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CAPITAL STRUCTURE AND FINANCIAL PERFORMANCE: EXAMINING THE
MEDIATION OF MARKET PERFORMANCE AND THE MODERATING OF
WOMEN ON BOARD
Eka Hariyani1*.
Zirman Zirman2.
Yesi Mutia Basri,3 Sri Indriani4 1,2,3.
Accounting Study Program.
Faculty of Economics and Business.
Universitas Riau.
Pekanbaru.
Indonesia *EmailL: eka.
hariyani@lecturer.
Keywords Abstract capital structure, market performance, financial performance, women on board, signaling theory.
PLS-SEM Article informations ReceivedL:
2024-11-20
Accepted:
2025-07-30
AvailableL Online:
2025-07-31
This study examines the relationship between capital structure, market performance, and financial outcomes, while evaluating the moderating role of female board participation and the mediating role of market valuation.
Using a quantitative approach with PLSSEM on cross-sectional data from Kompas100-listed companies, the findings show that capital structure has no significant effect on financial performance or market valuationAicontradicting traditional trade-off and signaling theories in an emerging market In contrast, market performance significantly influences financial outcomes, highlighting the importance of investor Female board presence strengthens the impact of capital structure on market performance, supporting the upper echelon theory regarding leadership diversity.
However, market performance does not mediate the link between capital structure and financial outcomes.
These results suggest that financial decisions are shaped by external perceptions and firm context rather than following a linear pattern.
Practically, firms are advised to adopt context-aware financing strategies, enhance transparency, and support inclusive governance for sustainable value creation.
INTRODUCTION
Capital structure represents a strategic component in financial management that determines the balance between debt and equity used to finance a firmAos operations.
Decisions regarding capital structure not only reflect managementAos risk preferences and policy directions, but also serve as a signal of the companyAos internal conditions to external investors.
According to the Trade-Off Theory (Kraus & Litzenberger, 1.
, there is an optimal level of debt usage that minimizes the cost of capital while maximizing firm value.
Conversely.
Signaling Theory (Spence, 1973.
Ross, 1.
emphasizes that financing decisions can be utilized by managers to convey signals to the market regarding the companyAos future prospects.
Nonetheless, empirical studies have shown mixed findings regarding the impact of capital structure on financial performance.
In emerging markets such as Indonesia, the relationship between capital structure and both financial and market performance remains inconsistent (Abor, 2005.
Salim & Yadav, 2.
Variations in debt management practices, financial access, and market conditions contribute to these inconsistencies.
Therefore, it Program Studi Akuntansi.
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PERFORMANCE AND THE MODERATING OF FEMALE BOARD REPRESENTATION
becomes necessary to explore more complex relationships, particularly by incorporating relevant mediating and moderating variables.
In this context, market performance is viewed as a potential mediating variable between capital structure and financial performance.
Market perceptions of corporate financial decisionsAias reflected by indicators such as stock price and the Price Earnings Ratio (PER)Ai play a crucial role in shaping firm value.
When the market responds positively to a firmAos capital structure policy, stock prices tend to rise, thereby enhancing financial performance through increased shareholder confidence and improved financing efficiency (Connelly et al.
, 2011.
Zhu & Westphal, 2.
However, this mediation pathway has not been extensively explored, particularly in the context of publicly listed firms in Indonesia.
A real-world example can be observed among companies listed in the Kompas100 index, where the relationship between capital structure, market value, and financial performance does not appear to be linear.
A striking case is PT Bukit Asam Tbk (PTBA), which experienced a significant drop in PER from 13.
56 in 2020 to 3.
19 in 2022, despite a substantial increase in total dividends from IDR 835 billion to IDR 7.
9 trillion and a rise in leverage from 328 to 0.
362 during the same period (PTBA, 2.
This suggests that rising debt and profit distribution are not automatically interpreted by the market as positive signals, and may not directly translate into improved financial performance.
Similar patterns are observed in other companies such as ITMG.
