Search Results (102)

The accelerated integration of digital technologies in schools over the past decade has significantly increased levels of technostress among teachers, impacting their psychological well-being and professional engagement. In this context, engagement and technostress emerge as critical constructs for understanding the well-being and quality of teaching in primary and secondary school teachers. However, the available evidence is fragmented across rural and urban contexts, making it difficult to gain a comprehensive understanding of this relationship. A systematic review was conducted following the PRISMA 2020 guidelines, including 13 studies published between 2015 and 2025, with a total of 6630 participants. The PEC model was used to define eligibility criteria and search strategies in five databases (Web of Science (n = 18), Scopus (n = 734), PsycNet (n = 32), SciELO (n = 0), PubMed (n = 135)). Methodological quality was assessed using the EACSH Scale, and the analysis integrated qualitative and quantitative descriptive approaches. A consistent inverse relationship was found between technostress and teaching engagement, moderated by contextual factors, educational level, and technological infrastructure. Technostress was associated with digital fatigue, reduced vigor, and lower professional dedication. Protective factors supporting engagement included digital self-efficacy, institutional support, adaptive emotion regulation, and a sense of meaning in work. Teachers in digitally demanding environments maintained high engagement when they had adequate personal and organizational resources. These findings highlight the urgent need for training and psychosocial support policies that mitigate technostress and strengthen teaching engagement across diverse territorial contexts. Within the broader landscape of digital transformation, including emerging artificial intelligence applications in education, this review underscores the importance of preparing teachers not only for technical proficiency but for sustainable digital practice. This literature review identifies research gaps on rural dynamics and the longitudinal nature of the phenomenon. Full article

This study investigates the factors influencing employee acceptance and actual AI use intensity (frequency and routinization) by integrating the Technology Acceptance Model with organizational and psychosocial variables. Data were collected via an online survey of Romanian Generation Z participants with work experience (N = 272) between 10 May and 25 May 2025, and analyzed using PLS-SEM with a moderated mediation model. Perceived usefulness emerged as the strongest driver of attitude, intention, and AI use intensity. Organizational AI readiness increased perceived usefulness and was positively associated with psychological safety. Trust influenced both intention and AI use intensity and partially mediated the relationship between perceived usefulness and intention. Technostress was negatively associated with attitudes and weakened the positive relationship between psychological safety and perceived ease of use. By shifting the focus from intention to AI use intensity, the study refines acceptance theory for AI-enabled work and clarifies how organizational context, trust, and digital strain shape sustained and routinized AI use in daily work. Practically, the findings suggest that organizations should communicate AI value and task fit, foster psychologically safe learning climates, build trust through transparency and guidance, and actively mitigate technostress through training, workload design, and clear expectations. Full article

Journalism is an inherently fast-paced and pressure-filled profession with features such as industry competition and reporting on traumatic events that can cause mental health issues for journalists. However, little work has examined the extent to which rapid implementation of new technologies might also contribute to the stress that journalists experience. In this study, I carried out qualitative interviews with working journalists to understand how they manage technostress in their work. The journalists’ experiences indicated that they approach technostress based on different levels within the decision-making process to adopt, reinvent, or reject an innovation. At the individual professional level, journalists used the strategies to adapt and alter technology for their needs and implement new tools when meeting timeliness, not just deadlines; at the social connection level, journalists built off educational encouragement through personal experimentation and engaged with mentors, coworkers, and audience for support; and at the foundational meaning level, journalists took breaks from technologies while acknowledging their downsides and kept humanity at the center of journalistic work. These findings contribute to diffusion of innovations theory by focusing on the ongoing decisions made to manage adverse impacts of a new tool being adopted. Further, the findings showcase that humanity remains central to the journalistic enterprise even in the technology-saturated digital age. Full article

This study introduces an innovative approach for quantifying the technostress phenomenon, drawing on textual narratives from the firm’s annual report. Based on a dataset covering the Standard and Poor’s 500 (S&P 500) index firms, we analyze 2532 10-K annual reports and highlight the key contributors of technostress across six different dimensions of technostress using a combined score. A major advantage of the new six-dimensional scoring framework is that it offers a set of objective metric proxies to capture technostress without bias, utilizing a refined list of 42 key clues derived through factor analysis. Also, it adopts natural language processing, revealing hidden patterns and anomalies that indicate technostress. We further validate this framework by applying fixed-effect regression models to examine the impact of technostress on productivity. The main results imply that the four technostress dimensions presented in techno-risks, insecurity, uncertainty, and invasion negatively impact firms’ productivity. This framework offers practical implications for firms, allowing them to generate a rich profile concerning the degree of technostress associated with existing practices, highlighting the crucial need for advanced interventions, facilitating comparisons with other firms from the same or different industries, as well as cross-country comparisons. Full article

