Abstract
This article investigates the moderating role of board size (BS), board independence (BI) and board gender diversity (BGD) in the relationships among capital structure (CS), ownership structure (OS) and the performance of non-financial listed firms in Ghana. A quantitative approach, using a panel-data design with endogeneity correction via two-step system GMM dynamic modelling, was employed to analyse financial data from 25 non-financial listed firms spanning 2010–2019. Findings indicated that total-debt-to-equity-ratio (TDTER), total-debt-to-assets-ratio (TDTAR), long-term-debt-ratio (LTDR) and financial risk (FR) significantly and negatively impacted FP. Conversely, total-equity-to-assets-ratio (TETAR), short-term-debt-ratio (STDR), cash conversion cycle (CCC), total assets turnover (TAT), tangibility (TANG), sales growth (GROW), firm size (SZ) and firm age (AGE) significantly and positively influenced FP. Bulk-shareholding (BSH) had a significantly positive effect on FP, while individual-shareholding (ISH) did not. BS, BI and BGD moderated/strengthened the relationships among CS, OS and FP. Findings/Results underscore the risk of high borrowing costs for highly-geared firms, advocating for corporate deleveraging, optimal CS and OS and improved governance practices. This study’s framework, though specific to Ghana, can be applied to other emerging economies, as it integrates previously unexplored/uncharted CG metrics of BS, BI and BGD into Agency Theory (AT), extending the theory’s scope, making it more rigorous/robust and generalisable. This theory extension-driven approach offers novel theoretical/conceptual/methodological insights, along with detailed, context-specific, practical/managerial and policy implications.
Introduction
Recent years have seen significant focus on the interplay among capital structure (CS), ownership structure (OS), corporate governance (CG) and firm performance (FP) in corporate finance and governance. Decisions on capital split between equity and debt, OS and CG are primary risk sources crucial for financial managers (Essel, 2023a; Sarpong-Danquah et al., 2022). Effective strategic financial management, excellent CG practices, sound CS and OS decisions can mitigate challenges leading to business failures and aid firms’ advancement and success (Essel & Addo, 2021). In emerging markets like Ghana, understanding these relationships is crucial due to unique economic, socio-cultural and institutional dynamics, with CG metrics like board size (BS), board independence (BI) and board gender diversity (BGD) being important moderators (Essel, 2023c, 2023d).
CS, the mix of debt and equity financing used by firms, is crucial for determining financial risk (FR), cost of capital and overall FP. Firms’ CS choices balance risk tolerance with return pursuits, aiming to optimise profit, growth sustainability and shareholder value (Essel, 2023a). Theoretically, an optimal CS minimises the Weighted Average Cost of Capital (WACC) while maximising shareholder wealth, yet practical challenges and the absence of a definitive model make identifying an optimal/ideal CS difficult. Firms, therefore, issue various financial instruments to find optimal blends that enhance value.
OS involves the distribution of ownership among various entities, including institutional investors, corporate investors, government, individuals, founders, managers, employees and foreign and domestic owners. OS impacts decision-making, risk-taking and long-term strategies. Increased FP often attracts external shareholders. This study examines OS through ownership concentration and ownership identity as outlined by Ongore (2011). Ownership concentration refers to the distribution of ownership stakes, with high concentration implying significant control by bulk-shareholders (BSH), while dispersed ownership indicates many small/individual shareholders (ISH). Some argue that lower ownership concentration mitigates managerial self-interest, while others believe high concentration ensures active governance by large shareholders. Ownership identity reveals the nature of shareholders, influencing CG strategy and FP (Ongore, 2011; Sarpong-Danquah et al., 2022).
The governance of firms through board structure elements like BS, BI and BGD adds complexity to the relationships among CS, OS and FP. These components of CG are crucial for oversight, supervision, monitoring, strategic decision-making and accountability, significantly influencing FP (Essel, 2023c). CG is essential for businesses in the competitive global market, guiding relationships among shareholders, management and other stakeholders (Essel & Addo, 2021). It aims to establish decision-making procedures that protect all participants’ interests and maximise firm value, involving oversight and accountability for daily operations. At the corporate level, governance includes setting and implementing objectives within supervisory and operational frameworks, ensuring organisations achieve their goals while safeguarding stakeholders’ interests (Essel, 2023d).
CS, OS and CG are crucial factors that significantly influence FP (Aboagye-Otchere & Boateng, 2023). A well-designed CS enhances financial flexibility, reduces WACC, and optimises capital allocation, improving profitability and shareholder value (Ahmed et al., 2024). Debt-financing offers advantages such as immediate capital, tax benefits and predictable payments, but excessive debt increases financial and insolvency risks, limiting growth prospects (Appiah et al., 2020). The optimal mix of debt and equity varies based on industry dynamics and market conditions.
OS impacts corporate decision-making and governance practices (Abedin et al., 2022). Concentrated ownership can lead to effective monitoring and alignment of interests between shareholders and management, fostering long-term value creation and reducing agency costs (Ongore, 2011).
However, it may also cause agency conflicts if majority shareholders prioritise their interests over minority shareholders (Sarpong-Danquah et al., 2022).
Strong CG practices promote transparency, accountability and integrity, enhancing investor confidence and reducing agency costs (Essel, 2023c). Effective governance mechanisms mitigate conflicts of interest, ensure proper risk management and protect shareholder interests (Essel, 2023d). Robust governance structures facilitate efficient decision-making, mitigate agency conflicts and foster accountability and ethical behaviour, contributing to sustained long-term value creation (Essel & Addo, 2021).
CS, OS and CG are interrelated and collectively influence FP. Optimal interplay among these components is essential for managing risk, sustaining competitive advantage and maximising shareholder value. Aligning these factors with strategic objectives and stakeholder interests enhances firms’ resilience, profitability and overall performance.
