Mediating Role of Capital Structure Between Determinants of Capital Structure and Firm Value: A Path Analysis Using Econometrics Model

Mediating Role of Capital Structure Between Determinants of Capital Structure and Firm Value: A Path Analysis Using Econometrics Model

Baroon A.^1*, Dayaratne D. A. I.², Menike L. M. C. S.³

¹Sri Lanka Institute of Advanced Technological Education, Sri Lanka

^2,3Sabaragamuwa University of Sri Lanka, Sri Lanka

*Corresponding Author

DOI: https://dx.doi.org/10.47772/IJRISS.2025.906000428

Received: 12 June 2025; Accepted: 17 June 2025; Published: 22 July 2025

ABSTRACT

This study investigates the mediating role of capital structure between the determinants of capital structure and firm value, with a focus on non-financial firms listed on the Colombo Stock Exchange. A sample of 90 firms from twelve industry sectors was analyzed for the period of 8 years from 2013 to 2020 using secondary data extracted from financial reports. The research categorizes variables into three constructs: firm value (dependent variable), capital structure (mediating variable), and determinants of capital structure (independent variables), which include firm-specific attributes such as profitability, asset tangibility, liquidity, firm size, growth opportunity, and earning volatility and corporate governance practices such as board size, board composition, managerial ownership, board meetings, and CEO position. Employing an econometric model and the Sobel test for multiple independent variables, the results reveal that capital structure significantly and partially mediates the effects of profitability on firm value. Moreover, profitability, board size, and board meetings exhibit significant direct effects on firm value. This study contributes to the literature by providing empirical evidence on the indirect effects of profitability via capital structure mediation on firm value in the Sri Lankan context and demonstrates how path analysis can be applied to panel data with multiple independent variables using econometrics models.

Keywords: Determinants of Capital Structure, Firm Value, Path Analysis, Econometrics Models, Sobel test

INTRODUCTION

Capital structure refers to the mix of debt and equity used by a firm to finance its operations and investments. It emphasizes the importance of finding the right balance between debt and equity to maximize firm value and satisfy stakeholders. The Modigliani and Miller theory is introduced as a foundational theory in capital structure, suggesting that financial leverage has no impact on firm value in an ideal market. However, alternative theories such as the trade-off theory, pecking order theory, and agency cost theory argue that real-world imperfections make capital structure decisions relevant. The purpose of the study is to examine the mediating role of capital structure between determinants of capital structure and firm value. This study seeks to contribute to the existing literature by investigating the mediating effects of capital structure on the relationship between determinants of capital structure and firm value. By adopting an econometric model and employing the Sobel test for multiple independent variables, the research aims to provide a novel approach to understanding the indirect effects of determinants of capital structure on firm value through capital structure mediation.

LITERATURE

ReviewCapital structure of a firm refers to the combination of debt and equity used to finance its operations and investments. It represents the sources of funding that support the company’s activities and assets. Debt, which includes loans, bonds, and borrowing, requires repayment with interest over a specified period. Equity, on the other hand, represents the ownership stake held by shareholders, obtained through the issuance of shares.

Firm value, also referred to as company value or enterprise value, represents the overall worth of a business entity. It is a measure of the economic value of a company, considering both its tangible and intangible assets. Firm value reflects the market’s perception of the company’s ability to generate future cash flows. The importance of firm value to business firms encompasses several key aspects.

Capital structure theories.

Modigliani and Miller’s Irrelevance Theory: This seminal theory asserts that, under certain ideal assumptions (perfect capital markets, no taxes, no bankruptcy costs, and information symmetry), capital structure decisions have no impact on firm value.

Trade-Off Theory: This theory posits that firms face a trade-off between the tax benefits of debt and the costs of financial distress. An optimal capital structure is achieved by balancing these opposing forces, leading companies to target a specific debt-to-equity ratio to maximize value.

Pecking Order Theory: formalized by Myers and Majluf (1984), this theory suggests that firms prefer internal financing (retained earnings) over external financing (debt and equity) leading to a hierarchy where retained earnings are used first, followed by debt, and equity issuance is the last resort.

Agency Cost Theory: This theory emphasizes the agency relationship between shareholders and managers. It posits that managers may make suboptimal financing decisions due to differing risk preferences.