ADRO, and MEDC, where low PERs persist despite strong net income (Indonesia Stock Exchange, 2.
Furthermore, the sharp increase in capital market investors in IndonesiaAifrom 3.
million in 2020 to 7.
48 million by the end of 2021, reflecting a growth of 92.
7%Aiindicates a growing demand for transparency and credibility of corporate disclosures (CNBC Indonesia.
In this environment, financing decisions and market signaling become increasingly critical in building investor trust.
Beyond financial aspects, corporate governance characteristics are also crucial in moderating the relationship between capital structure and market perception.
One governance issue receiving growing attention is the presence of women on corporate boards.
Based on Upper Echelon Theory (Hambrick & Mason, 1.
, the personal attributes of top executives can influence strategic decision-making processes and outcomes.
Female representation on boards is believed to enhance oversight quality, broaden strategic perspectives, and strengthen firm credibility in the eyes of the market (Post & Byron, 2015.
Terjesen et al.
, 2.
A study by Ben Saad & Belkacem .
even found that the presence of women directors may amplify the signaling effect of capital structure decisions.
However, empirical research that simultaneously examines the direct effect of capital structure on financial performance, with market performance as a mediator and female board representation as a moderator, remains limited in the Indonesian context.
This study aims to address this gap by empirically investigating the direct impact of capital structure on financial and market performance, while also testing the mediating role of market performance and the moderating effect of women on boards.
This research adopts a quantitative approach using Partial Least Squares Structural Equation Modeling (PLS-SEM), with a sample of firms included in the Kompas100 index over the 2018Ae2022 period.
The findings are expected to contribute to the literature on finance and corporate governance, and offer practical implications for corporate decision-makers in formulating financing strategies that are effective, credible, and responsive to market dynamics and the increasing demand for leadership diversity.
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HYPOTHESES DEVELOPMENT
Effect of Capital Structure on Financial Performance Capital structure is a strategic decision that influences the balance between a firmAos risk exposure and return generation.
According to the Trade-Off Theory (Kraus & Litzenberger, 1.
, firms seek to achieve an optimal mix of debt and equity to minimize the overall cost of capital while maximizing financial performance.
The use of debt provides tax advantages .
interest tax shield.
, but if not managed prudently, it can lead to increased financial risk and reduced profitability.
Financial performance reflects a companyAos ability to efficiently utilize its financial resources to generate profit.
It is commonly measured using indicators such as Return on Assets (ROA) and Return on Equity (ROE), which represent the firmAos efficiency in utilizing assets, returns to shareholders, and the capacity to derive earnings from operational revenues (Brigham & Houston, 2.
Numerous empirical studies support the linkage between capital structure and financial Abor .
and Hirdinis .
suggest that moderate leverage can enhance profitability by enabling business expansion without diluting ownership.
However, other findingsAisuch as those from Firmansyah et al.
and Muzakir .
Aihighlight that in emerging economies like Indonesia, high debt levels may increase financial distress without producing proportional improvements in profitability.
Considering the theoretical background and empirical evidence, the following hypothesis is H1: Capital structure significantly influences a firmAos financial performance.
Effect of Capital Structure on Market Performance Beyond internal performance, capital structure plays a pivotal role in shaping external perceptions, particularly market responses.
Based on Signaling Theory (Spence, 1973.
Ross, 1.
, managerial financing decisionsAisuch as increasing leverage or asset expansionAican serve as signals to investors regarding the firmAos future outlook.
A well-structured and strategically managed capital structure is likely to enhance investor confidence in the firmAos capacity for long-term value creation.
In this study, market performance is measured using Price to Book Value (PBV) and TobinAos QAiboth widely recognized as indicators of investor perceptions regarding valuation and market efficiency (Li et al.
, 2014.
Martani et al.
, 2.
Properly managed leverage may reflect the firmAos willingness to take calculated risks for expansion, while firm size often signals operational stability and resource capacity, which can enhance market credibility.
Previous studies have found a positive association between capital structure and market For example.
