Jul 2026· Humanities and Social Sciences Communications· 0 citations
TL;DR
It is found that AI dependence does not have a significant direct negative effect on innovative behavior, and inhibits innovation exclusively through a full mediation pathway by eroding employee self-efficacy, indicating that the suppression of innovation is caused not by the technology itself, but by the “deprivation of mastery experiences” that accompanies over-dependence.
Abstract
While the adoption of artificial intelligence (AI) is often heralded as a catalyst for cognitive augmentation, it simultaneously introduces a risk of cognitive offloading that may threaten human agency. Drawing on the human agency perspective of Social Cognitive Theory, this study investigates the paradoxical relationship between AI dependence and employee innovative behavior. We propose a moderated mediation model to explain how the dependence on “proxy agency” (AI dependence) has a reciprocal relationship with “personal agency” (self-efficacy) to influence innovation, and how AI ethical leadership functions as a contextual safeguard. We tested our hypotheses using data collected from 421 full-time employees in South Korea via a three-wave time-lagged research design. The results reveal a counterintuitive mechanism regarding the impact of AI. We found that AI dependence does not have a significant direct negative effect on innovative behavior. Instead, it inhibits innovation exclusively through a full mediation pathway by eroding employee self-efficacy. This indicates that the suppression of innovation is caused not by the technology itself, but by the “deprivation of mastery experiences” that accompanies over-dependence. Furthermore, we found that AI ethical leadership acts as a critical boundary condition. Under high levels of ethical leadership, the agency-eroding effect of AI dependence on self-efficacy was neutralized, thereby sustaining innovative behavior. These findings challenge technological determinism by highlighting the primacy of psychological resources and offer theoretical and practical insights for fostering a symbiotic human-AI relationship in the modern workplace.
As artificial intelligence (AI) permeates modern workplaces, the need for ethical governance, specifically corporate-responsible AI (CRAI), has become paramount. However, empirical research has been hampered by the lack of a validated instrument to assess CRAI from the perspective of employees. To bridge this gap, this study first creates and validates a novel measurement scale for CRAI through a rigorous multi-stage development process. Using this newly developed instrument, we subsequently investigate how CRAI fosters employees’ knowledge-sharing behavior (KSB) by integrating organizational behavior theories and knowledge-based perspectives. Data were collected using a three-wave, time-lagged design from 405 working professionals in South Korea. The findings indicate that CRAI positively influences KSB, with psychological safety serving as a partial mediator. This outcome suggests that employees’ sense of psychological safety acts as a key mechanism for translating ethical AI governance into collaborative knowledge exchange. Furthermore, the results reveal that organizationally prescribed perfectionism (OPP) moderates the relationship between CRAI and psychological safety; specifically, rigid performance demands undermine the trust-building potential of responsible AI practices. This study contributes to the literature by providing a psychometrically sound tool for future CRAI research and by demonstrating that ethical AI encourages knowledge sharing most effectively when synchronized with a supportive, rather than perfectionistic, work culture.
Byung‐Jik Kim, Yeon-Jun Choi, Julak Lee· Humanities and Social Scienc...· 0 citations
This study unpacks the “black box” phenomenon of artificial intelligence (AI) in Human Resource Management (HRM) by examining the dual structural mechanisms of algorithmic fairness and AI anxiety in shaping employees’ adaptive performance within the Ability-Motivation-Opportunity (AMO) framework. Using a quantitative explanatory design, primary data were collected from 285 full-time professionals in the Indonesian e-commerce industry through purposive sampling and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS 4. The findings reveal a significant full serial mediation mechanism in which algorithmic fairness influences adaptive performance entirely through trust in AI and AI readiness. Although fairness strengthens trust in AI, trust alone does not directly enhance adaptive performance; instead, it functions as a cognitive precursor that fosters AI readiness, which subsequently drives adaptive performance. Contrary to conventional assumptions that technological anxiety weakens performance, AI anxiety has a positive direct effect on adaptive performance, suggesting a proactive defensive coping mechanism among employees facing technological disruption and job insecurity. These findings imply that organizations should move beyond merely auditing algorithmic fairness and focus on developing employees’ cognitive readiness for AI adoption through transparent systems and targeted literacy programs that transform anxiety into proactive digital competence. This study contributes to algorithmic management literature by showing that AI readiness, rather than emotional trust alone, is the key functional link in the AI-HRM interface. However, the study is limited by its cross-sectional design, high R-squared values, and focus on a single industry
Tri Wahyu Wirjawan, Dadang Heri Kusumah, R. Sarah et al.· Fundamental and Applied Mana...· 0 citations
The rise of agentic AI systems, which are autonomous, proactive, and capable of multi-step task execution, has transformed how individuals interact with intelligent technologies. While these systems promise efficiency and enhanced decisionmaking, they also introduce new ethical vulnerabilities. This study investigates a paradoxical mechanism in AI-assisted academic task delegation: as social acceptance of AI delegation increases, individuals rely less on internal moral regulation. Drawing on Moral Disengagement Theory and Social Norms Theory, we test a normative substitution model using SEM data from 280 European university students and find that subjective norms function as both mediator and moderator, amplifying delegation intentions while reducing the influence of moral disengagement. Shame proneness emerges as a secondary moderator that buffers the normative pull for individuals with strong internal moral emotions. These findings highlight a critical socio-technical risk: proactive AI systems may unintentionally erode moral accountability as their use becomes socially normalized. We discuss implications for responsible agentic AI design, governance, and human-AI collaboration.
Yasser Al Helaly, A. Ashofteh· Annual International Compute...· 0 citations
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.
Yuanyuan Ji, Huaming Wu· Frontiers in Psychology· 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 investigated the mechanisms of artificial intelligence (AI) integration among knowledge workers by testing a dual-stage moderated mediation model. Based on the Ability-Motivation-Opportunity (AMO) framework and Job Demands-Resources (JD-R) theory, this study examines whether digital leadership and algorithmic transparency moderate the mediating effects of AI-induced job insecurity on human-algorithm symbiosis. This study used a cross-sectional quantitative design. Data were collected using purposive sampling from an expert niche consisting of 71 academic publishing managers, quality auditors, and academic staff in the higher education sector. Hypotheses were tested using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 with 5,000 bootstrap samples. Contrary to mainstream narratives, the hypothesized dual-stage moderated mediation was not supported. Interestingly, AI-oriented AMO practices and digital leadership positively predicted AI-induced job insecurity, contradicting the expected mitigating effect. However, this heightened insecurity failed to mediate or impede collaborative performance. The Phase 1 model yielded an acceptable R2 = 0.638, while the Phase 2 model (R2 = 0.562) revealed a robust and highly significant direct effect of AMO practices on the establishment of human-algorithm symbiosis. This study challenges conventional assumptions about the "dark side" of AI integration by revealing the phenomenon of "instrumental pragmatism" in highly autonomous knowledge workers. These findings demonstrate that in specific academic settings, professionals bypass psychological trauma (job insecurity) and structural rhetoric (leadership vision), directly translating practical HR interventions (AMO) into effective human-AI collaborations. This research urges a shift in HR strategy from complex change management to direct capability building
Erina Rulianti, Ahmad Gunawan, Giri Nurpribadi et al.· Fundamental and Applied Mana...· 0 citations