From threat to strength: how AI usage is paradoxically associated with resilience among accounting and finance employees with perceived work meaningfulness as a mediating mechanism
A moderated mediation model is developed in which perceived work meaningfulness acts as the mediator and job complexity serves as the moderator in the relationship between AI usage and employee resilience, showing that AI usage positively predicted employee resilience, and perceived work meaningfulness mediated this relationship.
Abstract
Introduction Artificial intelligence is increasingly reshaping accounting and finance work, yet its psychological implications for employees remain insufficiently understood. While existing studies have mainly emphasized the productivity and efficiency outcomes of AI, less is known about how AI usage relates to employee resilience in accounting and finance roles. To address this gap, this study examines the relationship between AI usage and employee resilience and explores the psychological mechanism and boundary condition underlying this relationship. Methods Based on the Job Demands-Resources (JD-R) theory, this study develops a moderated mediation model in which perceived work meaningfulness acts as the mediator and job complexity serves as the moderator. Data were obtained from employees in accounting and finance-related positions through a three-wave time-lagged survey. After matching responses across the three waves and screening invalid cases, 332 valid questionnaires were used for analysis. Results The results showed that AI usage positively predicted employee resilience, and perceived work meaningfulness mediated this relationship. Job complexity further moderated the relationship between AI usage and perceived work meaningfulness, such that the positive relationship was stronger under conditions of low job complexity. Discussion This study extends the JD-R framework to AI-enabled work contexts by conceptualizing AI usage as a work-related resource associated with employee resilience. The findings identify perceived work meaningfulness as a key psychological mechanism and job complexity as an important boundary condition, providing theoretical and practical implications for AI implementation in accounting and finance settings.
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.
Xiumei Ma, Junxi Lin· Journal of Theoretical and A...· 0 citations
It is proposed that, from employees’ perspective, AI reliance is associated with higher levels of involution through elevated performance expectations and anxiety, and the importance of protecting employee wellbeing, communicating realistic performance expectations, and maintaining workplace social capital when implementing AI in digitally enabled organizations.
Ruochen Huang· Frontiers in Psychology· 0 citations
This study examines whether adopting artificial intelligence (AI) strengthens organizational resilience when the operating environment is hostile, and identifies the internal conditions an organization must satisfy before an AI investment translates into resilience. Two mechanisms anchor the argument: employee digital literacy, which carries the effect, and an innovation-supportive climate, which conditions it. We surveyed 400 employees across three premium hotels in Baghdad, Iraq, sampled by operational level, and tested the model with structural equation modeling in AMOS 26.0. The results show that AI adoption alone yields only a modest direct gain in resilience. Slightly more than half of its total effect on organizational resilience operates through employee digital literacy rather than flowing directly, and this indirect path strengthens where employees work in a climate that rewards experimentation. For practice, the findings position AI as a potential resource rather than a self-contained solution: hotels that invest in the technology without a parallel investment in the people who operate it, and in the climate they work in, realize only part of the available return. Theoretically, we interpret the case through the resource-based view (RBV) and dynamic capabilities theory (DCT): AI adoption supplies the strategic resource, employee digital literacy is the dynamic capability that activates it, and innovation-supportive climate is the boundary condition governing how freely that capability is exercised. The framework should extend to other settings where organizations operate with limited resources under sustained instability
Hasan Mutashar Gbouri· Arab Economic and Business J...· 0 citations
This study aims to explore the impact of Human–AI Collaboration on the Innovation Behavior of employees in the banking sector, focusing on the mediation effect of Job Satisfaction and the moderation effect of AI Self-Efficacy. The study draws its concept of Human–AI Collaboration from the Job Demands–Resources (JD–R) Theory, which considers HACC as a strategic organizational resource that can boost employees' motivation and innovative performance. Quantitative, Cross Sectional. A quantitative, cross-sectional research design was used. Structured questionnaires were used to gather data on 384 employees of commercial banks who have experienced the use of AI in their work. To ensure the respondents had the relevant work experience with AI, purposive sampling was used and explore the direct, mediating, and moderating relationships, the proposed conceptual model was analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique by SmartPLS 4. The results show that Human–AI Collaboration has a significant positive impact on Job Satisfaction and Innovation Behavior. Job Satisfaction positively impacts Innovation Behavior and partially mediates the link between Human–AI Collaboration and Innovation Behavior, suggesting that a collaborative environment with AI can foster innovation by enhancing employee work satisfaction. The findings of this study could help bank executives, human resource managers, and policymakers understand the importance of human-focused implementation of AI, ongoing AI skill-building, and favorable organizational practices that boost employee satisfaction and innovation. To truly unlock the strategic benefits of Human–AI Collaboration, organizations must include investment in employee capability development within their AI investments. This study adds to the growing increasingly relevant literature on Human–AI Collaboration by combining technological and psychological aspects in one framework. It expands on the JD–R Theory by clarifying the strategic role of Human–AI Collaboration as a job resource that fosters innovation by Job Satisfaction, and shows the contingent role of AI Self-Efficacy in AI-enabled workplaces.