Signaling Theory: This theory suggests that firms choose their capital structure to signal their true value to the market. Issuing equity is seen as a negative signal, as it implies the firm’s stock is overvalued. In contrast, issuing debt is perceived positively, as it indicates confidence in future cash flows.

Market Timing Theory: This theory posits that firms consider market conditions when making capital structure decisions. Firms tend to issue equity when their stock is overvalued and use debt when their stock is undervalued, aligning with the idea of exploiting market mispricing.

Empirical Review

Khaki and Akin (2020) found that profitability is negatively correlated with leverage, whereas Doan (2019) discovered a positive influence of profitability on capital structure. Tangible assets have also been studied in relation to leverage. Khaki and Akin (2020) reported that firms with tangible assets tend to employ higher debt levels, contrasting with the findings of Briones and Chang (2017), who found a negative statistical relationship between tangibility and debt levels.

Growth opportunity in determining capital structure has been examined as well. Briones and Chang (2017) concluded that growth isn’t statistically significant, whereas Pratheepan and Banda (2016) revealed a significant positive relationship between growth and leverage. The impact of liquidity on leverage has been debated, with Molla (2019) finding no significant relationship, whereas Kasthury and Anandasayanan (2019) observed a significant negative impact of liquidity on leverage.

The effect of firm size on leverage has yielded varied results. Pratheepan and Banda (2016) established a positive relationship firm size and leverage, while Hussain and Miras (2015) documented a significant negative relationship between firm size and total debt ratio. Earnings volatility’s relationship with leverage also varies, as Akhtar and Oliver (2009) identified a negative association, whereas other studies (Booth et al., 2001; Deesomsak et al., 2004; Ellili and Farouk, 2011) highlighted a positive effect.

The impact of board size on capital structure demonstrated conflicting findings, with Ajanthan (2013) showing a negative relationship and Sheikh and Wang (2012) discovered that larger boards correlate with higher debt levels. Managerial ownership played a role in capital structure decisions, where Kulathunga et al. (2017) found that higher ownership led to a preference for equity financing. Sewpersadh (2019) found a positive correlation between CEO duality and leverage, while Sheikh and Wang (2012) suggested an insignificant effect.

Regarding the relationship between financial leverage and firm value, Panyagometh (2022) found no significant impact, a conclusion aligned with Winata et al. (2020), who also observed no direct effect of debt ratios on firm value. Conversely, Almahadin and Oroud (2019) reported that a higher debt ratio correlated with decreased firm value.

METHODOLOGY

This section will provide a comprehensive overview of the research design, including the conceptual framework, hypotheses, population and sample, data collection methods and tools, variable definition, econometric models, analytical tools.

Conceptual Framework

The study has developed a conceptual framework to illustrate the relationships between the determinants of capital structure and firm value mediated through capital structure and has formulated the hypotheses to investigate indirect effects of capital structure determinants on firm value.

Hypothesis

H₁: Determinants of capital structure have a statistically significant on capital structure

H₂: Capital structure has a statistically significant effect on firm value.

H₃: Capital structure, when incorporated with its determinants, has a statistically significant effect on firm value.

H₄: Determinants of capital structure have a statistically significant indirect effects on firm value mediated through capital structure.

Population and Sample

To conduct the study, the population consists of non-financial firms listed on the Colombo Stock Exchange. Financial firms are excluded due to their significant differences in financial statements and leverage ratios. A sample of 90 listed companies from twelve industry groups was selected based on data availability. The data covers an eight-year period from 2013 to 2020. Secondary data from the financial reports of the sample companies were collected to measure the variables.

Variables description

The variables were categorized into the constructs, including firm value, capital structure, and determinants of capital structure. The measurement methods for each variable are provided.

In this study, the determinants of capital structure are considered independent variables, while firm value is the dependent variable. Capital structure is treated as the mediating variable between its determinants and firm value.

Table 1: Details of constructs, variables, measurements, and reference

Econometric Models

The study aims to establish linear relationships between the variables using econometric models. Specific models are developed to test the formulated hypotheses and examine the mediating effects of capital structure.

