Abstract

Whether and how enterprise digital technology requirements affect employees constitutes a pivotal scholarly inquiry in the digital economy landscape. Grounded in the Cognitive Appraisal Theory of Stress and integrating the Job Demands-Resources (JD-R) Model with the Conservation of Resources (CoR) Theory, this study proposes employees may appraise enterprise digital technology requirements as either challenge stressors-proactively embracing demands to catalyze positive outcomes in proactive behaviors-or hindrance stressors-cognitively avoiding requirements thus eliciting negative consequences in turnover intention. Moreover, employees’ digital literacy levels fundamentally reconfigure their cognitive appraisals of these technological demands, thereby triggering differential behavioral cascades. Utilizing a three-wave longitudinal survey design with 332 validated responses from employees undergoing digital transformation, this study employed AMOS 23.0 for confirmatory factor analysis, SPSS 23.0 for hierarchical regression modeling, and the PROCESS macro for bootstrap mediation tests to substantiate the proposed theoretical framework. This study advances the dual-path framework of enterprise digital technology implementation by elucidating how digital literacy-as a personal resource-shapes divergent outcomes stemming from organizational digital demands. Furthermore, it extends the application of the Cognitive Appraisal Theory of Stress to digital contexts through empirical validation, and also to provide some inspiration and reference for relevant management practices.

Abstract

Technostress has emerged as a prevalent source of workplace stress as artificial intelligence (AI) becomes increasingly integrated into organizations. However, current literature predominantly focuses on the negative psychological impacts of technostress, highlighting the need for research into its potential benefits. To clarify the impact of technostress on employee innovative behavior, this study proposes a conceptual model grounded in transactional stress theory. Specifically, it investigates the mechanisms linking technostress to innovation behavior through the mediating roles of job anxiety and work engagement, with corporate digital responsibility (CDR) (i.e., organizational obligations in digital transformation encompassing data ethics, lawful technology use, and mitigation of digitalization's adverse effects) serving as a contextual boundary condition. Based on two-wave survey data from employees across Chinese manufacturing firms, this study conducted confirmatory factor analysis (CFA) and path regression analysis using Mplus 8.3. The results reveal that the effect of technostress on employee innovative behavior hinges on employees' appraisal of technostress. Specifically, when perceived as a hindrance, technostress triggers job anxiety, thereby inhibiting innovation behavior. When perceived as a challenge, it fosters work engagement, which enhances innovation behavior. Furthermore, CDR attenuates the positive relationship between technostress and job anxiety while amplifying its positive association with work engagement. Additionally, CDR moderates the mediating effect of job anxiety in the technostress-innovation behavior link. However, CDR does not significantly moderate the mediating role of work engagement, possibly because this mechanism is influenced by multiple contextual factors and requires further research in the future. This paper contributes to the technostress literature by clarifying how technostress affects employee innovative behavior through different mechanisms. This study holds important theoretical and practical significance for improving employees' adaptability and promoting the innovative development of enterprises amidst the technostress induced by AI.

Abstract

Background Despite growing concerns about art students’ over-reliance on generative AI tools, existing research has largely treated academic stress as a uniform construct, overlooking the possibility that different types of stressors may drive AI dependence through distinct psychological pathways. Methods Based on the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, this study examines the relationships between Challenge-Hindrance stressors (CHS) and AI dependence among art students. It explores the mediating roles of creative self-efficacy and positive experiences. A questionnaire survey was conducted among 580 art students using the Challenge-Hindrance Stress Scale, the Creative Self-Efficacy Scale, the Positive Experiences Scale, and the AI Dependence Scale. Results Path analysis revealed that both challenge stressors (β = 0.245, p < 0.001) and hindrance stressors (β = 0.322, p < 0.001) were positively associated with AI dependence; challenge stressors were negatively associated with creative self-efficacy (β = −0.155, p = 0.003) and positively associated with positive experiences (β = 0.268, p < 0.001); the negative association between hindrance stressors and creative self-efficacy was significant (β = −0.107, p = 0.043). Mediation analysis indicated that creative self-efficacy played a significant mediating role only in the path involving challenge stressors (indirect effect = 0.023, 95% CI [0.007, 0.044]) and was not significant in the path involving hindrance stressors; positive experiences significantly served as a mediator in the relationships between both challenge stressors (indirect effect = 0.042, 95% CI [0.020, 0.068]) and hindrance stressors (indirect effect = 0.019, 95% CI [0.003, 0.039]) on AI dependence. The chain mediation effects did not reach significance in either path. Conclusion The study found asymmetry in the dual pathways by which Challenge and Hindrance stressors relate to AI dependence. These findings extend the I-PACE model to the issue of AI dependence in arts education and provide empirical evidence and intervention insights for targeted prevention of problematic AI use.

