CEO Overconfidence, Debt Policy, and Financial Condition: The Moderating Role of Family Ownership in Indonesian Manufacturing Firms

Abstract

ating Role of Family Ownership onesian Manufacturing Firms a Mahardini1, Nana Umdiana2, Kodriyah3 Serang Raya, Banten, Indonesia e: [email protected] ndition is central to corporate sustainability, yet evidence on whether rship conditions the relationships of CEO overconfidence and debt financial condition remains limited, particularly in Indonesian ng firms. This study addresses this gap by positioning family ownership ary condition. From 850 firm-year observations of Indonesian listed ng firms during 2020-2024, 822 observations remained after residual- ning and were analyzed using multiple and moderated regressions. ndition was measured using the MFA Score, with the Altman Z-score as e. CEO overconfidence and debt policy are negatively associated with ndition, while family ownership attenuates both relationships. The ins significant across ownership levels, whereas the latter becomes at high family ownership. Results remain consistent across the full rnative outcome and CEO-overconfidence measures. Two-way fixed- mates support H1-H3, while H4 shows limited robustness. The study mily ownership as a boundary condition in managerial-behavioural and thways to financial condition. fidence; debt policy; family ownership; financial condition; companies.

Keywords: CEO overconfidence; debt policy; family ownership; financial condition; manufacturing companies

Introduction

Financial condition is central to corporate sustainability because it reflects a firm’s ability to maintain operations and meet financial obligations. In emerging markets, it is shaped by both financial factors and managerial characteristics (Horváthová & Mokrišová, 2018; Mișu & Madaleno, 2020). CEO overconfidence may encourage overly optimistic assessments, aggressive investment, and risk underestimation (Kaplan et al., 2022; Liang et al., 2020) while persistent high leverage may increase financial pressure when operating cash flows are insufficient (Zhen et al., 2020). Family ownership may condition both relationships because family owners balance economic objectives with control, reputation, and long-term continuity, although concentrated control may also create entrenchment concerns (Gottardo & Moisello, 2015, 2017). Yet CEO overconfidence, debt policy, and family ownership have largely been examined separately. Prior studies link CEO overconfidence to failure and financial distress, overconfidence-related agency costs to inside-debt incentives, and family involvement to leverage, survival, and distress risk (Galariotis et al., 2023; Gottardo & Moisello, 2017, 2019; Leng et al., 2021; Muñoz-Bullón et al., 2024; Wantriani & Susanti, 2025). Agency and entrenchment perspectives, together with Socioemotional Wealth (SEW) theory, further suggest that family control may shape managerial discretion and financing decisions (Gomez-Mejia et al., 2014; Gottardo & Moisello, 2017, 2019; Hsueh et al., 2023; Muñoz Bullón et al., 2024) . However, whether family ownership conditions the separate relationships of CEO overconfidence and debt policy with financial condition within a unified framework remains unclear. Addressing this gap, this study examines CEO overconfidence and debt policy as Financial condition is central to corporate sustainability, yet evidence on whether family ownership conditions the relationships of CEO overconfidence and debt policy with financial condition remains limited, particularly in Indonesian manufacturing firms. This study addresses this gap by positioning family ownership as a boundary condition. From 850 firm-year observations of Indonesian listed manufacturing firms during 2020-2024, 822 observations remained after residual- based screening and were analyzed using multiple and moderated regressions. Financial condition was measured using the MFA Score, with the Altman Z-score as an alternative. CEO overconfidence and debt policy are negatively associated with financial condition, while family ownership attenuates both relationships. The former remains significant across ownership levels, whereas the latter becomes insignificant at high family ownership. Results remain consistent across the full sample, alternative outcome and CEO-overconfidence measures. Two-way fixed- effects estimates support H1-H3, while H4 shows limited robustness. The study identifies family ownership as a boundary condition in managerial-behavioural and financing pathways to financial condition.

managerial-behavioural and financing pathways to financial condition, respectively, with family ownership as a boundary condition. Using Indonesian listed manufacturing firms, financial condition is measured primarily by the MFA Score (Çolak, 2021) and alternatively by the Altman Z-score (Altman, 1968) with firm size, ROA, and cash flow as controls. Robustness is assessed through alternative financialcondition measures, full-sample sensitivity tests, alternative CEO-overconfidence measures, and two-way fixed-effects models. The findings show that CEO overconfidence and debt policy are negatively associated with financial condition, while family ownership significantly changes the strength of both relationships. The study therefore extends prior research by showing that family ownership is relevant not only to leverage and financial risk but also to how managerial overconfidence and debt policy relate to financial condition. Because monitoring and entrenchment are not directly measured, these findings are interpreted as conditional associations rather than direct evidence of either governance mechanism.

