Jul 2026· Fundamental and Applied Management Journal· Vol 4, pp. 938-952· 0 citations· 26 references
TL;DR
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, urging a shift in HR strategy from complex change management to direct capability building.
Abstract
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
The findings support generationally differentiated HR interventions for strengthening human–AI collaboration in higher education and contribute to SDGs 4, 8, and 9.
Wahdaniaht Wahdaniah, Ayyub Yunus, Mujirin M. Yamin· Vifada Management and Social...· 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 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
The rapid integration of artificial intelligence (AI) into organizational decision-making has generated an urgent need to understand the human conditions that enable or inhibit employee acceptance of AI-driven decisions. Drawing on Mayer, Davis, and Schoorman's (1995) Integrative Trust Model, Social Exchange Theory (SET), and the Technology Acceptance Model (TAM), this study proposes and empirically tests a novel sequential dual-mediation model in which Leadership Style (Transformational vs. Transactional) influences employee Acceptance of AI-Driven Decisions through two parallel first-stage mediators — Perceived Fairness and Perceived Competence — and one sequential second-stage mediator — Employee Trust. Data were collected from 400 academic faculty and administrative staff across higher education institutions in Lebanon using a cross-sectional survey. Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS 4 was employed for analysis. The measurement model demonstrated strong reliability (Cronbach's α: 0.843–0.901) and validity (AVE: 0.571–0.634; HTMT < 0.85). Structural results indicate that transformational leadership exerts significantly stronger positive effects on perceived fairness (β = 0.423, p < 0.001) and perceived competence (β = 0.391, p < 0.001) than transactional leadership. Both mediators significantly predicted employee trust, which in turn significantly enhanced acceptance of AI-driven decisions (β = 0.447, p < 0.001). Full sequential mediation was confirmed for all leadership pathways. The model explained 54.3% of the variance in employee trust and 49.8% of the variance in AI-driven decision acceptance. Findings advance theory by providing the first empirical validation of a sequential dual-mediation model connecting leadership style to AI acceptance, and offer practical guidance for higher education administrators navigating AI adoption.
Sonia N. El-Khawand, Maria Frangieh· Journal of Intelligent Decis...· 0 citations
The study aims to investigate the role of culturally embedded emotional intelligence (EI) as a strategic human capital asset in facilitating innovative work behavior (IWB) in artificial intelligence (AI)-enabled organizations. The study proposes a relational-digital framework in which Indonesian emotional intelligence (IEI) impacts IWB through human-centered leadership (HCL), facilitated by AI utilization as structural digital infrastructure (Mayyora & Sumartik, 2024). The study used survey data collected from Indonesian organizations and applied structural equation modeling to examine relationships among culturally embedded EI, HCL, AI utilization, and IWB. The study found that IEI is a strong predictor of HCL, which, in turn, significantly impacts IWB, thus supporting the full relational mediation hypothesis. The study found that AI utilization was a direct predictor of IWB, but did not moderate the leadership-innovation relationship. The study found that relational legitimacy and psychological safety were more significant than technological intensity in facilitating innovation behavior in collectivist organizational contexts. The study extends existing knowledge of EI as culturally embedded strategic human capital, proposing that HCL is an aspect of relational infrastructure that facilitates innovation. The study offers a collectivist perspective on digital transformation, in which relational capabilities are primary drivers of innovation in AI-enabled organizations.
Maria Grace Herlina, S. Hamali, Karto Iskandar· Corporate & Business Strateg...· 0 citations
This conceptual paper examines how artificial intelligence (AI) reshapes leadership dynamics. It addresses a critical gap: the lack of an integrated framework explaining how AI leadership influences employees' psychological and social resources.
The paper develops the AI-augmented leadership–well-being–relational energy model. This framework challenges the job demands-resources model's static view, theorizing digital well-being as a dynamic regulatory mechanism linking leadership behaviors to relational energy.
The analysis finds that AI-empowering leadership enhances digital well-being and strengthens relational energy. Conversely, AI-monitoring leadership depletes these same vital resources.
The proposed model is conceptual and needs empirical validation across different cultural and organizational settings.
The model provides leaders and organizations with a critical, human-centered framework for the ethical integration of AI into leadership practice, aiming to foster more resilient and energized workplaces.
Socially, it promotes digitally healthy, resilient workplaces.
The primary contributions are extending the job-demands and resource model, uniquely re-conceptualizing digital well-being as a regulatory resource and relational energy as a collective amplifier and recasting the leader's role as a sociotechnical architect.
P. K. Nanda· Leadership & Organizatio...· 0 citations