Capital Structure and Corporates Financial Sustainability: Evidence from Listed Non-Financial Entities in Ghana

Abstract

This study examined the nexus between capital structure and the financial sustainability of 28 listed non-financial firms in Ghana. Panel data for the period 2008 to 2019 was used for the analysis. From the results, the panel studied was heterogeneous and cross-sectionally dependent. In addition, the variables investigated were first-differenced stationary and cointegrated in the long term. The elasticities of the predictors were explored via the common correlated effects mean group (CCEMG) estimator. From the findings, capital structure proxied by the debt and debt-to-equity ratio improved the firms’ financial sustainability via the increase in return on equity (ROE). Also, firm size and assets growth promoted the entities’ financial sustainability in all the panels; however, the association between operational efficiency and the corporates’ sustainability was heterogeneous across panels. Finally, asset tangibility significantly impacted the firms’ financial sustainability. Based on the findings, the study recommended that authorities should opt for a capital structure mix that would minimize costs and optimize the firms’ financial sustainability when making capital structure decisions.

1. Introduction

1.1. Corporate Financial Sustainability

Corporate sustainability is the ability of firms to effectively manage their operation while meeting the demands and expectations of customers and stakeholders over an extended period [1,2]. It has been more critical for organizations recently because shareholders and stakeholders are keen to know their entities’ crucial role [3,4]. Corporate sustainability is broadly grouped into social, environmental, and economic aspects [5]. These are known as the three pillars (3Ps), informally referring to people, the planet, and profits [6]. Even though each of the three pillars is equally significant, corporate finance researchers have mainly focused on the financial or economic side of corporate sustainability. According to Günther & Günther [7], financial sustainability refers to a firm’s approach to managing its finances so that its current financial success does not harm its potential to financially succeed in the future, including the success of other generations. Most firms view the financial pillar as their pillar of success because firms need to be profitable to be sustainable. However, financial sustainability cannot be put above the other two pillars, as corporate sustainability is made possible by incorporating all the pillars.

Risk-averse investors may perceive financial sustainability as a vital control parameter to shareholder value and as a secondary condition of investment decisions. Financial sustainability lowers refinancing and insolvency risks in an imperfect capital market with financing constraints and insolvency costs, resulting in risk-adjusted surplus returns [5]. In Northern Europe, Zabolotuyy [8] studied 12 food entities from 2005 to 2015. The study measured financial sustainability in terms of continuity and value. Whereas continuity was measured by interest expense/earnings before interest and tax, retained earnings/revenue, total liabilities/total assets, and current assets/current liabilities, the value, on the other hand, was measured as revenue/total assets, price/book value, total assets/current assets, and net profit/equity. The authors did not give any justifications for the choice of these surrogates. Gómez-Bezares et al. [9] investigated 65 establishments from the Financial Times Stock Exchange (FTSE) 350 index from 2006 to 2012. In the study, financial sustainability was considered a measure of the corporates’ sustainability, which was assessed by comparing the sustainable growth rate to the actual growth rate. It was disclosed that corporate sustainability entities had higher market-value-added (MVA) ratios, lower economic-value-added (EVA) ratios, lower book value/market value ratios, slower asset growth rate, and higher financial risk exposure. The study concluded that entities that integrated sustainability concerns into their undertakings could better harness their resources than others, improving their financial performance and shareholder value creation. Henock [10] conducted a study on 46 savings and credit cooperative societies (SACCOs) in Eastern Ethiopia. In the study, financial sustainability was measured by the relationship between adjusted financial revenue and adjusted operating expenses. The study also disclosed that deposit mobilization, return on assets, operational efficiency, debt-to-equity ratio, and donations were associated with the financial sustainability of the establishments. It is obvious from the above that the measurement of financial sustainability covers a wide range of financial indicators. Our exploration therefore measured the financial sustainability of the entities by return on equity (ROE), whereby an increase in ROE implies the firms became more financially sustainable, and a decrease in ROE means the corporates became less financially sustainable.

1.2. Background of the Study

Corporate sustainability has gained popularity in businesses of all sizes. Establishments such as McDonald’s Corporation (MCD), Inc. (WMT), and Walmart Stores, among others, have viewed sustainability as a top priority going forward [6]. Firms can reinvigorate their commitment to fundamental objectives such as efficiency, sustainable growth, and shareholder value by focusing on sustainability. This gives them a bigger goal and some deliverables to aim for. Due to the numerous benefits associated with sustainability, many firms are under pressure to adopt practices that would make them sustainable. Corporates cannot be sustainable without good capital structure decisions, according to Musah [11], because such decisions help them optimize their earnings and thus become sustainable. According to Ropafadzai [12], the issue of what a perfect capital structure is and how it helps promote an entity’s sustainability continues to be debated. Achchi [13] argued that bad capital structure decisions would likely raise the capital costs of entities, which might be consequential to their sustainability.

In contrast, the right capital structure decisions might boost a company’s long and short-term sustainability. Therefore, financial managers should select a blend of capital structures to mitigate costs and optimize corporate sustainability. This is a core goal of an organization’s monetary decisions, and it highlights the benefits of comprehending the capital structure of corporates. Among others, investigations by Jumanne [14] and Ahmad [15] inferred that capital structure improved the sustainability of establishments. However, research by Ngoc et al. [16] in London, a study by Adeyemi et al. [17] in Nigeria, an investigation by Kanwal et al. [18] in Pakistan, and explorative work by Amraoui et al. [19] in Morocco documented that capital structure reduced the sustainability of body corporates. These contradictions might result from the choice of econometric methods, sample selection, geographical location, time frame, and model specification, signposting that the debate about capital structure and the sustainability of firms is ceaseless and warrants further research. Therefore, subscribing to the Brundtland Commission’s (1987) submission on financial sustainability, corporates should be financially managed to promote current financial success without harming future financial success and that of future generations Günther and Günther [7]. Exploring the nexus between capital structure and the financial sustainability of listed non-financial entities in Ghana was deemed fitting.