Campbell and Mynguez-Vera .
and Hirdinis .
note that firms with strong capital structures and larger sizes tend to achieve higher market valuations.
However, these effects may be context-dependent and influenced by the level of information transparency and market efficiency (Chen et al.
, 2.
Based on this theoretical and empirical grounding, the second hypothesis is formulated as H2: Capital structure .
everage and firm siz.
positively influences market performance.
Effect of Market Performance on Financial Performance Market performance not only reflects investor sentiment but may also serve as a catalyst for enhanced financial performance.
In line with Signaling Theory (Spence, 1973.
Ross, 1.
, market indicators such as stock price or valuation ratios signal a firmAos internal conditions to external stakeholders.
Positive signalsAievident through high PBV or TobinAos Q valuesAi indicate strong investor expectations for profitability and future growth.
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PERFORMANCE AND THE MODERATING OF FEMALE BOARD REPRESENTATION
A favorable market perception can lead to easier access to external financing at lower capital costs (Li et al.
, 2.
, strengthen bargaining positions with stakeholders, and enhance longterm stakeholder confidence (Connelly et al.
, 2.
This, in turn, facilitates operational expansion, strategic investment, and managerial efficiencyAiultimately improving financial indicators such as ROA and ROE Empirical support for this linkage includes findings from Martani et al.
, who demonstrate that valuation metrics like PBV and stock returns are closely correlated with financial performance.
Similarly.
Abor .
argues that strong market perceptions regarding financial management reinforce firm value and financial efficiency.
Accordingly, the third hypothesis is presented:
H3: Market performance (PBV and TobinAos Q) positively influences financial performance (ROA and ROE).
Moderating Role of Female Board Representation in the Relationship Between Capital Structure and Market Performance Leadership characteristicsAiparticularly female representation on corporate boardsAi may act as a contextual factor that strengthens the relationship between capital structure and market perception.
Drawing on Upper Echelon Theory (Hambrick & Mason, 1.
, organizational outcomes are shaped by the cognitive frameworks and values of top executives.
In this regard, the inclusion of women on boards is believed to enrich strategic decision-making, including in financial structure management.
Women tend to exhibit more participative and risk-averse leadership styles compared to men (Campbell & Mynguez-Vera, 2.
, which may promote prudent and long-term financial decisions.
These characteristics can enhance investor confidence in the firmAos stability and credibility.
Studies by Post and Byron .
and Terjesen et al.
further confirm that gender-diverse boards contribute to stronger oversight and strategic communication, reinforcing the alignment between financial decisions and market responses.
Therefore, female board representation is posited to moderate the influence of capital structure on market performance.
This leads to the formulation of the fourth hypothesis:
H4: Female representation on the board of directors moderates the relationship between capital structure and market performance, strengthening the effect.
Mediating Role of Market Performance in the Relationship Between Capital Structure and Financial Performance Finally, this study examines the mediating role of market performance in the relationship between capital structure and financial performance.
The causal mediation approach proposed by Baron and Kenny .
, later refined by Zhao et al.
, outlines that mediation occurs when the independent variable .
apital structur.
affects the mediating variable .
arket performanc.
, which subsequently influences the dependent variable .
inancial performanc.
In essence, capital structure may exert both direct and indirect effects on financial performance through market perceptions.
An optimal capital structureAie.
, appropriate leverage levels and large firm sizeAimay send positive signals to investors about a companyAos long-term prospects (Spence, 1973.
Ross.
These signals are reflected in enhanced market performance, measured through PBV and TobinAos Q, which subsequently lead to improved access to capital and better financial This aligns with signaling theory, which posits that financial decisions convey key information to the market, shaping investor expectations (Connelly et al.
, 2.
Nevertheless, the strength of this mediation depends significantly on market efficiency and corporate transparency.
In inefficient markets or where information disclosure is limited, capital E ISSN 2721-1819 | P ISSN 2721-2416 CURRENT: Jurnal Kajian Akuntansi dan Bisnis Terkini.
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structure signals may be misunderstood or disregarded, weakening the mediating effect (Abeysekera, 2010.
