Taxpayers’ awareness and perception of machine learning in enhancing tax compliance in Indonesia

Angeline Shane (), Helena Jelivia Tan Wijaya () and Gatot Soepriyanto ()

Edelweiss Applied Science and Technology, 2025, vol. 9, issue 10, 801-814

Abstract: This study explores taxpayers’ awareness and perception of Machine Learning (ML) in the context of enhancing tax compliance in Indonesia. As the government advances digital tax systems, understanding how taxpayers respond to innovations becomes increasingly important. The research aims to identify whether familiarity, knowledge, and experience with ML influence users’ perceptions of ease of use and usefulness, and ultimately, their willingness to comply with tax regulations. Using a quantitative approach, data were collected through a structured questionnaire distributed to individual taxpayers. A total of 306 responses were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) via SmartPLS. The findings indicate that familiarity and experience positively affect perceived ease of use and usefulness, which in turn strongly influence tax compliance. Conversely, knowledge of ML does not show a significant impact. These results suggest that engagement with ML technologies is positively associated with tax compliance. This study provides valuable insights from the taxpayers’ perspective on how Indonesian tax authorities could design a more digital, accessible, and user-centered tax system.

Keywords: Machine learning; Tax compliance; Technology acceptance Model (TAM). (search for similar items in EconPapers)
Date: 2025
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