The relationships among CS, OS, CG and FP have garnered significant academic and industry/corporate interest, particularly in advanced economies. However, limited attention has been given to emerging markets like Ghana, which present unique economic, institutional and socio-cultural characteristics that can shape these relationships (Essel, 2023a; Sarpong-Danquah et al., 2022). Existing studies on these relationships have produced varied and inconclusive results, showing positive (Gill et al., 2011), negative (Hassan & Butt, 2009), or insignificant associations (Mardones & Cuneo, 2020), due to differences in data, methodologies and contexts. Many prior studies have used flawed econometric methodologies, such as static regression models, which neglect dynamism and fail to address issues like endogeneity, unobserved heterogeneity, heteroscedasticity, simultaneity and reverse causality, leading to biases and inconsistent outcomes.
Previous research has also overlooked the mediating or moderating roles of CG elements like BS, BI and BGD in the relationships among CS, OS and FP within Ghana’s specific context. Scholars like Galbreath (2018) and Saleh et al. (2020) argue that the direct relationships are overly simplistic and that CG elements could significantly influence these dynamics. This highlights a need for further research to explore these moderating and mediating effects, particularly in Ghana’s emerging economy, which is characterised by underdeveloped financial markets, weaker legal and regulatory frameworks, and macroeconomic variability. Understanding the specific mechanisms of BS, BI and BGD in influencing CS, OS and FP in Ghana remains underexplored and warrants further investigation.
A review of studies conducted in Ghana shows that researchers have investigated the influence of CS (Essel, 2023a), OS (Quaicoo & Bannor, 2023) and CG (Essel & Addo, 2021) separately on FP, focusing on specific industries, such as manufacturing, rural banks, universal banks, non-bank financial institutions (NBFIs), SMEs, Ghana Club 100, oil marketing companies (OMVs), and Ghana Free Zone Companies. They have largely neglected investigating the combined phenomenal effects of CS, OS and CG on FP across all firms listed on the Ghana Stock Exchange (GSE). This study aims to address these gaps by examining the complex relationships among CS, OS and FP in Ghana, via the moderating roles of BS, BI and BGD. This research will provide valuable insights for policymakers, investors and practitioners/managers in emerging economies, facilitating evidence-based decision-making and effective policy formulation to enhance FP and value creation.
This present study’s focal objective is to empirically investigate the moderating influence of BS, BI and BGD in the intricate relationships among CS, OS and FP, concentrating on all the 36 firms listed on the GSE.
The research questions for this study are as follows:
What is the relationship between CS and the performance of listed firms in Ghana? What is the relationship between OS and the performance of listed firms in Ghana? Do the board structure metrics of BS, BI and BGD moderate the relationships among CS, OS and the performance of listed firms in Ghana? What type of moderation effect/influence exists (strengthening or weakening effect) among CS, OS and CG metrics of BS, BI and BGD in Ghana?
This study significantly enhances the finance literature in several ways.
First, it deepens understanding of financial performance dynamics in Ghana’s emerging market, distinct from developed economies, shedding light on how CS and OS impact CG and performance.
Unlike prior studies in Ghana and other African countries, which often examine isolated relationships (e.g., CS → FP or OS → FP) within specific sectors such as banking, SMEs, or oil marketing, this study introduces a comprehensive conceptual framework that simultaneously integrates CS, OS and FP. CG metrics are incorporated as moderating factors, reflecting their influence on these relationships. This research is among the first to empirically investigate these interdependencies across all non-financial firms listed on the GSE. The resulting model offers a holistic and contextually relevant representation of the complex drivers of FP in emerging market settings.
Third, it pioneers the exploration of CG metrics (BS, BI and BGD) as moderators in the relationship between CS, OS and FP within Ghana’s context, filling a critical gap in empirical research, and providing a more comprehensive understanding of how internal governance mechanisms (BS, BI and BGD) influence performance outcomes. By adding these moderating variables, the study enhances AT by offering insights on how effective boards can mitigate agency costs, especially in emerging economies where governance practices may vary. The integration of these uncharted CG metrics into AT provides a new dimension of theoretical inquiry that can be explored in similar contexts globally.
Fourth, its country-specific framework can serve as a model for similar emerging market economies, enriching finance literature methodologically, conceptually and theoretically by incorporating and integrating the unexploited/uncharted CG metrics of BS, BI and BGD into the AT, extending and enriching the theory’s scope, making the theory more rigorous, robust and generalisable.
Fifth, by extending AT through empirical insights into governance mechanisms, it informs stakeholders, policymakers, corporate financial managers and investors in Ghana and similar market economies on optimising corporate strategies and governance for improved financial outcomes and value creation.
Sixth, the study provides valuable managerial insights for optimising CS and OS while enhancing CG. Managers of non-financial firms are advised to reduce reliance on excessive debt, such as high total-debt-to-equity-ratio (TDTER), total-debt-to-assets-ratio (TDTAR) and long-term-debt-ratio (LTDR), due to FR, and instead focus on equity-financing (TETAR) and efficient use of short-term-debt-ratio (STDR) to boost FP. Additionally, a well-structured board—defined by optimal size, independence, and gender diversity—can enhance decision-making, CS, and governance practices. Firms with bulk-shareholding (BSH) tend to perform better, suggesting the benefits of concentrated ownership, but managers must balance OS to sustain long-term performance and minimise agency conflicts.
Lastly, the research offers critical policy insights for regulators and CG bodies, particularly in emerging economies. Policymakers should promote corporate deleveraging and encourage equity financing to reduce risks associated with high-debt levels. Strengthening CG codes by recommending or mandating diversity and independence on boards can enhance accountability and performance, particularly by focusing on BGD and independence. Additionally, regulators could encourage BSH and institutional investment while ensuring ISH protections to prevent governance abuses. These insights are not only relevant to Ghana but also applicable to other emerging markets, contributing to broader discourse on governance and corporate performance.
The outstanding sections of this article are structured in the following manner: The second section reviews the theoretical and empirical literature and formulates the study’s hypotheses. Research methodology follows in section three. A presentation and discussion of the research results are provided in section four. Conclusion, theoretical and managerial implications and relevance are offered in the fifth section. Limitations and areas for further research end the study in section six.