This study was motivated by the recent developments in the Ghana stock market, where several companies have been completely phased out from the books of the stock regulator. While firms such as Mechanical Lloyd Plc and PZ Cussons were delisted so that they could re-strategize to attain operational efficiency and become more sustainable, others such as Pioneer Kitchenware, African Champion Industries, Golden Web, and Transactions Solutions, among others, were compulsorily phased out under rule 13(1) of the GSE’s listing rules that states that “the Council may at any time and in circumstances as it thinks fit, suspend or cancel a listing and shall do so to protect investors and to ensure an orderly market”. A key reason that led to the delisting of these entities is sustainability issues, of which capital structure is a significant component. Therefore, a study was deemed appropriate to examine the connection between capital structure and the financial sustainability of the firms to help raise policy options to improve the well-being of the entities. Also, inflation has been on the rise over the period studied, which has raised interest rates, resulting in a high cost of capital. Due to this threat, authorities need to pay proper attention to issues that have a material influence on their firms’ capital structure and sustainability, which our study’s recommendations provide. Finally, our research on the association between capital structure and the financial sustainability of listed non-financial entities was motivated by the relevance of capital structure efficiency and the tightening of corporate sustainability policies.

This study contributes to the literature in the following ways; firstly, preceding investigations into capital structure and the financial sustainability of corporates combined businesses from diverse sectors for their analysis. This does not seem right because firms have different needs across various sectors. Firms in the financial industry have minimal investments in capital expenditure and will reduce their appetite for debt. In contrast, firms in the industrial sector have huge capital expenditure needs and may require debt or equity financing. Therefore, mixing these firms might result in biased outcomes. Our study averts this situation by focusing on only Ghana’s listed non-financial establishments. Secondly, many investigations into capital structure and the financial sustainability of corporates were carried out on an aggregate panel. This research is different from those because it further categorizes the establishments into those dealing with consumer goods (CG) and those dealing with non-consumer goods (NCG). This aided in minimizing the “lump sum” issue and helped in a deeper examination of the links within the series. Thirdly, while preceding investigations did not factor in cross-sectional dependence in their analysis, our study factors helped produce valid and reliable outcomes. According to Chen et al., Obobisa et al., and Tackie et al. [20,21,22], the negligence of cross-sectional reliance could result in biased estimates and conclusions. Therefore, accounting for the above issue was well in line. Finally, previous studies in Ghana only focused on the impact of capital structure on the financial sustainability of entities without considering the causes. This study is unique because it locates the causes within the variables studied.

A well-outlined analytical process was followed in the study’s conduct. First, tests were performed to affirm dependencies or independencies in the residual terms. This was followed by a test to confirm heterogeneity or otherwise in the slope parameters. In the third phase, the order of integration of the variables was assessed via stationarity tests. Then, cointegration tests were performed to ascertain whether the variables were materially connected in the long term. In the fifth stage, the elasticity coefficients of the series were explored, while tests to examine the causal paths within the series were conducted at the final phase. From the findings, the capital structure was a significantly positive determinant of the firms’ financial sustainability. This implies that the capital structure decisions of the firms helped to improve their financial sustainability. Existing and potential shareholders may use this information to make vital investment decisions.

The study contributes to the academic community by adding to the existing pool of literature on the nexus between capital structure and the financial sustainability of corporates. This will serve as reference material for further studies on the topic of concern. The study will also assist policymakers in developing efficient frameworks to help monitor and regulate the capital structure and the sustainability of entities. The other parts of the exploration are structured as follows: Part Two discusses the literature review, while Part Three looks at the research methodology. Part Four describes the analytical findings of the research, while Part Five focuses on the discussions resulting from the study. Finally, Part Six outlines the conclusions and policy recommendations of the exploration.

2. Literature Review

2.1. Theoretical Review

Sathyamoorthi [23] defined capital structure as the blend of equity and obligations used to fund the resources of corporates. Kennon [24] and Peavler [25] also described capital structure as how institutions prefer to finance their operations by combining equity, debt, or internal funds. The theories affiliated with capital structure are many. However, this research was confined to the Modigliani and Miller (M & M) trade-off, agency cost, and the pecking order theories. Modigliani and Miller [26] suggested that capital structure is immaterial. They took the view that if a company’s operating income and future expectations were similar, then the valuation of a geared business would be the same as the valuation of a business that is not geared. Under the M & M concept, leverage does not influence the market value of companies. Modigliani and Miller [26] tested their irrelevancy hypothesis on capital structure by integrating tax advantages as determinants of the capital structure of corporates.

From the concept, debt financing may be important in forecasting the profitability of establishments in many jurisdictions due to the interest costs of debt allowable for tax-deductible purposes. According to the authors, debts are relevant if tax advantages are known, and they suggested that entities can use any amount of debt. They like to aid in advance their performances due to the tax-deductible advantages of interest payments. Kraus and Litzenberger [27] viewed the balance between the dead-weight bankruptcy costs and the tax savings benefits of obligations as the subject of the trade-off theory. According to this hypothesis, the advantages of debt tax are potentially part of the merits affiliated with debt. At the same time, liquidation costs are part of the losses affiliated with debt. Under this concept, each business has an ideal debt-to-equity ratio that exploits its value and diminishes its capital cost. The concept predicts a positive association between leverage and the profitability of entities [27].

According to the agency cost theory, managers value their personal goals instead of maximizing stockholders’ returns [28]. According to the theory, better firm output correlates with a higher debt level. Managers under this concept are expected to work effectively to satisfy debt burdens. This thus makes geared businesses safer for stockholders because the rates of obligations could be used to track the effectiveness of managers [29]. This hypothesis proposes a favorable association between capital structure and an entity’s financial sustainability. The pecking order hypothesis questions the existence of a distinct optimal gearing ratio by following a hierarchical pattern of funding activities [30]. According to this hypothesis, companies do not seek external financing until all retained earnings are used. Afterwards, they turn to the debt market, and equity capital is their last resort [31]. Theoretically, businesses use less expensive internal funds and are thus not influenced by the charges associated with outside finances. It is asserted under this concept that profitable businesses use minimum debt. If the profit of the businesses rises, their debt levels decrease because they will have more resources to defray the obligations, thus minimizing interest expenses and surging profitability. This theory predicts an adverse connection between capital structure and the profitability of establishments.