Remote and hybrid teaching have become enduring features of European higher education, yet their implications for teachers’ well-being are often examined in fragmented ways. This study investigated a systemic imbalance across five interdependent domains—physical, emotional, cognitive, social, and existential well-being—among Lithuanian higher education teachers, interpreted through the Job Demands–Resources framework and Self-Determination Theory. Using a mixed-methods design, data were collected from 385 teachers via a structured online questionnaire that included demographic variables, 10-point imbalance ratings across the five domains, and open-ended questions. Quantitative analyses (descriptive statistics and correlational pattern exploration) were complemented by thematic analysis of teachers’ narratives. Results indicate a widespread multidimensional disruption: elevated stress and emotional exhaustion, substantial physical strain associated with inadequate home workspaces, cognitive overload linked to multi-platform teaching, reduced collegial connection, blurred work–life boundaries, and challenges to professional meaning. Strain was unevenly distributed, with higher vulnerability associated with gender and caregiving demands, early-career status, limited ergonomic conditions, and weak institutional support. The findings support a systemic interpretation in which intensified demands, reduced resources, and frustrated psychological needs jointly drive well-being imbalance. Sustainable remote/hybrid teaching therefore requires institution-level measures (workload regulation, training, ergonomic support, and boundary-setting policies) rather than reliance on individual coping alone. Full article

The primary objective of this study is to examine factors that influence Mobile Learning adoption and effectiveness in the case of higher education, underpinned by the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2) model. The study was conducted at some of the top Saudi universities. A survey method was used in this study, and the questionnaire includes 12 factors, which include enjoyment, hedonic motivation, and performance expectancy, among others, whose relationships with behavioral intention towards using m-learning and in turn, its actual use, were investigated. Additionally, two mediating factors, namely technostress and exhaustion, were included to examine the effects of students’ engagement with m-learning. The questionnaire was distributed to 500 undergraduate students, and 264 replied. Based on the findings, the traditional UTAUT2 factors had significant effects on students’ intention and use behaviors, which were mediated by technostress and exhaustion. The findings indicated that the effective management of mediators is important to improve student engagement in m-learning and that m-learning strategies can be developed through the information provided. This study validated the UTAUT2 model in the case of m-learning, with a 70% success rate in predicting IT adoption, which exceeded other models. Through the study findings, educators and policymakers can find ways to optimize m-learning platforms. Furthermore, it is recommended that focus is placed on the long-term effects of technostress and exhaustion on the students’ adoption of and engagement with m-learning. Full article

Aging may lead to a gradual decline in work ability, but other factors, such as worker motivation, also play a crucial role. This study, based on the Job Demands–Resources model, examined whether work engagement, an indicator of high motivation, is positively linked to work ability in older white-collar workers. We also explored the influence of job resources (control and social support) and demands (workload and techno-complexity) on work ability, mediated by work engagement. Structured interviews were conducted with 230 bank and finance workers aged over 50, and structural equation modeling was employed to investigate our hypotheses. Work engagement was positively associated with work ability. Control and social support improved work engagement and indirectly enhanced work ability. Among job demands, techno-complexity reduced work engagement but did not affect work ability. Workload weakened the positive effects of control on work engagement and work ability. Our findings suggest that promoting work engagement in older white-collar workers by increasing autonomy, fostering a supportive environment, and reducing overload and techno-complexity can help protect and enhance their work ability. Full article

In the context of the increasing digitalisation of education, teachers are facing growing technological demands that may affect their professional well-being. The aim of this study was to examine the relationship between digital competencies, technostress, and teachers’ professional well-being. The research was conducted on a sample of primary school teachers using validated questionnaires. The data were analysed using descriptive statistics, Pearson correlation analysis, multiple regression analysis, and one-way analysis of variance. The results showed a statistically significant negative relationship between digital competencies and technostress, as well as a positive relationship between digital competencies and professional well-being. Digital competencies proved to be a significant positive predictor of professional well-being, while technostress did not make a significant independent contribution. Differences in the level of technostress were also found with regard to teachers’ years of work experience. In conclusion, the results highlight the importance of strengthening digital competencies as a key resource for maintaining teachers’ professional well-being in a digital environment. Full article

