ARTIFICIAL INTELLIGENCE IN TAX ADMINISTRATION AND CORPORATE TAX COMPLIANCE: EVIDENCE FROM JORDAN

Abstract

Background: Artificial Intelligence (AI) has changed tax administration worldwide, but the evidences from developing countries is restricted. Jordan’s Income and Sales Tax Department (ISTD) generated AI-led audit systems in 2020, offering an appropriate context to examine their effect on compliance.

Objectives: To evaluate how perceived AI adoption affects corporate tax compliance through detection possibility, procedural fairness and compliance intention, whereas considering corporate size, digitalization and professional role.

Methods: A cross-sectional survey was performed among 320 respondents (160 financial managers, 160 external auditors/accountants). Data were analyzed using Structural Equation Modeling (SEM) to test mediation and moderation effects.

Results: Perceived AI intensity significantly improved detection probability and procedural fairness, which increased compliance intention and self-reported compliance. Mediation impacts of fairness and detection were confirmed. Moderation analyses showed stricter compliance impacts among smaller corporates, higher digitalized corporates and financial managers compared to auditors.

Conclusions: AI adoption enhances corporate tax compliance in Jordan through strengthening fairness and detection perceptions. Policymakers should confirm transparency, algorithmic governance and stakeholder involvements, whereas corporates must invest in digitalization and internal tax governance to consistent with AI-enabled systems.