2.2. Capital Structure and Firms’ Financial Sustainability

Innumerable studies on capital structure and corporates’ financial sustainability have been conducted with differing discoveries. For example, six listed companies were investigated by Mauwa et al. [32] in Rwanda. From the study’s revelations, capital structure reduced the financial sustainability of companies, which contrasts that with the findings of Merugu and Ravindar [33], who affirmed an insignificant relationship between capital structure and corporates’ financial sustainability. An investigation of 739 establishments by Ngoc et al. [16] in London confirmed capital structure as an inverse predictor of the corporates’ financial sustainability. Six Nigerian entities were analyzed by Adeyemi et al [17]. Outcomes from the research affirmed a converse relationship between capital structure and the businesses’ return on assets and equity. Kanwal et al [18] investigated 213 quoted Pakistani companies and confirmed that capital structure reduced the corporates’ return on assets, equity, and price-earnings ratios. Assad [34] performed research on 30 companies in London. From the results, the capital structure was a noteworthy predictor of the financial sustainability of the companies. This disclosure contradicts that of Sharifa and Hafinaz [35], who uncovered an inconsequential affiliation between capital structure and the financial sustainability of corporates. In India, 18 listed cement firms were investigated by Merugu and Ravindar [33]. Disclosures from the research affirmed capital structure as a trivial determinant of the firms’ financial sustainability. Seven listed companies in Nigeria were analyzed by Suleiman and Ahmed [36]. It was unveiled that capital structure had a negligible impact on the financial sustainability of the businesses.

Achchi [13] published a report on quoted companies in Sri Lanka. The study identified debt to assets and debt to equity as immaterial predictors of the corporates’ financial sustainability. Singh and Singh [37] investigated ten cement companies in India and confirmed capital structure as a positive predictor of the companies’ return on equity. Fifty-three listed companies were analyzed in Morocco by Amraoui et al. [19]. It was revealed that capital structure abated the financial sustainability of the entities. Ten companies in Malaysia were investigated by Yong [38]. The study revealed a strongly beneficial relationship between capital structure and the companies’ return on assets and return on equity. Ubesie [39] investigated several firms in Nigeria and identified the capital structure as a vital predictor of the firms’ return on assets but not their return on equity and earnings per share. Muhammad [40] analyzed 100 companies in Pakistan and disclosed capital structure as a favorable predictor of the companies’ financial sustainability. This was in contrast to Cole et al [41], who identified an adverse link between capital structure and financial sustainability. Thirty-six cited businesses in Malaysia were investigated by Sharifa and Hafinaz [35]. The study’s findings established capital structure as a trivially adverse determinant of the entities’ return on assets, return on equity, and net profit margin.

An investigation of 100 small and medium-sized enterprises in Tanzania was undertaken by Salamba [42]. The research findings affirmed capital structure as a favorable determinant of the corporates’ financial sustainability. Hassan et al. [43] investigated five institutions in Pakistan. Findings from the study confirmed a converse affiliation between capital structure and the financial sustainability of the firms. Thirty-six listed companies were investigated by Gichuhi [44] in Kenya. It was revealed that capital structure had a marginal correlation with the financial sustainability of the corporations. Research by Mbahijona [45] was undertaken on 21 entities in Namibia. Disclosures of the study confirmed a detrimental relationship between capital structure and the financial sustainability of the companies. Chin et al. [46] studied 183 establishments in Malaysia. The study’s findings confirmed capital structure as a favorable predictor of the firms’ financial sustainability. Ropafadzi [12] researched on 52 entities in South Africa. From the findings, the capital structure was negatively associated with the firms’ financial sustainability. Several chosen Indian corporates were analyzed by Sharma and Verma [47]. Findings of the study affirmed a strongly adverse relationship between capital structure and the financial sustainability of the firms. In India, 17 entities were investigated by Sushil and Neeti [48]. It was confirmed that capital structure correlated with the firms’ financial sustainability. Shehryar [49] analyzed 50 corporates in Italy and found an inverse relationship between capital structure and firms’ financial sustainability.

An analysis of 17 corporates in Bahrain was undertaken by Ahmad [15]. Findings of the study confirmed capital structure to be a noticeably positive predictor of the entities’ financial sustainability. Five companies in Kenya were studied by Mutwiri [50]. It was revealed that capital structure positively impacted the corporates’ financial sustainability. This finding was in contrast with Manjuru [51] and Mugambi [52] for Kenya. A study was undertaken by Schulz [53] of 3,363 Dutch small and medium-sized enterprises. It was deduced that capital structure abated the financial sustainability of the firms. Seven companies in Nigeria were investigated by Kakanda et al. [54]. The study confirmed capital structure as a positive determinant of the entities’ financial sustainability. Sultan and Adam [55] studied four establishments in Iraq. Based on the findings, capital structure was a strongly positive predictor of the entities’ financial sustainability. Several complainant firms in Pakistan were analyzed by Cheema et al. [56]. It was deduced that capital structure had a relevant influence on the companies’ return on assets and return on equity. Nikoo [57] analyzed 17 listed firms on the Tehran bourse. The research results confirmed a strongly adverse relationship between capital structure and the establishment’s return on assets and return on equity. This finding coincides with those of Cole et al [41] and Mumtaz et al. [58] but conflicts with those of Oladele et al and and Olaniyi et al [59,60]. In Sri Lanka, 82 listed establishments were investigated by Nadeesha and Pieris [61]. Findings of the research confirmed an essentially positive association between capital structure and the firms’ financial sustainability.

In Iran, 123 companies were investigated by Boroujeni et al. [62]. The study’s findings verified capital structure as a moderately positive determinant of the entities’ financial sustainability. Ujah and Brusa [63] investigated 559 US companies and confirmed capital structure as a vital predictor of the financial sustainability of the firms. Previous studies of the association between capital structure and firms’ financial sustainability have yielded varying outcomes. These conflicting discoveries might result from the geographical location, variable selection, time frame, study sample, choice of econometric methods, and model specification, among others. This signifies that further studies of the relationship between capital structure and the financial sustainability of entities are warranted. Therefore, undertaking a study to explore the nexus within the series, using listed non-financial establishments in Ghana as evidence, was deemed appropriate.

3. Materials and Methods

3.1. Data Source and Summary Statistics

Panel data from 2008 to 2019 was used for the analysis. The time frame of 2008 to 2019 was chosen because of data limitations. The study was confined to the period between 2008 and 2019 because of data availability. Authors were not able to obtain data for periods before 2008 and periods after 2019 for most firms because they were either non-existent or their reports could not be found on the stock regulator’s official website. The researchers had no option but to limit themselves to that period in gathering data for all the variables. The Ghana Stock Exchange (GSE), where the companies’ information was obtained, was considered because it provides the most accurate and reliable information on Ghana’s quoted companies. In addition, information sent by businesses to this platform is diligently checked by prominent accountants. More details on the series studied are exhibited in

Table 1. Measurement of Study Variables.

