The influence of income tax framework on tax evasion intention: The mediating role of taxpayer preparedness

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Type of the article: Research Article

Abstract
Lebanon is characterized by a fragile economy, rising deficit, and low compliance. Tax deviance poses a major drain to public finances and undermines fiscal sustainability, particularly in the Lebanese context, where the quality of the tax framework directly influences taxpayer behavior. Hence, understanding the mechanisms by which tax legislation and administration shape the intent to defraud is crucial for strengthening tax compliance. This paper examines the influence of the tax framework, namely tax legislation and administration, on tax evasion intentions, with a focus on the mediating role of taxpayer preparedness. The analysis adopts a quantitative approach, using a questionnaire administered to 318 SMEs registered as taxpayers in the Akkar area, employing simple random sampling. The data were analyzed using exploratory and confirmatory factor analyses, followed by structural equation modeling to test the hypotheses. The results reveal that tax legislation has a direct effect on evasion intention (β = 0.466; p < 0.001), while tax administration exerts a moderate direct influence (β = 0.148). Taxpayer preparedness emerged as a primary determinant with a strong direct impact (β = 0.744) and a proven mediating role between tax legislation (β = 0.098), tax administration (β = 0.012), and tax evasion. These findings corroborated that evasion is a causal process in which individual preparedness is key to enhancing compliance. Effective tax policies must incorporate targeted actions on tax education and procedural simplification. Consequently, the study advocates for a transition from coercive to educational tax policies, emphasizing that a comprehensive institutional overhaul is required to rebuild trust in Lebanon’s fiscal system.

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    • Figure 1. Conceptual framework
    • Figure 2. Second-order structural modeling and goodness of fit
    • Table 1. Demographics
    • Table 2. Operational definition and references
    • Table 3. Normality distribution
    • Table 4. Convergent validity (component matrix)
    • Table 5. Divergent validity (Correlations and VIF)
    • Table 6. Regression weights
    • Table 7. Hypotheses testing and regression weights
    • Table A1. Questionnaire
    • Conceptualization
      Tamima Elhassan, Mahmoud Edelby, Mazen Massoud, Rasha Saleh
    • Data curation
      Tamima Elhassan, Mahmoud Edelby, Mazen Massoud
    • Formal Analysis
      Tamima Elhassan, Mahmoud Edelby, Rasha Saleh, Nahed Taha Rizk
    • Funding acquisition
      Tamima Elhassan, Nahed Taha Rizk
    • Investigation
      Tamima Elhassan, Mahmoud Edelby, Mazen Massoud, Rasha Saleh
    • Methodology
      Tamima Elhassan, Mazen Massoud, Rasha Saleh
    • Project administration
      Tamima Elhassan, Mazen Massoud, Rasha Saleh, Nahed Taha Rizk
    • Resources
      Tamima Elhassan, Nahed Taha Rizk
    • Supervision
      Tamima Elhassan, Nahed Taha Rizk
    • Validation
      Tamima Elhassan, Mahmoud Edelby, Mazen Massoud, Rasha Saleh, Nahed Taha Rizk
    • Visualization
      Tamima Elhassan, Mahmoud Edelby, Mazen Massoud, Rasha Saleh
    • Writing – original draft
      Tamima Elhassan, Rasha Saleh
    • Writing – review & editing
      Tamima Elhassan, Mazen Massoud, Rasha Saleh, Nahed Taha Rizk
    • Software
      Mahmoud Edelby, Rasha Saleh