the contextual and fluctuating nature of such demands. The variations in findings regarding the mediating role of resources suggest a need for more in-depth research to explore which resources are most relevant and effective in addressing the evolving digital demands faced by healthcare professionals in today’s workplace. Consequently, the authors contribute to health services research and literature by clarifying the complex and multifaceted understanding of psychological and organizational resources as crucial factors in navigating digital job demands. The findings of this paper offer essential practical implications for health organizations and health managers, by highlighting the importance of managing both personal and organizational resources to secure health workers’ wellbeing in a quickly evolving work environment. Digital innovations Resources Digital job demands Healthcare workers Employee wellbeing Figures Figure 1 Background Digital innovations (DIs) are being adopted at an accelerating rate to address healthcare sector challenges such as heavy workloads, staff shortages, financial constraints, and pandemic-related pressures, aiming to create a more inclusive, accessible, and effective healthcare system (1,2). This rapid adoption of Dis however, has had dual impacts on healthcare workers' workplace wellbeing. On the positive side, DIs have been argued to create several beneficial outcomes, such as, though not limited to, enhancing healthcare resilience when facing environmental uncertainty and unpredictability, such as the COVID-19 pandemic (3,4). Conversely, digitalization has also introduced various negative effects, for instance increasing or duplicating workloads, or limiting the time available for workers to adequately learn new systems (5). Previous studies on the impact of digitalization in healthcare sector have frequently focused on the patient (6,7) or organizational outcomes (8,9), while failing to pay adequate attention to employee´s wellbeing. The understanding of digital innovations and its impact on healthcare workers is of importance as healthcare workers' wellbeing not only influences individual outcomes but also predicts organizational outcomes such as absenteeism and voluntary turnover (10–12), as well as significantly impacts patient care quality and outcomes (13–17). Thus, there is a clear gap and need for further knowledge and understanding of how digital innovations and workplace conditions in the healthcare sector influence healthcare workers' wellbeing. Organizational studies highlight that fostering resources across individual, group, leader, and organizational levels significantly enhances employee wellbeing and performance, particularly when facing workplace demands (18). The Job Demands-Resources (JD-R) model of organization behavior by Demerouti et al. (19) investigates the dynamic of resources and demands and their outcome on employees’ wellbeing and performance. The JD-R model consists of two complementary processes: a health impairment process, where excessive demands may lead to strain, and, importantly, a motivational process, where resources at various levels, organizational, social, and personal, actively promote engagement, resilience, and wellbeing (19,20). Within the JD-R framework, digital technologies can introduce new workplace demands, potentially impairing wellbeing and performance (21). However, resources, including organizational and personal resources, are argued to act as buffers, effectively reducing the negative impacts of work demands and enhancing employee wellbeing (19,22). This perspective aligns closely with research on technostress, which indicates that negative impacts from DIs are influenced by mediating factors, notably the availability and effectiveness of resources and coping mechanisms (23). In other words, adaptive coping strategies significantly mitigate these adverse effects, whereas maladaptive coping strategies exacerbate them (24). Research shows that organizational and personal resources are critical assets for healthcare workers to manage stress effectively; conversely, insufficient or depleted resources heighten stress and risk of burnout (25,26). Although extensive research using JD-R model has examined how various demands and resources influence employee outcomes, there remains a notable knowledge gap regarding whether commonly emphasized resources continue to be effective in today’s rapidly digitalizing healthcare environment. As DIs reshape work processes, it becomes increasingly essential to reassess the relevance and effectiveness of key psychological and organizational resources in buffering emerging digital job demands (DJDs). Thus, this study aims to address this gap by investigating: To what extent do resources mediate the relationship between digital work demands and healthcare workers' wellbeing in digitalized workplaces? By addressing the research question above, this study aims to contribute to theoretical insights into the reassessment of key resources in the face of the evolving digital demands in healthcare, as well as practical guidance for health organizations aiming to support employee wellbeing in a digitalized healthcare workplace. We tested our model and hypotheses, as shown in Figure 1, using survey data collected from healthcare workers. Review of literature and hypothesis development Much of previous DIs research on wellbeing of employees emphasizes negative outcomes such as burnout and technostress (23,27), leaving gaps in the understanding of how to mitigate the negative impacts of digitalization on the healthcare workers' wellbeing through examining the role of resources on positive wellbeing outcome (28). To address this gap, this study has adopted the JD-R model (19). This study emphasizes the motivational process of JD-R model, which posits that resources foster positive wellbeing outcomes like engagement and satisfaction (19,20) and buffer the negative impacts of the demands on wellbeing (29)—see Figure 1. Employee wellbeing General theories of individual wellbeing are often derived from two major viewpoints. First, the hedonic view, which emphasizes the pursuit of pleasure and subjective happiness while minimizing pain as an essential components of wellbeing. Second, the eudaimonia approach emphasizes finding meaning and self-realization as central to wellbeing (30). Although some studies of wellbeing have focused on the hedonic and some on the eudemonic perspective, the findings of these studies have revealed that individuals who pursue both hedonic and eudemonic well-being often experience greater overall well-being and live a “full life” (31,32). This notion suggests the importance of including both perspectives in understanding employee wellbeing. Simultaneously, wellbeing is a complex and multifaceted construct that extends beyond ill-health avoidance to positive states (33). Subsequently, in this paper, wellbeing is formulated and understood as a dynamic balance of psychological, physical, and social resources in response to external pressures, resulting in positive outcomes encompassing pleasure and life satisfaction (hedonic) , as well as purpose and personal growth (eudemonic) (34,35). This definition can be applied to workplace wellbeing, and it is aligned with the JD-R model, which emphasizes the balance between resources and demands as critical to employee wellbeing and motivation. Following this definition, this study adopts two notions of ‘thriving at work’ and ‘job satisfaction’ as indicators of employees’ workplace wellbeing. First, j ob s atisfaction (JS) reflects the hedonic aspect of workplace wellbeing by emphasizing pleasure, contentment, and satisfaction from work (36). JS suggests the happiness and fulfillment of individuals derive from their work and has previously been found to closely relate to organizational outcomes such as productivity and retention and individuals’ overall wellbeing (37,38). The increasing integration of DIs in the workplace necessitates an agile approach in examining their impact on JS, as DIs often introduce job demands that can negatively influence satisfaction (39–41). Previous research examining factors affecting physician job satisfaction reveals that in their findings, electronic health records have been found to reduce professional satisfaction due to time-intensive tasks and interference with patient care (14). However, there is still a lack of further understanding of the role of digital job demands on job satisfaction for a broader sample that includes health workers who fall outside the physician category. Second, Thriving at Work (TAW) is defined as the psychological state of experiencing vitality, reflecting energy and passion, and learning involving growth and skill acquisition (42,43). Thriving as growing in terms of both learning and vitality captures both the hedonic (vitality) and eudaimonic (learning) aspects of psychological functioning and development (44,45). Previous research on TAW has found TAW to positively and significantly relate to better mental and physical health, life satisfaction, job performance, and work engagement (46–48). The study of Walumba et al. (46) offered valuable insights into the multilevel understanding of TAW for creating sustainable organizational performance. In line with previous studies arguing digital innovation's usefulness in furthering sustainable organizations, the gap in its understanding is still underexplored. Therefore, this study furthers this knowledge by examining the role of digital job demands on TAW in levering overall sustainable organizational performance. In doing so, and in line with Walumba et al. (46) and Zhai et al. (47), this study offers further knowledge on how health organizations can promote work performance through healthcare workers´ wellbeing. Job demands and digital context Job demands are aspects of work requiring sustained physical, emotional, or cognitive effort that potentially disrupts wellbeing by consuming energy (19,29). Examples include workload and work pressure (29). DIs can act as both resources (e.g., automating tasks) and demands (e.g., increasing overload) (49). Drawing on the definition of DIs from Hund et al. (50), this paper defines DIs in healthcare as the creation or adoption and exploitation of novel technologies, processes, or systems—such as electronic health records (EHRs), telemedicine, and AI-driven diagnostics—that leverage digital tools and data to improve healthcare delivery, patient outcomes, and operational efficiency (50). In this study, following Scholze and Hecker’s (2024) definition, DJDs refer to job demands arising from using DIs, such as information overload, system inefficiencies, and new skill requirements (21). Previous studies have found that DJDs can reduce job satisfaction and commitment (51), blur work-life boundaries (52), and increase turnover intentions and workplace detachment (21). A recent meta-analysis study on technostress and employee wellbeing revealed that digital stressors like technostress exacerbate exhaustion and diminish well-being (53). Though the meta-study by Wang et al. (53) is not in the healthcare setting, it provides insights and gaps valuable for healthcare settings, a gap this study aims to contribute to. While much of DIs research focuses on singular technologies (e.g., mobile devices (54), this study adopts a broader perspective, focusing not only on a single technology but on the DJDs caused by various technologies that healthcare workers use in their daily work tasks. Additionally, limited research has explored the DJDs within the healthcare sector (49). Based on the JD-R model, demands are context dependent and can vary from organization to organization and sector to sector (29). Thus, this study brings attention to digital system overload (DSO) and digital work overload (DWO), which have been argued to be relevant to the healthcare sector. Previous research have shown major challenges related to digital systems in healthcare (1,55), and work overload is also a common issue in the sector (56). DSO reflects the complexity and feature excess of digital tools, such as electronic health records (EHRs), which can overwhelm users and reduce usability (49). Not surprisingly, EHRs’ time-intensive navigation disrupts patient care, which also results in lowering professional satisfaction (14). DWO occurs when digital tools, intended to save labor, escalate tasks, volume, and pace (51). Previous findings have shown that frequent use of digital technologies, while improving efficiency, often increases perceived workload (57), reducing vitality (24). Subsequently, in this study we propose that DJDs, termed DWO and DSO, are negatively associated with employee wellbeing, specified as JS and TAW. The suggested hypotheses are formulated as follows: H1 : DWO (a) and DSO (b) are negatively associated with JS . H 2 : DWO (a) and DSO (b) are negatively associated with TAW. Organizational and individual resources