This paper aims to examine the relationship between employees' use of artificial intelligence (AI) for work and digital-enabled innovative performance (DEIP) from the perspective of employee engagement and trust in AI. Based on these perspectives, this study identifies specific solutions for achieving high levels of DEIP.
Drawing on job demands-resources (JD-R) theory and analyzing data from 431 employees, this paper proposes a research model to investigate how employee AI use affects employees' DEIP through partial least squares structural equation modeling and highlights the configurations of causal conditions associated with DEIP through fuzzy-set qualitative comparative analysis (fsQCA).
The results show that AI use for work exerts the strongest positive impact on employees' behavioral engagement, followed by emotional and cognitive engagement. Employee engagement (three types mention before) play a partial mediating role between work-related AI and DEIP. Furthermore, both human-like and functionality trust in AI positively moderate the relationship between work-related AI and behavioral engagement. Finally, a total of four solutions leads to a high level of DEIP.
Organizations should enhance employees' engagement and trust in AI through training and supportive implementation strategies. Managers should adopt context-sensitive approaches that align AI use, engagement and trust to improve DEIP.
Firstly, this study enriches the literature on DEIP and JD-R theory by exploring the AI-performance link via employee engagement. Secondly, this paper supplements work-related AI literature by clarifying AI trust's boundary conditions. Thirdly, this paper contributes to the performance literature by identifying key solutions for DEIP from a configuration perspective.
Liang Ma, Zhihao Qi, Xin Zhang et al.· Internet Research· 0 citations
The role of employee resilience as psychological capital in mediating the relationship of organizational resources to proactive work behavior of lecturers is very critical. Especially in the context of Sustainable Development Goal 4 on quality education. This research was quantitative with an explanatory approach based on the Conservation of Resources Theory. The research subjects were 240 permanent lecturers from 25 private universities in West Kalimantan, Indonesia, a geographically peripheral area with significant institutional and resource limitations. The data were analyzed by employing Partial Least Squares Structural Equation Modelling (PLS-SEM) to test the direct and indirect relationships among the suggested variables and the mediating role of employee resilience. The findings indicate that perceived organizational support (β = 0.284), empowering leadership (β = 0.385) and job autonomy (β = 0.237) have positive and significant influence on employee resilience with a Variance Accounted For (VAF) of 54.2%. Furthermore, employee resilience significantly mediates the transformation of these organizational resources into proactive work behavior, with significant indirect effects of 0.061, 0.083 and 0.051, respectively. These findings indicate that the development of psychological capital is a stronger determinant than structural resource provision in fostering sustainable faculty proactivity. Therefore, improving quality education in resource-constrained regions requires strengthening empowering leadership, organizational support and lecturers’ professional autonomy alongside infrastructure development and institutional capacity building to encourage sustainable academic performance and innovation.
B. G. Dimmera, Nurul Komari, Titik Rosnani· International Research Journ...· 0 citations