Do CEO duality and firm size influence earnings management practices? Evidence from Indonesian manufacturing listed firms

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Type of the article: Research Article

Abstract
This study provides empirical evidence on the influence of the corporate governance structure and firm characteristics on earnings management practices within Indonesia’s manufacturing sector. Specifically, it examines whether firms with family-linked CEO duality and varying firm sizes influence the level of discretionary accruals in financial reporting. Adopting agency theory and positive accounting theory, the study explores how internal governance alignment and organizational scale relate to managers’ earnings management practices. Using a quantitative research approach, the study analyzes 840 firm-year observations from 140 listed manufacturing companies on the Indonesia Stock Exchange (IDX) for the period 2018–2023, selected based on data completeness and analyzed using panel data regression. The manufacturing sector is chosen due to its economic significance and vulnerability to earnings management in emerging markets such as Indonesia. The findings reveal that CEO duality, defined as a familial relationship between the board of directors and commissioners, significantly reduces earnings management practices, suggesting improved internal monitoring and interest alignment. In contrast, larger-sized firms are associated with higher levels of earnings management, possibly due to greater financial complexity and performance pressure. Other governance mechanisms and financial indicators, such as board size, audit committee independence, and leverage, are included as control variables. Overall, the results suggest that targeted governance structures and firm attributes play a crucial role in shaping financial reporting quality, offering practical implications for investors, regulators, and corporate policymakers concerned with earnings transparency.

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    • Table 1. Sample selection
    • Table 2. Measurement of variables
    • Table 3. Descriptive statistics
    • Table 4. Chow test
    • Table 5. Hausman test
    • Table 6. Breusch and Pagan Lagrangian multiplier (LM test)
    • Table 7. Normality test
    • Table 8. Multicollinearity test
    • Table 9. VIF test
    • Table 10. Autocorrelation test
    • Table 11. Heteroskedasticity test
    • Table 12. Estimation results
    • Table 13. F-test result
    • Table 14. Coefficient of determination
    • Conceptualization
      Daniel Jonathan, Lindrianasari
    • Data curation
      Daniel Jonathan
    • Investigation
      Daniel Jonathan
    • Methodology
      Daniel Jonathan, Lindrianasari
    • Project administration
      Daniel Jonathan
    • Software
      Daniel Jonathan
    • Visualization
      Daniel Jonathan
    • Writing – original draft
      Daniel Jonathan, Lindrianasari
    • Writing – review & editing
      Daniel Jonathan, Lindrianasari
    • Supervision
      Lindrianasari
    • Validation
      Lindrianasari