AI-DRIVEN USER INTERACTION PATTERN ANALYSIS IN CLOUD ACCOUNTING

Abstract

This research is based on the behavioral effects of the adoption of artificial intelligence (AI) in cloud accounting systems and examines its effects on efficiency, errors, user trust, satisfaction, and adoption intention. A quantitative and cross-sectional survey design was applied, which involved the collection of data concerning 150 accounting professionals that work with such platforms as QuickBooks Online, Xero, and Oracle Cloud ERP. Eight dimensions were measured by a structured questionnaire: perceived usefulness, ease of use, behavioral interaction patterns, trust, accuracy, efficiency, satisfaction, and adoption intention. The findings indicate that AI characteristics considerably increase efficiency, optimize operations, and minimize errors, and at the same time, promote a sense of reliability in the quality of financial outputs. The multivariate analysis also showed that workflow integration and effort reduction are some of the most predictive factors of adoption and satisfaction. Internal consistency was established as high and descriptive results indicated that perceptions were always positive on constructs. Despite the overall positive rating of trust, there was still a reserved attitude towards the use of AI in making high-value financial recommendations, which should be regarded as the role of human responsibility. This research forms an extension to the literature of Technology Acceptance Model (TAM) and Human-Computer Interaction (HCI), and the research constructs of perceived explainability and workflow fit are major antecedents of adoption in the accounting context. Software developers, accounting companies, and policymakers can apply this in practice because it requires transparent, auditable, and user-friendly solutions based on AI. Although constrained by self-reporting and sampling limitation, this study forms a base of longitudinal, experimental and cross-cultural studies on AI adoption in accounting in the future.