In a broad sense, resources are defined as “anything perceived by the individual to help attain his or her goals” (25). Job resources are physical, psychological, social, or organizational aspects that (a) aid work goal achievement, (b) reduce demands and their costs, and (c) promote growth and development (19). Nielsen et al. (58) categorize resources into four types: i) individual-level (e.g., self-efficacy, competence), ii) group-level (e.g., social support, teamwork), iii), leader-level (e.g., leadership style, leader-member exchange), and iv), organizational-level (e.g., job design, management practices) (58). These resources can act both as a booster that increases the wellbeing outcome and as buffers by mitigating the impact of work demands (29). Therefore, resources can enhance wellbeing by enabling effective performance and coping (58). However, to the authors’ knowledge, a dart of health services research studies have yet to explore resources as buffers for the DJDs. Consequently, as depicted in the study's conceptual model, illustrated in Figure 1, this paper focuses on two types of resources as a buffer: organizational-level resources, such as digital innovation support (DIS) and autonomy, and individual level resources, such as psychological resilience. First, and as already mentioned, the study has focused on two organizational level resources, namely DIS, such as technical assistance for digital tools, and autonomy, which entails the level of control over task execution. Both DIS and autonomy are factors previous studies have argued are beneficial to managing DJDs and achieving goals (58,59) through enhancing technological coping (26,60) and fostering control (20), respectively. Second, the study focuses also on personal or individual level resources , as shown in Figure 1, namely psychological r esilienc e. Psychological resilience is adaptability to stressors, which may enable coping and buffer demand effects (25,59). Thus, it is proposed here that DIS, autonomy, and psychological resilience mediate DJDs’ impact on wellbeing, mitigating negative effects and fostering positive outcomes, purposing the following hypothesis: H 3 : DIS (a), autonomy (b), and psychological resilience (c) are positively associated with JS . H 4 : DIS (a), autonomy (b), and psychological resilience (c) are positively associated with TAW . Mediating role of digital innovation support (DIS) Organizational resources like DIS—technical assistance and resources for digital tools (e.g., IT help for EHRs)—can enhance wellbeing by aiding goal achievement and reducing demand costs (61). Although DIs aim to streamline work, their demands can disrupt satisfaction and vitality without support (60). A supportive climate is suggested to boost engagement with technology while training and assistance enhance self-efficacy (60,62,63). Effective DIS resolves technical issues swiftly, minimizing disruptions (64), and mirrors general support’s positive effects on satisfaction (60,65). However, the mediating role of DIS in the healthcare sector is underexplored (28). Therefore, the following hypotheses are proposed: H5a : DIS mediates the relationship between DWO and JS. H5b : DIS mediates the relationship between DWO and TAW. H5c : DIS mediates the relationship between DSO and JS. H5d : DIS mediates the relationship between DSO and TAW. Mediating role of autonomy Autonomy is understood as one’s ability to self-govern one´s work, which includes work scheduling, decisions, and methods (66). Autonomy is deemed as a cornerstone of JD-R resources that enhances wellbeing by fostering goal achievement and intrinsic motivation (19,61). It satisfies one of the core psychological needs (67), boosting job satisfaction and thriving (41). Autonomy-supportive environments improve engagement and psychological health, while controlling ones diminish vitality. In healthcare’s digital context, autonomy may empower workers to manage DJDs, such as, though not limited to, EHR complexity, offering flexiblity, preserving satisfaction, and learning (68). For instance, nurses with scheduling control report higher thriving amidst technological pressures (58). In line, a study by Zhang et al. (41) on Chinese healthcare personnel found that perceived job resources, such as autonomy and support, are positively associated with thriving at work (41). Yet, its mediating role in mitigating DJDs’ effects on positive wellbeing remains underexplored in the health services research (69). Thus, the following hypotheses are formulated: H 6 a : Autonomy mediates the relationship between DWO and JS . H 6 b : Autonomy mediates the relationship between DWO and TAW . H 6 c : Autonomy mediates the relationship between DSO and JS . H 6 d : Autonomy mediates the relationship between DSO and TAW . Mediating role of psychological resilience Psychological resilience is defined as a positive psychological capacity to bunce back after stressful situation (70). Previous studies that have explored the resilience of employees, have found that psychological resilience is a crucial positive resource to overcome stressors and challenges at work (71,72). This is because psychological resilience reframes challenges as growth opportunities (73). In healthcare, resilient workers thrive amidst job stressors (74,75), reporting higher job satisfaction and performance (73). Previous research reveals that DJDs, such as system complexity or task overload, are moderated by psychological resilience through sustained energy and learning, which is achieved by buffering their disruptive effects (76). Despite its relevance, studies exploring psychological resilience as a mediating factor in digital healthcare contexts, are underexplored (10,77). This is particularly evident in studies focusing on discussing psychological resilience in terms of fostering positive outcomes rather than merely reducing strain (58). A meta-analysis study indicates that personal resources received less attention from research in facing demands as compared to organization resources (58). Psychological resilience as a personal resource in coping with the negative impacts of digitalization has received less attention than other individual factors such as personality traits or self-efficacy (28). This evident gap in health services research provides compelling evidence for furthering knowledge on examining psychological resilience as a mediator between the relationships of DJDs, TAW, and JS. We therefore propose the following hypotheses, as formulated: H 7 a : Psychological r esilience mediates the relationship between DWO and JS . H 7 b : Psychological r esilience mediates the relationship between DWO and TAW . H 7 c : Psychological r esilience mediates the relationship between DSO and JS . H 7 d : Psychological r esilience mediates the relationship between DSO and TAW . Methodology The aim of this study has been to furthering knowledge on the role of digital job demands on healthcare employee´s wellbeing. As such, the study has focused on examining psychological and organizational resources as buffers in tackling digital job demands (DJDs). Consequently, the study builds upon the JD-R model, and as depicted in the study's conceptual model, Figure 1. Data were collected via a structured questionnaire in a two-phase process to investigate the role of DJDs on healthcare workers’ wellbeing, with analysis conducted using covariance-based structural equation modeling (CB-SEM). Participants and procedure Following the JD-R model context-specific approach (29), a two-phase data collection was employed. Phase 1 : Expert Review and Pilot Study. Ten healthcare experts (nurses and doctors from various hospital units) reviewed the questionnaire for face validity, refining DJDs and resources’ constructs. First, drawing on technostress (64) and technology overload literature (49), an initial list of constructs for DJDs (e.g., techno-invasion, information overload) was created. Then, experts were asked to choose the most relevant DJDs based on their work. They went through the items of each construct and prioritized system overload, work overload, and information overload, respectively, as being the most relevant. Then, a pilot study (n = 60) via Prolific was conducted, and subsequently, items and wording were refined, and feasibility was confirmed. Phase 2 : Main Study. We recruited 292 UK healthcare workers via Prolific (December 2024), ensuring data quality with attention checks and eligibility criteria (e.g., active healthcare role) (78). The healthcare workers´ role included doctors, nurses, paramedics, emergency dispatchers, or medical services personnel. This sample size supports CB-SEM’s requirements of minimum N ~ 200 (88). No missing data were observed across the 292 responses. The sample sociodemographic characteristic is shown in Appendix A. The demographic shows that most participants were primarily women (77%), nurses (65%), held a bachelor’s degree (55%), and worked full-time (65%). Leadership roles were common (35% Team Leaders). Ethical Considerations Data collection was conducted in accordance with the ethical guidelines provided by the Norwegian Agency for Shared Services in Education and Research (SIKT) that is in line with Helsinki declaration. Since the study does not involve personal data, traceable IP addresses, or clinical intervention, SIKT approved the study without assigning a specific reference number. Data were collected through Nettskjema, a secure Norwegian online survey platform that automatically anonymizes responses. Kristiania University College is the responsible institution for data management, and the collected data will be securely stored on Kristiania's servers in protected and locked facilities. Participants were informed on the first page of the survey, prior to consenting, that their participation was entirely voluntary and that they had the right to withdraw at any point without consequence. After reviewing the procedure, project content, and mentioned information on anonymity and data collection and storage, Participants provided informed consent by clicking "Next" to proceed with the survey. Measurement instruments The study survey was developed by adapting items from established measures used in previous studies (see Table 1). The survey used in this study is part of a larger research project and has not been previously published. All constructs were measured using a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree), asking the respondent to answer while reflecting on digital technologies they are using most in their daily healthcare work, such as Electronic Health Records (EHRs), Telemedicine platforms, Clinical Decision Support Systems (CDSS), etc. Measures were adopted from validated instruments (see Procedure). Table 1 details constructs, sub-constructs, item counts, examples of items, and sources. [ Insert Table 1here ] Data analysis The conceptual model and the hypothesized relationship were tested using CB-SEM in R 4.4.3 software. Analyses were conducted based on the psych (79), lavaan (80), semTools (81) and polycor (82) R packages. Analysis followed a two-step process. In the first step, the measurement model was assessed. Maximum likelihood estimation based on polychoric correlations was applied (Appendix B) as all variables were ordinal (83). Two items from TAW were removed (thrivev2, thrivev4) due to high correlations with items in JS. When the measurement model assessment was satisfactory, the second step was to assess the structural model. In step 2, CB-SEM tested the hypothesized relationships (H1–H7). First, model fit was evaluated using Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and coefficient of determination (R²). Then, the direct relation (H1-H4) and the mediations (H5-H7) were evaluated with 5,000 bootstrap samples for mediation analysis (84). Results Measurement model results In assessing the psychometric properties of the factors used in this study, a confirmatory factor analysis (CFA) was performed using the robust maximum likelihood estimation (MLR). CFA showed an overall satisfactory model fit (CFI = 0.910, TLI = 0.900, RMSEA = 0.068, and SRMR = 0.071). These values indicate a good model fit, suggesting that the measurement model adequately represents the data. Table 2 summarizes the measurement model, mean, and standard deviation. Reliability was estimated using composite reliability (CR) rather than Cronbach’s alpha (CA) due to CA limitations (85). All loadings meet acceptable thresholds and are statistically significant (CR >0.7) (86), showing good item-construct relationships, except for two items at the borderline (aut1: 0.48 and thrivel4: -0.47). These items were removed from the data set in step 2 of the analysis. Convergent validity was assessed through factor loading and average variance extracted (AVE) ≥ 0.50, indicating satisfactory properties and statistically significant values and, thus, good convergent validity (85). [ Insert Table 2 here ] As illustrated in Table 3, discriminant validity was assessed using the Fornell–Larcker criterion by comparing each construct’s AVE with the squared correlations between constructs (87). All constructs met the criterion, with AVE values exceeding the squared correlations with other constructs, except for a marginal case between TAW (AVE = 0.501) and JS (squared correlation = 0.519). Given the conceptual proximity between these two constructs, this result is interpreted as acceptable (88). Table 3 Discriminant validity, squared correlations, AVE and Multicollinearity (VIF) Construct 1 2 3 4 5 6 7 VIF 1. DWO 1.00 1. 