Definition	Measurement	Proxy
Return on Equity (ROE)	Net Income/Total Equity	Financial Sustainability
Debt Ratio (DR)	Total Liabilities/Total Assets	Capital Structure
Debt to Equity (DE)	Total Liabilities/Total Equity	Capital Structure
Firm Size (SIZE)	Natural Log of Total Assets	Control Variable
Assets Growth (GRO)	(Sizet-Sizet−1)/Sizet−1	Control Variable
Operational Efficiency (EFF)	Gross Revenue/Total Assets	Control Variable
Assets Tangibility (TAN)	Tangible Assets/Total Assets	Control Variable

. Descriptive statistics on the series are shown in

Table 2. Descriptive Statistics on Study Variables.

Panel	Statistics	ROE	DR	DE	SIZE	GRO	EFF	TAN
	Mean	−0.0100	45.0708	2.7324	4.9030	0.0372	1.1991	0.7474
	Maximum	0.0800	42.000	19.1717	8.7335	0.9766	7.9236	0.9151
	Minimum	−0.0195	3.9635	4.9730	2.0261	−0.6711	0.1061	0.0091
	Std.Dev.	6.2300	462.1888	9.8104	1.4049	0.1663	1.0582	0.4638
CG	Skewness	12.8455	12.1303	10.2215	0.7079	1.4015	2.8452	4.6599
	Kurtosis	166.0060	152.4217	119.9583	3.6067	14.3413	14.1169	47.9626
	JB	190,616.8	160,408.0	98,680.14	16.6090	955.3716	1091.770	14,759.47
	Prob.	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***	0.000 ***
	Mean	0.0279	0.7709	1.3964	4.6566	0.0341	1.2697	0.7934
	Maximum	0.0951	11.1992	26.2221	8.7056	0.6683	5.5267	0.1000
	Minimum	−0.0715	2.8055	4.6981	0.6624	−0.0594	0.0343	0.0937
	Std.Dev.	1.4440	2.0576	5.9874	1.5170	0.0847	1.2509	0.2100
NCG	Skewness	1.1400	−1.3564	−7.5699	2.2448	4.4609	1.8951	−1.0507
	Kurtosis	59.3500	22.7543	91.7919	4.0423	27.3829	5.4176	3.5043
	JB	22,263.62	2783.144	56,792.45	15.6907	4718.866	141.4681	32.6895
	Prob.	0.000 ***	0.000 ***	0.000 ***	0.043 **	0.000 ***	0.000 ***	0.000 ***
	Mean	0.0024	22.9209	2.0644	4.7798	0.0356	1.2344	0.7704
	Maximum	0.0951	42.000	26.2221	8.7335	0.9766	7.9236	0.9151
	Minimum	−0.0715	2.8055	4.6981	0.6624	−0.6711	0.0343	0.0091
	Std.Dev.	4.4100	−12.8055	−64.6981	1.4651	0.1317	1.1574	0.3602
Sample	Skewness	18.2484	327.0850	8.1423	0.4268	2.0121	2.2886	4.7213
	Kurtosis	334.0030	17.2386	7.5130	3.3555	20.4409	8.5965	64.4464
	JB	1,552,530.0	1,307,766.0	275,401.5	11.971	4485.324	731.8045	54,107.49
	Prob.	0.000 ***	0.000 ***	0.000 ***	0.003 ***	0.000 ***	0.000 ***	0.000 ***

Notes: JB stands for Jarque–Bera; CG denotes firms in consumer goods, NCG represents firms in non-consumer goods; and ***,** denote significance at the 1% and the 5% levels, respectively.

. From the table, the debt ratio had the highest average value, while the return on equity had the lowest average value. Also, the distributions of all the series tended positively to the right. In terms of Kurtosis, the variables had leptokurtic-shaped distributions. Based on the skewness and kurtosis outcomes, none of the variables was normally distributed. These discoveries were in line with the Jarque–Bera test, which also confirmed the distributions of the variables to be non-normal. Moreover, the variance inflation factor and tolerance tests displayed in

Table 3. Multi-Collinearity and Normality Tests Results.

Variable	CG		NCG		Sample
	VIF	Tolerance	VIF	Tolerance	VIF	Tolerance
DR	1.01	0.9900	1.06	0.9434	1.01	0.9900
DE	1.15	0.8696	1.02	0.9804	1.06	0.9434
SIZE	1.02	0.9804	1.14	0.8772	1.02	0.9804
GRO	1.09	0.9174	1.03	0.9709	1.04	0.9615
EFF	1.16	0.8621	1.05	0.9524	1.07	0.9346
TAN	1.08	0.9259	1.07	0.9346	1.03	0.9709
Mean VIF	1.09		1.06		1.04

Notes: VIF implies variance inflation factor; CG denotes firms in consumer goods, and NCG represents firms in non-consumer goods.

revealed no collinearity between the regressors.

3.2. Model Specification

According to Ngoc et al. [16] and Shehryar [49], capital structure has a crucial effect on the financial sustainability of companies. To examine the link between capital structure and the financial sustainability of the entities studied more extensively, a model encompassing seven distinct variables was established for estimation. In the model, financial sustainability was the criterion variable represented by return on equity (ROE). In contrast, the debt ratio (DR) and the debt-to-equity ratio (DE) were the surrogates of capital structure. Because of the consequences of omitted variable bias, the study controlled for firm size (SIZE), assets growth (GRO), operational efficiency (EFF), and assets tangibility (TAN). The model formulated to study the nexus within the series was specified as follows:

$$RO{E}_{it}={\mathsf{\alpha }}_i+{\beta }_{1}D{R}_{it}+{\beta }_{2}D{E}_{it}+{\displaystyle \sum }_{j=1}^4{\theta }_j^{\prime }{Z}_{it}+{\epsilon }_{i,t}$$

(1)

where ROE represents financial sustainability, and β₁ and β₂ are, respectively, the parameters of capital structure surrogated by debt ratio and debt to equity ratio. ${\theta }_j^{\prime }$ symbolizes the coefficient of control variables firm size, assets growth, operational efficiency, and assets tangibility represented by Z. Finally, ${\mathsf{\alpha }}_i$ and ${\epsilon }_{i,t}$ are the constant and error terms of firm I in time t, respectively. The study predicted positive signs for β₁ and β₂ because large entities have more taxable income benefits, which helps them maintain more funds to pay for their obligations without consequences [12], thereby boosting their financial sustainability. β₃ was also predicted to have a positive effect because large organizations use new technology and are more successful in controlling their expenditures to raise their financial sustainability [49]. Furthermore, β₄ was anticipated to have a positive effect in those corporates that grow have the potential to gain economies of scale over their competitors, which could add to their financial sustainability [12]. β₅ was predicted to have a positive effect since the efficient management of companies is anticipated to make them more sustainable [64]. Finally, an adverse indication was projected for β₆ because tangibles are usually employed as securities by firms to secure debt finance. However, debt has high associated costs that negatively affect financial sustainability [65].

