Jul 2026· Fundamental and Applied Management Journal· Vol 4, pp. 953-963· 0 citations· 30 references
TL;DR
It is shown that AI readiness, rather than emotional trust alone, is the key functional link in the AI-HRM interface, and 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.
Abstract
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
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
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.
Byung‐Jik Kim, Yeon-Jun Choi, Julak Lee· Humanities and Social Scienc...· 0 citations
As generative artificial intelligence agents become embedded in higher education, understanding students' continuance intention (CUI) is critical. To address the underexplored mechanisms regarding how campus AI environments are associated with user trust, this study adopts the Stimulus-Organism-Response (S-O-R) framework to investigate a dual-path trust mechanism: AI System-like Trust (AST) and AI Human-like Trust (AHT). Through a hybrid methodology combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and non-parametric Random Forest classification, this study analyzes university students' CUI toward campus GenAI. The findings demonstrate that Information Quality (IQ) and Service Quality (SQ) are positively associated with both AST and AHT pathways. Conversely, Facilitating Conditions (FC) and Performance Expectancy (PE) do not exhibit independent linear associations within the simultaneous structural estimation. Rather than indicating an empirical absence of functional value, this configuration uncovers a holistic evaluation mechanism among digital natives, where basic functional affordances operate as baseline hygiene constraints whose variance is subsumed by quality attributes. Notably, while both AST and AHT exert concurrently significant linear driving forces on CUI within the structural path framework, the exploratory machine learning analysis clarifies that under recursive partitioning, AHT exhibits a higher relative predictive weight. Furthermore, a localized asymmetry operates within the linear mediation channels, where SQ selectively mobilizes AHT over AST to drive continuous usage. Post-hoc inspections imply that PE operates through a non-linear threshold dynamic, transitioning into an active predictive catalyst primarily within the positive spectrum of user perception. Our study contributes to campus AI services evaluation by highlighting that within the contemporary GenAI ecosystem, students' evaluative focus prioritizes relational and epistemic quality over functional accessibility. Campus application administrators and developers are advised to prioritize content rigor and responsive, empathetic interaction design to cultivate the relational trust relevant for sustained engagement.
Bingyao Li, T. Yu· Frontiers in Psychology· 0 citations
Artificial intelligence (AI) is increasingly embedded in human resource management (HRM), yet its performance consequences depend on whether employees perceive AI-enabled HR systems as fair, transparent, privacy-protective, and trustworthy. We examine the direct effects of four AI-HRM dimensions—algorithmic fairness (bias mitigation), transparency, privacy protection, and trust—on employee job performance and test innovation as a mediating mechanism in the United Arab Emirates (UAE) public sector. Guided by the Resource-Based View, we administered a cross-sectional survey to employees of Dubai Municipality. Of 382 returned questionnaires, 376 valid responses were analyzed using SPSS and Partial Least Squares Structural Equation Modeling. Fairness, transparency, and privacy produced statistically significant direct coefficients, but the effects were negligible in magnitude: fairness and transparency were negative, privacy was positive, and trust was nonsignificant. Innovation generated substantially larger indirect effects from fairness and transparency, with transparency producing the dominant pathway (β = .792, p < .001). Fairness and transparency exhibited competitive mediation, privacy showed a direct-only effect, and trust showed negative indirect-only mediation. These findings indicate that AI-HRM creates performance value primarily when ethical and governance capabilities are converted into innovative work practices. Because the job-performance model reached the boundary value R² = 1.000 and the innovation-to-performance effect size was exceptionally large, we interpret the model-quality statistics cautiously and treat them as priorities for robustness re-estimation.
Ebtesam Abdulla AlDhanhani, Ahmad Nur Aizat Bin Ahmad· International journal of com...· 0 citations
The rapid integration of Artificial Intelligence (AI) in banking, Customer Relationship Management (CRM) has undergone several changes, which have also led to ethical concerns and implications on customer outcomes. While previous studies have studied AI adoption and trust independently, little attention has been placed on how ethical AI dimensions interact and affect customer retention via trust mechanisms. In this sense the objective of our study is to investigate ethical AI practices (using the EASTL framework) and its impact on Customer Retention (CR), with Sustainable Customer Trust (SCT) as mediator factor in private sector banks. A quantitative research design was taken by implementing structured questionnaires for 361 respondents. The model consists of five major ethical AI constructs-Normative Ethical Alignment, Institutional Accountability Mechanisms, Algorithmic Transparency and Explainability, Data Security and Privacy, and Regulatory and Social Legitimacy. The data were processed by Partial Least Squares Structural Equation Modeling (PLS-SEM) to calculate the direct and indirect relationships. The model accounts for 48.4% for Variance in Sustainable Customer Trust and 32.1% for Customer Retention. Algorithmic Transparency, Data Security, Institutional Accountability and Regulatory Legitimacy are the major factors that directly and indirectly affect customer retention via trust suggesting partial mediation. But Normative Ethical Alignment was not significant. Sustainable Customer Trust formed a vital mediating factor of ethical AI practices on the customer retention and its influence from the one-sided effect. The research shows that ethical AI is not only a compliance obligation, but a strategic lever for building trust and long-term customer relationships. These results present useful insights for banks and policymakers into how to develop open and trustworthy AI systems that are responsible and reliable to instill customer trust and sustainability within digital banking environments.
Herbert Kimura, Alaaeddine Ramadan, Najla A. Al-Thani et al.· Frontiers in Artificial Inte...· 0 citations
Public administration increasingly relies on artificial intelligence (AI) to optimize processes, personalize services, and improve decision-making; however, evidence on human resources management (HRM) for AI readiness remains limited. This article examines how six HRM-related AI readiness factors relate to four AI readiness components (“Adaptability”, “Proactivity”, “Resilience”, “Ethical governance commitment”), with human resources (HR) development as a central capability-building lever. Data were collected through an online survey of 112 senior public managers in Slovenia conducted between May and July 2025 and analyzed using partial least squares structural equation modeling (PLS-SEM) to test the direct relationships between HRM factors and AI readiness components. “Human resources (HR) development” is the only factor positively associated with all AI readiness components, but the findings are exploratory due to modest effect sizes, low reliability for some constructs, and limited discriminant validity. “Leadership style” is associated with “Resilience” and “Ethical governance commitment,” while “Change management” shows a smaller association with “Proactivity.” Factors such as “HR planning, recruiting, hiring,” “Communication and collaboration,” and “Organizational culture” do not show statistically significant direct associations in the tested model. The findings support conceptualizing AI readiness as a human-centered, multidimensional organizational capability and suggest that capability building through “HR development”, supported by leadership and targeted change practices, may help strengthen AI readiness for AI-enabled change in public administration.
Nina Tomaževič, Matej Babšek, Dejan Ravšelj et al.· Frontiers in Human Dynamics· 0 citations