Chen et al.
, 2.
Thus, the fifth hypothesis is articulated as follows:
H5: Market performance mediates the relationship between capital structure and financial performance.
RESEARCH METHOD
Research Design and Sampling Technique This study adopts an explanatory research design, aiming to examine the causal relationships between capital structure as the independent variable and market and financial performance as dependent variables.
The explanatory approach is appropriate, as this research does not merely describe or explore phenomena but empirically tests inter-variable effects based on established theoretical frameworks, including Signaling Theory and Upper Echelon Theory.
The unit of analysis is publicly listed firms included in the Kompas100 Index on the Indonesia Stock Exchange (IDX) during the 2018Ae2022 period.
A purposive sampling technique was employed using the following inclusion criteria: .
firms consistently listed in the Kompas100 Index for five consecutive years.
availability of complete annual financial and .
disclosure of information related to dividend policy, capital structure, board composition.
CEO background, and total assets.
Based on these criteria, a sample of 35 companies was selected, resulting in a panel dataset of 175 firm-year observations .
companies y 5 year.
Operational Definitions and Variable Measurements This study investigates four key variables: capital structure, market performance, financial performance, and woman on board (WOB) as a moderating variable.
The capital structure variable is measured using two formative indicators: leverage (LEV) and firm size (SIZE).
Leverage reflects the proportion of debt relative to total assets, indicating the firmAos financial risk, while firm size is represented by the natural logarithm of total assets, indicating the operational scale of the firm (Hirdinis, 2019.
Nafiah & Sopi, 2.
These two indicators jointly capture the firmAos financing capacity and strategy.
Market performance is assessed using the dividend payout ratio (DPR) and the price-toearnings ratio (PER).
DPR reflects the firm's profit distribution policy to shareholders, signaling the sustainability of earnings (Setyorini & Sulhan, 2.
, while PER represents investor valuation of the firm relative to its earnings per share, serving as a proxy for market response to financial performance (Pushpa Bhatt & Sumangala, 2.
Financial performance, the main endogenous variable, is constructed from return on assets (ROA) and return on equity (ROE).
ROA indicates asset utilization efficiency, and ROE reflects the firmAos ability to generate shareholder returns (Brigham & Houston, 2021.
Weston & Copeland, 1.
These indicators provide a comprehensive view of financial effectiveness from both managerial and shareholder perspectives.
The moderating variable, woman on board (WOB), is measured by the percentage of women on the board of directors relative to total board members (Fitroni & Feliana, 2.
WOB is used to test whether gender diversity at the strategic leadership level strengthens the relationship between capital structure and market performance, in line with Upper Echelon Theory (Hambrick & Mason, 1.
, which asserts that executive demographic characteristics influence organizational policy and external perceptions.
This study adopts a formative measurement model, wherein constructs are formed by their respective indicators (Diamantopoulos & Winklhofer, 2.
When relevant, higher-order Program Studi Akuntansi.
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constructs are measured using a reflectiveAeformative second-order approach.
Variable measurements are as follows:
Firm Value Ae Measured by the Price to Earnings Ratio (PER):
PER = Stock Price / Earnings per Share (EPS) This metric captures investor expectations of the firmAos future profitability (Damodaran, 2.
Dividend Policy Ae Measured by the Dividend Payout Ratio (DPR):
DPR = Dividends per Share / Earnings per Share This formative indicator reflects the firm's preference for distributing earnings (Baker & Powell, 2.
Debt Policy Ae Measured by the leverage ratio:
Leverage = Total Liabilities / Total Assets This ratio indicates the portion of assets financed by debt and reflects the firmAos funding structure (Myers, 2.
Woman on Board (WOB) Ae Measured by the proportion of female board members:
WOB = (Number of Female Directors / Total Board Member.
y 100% Firm Size Ae Measured as the natural logarithm of total assets:
Firm Size = Ln(Total Asset.
Larger firms typically enjoy easier access to external financing, lower bankruptcy risk, and greater transparency, leading to more stable capital structures (Rajan & Zingales.