3.3. Econometric Approaches

The analysis started by testing for cross-sectional dependence (CD) or otherwise in the panels via the Breusch-Pagan LM test, Pesaran scaled LM test, and the Pesaran CD test. Afterwards, the Pesaran and Yamaga [66] test was conducted to examine heterogeneity or otherwise in the slope coefficients. In the third phase, the Cross-sectionally Augmented Dickey-Fuller (CADF) and the Cross-sectional, Pesaran, and Shin (CIPS) tests were performed to assess the unit root attributes of the series. Fourthly, the Pedroni [67,68] and the Westerlund and Edgerton [69] tests were performed to study the cointegration features of the series. Afterwards, the common correlated effects mean group (CCEMG) estimator, which is robust to cross-sectional correlations, was employed to explore the elastic effects of the predictors on the response variable. The idea of the CCEMG estimator is to approximate the projection space of unobserved common factors by including the cross-sectional averages of the variables in the regression equation. The focus of the estimator is to obtain consistent estimates of parameters related to observable variables. This estimator was used because it is robust to cross-sectional dependence, slope heterogeneity, and exogenous or endogenous regressors [70]. The CCE model is stated as:

$$y_{it}={\mathsf{\alpha }}_i+{\beta }_i{x}_{it}+c_i{f}_t+{\delta }_i{\overline{x}}_t+{\eta }_i{\overline{y}}_t+u_{it}$$

(2)

where

$${\overline{x}}_t=\frac{1}{N}{{\displaystyle \sum }}_{i=1}^N{x}_{it},{\overline{y}}_t=\frac{1}{N}{{\displaystyle \sum }}_{i=1}^N{y}_{it}$$

(3)

Using Equation (2), the following CCE model was formulated for the study:

$$RO{E}_{it}={\mathsf{\alpha }}_i+{\beta }_{1}D{R}_{it}+{\beta }_{2}D{E}_{it}+{{\displaystyle \sum }}_{j=1}^4{\theta }_j^{\prime }{Z}_{it}+c_i{f}_t+{\delta }_i{\overline{x}}_t+{\eta }_i{\overline{y}}_t+{\mu }_{it}$$

(4)

where $f_{t }$ denotes unobserved common factors and ROE, DR, DE, and Z with their respective parameters have already been defined in Equation (1). At the last stage, the Dumitrescu and Hurlin [71] panel causality test (hereafter DH causality test), which is vigorous to cross-sectionally correlated panels, was used to study the causal relationship between the series. The test is expressed as follows:

$$Y_{it}={\gamma }_i+{{\displaystyle \sum }}_{m=1}^M{\alpha }_i{}^{\left(m\right)}{Y}_{it-m}+{{\displaystyle \sum }}_{m=1}^M{\delta }_i{}^{\left(m\right)}{X}_{it-m}+{\epsilon }_{it}$$

(5)

where X and Y are the regressors and the regressand, respectively, $M$ denotes the lag orders, ${\gamma }_i$ represents the distinct fixed effects, and ${\alpha }_i{}^{\left(m\right)}$ and ${\delta }_i{}^{\left(m\right)}$ indicate the lag and slope coefficients that differentiate across groups.

4. Results and Discussions

4.1. Cross-Sectional Dependence and Heterogeneity Tests Results

Different firms may interact with each other through socio-economic activities such as investments and trade. These activities may cause cross-sectional dependence amongst the firms. In addition, dependencies may also happen because of model misspecification and shocks such as the global financial crisis [72]. As indicated by Pesaran [73] and Phillips and Sul [74], the ignorance of dependencies in panel data analysis may lead to erroneous estimates and inferences. Therefore, the cross-sectional dependence tests indicated in

Table 4. Cross-Sectional Dependence Tests Results.

CD Tests	CG		NCG		Sample
Test Type	Value	Prob.	Value	Prob.	Value	Prob.
Breusch-Pagan LM	129.6436	0.005 ***	101.0451	0.002 ***	486.9762	0.000 ***
Pesaran scaled LM	2.8645	0.004 ***	0.7446	0.046 **	3.9634	0.000 ***
Pesaran CD	−0.3501	0.026 **	−0.4043	0.086 *	2.4220	0.015 **
Heterogeneity Test
$\tilde{\mathsf{\Delta }}$	6.344	0.005 ***	8.231	0.000 ***	5.245	0.007 ***
${\tilde{\mathsf{\Delta }}}_{adj}$	11.145	0.000 ***	13.067	0.013 **	14.415	0.000 ***

Note: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods; ***, **, * represent significance at the 1%, 5%, and the 10% levels, respectively; $\tilde{\mathsf{\Delta }}$ signifies delta tilde and ${\tilde{\mathsf{\Delta }}}_{adj}$ denotes adjusted delta tilde.

were conducted as a first step. From the findings of the tests, the null hypothesis of no cross-sectional dependence between the model’s residuals could not be accepted. This suggests that there could be spillover effects from one establishment to the other due to the strong bonds between the entities. Empirical investigations by Li et al. [75] and Ali and Eneizan [76] support the above finding. In the second stage, heterogeneity or otherwise in the slope parameters was examined via the tests exhibited in Table 4. From the findings, the slope coefficients were heterogeneous, aligning with the studies of Tackie et al., Sun et al., Musah et al., and Phale et al. [77,78,79,80,81,82]. Based on the above discoveries, econometric methods robust to cross-sectional dependence and heterogeneity were used for the ensuing analysis. After the heterogeneity and cross-sectional dependence tests, the researchers proceeded to investigate the integration order of the series in the next.

4.2. Unit Root and Cointegration Tests Results

Most estimators require variables to be integrated in a certain order. Hence, the negligence of the unit root assumption in regression analysis could lead to inaccurate estimates and conclusions. Therefore, as a third step, the CIPS and the CADF tests were performed to assess the stationarity features of the variables. As shown in the test outcomes in

Table 5. CIPS and CADF Unit Root Tests Results.

