Jul 2026· International Journal of innovative inventions in Social Science and Humanities· Vol 03· 0 citations
TL;DR
The results show that organizational readiness is the strongest predictor of adoption intention, followed by technology readiness and the regulatory environment, while perceived trust partially mediates the relationship between readiness and intention.
Abstract
This study examines the determinants of artificial intelligence adoption intention in Qatar’s financial sector by integrating individual, organizational, and regulatory perspectives within a mixed-methods sequential explanatory design. Drawing on survey data from 215 validated responses collected from financial professionals across conventional, Islamic, foreign, and FinTech institutions, the analysis employs partial least squares structural equation modeling (PLS-SEM) to test the effects of technology readiness, organizational readiness, regulatory environment, perceived trust, and fairness perception on intention to adopt AI. The results show that organizational readiness is the strongest predictor of adoption intention, followed by technology readiness and the regulatory environment, while perceived trust partially mediates the relationship between readiness and intention. Multi-group analysis further reveals heterogeneity across bank types and levels of AI exposure, with stronger effects observed in FinTech and digitally oriented institutions. The findings suggest that AI adoption in Qatar depends not only on technical capability, but also on institutional preparedness and ethical legitimacy. The study contributes to the literature on financial innovation by extending readiness-based models to a Gulf context shaped by state-led modernization, regulated experimentation, and the growing need for responsible AI governance.
The rapid integration of artificial intelligence (AI) into organizational practices has intensified scholarly interest in employees’ readiness and behavioral intentions toward AI adoption, particularly in emerging economies such as Vietnam. While prior studies have largely emphasized technological and organizational determinants, relatively limited attention has been paid to individual-level and contextual factors shaping AI adoption within developing digital environments. This study examines the determinants of employees’ behavioral intentions to adopt AI tools in Vietnamese technology firms, drawing on an integrated framework that combines the Technology Acceptance Model (TAM) and the Technology-Organization-Environment (TOE) framework, with the inclusion of personality traits and social influence. A quantitative research design was employed, using survey data collected from 231 employees working in technology-related roles across Vietnam. Structural Equation Modeling (SEM) was applied to test the proposed relationships among key constructs. The findings reveal that perceived ease of use plays a pivotal mediating role, significantly influenced by organizational support, environmental factors, and individual personality traits. Moreover, perceived usefulness and attitude toward using AI emerge as the strongest direct predictors of behavioral intention. Notably, and contrary to much of the existing literature, technological characteristics and social influence do not exhibit statistically significant effects on employees’ adoption intentions in this context. These findings contribute empirical evidence from an emerging market and suggest that internal organizational readiness and individual psychological factors may outweigh purely technological considerations in shaping AI adoption. From a practical perspective, the study highlights the importance of employee-centered training, supportive organizational cultures, and targeted change management strategies for enhancing AI adoption in Vietnamese technology firms.
Nhung Thi Hong Tran, T. Nguyen, Hong Duyen· Tạp chí Khoa học Đại học Côn...· 0 citations
This study addresses the fragmented and inconsistent findings regarding consumer adoption of Artificial Intelligence (AI) in the banking sector. Through a meta-analysis of existing empirical research, it identifies and evaluates the most influential determinants of AI adoption.
A meta-analysis was conducted using 58 empirical studies with data from 23,434 respondents across more than 20 countries. Seventeen drivers were examined, including traditional constructs (e.g., perceived usefulness, ease of use, trust, attitude) and post-adoption variables (e.g. satisfaction, customization, facilitating conditions). Moderation analysis assessed the influence of cultural and contextual variables using hierarchical meta-regression.
Results confirm the central role of perceived usefulness, trust, attitude, and satisfaction in AI adoption, reinforcing the technology adoption models frameworks. Cultural and contextual moderators, such as Power Distance, Masculinity, Uncertainty Avoidance, Long-Term Orientation, and Human Development Index, significantly influenced these effects, highlighting the importance of sociocultural context.
This research offers the first meta-analytic synthesis of consumer AI adoption in banking, integrating cognitive and post-adoption perspectives. Incorporating cultural and contextual moderators, it enhances theoretical generalization and provides actionable guidance for banks and policymakers implementing AI-based financial solutions.