Agency Theory

Agency Theory views managers as agents whose interests may diverge from those of shareholders, particularly under information asymmetry and substantial managerial discretion (Ghozali, 2020; Jensen & Meckling, 1976; Louis & Urcan, 2015). CEO overconfidence can intensify agency problems when managers overestimate returns, underestimate risks, and undertake aggressive investment or financing decisions that increase agency costs and financial pressure (Galariotis et al., 2023). The theory therefore supports examining both CEO overconfidence and debt policy as determinants of financial condition and ownership structure as a context that may alter these relationships.

Upper Echelon Theory

Upper Echelons Theory posits that organizational outcomes reflect executives’ cognitive characteristics, values, and experiences (Abatecola & Cristofaro, 2020; Hambrick & Mason, 1984). CEO overconfidence may lead executives to overestimate returns and underestimate risks, encouraging excessive investment, expansion, or debt utilization (Abdolhosseini & Wooi, 2024; Abdulwahab et al., 2025). These decisions may cause inefficient resource allocation, performance volatility, and financial pressure, thereby weakening financial condition. Thus, the theory links CEO overconfidence to financial condition through cognitively shaped strategic and financial decisions.

Trade-off Theory

Trade-off Theory explains capital structure as a balance between the benefits and costs of debt, particularly tax advantages versus agency and financial-distress costs, implying an optimal debt level (Abdeljawad & Farhood, 2025; Shiro & Biiranee, 2025). Beyond a sustainable level, higher leverage increases interest and debt-payment obligations, bankruptcy risk, and financial pressure, potentially constraining profitability and financial stability (Khan & Qasem, 2024; Sunardi et al., 2020). Accordingly, the theory supports an association between excessive debt utilization and weaker financial condition.

Socioemotional Wealth (SEW)

Socioemotional Wealth (SEW) reflects the non-financial value family owners derive from identity, control, reputation, and intergenerational continuity (Gómez-Mejía et al., 2007). These priorities may encourage conservative financing, while concentrated ownership may reinforce family influence and managerial preferences under limited oversight (BaixauliSoler et al., 2021; Jansen et al., 2023; Michiels & Molly,

2017). Thus, SEW supports family ownership as a boundary condition linking CEO overconfidence and debt policy to financial condition (Gottardo & Moisello, 2017, 2019; Muñoz-Bullón et al., 2024). As monitoring and entrenchment are not directly measured, moderation reflects conditional association rather than evidence of either mechanism. The Impact of CEO Overconfidence on Financial Condition

Overconfident CEOs may overestimate expected returns and underestimate risks, encouraging excessive investment, debt utilization, and other aggressive decisions that can misallocate resources and weaken financial condition (Guluma, 2021; Xiao et al., 2021). Agency Theory highlights the potential agency costs of such decisions, while Upper Echelons Theory explains how managerial cognitive characteristics shape strategic and financial choices (Chua et al., 2025). Prior studies further link CEO overconfidence to corporate risk-taking and adverse financial outcomes (Leng et al., 2021; Mardjono et al., 2026; Wantriani & Susanti, 2025). Accordingly:

H1: CEO overconfidence has a significant negative effect on financial condition. The Impact of Debt Policy on Financial Condition

Trade-off Theory suggests that debt is beneficial only while its tax and financing advantages exceed expected distress costs. Excessive leverage increases interest burdens, bankruptcy risk, and financing costs, while agency conflicts among managers, shareholders, and creditors may further encourage inefficient investment or risk shifting (Khan & Qasem, 2024); (Marimuthu & Abbana, 2023). Hence, greater debt utilization beyond the firm's financial capacity is expected to weaken financial condition. Therefore:

H2: Debt policy has a significant negative effect on financial condition.

Family Ownership Moderates the Effect of CEO Overconfidence on Financial Condition

Family ownership may condition the relationship between CEO overconfidence and financial condition by shaping managerial discretion, monitoring, and family interests. Agency Theory highlights potential Type II conflicts under concentrated control, Upper Echelons Theory emphasizes the role of managerial discretion, and SEW underscores family owners’ interests in reputation and long-term continuity (Hsueh et al., 2023; Muñoz Bullón et al., 2024). Together, these perspectives suggest that the relationship between CEO overconfidence and financial condition may vary with family ownership. Accordingly:

H3: Family ownership moderates the relationship between CEO overconfidence and financial condition.