Panel	Variable	CIPS		CADF
		Levels	First Difference	Levels	First Difference
	ROE	−2.822	−2.091 *	−2.036	−2.655 *
	DR	−2.647	−1.287 *	−1.742	−1.571 *
	DE	−2.586	−1.492 *	−2.229	−1.822 **
CG	SIZE	−3.362	−2.382 ***	−1.569	−1.608 *
	GRO	−3.026	−2.096 *	−1.592	−1.480 **
	EFF	−3.326	−2.203 **	−1.638	−2.141 *
	TAN	−2.894	−2.145 *	−1.311	−1.583 *
	ROE	−2.726	−1.083 *	−2.281	−2.648 *
	DR	−2.852	−2.358 ***	−1.868	−1.888 **
	DE	−3.801	−2.480 **	−2.377	−3.215 ***
NCG	SIZE	−3.378	−2.202 **	−1.563	−2.162 ***
	GRO	−3.339	−2.196 **	−2.210	−2.491 **
	EFF	−2.742	−1.522 *	−2.242	−1.917 *
	TAN	−3.005	−2.313 ***	−1.414	−1.964 **
	ROE	−2.587	−1.793 *	−1.899	−2.332 *
	DR	−2.672	−2.221 ***	−2.030	−1.715 ***
	DE	−3.543	−2.157 **	−2.084	−3.052 ***
Sample	SIZE	−4.071	−2.213 ***	−1.123	−1.727 **
	GRO	−4.220	−2.226 ***	−1.960	−2.334 **
	EFF	−2.757	−1.444 *	−2.360	−2.092 *
	TAN	−2.525	−2.215 ***	−1.589	−1.452 ***

Notes: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods and ***, **, * denote significance at the 1%, 5%, and the 10% levels, respectively.

, the entire variables became stable after the first differentiation, supporting the works of Musah et al., Donkor et al., and Li et al. [75,78,83]. The finding also suggests that the series could be cointegrated in the long run. Therefore, as a third step, the Pedroni residual cointegration test and the Westerlund and Edgerton bootstrap cointegration test were performed to establish whether the series were cointegrated in the long term. From the test findings in

Table 6. Pedroni Residual Co-Integration Test Results.

Test Statistic	CG		NCG		Sample
	Value	Prob.	Value	Prob.	Value	Prob.
Within-Dimension
Panel v-Statistic	−4.5828	1.000	−4.0093	1.0000	−6.4810	1.000
Panel rho-Statistic	4.8948	1.000	4.6819	1.0000	6.9223	1.000
Panel PP-Statistic	−12.2974	0.000 ***	−6.9638	0.000 ***	−17.3912	0.000 ***
Panel ADF-Statistic	−2.9119	0.002 ***	−2.6551	0.004 ***	−4.1181	0.000 ***
Between-Dimension
Group rho-Statistic	5.3732	1.000	6.1299	1.000	8.1339	1.000
Group PP-Statistic	−10.5599	0.000 ***	−3.7652	0.000 ***	−10.1294	0.000 ***
Group ADF-Statistic	−5.7236	0.000 ***	−0.8506	0.098 *	−4.6486	0.000 ***

Notes: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods and ***, * denote significance at the 1% and the 10% levels, respectively.

and

Table 7. Westerlund and Edgerton Panel Cointegration Test Results.

Panel	Statistic	Value	Z-Value	P-Value	Robust P-Value
	Gt	−1.798	0.659	0.045 **	0.051 *
CG	Ga	−4.741	2.703	0.097 *	0.042 **
	Pt	−5.987	0.049	0.051 *	0.033 **
	Pa	−4.186	0.987	0.008 ***	0.000 ***
	Gt	−0.844	3.162	0.019 **	0.000 ***
NCG	Ga	−2.623	3.108	0.045 **	0.021 **
	Pt	−3.917	0.771	0.084 *	0.043 **
	Pa	−2.342	1.142	0.023 **	0.006 ***
	Gt	−0.192	4.246	0.098 *	0.011 **
Sample	Ga	−1.480	2.972	0.099 *	0.060 *
	Pt	−1.762	1.313	0.005 ***	0.000 ***
	Pa	−0.880	1.236	0.092 *	0.031 **

Notes: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods and ***, **, * denote significance at the 1%, 5%, and the 10% levels, respectively.

, the null hypothesis of no cointegration within the series could not be accepted. This implies the variables were substantially affiliated in the long run. Investigations by Musah et al., Erdogan et al., and Bashir et al. [80,84,85] affirmed the above finding. Based on this finding, the researchers proceeded to estimate the elasticities of the covariates at the next stage.

4.3. Regression and Causality Results

After confirming the cointegration association within the series, the researchers explored the covariates’ elasticities at the fifth stage. From the discoveries in

Table 8. CCEMG Estimation Results with ROE as the Response Variable.

Variable	CG		NCG		Sample
	Coefficient	Prob.	Coefficient	Prob.	Coefficient	Prob.
DR	0.7904	0.072 *	0.3056	0.006 ***	0.8454	0.027 **
DE	1.3160	0.028 **	0.1529	0.039 **	0.6642	0.068 *
SIZE	0.2346	0.056 *	0.1146	0.064 *	1.1312	0.003 ***
GRO	2.9033	0.035 **	0.4487	0.018 **	0.4907	0.049 **
EFF	2.3236	0.007 ***	0.2215	0.171	0.5449	0.093 *
TAN	−2.0992	0.209	−0.3977	0.290	0.6543	0.536
Wald chi2	210.79	0.028 **	195.70	0.045 **	182.74	0.067 *
RMSE	0.025		0.043		0.061

Notes: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods and ***, **, * denote significance at the 1%, 5% and the 10% levels, respectively.

, the capital structure significantly positively affected the firms’ financial sustainability. Ceteris paribus, a percentage rise in debt ratio improved the firms’ financial sustainability by 0.8454%, 0.7904%, and 0.3056%, respectively in the panels. Also, a 1% increase in debt-to-equity ratio raised the financial sustainability of the firms by 0.6642%, 1.3160%, and 0.1529%, respectively. These findings imply that the firms’ capital structure helped promote their financial sustainability. Since the debt and debt-to-equity ratios are measures of leverage, it can be hypothesized that higher levels of leverage in the firms’ capital structure were associated with a stronger firm sustainability. The findings might also suggest that the companies were not more dependent on debt but relied on equity or internal funds that are less volatile because they are not linked to fixed principal and interest payments that could reduce the viability of the corporates.