Literature Review, Hypothesis Development and Conceptual Framework
Theoretical Literature
The hypotheses concerning the influence of CS and OS on the moderating roles of BS, BI and BGD are underpinned by a range of theoretical frameworks, including Agency Theory (AT), Stakeholder Theory (SKT), Institutional Theory (IST), Resource Dependence Theory (RDT), Transaction Cost Economics (TCE), Pecking Order Theory (POT) and Market Timing Theory (MTT). These theories collectively offer a conceptual foundation for understanding the mechanisms through which CS and OS interact with key CG attributes. However, this study adopts AT as its primary theoretical lens, with all hypotheses formulated based on AT’s core principles and supported by relevant empirical findings in the finance literature. By employing the agency perspective, the study contributes to a deeper understanding of how CG mechanisms—specifically BS, BI and BGD—moderate the relationship between CS and OS and FP, particularly within the context of emerging markets.
Underpinning Theory—AT
The study employs AT as its primary theoretical lens to examine the relationships among CG metrics—BS, BI and BGD—and the constructs of CS, OS and FP. AT, which centres on the principal-agent relationship between shareholders (principals) and management (agents), offers a suitable framework for analysing how CG mechanisms can align managerial interests with those of shareholders and mitigate the conflicts and costs associated with agency problems.
This theoretical framework is particularly relevant in the context of emerging economies such as Ghana, where CG practices remain underdeveloped, and agency problems are pronounced. These challenges stem from high levels of information asymmetry, weak legal and regulatory institutions, limited enforcement mechanisms, and a prevalence of concentrated OS. Within such settings, evaluating the moderating role of CG mechanisms is essential to understanding their effectiveness in reducing agency conflicts and enhancing FP.
AT, a well-established framework in the fields of economics, finance, management and CG, assumes that agents are self-interested, possess bounded rationality, exploit informational advantages, and often pursue goals divergent from those of principals. This divergence gives rise to agency costs when principals delegate decision-making authority to agents. The foundational work by Jensen and Meckling (1976), Theory of the Firm: Managerial Behaviour, Agency Costs and Ownership Structure, introduced the concept of agency costs and highlighted the fundamental issues of goal incongruence and asymmetric information. Subsequent scholarly contributions by Fama (1980), Holmstrom (1980) and Fama and Jensen (1983) further elaborated on the monitoring functions of boards and mechanisms for reducing agency-related inefficiencies.
In the Ghanaian context, AT offers valuable insights into the interactions among shareholders, managers and other stakeholders, particularly in relation to financing and governance decisions. It posits that CS decisions are shaped by the competing interests of shareholders and managers, while ownership dispersion influences the degree of agency conflict. Concentrated ownership may facilitate closer monitoring and enhanced alignment of interests, whereas dispersed ownership can exacerbate agency problems due to weakened oversight.
The effectiveness of CG mechanisms—specifically BS, BI and BGD—is therefore critical in mitigating agency conflicts and influencing FP. A well-constituted board, in terms of size, independence and diversity, is posited to play a key role in monitoring managerial actions and safeguarding shareholder interests. In emerging markets like Ghana, where governance frameworks are still evolving, the role of such governance attributes becomes increasingly important.
The study also highlights that agency costs can be addressed through CS decisions, such as employing higher leverage to discipline managerial behaviour by exposing firms to increased scrutiny from debt holders. While this may align managerial incentives with shareholder interests, excessive leverage introduces the risk of financial distress, necessitating a careful balance in CS design.
In sum, the study asserts that strong CG mechanisms can enhance a firm’s access to external financing, reduce agency conflicts, and ultimately improve firm value. By grounding its hypotheses and empirical analyses in AT, the research offers a theoretically informed framework for understanding the complex interplay among governance, ownership, financing and performance in the context of Ghana’s emerging economy.
Empirical Literature and Hypotheses Development
Capital Structure and Firm Performance
CS, defined as the composition of debt, preference shares and ordinary equity within a firm’s financial framework, plays a critical role in optimising resource allocation and enhancing returns to stakeholders. It is a central determinant of FP, though empirical evidence on the CS–FP relationship remains inconclusive, largely due to variations in methodological approaches, industry contexts, and measurement metrics.
Recent empirical studies present mixed findings. Nikhil et al. (2024) reported that debt exerts a negative impact on FP among listed non-financial institutions in India, with the adverse effect being more pronounced in highly profitable firms.
In the Sub-Saharan African (SSA) context, Bawuah (2024) found that while long-term debt enhances FP, both short-term and total debt are detrimental; moreover, CG attributes such as BI and BGD were shown to positively moderate these relationships. Similarly, Tesema (2024) identified a negative association between total and long-term debt and FP among Ethiopian manufacturing firms.
In Bangladesh, Ahmed et al. (2024) observed that all forms of debt negatively influence return on assets (ROA); however, short-term and total debt displayed a positive association with return on equity (ROE), underscoring the complex and context-specific nature of the CS–FP nexus. Dabi et al. (2023), examining Ghanaian microfinance institutions (MFIs), found that while asset size positively affects FP, CS variables significantly impact FP but have no notable effect on financial self-sufficiency or institutional stability.
From a broader regional perspective, Toader et al. (2022) discovered that profitability and liquidity negatively influence leverage in Romanian, Bulgarian and Hungarian firms, whereas institutional determinants were positively associated with leverage. Additionally, Cuevas-Vargas et al. (2022) demonstrated that CS has a significant effect on innovation, which in turn indirectly shapes the performance of small and medium-sized enterprises (SMEs) in Mexico.
Drawing on the theoretical foundations of AT and supported by evidence from the extant empirical literature, the following hypotheses are formulated to examine the relationship between CS and FP, with consideration given to the potential moderating role of CG mechanisms.
H1: There is a positive relationship between TDTER and FP (ROA and TQ).
H2: There is a positive relationship between TETAR and FP (ROA and TQ).
H3: There is a positive relationship between TDTAR and FP (ROA and TQ).
H4: There is a positive relationship between LTDR and FP (ROA and TQ).
H5: There is a positive relationship between STDR and FP (ROA and TQ).
Ownership Structure and Firm Performance
OS exerts a significant influence on FP and stock market liquidity (SML) through a variety of mechanisms that reflect differing ownership types, governance dynamics and contextual factors. Empirical studies reveal that OS can both enhance and impede financial outcomes, depending on the nature and composition of ownership and the institutional environment.
