Determinants of taxpayer satisfaction with e-tax filing services: New evidence from artificial neural networks approach
Main Article Content
Abstract
This paper aims to assess the factors affecting taxpayer satisfaction with e-tax filing services in the export-import and logistics industries. It employs a mixed-analytical approach combining Multiple Linear Regression (MLR) and Artificial Neural Networks (ANN) to capture both linear and nonlinear relationships between predictors and the dependent variable, thereby improving prediction accuracy. The results indicate that combining MLR and ANN provides a more precise measure of the relative influence of each predictor on taxpayer satisfaction. The research identifies key factors influencing e-taxpayer satisfaction, including Accessibility, Appearance, Safety, Effectiveness, and Interactivity. These factors are categorized into two groups: high-impact (Appearance, Accessibility, Safety) and low-impact (Interactivity, Effectiveness). The findings have significant theoretical and managerial implications, suggesting that tax authorities should design user-friendly interfaces for their electronic platforms and prioritize security to enhance taxpayer satisfaction. This study's innovative use of ANN combined with MLR provides new insights compared to prior research.
Keywords
Artificial Neural Networks; E-taxpayer satisfaction; Export-import; Logistics; Multiple Linear Regression
Article Details
Field of Economic (JEL Codes)
M15 - IT Management - Business Administration, M30 - General - M38 - Government Policy and Regulation - Marketing and Advertising
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