Abstract

The widespread application of artificial intelligence (AI) in electronic commerce platforms has profoundly reshaped frontline employees’ service patterns, psychological experiences, and innovation behaviors. This is especially salient in electronic commerce, where AI systems have made human–AI collaboration a defining feature of frontline service work on digital platforms. Existing research has predominantly focused on either the positive or negative effects of AI, failing to fully explain how employees’ cognitive appraisals of AI technology influence their service innovation behavior through distinct psychological pathways. To address this research gap, this study integrates the transactional theory of stress and the conservation of resources theory into a dual-path model, investigating how challenge appraisal and hindrance appraisal of AI-related work uncertainty respectively are associated with service innovation behavior through two mediating pathways: work engagement and emotional exhaustion. Using partial least squares structural equation modeling (PLS-SEM) to analyze 317 valid responses collected from frontline employees on electronic commerce platforms in China, the results indicate that challenge appraisal is positively associated with service innovation behavior, with work engagement serving as a significant mediator; conversely, hindrance appraisal is negatively associated with service innovation behavior, with emotional exhaustion acting as a significant mediator. This study provides an integrated perspective on the differentiated pathways through which employees’ cognitive appraisals of AI-related work uncertainty are linked to innovation behavior, articulating a complete explanatory chain from cognitive appraisal to psychological resources to behavioral outcomes. The findings offer practical implications for employees, organizations, and governments to foster service innovation in human–AI collaborative environments in electronic commerce platforms.

Abstract

Drawing on the Challenge–Hindrance Stressor Framework (CHSF), we examined how AI-supported work functions as a daily stressor that can elicit both engagement and disengagement responses depending on individual challenge and hindrance appraisals. Using a 10-day diary study design conducted in the US (N = 173), we tested dual pathways through which daily AI-supported work influenced employee behavioral and attitudinal responses. On days when employees perceived having more AI-supported work, they were more likely to appraise it as a challenge, which in turn was associated with higher levels of task crafting and innovative work behavior. At the same time, AI-supported work was also appraised as a hindrance, triggering job replacement anxiety and resulting in higher levels of organizational cynicism. Furthermore, AI system evaluation, a general attitude toward AI systems, moderated the hindrance pathway such that individuals with more favorable AI evaluations were less likely to perceive AI-supported work as a hindrance. Our findings extend the CHSF to the context of AI, highlighting that the same AI stimulus can evoke distinct psychological and behavioral responses depending on appraisal and attitude. We discuss implications for theory and for organizations seeking to implement AI in ways that enhance positive employee outcomes while mitigating adverse effects, thereby contributing to a more human-centric approach to AI integration at work.

Abstract

Generative AI promises academic efficiency yet often delivers flawed answers, leaving users “irritated but engaged.” To explain this paradox, we merge the Transactional Model of Stress and Coping with Cognitive Dissonance Theory and survey Indonesian and Taiwanese academics who had used ChatGPT for at least a month ( N = 388). Partial Least Squares analysis shows that response failures and low AI literacy sharply raise frustration; frustration, in turn, both directly sustains continuance intention and indirectly does so through heightened resistance to change. The indirect route dominates in Indonesia, where higher switching costs foster inertia, whereas Taiwanese users convert frustration into exploratory recommitment. These findings re-cast resistance as an adaptive buffer rather than a mere barrier and reveal culture-specific coping paths that keep imperfect AI in daily workflows. The study advances stress-and-dissonance theory integration and guides institutions toward balanced strategies combining accuracy auditing, literacy scaffolding, and context-sensitive expectation management.

