The Role of Technology Trust in Moderating AI Literacy, Digital Tax Literacy, and Coretax Utilization on Taxpayer Compliance - Listya Devi Junaidi; Ratna Dina Marviana; Aryati Juliana Sulaiman (Indonesian Journal of Taxation and Accounting, 2026)
Purpose – This study aims to examine the effect of Artificial Intelligence (AI) literacy, digital tax literacy, and Coretax utilization on MSME taxpayer compliance in Medan, as well as to analyze the moderating role of technology trust in these relationships. Methods – This research adopts a quantitative approach using an explanatory survey design. Data were collected from 250 MSME actors in Medan who have utilized digital tax systems. The sampling technique used was purposive sampling based on specific criteria. Data analysis was conducted using Structural Equation Modeling Partial Least Squares (SEM-PLS) to test the proposed hypotheses and moderation effects. Findings – This study found that AI literacy, digital tax literacy, and the use of Coretax have a positive and significant effect on MSME taxpayer compliance, with Coretax utilization as the strongest predictor with an effect size of 0.079 (7.9%). This model demonstrates acceptable predictive relevance (Q² = 0.127), although the effect sizes of the individual predictors are generally small. Conversely, trust in technology does not moderate the relationship between the independent variables and compliance, as all interaction effects are insignificant and exhibit negligible effect sizes. These results suggest that competency-based factors play a more prominent role than conditional factors in driving compliance behavior during the early phase of digital tax system implementation. Research implications – The findings contribute to the extension of the Technology Acceptance Model by highlighting that technology-related competencies particularly digital tax literacy serve as key determinants of taxpayer compliance in the context of digital taxation. The absence of moderating effects suggests that technology trust is better conceptualized as a direct antecedent rather than a boundary condition within the model. Practically, the results imply that policymakers should prioritize improving taxpayers’ digital and AI literacy through targeted education and training programs to enhance voluntary compliance. Additionally, future research is encouraged to adopt longitudinal designs and incorporate broader variables within an extended TAM framework to better capture the dynamics of technology adoption and compliance behavior in evolving digital tax environments. Originality – This study contributes to the literature by integrating AI literacy, digital tax literacy, Coretax utilization, and technology trust into a single comprehensive model within the context of the newly implemented Coretax system in Indonesia. It provides preliminary evidence on the effectiveness of digital tax transformation in 2025, particularly in MSMEs in Medan, which has been rarely explored in previous studies.