Frank & Goyal, 2.
Data Analysis Technique A quantitative approach is applied using Partial Least Squares Structural Equation Modeling (PLS-SEM), with SmartPLS 4 as the analytical software.
PLS-SEM is selected for its robustness in handling non-normal data distributions, moderate sample sizes, and complex models involving moderation and mediation effects (Hair et al.
, 2.
Furthermore, it is wellsuited for formative measurement models, where indicators define constructs rather than reflect In formative models, changes in a single indicator may alter the meaning of the entire construct, and indicators are not required to be correlated (Diamantopoulos & Winklhofer.
Hence, construct validity is assessed based on indicator significance and multicollinearity, rather than internal consistency measures such as CronbachAos Alpha.
The use of formative indicators in this study aims to accurately capture multifaceted constructs such as capital structure, market performance, and financial performance, each comprising heterogeneous, non-substitutable dimensions.
This modeling approach allows for a more realistic and context-sensitive interpretation of each indicatorAos contribution to the respective constructs.
PLS-SEM Analysis Procedures Measurement Model Assessment (Formative Construct.
Variance Inflation Factor (VIF): To ensure no high multicollinearity exists among indicators (VIF < .
Indicator Significance: Evaluated through outer weights and p-values to determine the contribution of each indicator to the construct.
Content Validity: Justified through theoretical rationale and empirical references.
Structural Model Assessment:
RA (Coefficient of Determinatio.
: Measures the explanatory power of endogenous .
QA (Predictive Relevanc.
: Assessed via blindfolding procedures to evaluate the modelAos predictive capability.
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Path Coefficients: Estimated using bootstrapping with 5,000 resamples to test the significance of relationships.
fA (Effect Siz.
: Assesses the strength of each predictor variableAos effect on the dependent variable.
Hypothesis Testing:
Hypotheses are tested based on t-statistics and p-values, with a significance threshold of p < 0.
Moderation effects .
nteraction between WOB and capital structur.
and mediation effects are examined using the bootstrapping procedure for indirect effects (Preacher & Hayes, 2.
RESEARCH RESULTS AND DISCUSSION Descriptive Statistics Before testing the hypotheses, descriptive statistics are presented to provide an overview of the characteristics of the data used in this study, as shown in Table 1.
Table 1
Descriptive Statistics Medi Observed Variable Women on Board
0,191
-0,831
Capital Structure 0,129 -2,688 Financial Performance 0,312 -3,628 Market Performance 0,066 -1,630 Source: Output of SmartPLS 4, 2025 Observed Excess Skewn Cramyr-von Mises test statistic 2,383 -0,193 0,976 2,153 2,332 0,267 -0,131 0,385 5,003 7,210 2,025 2,945 4,207 1,358 0,994 0,500 Descriptive Analysis Results The descriptive analysis of the latent constructs in this study reveals that most variables exhibit acceptable data distribution for the PLS-SEM approach.
The Women on Board variable shows a slightly right-skewed distribution .
kewness = 0.
, yet with near-zero kurtosis (Ae .
, indicating a relatively normal distribution shape.
Capital Structure appears to have the most balanced distribution, with skewness close to zero (Ae0.
and low kurtosis .
suggesting evenly spread values with minimal presence of outliers.
In contrast.
Market Performance shows a mild deviation toward a right-skewed distribution .
kewness = 0.
and a slightly peaked shape .
urtosis = 1.
, but still within acceptable limits.
The variable showing the most significant deviation from normality is Financial Performance, which has a high skewness .
and excessive kurtosis .
indicating a predominance of low values alongside a few extreme high values .
This distribution is further evidenced by the wide range of values, from Ae3.
628 to 5.
003, and the highest CramyrAevon Mises statistic among the constructs, confirming its departure from normal Nevertheless, since PLS-SEM does not require the assumption of normality (Hair et al.
, these findings do not compromise the validity of model estimation.