39 2. DSO 0.319 1.00 1.47 3. DIS 0.055 0.127 1.00 1. 19 4. Resilience 0.004 0.033 0.054 1.00 1.07 5. TAW 0.001 0.026 0.131 0.046 1.00 - 6. JS 0.001 0.040 0.102 0.092 0.519 1.00 - 7. Autonomy 0.025 0.011 0.002 0.001 0.065 0.026 1.00 1.0 2 AVE 0.734 0.532 0.596 0.721 0.501 0.766 0.602 Note: AVE: average variance extracted; VIF: Variance Inflation Factor; DWO: digital work overload; DSO: digital system overload; DIS: digital innovation support; Resilience: psychological resilience; TAW: Thriving at work; JS: job satisfaction Structural e quation m odeling (SEM) Before measuring the structure model, the multicollinearity was tested using the variance inflation factor (VIF) (Table 3). VIF values are between 1.02 and 1.47, well below the acceptable threshold of 3.0 (85). The structural model showed an acceptable fit ( CFI = 0.902, TLI = 0.892, RMSEA = 0.071, except SRMR = 0.114). Given the elevated SRMR value, three items with high cross-loadings were removed (aut2, thrivev3, and res1). This adjustment substantially improved the model fit (CFI = 0.932, TLI = 0.922, RMSEA = 0.064, and SRMR = 0.062). These values meet the commonly accepted thresholds for good model fit (CFI and TLI ≥ 0.90, RMSEA, and SRMR ≤ 0.08 (89). R² values indicated that the model accounted for 20% of the variance in Job Satisfaction and 22.9% in TAW. Table 4 presents standardized path coefficients. DWO and DSO were not significantly associated with JS or TAW (p > .05). In contrast, DIS, psychological resilience, and autonomy had significant positive effects on both wellbeing outcomes (p .05). Table 4 Structural model direct relations Hypothesis Path Std. Coef (β) p-value H1a DWO à JS 0.087 0.282 H1b DSO à JS -0.141 0.096* H2a DWO à TAW 0.070 0.372 H2b DSO à TAW -0.086 0.294 H3a DIS à JS 0.250 0.000*** H3b Resilience à JS 0.208 0.003*** H3c Autonomy à JS 0.160 0.010** H4a DIS à TAW 0.336 0.000*** H4b Resilience à TAW 0.096 0.153 H4c Autonomy àTAW 0.266 0.000*** Note. DWO: digital work overload; DSO: digital system overload; DIS: digital innovation support; JS: Job Satisfaction; TAW: thriving at work. p < .05 values Subsequently, mediation effects were tested with 5000 bootstrap samples (BCa method) in two steps (90). First, the indirect effect was estimated. Then, the statistical significance of each mediator was tested. Results are illustrated in Table 5. Mediation analysis that was supported was the indirect effect of DSO on JS through DIS (β = −0.085, 95% CI [−0.181, −0.031], p = .015) and the indirect effect of DSO on TAW through DIS (β = −0.114, 95% CI [−0.229, −0.051], p = .005). Table 5 Results of Mediation Analysis Using Bootstrap Estimates [Insert Table 5 here] Discussion Theoretical implications The findings of this study prompt a reassessment of how key psychological and organizational resources function in the face of evolving digital job demands in healthcare. Two key areas of discussion are raised. First, the marginal effects of DSO and DWO suggest that these demands do not uniformly impact job satisfaction or thriving at work among healthcare workers. This can be interpreted through the lens of the Challenge–Hindrance framework within the JD-R model ( 91 ). According to this framework, challenge demands may foster motivation and personal growth, while hindrance demands tend to generate strain and disengagement. However, even challenging demands can negatively affect well-being in the long run, despite short-term motivational benefits ( 92 ). Consistent with this, prior research has suggested that digital job demands such as ,system complexity and information overload, an function as either challenges or hindrances depending on contextual and individual factors ( 93 , 94 ). This perspective may help explain this study's unexpected positive association between digital work overload and job satisfaction. Similarly, Podsakoff et al. ( 91 ) note that hindrance stressors negatively affect performance via strain, while challenge stressors can exert both positive and negative effects through motivation and burnout pathways ( 92 ). Nonetheless, these findings contrast with the broader technostress literature, which consistently shows that stressors such as techno-overload diminish job satisfaction ( 64 ). One potential explanation for the findings of this study, is the dual nature of digital job demands, where initial feelings of engagement or productivity may mask the cumulative negative effects on well-being over time ( 29 ). Second, the findings confirm previous studies on the role of resources in supporting positive wellbeing outcomes, consistent with research in other sectors ( 67 , 95 , 96 ). However, the effect of psychological resilience on TAW was not supported, unlike previous research in other contexts that indicated a relationship between psychological resilience and TAW ( 97 ). The mediating roles of resources in buffering the potential negative impacts of DJDs yielded mixed results. Although prior research has shown that autonomy plays a significant role in mitigating the effects of job demands ( 98 ), this was not supported in our study concerning DJDs and healthcare workers' wellbeing. This may be due to contextual factors. For instance, a multilevel study by Reyes-Luján et al. ( 98 ) on N = 1,232 hospital workers, showed that workplace autonomy can positively or negatively affect burnout depending on role ambiguity and the worker’s age ( 98 ). Similarly, the structured nature of the healthcare sector, characterized by strict protocols and compliance requirements, may limit the degree of autonomy healthcare workers experience in using digital technologies ( 92 ). Psychological resilience also did not demonstrate a mediating effect. While psychological resilience is often considered a protective factor in high-stress environments, it may not significantly buffer the effects of DJDs on positive wellbeing outcomes in this context. This suggests that not all personal resources function uniformly across different demands or workplace conditions. In contrast, DIS exhibited an indirect-only mediation effect. This indicates that DIS plays a key role in transmitting the effects of DSO on JS and TAW. While the total (direct) effect of digital system overload on wellbeing was not significant, the significant indirect (partial mediation) path through DIS suggests that this relationship may still be meaningful. As such, DSO is operating primarily through support mechanisms rather than directly on healthcare worker´s wellbeing ( 99 ). Practical implications This study builds on JD-R literature by examining how specific organizational and psychological resources mediate the relationship between digital job demands (DJDs) and healthcare workers’ wellbeing. The findings suggest that DJDs do not always directly and negatively affect positive wellbeing outcomes, at least not in the short term. This aligns with the JD-R model, which posits that job demands may function either as challenges or hindrances, depending on the context. Importantly, the results call for a reassessment of whether traditionally valued resources (e.g., autonomy or psychological resilience) continue to buffer digital demands effectively in today’s rapidly evolving healthcare settings. The mixed findings suggest that some resources may be less impactful in the face of complex digital pressures, highlighting the need to identify which resources are most relevant in contemporary digital work environments. This has important practical implications for organizations, guiding where to focus efforts in creating and enhancing the types of resources most effective in balancing the potential negative impact of digitalization. Moreover, this study contributes to the literature on digital transformation by moving beyond traditional technology acceptance models like TAM and UTAUT ( 100 ), which primarily focus on system usability and adoption. Instead, it emphasizes how digital innovations reshape the work environment by introducing new demands and altering the effectiveness of available resources. This broader perspective encourages healthcare organizations to evaluate digital transformation not just by implementation success, but by how it affects employee wellbeing, motivation, and the overall dynamics of the workplace. Finally, the mixed mediating effects point to underlying mechanisms in how healthcare workers experience and respond to digital innovations. Digital technologies are not neutral tools simply imposed on staff—they are embedded in socially constructed work systems, where employees actively interpret, adapt to, and shape the role of technology in their daily tasks. This perspective challenges linear models of technology use and underscores the importance of health organizations and managers allowing for employee agency in navigating digital change. Limitations and future research This study has three main limitations. First, this study relied on cross-sectional self-reported data from a specific group of healthcare workers in the UK, which limits causal interpretations and generalizability across other healthcare roles or sectors. However, all the questions were from validated instruments, and robust statistical methods were used to analyze the data, reducing response bias. Second, while the study included key psychological and organizational resources, it did not capture all possible factors that may influence how digital job demands impact wellbeing, such as leadership behavior or organizational culture. Third, although the structural equation modeling approach provides comprehensive insights into complex relationships, the interpretation of mediation effects can still be influenced by unmeasured variables or contextual nuances not captured in the model. Accordingly, several areas warrant further investigation to deepen our understanding of the conditions under which digital job demands function as either enablers or barriers to employee wellbeing. First, the study can be replicated in other sectors and compared to population data to explore possible discrepancies or variation in healthcare across other nations. Additionally, other resources such as leadership style and other DJDs, such as information overload, can be further investigated. Future research should explore how contextual factors, such as job role, industry, and organizational culture, shape employees’ perceptions of digital demands as either challenges or hindrances ( 93 , 94 ). The majority of the sample in this study are women and nurses, and as a result, future research can further investigate the gender and occupational differences as factors in furthering knowledge of how to navigate digital demands in healthcare sector. Another possibility is to undertake a longitudinal study, as a longitudinal perspective is worthwhile, especially as resources fluctuate. This is evident as having more or less of the same resources ( 101 ) and job demand, can in turn become a challenge. In the long term, resources can also act as a hindrance and negatively impact the wellbeing of health workers ( 29 ). Additionally, rather than viewing employees as passive recipients of digital technologies, our findings suggest that future research should explore how employees actively navigate digital work environments, including how they leverage resources, cope with job demands, and exercise agency in response to digitalization. Conclusion This study offers intriguing insights into how digital job demands and resources influence employee wellbeing in healthcare. The findings highlight the complexity of digital systems and their varied impact on healthcare workers. While digital job resources support job satisfaction and thriving, the influence of digital job demands appears to be more context-dependent. The lack of significant effects for some digital job demands, along with mixed findings on the mediating role of resources, suggests a need for a deeper understanding of the specific pressures created by digitalization, and which resources are genuinely effective in mitigating them. Theoretically, this study contributes to a better understanding of how digital innovations interact with personal and organizational resources in shaping employee wellbeing. Practically, it provides direction for healthcare organizations in navigating the dual impacts of digitalization, both enabling and challenging, while aiming to support