Jul 2026· Academia Research Journal· 0 citations· 21 references
TL;DR
Results have demonstrated that good practice in teaching and learning of AI requires responsible governance, and staff engagement, ethical practice, and access to continual AI-related information are needed to build confidence and attain institutional good practice.
Abstract
Artificial Intelligence (AI) is affecting human resource (HR) practices internationally, but little is known about AI adoption in Global South countries, such as Nepal. This research analyzes employees' attitudes towards AI implementation in HR processes and its consequences for trust, job performance, and organizational outcomes. For this research, the constructs were defined as: trust was operationalized as employees' beliefs about the reliability and integrity of AI-based HR decisions; fairness was conceptualized as justice and bias in AI-based processes; and transparency was conceptualized as the degree to which a user's perceptions of disclosure and openness of the mechanisms behind AI algorithms and decisions. These constructs were measured using a number of Likert-scale items adapted from validated scales. This enabled the evaluation of each construct and was included in the overall mean scores for trust, fairness, and transparency (m = 3.51). A Systematic Review and a causal Configurational explanation were used to conduct the research, which involved 41 employees from the service, banking, IT, and telecommunications industries in Nepal. The evidence shows a moderate level of AI-HR adoption (mean=3.69) and a moderate level of trust, fairness, and transparency (mean=3.51) within participants. The results also indicate that employees held favorable views regarding the impact of AI on their individual performance (M=3.98) and on organizational outcomes (M=3.96). Despite a high level of digital readiness (mean = 3.98), a number of concerns, including decline of human decision making (56.1%), job loss (51.2%), and privacy (46.3%), were expressed. Results have demonstrated that good practice in teaching and learning of AI requires responsible governance. Employee achievement is also positively associated with the company's success (r = 0.73, p < 0.01). Employee job (β=0.42, p<0.001) and AI utilization (β=0.34, p=0.014) were significant predictors of organizational performance in the regression analysis. Staff engagement, ethical practice, and access to continual AI-related information are needed to build confidence and attain institutional good practice.
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
Assessing the effects of technology reliability (RL), credibility (CR) and technical competence (TEC) on HR professionals’ trust and, subsequently, their intent to deploy AI tools reveals that technology RL, CR and TEC each enhance trust in AI.
R. Arora, Neha Kumari Siradhana· South Asian Journal of Human...· 0 citations
It is indicated that effectively implementing AI in management requires parallel development of employee competencies, building trust in technology and implementing ethical and supervisory standards, in which technology plays a supporting role rather than replacing the human factor.
Małgorzata Oleś-Filiks· European Conference on Knowl...· 0 citations
The Unified Theory of Acceptance and Use of Technology (UTAUT) has been widely used to explain technology adoption, although its suitability for complex, learning-oriented systems such as Artificial Intelligence (AI) is contested. This systematic literature review, following PRISMA 2020, reviews 71 empirical and review articles published from 2020 to 2025 that apply UTAUT or its extensions to AI adoption in a variety of domains such as education, healthcare, finance and banking, public administration, and retail and services. The review identifies three common limitations of traditional UTAUT in the AI context: little attention to the opaque “black box” decision-making of AI, little consideration of relational user AI interactions, and little consideration of ethical issues such as algorithmic bias and fairness. To address these shortcomings, it proposes an integrated framework where Perceived Intelligence captures users’ evaluation of an AI system’s learning ability, adaptability, and predictive accuracy. Perceived Intelligence is positioned as an intermediary between core UTAUT constructs, in particular Performance Expectancy and Effort Expectancy, and Behavioural Intention. Trust in AI is positioned as a critical antecedent of Perceived Intelligence and disaggregated into competence-based, transparency-based, and privacy-based dimensions. The review offers a theoretically grounded UTAUT extension for AI adoption, specifies hypothesized structural paths, and examines sectoral variation in ethical and competency concerns, providing a testable model for future structural equation modelling and practical guidance for trust-centred AI implementation.
Muhammad Shafeeq Mohd Sapian, Haslinda Musa, N. Rashid et al.· International journal of res...· 0 citations
AI is being increasingly integrated into HRM, modifying recruitment, performance analysis, learning and development, and managerial decisions. This paper uses eighteen recent organizational psychology studies and focuses on information systems and human-computer interaction. It provides a synthesis on what triggers trust between employees and AI, the human-algorithm workload balance, and fairness concerns regarding AI in people management and assessment. The review concludes that trust in AI is separate from trust in human colleagues, that the process shapes trust and the delegation of trust can improve employees’ performance and overall satisfaction, even when the trust is not complete. This review also notes that trust, explainability, accountability, and the varying perceptions of fairness limit the deployment of AI. Task Division, trust construction, varying perceptions of fairness, and the organizational structure are the main components of the Human-AI and HRM collaboration system, which this review seeks to examine. It also identifies research gaps.
Nidhi Goel· International Journal For Mu...· 0 citations
This study aims to examine the impact of artificial intelligence (AI)-driven workflows on efficiency and collaboration, shaping employees’ attitudes and intentions. In addition, it theoretically contributes by linking AI adoption to different levels of collaboration, showing how trust and risk influence engagement.
This study conducted a survey with remote work employees in Indian information technology (IT) firms and received 386 respondents. The study further extended the unified theory of acceptance and use of technology (UTAUT2) model, and for a comprehensive analysis, partial least squares structural equation modeling using SmartPLS4 was used.
The study findings underline the significant impact of AI adoption on employees’ attitudes and intentions. Results also demonstrate how trust and risk perceptions determine the depth of collaboration in remote AI-enabled work environments. It further provides insights into how traditional job practices adapt to an AI-integrated work environment.
Finally, this study contributes to understanding organizational adaptation in an AI-enabled environment and gives practical and managerial insights for organizational leaders, practitioners and policymakers while ensuring a trust- and ethics-focused AI system in remote work. The findings contribute to collaboration theory by empirically showing how trust enables, and risk constrains, effective collaborative engagement in remote work.
The rapid use of AI in remote work scenarios in Indian IT firms influences collaboration and work efficiency. However, this scenario is hindered by certain challenges related to stakeholder and employee trust and ethical concerns. This study provides a novel integration of UTAUT2 with collaboration frameworks, emphasizing the theoretical link between AI adoption, trust, risk and collaboration levels.
Suman Kumar, M. Moslehpour, A. Walawalkar et al.· Journal of Information, Comm...· 0 citations
