Ai-Driven Digital Transformation and Performance Efficiency in Accounting Information Systems
TL;DR
The findings reveal that AI-driven digital transformation significantly enhances accounting system performance efficiency, particularly in processing speed, reporting accuracy, and real-time financial decision support.
Abstract
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.