Adoption of AI-Based Tax Filing Systems among Indian Taxpayers

Abstract

The growing integration of artificial intelligence (AI) in public sector governance has significantly transformed tax administration by enhancing efficiency, accuracy, and taxpayer engagement. In India, the adoption of AI based tax filing systems marks a shift toward automated, data-driven compliance mechanisms aimed at reducing errors and administrative burden. However, taxpayer acceptance of such systems depends on multiple technological and behavioral factors. This study examines the determinants influencing the adoption of AI-based tax filing systems among Indian taxpayers using an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework.
A quantitative, cross-sectional research design was employed, with primary data collected from 300 Indian taxpayers through a structured questionnaire. Key constructs analyzed include performance expectancy, effort expectancy, social influence, facilitating conditions, and trust in AI-based tax systems, with behavioral intention as the dependent variable. Data analysis was conducted using SPSS and AMOS, applying reliability analysis, exploratory and confirmatory factor analysis, and structural equation modeling.
The results indicate that performance expectancy and trust in AI systems are the most significant predictors of behavioral intention, followed by facilitating conditions and effort expectancy. Social influence also shows a positive but comparatively weaker effect. The proposed model explains 68 percent of the variance in behavioral intention. The study highlights that effective implementation of AI-based tax filing systems requires not only technological sophistication but also institutional trust, transparency, and digital readiness. The findings offer valuable implications for policymakers and tax authorities in designing inclusive, citizen-centric AI-enabled tax governance frameworks.