Family Ownership Moderates the Relationship Between Debt Policy and Financial Condition

Family ownership may condition the relationship between debt policy and financial condition by shaping financing decisions and risk preferences. Agency Theory emphasizes the role of concentrated ownership in aligning owner-manager interests, while Trade-off Theory and SEW suggest that family owners’ concerns for control, reputation, and long-term continuity may influence debt utilization and financial risk (Gottardo & Moisello, 2015, 2019; Muñoz-Bullón et al., 2024).These considerations suggest that the relationship between debt policy and financial condition may vary with family ownership. Therefore:

H4: Family ownership moderates the relationship between debt policy and financial condition.

The conceptual framework illustrating the relationships among variables and the formulation of hypotheses H1-H4 is presented in Figure 1.

Methods

H3 H4

CEO Overconfidence (X1)

H1

H2

Debt Policy (X2)

Control Variables

Firm Size (X3)

ROA (X4)

Cash Flow (X5)

This quantitative associative study uses annual panel data from manufacturing firms listed on the Indonesia Stock Exchange (IDX) during 2020-2024 (Chandrarin, 2017). Secondary data were obtained from annual reports. Of 228 firms, purposive sampling retained firms that were continuously listed, published complete annual reports, were not delisted, and disclosed sufficient ownership information to identify family ownership. Fifty-eight firms were excluded, leaving 170 firms and an initial balanced panel of 850 firmyear observations. Before hypothesis testing, standardized residuals from baseline Model 1 were screened once using an a priori cutoff of ±3. Twenty-eight observations (3.29%) exceeded this threshold and were excluded from the primary analysis, producing a common unbalanced sample of 822 firm-year observations for Models 1-4. The rule was applied independently of coefficient direction or significance. As a sensitivity test, all models were also re-estimated using the complete 850-observation sample; full estimates are reported in Table 4.

Financial Condition

Financial condition reflects a firm’s ability to maintain its financial position and meet current and future obligations, while financial distress represents a more severe and prolonged deterioration in this condition (Brigham & Daves, 2019; Mahardini & Bandi, 2023). This study measures financial condition using the continuous MFA Score (Çolak, 2021), where higher scores indicate stronger financial condition and lower scores indicate greater distress risk. Although the MFA classifies firms as distressed (< -0.02), grey area (-0.02 to 0.56), or financially healthy (> 0.56), the continuous score is used in regression analyses to preserve variation and statistical information, while the cut-offs are used to classify firms as financially distressed (FD) or nonfinancially distressed (NFD). MFA score = 0.24X₁ − 0.14X₂ − 0.03X₃ + 3.76X₄ − 0.72X₅ + 0.20X₆ + 1.14X₇ Note: X₁ = (cash equivalents + securities + short term trade receivables) / short term liabilities X₂ = short term liabilities / current assets X₃ = total liabilities / equity

Dependent Variable

Financial Condition (Y)

X₄ = EBITDA / total assets X₅ = financial expenses / net sales X₆ = net profit (loss) / net sales X₇ = retained earnings / total assets

CEO Overconfidence

CEO overconfidence is measured using the composite index adapted from (Liang et al., 2020), comprising five equally weighted binary indicators: overinvestment, gender, tenure, expertise, and educational level. Scores range from 0 to 5; CEOs scoring at least 3 are classified as overconfident (1), otherwise 0.

Overinvestment. Overinvestment is based on the investment-growth specification used by (Ma’sum et al., 2022) and (Wantriani & Susanti, 2025), following (Richardson, 2006). Expected investment is estimated from asset growth and sales growth; observations with residuals above the sample median are coded as overinvesting (1), otherwise 0.

∆ Asset it

∆ Sales it

Sales it−1 + εi,t Gender. Male executives are generally more prone to overconfidence than female executives, particularly in investment decisions. Gender is therefore coded 1 for male CEOs and 0 for female CEOs (Liang et al., 2020).

Asset it−1 = β0 + β1

Tenure. Managerial experience may reduce overconfidence through learning from past experience. Tenure is coded 1 when the CEO’s tenure is below the sample median and 0 otherwise (Liang et al., 2020).