Tareq Rasul, F. Santini, Cláudio Hoffmann Sampaio et al.· International Journal of Ban...· 0 citations
This study investigates the factors influencing customers’ intentions to adopt Artificial Intelligence (AI) banking services in Tunisa. Building on an integrated framework combining the Technology Acceptance Model, Unified Theory of Acceptance and Use of Technology, Theory of Planned Behavior, Perceived Risk Theory, and Diffusion of Innovation Theory, the study investigates how perceived usefulness, perceived ease of use (, awareness, knowledge, attitude, subjective norms, trust, and perceived risk influence AI adoption intentions. Using survey data from 400 Tunisian retail banking customers and applying regression analysis, the findings show that perceived usefulness, perceived ease of use, subjective norms, and trust positively and significantly affect customers’ intention to adopt AI banking services, whereas perceived risk exerts a negative effect. In contrast, awareness, attitude, and knowledge do not appear to significantly influence adoption intentions. The results suggest that functional benefits, ease of interaction, social influence, and confidence in AI systems are more decisive drivers of adoption than cognitive familiarity in an emerging market context. In addition, analysis of variance (ANOVA) results indicate that age and educational level significantly moderate AI adoption behavior, highlighting the importance of demographic heterogeneity in shaping AI acceptance. This study offers valuable insights for banks and policymakers seeking to accelerate digital financial innovation
Rahma Khattab, Sami Bacha· Arab Economic and Business J...· 0 citations
The UTAUT Model was the strongest predictor of behavioral intention, followed by Personal Concern and Top Management Support, while Perceived Risk had no significant influence and the model demonstrated strong explanatory and predictive capability.
Roselyn Ann Quijano, Jimnanie Manigo· International Journal For Mu...· 0 citations
Abstract* Background The increasing adoption of artificial intelligence (AI) in public administration requires local government employees to possess not only technological competence but also sufficient empowerment to effectively utilize AI in delivering high quality public services. However, limited evidence explains how organizational readiness, public service values, learning culture, and technology related psychological factors jointly influence employees’ perceived work empowerment in AI enabled public organizations, particularly in emerging economies such as Indonesia. Methods This study employed a quantitative cross sectional survey involving 687 local government employees in Indonesia. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) to examine the direct effects of AI Governance Preparedness, Public Service Orientation, Learning Culture, and Technology Trust on Perceived Work Empowerment, as well as the moderating role of Technology Trust. Results The findings indicate that AI Governance Preparedness, Public Service Orientation, Learning Culture, and Technology Trust all have positive and significant effects on Perceived Work Empowerment. Technology Trust emerged as the strongest predictor, followed by Learning Culture. In contrast, Technology Trust did not significantly moderate the relationships between AI Governance Preparedness and Perceived Work Empowerment or between Public Service Orientation and Perceived Work Empowerment. Moreover, Technology Trust significantly weakened the positive relationship between Learning Culture and Perceived Work Empowerment. Conclusions Employee empowerment in AI enabled public administration is shaped by the combined influence of organizational readiness, public service values, organizational learning, and employees’ trust in AI technologies. Technology Trust functions primarily as an independent psychological resource rather than as a consistent boundary condition that strengthens organizational factors influencing perceived work empowerment. Recommendations Local governments should strengthen trustworthy AI governance, organizational learning, and employees’ digital competencies through transparent implementation and continuous training. Future research should adopt longitudinal designs and extend comparisons across public sector institutions and countries.
Muhammad Noor, Adam Idris, Annisa Wahyuni Arsyad et al.· F1000Research· 0 citations
Artificial intelligence (AI) has emerged as an important strategic tool for improving marketing performance and enhancing the competitiveness of small and medium-sized enterprises (SMEs). Despite its growing adoption, limited empirical evidence explains how different dimensions of organizational readiness influence AI usage in SMEs, particularly in emerging economies. This study investigates the effects of Technological Infrastructure Readiness (TIR), Human Resource Readiness (HRR), and Organizational Culture and Change Management Readiness (OCCMR) on AI Usage Behavior, with Behavioral Intention serving as a mediating variable. The proposed research framework integrates the Unified Theory of Acceptance and Use of Technology 2 (UTAUT2), the Technology–Organization–Environment (TOE) framework, the Resource-Based View (RBV), and Organizational Readiness Theory. Data were collected from 402 SME owners and managers in Bangkok, Thailand, and analyzed using covariance-based structural equation modeling (CB-SEM). The measurement model demonstrated satisfactory reliability and validity, while the structural model exhibited an excellent fit (χ²/df = 1.91, CFI = .983, TLI = .978, RMSEA = .047). The results indicate that Technological Infrastructure Readiness, Human Resource Readiness, and Organizational Culture and Change Management Readiness significantly influence Behavioral Intention. In addition, Technological Infrastructure Readiness, Organizational Culture and Change Management Readiness, and Behavioral Intention have significant positive effects on AI Usage Behavior, whereas the direct effect of Human Resource Readiness on AI Usage Behavior is not significant. Behavioral Intention is the strongest predictor of AI Usage Behavior (β = .591, p < .001) and significantly mediates the relationships between organizational readiness and AI adoption.
Wanlop Aruntammanak, Yod Sukamongkol· Decision Science Letters· 0 citations
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