Abstract

This study investigates how Chinese pre-service teachers of English as a foreign language (EFL) develop self-regulated teaching competencies in the face of disruptions posed by generative artificial intelligence (GenAI) and structural limitations within teacher education programs. As GenAI increasingly influences EFL instruction, uncertainties surrounding teachers' professional roles intensify. Meanwhile, the limited availability of practical teaching internships in China compels pre-service teachers to independently cultivate essential skills, rendering self-regulated teaching pivotal. Drawing on the Conservation of Resources (COR) theory, we examined the interplay among academic resilience, cognitive appraisals (challenge versus hindrance), and self-regulated teaching. Employing Covariance-Based Structural Equation Modeling (CB-SEM), we analyzed data from 517 participants across six universities in three Chinese provinces. Findings revealed that academic resilience significantly predicted self-regulated teaching, with challenge appraisals mediating this relationship, suggesting that resilience facilitates a growth-oriented interpretation of stress. While resilience was linked to reduced hindrance appraisal, this pathway did not significantly influence self-regulated teaching. Age was associated with more positive cognitive framing (older participants reported higher challenge appraisals), but had no direct effect on self-regulated teaching; gender showed no significant impact. These insights underscore the value of fostering academic resilience and challenge-based cognitive strategies to better prepare pre-service EFL teachers for the dual demands of technological transformation and institutional constraint.

Abstract

Purpose The increasing integration of Artificial Intelligence (AI) within enterprises is generates significant technostress among employees, potentially influencing their intention to adopt AI. However, existing research on the psychological effects of this phenomenon remains inconclusive. Drawing on the Affective Events Theory (AET) and the Challenge–Hindrance Stressor Framework (CHSF), the current study aims to explore the “black box” between challenge and hindrance technology stressors and employees’ intention to adopt AI, as well as the boundary conditions of this mediation relationship. Methods The study employs a quantitative approach and utilizes three-wave data. Data were collected through the snowball sampling technique and a structured questionnaire survey. The sample comprises employees from 11 distinct organizations located in Guangdong Province, China. We received 301 valid questionnaires, representing an overall response rate of 75%. The theoretical model was tested through confirmatory factor analysis and regression analyses using Mplus and the Process macro for SPSS. Results The results indicate that positive affect mediates the positive relationship between challenge technology stressors and AI adoption intention, whereas AI anxiety mediates the negative relationship between hindrance technology stressors and AI adoption intention. Furthermore, the results reveal that technical self-efficacy moderates the effects of challenge and hindrance technology stressors on affective reactions and the indirect effects of challenge and hindrance technology stressors on AI adoption intention through positive affect and AI anxiety, respectively. Conclusion Overall, our study suggests that AI-driven challenge technology stressors positively impact AI adoption intention through the cultivation of positive affect, while hindrance technology stressors impede AI adoption intention by triggering AI anxiety. Additionally, technical self-efficacy emerges as a crucial moderator in shaping these relationships. This research has the potential to make a meaningful contribution to the literature on AI adoption intention, deepening our holistic understanding of the influential mechanisms involved. Furthermore, the study affirms the applicability and relevance of Affective Events Theory (AET) and the Challenge-Hindrance Stressor Framework (CHSF). In practical terms, the research provides actionable insights for organizations to effectively manage employees’ AI adoption intention.

Abstract

Purpose Although generative artificial intelligence (GAI) has become increasingly integrated into higher education, its potential psychological costs for teachers remain insufficiently understood. Drawing on the Transactional Model of Stress and Coping (TMSC), this study examined how GAI use influences teachers’ innovative behavior through sequential cognitive and emotional mechanisms while exploring heterogeneity in teachers’ psychological responses. Methods A three-wave time-lagged survey was conducted among 898 university teachers from public higher education institutions in Northeast China. Structural equation modeling (SEM) was used to examine the proposed sequential mediation model, and latent profile analysis (LPA) was performed to identify distinct psychological response patterns. Results Within the present study, greater generative AI use was associated with lower levels of teachers’ innovative behavior. Job insecurity and AI anxiety independently mediated this relationship and jointly formed a significant sequential mediation pathway. In addition, five distinct psychological profiles were identified. Teachers characterized by lower job insecurity and AI anxiety exhibited significantly higher levels of innovative behavior than those experiencing greater psychological risks. Conclusion By integrating variable-centered and person-centered approaches within the TMSC framework, this study advances understanding of the psychological mechanisms and heterogeneous responses underlying teachers’ adaptation to generative AI. The findings contribute to the emerging literature on the psychological consequences of AI adoption and provide practical implications for promoting sustainable teacher innovation and effective psychological management during AI-enabled educational transformation.