However, the presence of outliers, particularly in the Financial Performance variable, should be taken into account when interpreting the results and formulating conclusions.
Future studies employing parametric methods or assuming normality may require additional treatments such as data transformation or outlier trimming to ensure more robust and representative findings.
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PERFORMANCE AND THE MODERATING OF FEMALE BOARD REPRESENTATION
Measurement Model Assessment Outer Weight Results To evaluate the validity and contribution of indicators in the formative constructs, an outer weight analysis was conducted as part of the measurement model assessment.
Outer weight values indicate the relative contribution of each indicator to its corresponding formative In the context of formative modeling, a significant outer weight implies that the indicator contributes statistically to the formation of the latent construct.
The results of the outer weight analysis are presented in Table 2.
Table 2
Outer Weight Results Original sample (O) DPR -> Market
Performance 1,012 PER -> Market
Performance -0,250 LEV -> Capital Structure 0,676 SIZE -> Capital Structure 1,008 ROA -> Financial Performance 0,672 ROE -> Financial Performance 0,478 Source: Output of SmartPLS 4, 2025
Sample mean (M) Standard
(STDEV)
T statistics (|O/STDEV|) P values 1,005 0,047 21,694 0,000 -0,212 0,143 1,749 0,080 0,470 0,559 1,210 0,226 0,725 0,406 2,479 0,013 0,714 0,313 2,144 0,032 0,359 0,406 1,176 0,240 Outer Weight Results The analysis results indicate that within the Market Performance construct, the Dividend Payout Ratio (DPR) contributes significantly, with an outer weight of 1.
012, a t-statistic of 694, and a p-value of 0.
This confirms DPR as the primary indicator shaping Market Performance.
Conversely, the Price Earning Ratio (PER) has a negative weight of Ae0.
250 and a p-value of 0.
080, indicating it is statistically insignificant at the 5% level.
Nevertheless, in formative measurement models, non-significant indicators may still be retained if they have strong theoretical justification and exhibit substantial practical relevance (Hair et al.
, 2.
For the Capital Structure construct.
Firm Size (SIZE) shows a statistically significant contribution .
eight = 1.
p = 0.
, while Leverage (LEV) does not .
eight = 0.
This suggests that firm size plays a more dominant role in defining capital structure within the current model.
This finding is consistent with previous empirical studies which argue that larger firms tend to have more stable capital structures and better access to external financing (Rajan & Zingales, 1.
Similarly, in the Financial Performance construct.
Return on Assets (ROA) demonstrates a significant contribution .
eight = 0.
p = 0.
, whereas Return on Equity (ROE) is not statistically significant .
eight = 0.
p = 0.
These results indicate that operational profitability, as measured by ROA, better represents financial performance in this model than ROE.
In formative measurement frameworks, statistical insignificance alone does not necessarily warrant the removal of an indicator.
If an indicator is conceptually important and supported by theoretical or empirical evidence, it may still be considered relevant (Diamantopoulos & Siguaw, 2.
Therefore, the decision to retain or exclude an indicator should consider both statistical results and theoretical justification.
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Outer Loading Results To evaluate the convergent validity of reflectively measured constructs in the model, an outer loading analysis was conducted.
Outer loading values represent the degree of association between each indicator and its related latent variable.
An indicator is generally regarded as having satisfactory convergent validity when its loading exceeds 0.
70, indicating that over half of its variance is captured by the latent construct (Hair et al.
, 2.
Nonetheless, indicators with loading values between 0.
40 and 0.
70 can still be retained if their presence contributes meaningfully to the constructAos overall reliability and validity.
The outer loading results for each reflective indicator in the model are presented in Table 3 below.
Table 3
Outer Loadings Indicators Capital Structure Financial Performance Market Performance DPR
0,969
PER
-0,075
LEV
0,327
SIZE
0,773
ROA
0,910
ROE
0,813
Source: Output of SmartPLS 4, 2025 The outer loading analysis for the formative constructs shows that the indicator Firm Size (SIZE) has a strong contribution to the Capital Structure construct .
oading = 0.