their workforce. Overall, this study contributes to the growing field of digital health services research by emphasizing the importance of reassessing which resources are most relevant and effective in today’s digitally evolving healthcare environment. Abbreviations DIs: Digital innovations; DJDs: Digital job demands; EHR: Electronic health records; JD-R model: Job Demands-Resources model; TAW: Thriving at work; JS: Job satisfaction; DIS: Digital innovations support; DWO: Digital work overload; DSO: Digital system overload; TAM: Technology Acceptance Model; UTAUT: Unified Theory of Acceptance and Use of Technology; SEM: Structural equation modelling; CB-SEM: Covariance based structural equation modelling; CFA: Confirmatory factor analysis; MLR: Maximum likelihood estimation; SD: Standard deviation; CR: Composite reliability ; AVE: Average variance extracted; CFI: Comparative Fit Index (CFI); TLI: Tucker-Lewis Index ; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Square Residual; R²: Coefficient of determination; CDSS: Clinical decision support systems; CI: Confidence interval; BCa CI: Bias-Corrected and Accelerated Confidence Interval Declarations Ethics approval and consent to participate: This study was conducted in accordance with the ethical guidelines of the Norwegian Agency for Shared Services in Education and Research (SIKT), which align with the Declaration of Helsinki. As the study did not involve the collection of personal data, traceable IP addresses, or any clinical intervention, SIKT evaluated the project as ethically acceptable and determined that a formal reference number was not required. Kristiania University College served as the responsible institution for data management. Informed consent was obtained from all participants. On the first page of the online survey, participants were informed about the purpose of the study, the voluntary nature of participation, data anonymity, data storage procedures, potential reuse of data for research purposes, and their right to withdraw at any time without consequence. In accordance with the Norwegian Personal Data Act §§2 and 8 no. 1, participants provided free and informed consent by clicking "Next" to proceed with the survey. Consent for publication: Not applicable. Availability of data and materials: The questionnaire and all items were newly designed for this study and have not been previously published or used in other publications. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request. Competing interests: The author declares that there are no competing interests. Funding: Not applicable Author contributions: PA contributed to the preparation, development, analysis, and mainly drafted the manuscript. BRM contributed to the development of questionnaire, and to the manuscript draft. EN contributed to the study design, initial analysis and to the manuscript draft. All authors approved the final draft. Acknowledgements The authors would like to thank Mohsen Taheri Shalmani for his guidance regarding analysis in R software and Professor Moutaz Haddara for his feedback on the article’s draft. 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Available from: https://www.jstor.org/stable/41410412 Halbesleben JRB, Neveu JP, Paustian-Underdahl SC, Westman M. Getting to the “COR”: Understanding the Role of Resources in Conservation of Resources Theory. J Manag [Internet]. 2014 Jul 1 [cited 2025 Apr 2];40(5):1334–64. Available from: https://doi.org/10.1177/0149206314527130 Tables Tables 1, 2 and 5 are available in the Supplementary Files section. Additional Declarations No competing interests reported. Supplementary Files Table125MeasurementinstrumentsdetailsMeasurementmodelresultsResultsofmediationanalysis.docx AppendixADescriptivestatistics.docx AppendixBPolychoricCorrelationsforConstructIndicators.docx QuestionnairePABMEN2025.pdf Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Wellbeing","fulltext":[{"header":"Background","content":"\u003cp\u003eDigital innovations (DIs) are being adopted at an accelerating rate to address healthcare sector challenges such as heavy workloads, staff shortages, financial constraints, and pandemic-related pressures, aiming to create a more inclusive, accessible, and effective healthcare system (1,2). This rapid adoption of Dis however, has had dual impacts on healthcare workers\u0026apos; workplace wellbeing. On the positive side, DIs have been argued to create several beneficial outcomes,\u0026nbsp;such as, though not limited to, enhancing healthcare resilience when facing environmental uncertainty and unpredictability, such as the COVID-19 pandemic\u0026nbsp;(3,4). Conversely, digitalization has also introduced various negative effects,\u0026nbsp;for instance\u0026nbsp;increasing or duplicating workloads, or limiting the time available for workers to adequately learn new systems\u0026nbsp;(5). Previous studies on the impact of digitalization in healthcare sector have frequently focused on the patient\u0026nbsp;(6,7)\u0026nbsp;or\u0026nbsp;organizational outcomes\u0026nbsp;(8,9),\u0026nbsp;while failing to pay adequate attention to employee\u0026acute;s wellbeing. The understanding of digital innovations and its impact on healthcare workers is of importance as healthcare workers\u0026apos; wellbeing not only influences individual outcomes but also predicts organizational outcomes such as absenteeism and voluntary turnover\u0026nbsp;(10\u0026ndash;12), as well as significantly impacts patient care quality and outcomes\u0026nbsp;(13\u0026ndash;17). Thus, there is a clear gap and need for further knowledge and understanding of how digital innovations and workplace conditions in the healthcare sector influence healthcare workers\u0026apos; wellbeing.\u003c/p\u003e\n\u003cp\u003eOrganizational studies highlight\u0026nbsp;that fostering resources across individual, group, leader, and organizational levels significantly enhances employee wellbeing and performance, particularly when facing workplace demands (18). The Job Demands-Resources (JD-R) model of organization behavior by Demerouti et al. (19) investigates the dynamic of resources and demands and their outcome on employees\u0026rsquo; wellbeing and performance. The JD-R model consists of two complementary processes: a health impairment process, where excessive demands may lead to strain, and, importantly, a motivational process, where resources\u0026nbsp;at various levels,\u0026nbsp;organizational, social, and personal,\u0026nbsp;actively promote engagement, resilience, and wellbeing\u0026nbsp;(19,20).\u0026nbsp;Within the JD-R\u0026nbsp;framework, digital technologies\u0026nbsp;can\u0026nbsp;introduce new workplace demands, potentially impairing wellbeing\u0026nbsp;and performance\u0026nbsp;(21).\u0026nbsp;However,\u0026nbsp;resources, including organizational and personal resources,\u0026nbsp;are argued to\u0026nbsp;act as buffers, effectively reducing the negative impacts\u0026nbsp;of work demands\u0026nbsp;and enhancing employee\u0026nbsp;wellbeing\u0026nbsp;(19,22).\u0026nbsp;This perspective aligns closely with research on technostress, which indicates that negative impacts from\u0026nbsp;DIs\u0026nbsp;are influenced by mediating factors, notably the availability and effectiveness of resources and coping mechanisms\u0026nbsp;(23).\u0026nbsp;In other words, adaptive coping strategies significantly mitigate these adverse effects, whereas maladaptive coping strategies exacerbate them\u0026nbsp;(24).\u0026nbsp;Research shows that\u0026nbsp;organizational and personal resources are critical assets for healthcare workers to manage stress effectively; conversely, insufficient or depleted resources heighten stress and risk of burnout\u0026nbsp;(25,26).\u0026nbsp;Although extensive research using JD-R\u0026nbsp;model has examined how various demands and resources influence employee outcomes, there remains a notable knowledge gap regarding whether commonly emphasized resources continue to be effective in today\u0026rsquo;s rapidly digitalizing healthcare environment. As\u0026nbsp;DIs\u0026nbsp;reshape work processes, it becomes increasingly essential to reassess the relevance and effectiveness of key psychological and organizational resources in buffering emerging digital job demands\u0026nbsp;(DJDs). Thus, this study aims to address this gap by investigating:\u0026nbsp;To what extent do resources mediate the relationship between digital work demands and healthcare workers\u0026apos; wellbeing in digitalized workplaces?\u003c/p\u003e\n\u003cp\u003eBy addressing the research question above, this study aims to contribute to theoretical insights into the reassessment of key resources in the face of the evolving digital demands in healthcare, as well as practical guidance for health organizations aiming to support employee wellbeing in a digitalized healthcare workplace. We tested our model and hypotheses, as shown in Figure 1, using survey data collected from healthcare workers.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eReview of literature and hypothesis development\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMuch of previous DIs research on wellbeing of employees emphasizes negative outcomes such as burnout and technostress (23,27), leaving gaps in the understanding of how to mitigate the negative impacts of digitalization on the healthcare workers\u0026apos; wellbeing through examining the role of resources on positive wellbeing outcome (28).\u0026nbsp;To address this gap, this study has adopted the\u0026nbsp;JD-R\u0026nbsp;model\u0026nbsp;(19).\u0026nbsp;This study emphasizes the motivational process of JD-R model, which posits that resources foster positive wellbeing outcomes like engagement and satisfaction \u0026nbsp;(19,20)\u0026nbsp;and buffer the negative impacts of the demands on wellbeing\u0026nbsp;(29)\u0026mdash;see Figure 1.\u003c/p\u003e\n\u003cp\u003eEmployee wellbeing\u003c/p\u003e\n\u003cp\u003eGeneral theories of individual wellbeing are often derived from two major viewpoints. First, the hedonic view, which emphasizes the pursuit of pleasure and subjective happiness while minimizing pain as an essential components of wellbeing. Second, the eudaimonia approach emphasizes finding meaning and self-realization as central to wellbeing (30). Although some studies of wellbeing have focused on the hedonic and some on the eudemonic perspective, the findings of these studies have revealed that individuals who pursue both hedonic and eudemonic well-being often experience greater overall well-being and live a \u0026ldquo;full life\u0026rdquo; (31,32). This notion suggests the importance of including both perspectives in understanding employee wellbeing. Simultaneously, wellbeing is a complex and multifaceted construct that extends beyond ill-health avoidance to positive states (33). Subsequently, in this paper, wellbeing is formulated and understood\u0026nbsp;as\u0026nbsp;\u003cem\u003ea dynamic balance of psychological, physical, and social resources in response to external pressures,\u0026nbsp;\u003c/em\u003e\u003cem\u003eresulting in positive outcomes\u0026nbsp;\u003c/em\u003e\u003cem\u003eencompassing pleasure and life satisfaction\u003c/em\u003e\u003cem\u003e\u0026nbsp;(hedonic)\u003c/em\u003e\u003cem\u003e, as well as purpose and personal growth\u003c/em\u003e \u003cem\u003e(eudemonic)\u003c/em\u003e (34,35).\u0026nbsp;This definition can be applied to workplace wellbeing, and it is aligned with the JD-R model, which emphasizes the balance between resources and demands as critical to employee wellbeing and motivation. Following this definition, this study adopts two notions of \u0026lsquo;thriving at work\u0026rsquo; and \u0026lsquo;job satisfaction\u0026rsquo; as indicators of employees\u0026rsquo; workplace wellbeing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eFirst,\u0026nbsp;\u003cem\u003ej\u003c/em\u003e\u003cem\u003eob\u0026nbsp;\u003c/em\u003e\u003cem\u003es\u003c/em\u003e\u003cem\u003eatisfaction\u003c/em\u003e\u003cem\u003e\u0026nbsp;(JS)\u003c/em\u003e reflects the hedonic aspect of workplace wellbeing by emphasizing pleasure, contentment, and satisfaction from work (36). JS suggests the happiness and fulfillment of individuals derive from their work and has previously been found to closely relate to organizational outcomes such as productivity and retention and individuals\u0026rsquo; overall wellbeing (37,38). The increasing integration of DIs in the workplace necessitates an agile approach in examining their impact on JS, as DIs often introduce job demands that can negatively influence satisfaction (39\u0026ndash;41). Previous research examining factors affecting physician job satisfaction reveals that in their findings, electronic health records have been found to reduce professional satisfaction due to time-intensive tasks and interference with patient care (14). However, there is still a lack of further understanding of the role of digital job demands on job satisfaction for a broader sample that includes health workers who fall outside the physician category. Second,\u0026nbsp;Thriving at Work\u0026nbsp;(TAW)\u0026nbsp;is\u0026nbsp;defined as the psychological state of experiencing vitality, reflecting energy and passion, and learning involving growth and skill acquisition\u0026nbsp;(42,43). Thriving as growing in terms of both learning and vitality captures both the hedonic (vitality) and eudaimonic (learning) aspects of psychological functioning and development\u0026nbsp;(44,45). Previous research on\u0026nbsp;TAW has found TAW to positively and significantly relate to better mental and physical health, life satisfaction, job performance, and work engagement\u0026nbsp;(46\u0026ndash;48). The study of Walumba et al. (46) offered valuable insights into the multilevel understanding of TAW for creating sustainable organizational performance. In line with previous studies arguing digital innovation\u0026apos;s usefulness in furthering sustainable organizations, the gap in its understanding is still underexplored. Therefore, this study furthers this knowledge by examining the role of digital job demands on TAW in levering overall sustainable organizational performance. In doing so, and in line with Walumba et al. (46) and Zhai et al.