Model-1 focuses on the impact of firm specific attributes such as profitability (PRO), asset tangibility (TAN), growth opportunity (GO), firm size (SIZ), liquidity (LIQ), and earnings volatility (EV) and variables of corporate governance practices, such as board size (BS), board composition (BC), managerial ownership (MO), board meetings (BM), and CEO position (CP) on capital structure measured by long-term debt to total assets ratio. This model identifies determinants of capital structure that have a statistically significant

LDA_it=α+ß₁PRO_it+ß₂TAN_it+ß₃GO_it+ß₄SIZ_it+ß₅LIQ_it+ß₆EV_it+ß₇BS_it+ß₈BC_it+ß₉MO_it+ß₁₀BM_it+ß₁₁CP_it+µ_it

Model 2 is designed to apply regression analysis in estimating the impact of capital structure as the mediating variable on firm value, measured by price-earnings ratio, and this model corresponds to hypothesis H₂. The path analysis can be conducted under the condition of finding a significant effect of the mediating variable.

PER_it =   α2 + ß2LDA_it + µ2_it  

Model 3 focuses on only the direct effects. The ultimate objective is the path analysis based on Baron and Kenny approach. This model corresponds to hypothesis H₃

Model 4 represents the indirect effects of the independent variables on firm value via the mediating variable, capital structure. This model is consistent with hypothesis H₄.

Sobel Test for panel data

The Sobel test is one of the general ways to assess the significance of the indirect effect in the mediation analysis. It allows determination of whether the mediating variable actually mediates the relationship between the determinants of capital structure and firm value.

The Sobel test statistic (Z) is calculated using the formula given below:

Z = (a × b) / √(b^2 × SE_a^2 + a^2 × SE_b^2)

Where:

a: represents the unstandardized coefficient of the independent variable on the mediating variable (Model-1).

b: represents the unstandardized coefficient of the mediating variable on the dependent variable (Model-2).

SE_a: represents the standard error of the coefficient a.

SE_b: represents the standard error of the coefficient b.

To interpret the Sobel test statistic, the study compares its absolute value to the critical value corresponding to the desired level of significance-often α = 0.05, which indicates a 95% confidence level. If the absolute value of the test statistic exceeds the critical value-usually 1.96 for a 95% confidence level-then the mediating effect is considered statistically significant.

Conversion of a standardized coefficient into an unstandardized coefficient

To convert a standardized coefficient (also known as a beta coefficient) back into an unstandardized coefficient in a regression analysis, the following formula can be used

b=β x (SD_y/SD_x)​​

The formula states that the unstandardized coefficient (b) is obtained by multiplying the standardized coefficient (β) by the ratio of the standard deviation of the dependent variable (SD_y​) to the independent variable (SD_x​).

Where:

b: unstandardized coefficient.

β: standardized coefficient.

SD_y ​: standard deviation of the dependent variable.

SD_x ​: standard deviation of the independent variable.

Analysis Tools and Techniques 

The techniques of inferential data analysis have been used that include multiple regression analysis, and path analysis. The analysis is sequential, starting with the testing of hypotheses H₁ to H₄ and then the identification of the determinants of capital structure. Data analysis was done using EViews 14 software widely used in econometrics for handling time-series and panel data.

RESULTS AND DISCUSSION

Analysis on the determinants of capital structure

Table 2 displays regression of long-term debt to total assets ratio on variables related to firm-specific attributes and corporate governance practice. The purpose of this analysis is to examine the determinants of capital structure which have a statistically significant effects on capital structure.

Table 2: Regression of long-term debt to total assets ratio on variables related to firm-specific attributes and corporate governance practice.

(PRO-Profitability, TAN-Asset tangibility, GO-Growth opportunity, SIZ-Firm size,

LIQ-Liquidity, EV-Earnings volatility, BS-Board size, BC-Board composition,

BM-Board meeting, MO- Managerial ownership, CP-CEO position)

The above table shows that profitability has a negative impact on the long-term debt to total assets ratio. Some studies found a negative correlation, meaning that the more profitable companies tend to carry less debt, as shown by Khaki and Akin (2020), Çekrezi (2013), Malinic et al. (2013), and Hossain and Ali (2012). Asset Tangibility has a significant positive relation to long-term debt to total assets ratio. Firms that have more tangible are likely to use more debt finance. Some studies (Khaki and Akin, 2020; Hussain and Miras, 2015; Yasin, 2014; Ghazouani, 2013) indicated a positive relationship, suggesting that firms with tangible assets tend to employ higher levels of debt.