, while Leverage (LEV) has a weaker contribution .
oading = 0.
This indicates that firm size more accurately represents the capital structure in this model.
For the Financial Performance construct, both Return on Assets (ROA) and Return on Equity (ROE) have high loading values .
910 and 0.
813, respectivel.
, reinforcing the validity of this construct in reflecting corporate In contrast, within the Market Performance construct, only the Dividend Payout Ratio (DPR) demonstrates a very strong contribution .
oading = 0.
, while the Price Earning Ratio (PER) shows a very low and negative value (Ae0.
, suggesting that this indicator is not empirically relevant for forming the construct.
In formative measurement models, indicators with low loading values such as LEV and PER should not be automatically removed.
their theoretical contribution and potential for multicollinearity should be carefully assessed (Diamantopoulos and Winklhofer, 2001.
Hair et al.
, 2.
Collinearity Assessment Prior to analyzing the structural model, a multicollinearity check was performed to confirm that the independent constructs were not highly correlated.
This assessment utilized the Variance Inflation Factor (VIF) to detect any potential multicollinearity problems.
VIF values that surpass specific cutoff points may suggest problematic levels of multicollinearity requiring further attention.
The detailed results of the VIF analysis are shown in Table 4.
Table 4 Collinearity (Inner Mode.
WOB-> Market Performance WOB x Capital Structure -> Market Performance Capital Structure -> Financial Performance Capital Structure -> Market Performance Market Performance -> Financial Performance Source: Output of SmartPLS 4, 2025 Collinearity Test Results Program Studi Akuntansi.
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1,019
1,001
1,001
1,018
1,001
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The multicollinearity test results between formative constructs and interaction terms indicate that all VIF values are well below the critical threshold of 3.
3, suggesting no multicollinearity issues that could compromise model estimation.
The highest VIF value recorded is only 1.
n the path from Board Capital to Market Performanc.
, while most others are close to 1, including the interaction construct (Board Capital y Capital Structur.
and the path from Market Performance to Financial Performance.
These findings suggest that each construct contributes uniquely and independently to the structural model, and that the formative model satisfies the statistical requirement for discriminant validity at the indicator level (Diamantopoulos & Siguaw, 2006.
Hair et al.
, 2.
Structural Model Assessment Predictive Relevance and Explanatory Power of the Model After confirming that the formative measurement model meets the criteria for validity, the next step is to assess the structural model in order to test the relationships between This assessment involves evaluating the RA values, the significance of path coefficients based on bootstrapping results, and the QA_predict values to determine the modelAos predictive ability for endogenous constructs.
Table 5 QA_predict.
RMSE.
MAE, and R Square Financial Performance Market Performance Source: Output of SmartPLS 4, 2025
QApredict
0,015
0,037
RMSE
1,247
1,020
MAE
0,676
0,776
R-square 0,168 0,094 Predictive Relevance and Explanatory Power Based on the evaluation of the modelAos predictive ability using PLS Predict, the QA_predict value for the Financial Performance construct is 0.
015, while for Market Performance it is 0.
The QA_predict for Financial Performance falls below the minimum threshold of 0.
02, indicating that the model lacks sufficient predictive relevance for this In contrast, the QA_predict for Market Performance slightly exceeds the minimum threshold, suggesting low but acceptable predictive power (Hair et al.
, 2.
The RMSE (Root Mean Square Erro.
and MAE (Mean Absolute Erro.
values for both constructs indicate relatively high levels of prediction error.
For Financial Performance, the RMSE is 1.
247 and MAE is 0.
676, while for Market Performance.
RMSE is 1.
020 and MAE is These values suggest that although the model shows limited predictive capability, its accuracy remains suboptimal and requires improvement.
From an explanatory perspective, the RA value for Financial Performance is 0.
168, meaning that 8% of the variance in this construct is explained by the predictor variables in the model.
According to Hair et al.
, an RA value of 0.
19 or higher is considered weak.
thus, a value 168 falls into the category of very low explanatory power.