El Houcine (2023) demonstrated that institutional ownership (IO) significantly influences SML in the post-crisis period, whereas family ownership (FO) affects SML consistently across the study timeframe. Buachoom et al. (2023) provided evidence that female directors (FD) help mitigate the adverse effects of FO on firm value; additionally, BI was found to enhance firm value, while CEO duality exerted a negative influence. In the Ghanaian context, Aboagye-Otchere and Boateng (2023) reported that foreign ownership positively moderates the relationship between CS and FP, whereas state ownership exerts a negative effect.
Further contributing to this literature, Quaicoo and Bannor (2023) identified that the separation of ownership and management (SOM) significantly impacts asset-related performance in Ghanaian poultry enterprises, although it does not influence other performance dimensions. Similarly, Abedin et al. (2022) found that institutional investors positively affect FP in Bangladeshi firms, lending support to the active monitoring hypothesis, which posits that institutional shareholders can play a critical role in reducing agency costs and improving governance outcomes.
Wang (2022) revealed that the impact of OS on stock turnover rate is nonlinear and moderated by both SML and profitability. Specifically, low SML exacerbates the negative influence of block shareholding (BSH) and shifts the effect of IO from positive to negative. Conversely, operational deficits were shown to lessen the negative consequences of BSH while also diminishing the positive impact of IO. In a related domain, Fuadah et al. (2022) observed that foreign and public OS positively affect environmental, social and governance (ESG) disclosure in Indonesian firms, whereas state and FO do not. While ESG disclosure contributes to increased firm value, it does not have a direct effect (DE) on FP.
Collectively, these studies underscore the multifaceted and context-dependent effects of OS on financial performance and related corporate outcomes. Drawing on the theoretical underpinnings of AT and supported by the diverse empirical findings in the literature, the following hypotheses are developed to examine the effects of key OS variables—specifically BSH and institutional shareholding (ISH)—on FP.
H6: There is a positive relationship between BSH and FP (ROA and TQ).
H7: There is a positive relationship between ISH and FP (ROA and TQ).
The Moderating Role of CG Metrics of BS, BI and BGD in the Relationships Between CS, OS and FP
CG plays a pivotal role in shaping share prices, shareholder value, SML and FP. However, empirical evidence regarding the relationships among CG, CS, OS and FP remains mixed and, at times, inconsistent. While some studies report positive associations (e.g., Coles et al., 2008), others reveal negative (Beiner et al., 2006) or statistically insignificant relationships (Piri & Nateghian, 2015), reflecting the complexity and contextual dependence of CG’s influence.
Awad et al. (2023) found that BS and BGD positively influence firm value in non-financial firms listed on stock exchanges within the Gulf Cooperation Council (GCC), suggesting that larger boards and enhanced female representation contribute to improved profitability. In the Ghanaian context, Essel (2023d) demonstrated that dividend per share (DPS) and dividend payout ratio (DPR) have a positive impact on FP, with BI moderating the dividend policy–performance relationship, unlike BS and BGD. In a related study, Essel (2023c) reported a positive association between corporate social responsibility (CSR) and FP, with BS, BI and BGD serving as moderating variables—findings that align with the tenets of SKT and Carroll’s Pyramid of CSR.
Abedin et al. (2022) provided further evidence of CG’s mediating role by showing that BS and BI mediate the positive relationship between IO and FP, emphasising the governance-enhancing function of board attributes. Meanwhile, Anas et al. (2022) reported that BGD negatively affects firm value in Indian firms listed on the S&P BSE SENSEX 50; however, it positively moderates the relationship between BS and firm value, pointing to the nuanced role of gender diversity in board effectiveness.
Additional studies underscore the context-specific nature of CG effects. Sobhan (2021) found that BS, BGD and firm size (SZ) positively affect FP in financial institutions in Bangladesh, whereas BI, board meeting frequency (BMET) and firm age (AGE) do not exhibit significant effects. Varghese and Sasidharan (2021) observed contrasting patterns in China and India: in China, BS negatively influences firm value, and BI has a detrimental effect; in India, however, BI is positively associated with firm value, while leverage negatively affects it. Moreover, SZ was found to negatively impact firm value in China, highlighting the divergent effects of governance and control variables across institutional environments.
Collectively, these studies underscore the significant yet varied influence of CG mechanisms on FP, depending on institutional, cultural and economic contexts. Grounded in the theoretical foundations of AT and supported by empirical findings from the extant literature, the following hypotheses are developed to explore the effects of CG variables—specifically BS, BI and BGD—on FP.
H8: BS moderates the relationship between CS and OS metrics and FP (ROA and TQ).
H9: BI moderates the relationship between CS and OS metrics and FP (ROA and TQ).
H10: BGD moderates the relationship between CS and OS metrics and FP (ROA and TQ).
Conceptual Framework (Research Model)
The schematic framework illustrated in Figure 1 suggests that CS components (TDTER, TETAR, TDTAR, LTDR, STDR) and OS factors (BSH and ISH), along with related control variables (cash conversion cycle [CCC], total assets turnover [TAT], tangibility [TANG], GROW, SZ, AGE, FR), may impact FP positively or negatively via the moderating influence of BS, BI and BGD, as identified in the economics and finance existing literature.
Conceptual Framework (Research Model) of the Nexuses Among CS, OS, BS, BI, BGD and FP.
Methodology
Sample and Data
This study used financial data from 25 non-financial firms listed on the GSE from 2010 to 2019, yielding 250 balanced panel firm-year observations. The sectoral/industry classification of all listed firms on the GSE is presented in Table 1. The period was chosen due to data availability and specific market conditions on the GSE, excluding Ghana’s COVID-19 pandemic period, in which Ghana recorded its first case on 12th March 2020, with two imported cases involving individuals who had returned to Ghana from Norway and Turkey. The research aimed to evaluate the Ghanaian capital market’s performance, excluding financial firms (banks and insurance companies), which, as of October 2024, lists 42 equities and 2 corporate bonds from 37 firms, with a market capitalisation of GH¢87.2 billion.
Industry Classification of Firms Listed on the GSE.
Currently (October 2024), the GSE lists ordinary shares, preference shares and exchange-traded funds (ETFs). Atlantic Lithium Limited, listed on 13th May 2024, was excluded from the dataset in view of data availability. Data were sourced from the audited annual reports in the GSE Fact Book and websites of the listed firms.
