Identification and Prioritization of Factors Affecting Whistleblowing Behavior on Tax Violations Using the ISM Structural Equation Modeling Technique

نویسندگان

1 Department of Accounting ,TO.C. , Islamic Azad University, Tonekabon,Mazandaran,iran

2 Department of Accounting ,Cha.C. , Islamic Azad University, chalus,Mazandaran,iran

3 Department of Accounting ,UAE.C.,Islamic Azad University , Dubai,United Arab Emirates

4 Department of Accounting ,ST.C. , Islamic Azad University Tehran,Iran

doi
10.22034/ijfma.2026.78911.2328
چکیده

Whistleblowing behavior on tax violations is a process through which individuals disclose fraud and income concealment in order to protect public interests and achieve tax justice. This behavior is influenced by a set of individual, organizational, and social factors and can play an important role in reducing tax corruption and enhancing public trust. The present study, conducted in the summer of 2025, employed a qualitative–quantitative approach; in the qualitative part, the grounded theory method was used, and in the quantitative part, the FDAHP technique was applied. Data were collected through interviews with 15experts. The results of the qualitative phase led to the identification of 32initial components, which, after three rounds of fuzzy Delphi, were refined into 25final components, including factors such as the existence of secure reporting channels, legal and regulatory frameworks, financial reward programs, strengthening tax justice, reducing pressure on compliant taxpayers, and fear of retaliation. Moreover, the findings of structural equation modeling indicated that the most influential variables included legal and regulatory frameworks, training and awareness-raising, financial reward programs, severity and magnitude of violation, trust in supervisory institutions, justice- oriented motivations, organizational support for whistleblowers, protection of identity and personal security, and the enhancement of transparency and public trust. Finally, technological and informational infrastructures were identified as the most affected variables.