ANTECEDENTS OF AI-DRIVEN ACCOUNTING SYSTEM USAGE AND ITS IMPACT ON SMES PERFORMANCE: IN THE CONTEXT OF INDUSTRY 4.0 OF INDIA

Studies have shown that companies are working on creating digital technology to make better decisions and stay ahead of their rivals. However, these studies haven’t explored deeply how companies use digital technology to enhance their operations and impact value, particularly for small and medium-sized enterprises (SMEs). In this context, accounting information has played a crucial role in the decision-making process of businesses, and the broad use of digital technology has allowed for more efficient and effective handling of accounting data related to these activities. An online accounting system called the AI-Driven Accounting System generates the data required for research while enabling the reporting, recording, and processing of enormous amounts of money. The current study focuses on the influence of the TOE, or technological, organizational, and environmental characteristics framework, on AI-Driven Accounting System usage (AASU), which, in turn, affects AI-Driven Accounting System performance (AASP), in light of the significance of AI-Driven Accounting Systems for improving the financial performance of SMEs. In order to examine the variables influencing SMEs’ adoption of AI-Driven accounting systems, this study created an integrated model. The data collection tool for this study was a questionnaire created from a review of the literature. After analyzing data from 183 working professionals of SMEs in Gujarat and Haryana, India, structural equation modeling (SEM) was performed using IBM SPSS AMOS, Ver-20. The results demonstrate that compatibility of using technology, organizational preparedness, management at senior level, and government support—all these variables had an influence on using AI-Driven Accounting System (AASU) except in the Relative Advantage of Technology context. AASU also had a positive and substantial impact on AI-Driven accounting system performance (AASP). AASU has been demonstrated to be an effective technology for the management of large volumes of data, so it is expected that its usage will increase in the near future.