Liquidity exhibits its significant impact on long-term debt to total assets ratio as well. That is, a firm with a higher degree of liquidity shows a lower amount of long-term debt to total assets ratio. Some researchers documented significant negative influence of liquidity on the firms’ leverage, for instance, Kasthury and Anandasayanan (2019) and Hussain and Miras (2015) reported that the firms with higher liquidity has lower leverage. Firm size, growth opportunity and earnings volatility have no significant effects on capital structure.

These findings further tend to show that board size has a positive significant effect on long-term debt to total assets ratio, meaning that larger boards are more likely to show more debt. Sheikh and Wang, 2012 postulate that firms with larger boards are likely to have high levels of debts.

The study indicated that board composition has a negative significant impact on long-term debt to total assets ratio. This may indicate that a board composed of a mix of non-executive directors is more conservative in making their financing decisions, which may provide a lower level of long-term debt for the company.

Board meetings is also proved to contribute positively and significantly to the long-term debt to total assets ratio. Rajendran (2012) reported that the more frequent board meeting leads to be higher level of leverage.

The study also identifies the impact of the tenure of the CEO on the ratio of long-term debt to total assets. Firms where the CEO also held the position for a long period tend to have a higher long-term debt to total assets ratio. On the other hand, the research findings indicate that managerial ownership did not illustrate statistically significant effect on long-term debt ratio.

From the regression analysis, profitability, asset tangibility and liquidity, are the determinants of capital structure in relation to firm specific attributes and board size, board composition, board meeting, and CEO position are the determinants of capital structure in relation to corporate governance practices in Sri Lankan context.

Analysis of effects of capital structure on firm value

Table 3 presents a regression analysis carried out to establish the relationship between capital structure represented by the long-term debt to asset ratio and firm value represented by the price earnings ratio. The analysis in this context is a method to establishing whether capital structure mediates the relationship between its determinants and firm value.

Table 3: Regression analysis of price earnings ratio on long-term debt to assets ratio.

The coefficient and p-value suggest that there is a significant negative relation between long-term debt and firm value. Further, the t-statistic confirms that this relationship is statistically significant. This analysis brings out the importance of capital structure in understanding firm value and sets the stage for further investigation into the mediating role of capital structure in the relationship between its determinants and firm value.

Analysis of direct effects of determinants of capital structure on firm value

Table 4 presents the regression results of the price-earning ratio on the determinants of capital structure, including capital structure measured by long-term debt to assets ratio, in the table below. The main motivation for this regression analysis is that, according to Baron and Kenny (1986), such analysis allows for an examination of the direct effects of determinants of capital structure on firm value.

Table 4: Regression analysis of price earnings ratio on determinants of capital structure, incorporating capital structure

(LDA- Long-term debt to assets ratio, PRO-Profitability, TAN-Asset tangibility,

LIQ-Liquidity, BS-Board size, BC-Board composition, BM-Board meeting, CP-CEO position)

The above table’s standardized coefficients are the direct effects of determinants of capital structure on firm value except for the long-term debt to asset ratio. All the above-mentioned four variables- profitability, board size, board meetings, and CEO position-exhibit statistically significant effects on firm value, hence, are qualified and satisfy the “sufficient” condition required for performing a path analysis. The mediating effects can be derived with the help of a standardized coefficient of long-term debt to assets ratio denoted as ß_m.

Analysis of indirect effects of determinants of capital structure on firm value.

Table 5 presents the regression analysis of the long-term debt to total assets ratio which measures capital structure, on identified determinants of capital structure. The purpose of conducting this regression analysis is to calculate indirect effects of the determinants of capital structure on firm value.

Table 5: Regression analysis of long-term debt to total assets ratio on identified determinants of capital structure

(PRO-Profitability, TAN-Asset tangibility, LIQ-Liquidity, BS-Board size,

BC-Board composition, BM-Board meeting, CP-CEO position)

The above table was used to determine the mediation effects or indirect effects with the help of standardized coefficients. It was found that variables such as profitability, asset tangibility, liquidity, board size, board composition, and board meetings showed statistical significance, which is a prerequisite for path analysis.

The following, Table 6 and Table 8, show the path analysis results that investigate the indirect effect of capital structure determinants on firm value through capital structure. In testing the significance of the indirect effect, the Sobel test was done. If the Sobel test statistic is greater than 1.96, then a particular determinants of capital structure has a significant effect on firm value mediated by capital structure.