Econometric Estimation Technique
This study investigates the intricate relationships between CS, OS, CG metrics and FP among 25 non-financial firms listed on the GSE. To rigorously analyse these relationships within the Ghanaian capital market context, the study adopts the two-step system-GMM estimator developed by Blundell and Bond (1998). This dynamic panel estimation technique is widely endorsed in empirical literature (Essel, 2023a, 2023b, 2023c, 2023d; Essel & Brobbey, 2021) due to its capacity to address a range of econometric challenges, including endogeneity, heteroscedasticity, small sample bias, Nickell bias, measurement error, reverse causality, simultaneity and autocorrelation.
The system-GMM estimator integrates both differenced and level equations, accommodating the dynamic interplay between CS, OS, CG and FP while controlling for unobserved heterogeneity via time-invariant firm-specific effects. Lagged dependent variables are employed as instrumental variables to mitigate endogeneity and control for reverse causality. To ensure the robustness of the model, four diagnostic tests were conducted: Arellano and Bond’s (1991) AR(1) and AR(2) tests for serial correlation, the Sargan and Hansen tests for overidentifying restrictions, and a cross-sectional dependence test (CD3) to account for contemporaneous correlation. These diagnostics confirm the validity of the instruments and the appropriateness of the model specification.
Although multiple panel-data estimation techniques, such as OLS, POLS, GLS, 2SLS, FE, RE, Level-GMM and Difference-GMM, are available, each with specific advantages and limitations, the system-GMM approach is preferred due to its precision and robustness. Nevertheless, its limitations, including sensitivity to instrument proliferation and potential challenges in addressing omitted variable bias and unobserved heterogeneity, are acknowledged. To address these concerns, the study complements the system-GMM analysis with robustness checks using fixed effects (FE) and random effects (RE) models, thereby reinforcing the validity, credibility, consistency and robustness of the empirical findings.
Regarding the moderating influence of BS, BI and BGD, this inquiry deployed moderated/interactive multivariate linear multiplicative regression analyses. A moderator methodically alters the form or strength of the association between a prognostic/predictor variable and an outcome/dependent variable (Baron & Kenny, 1986).
Integrating interaction terms into multivariate linear regression models changes how the coefficients of the component variables are interpreted; each individual variable’s coefficient reflects its effect only when the other variables are held at zero (Fricker et al., 2019). It is important to note that this specific effect does not necessarily indicate a central tendency or ‘main effect,’ as the scenario where other variables are zero can be extreme or unfeasible.
Large changes in individual term coefficients when interaction terms are introduced simply reflect the newly conditional nature of those coefficients and do not imply collinearity between the individual term and the interaction term (Fricker et al., 2019). This does not undermine the validity of the multivariate linear regression equations (Fricker et al., 2019).
In this study, the mean centring technique was employed before introducing interaction terms to mitigate concerns of structural multicollinearity—where new regressors are derived from existing ones, like causing the predictor x2 from the predictor x potentially causing mathematical issues. Mean centring involves calculating the mean of each prognostic variable and subtracting this mean from all observed values of that variable. The resulting centred variables are then used in the regression equations. Despite other standardisation methods being available, mean subtraction is advantageous because it does not alter the interpretation of coefficients; they continue to represent the mean change in the outcome variable given a one-unit change in the prognostic variable.
Overall, the study employs a rigorous econometric approach to analyse the relationships among CS, OS, CG and FP within the Ghanaian capital market context, addressing various methodological challenges and ensuring the reliability/validity and robustness of the study findings.
Empirical Model Specification
This study employed two measures of financial performance: ROA and Tobin’s Q (TQ). ROA is an accounting/profitability-based measure prone to managerial manipulations, while TQ, a stock market metric, reflects investors’ subjective assessments and is less susceptible to manipulations (Essel, 2023a, 2023b, 2023c, 2023d; Essel & Brobbey, 2021). Using both measures helps to mitigate variable mismatch issues and allows for a robustness check, with each measure’s strengths recompensing for the other’s limitations.
The study initially modelled ROA and TQ for each firm i at time t based on the measurement prognostic variables of CS and OS, along with relevant control variables, excluding the moderation variables of CG, that is, investigating only the DE. Given the dynamic structure of the two-step system-GMM methodology employed and the inclusion of seven prognostic variables, resulting in a total of 14 estimated models for ROA and TQ, only the models incorporating the first prognostic variable for both ROA and TQ are presented in the main manuscript to enhance readability. The remaining models are provided in the Appendix for reference.
At time ‘t’, the second set of models regressed the TQ for firm ‘i’ on CS, OS, and their respective control variables as follows:
The final equation incorporates the modelled ROA and TQ for firm i at time t, including the CS, OS prognostic variables, related control variables and the introduction of interaction/moderating terms as follows:
The same multiplicative interactive/moderating effect is replicated using TQ as the outcome/dependent variable, as follows:
In these models, a moderation effect is detected if the coefficients of the interaction between the predictors and the moderators are statistically significant, altering the strength of the relationship between the predictors and the outcome variables (Essel, 2023c, 2023d). Specifically, when BS, BI and BGD strengthen the effect of the predictors, the interaction terms will exhibit the same sign as the predictor variables in this case, positive (+); conversely, if they weaken their effect, the interaction terms will display the opposite sign, that is, negative (–) (Essel, 2023c, 2023d).
In this study, BS, BI and BGD were strictly treated as pure moderators and not quasi-moderators. Pure moderators interact with the predictors to modify the form or strength of their relationships with the outcome variables without directly influencing the outcome variables (i.e., indirect effect [IE] only and absence of DE). This distinction implies that pure moderators, unlike quasi-moderators, enter into interactions that alter the relationship dynamics without directly impacting the outcome variables themselves (Essel, 2023c, 2023d). The selection of these variables was informed by data availability and the unique characteristics of the Ghanaian capital market, aiming to capture the complex interrelationships among CS, OS and CG mechanisms, as well as to reflect the financing behaviour and FP dynamics of non-financial firms in Ghana. Table 2 details the justification for the inclusion of each major variable group and specific indicators, grounded in both theoretical relevance (primarily AT) and Ghana-specific corporate and institutional realities.
