This study investigates the impact of Artificial Intelligence (AI)-driven digital transformation on performance efficiency in Accounting Information Systems (AIS) within emerging economies, with Nigeria as the focal context. The study is anchored on the Technology Acceptance Model (TAM), Diffusion of Innovation (DOI), and Resource-Based View (RBV), which collectively explain technology adoption behavior, diffusion patterns, and performance outcomes.
A cross-sectional descriptive and explanatory survey design was adopted. Primary data were collected from 300 accounting and finance professionals drawn from banking, manufacturing, telecommunications, and public sector organizations across Lagos, Abuja, Port Harcourt, and Ibadan. Data were obtained through a structured Likert-scale questionnaire and analyzed using SPSS version 27.
The findings reveal that AI-driven digital transformation significantly enhances accounting system performance efficiency, particularly in processing speed (β = 0.47, p < .001), reporting accuracy (β = 0.43, p < .001), and real-time financial decision support (β = 0.45, p < .001). Results further indicate that AI integration improves automation of financial workflows, strengthens data consistency, and enhances system responsiveness.
However, the study identifies key barriers including high implementation costs (86.9%), inadequate technical skills (84.1%), cybersecurity risks (82.3%), and infrastructural limitations (75.6%), which collectively slow full-scale AIS transformation.
The study concludes that AI-driven digital transformation is a significant predictor of accounting information system performance efficiency in emerging economies. Theoretically, the study extends TAM and DOI by demonstrating their relevance in AIS transformation, while RBV explains how AI enhances organizational capability. Practically, the study provides actionable insights for policymakers, system developers, and organizational leaders on optimizing AI integration in accounting systems.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations
The increasing prevalence of non-communicable diseases (NCDs) continues to place significant pressure on healthcare systems, particularly in low- and middle-income regions where access to early diagnostic infrastructure remains limited. Conventional healthcare approaches are often reactive, detecting diseases after substantial progression and reducing opportunities for timely intervention. This challenge highlights the need for predictive, affordable, and data-driven healthcare solutions that can support early diagnosis and prevention.
This study proposes a conceptual framework that integrates metabolomics with artificial intelligence (AI) to support predictive health systems in resource-constrained environments. Metabolomics enables comprehensive characterization of small-molecule metabolites, providing valuable insights into physiological and pathological changes. When combined with machine learning approaches, metabolomic datasets can be analyzed to identify potential biomarkers, classify disease risks, and generate personalized healthcare insights.
The proposed framework presents a multi-layered architecture consisting of metabolomic data acquisition, preprocessing, feature engineering, AI-based predictive modeling, and clinical decision-support outputs. The model emphasizes scalability through the integration of portable diagnostic technologies, cloud-based analytics, edge computing, and decentralized healthcare delivery approaches. It also considers critical implementation challenges, including data harmonization, infrastructure limitations, algorithmic bias, and ethical governance.
Furthermore, the framework highlights the need for empirical validation through pilot studies, technology assessment, and multi-site evaluation to determine its feasibility, reliability, and applicability across diverse healthcare settings. By integrating biological data analysis, computational intelligence, and responsible innovation principles, this study provides a pathway toward accessible predictive and precision public health systems for underserved populations.
Overall, this research contributes to the advancement of AI-enabled healthcare by proposing a scalable and context-sensitive model that bridges metabolomics, artificial intelligence, and healthcare delivery requirements in resource-constrained environments.
David Sunday Araoti· Journal of Artificial Intell...· 0 citations