A Model for Preventing Tax Evasion in the Iranian Tax System

Preventing tax evasion is a crucial and significant issue in the economy of any country, requiring effective policymaking and actions. Accordingly, considering the subject's importance, this research aimed to present a model for preventing tax evasion in 2023. A mixed-methods research approach (qualitative-quantitative) was chosen. The study population included large taxpayers, experts, managers, and specialists in the country's taxation field. The qualitative part of the research was conducted through semi-structured in-depth interviews with 13 experts selected through purposive sampling. The sample for the quantitative part was determined randomly based on Cochran's formula, resulting in 384 individuals, and to ensure greater reliability and reduce sampling error, 390 individuals were selected. The data collection tools included interviews, while in the quantitative phase, a researcher-made questionnaire derived from the research model containing 202 items was used. Data analysis in the qualitative section was performed using grounded theory, and in the quantitative section, the partial least squares method was applied. The findings indicated that in the qualitative section, after three coding stages, the research model included six main categories (core, causal conditions, intervening conditions, contextual conditions, strategies, and outcomes), 12 subcategories, and 202 concepts. The results of the quantitative section also confirmed the validity of the model. Overall, based on the results, this research model serves as a suitable framework for enhancing managers' understanding of the concepts and categories influencing the prevention of tax evasion. Therefore, it is recommended to utilize the proposed model to maximize government tax revenue by implementing the model's strategies.