Variable Inclusion Justification.
Detailed measurements, definitions and empirical literature sources for the study’s variables are presented in Table 3.
Measurement of Variables.
Results and Discussion
Regression Assumptions Testing
The authors verified that the standardised multivariate linear regression models meet all the prerequisite assumptions (normality, heteroscedasticity, autocorrelation, endogeneity and heterogeneity) essential for multiple linear regression analyses, ensuring no violations that could lead to biased/spurious/inconsistent outcomes. These assumption tests ensure the models’ suitability/appropriateness for regression analyses. Detailed results of the regression assumption testing are provided in Table 4.
Regression Assumptions Testing Results.
Descriptive Statistics
Table 5 summarises the descriptive statistics for various variables.
The average profitability (ROA) was 18%, indicating an 18% return on investments for firms listed on the GSE. The average TQ was 0.75, suggesting that most firms did not break even, as their replacement costs were lower than their market values.
Descriptive Summary Statistics of Dependent and Independent Variables.
In terms of CS, firms predominantly used external debt-financing over internal or external equity-financing, with short-term debt being the majority. The relevant ratios were: TDTER at 69.67%, TETAR at 21.39%, TDTAR at 78.61%, LTDR at 34.95% and STDR at 43.66%, aligning with the POT and supporting empirical findings like those of Essel (2023a).
Firms recorded BSH (shareholdings exceeding 5%) at 73.91% and ISH (shares held by private individuals) at 26.09%, indicating a higher presence of bulk investors. This predominance can significantly affect stability, governance, market perception, liquidity and strategic direction, offering benefits such as improved stability and governance but also potential conflicts of interest between BSH and ISH.
The average CCC was 3 months, reflecting effective working capital management. The average TAT was 65.88%, and the average TANG was 67.44%, indicating that 66% of sales were generated by total assets, and 67% of assets were fixed assets. The average sales growth (GROW) rate was 23%, and the average SZ was 3.86. Firms had a mean FR of 2.53, with a range from 1.91 to 3.09, indicating a fairly good solvency level.
Correlation Analysis
A Pearson’s correlation test was conducted to identify multicollinearity issues and assess relationships between predictor and outcome variables in the dataset. Using mean centring and the Variance Inflation Factor (VIF) to stabilise regression coefficients, the correlation analysis found that all predictors were suitable for inclusion in the regression models, as their correlations were below 0.6. Additionally, all the predictors exhibited the highest VIF values of 1.25, 1.65, 1.68, 1.14, 1.57, 1.95, 1.58, 1.75, 1.88, 1.98, 1.89, 1.74, 1.28 and 1.44 for the explanatory, moderating and control variables as shown in Table 6. These VIFs are well below the threshold of 4, indicating the absence of multicollinearity (Kennedy, 1998). The analysis revealed that highly-leveraged firms, indicated by TDTER, TDTAR, LTDR and FR, performed poorly in terms of ROA and TQ, likely due to high borrowing costs affecting profitability (ROA) and market value (TQ). Conversely, except for ISH, which exhibited insignificant correlations, all the other prognostic and control variables, namely TETAR, STDR, BSH, TAT, TANG, CCC, GROW, SZ and AGE, showed significantly positive associations with ROA and TQ, indicating better financial performance.
Correlation Analysis with Mean Centring for Dependent and Independent Variables.
Empirical Baseline Regression Results and Discussion
To examine the relationship between CS, OS, CG and other control variables with the performance of listed companies in Ghana, multivariate linear regression analysis, specifically two-step system-GMM, was employed. The Blundell and Bond (1998) estimator tests confirmed statistical validity, as the Hansen and Sargan (1958) tests indicated no over-identification issues, AR(1) and AR(2) tests passed for no first and second-order serial/auto correlation in errors, and the CD3 test showed no contemporaneous or cross-sectional dependence. The adjusted R2 values for all 28 models depicted in Tables 7–10 were substantial, indicating that the predictors explained a significant portion of the variance in the outcome variables. The F-statistic values for all 28 models were also reasonable, showing satisfactory overall performance and model fit, confirming the statistical validity of all regression results.
The research initially analysed the DE of the predictors on the performance of listed companies in Ghana without considering the moderation-interaction influence.
Subsequently, it evaluated the IE of the predictors on FP with the inclusion of moderation-interaction effects. The baseline regression results without moderation-interaction are shown in Tables 7 and 8, with ROA and TQ as outcome variables. Tables 9 and 10 display the results incorporating moderated interactions, also using ROA and TQ as outcome variables.
In the non-moderated regression results, Table 7 (Models 1–7) with ROA as the outcome variable and Table 8 (Models 8–14) with TQ as the outcome variable reveal that TDTER, TDTAR, LTDR and FR displayed negative correlations with FP, whereas TETAR, STDR, BSH, ISH, CCC, TAT, TANG, GROW, SZ and AGE showed positive correlations with FP. The regression results align with the theoretical proposition of the AT by highlighting the impact of CS and OS on FP. Negative correlations between TDTER, TDTAR, LTDR and FP suggest that high-debt levels increase agency costs of debt, potentially leading to suboptimal decisions under financial pressure. Conversely, positive correlations with TETAR and STDR imply that equity financing and short-term debt enhance FP by aligning managers’ and shareholders’ interests and imposing financial discipline. The positive effect of BSH supports the idea that large shareholders provide better monitoring, reducing agency costs.
System-GMM Regression Results of Capital and Ownership Structure Effect on Return on Assets (ROA) as Dependent Variable Without the Moderation Effect.
The system-GMM results from Tables 7 and 8 show a significantly ( p < .05) negative relationship between TDTER and FP (ROA and TQ), with β1 = –0.0586 for ROA and β1 = –0.0722 for TQ. This indicates that highly geared firms experienced a reduction of 0.0586 units in profitability (ROA) and 0.0722 units in market value (TQ) for every 1% increase in total debt. This suggests that such firms had insufficient shareholder equity to cover their debts, and the high cost of debt-financing negatively impacted their profitability (ROA) and market value (TQ).