Expertise. CEO expertise reflects the executive’s educational specialization, which may shape managerial decisions and corporate outcomes (Gounopoulos et al., 2021; Tanjaya & Santoso, 2020). It is coded 1 for CEOs with an educational background in business or economics and 0 otherwise (Gounopoulos et al., 2021).

Educational level. Higher educational attainment may strengthen managerial knowledge and human capital but may also increase confidence in managerial abilities (Liang et al., 2020). As a component of the composite overconfidence index, educational level is coded 1 for CEOs holding a master’s or doctoral degree and 0 for those with a bachelor’s degree or lower (Liang et al., 2020).

CEO overconfidence. The five binary indicators are summed into a score ranging from 0 to 5. Following (Liang et al., 2020). CEOs with scores ≥3 are classified as overconfident, while those with scores <3 are classified as non-overconfident.

Sensitivity Analysis of CEO Overconfidence Measurement.

Measurement sensitivity is assessed by re-estimating Models 1 and 2 with alternative composite-score thresholds of 2 and 4 and overinvestment alone as a behavioural proxy, while retaining the same sample, dependent variable, and controls as the baseline specification. The relevant CO×FO interaction is reconstructed for each alternative measure, and the results are compared with the baseline to assess the robustness of the main inferences.

Debt policy

Debt policy is measured by the Debt-to-Asset Ratio (DAR), defined as total liabilities divided by total assets and expressed as a percentage (Ogachi et al., 2020; Samara & Sutandi, 2024). Values above 100% are retained because they represent firms whose liabilities exceed assets and therefore have negative equity.

Total Liabilities

Total Assets x 100% Family ownership

DAR =

Family ownership is identified from annual reports by tracing the founding family’s ultimate direct and indirect (pyramid) ownership and is measured as the proportion of outstanding ordinary shares ultimately attributable to the family. Indirect ownership is determined by tracing ownership chains and calculating the attributable proportion from the ownership percentages along each chain, while overlapping direct and indirect interests are counted only once (Madyan et al., 2019). Ownership data were rechecked to verify the tracing and eliminate double counting, resulting in family ownership values ranging from 0% to 100%.

Ultimate attributable ordinary shares controlled by the founding family

Total outstanding ordinary shares x 100% Firm size. Firm size reflects a firm’s operational scale and financial capacity, with larger asset bases generally associated with greater financial resilience and lower distress risk (Hamzah et al., 2024). It is measured as the natural logarithm of total assets.

FO =

Return on Assets (ROA). ROA measures profitability by reflecting a firm’s efficiency in using its assets to generate profits (Dirman, 2020; Kasmir, 2021). ROA is calculated as follows (Kasmir, 2021).

𝐸𝑎𝑟𝑛𝑖𝑛𝑔 𝑎𝑓𝑡𝑒𝑟 𝑡𝑎𝑥

𝑇𝑜𝑡𝑎𝑙 𝑎𝑠𝑠𝑒𝑡𝑠 Cash flow. Cash flow reflects cash generated and used in operating, investing, and financing activities. Insufficient operating cash flow may weaken investor confidence in a firm’s ability to sustain operations and increase financialdistress risk (Ramadhanti & Subagyo, 2022). Cash flow is measured as follows:

ROA =

Cash Flow Operating

Total Assets Data Analysis

Cash flow (CFOTA) =

The primary hypotheses are tested using pooled multiple regression and Moderated Regression Analysis (MRA), with the MFA Score as the primary outcome. Robustness is evaluated by replacing the MFA Score with the Altman Z-score and by re-estimating all four models using two-way firm and year fixed effects with standard errors clustered at the firm level. The latter addresses time-invariant firm heterogeneity, common year shocks, heteroskedasticity, and within-firm serial correlation. All coefficients are interpreted as statistical associations rather than definitive causal effects. The research models are specified as follows:

Main model

Model 1 - MFA Score, direct effects FC1 = α + β1CO + β2DP + β3Size + β4ROA + β5CF + Ɛ

Model 2 - MFA Score, moderation model

FC1= α + β1CO + β2DP + β3FO + β4(CO*FO) + β5(DP*FO) + β6Size + β7ROA + β8CF + Ɛ

Two complementary robustness procedures are employed. Models 3-4 replace the MFA Score with the Altman Z-score to assess whether the findings depend on the measurement of financial condition, while Models 1-4 are re-estimated with firm and year fixed effects and firm-clustered standard errors to account for unobserved firm heterogeneity, common year shocks, and within-firm error dependence. Altman Z-scores above 2.99 indicate financial soundness, 1.81–2.99 the grey zone, and below 1.81 elevated bankruptcy risk (Altman, 1968).