\u0026nbsp;(47), this study offers further knowledge on how health organizations can promote work performance through healthcare workers\u0026acute; wellbeing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eJob demands and digital context\u003c/p\u003e\n\u003cp\u003eJob demands are aspects of work requiring sustained physical, emotional, or cognitive effort that potentially disrupts wellbeing by consuming energy (19,29). Examples include workload and work pressure (29). DIs can act as both resources (e.g., automating tasks) and demands (e.g., increasing overload) (49).\u0026nbsp;Drawing on the definition of DIs from Hund et al. (50), this paper defines DIs in healthcare as the creation or adoption and exploitation of novel technologies, processes, or systems\u0026mdash;such as electronic health records (EHRs), telemedicine, and AI-driven diagnostics\u0026mdash;that leverage digital tools and data to improve healthcare delivery, patient outcomes, and operational efficiency\u0026nbsp;(50).\u0026nbsp;In this study, following Scholze and Hecker\u0026rsquo;s (2024)\u0026nbsp;definition,\u0026nbsp;DJDs\u0026nbsp;refer to job demands arising from using DIs, such as information overload, system inefficiencies, and new skill requirements (21). Previous studies have found that DJDs can reduce job satisfaction and commitment\u0026nbsp;(51), blur work-life boundaries\u0026nbsp;(52), and increase turnover intentions and workplace detachment (21). A recent meta-analysis study on technostress and employee wellbeing revealed that digital stressors like technostress exacerbate exhaustion and diminish well-being\u0026nbsp;(53). Though the meta-study by Wang et al. (53) is not in the healthcare setting, it provides insights and gaps valuable for healthcare settings, a gap this study aims to contribute to.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWhile much of DIs research focuses on singular technologies (e.g., mobile devices (54),\u0026nbsp;this study adopts a broader perspective, focusing not only on a single technology but on the DJDs caused by various technologies that healthcare workers use in their daily work tasks. Additionally, limited research has explored the DJDs within the healthcare sector (49). Based on the JD-R model, demands are context dependent and can vary from organization to organization and sector to sector\u0026nbsp;(29). Thus, this study brings attention to\u0026nbsp;digital system overload (DSO) and digital work overload (DWO), which have been argued to be relevant to the healthcare sector. Previous research have shown major challenges related to digital systems in healthcare\u0026nbsp;(1,55), and work overload is also a common issue in the sector\u0026nbsp;(56).\u0026nbsp;\u003cem\u003eDSO\u003c/em\u003ereflects the complexity and feature excess of digital tools, such as electronic\u0026nbsp;health records (EHRs), which can overwhelm users and reduce usability\u0026nbsp;(49). Not surprisingly, EHRs\u0026rsquo; time-intensive navigation disrupts patient care, which also results in lowering professional satisfaction\u0026nbsp;(14).\u0026nbsp;\u003cem\u003eDWO\u003c/em\u003e occurs when digital tools, intended to save labor, escalate tasks, volume, and pace (51). Previous findings have shown that frequent use of digital technologies, while improving efficiency, often increases perceived workload\u0026nbsp;(57), reducing vitality\u0026nbsp;(24).\u0026nbsp;Subsequently, in this study we propose that DJDs, termed DWO and DSO, are negatively associated with employee wellbeing, specified as JS and TAW. The suggested hypotheses are formulated as follows:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH1\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO (a) and DSO (b)\u003c/em\u003e\u003cem\u003eare\u003c/em\u003e\u003cem\u003e\u0026nbsp;negatively associated with\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e2\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO (a) and DSO (b)\u003c/em\u003e\u003cem\u003eare\u003c/em\u003e\u003cem\u003e\u0026nbsp;negatively associated with\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eOrganizational and individual resources\u003c/p\u003e\n\u003cp\u003eIn a broad sense, resources are defined as \u0026ldquo;anything perceived by the individual to help attain his or her goals\u0026rdquo; (25). Job resources are physical, psychological, social, or organizational aspects that (a) aid work goal achievement, (b) reduce demands and their costs, and (c) promote growth and development (19).\u0026nbsp;Nielsen et al. (58) categorize resources into four types:\u0026nbsp;i)\u0026nbsp;individual-level (e.g., self-efficacy, competence),\u0026nbsp;ii)\u0026nbsp;group-level (e.g., social support, teamwork),\u0026nbsp;iii),\u0026nbsp;leader-level (e.g., leadership style, leader-member exchange), and\u0026nbsp;iv),\u0026nbsp;organizational-level (e.g., job design, management practices)\u0026nbsp;(58).\u0026nbsp;These resources can act both as a booster that increases the wellbeing outcome and as buffers by mitigating the impact of work demands\u0026nbsp;(29). Therefore, resources\u0026nbsp;can\u0026nbsp;enhance wellbeing by enabling effective performance and coping\u0026nbsp;(58).\u0026nbsp;However, to the authors\u0026rsquo; knowledge,\u0026nbsp;a dart of health services research studies\u0026nbsp;have yet to\u0026nbsp;explore resources as\u0026nbsp;buffers for\u0026nbsp;the\u0026nbsp;DJDs. Consequently, as depicted in the study\u0026apos;s conceptual model, illustrated in Figure 1, this paper focuses on two types of resources as a buffer: organizational-level resources, such as digital innovation support (DIS) and autonomy, and individual level resources, such as psychological resilience. First, and as already mentioned, the study has focused on two\u0026nbsp;organizational\u0026nbsp;level\u0026nbsp;resources, namely DIS, such as\u0026nbsp;technical assistance for digital tools,\u0026nbsp;and autonomy,\u0026nbsp;which entails the level of\u0026nbsp;control over task execution. Both DIS and autonomy are factors previous studies have argued are beneficial to\u0026nbsp;managing DJDs and achieving goals\u0026nbsp;(58,59)\u0026nbsp;through enhancing technological coping\u0026nbsp;(26,60)\u0026nbsp;and fostering control (20),\u0026nbsp;respectively.\u0026nbsp;Second, the study focuses also on\u0026nbsp;\u003cem\u003epersonal\u003c/em\u003e\u003cem\u003e\u0026nbsp;or individual level\u003c/em\u003e\u003cem\u003e\u0026nbsp;resources\u003c/em\u003e\u003cem\u003e, as shown in Figure 1, namely psychological r\u003c/em\u003e\u003cem\u003eesilienc\u003c/em\u003e\u003cem\u003ee. Psychological resilience is\u0026nbsp;\u003c/em\u003eadaptability to stressors, which may\u0026nbsp;enable\u0026nbsp;coping and buffer demand effects\u0026nbsp;\u0026nbsp;(25,59). Thus, it is\u0026nbsp;proposed\u0026nbsp;here\u0026nbsp;that\u0026nbsp;DIS,\u0026nbsp;autonomy, and\u0026nbsp;psychological\u0026nbsp;resilience mediate DJDs\u0026rsquo; impact on wellbeing, mitigating negative effects and fostering positive outcomes,\u0026nbsp;purposing the following hypothesis:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e3\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDIS (a), autonomy (b), and psychological resilience (c)\u003c/em\u003e\u003cem\u003eare positively associated\u003c/em\u003e\u003cem\u003e\u0026nbsp;with\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e4\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003eDIS (a), autonomy (b), and psychological resilience (c)\u003c/em\u003e\u003cem\u003eare positively associated\u003c/em\u003e\u003cem\u003e\u0026nbsp;with\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW\u003c/em\u003e\u003cem\u003e.\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMediating role of digital innovation support (DIS)\u003c/p\u003e\n\u003cp\u003eOrganizational resources like\u0026nbsp;DIS\u0026mdash;technical assistance and resources for digital tools (e.g., IT help for EHRs)\u0026mdash;can enhance wellbeing by aiding goal achievement and reducing demand costs (61). Although DIs aim to streamline work, their demands can disrupt satisfaction and vitality without support (60). A supportive climate is suggested to boost engagement with technology while training and assistance enhance self-efficacy \u0026nbsp;(60,62,63). Effective DIS resolves technical issues swiftly, minimizing disruptions (64), and mirrors general support\u0026rsquo;s positive effects on satisfaction (60,65). However, the mediating role of DIS in the healthcare sector is underexplored\u0026nbsp;(28). Therefore, the following hypotheses are proposed:\u003cbr\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH5a\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: DIS mediates the relationship between DWO and JS.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH5b\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: DIS mediates the relationship between DWO and TAW.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH5c\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: DIS mediates the relationship between DSO and JS.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH5d\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: DIS mediates the relationship between DSO and TAW.\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMediating role of autonomy\u003c/p\u003e\n\u003cp\u003eAutonomy\u0026nbsp;is understood as one\u0026rsquo;s ability to self-govern one\u0026acute;s work, which includes work scheduling, decisions, and methods (66). Autonomy is deemed as a cornerstone of JD-R resources that enhances wellbeing by fostering goal achievement and intrinsic motivation (19,61). It satisfies one of the core psychological needs (67), boosting job satisfaction and thriving (41). Autonomy-supportive environments improve engagement and psychological health, while controlling ones diminish vitality. In healthcare\u0026rsquo;s digital context, autonomy may empower workers to manage DJDs, such as, though not limited to, EHR complexity, offering flexiblity, preserving satisfaction, and learning (68). For instance, nurses with scheduling control report higher thriving amidst technological pressures (58). In line, a study by Zhang et al. (41) on Chinese healthcare personnel found that perceived job resources, such as autonomy and support, are positively associated with thriving at work (41). Yet, its mediating role in mitigating DJDs\u0026rsquo; effects on positive wellbeing remains underexplored in the health services research (69). Thus, the following hypotheses are formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e6\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ea\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Autonomy mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e6\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Autonomy mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e6\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ec\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Autonomy mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDSO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e6\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ed\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e: Autonomy mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDSO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW\u003c/em\u003e\u003cem\u003e.