Consequently, H1 is rejected, aligning with the findings of Essel (2023a) and Dodoo et al. (2021), but differing from those of Boadi et al. (2013). Similarly, results from Tables 7 and 8 show a significantly ( p < .05) negative relationship between TDTAR and FP (ROA and TQ), with β1 = –0.0628 for ROA and β1 = –0.0757 for TQ. This suggests that the higher the debt-financing deployed by listed firms, the lower the profitability (ROA) and market value (TQ), as a 1% rise in total debt resulted in a 0.0628 reduction in ROA and a 0.0757 dip in market value (TQ). This implies that TDTAR and FP displayed a significantly negative relationship, leading to the rejection of H3. This result aligns with the findings of Essel (2023a) and Appiah et al. (2020), but contradicts the findings of Yakubu et al. (2017).
Tables 7 and 8 also show a significantly ( p < .05) negative relationship between LTDR and FP (ROA and TQ), with β1 = –0.0951 for ROA and β1 = –0.0962 for TQ. This indicates that listed firms that relied more on long-term debt financing performed poorly in terms of ROA and TQ, as a 1% increase in long-term debt led to a 0.0951 unit decrease in ROA and a 0.0962 unit decrease in TQ. This suggests that the high-interest costs associated with long-term debt financing negatively impacted the profitability (ROA) and market values (TQ) of listed firms in Ghana. Consequently, LTDR and FP exhibited a significantly negative relationship, leading to H4.
System-GMM Regression Results of Capital and Ownership Structure Effect on Tobin’s Q (TQ) as Outcome Variable Without the Moderation Effect.
This finding is consistent with the results of Essel (2023a) and Appiah et al. (2020), but contrasts sharply with the findings of Nsiah et al. (2019).
The study findings, as presented in Tables 7 and 8, indicated that, although Ghanaian listed firms predominantly relied on debt-financing over equity-financing, the majority of this debt was short-term. STDR was significantly ( p < .05) positively associated with FP (ROA and TQ), with β1 = 0.0775 for ROA and β1 = 0.0892 for TQ. This implies that a 1% increase in short-term debt resulted in a 0.0775 unit increase in ROA and a 0.0892 unit increase in TQ, suggesting that firms utilising more short-term debt-financing performed better in terms of ROA and TQ. This superior performance can be attributed to the easy access to relatively inexpensive short-term funding options on the Ghanaian money market, highlighting the underdeveloped state of the Ghanaian capital market (GSE). Therefore, H5 is confirmed. This finding aligns with the results of Essel (2023a) but contradicts those of Nsiah et al. (2019).
The study’s findings in Tables 7 and 8 demonstrate a statistically significant ( p < .05) and positive relationship between TETAR and FP (ROA and TQ), with β1 = 0.0843 for ROA and β1 = 0.0943 for TQ. This indicates that firms relying more on equity financing performed better in terms of ROA and TQ. The overall effect suggests that profitable listed firms utilised more internal equity-financing ( ploughing back retained earnings kept in surplus accounts) and/or external equity-financing (raising of additional equity via new shares issuance to fresh shareholders or to already existing shareholders in the form of right issues), as these financing sources had relatively lower costs—opportunity cost for internal-financing utilisation and dividends payments to shareholders or capital appreciation for external equity-financing. Therefore, H2 is supported. This finding is consistent with the research of Essel (2023a).
Concerning the OS prognostic variables, results from Tables 7 and 8 revealed that BSH had a significant ( p < .05) positive effect on FP (ROA and TQ), with β1 = 0.0512 for ROA and β1 = 0.0713 for TQ, while ISH exhibited an insignificant relationship with FP (ROA and TQ). This suggests that companies with larger shareholders or concentrated ownership tend to perform better in terms of both profitability (ROA) and market valuation (TQ), as a 1% rise in BSH resulted in a 0.0512-unit improvement in ROA and a 0.0713 enhancement in TQ.
Contrary, ISH demonstrates an insignificant relationship with FP (ROA and TQ), indicating that the level of individual ownership does not have a substantially noticeable impact on listed FP in Ghana. This highlights the importance of OS in shaping FP and provides actionable insights for investors, regulators, and company management. Concentrated ownership through BSH improves monitoring, governance, and FP, making such companies attractive for investors. Regulators should encourage BSH structures, while management should implement effective governance to attract bulk shareholders. The study results/findings support H6 but do not support H7. These findings are consistent with the findings of Sarpong-Danquah et al. (2022).
The empirical findings in Tables 7 and 8 show that, apart from FR, which had a significantly negative association with FP (ROA and TQ), all six other control variables (CCC, TAT, TANG, GROW, SZ and AGE) exhibited significantly positive relationships with FP (ROA and TQ) across all 14 non-moderated regression models.
CCC’s positive relationship with FP (ROA and TQ) suggests that profitable firms on the GSE used effective working capital strategies by maintaining sufficient current asset investments, thus improving ROA and TQ. This aligns with the findings of Essel (2023a) and Agyei et al. (2020).
Additionally, TAT’s significant and positive association with FP (ROA and TQ) indicates that firms with higher sales from total asset deployment achieved better ROA and TQ. This is consistent with the findings of Essel (2023a) and Agyei et al. (2020).
TANG also exhibits a significant and direct relationship with FP (ROA and TQ), indicating that firms with substantial investments in non-current assets experience higher ROA and TQ levels. This finding aligns with the results of Essel (2023a) and Agyei et al. (2020).
GROW shows a significant and positive association with FP (ROA and TQ), suggesting that high sales growth leads to increased ROA and TQ. This result is consistent with the findings of Essel (2023a) and Agyei et al. (2020).
Additionally, SZ had a significant and positive correlation with FP (ROA and TQ), implying that larger firms are more profitable due to economies of scale and better access to external funding. This is consistent with Essel (2023a), Yakubu et al. (2017) and Agyei et al. (2020).
Similarly, AGE shows a significant and positive relationship with FP (ROA and TQ), indicating that older firms are more profitable due to operational and managerial efficiencies and competencies developed over the years. This aligns with Essel (2023a) and Agyei et al. (2020).
Conversely, FR has a significant and negative association with FP (ROA and TQ), suggesting that firms with a lower risk of bankruptcy (FR-value of 1.8 and above) perform better in terms of ROA and TQ. This finding is consistent with Essel (2023a).