The rapid advancement of Artificial Intelligence (AI) has profoundly transformed organizational systems, reshaping how employees interact with technology and adapt to changing work environments. However, the systemic mechanisms through which AI-induced technostress influences employee career growth remain insufficiently understood. Grounded in a socio-technical systems perspective, this study conceptualizes organizations as adaptive systems where technological, organizational, and human subsystems dynamically interact. We propose a dual-path framework that distinguishes between challenge-related technostressors (a resource-gain process) and hindrance-related technostressors (a resource-loss process), elucidating how AI-related pressures can simultaneously foster and hinder career development. Furthermore, employee resilience and organizational AI support are incorporated as systemic moderators that modulate the intensity of these effects within the human–AI–organization system. Using two-stage survey data from 326 matched pairs of employees and supervisors, results largely support the proposed model, with some pathways showing marginal significance. The findings reveal that AI challenge-related technostressors stimulate proactive adaptation and skill development, whereas hindrance-related technostressors generate anxiety and insecurity, thereby impeding growth. This research extends systems theory by demonstrating how technostressors function as an emergent property of human–technology interactions and provides actionable insights for designing more adaptive and resilient socio-technical work systems. Full article

With the ubiquitous connectivity and exposure of social network service (SNS), the stressors it causes have received extensive attention in the academic community. Unlike previous studies, this research focuses on the cross-cultural dimension and explores the different effects of multiple SNS-generated stressors on user behavior outcomes. Based on the “Stressors-Strain-Outcome” (SSO) theoretical framework, we constructed a “technical stressors—exhaustion—reduced SNS usage intention” pathway to systematically investigate five types of technical stressors. These were perceived information overload, perceived social overload, perceived compulsive use, perceived privacy concern, and perceived role conflict. We introduce “fear of missing out” (FOMO) as a moderating variable to explore its moderating role in SNS exhaustion and reduced SNS usage intention. In this study, we took SNS users from China and the United States as the research subjects (338 samples from China and 346 samples from the United States), and conducted empirical tests using structural equation models and multiple comparative analyses. The results show that there are significant cultural differences between Chinese and American users in terms of the perceived intensity of technostress, the path of stress transmission, and the moderating effect of FOMO. Against the background of collectivist culture in China, perceived information overload, privacy concerns, and role conflicts have a significant positive impact on SNS exhaustion, and SNS exhaustion further positively drives the intention to reduce usage of SNS. However, the direct impacts of perceived social overload and perceived compulsive usage are not significant, and FOMO does not play a significant moderating role. In the context of the individualistic culture found in the United States, only perceived information overload and perceived social overload have a significant positive impact on SNS exhaustion, and FOMO significantly negatively moderates the relationship between exhaustion and reduced SNS usage intention, as high FOMO levels will strengthen the driving effect of exhaustion on reduced usage intention. The innovation this study exhibits lies in verifying the applicability of the SSO model in social media behavior research from a cross-cultural perspective, revealing the cultural boundaries of the FOMO moderating effect, and enriching the cross-cultural research system of reduced usage intention of SNS. The research results not only provide empirical support for a deep understanding of the psychological mechanisms of users’ SNS usage behaviors in different cultural backgrounds, but also offer important references that SNS enterprises can use to formulate differentiated operation strategies and optimize cross-cultural user experiences. Full article