Moreover, since large firms face a relatively low risk of default and suffer less from informational asymmetries, the investigated entities were able to use this as a competitive advantage over their counterparts. This aided them in gaining economy of scale and other benefits that helped to boost their operations and, subsequently, their sustainability. Based on this conclusion, the hypothesis that capital structure had a significantly positive effect on the companies’ financial sustainability could not be rejected. The conclusion coincides with the trade-off hypothesis that proposes a favorable connection amid debt and companies’ financial sustainability. According to the theory, entities have more taxable revenue to protect and could thus maintain more cash to back their operations without any financial distress. The finding also coincides with those of Kakanda et al. and Gichuhi [44,54], who attested the trade-off predictions in their studies. The conclusion is contrary to the pecking order theory, which proposes an adverse affiliation between profitability and the capital structure of companies. The finding of an unfavorable relationship between capital structure and companies’ financial sustainability by Mbahijona and Ropafadzi [12,45] also conflicts with the above finding. Finally, the conclusion contradicts the Modigliani and Miller proposition that capital structure is immaterial and does not influence the market value of companies.

Furthermore, size significantly positively explained the financial sustainability of the body corporates. Specifically, a percentage surge in size promoted the firms’ sustainability by 1.1312%, 0.2346%, and 0.1146%, respectively. These findings suggest that management implemented policies that were effective enough to boost the operations and scope of the entities, thereby advancing their levels of sustainability. The result supports Doğan and Topal, Isik and Tasgin, and Akben-Selcuk [86,87,88], but contrasts with Navleen and Jasmindeep’s [89] finding that size was irrelevant to the financial sustainability of corporates. In addition, asset growth positively predicted the companies’ financial sustainability in the panels. All factors held constant, a 1% rise in growth raised the firms’ financial sustainability by 0.4907%, 2.9033%, and 0.4487%, respectively. This indicates that the companies adopted tactics that were sufficiently effective to propel the operations of the firms. This helped the firms to gain more revenue to boost their resource base, resulting in their growth and sustainability. The finding is consistent with Lazăr [90], but conflicts with Bhutta and Hasan [91].

Furthermore, operational efficiency was a materially positive predictor of the firms’ financial sustainability in the aggregate and the consumer goods panels but immaterially predicted the sustainability of the panel for non-consumer goods. Ceteris paribus, a percentage rise in operational efficiency spurred the financial sustainability of the aggregate and the consumer goods panels by 0.5449% and 2.3236%, respectively. This indicates that management was competent in using the firms’ assets to help improve their sustainability. Empirical investigations by Gichuhi and Mehmet and Nuri [44,92] provided support for this finding, but those of Cuong et al., Mouna et al. [93,94] contradicted the above finding. Moreover, asset tangibility had a trivial effect on the corporates’ financial sustainability in all the panels. This suggests that the tangible assets base of the establishments did not contribute substantially to their financial sustainability. The findings collaborate with those of Navleen and Jasmindeep, and Demis [89,95] but contradict the assertion of Rajan and Zingales [96] that tangible asset-intensive firms can reduce agency costs of debt due to the ease of collateralization of these assets, and reduced agency costs of debt will result in higher financial sustainability. Finally, the Wald chi2 value of 182.74 with a probability of 0.067 indicates a good sign of model fitness. At the same time, the root means square error (RMSE) value of 0.061 signposts that the return on equity model had a very high predictive power. Return on invested capital (ROIC) was employed as a surrogate of financial sustainability to help check the robustness of the ROE results. Based on

Table 9. CCEMG Estimation Results with ROIC as the Response Variable.

Variable	CG		NCG		Sample
	Coefficient	Prob.	Coefficient	Prob.	Coefficient	Prob.
DR	2.0732	0.038 **	0.7615	0.077 *	1.2521	0.077 *
DE	2.8124	0.004 ***	1.7096	0.032 **	2.6573	0.039 **
SIZE	4.5147	0.026 **	3.1154	0.032 **	1.7254	0.047 **
GRO	1.4132	0.023 **	1.6081	0.043 **	0.9244	0.059 *
EFF	1.5805	0.004 ***	0.7617	0.007 ***	2.1216	0.022 **
TAN	−2.7715	0.097 *	0.9229	0.317	1.8723	0.953
Wald chi2	256.74	0.002 ***	147.63	0.021 **	211.50	0.001 ***
RMSE	0.011		0.016		0.042

Notes: CG denotes firms in consumer goods, NCG represents firms in non-consumer goods and ***, **, * denote significance at the 1%, 5% and the 10% levels, respectively.

, the parameter estimates in terms of weight were dissimilar to those under the ROE. However, in terms of the effect, they were the same. Also, the root means square errors under the two proxies of financial sustainability showed a good indication of model fitness for all the panels. This signposts that the results were robust. The elastic effects of the predictors on the response variable are also shown in Figure 1.

Since the affirmation of long-term connection within series does not guarantee causation, the Dumitrescu-Hurlin panel causality test was conducted to unearth the causalities between the variables. From the results in

Table 10. Dumitrescu Hurlin Panel Causality Tests Results.

	CG		NCG		Sample
Null Hypothesis	W-stat.	Prob.	W-stat.	Prob.	W-stat.	Prob.
DR⇏ROE	4.7718	0.072 *	2.5814	0.061 *	4.7221	0.009 ***
ROE⇏DR	4.4952	0.057 *	2.9923	0.013 **	3.7434	0.038 **
DE⇏ROE	15.491	0.072 *	4.2348	0.076 *	2.5805	0.008 ***
ROE⇏DE	7.188	0.053 *	7.6470	0.033 **	5.6284	0.035 **
SIZE⇏ROE	3.8337	0.027 **	5.1203	0.049 **	3.222	0.013 **
ROE⇏SIZE	7.5932	0.032 **	4.6425	0.001 ***	4.5803	0.006 ***
GRO⇏ROE	3.8452	0.002 ***	8.5766	0.056 *	5.0297	0.052 *
ROE⇏GRO	0.6054	3.145	1.6464	0.639	1.4309	0.836
EFF⇏ROE	5.5909	0.039 **	1.2544	0.167	5.7455	0.038 **
ROE⇏EFF	5.5255	0.071 *	1.1141	0.742	1.4281	0.841
TAN⇏ROE	0.4473	0.154	0.799	0.423	0.6614	0.154
ROE⇏TAN	0.8480	0.796	1.539	0.758	0.154	0.752

Notes: ROE is the response variable and ***, **, * denote significance at the 1%, 5% and the 10% levels, respectively.