The moderated centring interaction linear multiplicative regression results reveal that all three CG elements moderated/strengthened the relationships between the study’s predictors and FP, as shown by the significant and positive interaction effects in Tables 9 and 10. These results/findings support H8, H9 and H10, respectively, for FP (ROA and TQ). This indicates that effective governance practices can enhance the positive impacts of equity-financing (TETAR) and short-term debt (STDR) on FP by mitigating agency conflicts and ensuring better oversight. The results suggest that larger, independent and diverse boards can reduce agency costs associated with high-debt levels (TDTER, TDTAR, LTDR) and concentrated ownership, thereby improving transparency, accountability and decision-making. This alignment with AT highlights the critical role of robust CG mechanisms in optimising CS and OS decisions, ultimately enhancing FP and shareholder value.
System-GMM Regression Results of Capital and Ownership Structure Effect on Return on Assets (ROA) as Dependent Variable with the Moderation Effect.
This study’s theoretical proposition, as formulated in the hypotheses, has been validated by the significant interactions shown in the results presented in Tables 9 and 10. Additionally, the complementary relationships between the three CG metrics and CS and OS have been confirmed through the synergistic impact of the moderated interaction. This is evidenced by the statistically significant and positive association between the moderated-interaction terms and the listed firms’ performance in Ghana. The implication of these findings is that the interaction terms have a significant marginal effect on FP for listed firms in Ghana. The study’s results suggest that incorporating the moderators into the regression models positively influences the relationship between the predictors and the outcome variables (ROA and TQ), thereby significantly enhancing firm value (shareholders’ wealth). This study’s findings are in line with AT and underscore the pivotal role of strong CG mechanisms in refining CS and OS decisions, ultimately boosting FP and increasing shareholder value.
System-GMM Regression Results of IC Effect on Tobin’s Q (TQ) as Dependent Variable with the Moderation Effect.
Sensitivity Analysis (Robustness Checks)
This study examined the impact of CS and OS on the performance of listed firms in Ghana, considering the moderating influence of BS, BI and BGD. The analysis employed a robust dynamic panel two-step system-GMM, which controls for endogeneity, unobserved heterogeneity, heteroscedasticity, simultaneity and reverse causality. To ensure the robustness of the findings, sensitivity analyses were conducted using an alternative/static econometric estimation technique, specifically FE and RE models, which deal with omitted variable bias, an econometric short fall which is not usually dealt with by system-GMM. Additionally, alternative measures of FP, such as ROE, SZ and two additional control variables, namely cash flow performance (CFP) and capital intensity (CI), defined in Table 3, were computed and incorporated into the regression analyses. The results, presented in Table 11, demonstrate consistency in expected theoretical signs and conventional significance levels with those reported in Tables 9 and 10, thereby confirming the stability, credibility, reliability, validity and robustness of the study’s findings.
Sensitivity Analyses (Robustness Checks): Fixed Effect (FE) and Random Effect (RE) Regression Results of Capital and Ownership Structure and Other Prognostic Variables Effect on FP Proxy by Return on Equity (ROE) as the Outcome/Dependent/Response Variable.
Theoretical Implications
The theoretical implication of this study suggests that the relationships among CS, OS and FP are influenced by certain control variables. These variables, though not the main focus, impact both the outcome and the prognostic variables, making their inclusion in the research model essential. Additionally, moderators can affect the strength and direction of the relationship between prognostic and outcome variables, either enhancing or weakening it. Furthermore, mediators could play a role by partially (complementarily or competitively) or fully explaining the effect of the prognostic variables on the outcome variables. While this study investigated the role of moderators, it did not explore mediators. Future research could examine the mediator’s influence to provide further insights into the CS-OS-CG-FP nexus in Ghana.
Conclusions
This study examined the impact of CS and OS on the financial performance of listed firms in Ghana, with a focus on the moderating influence of BS, BI and BGD. The results indicated that high levels of total debt, particularly long-term debt and FR, negatively affect FP. Conversely, equity-financing, short-term debt and factors such as efficient asset management (TAT and TANG), sales growth, SZ and age positively impact performance. BSH was found to significantly enhance FP, while individual-shareholding (ISH) did not. Additionally, BS, BI and BGD positively moderated the relationship between CS, OS and FP, reinforcing the importance of strong CG practices.
Managerial Implications
This present study, therefore, makes the following managerial recommendations: Managers should meticulously evaluate their CS decisions to achieve an optimal mix, prioritising equity and short-term debt over long-term debt. They should pursue corporate deleveraging by encouraging equity investment and utilising internal funds to enhance FP. Efficient management of assets and working capital, as well as focusing on growth opportunities and leveraging SZ and age, can further improve financial outcomes. Companies should also strive to attract and maintain bulk shareholders, who can contribute to better governance and oversight. Implementing strong board structures, including diverse and independent boards, can further strengthen the positive effects of CS and OS on FP, leading to improved operational efficiency and financial results.
Policy Implications
Policymakers are advised to revamp Ghana’s bourse, encourage firms to improve profitability and ensure appropriate CG mechanisms. They should encourage practices that promote equity financing and the use of short-term debt over long-term debt to support the financial health of firms. Regulations should be designed to facilitate the development of efficient asset management and working capital practices within firms. Additionally, policies that incentivise BSH can enhance CG and FP. Strengthening CG frameworks, particularly by promoting diverse and independent boards, will also be crucial. Such measures can help create a more robust and competitive business environment in Ghana, fostering sustainable growth and development in the emerging economy.
Limitations and Areas for Future Research
This study is not without limitations. The research focused exclusively on listed non-financial firms in Ghana, overlooking listed financial firms and non-listed firms. To enhance the generalisability of the findings, future studies should include non-listed non-financial firms to provide a more comprehensive understanding of the effects of CS, OS and CG on FP in Ghana.
Footnotes
Data Availability Statement
The data covering this research is available from the corresponding author upon reasonable request.
Declaration of Conflicting Interests
The author declared no potential conflicts of interest with respect to the research, authorship and/or publication of this article.
Funding
The author received no financial support for the research, authorship and/or publication of this article.