Meanwhile.
Market Performance has an RA of 0.
094, indicating that only 9.
4% of its variation is accounted for by the modelAi suggesting a very weak explanatory capacity.
Overall, the model demonstrates slightly stronger explanatory power for Financial Performance than for Market Performance, though both constructs exhibit limited predictive ability, highlighting the need for further model refinement in terms of both predictor constructs and indicator validity.
Hypothesis Testing Results Following the evaluation of the formative modelAithrough multicollinearity assessment (VIF) and the significance of outer weightsAithe next step is to conduct hypothesis testing within the structural model.
This analysis aims to assess the strength and direction of the E ISSN 2721-1819 | P ISSN 2721-2416 CURRENT: Jurnal Kajian Akuntansi dan Bisnis Terkini.
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relationships between latent constructs, both exogenous and endogenous.
Hypothesis testing was performed by analyzing the path coefficients, t-statistics, and their associated p-values, derived from PLS algorithm estimation and bootstrapping.
The results are summarized in Table Figure 1 presents the estimation output of the structural model using the formative approach via PLS-SEM.
Arrows between constructs illustrate the direction of causal relationships, while the numerical values along the paths represent the estimated coefficients, indicating the magnitude of influence between constructs.
The model also illustrates the formative indicator structure and the relative contributions of each indicator to its corresponding construct Figure 1 Structural Model Estimation Results (PLS-SEM) Table 6 Hypothesis Testing Results Ae Mean.
Standard Deviation (STDEV).
T-values, and Pvalues Origina
(O)
Hypothesis H1 Capital Structure -> Financial Performance 0,137 H2 Capital Structure -> Market Performance -0,022 H3 Market Performance -> Financial Performance 0,389 H4 WOB x Capital Structure -> Market Performance 0,310 H5 Capital Structure -> Market Performance -> Financial Performance -0,009 Source: Output of SmartPLS 4, 2025
Sample (M) Standar
(STDEV
(|O/ST
DEV|)
Conclusion P values 0,141 0,103 1,332 0,183 -0,014 0,096 0,232 0,816 0,408 0,093 4,168 0,000 0,243 0,137 2,260 0,024 Rejected Rejected Accepted Accepted Rejected -0,006 0,041 0,215 0,830 H1 tested the impact of capital structure on financial performance.
The results reveal a statistically insignificant relationship ( = 0.
t = 1.
p = 0.
, leading to the rejection of H1.
This finding suggests that capital structure does not directly influence financial performance within the observed sample.
Program Studi Akuntansi.
Fakultas Ekonomi dan Bisnis.
Universitas Riau Eka Hariyani.
Zirman Zirman.
Yesi Mutia Basri.
Sri Indriani CAPITAL STRUCTURE AND FINANCIAL PERFORMANCE: EXAMINING THE MEDIATION OF MARKET
PERFORMANCE AND THE MODERATING OF FEMALE BOARD REPRESENTATION
H2 examined the influence of capital structure on market performance.
The analysis revealed a very weak and non-significant relationship ( = Ae0.
t = 0.
p = 0.
, thus H2 is also rejected.
This implies that the market does not perceive capital structure policy as a major determinant of firm value.
H3 posited that market performance positively affects financial performance.
The results support this hypothesis ( = 0.
t = 4.
p < 0.
, indicating a significant relationship.
This aligns with signaling theory, where favorable market perceptions are reflected in improved financial outcomes.
H4 assessed the moderating role of women on boards (WOB) in the relationship between capital structure and market performance.
The interaction effect was found to be significant ( = 0.
t = 2.
p = 0.
, thereby supporting H4.
This suggests that gender diversity in the boardroom enhances the influence of capital structure on market perceptions.
H5 evaluated the mediating role of market performance in the relationship between capital structure and financial performance.
The results indicate a non-significant mediation effect ( = Ae0.
t = 0.
p = 0.
, leading to the rejection of H 5.
Hence, market performance does not serve as an effective mediator in this context.
Discussion