Can CEO overconfidence influence tax avoidance with thin capitalization as a moderating variable? - Mustika Dewi; Eva Herianti; Djusmalinar (BASKARA : Journal of Business and Entrepreneurship, 2026)
This study examines the effect of CEO overconfidence on tax avoidance, using thin capitalisation as a moderating variable. Tax avoidance remains a critical concern because differences in tax rates across countries create opportunities for companies to reduce tax liabilities. This study uses a quantitative approach using a population of manufacturing companies listed on the Indonesia Stock Exchange (IDX) during 2020–2023. The study employed purposive sampling based on several specific criteria. Manufacturing companies were selected as research samples due to their high profit orientation, large tax burden, and the complexity of operational transactions. A panel data regression analysis approach was used in this study to test the hypotheses. The results indicate that CEO overconfidence has a positive and significant effect on tax avoidance. Furthermore, thin capitalisation amplifies the effect of CEO overconfidence on tax avoidance. Firms with higher levels of debt relative to equity and confident CEOs tend to implement tax avoidance strategies more effectively within legal constraints. This study sheds light on the interaction between managerial psychological factors and capital structure in influencing tax avoidance, thereby contributing to the existing literature on corporate governance and taxation. These findings offer guidance for corporate decision-makers in designing capital structure policies that balance operational financing needs and tax compliance, helping managers assess the risks associated with debt financing and tax strategies. Companies need to strengthen governance and oversight of CEO decisions and control debt structures. Regulators ought to strengthen regulations concerning thin capitalisation. Investors should consider CEO character when assessing risk. Future research should explore the inclusion of additional variables and expand the range of sectors examined to improve the generalisability of the findings.