Emotional intelligence components and ethical judgment levels of independent auditors: The moderating role of extraversion

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Type of the article: Research Article

 Auditors’ ethical judgments are central to the credibility of financial reporting and the preservation of public trust. While technical expertise is essential, it is not sufficient on its own, as auditors often work under intense pressure, uncertainty, and complex social interactions where emotions play a significant role in shaping decisions. Drawing on Rest’s four-component model of moral functioning, the study investigates how four EI subdimensions (emotional evaluation, empathic sensitivity, positive emotional management, and positive use of emotions) shape auditors’ ethical decision-making. Data were collected from 207 auditors through validated survey instruments and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The population of the study consists of participants who have an independent auditor certificate and operate in Türkiye. Empirical study was conducted over a three-month period, beginning in December 2024 and ending in February 2025. Results indicate that emotional evaluation and the positive use of emotions significantly predict ethical judgment, underscoring the importance of recognizing emotional cues and leveraging positive affect in promoting ethical vigilance. By contrast, empathic sensitivity and emotional management did not demonstrate significant effects, suggesting that empathy and self-regulation may play a lesser role in highly regulated professional contexts. Extraversion did not moderate any of the hypothesized relationships, challenging assumptions about the amplifying role of personality traits in the application of EI. The findings extend emotional intelligence theory by highlighting its selective and context-dependent impact on ethics, while also offering practical insights for auditor training and regulatory policies aimed at strengthening professional skepticism and ethical conduct.

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    • Figure 1. Structural equation model
    • Figure 2. Structural equation modeling analysis result
    • Table 1. Literature review
    • Table 2. Demographic information
    • Table 3. Factor loadings, reliability levels and explained average variances of the scales
    • Table 4. Fornell and Larcker test results
    • Table 5. Discriminant validity results according to the HTMT criterion
    • Table 6. PLS-SEM analysis results
    • Table A1. Demographic information
    • Table A2. Scale groups and questions
    • Conceptualization
      Yusuf Kurt
    • Data curation
      Yusuf Kurt
    • Investigation
      Yusuf Kurt
    • Methodology
      Yusuf Kurt
    • Writing – original draft
      Yusuf Kurt
    • Formal Analysis
      Václav Kupec
    • Project administration
      Václav Kupec
    • Resources
      Václav Kupec
    • Supervision
      Václav Kupec
    • Writing – review & editing
      Václav Kupec, Alp Aytaç
    • Funding acquisition
      Alp Aytaç
    • Validation
      Alp Aytaç
    • Software
      Alp Aytaç
    • Visualization
      Alp Aytaç