Abstract

The deepening integration of generative AI into design education is driving a paradigm shift in human-computer AI collaboration (HAIC) -from mere tool assistance toward intelligent synergy. However, its dual impact on the competency development of design students and the underlying mechanisms remain insufficiently understood. Grounded in the Theory of Stress Appraisal, this study constructs a research model to examine the double-edged sword effect of HAIC on Design Competence (DC), introducing teacher AI Leadership (AIL) as a moderating variable. The aim is to reveal the psychological mechanisms and boundary conditions through which HAIC influences DC. Employing a questionnaire survey, this study conducted an empirical investigation involving 483 design students in East China. Data analysis was performed using SPSS 24.0 and Amos 23.0, encompassing reliability and validity tests, regression analysis, and Bootstrap mediation analysis. The results indicate that HAIC influences DC through dual pathways: it positively enhances DC by fostering Challenge Appraisal (CA) (β = 0.283, p < 0.01), which in turn positively predicted competence development (β = 0.363, p < 0.01), yielding a significant indirect effect of 0.103 (p < 0.01). Simultaneously, HAIC triggered Hindrance Appraisal (HA) (β = 0.271, p < 0.01), which negatively predicted competence development (β = −0.229, p < 0.01), yielding a significant indirect effect of −0.062 (p < 0.01). This constitutes a partial mediating mechanism characterized by “one positive, one negative” effect. Furthermore, Teacher AIL exhibits an asymmetric moderating effect: it significantly buffers the positive impact of HAIC on HA (β = −0.112, p < 0.01), indicating that high AIL can mitigate cognitive load and anxiety arising from technological uncertainty, yet it does not significantly amplify the positive pathway via CA (β = 0.001, p > 0.05). The findings suggest that the impact of HAIC on DC is not linear but rather a dialectical process contingent upon individual cognitive appraisal. The core value of Teacher AIL lies in “risk mitigation” rather than “creativity stimulation,” offering a theoretical foundation for the intelligent transformation of educational design. At the practical level, it is imperative to facilitate from knowledge deliverers to cognitive facilitators in AI-augmented design education. By systematically developing AI instructional competencies and reforming assessment systems, educators can guide students toward establishing a healthy relationship with HAIC, thereby achieving the synergistic advancement of technological empowerment and humanistic literacy.

Abstract

Currently, there is a growing trend for college and university teachers to use AI in their teaching work. However, existing research explores the impact of teachers’ usage of AI in the workplace on students. Based on resource preservation theory, this study examined the mechanism of the usage of AI in teaching on students’ innovative behavior and well-being with a sample of 356 college students from Zhejiang Province. The study found that the usage of AI in teaching significantly and positively affected students’ innovative behavior and well-being, with students’ positive emotion playing a mediating role. Students’ AI trust not only moderated the effect of the usage of AI in teaching on positive emotion, but also moderated the mediating role of positive emotion. The findings have important implications for teachers’ instructional management and practice.

Abstract

Background Generative artificial intelligence is profoundly reshaping knowledge work, yet the cognitive mechanisms through which AI use relates to employee innovation performance remain underexplored. Objective Integrating Conservation of Resources theory and Cognitive Appraisal Theory, this study proposes that generative AI use is associated with innovation performance through a three-stage partially serial mediation pathway: resource acquisition (cognitive divergence), resource integration (cognitive elaboration), and resource activation (challenge appraisal), with work stress moderating each stage. Methods Survey data from 612 knowledge workers were analyzed using structural equation modeling and Monte Carlo bootstrap methods. Results (1) Generative AI use is positively associated with employee innovation performance; (2) the three-stage serial mediation model received support, with indirect effects accounting for 72.9% of the total effect; and (3) work stress moderation exhibits a stage-dependent pattern: non-significant at the resource acquisition stage but significantly strengthening mediation efficiency at the integration and activation stages. Conclusion This study advances a chain mediation framework that offers a more coherent theoretical account of AI-enabled cognitive transformation and provides organizations with specific intervention points for shifting from tool adoption to cognitive empowerment.

Abstract

<jats:p>Academic stress is prevalent among university students and affects their evaluation of educational environment quality, fairness, and supportiveness. Based on the challenge–hindrance stressor framework and transactional stress-coping model, this study explores how challenge and hindrance stressors (HSs) shape perceived decent education (DE), focusing on the mediating role of artificial intelligence use (AIUSE) and moderating effect of interpersonal interaction (II). Using partial least squares structural equation modeling (PLS-SEM) to analyze survey data from 520 university students, the results show that both stressors positively predict AIUSE, which in turn improves perceived DE and mediates the stressor-DE relationship. II negatively moderates the AIUSE–DE link: the positive effect weakens as II increases. Moderated mediation analysis indicates that the indirect effects via AIUSE are only significant at low II levels. These findings highlight AI-enabled learning as an adaptive coping strategy and the necessity of integrating technological and interpersonal resources to enhance student well-being in higher education.</jats:p>