\u003cbr\u003e\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eMediating role of psychological resilience\u003c/p\u003e\n\u003cp\u003ePsychological resilience is defined as a positive psychological capacity to bunce back after stressful situation (70). Previous studies that have explored the resilience of employees, have found that psychological resilience is a crucial positive resource to overcome stressors and challenges at work (71,72). This is because psychological resilience reframes challenges as growth opportunities (73). In healthcare, resilient workers thrive amidst job stressors (74,75), reporting higher job satisfaction and performance (73). Previous research reveals that DJDs, such as system complexity or task overload, are moderated by psychological resilience through sustained energy and learning, which is achieved by buffering their disruptive effects (76). Despite its relevance, studies exploring psychological resilience as a mediating factor in digital healthcare contexts, are underexplored (10,77). This is particularly evident in studies focusing on discussing psychological resilience in terms of fostering positive outcomes rather than merely reducing strain (58). A meta-analysis study indicates that personal resources received less attention from research in facing demands as compared to organization resources (58). Psychological resilience as a personal resource in coping with the negative impacts of digitalization has received less attention than other individual factors such as personality traits or self-efficacy (28). This evident gap in health services research provides compelling evidence for furthering knowledge on examining psychological resilience as a mediator between the relationships of DJDs, TAW, and JS. We therefore propose the following hypotheses, as formulated:\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e7\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ea\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003ePsychological r\u003c/em\u003e\u003cem\u003eesilience mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e7\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003eb\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003ePsychological r\u003c/em\u003e\u003cem\u003eesilience mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDWO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e7\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ec\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003ePsychological r\u003c/em\u003e\u003cem\u003eesilience mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDSO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eJS\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eH\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003e7\u003c/em\u003e\u003c/strong\u003e\u003cstrong\u003e\u003cem\u003ed\u003c/em\u003e\u003c/strong\u003e\u003cem\u003e:\u0026nbsp;\u003c/em\u003e\u003cem\u003ePsychological r\u003c/em\u003e\u003cem\u003eesilience mediates the relationship between\u0026nbsp;\u003c/em\u003e\u003cem\u003eDSO\u003c/em\u003e\u003cem\u003e\u0026nbsp;and\u0026nbsp;\u003c/em\u003e\u003cem\u003eTAW\u003c/em\u003e\u003cem\u003e.\u003c/em\u003e\u003c/p\u003e"},{"header":"Methodology","content":"\u003cp\u003eThe aim of this study has been to furthering knowledge on the role of digital job demands on healthcare employee\u0026acute;s wellbeing. As such, the study has focused on examining psychological and organizational resources as buffers in tackling digital job demands (DJDs). Consequently, the study builds upon the JD-R model, and as depicted in the study\u0026apos;s conceptual model, Figure 1. \u0026nbsp;Data were collected via a structured questionnaire in a two-phase process to investigate the role of DJDs on healthcare workers\u0026rsquo; wellbeing, with analysis conducted using covariance-based structural equation modeling (CB-SEM).\u003c/p\u003e\n\u003cp\u003eParticipants and procedure\u003c/p\u003e\n\u003cp\u003eFollowing the JD-R model context-specific approach (29), a two-phase data collection was employed. \u003cem\u003ePhase 1\u003c/em\u003e: Expert Review and Pilot Study. Ten healthcare experts (nurses and doctors from various hospital units) reviewed the questionnaire for face validity, refining DJDs and resources\u0026rsquo; constructs. First, drawing on technostress (64) and technology overload literature (49), an initial list of constructs for DJDs (e.g., techno-invasion, information overload) was created. Then, experts were asked to choose the most relevant DJDs based on their work. They went through the items of each construct and prioritized system overload, work overload, and information overload, respectively, as being the most relevant. Then, a pilot study (n = 60) via Prolific was conducted, and subsequently, items and wording were refined, and feasibility was confirmed. \u003cem\u003ePhase 2\u003c/em\u003e: Main Study. We recruited 292 UK healthcare workers via Prolific (December 2024), ensuring data quality with attention checks and eligibility criteria (e.g., active healthcare role) (78). The healthcare workers\u0026acute; role included doctors, nurses, paramedics, emergency dispatchers, or medical services personnel. This sample size supports CB-SEM\u0026rsquo;s requirements of minimum \u003cem\u003eN\u0026nbsp;\u003c/em\u003e~ 200 (88). No missing data were observed across the 292 responses. The sample sociodemographic characteristic is shown in Appendix A. The demographic shows that most participants were primarily women (77%), nurses (65%), held a bachelor\u0026rsquo;s degree (55%), and worked full-time (65%). Leadership roles were common (35% Team Leaders).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eEthical Considerations\u003c/p\u003e\n\u003cp\u003eData collection was conducted in accordance with the ethical guidelines provided by the Norwegian Agency for Shared Services in Education and Research (SIKT) that is in line with Helsinki declaration. Since the study does not involve personal data, traceable IP addresses, or clinical intervention, SIKT approved the study without assigning a specific reference number. Data were collected through Nettskjema, a secure Norwegian online survey platform that automatically anonymizes responses. Kristiania University College is the responsible institution for data management, and the collected data will be securely stored on Kristiania\u0026apos;s servers in protected and locked facilities. Participants were informed on the first page of the survey, prior to consenting, that their participation was entirely voluntary and that they had the right to withdraw at any point without consequence. After reviewing the procedure, project content, and mentioned information on anonymity and data collection and storage, Participants provided informed consent by clicking \u0026quot;Next\u0026quot; to proceed with the survey.\u003c/p\u003e\n\u003cp\u003eMeasurement instruments\u003c/p\u003e\n\u003cp\u003eThe study survey was developed by adapting items from established measures used in previous studies (see Table 1). The survey used in this study is part of a larger research project and has not been previously published. All constructs were measured using a 7-point Likert scale (1 = Strongly Disagree, 7 = Strongly Agree), asking the respondent to answer while reflecting on digital technologies they are using most in their daily healthcare work, such as Electronic Health Records (EHRs), Telemedicine platforms, Clinical Decision Support Systems (CDSS), etc. Measures were adopted from validated instruments (see Procedure). Table 1 details constructs, sub-constructs, item counts, examples of items, and sources.\u003c/p\u003e\n\u003cp\u003e[ Insert Table 1here ]\u003c/p\u003e\n\u003cp\u003eData analysis\u003c/p\u003e\n\u003cp\u003eThe conceptual model and the hypothesized relationship were tested using CB-SEM in R 4.4.3 software. Analyses were conducted based on the psych (79), lavaan (80), semTools (81) and polycor (82) R packages. Analysis followed a two-step process. In the first step, the measurement model was assessed. Maximum likelihood estimation based on polychoric correlations was applied (Appendix B) as all variables were ordinal (83). Two items from TAW were removed (thrivev2, thrivev4) due to high correlations with items in JS. When the measurement model assessment was satisfactory, the second step was to assess the structural model. In step 2, CB-SEM tested the hypothesized relationships (H1\u0026ndash;H7). First, model fit was evaluated using Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square Error of Approximation (RMSEA), Standardized Root Mean Square Residual (SRMR), and coefficient of determination (R\u0026sup2;). Then, the direct relation (H1-H4) and the mediations (H5-H7) were evaluated with 5,000 bootstrap samples for mediation analysis (84).\u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eMeasurement model results\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn assessing the psychometric properties of the factors used in this study, a confirmatory factor analysis (CFA) was performed using the robust maximum likelihood estimation (MLR). CFA showed an overall satisfactory model fit (CFI = 0.910, TLI = 0.900, RMSEA = 0.068, and SRMR = 0.071). These values indicate a good model fit, suggesting that the measurement model adequately represents the data. Table 2 summarizes the measurement model, mean, and standard deviation. Reliability was estimated using composite reliability (CR) rather than Cronbach\u0026rsquo;s alpha (CA) due to CA limitations (85). All loadings meet acceptable thresholds and are statistically significant (CR \u0026gt;0.7) (86), showing good item-construct relationships, except for two items at the borderline (aut1: 0.48 and thrivel4: -0.47). These items were removed from the data set in step 2 of the analysis. Convergent validity was assessed through factor loading and average variance extracted (AVE) \u0026ge; 0.50, indicating satisfactory properties and statistically significant values and, thus, good convergent validity (85). \u0026nbsp;\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e[ Insert Table 2 here ]\u003c/p\u003e\n\u003cp\u003eAs illustrated in Table 3, discriminant validity was assessed using the Fornell\u0026ndash;Larcker criterion by comparing each construct\u0026rsquo;s AVE with the squared correlations between constructs (87). All constructs met the criterion, with AVE values exceeding the squared correlations with other constructs, except for a marginal case between TAW (AVE = 0.501) and JS (squared correlation = 0.519). Given the conceptual proximity between these two constructs, this result is interpreted as acceptable (88).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 3\u003c/strong\u003e Discriminant validity, squared correlations, AVE and Multicollinearity (VIF)\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"577\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003eConstruct\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eVIF\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e1. DWO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.\u003c/em\u003e\u003cem\u003e39\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e2. DSO\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.319\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.47\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e3. DIS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.055\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.127\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.