Table 6: Indirect effects of determinants of capital structure on firm value mediated through capital structure.

(PRO-Profitability, TAN-Asset tangibility, LIQ-Liquidity, BS-Board size, BC-Board composition,

BM-Board meeting, CP-CEO position)

The above table displays the indirect effects of capital structure determinants on firm value, which are mediated through capital structure.

The results in the above table were obtained using revised econometrics Model-1, Model-3 and Model-4, as presented below.

Model-1 (Table 5)

LDA_it=α1+ß1₁PRO_it+ß1₂TAN_it+ß1₃LIQ_it+ß1₄BS_it+ß1₅BC_it+ß1₆BM_it+ß1₇CP_it+µ1_it

Model-3(Table 4)

PER_it=α3+ß3₁PRO_it+ß3₂TAN_it+ß3₃LIQ_it+ß3₄BS_it+ß3₅BC_it+ß3₆BM_it+ß3₇CP_i+ß_mLDA_it +µ3_it

Model-4(Table 6)

PER_it=α4+ß_m*ß1₁PRO_it+ß_m*ß1₂TAN_it+ß_m*ß1₃LIQ_it+ß_m*ß1₄BS_it+ß_m* ß1₅BC_it +ß_m*ß1₆BM_it +ß_m*ß1₇CP_it +µ4_it

Sobel Test for determinants of capital structure

Table 7 shows the Sobel test statistic. The Sobel test is a common statistical method of testing the significance of the indirect effect in mediation analysis. It helps to determine whether the mediating variable indeed mediates the relationship between the determinants of capital structure and firm value. The mediating effect is considered statistically significant if the absolute value of Sobel’s test statistic exceeds the critical value, usually 1.96 for a 95 percent confidence level. Since output from EViews 14 does not show unstandardized coefficients, standardized coefficients or the beta coefficient of capital structure determinants has been converted into an unstandardized coefficient in the regression analysis.

Table 7: Sobel test statistic

(PRO-Profitability, TAN-Asset tangibility, LIQ-Liquidity, BS-Board size, BC-Board composition, BM-Board meeting, CP-CEO position)

Table 8: Summary of direct effects, indirect effects, and significance between independent variables (Determinants of capital structure) and firm value (price – earning ratio).

(PRO-Profitability, TAN-Asset tangibility, LIQ-Liquidity, BS-Board size, BC-Board composition, BM-Board meeting, CP-CEO position)

The Sobel statistical values confirm that profitability has significant indirect effects on firm value, which is mediated by capital structure since profitability has greater than 1.96 in the Sobel test value. This would, therefore, imply that a change in profitability may cause changes in the capital structure, which in turn influences the firm value.

If, on the other hand, in the approach of Baron and Kenny, a mediating variable does not have an effect on the dependent variable significantly, it means that mediating variable fully mediates the effect of the independent variables on dependent variable. In cases when mediating variable has an effect on dependent variable, it indicates that mediating variable only partially mediates the effects of independent variables on dependent variable. Capital structure, as a mediating variable, measured by LDA, has a significant effect on firm value measured by PER when integrated with its determinants. Therefore, the capital structure only partially mediates the effects of profitability on firm value.

CONCLUSION

This study examined the mediating role of capital structure between determinants of capital structure and firm value. The results indicated that variables such as profitability, tangibility, liquidity, board size, board composition, and board meetings had significant impacts on capital structure. Furthermore, profitability, board size, and board meetings were found to have significant direct effects on firm value. The analysis also revealed that capital structure, as a mediating variable, measured by LDA, has a significant effect on firm value measured by PER when integrated with its determinants. Therefore, capital structure is only able to partially mediate the effect of profitability, on firm value. This study has contributed by exploring indirect effects and employing an econometric model to demonstrate the Sobel test for multiple independent variables.

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APPENDIX

A: Conversion of Standardized Coefficient to Unstandardized coefficient and calculation of Sobel Test

Formula

b=β x (SD_y/SD_x)​​

The formula states that the unstandardized coefficient (b) is obtained by multiplying the standardized coefficient (β) by the ratio of the standard deviation of the dependent variable (SD_y​) to the independent variable (SD_x​).

Where:

b: unstandardized coefficient.

β: standardized coefficient.

SD_y ​: standard deviation of the dependent variable.

SD_x ​: standard deviation of the independent variable.