2026· International Journal of Social Science & Economic Research· 0 citations· 25 references
TL;DR
A quantitative research model for examining artificial intelligence (AI)-enabled fraud detection readiness among small and medium-sized enterprises (SMEs) in Sarawak, Malaysia and provides a replicable model for studying digitally underserved regional economies is developed.
Abstract
This manuscript develops a quantitative research model for examining artificial intelligence
(AI)-enabled fraud detection readiness among small and medium-sized enterprises (SMEs) in
Sarawak, Malaysia. Rather than treating AI adoption solely as a general technology acceptance
issue, the paper positions adoption readiness as a multi-layered condition shaped by
technological fit, organizational mobilisation, institutional facilitation, and user-level
acceptance. Drawing on the Technology-Organization-Environment (TOE) framework and the
Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model explains
how relative advantage, compatibility, cost, security, task-technology fit, business
innovativeness, top management support, government support, effort expectancy, and social
influence may affect SMEs' intention to adopt AI in accounting. Trust and firm size are
incorporated as boundary conditions to capture behavioral uncertainty and resource asymmetry
in regional SME settings. The manuscript further specifies a survey-based methodological
protocol involving purposive sampling, a seven-point Likert-scale questionnaire, pilot testing,
SPSS-based data screening, and Partial Least Squares Structural Equation Modelling (PLSSEM) using SmartPLS. By emphasizing readiness, measurement logic, and empirical testability,
the paper offers a methodological contribution to AI accounting research and provides a
replicable model for studying digitally underserved regional economies.
The study proposes that technological factors, organizational factors, environmental support, and individual-level perceptions influence AI adoption intention, and incorporates trust and firm size as moderating variables to account for behavioral uncertainty and resource heterogeneity among SMEs.
Asri Firdaus De Rozario, Dr. Rahmat Aidil Djubair· International Journal of Edu...· 0 citations
This study contributes a trust-centered, infrastructure-aware AI adoption pathway specifically designed for emerging economies, offering policymakers, fintech developers, and financial institutions a pragmatic roadmap for responsible AI-enabled fraud management in Nepal.
Y. Pant, Aditya Pudasaini, R. Shrestha et al.· Islington Journal of Multidi...· 0 citations
This study aims to support small and medium enterprises (SMEs) in making informed e-commerce adoption decisions by identifying key business constructs and developing a machine learning–based decision support tool.
A framework grounded in the technology–organization–environment (TOE) framework and perceived strategic value (PSV) principle is proposed. An extended multi-objective micro genetic algorithm (MmGA) is employed to identify influential business constructs, including perceived benefits, perceived obstacles, competitive pressure, government support, operational support, firm size and business models. Classification models are trained using survey data collected from manufacturing SMEs.
The proposed MmGA-based model provides e-commerce adoption predictions with useful insights into technological readiness, organizational capability and managerial intent affecting adoption decisions.
The study relies on self-reported survey data, which may introduce recall and social desirability bias and limits generalizability across regions and time. In addition, the scope focuses on e-commerce pre-adoption stages. Future work can extend the proposed multi-objective model to post-adoption factors such as scalability and customization.
The developed decision support tool assists SME managers and policymakers in evaluating adoption readiness and prioritizing strategic factors before entering e-commerce activities.
While the TOE and PSV frameworks are well-established individually, the primary novelty of this study lies in their integration into a multi-objective machine learning paradigm. Unlike traditional regression models that analyze constructs in isolation, our approach treats e-commerce adoption as a multi-objective optimization problem (MOP). This allows for the simultaneous optimization of predictive performance and model parsimony, aiming to undertake the non-linear trade-offs between objective organizational readiness (TOE) and subjective strategic intent of decision-makers (PSV). The extended MmGA model serves as a useful prediction tool with a parsimonious and actionable framework for SME managers in decision support of e-commerce adoption.
Seng-Chee Lim, Mohammed Falah Mohammed, Choo Jun Tan et al.· International Journal of Int...· 0 citations
The results suggest that the sustainability benefits of AI emerge when contextual readiness and psychological assurance jointly enable organizations to move beyond adoption intention toward sustained AI utilization, encompassing economic, operational, and environmental dimensions.
Artificial intelligence (AI) is increasingly transforming banking, yet responsible adoption depends not only on technical deployment but also on organizational readiness, governance capacity, monitoring practices, and the capacity to scale AI responsibly. This study examines AI adoption, governance readiness, maturity, perceived benefits, adoption barriers, and scaling intention in the Albanian banking system. Based on a cross-sectional survey of 85 professionals from 15 institutions, including all 12 banks operating in Albania and 3 additional financial institutions, the study applies the Technology–Organization–Environment framework together with principles of responsible AI governance. The analysis uses reliability and validity diagnostics, common-method diagnostics, robust OLS regressions, institution-clustered inference, bootstrap confidence intervals, PLS-SEM robustness analysis, and sensitivity checks. The findings show that AI adoption is visible but uneven: more than half of respondents reported active or pilot AI use, while integration, monitoring, and benefit measurement remain less developed. Data and Technology Readiness and Environmental/Regulatory Pressure were positively associated with Capability-Governance Readiness, which was positively associated with Perceived Benefits. Perceived Benefits were positively associated with Intention to Invest in or Scale AI, whereas Adoption Barriers showed no statistically significant association with scaling intention. The study provides exploratory evidence from a small banking system, indicating that responsible AI development requires the alignment of technological foundations, organizational capability, governance structures, monitoring routines, responsible-use orientation, and benefit-measurement practices.
Laureta Domi, Majlinda Godolja, K. Sevrani· Administrative Sciences· 0 citations
Positive effect of QM on AI adoption is amplified by high innovation sustainability and chief executive officers (CEOs) with IT backgrounds, and is particularly pronounced in large, non-state-owned firms within highly competitive industries and the eastern regions of China.