Alternative Model Robustness Test Model 3 - Altman Z-score, direct effects FC2= α + β1CO + β2DP + β3Size + β4ROA + β5CF + Ɛ

Model 4 - Altman Z-score, moderation model FC2 = α + β1CO + β2DP + β3FO + β4(CO*FO) + β5(DP*FO) + β6Size + β7ROA + β8CF + Ɛ

Note: FC1 represents financial condition measured using the primary proxy, while FC2 represents financial condition measured using an alternative proxy in the robustness test.

The calculation of FC2 (Z-score) is as follows.

Z = 1.2X₁ + 1.4X₂ + 3.3X₃ + 0.6X₄ + 1.0X₅ Note: X₁= working capital / total assets X₂= retained earnings / total assets X₃= EBIT / total assets X₄= market value of equity / book value of total debt X₅ = sales / total assets

Z-scores above 2.99 indicate a healthy financial condition, scores between 1.81 and 2.99 represent the grey zone, and scores below 1.81 indicate high bankruptcy risk.

Panel Model Robustness Analysis

Models 1-4 are re-estimated using two-way fixed effects to control for time-invariant firm heterogeneity and common year shocks, with firm-clustered standard errors accounting for heteroskedasticity and within-firm serial correlation. The panel specifications are as follows:

Panel Model 1 - MFA Score, direct effects FC1it = αi + λt + β1COit + β2DPit + β3Sizeit + β4ROAit + β5CFit + εit

Panel Model 2 - MFA Score, moderation model FC1it = αi + λt + β1COit + β2DPit + β3FOit + β4(CO×FO)it + β5 (DP×FO)it + β6Sizeit + β7ROAit + β8CFit + εit

Panel Model 3 - Altman Z-score, direct effects FC2it = αi + λt + β1COit + β2DPit + β3Sizeit + β4ROAit + β5CFit + εit

Panel Model 4 - Altman Z-score, moderation model FC2it = αi + λt + β1COit + β2DPit + β3FOit + β4(CO×FO)it + β5 (DP×FO)it + β6Sizeit + β7ROAit + β8CFit + εit

Notes: i and t denote firm and year; αᵢ and λₜ denote firm and year fixed effects; and εᵢₜ is the idiosyncratic error term. Standard errors are clustered at the firm level.

Result and Discussion

The initial 850 firm-year observations were screened once using standardized residuals from Model 1 (|SR| > 3). Excluding 28 observations (3.29%) yielded a common analytical sample of 822 observations for Models 1-4. All

Result and Discussion

Conclusion

service capacity, or actual default. Finally, the Indonesian listed manufacturing sample during 2020-2024, including the COVID-19 disruption and recovery, limits generalizability.

Recommendations for Future Research

Future research could strengthen causal identification through lagged variables, instrumental variables, natural experiments, or dynamic panels. It could also employ alternative CEO-overconfidence measures, richer indicators of family involvement and governance, and complementary financial-condition outcomes, including default, debt-service, and market-based measures. Broader industries, private firms, longer periods, and cross-country samples could further assess generalizability.

Conclusion

This study examines the relationships of CEO overconfidence and debt policy with the financial condition of Indonesian listed manufacturing firms and the moderating role of family ownership. Using 822 firm-year observations from 2020-2024, the results show that CEO overconfidence and debt policy are negatively associated with financial condition, while family ownership attenuates both relationships. The findings remain consistent across the full sample, the Altman Zscore, and alternative CEO-overconfidence measures. Fixedeffects results support H1-H3, whereas H4 shows limited robustness. Overall, family ownership represents a boundary condition in both managerial-behavioural and financing

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Author contributions

The first author developed the research concept, collected and analyzed the data, interpreted the findings, and prepared the manuscript. The second author contributed to the research methodology, validation of the analytical results, and improvement of the manuscript. The third author conducted the literature review, synthesized prior studies, assessed the findings, and contributed to manuscript editing and revision. All authors reviewed and approved the final manuscript and accepted responsibility for its content.

Acknowledgements

The authors sincerely thank the institutions, academic colleagues, and other parties who supported the research and manuscript preparation through assistance with data collection, feedback, and technical support.

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Author Contributions

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Acknowledgements

See the concluding and declaration material reproduced from the final PDF above.

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