\u003c/em\u003e\u003cem\u003e19\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e4. Resilience\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.033\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.054\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.07\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e5. TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.131\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.046\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e6. JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.040\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.102\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.092\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.519\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e-\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e7. Autonomy\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e0.025\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.011\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.065\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.026\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e1.00\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cem\u003e1.0\u003c/em\u003e\u003cem\u003e2\u003c/em\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 111px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAVE\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 64px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.734\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.532\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.596\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.721\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.501\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.766\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.602\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 50px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote:\u003cem\u003e\u0026nbsp;\u003c/em\u003eAVE: average variance extracted; VIF: Variance Inflation Factor; DWO: digital work overload; DSO: digital system overload; DIS: digital innovation support; Resilience: psychological resilience; TAW: Thriving at work; JS: job satisfaction\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStructural\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003ee\u003c/strong\u003e\u003cstrong\u003equation\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003em\u003c/strong\u003e\u003cstrong\u003eodeling\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;(SEM)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eBefore measuring the structure model, the multicollinearity was tested using the variance inflation factor (VIF) (Table 3). VIF values are between 1.02 and 1.47, well below the acceptable threshold of 3.0 (85). The structural model showed an acceptable fit ( CFI = 0.902, TLI = 0.892, RMSEA = 0.071, except SRMR = 0.114). Given the elevated SRMR value, three items with high cross-loadings were removed (aut2, thrivev3, and res1). This adjustment substantially improved the model fit (CFI = 0.932, TLI = 0.922, RMSEA = 0.064, and SRMR = 0.062). These values meet the commonly accepted thresholds for good model fit (CFI and TLI \u0026ge; 0.90, RMSEA, and SRMR \u0026le; 0.08 (89). R\u0026sup2; values indicated that the model accounted for 20% of the variance in Job Satisfaction and 22.9% in TAW. Table 4 presents standardized path coefficients. DWO and DSO were not significantly associated with JS or TAW (p \u0026gt; .05). In contrast, DIS, psychological resilience, and autonomy had significant positive effects on both wellbeing outcomes (p \u0026lt; .05), supporting H3 and H4 with one exception: Psychological resilience was not significantly associated with TAW (p \u0026gt; .05).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable 4\u003c/strong\u003e Structural model direct relations\u0026nbsp;\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"552\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHypothesis\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 167px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePath\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eStd. Coef (\u0026beta;)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ep-value\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eH1a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDWO\u0026nbsp;\u0026agrave;\u0026nbsp;JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.087\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.282\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 120px;\"\u003e\n \u003cp\u003eH1b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDSO\u0026nbsp;\u0026agrave;\u0026nbsp;JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.141\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.096*\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH2a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDWO\u0026nbsp;\u0026agrave;\u0026nbsp;TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.070\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.372\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH2b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDSO\u0026nbsp;\u0026agrave;\u0026nbsp;TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e-0.086\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.294\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH3a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDIS\u0026nbsp;\u0026agrave;\u0026nbsp;JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.250\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH3b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eResilience\u0026nbsp;\u0026agrave;\u0026nbsp;JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.208\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.003***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH3c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eAutonomy\u0026nbsp;\u0026agrave;\u0026nbsp;JS\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.160\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.010**\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH4a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eDIS\u0026nbsp;\u0026agrave;\u0026nbsp;TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH4b\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eResilience\u0026nbsp;\u0026agrave;\u0026nbsp;TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 120px;\"\u003e\n \u003cp\u003eH4c\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 167px;\"\u003e\n \u003cp\u003eAutonomy\u0026nbsp;\u0026agrave;TAW\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 149px;\"\u003e\n \u003cp\u003e0.266\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 117px;\"\u003e\n \u003cp\u003e0.000***\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003eNote. DWO: digital work overload; DSO: digital system overload; DIS: digital innovation support; JS: Job Satisfaction; TAW: thriving at work. p \u0026lt; .05 values\u003c/p\u003e\n\u003cp\u003eSubsequently, mediation effects were tested with 5000 bootstrap samples (BCa method) in two steps (90). First, the indirect effect was estimated. Then, the statistical significance of each mediator was tested. Results are illustrated in Table 5. Mediation analysis that was supported was the indirect effect of DSO on JS through DIS (\u0026beta; = \u0026minus;0.085, 95% CI [\u0026minus;0.181, \u0026minus;0.031], p = .015) and the indirect effect of DSO on TAW through DIS (\u0026beta; = \u0026minus;0.114, 95% CI [\u0026minus;0.229, \u0026minus;0.051], p = .005).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eTable\u0026nbsp;\u003c/strong\u003e\u003cstrong\u003e5\u003c/strong\u003e\u0026nbsp; Results of Mediation Analysis Using Bootstrap Estimates\u003c/p\u003e\n\u003cp\u003e[Insert Table 5 here]\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eTheoretical implications\u003c/p\u003e \u003cp\u003eThe findings of this study prompt a reassessment of how key psychological and organizational resources function in the face of evolving digital job demands in healthcare. Two key areas of discussion are raised. First, the marginal effects of DSO and DWO suggest that these demands do not uniformly impact job satisfaction or thriving at work among healthcare workers. This can be interpreted through the lens of the Challenge\u0026ndash;Hindrance framework within the JD-R model (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e). According to this framework, challenge demands may foster motivation and personal growth, while hindrance demands tend to generate strain and disengagement. However, even challenging demands can negatively affect well-being in the long run, despite short-term motivational benefits (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Consistent with this, prior research has suggested that digital job demands such as ,system complexity and information overload, an function as either challenges or hindrances depending on contextual and individual factors (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). This perspective may help explain this study's unexpected positive association between digital work overload and job satisfaction. Similarly, Podsakoff et al. (\u003cspan citationid=\"CR91\" class=\"CitationRef\"\u003e91\u003c/span\u003e) note that hindrance stressors negatively affect performance via strain, while challenge stressors can exert both positive and negative effects through motivation and burnout pathways (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e). Nonetheless, these findings contrast with the broader technostress literature, which consistently shows that stressors such as techno-overload diminish job satisfaction (\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e). One potential explanation for the findings of this study, is the dual nature of digital job demands, where initial feelings of engagement or productivity may mask the cumulative negative effects on well-being over time (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSecond, the findings confirm previous studies on the role of resources in supporting positive wellbeing outcomes, consistent with research in other sectors (\u003cspan citationid=\"CR67\" class=\"CitationRef\"\u003e67\u003c/span\u003e, \u003cspan citationid=\"CR95\" class=\"CitationRef\"\u003e95\u003c/span\u003e, \u003cspan citationid=\"CR96\" class=\"CitationRef\"\u003e96\u003c/span\u003e). However, the effect of psychological resilience on TAW was not supported, unlike previous research in other contexts that indicated a relationship between psychological resilience and TAW (\u003cspan citationid=\"CR97\" class=\"CitationRef\"\u003e97\u003c/span\u003e). The mediating roles of resources in buffering the potential negative impacts of DJDs yielded mixed results. Although prior research has shown that autonomy plays a significant role in mitigating the effects of job demands (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e), this was not supported in our study concerning DJDs and healthcare workers' wellbeing. This may be due to contextual factors. For instance, a multilevel study by Reyes-Luj\u0026aacute;n et al. (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e) on \u003cem\u003eN\u003c/em\u003e\u0026thinsp;=\u0026thinsp;1,232 hospital workers, showed that workplace autonomy can positively or negatively affect burnout depending on role ambiguity and the worker\u0026rsquo;s age (\u003cspan citationid=\"CR98\" class=\"CitationRef\"\u003e98\u003c/span\u003e). Similarly, the structured nature of the healthcare sector, characterized by strict protocols and compliance requirements, may limit the degree of autonomy healthcare workers experience in using digital technologies (\u003cspan citationid=\"CR92\" class=\"CitationRef\"\u003e92\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePsychological resilience also did not demonstrate a mediating effect. While psychological resilience is often considered a protective factor in high-stress environments, it may not significantly buffer the effects of DJDs on positive wellbeing outcomes in this context. This suggests that not all personal resources function uniformly across different demands or workplace conditions. In contrast, DIS exhibited an indirect-only mediation effect. This indicates that DIS plays a key role in transmitting the effects of DSO on JS and TAW. While the total (direct) effect of digital system overload on wellbeing was not significant, the significant indirect (partial mediation) path through DIS suggests that this relationship may still be meaningful. As such, DSO is operating primarily through support mechanisms rather than directly on healthcare worker\u0026acute;s wellbeing (\u003cspan citationid=\"CR99\" class=\"CitationRef\"\u003e99\u003c/span\u003e).\u003c/p\u003e \u003cp\u003ePractical implications\u003c/p\u003e \u003cp\u003eThis study builds on JD-R literature by examining how specific organizational and psychological resources mediate the relationship between digital job demands (DJDs) and healthcare workers\u0026rsquo; wellbeing. The findings suggest that DJDs do not always directly and negatively affect positive wellbeing outcomes, at least not in the short term. This aligns with the JD-R model, which posits that job demands may function either as challenges or hindrances, depending on the context. Importantly, the results call for a reassessment of whether traditionally valued resources (e.g., autonomy or psychological resilience) continue to buffer digital demands effectively in today\u0026rsquo;s rapidly evolving healthcare settings. The mixed findings suggest that some resources may be less impactful in the face of complex digital pressures, highlighting the need to identify which resources are most relevant in contemporary digital work environments. This has important practical implications for organizations, guiding where to focus efforts in creating and enhancing the types of resources most effective in balancing the potential negative impact of digitalization.\u003c/p\u003e \u003cp\u003eMoreover, this study contributes to the literature on digital transformation by moving beyond traditional technology acceptance models like TAM and UTAUT (\u003cspan citationid=\"CR100\" class=\"CitationRef\"\u003e100\u003c/span\u003e), which primarily focus on system usability and adoption. Instead, it emphasizes how digital innovations reshape the work environment by introducing new demands and altering the effectiveness of available resources. This broader perspective encourages healthcare organizations to evaluate digital transformation not just by implementation success, but by how it affects employee wellbeing, motivation, and the overall dynamics of the workplace.