, there was a two-way causal affiliation between debt ratio and return on equity and between debt-to-equity ratio and return on equity in the panels. This finding indicates that their sustainability levels also decreased as the entities progressed toward more debt commitments. Similarly, a decline in the sustainability levels of the firms also restricted their chances of sourcing more debt funding to back their undertakings. The finding collaborates with Shehryar and Ngoc et al. [16,49]. Still, it contradicts that of Suleiman and Ahmed, Merugu and Ravindar [33,36], who confirmed no causal relation between capital structure and firms’ financial sustainability.

In addition, a bilateral causal association between size and return on equity was found in the panels. This indicates that as the companies grew, their sustainability levels also increased. Likewise, an increase in the sustainability levels of the firms also triggered the enlargement in terms of size. Empirical investigations by Agiomirgianakis et al. and Akinyomi and Olagunju [97,98] support the above finding, while those of Muthusi and Avdalović [99,100] conflict with the finding of the study.

Furthermore, a one-directional causal movement from growth to return on equity was disclosed in the panels. This implies that growth in the companies’ activities helped them to be more sustainable. However, the firms’ sustainability level did not cause their activities to grow. An investigation by Ajayi and Zahiruddin [101] agrees with this finding, while that of Bhutta and Hasan [91] is not consistent with the finding. In addition, the panel for consumer goods disclosed a mutual affiliation between operational efficiency and return on equity. The causality between operational efficiency and return on equity in the aggregate panel was unidirectional, running from operational efficiency to return on equity. Still, in the panel for non-consumer goods, no causal connection existed among the variables. The finding in the panel for consumer goods indicates that management consciously made good use of the companies’ resources to make them more sustainable. Likewise, a rise in the firms’ sustainability influenced management to be more effective. Explorations by Cuong et al. and Al Shahrani and Zhengge [93,102] provided support to the above finding, while those of Gichuhi and Mehmet and Nuri [44,92] contradicted the outcome of the study. The finding in the whole sample symbolizes that changes in the entities’ return on equity resulted from how management efficiently put the firms’ assets to good use. However, the level of viability of the firms did not aid in the good use of their resources. This outcome coincides with that of Mistry and Bashar and Islam [103,104] but conflicts with that of Warrad and Rsina, and Santosuosso [105,106]. The outcome in the panel for non-consumer goods signposts that the firms’ sustainability was not dependent on their operational efficiency.

Similarly, the operational efficiency of the entities did not depend on the firms’ level of sustainability. This finding supports that of Rahel and Maru [107] but contradicts that of Al-Jafari and Al Samman and Guruswamy and Marew [108,109]. Lastly, assets tangibility and return on equity had no causal affiliation in the panels. This implies the firms’ sustainability levels were not dependent on the volume of their tangible assets. Likewise, the tangible resource base of the firms was also not dependent on the sustainability levels of the entities. Empirical investigations by Navleen and Jasmindeep and Demis [89,95] support the above finding, whilst those of Birhan and Kristina [110] and Dejan [111] conflict with the finding of the study. The causalities between the variables are illustrated in Figure 2.

5. Conclusions and Policy Recommendations

This study investigated the connection between capital structure and the financial sustainability of 28 listed non-financial entities in Ghana. Panel data for the period 2008 to 2019 was used for the analysis. From the results, the investigated panels were heterogeneous and cross-sectionally correlated. In addition, all the series were first differenced stationary and cointegrated in the long term. The common correlated effects mean group (CCEMG) estimator was adopted to explore the elastic effects of the predictors on the response variable. From the results, capital structure positively predicted the firms’ financial sustainability. This supports the trade-off theory of capital structure but contrasts with the pecking order and Modigliani and Miller’s propositions on capital structure. In addition, firm size and asset growth raised the entities’ financial sustainability in all the panels. However, operational efficiency was a significantly positive determinant of the financial sustainability of the aggregate and the panel for consumer goods but an insignificantly positive predictor of the sustainability of the panel for non-consumer goods. Finally, asset tangibility was an immaterial determinant of the financial sustainability of the establishments. Regarding the causal relations amid the variables, there was a mutual affiliation between debt ratio and return on equity in all the panels. A bilateral causal connection between return on equity and debt to equity ratio was also discovered in the panels. Similarly, a reciprocal link between size and return on equity was revealed for all the panels. In addition, a single-headed causality from assets growth to return on equity was disclosed for all the panels.

Furthermore, a two-way causal connection between operational efficiency and return on equity was discovered for the panel of consumer goods. The causality between the variables in the aggregate panel was unidirectional, running from operational efficiency to return on equity. However, no causal relationship was identified amongst the variables in the panel for non-consumer goods. Lastly, there was no causal relation between assets tangibility and return on equity in all the panels. According to the results, the capital structure was a significantly positive determinant of the firms’ financial sustainability. This implies that the firms’ capital structure decisions helped promote their financial sustainability. The discovery also suggests the firms took advantage of the tax-deductible advantages on interest payments to use more debts to advance their operations and sustainability. The apparent implication of the findings to policymakers is that they must be able to establish the ideal macroeconomic circumstances that will make it easier for businesses to acquire the perfect debt financing, hence reducing potential information asymmetries such as adverse selection and moral hazard. The firms should also seek the guidance of experts with in-depth knowledge of corporate financial issues when determining the mode of finance to use. This will help them choose the best capital structure mix to minimize costs and optimize shareholders’ wealth. The government should regulate the financial sector through various monetary policies to limit borrowing costs. This point is made because many companies rely on facilities to finance their activities. Hence, the surging interest rate affects their operations to a large extent.

Moreover, managers of organizations should adopt effective funding strategies that could boost the firms’ sustainability. This means that the managers should not entertain funding sources that could reduce the entities’ levels of sustainability. In addition, capacity building in investment and financial management should be in place, particularly for directors and management. This will help improve the firms’ managerial skills and financial results. Capital cost should be paramount when choosing the best capital structure to optimize the value of the firm. Therefore, managers of companies should weigh the benefits and costs associated with debts before opting for them.

According to Ibhagui and Olokoyo [111], size is a crucial factor, as large-sized firms are better able to reap the benefits of leverage than their smaller counterparts. Thus, when deciding whether increasing leverage is a viable option for firms, management and policymakers should particularly consider the influence of firm size in their decision-making process as it could be the ‘game changer’ and deciding factor on the impact that leverage will ultimately have on firms performance [111]. Finally, other issues which unfavorably influence the corporates’ capital structure should be factored into the firms’ long- and short-term decisions. Data constraints were the main hurdle of this exploration. The study was confined to the period from 2008 to 2019. Therefore, it is suggested that when more data becomes available, similar studies on the topic investigated could be conducted to corroborate the findings of our study.