In the era of sustainable digital transformation, organizations increasingly rely on artificial intelligence (AI) to enhance efficiency, innovation, and long-term competitiveness. However, employees’ psychological barriers, including technostress and innovation resistance, continue to constrain successful and sustainable AI adoption. Grounded in Social Exchange Theory (SET), Conservation of Resources Theory (COR), Diffusion of Innovation Theory (DOI), and the Technology Acceptance Model (TAM), this study develops an integrated model linking perceived organizational support (POS)—comprising emotional, informational, and instrumental dimensions—to employees’ sustainable AI adoption through the dual mediating roles of technostress and innovation resistance. Based on 426 valid responses collected from multiple industries, a triadic hybrid approach combining Structural Equation Modeling (SEM), Artificial Neural Networks (ANNs), and Necessary Condition Analysis (NCA) was applied to capture both linear and nonlinear mechanisms. The results reveal that Informational Support (IFS) is the most influential factor and constitutes the sole necessary condition for high-level AI adoption, while emotional and instrumental support indirectly promote sustainable adoption by mitigating employees’ stress and resistance. This study contributes to sustainable management and AI adoption research by providing insights into the potential hierarchical and threshold patterns of organizational support systems in digital transformation. It also provides managerial implications for designing transparent, empathetic, and resource-efficient support ecosystems that foster employee-driven intelligent transformation. Full article

This study investigates how technostress and work intensification jointly influence employee health harm through two distinct stressor-strain pathways within Pakistan’s manufacturing sector. The proposed model specifies two mechanisms, (1) technostress induces IT strain that contributes to health harm, moderated by user satisfaction; and (2) work intensification heightens emotional exhaustion that similarly leads to health harm, moderated by perceived organizational support. Grounded in Conservation of Resources (COR) theory, the framework explains how cumulative digital and organizational demands deplete employee resources, amplifying both psychological and physical harm. A cross-sectional quantitative design was employed, utilizing a structured self-administered questionnaire administered to mid and senior-level employees across manufacturing firms. A total of 252 valid responses were analyzed through Partial Least Squares Structural Equation Modeling (PLS-SEM) using Smart PLS 4. Results revealed that both IT strain and emotional exhaustion significantly mediated the effects of technostress and work intensification, respectively, on health harm. Moreover, user satisfaction significantly moderated the IT strain-health harm relationship, indicating that higher satisfaction with digital tools mitigates the adverse impact of technological stress. Similarly, organizational support weakened the association between emotional exhaustion and health harm, underscoring its protective role in high-pressure work settings. This study offers theoretical advancement by integrating fragmented stressor-strain models and offers practical recommendations to foster digital well-being and supportive organizational work cultures in evolving industrial contexts. Full article

Background: The COVID-19 pandemic has accelerated the adoption of smart working and digital connectivity, transforming workplace interactions. While this shift offers flexibility and autonomy, it also raises concerns about workload management, psychological health, and work–life balance. Method: This study utilized a sample of 1185 employees from a multinational company operating in Italy to examine the impact of digital connectivity on work engagement, technostress, and job performance, framed within the Job Demands-Resources (JD-R) model. Participants completed surveys measuring digital connectivity, work engagement, technostress, and job performance. Results: The findings revealed moderate levels of digital connectivity and technostress, alongside high levels of work engagement and job performance. Mediation analysis indicated that work engagement positively mediated the relationship between digital connectivity and job performance, while technostress negatively impacted job performance. Notably, the two mediators operated independently rather than sequentially. Value: this research highlights the dual nature of digital connectivity as both a resource and a demand in the workplace. It underscores the importance of implementing strategies to enhance work engagement while addressing technostress. Organizations can benefit from adopting “right to disconnect” policies and flexible work arrangements to foster a healthier work environment, ultimately improving employee well-being and productivity in the digital age. Full article

Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article

This study investigates the influence of technostress (Tech) on the well-being (WB) of employees in manufacturing sectors employing Industry 4.0 in Turkey, examining the effect of work exhaustion (WE) as a mediator in the association between technostress and well-being. How digital leadership (Dg) moderates these relationships is analyzed and discussed accordingly. This article also presents strategies for digital leaders to mitigate employees’ technostress in the digital transformation era and discusses their positive role. Using the Job Demands–Resources (JD-R) framework and Conservation of Resources (COR) theory, data were gathered from 329 workers employed at three manufacturing firms located in Istanbul. Structural equation modeling (SEM) was employed to test this study’s hypothesis. The results indicate that increased technostress notably reduces employee well-being, primarily because it heightens work exhaustion. Moreover, robust digital leadership effectively lessens these negative impacts, underscoring its value in managing technological stress. This research explains the importance of the Sustainable Development Goal (SDG 3) for better health and well-being practices in workplaces. It suggests practical implications for organizations, including developing digital leadership skills, routinely assessing technostress, and applying targeted actions to sustain employee health during digital shifts. Full article