\u003c/p\u003e \u003cp\u003eFinally, the mixed mediating effects point to underlying mechanisms in how healthcare workers experience and respond to digital innovations. Digital technologies are not neutral tools simply imposed on staff\u0026mdash;they are embedded in socially constructed work systems, where employees actively interpret, adapt to, and shape the role of technology in their daily tasks. This perspective challenges linear models of technology use and underscores the importance of health organizations and managers allowing for employee agency in navigating digital change.\u003c/p\u003e \u003cp\u003eLimitations and future research\u003c/p\u003e \u003cp\u003eThis study has three main limitations. First, this study relied on cross-sectional self-reported data from a specific group of healthcare workers in the UK, which limits causal interpretations and generalizability across other healthcare roles or sectors. However, all the questions were from validated instruments, and robust statistical methods were used to analyze the data, reducing response bias. Second, while the study included key psychological and organizational resources, it did not capture all possible factors that may influence how digital job demands impact wellbeing, such as leadership behavior or organizational culture. Third, although the structural equation modeling approach provides comprehensive insights into complex relationships, the interpretation of mediation effects can still be influenced by unmeasured variables or contextual nuances not captured in the model.\u003c/p\u003e \u003cp\u003eAccordingly, several areas warrant further investigation to deepen our understanding of the conditions under which digital job demands function as either enablers or barriers to employee wellbeing. First, the study can be replicated in other sectors and compared to population data to explore possible discrepancies or variation in healthcare across other nations. Additionally, other resources such as leadership style and other DJDs, such as information overload, can be further investigated. Future research should explore how contextual factors, such as job role, industry, and organizational culture, shape employees\u0026rsquo; perceptions of digital demands as either challenges or hindrances (\u003cspan citationid=\"CR93\" class=\"CitationRef\"\u003e93\u003c/span\u003e, \u003cspan citationid=\"CR94\" class=\"CitationRef\"\u003e94\u003c/span\u003e). The majority of the sample in this study are women and nurses, and as a result, future research can further investigate the gender and occupational differences as factors in furthering knowledge of how to navigate digital demands in healthcare sector. Another possibility is to undertake a longitudinal study, as a longitudinal perspective is worthwhile, especially as resources fluctuate. This is evident as having more or less of the same resources (\u003cspan citationid=\"CR101\" class=\"CitationRef\"\u003e101\u003c/span\u003e) and job demand, can in turn become a challenge. In the long term, resources can also act as a hindrance and negatively impact the wellbeing of health workers (\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e). Additionally, rather than viewing employees as passive recipients of digital technologies, our findings suggest that future research should explore how employees actively navigate digital work environments, including how they leverage resources, cope with job demands, and exercise agency in response to digitalization.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eThis study offers intriguing insights into how digital job demands and resources influence employee wellbeing in healthcare. The findings highlight the complexity of digital systems and their varied impact on healthcare workers. While digital job resources support job satisfaction and thriving, the influence of digital job demands appears to be more context-dependent. The lack of significant effects for some digital job demands, along with mixed findings on the mediating role of resources, suggests a need for a deeper understanding of the specific pressures created by digitalization, and which resources are genuinely effective in mitigating them. Theoretically, this study contributes to a better understanding of how digital innovations interact with personal and organizational resources in shaping employee wellbeing. Practically, it provides direction for healthcare organizations in navigating the dual impacts of digitalization, both enabling and challenging, while aiming to support their workforce. Overall, this study contributes to the growing field of digital health services research by emphasizing the importance of reassessing which resources are most relevant and effective in today\u0026rsquo;s digitally evolving healthcare environment.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eDIs: Digital innovations; DJDs: Digital job demands; EHR: Electronic health records; JD-R model: Job Demands-Resources model; TAW: Thriving at work; JS: Job satisfaction; DIS: Digital innovations support; DWO: Digital work overload; DSO: Digital system overload; TAM: Technology Acceptance Model; UTAUT: Unified Theory of Acceptance and Use of Technology; SEM: Structural equation modelling; CB-SEM: Covariance based structural equation modelling; CFA: Confirmatory factor analysis; MLR: Maximum likelihood estimation; SD: Standard deviation; CR: Composite reliability ; AVE: Average variance extracted; CFI: Comparative Fit Index (CFI); TLI: Tucker-Lewis Index ; RMSEA: Root Mean Square Error of Approximation; SRMR: Standardized Root Mean Square Residual; R\u0026sup2;: Coefficient of determination; CDSS: Clinical decision support systems; CI: Confidence interval; BCa CI: Bias-Corrected and Accelerated Confidence Interval\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the ethical guidelines of the Norwegian Agency for Shared Services in Education and Research (SIKT), which align with the Declaration of Helsinki. As the study did not involve the collection of personal data, traceable IP addresses, or any clinical intervention, SIKT evaluated the project as ethically acceptable and determined that a formal reference number was not required. Kristiania University College served as the responsible institution for data management. Informed consent was obtained from all participants. On the first page of the online survey, participants were informed about the purpose of the study, the voluntary nature of participation, data anonymity, data storage procedures, potential reuse of data for research purposes, and their right to withdraw at any time without consequence. In accordance with the Norwegian Personal Data Act §§2 and 8 no. 1, participants provided free and informed consent by clicking \"Next\" to proceed with the survey.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe questionnaire and all items were newly designed for this study and have not been previously published or used in other publications. The datasets generated and/or analyzed during the current study are available from the corresponding author on reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares that there are no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contributions:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePA contributed to the preparation, development, analysis, and mainly drafted the manuscript. BRM contributed to the development of questionnaire, and to the manuscript draft. EN contributed to the study design, initial analysis and to the manuscript draft. All authors approved the final draft.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank\u0026nbsp;Mohsen Taheri Shalmani for his guidance regarding analysis in R software and\u0026nbsp;Professor Moutaz Haddara for his feedback on the article’s draft.\u0026nbsp;In addition, the authors acknowledge that AI was used in this paper for the purpose of proof-reading the language and can attest that the content of this manuscript is not generated by AI.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors details:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e1\u003c/sup\u003eSchool of Economics, Innovation, and Technology. Kristiania University of Applied Sciences, Oslo, Norway\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eBamel U, Talwar S, Pereira V, Corazza L, Dhir A. 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Available from: https://doi.org/10.1177/0149206314527130\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cp\u003eTables 1, 2 and 5 are available in the Supplementary Files section.\u003c/p\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Digital innovations, Resources, Digital job demands, Healthcare workers, Employee wellbeing","lastPublishedDoi":"10.21203/rs.3.rs-6448853/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6448853/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eDigital innovations (DIs) are constantly reshaping healthcare, affecting healthcare workers\u0026rsquo; practices and wellbeing both positively and negatively. To balance this dual impact, it is essential to understand the specific demands introduced by DIs and assess whether existing personal and organizational resources are remain effective in addressing them. The aim of this study is to contribute to healthcare services research by examining the relevance and effectiveness of key psychological and organizational resources in buffering digital job demands (DJDs) in today\u0026rsquo;s evolving healthcare context.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eData were collected from \u003cem\u003eN\u0026thinsp;=\u003c/em\u003e\u0026thinsp;292 healthcare workers in the UK using an online quantitative survey platform by adopting items from established measurements. Covariance-Based Structural Equation Modelling (CB-SEM) was used to test the hypothesized relationships amnong the vriables, with the help of R 4.4.3 software.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eThe direct relationship of digital system overload, a DJDs, was found to have a negative and significant relation to employee job satisfaction (Beta = -0.141). In addition, the direct relationship of digital innovation support on job satisfaction (Beta\u0026thinsp;=\u0026thinsp;0.250) and thriving at work (Beta\u0026thinsp;=\u0026thinsp;0.336) was supported. The direct relationship of resilience on job satisfaction (Beta\u0026thinsp;=\u0026thinsp;0.208) was supported. As was the direct relationship between autonomy and job satisfaction (Beta\u0026thinsp;=\u0026thinsp;0.160) and thriving at work (Beta\u0026thinsp;=\u0026thinsp;0.266). The remaining direct relationship found no support. Finnaly, the results shows that digital innovation support mediates the relationship between digital system overload and job satisfaction (Beta = -0.085), and the relationship between digital system overload and thriving at work (Beta = -0.144). The remaining proposed mediating relationship found no support.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThe results confirm the positive impact of psychological and organizational resources on healthcare workers\u0026rsquo; positive well-being outcomes, with the exception of psychological resilience. In addition, the direct effects of identified DJDs on well-being were not supported, possibly due to the contextual and fluctuating nature of such demands. The variations in findings regarding the mediating role of resources suggest a need for more in-depth research to explore which resources are most relevant and effective in addressing the evolving digital demands faced by healthcare professionals in today\u0026rsquo;s workplace. Consequently, the authors contribute to health services research and literature by clarifying the complex and multifaceted understanding of psychological and organizational resources as crucial factors in navigating digital job demands. The findings of this paper offer essential practical implications for health organizations and health managers, by highlighting the importance of managing both personal and organizational resources to secure health workers\u0026rsquo; wellbeing in a quickly evolving work environment.\u003c/p\u003e","manuscriptTitle":"Navigating Digital Demands: A Reassessment of Resources for Healthcare Workers’ Workplace Wellbeing","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-13 13:57:58","doi":"10.21203/rs.3.rs-6448853/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"4b24de1d-512a-48fd-ab2b-1efb086aa6fa","owner":[],"postedDate":"May 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2025-10-29T09:38:55+00:00","versionOfRecord":[],"versionCreatedAt":"2025-05-13 13:57:58","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6448853","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6448853","identity":"rs-6448853","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}