The Mediating Effect of Working Capital on the Relationship between Debt Financing and Financial Performance: Evidence from Listed Non-Financial Firms in the East Africa Community

The purpose of this study was to investigate the mediating effect of working capital on the relationship between debt financing on financial performance among East African Community-listed non-financial enterprises. To achieve this goal, secondary data was collected from four East African states. Throughout the 2011–2020 study periods, the research employed an explanatory research design. The study operationalized debt financing by debt/assets ratio and debt/equity ratio. Financial performance was operationalized by return on assets (ROA) and earnings per share (EPS). Working capital was operationalized as current ratio and quick ratio. The findings indicated that in Tanzania, the current ratio acts as a mediator in the relationship between return on asset and debt/asset ratio. However, in Kenya, Rwanda, and Uganda, current ratio did not act as a mediator in the relationship between return on asset and debt to asset ratio. The study’s findings have expanded our understanding of previously uncharted territory. It validates theories of financial management that suggest concepts related to organization characteristics, working capital, and debt financing affect financial performance. It is suggested that future study concentrate on non-East African Community states. By doing so, it will be possible to ascertain whether the study’s conclusions apply to other parts of Africa.

Introduction

Background

One of the most highly contested financial topics in corporations around the world is financial performance (FP). Its significance stems from the fact that the firm’s immediate aim is what drives its existence (Omaliko & Okpala, 2020). Investments that require a significant outflow of cash produce financial performance (Subagyo, 2021). Consequently, in order to finance investment initiatives intended to boost financial performance, corporations are compelled to borrow money. Debt financing is an essential capital earning source for the company because the retaining of earnings may not be available or may not be sufficient to support business operations (Koseet al., 2020).

According to Adeniji (2008), WC continues to be the financial resource utilized by institutions in their institutional operations. A company’s working capital details the excess of its short-term liabilities over its short-term assets (Akinsulire, 2008). Working capital, as per Finkler (2010), is a corporation’s method of maximizing performance, calculated as current liabilities to current assets proportion. When determining the allocation of financial resources within their organizations, finance managers must take into account several critical factors, including working capital (Songet al., 2018). The ability of an organization to meet its operational obligations depends on its assets and liabilities (Harris, 2005). Effective management of working capital is considered vital to organizational operations, significantly influencing efficiency in both the short and long term (Akotoet al., 2013).

The working capital ratios used to measure it are the current ratio, net-working capital ratio, and quick ratio. For businesses, net liquid assets divided by total short-term obligations equals working capital. The absolute liquid/cash ratio is one of the other ratios that may be used to assess a company’s liquidity (Altaf & Shah, 2018), fast ratio or acid test (Alarussi & Alhaderi, 2018), and current ratios. This investigation intends to utilize the quick ratio and current ratio, which have been extensively utilized in prior research, as indicators of working capital (Alarussi & Alhaderi, 2018; Altaf & Shah, 2018). The quick ratio compares the current assets less inventories and prepaid expenses to the firm’s current liabilities. A high ratio indicates that the company can fulfill its current obligations without having to liquidate its inventory or secure further funding. Conversely, a low ratio suggests that a company has low current liabilities or cannot pay them off without inventory or further funding. The current ratio, on the other hand, contrasts a company’s liquid assets and liquid liabilities. It evaluates a company’s ability to use its current assets in paying off its current liabilities. A high proportion suggests that a business can use its existing assets in covering institutional current liabilities with ease. Conversely, a low ratio suggests that the company is not able to meet its present liabilities with its current assets. In this study, working capital was operationalized as current ratio and quick ratio.

Problem Statement

Important concepts for businesses include debt financing and financial performance. This is due to the fact that they are essential to a business’ capacity to grow and increase shareholder value that continues to be a primary objective of the company. Firm managers have acknowledged this and implemented a number of initiatives to remedy the shortcomings in the working capital and debt financing of listed companies (Noreen, 2013). Businesses still struggle to reach their financial performance goals, even with the greatest debt financing arrangements and adequate working capital. Because of this, managers of the company are unable to determine how debt financing affect the company’s financial performance. The inability of firm managers to establish optimal debt financing is related to the challenge of precisely figuring out the best financing arrangement for their businesses to boost financial performance (Noreen, 2013).

Globally, Vamishan (2014) examined how debt ratios affected Tehran stock market performance and concluded that there is a negative correlation between debt ratios and interest per share, there was also a significant positive correlation with firms’ success. Different research by Barakat (2014) came to the conclusion that there exists an association that is inverse amongst financial performance and leverage. Doan (2020) focused on how financial performance is influenced by debt financing in 102 Ho Chi Minh Exchange listed firms, Vietnam. Debt financing had a significant impact on performance, as per the research.

In Africa, Olaoyeet al. (2019) examined how working capital, and leverage affected the financial performance of 35 manufacturing companies listed on the Nigerian Stock Exchange, coming to the conclusion that both working capital and debt financing possess a favorable influence towards economic success. Ogobeet al. (2013) examined the influence by debt financing towards the profitability of Ghanaian listed companies and came to the conclusion that debt financing increases return on assets (ROA). Similar research was done in Tunisia by Hasanet al. (2014), coming to a determination that financial leverage has a detrimental effect on economic performance. The investigation used pooled panel regression analysis.

Further, as indicated above, many of the previous studies have not considered variables that moderate or mediate the association of debt financing and economic performance. This indicates that the adoption of different measures of debt financing and bringing in moderators and intervening variable in its relationship with financial performance would be crucial. This would show how the different measures bring out the relationship. Further, the inclusion of moderating and intervening variables would add to the literature to show how the union amongst debt financing and economic performance among banks would differ from other studies that have not adopted such variables. This conceptual gap was the key gap that existed in the research area motivated the researcher into undertaking this research. This research answers the question: What is the mediating effect of working capital on the relationship between debt financing and financial performance of listed non-financial firms in East Africa Community?

Literature Review

Theoretical Review

This study was anchored on capital structure irrelevance theory. Modigliani and Miller (1958) masterminded this concept. It investigates into how capital structure affects how much a company is worth. The idea claims that in a perfect market transaction, expenses, taxation, and insolvency don’t really exist. The main argument against the hypothesis is that a company’s worth is8not determined by ratio of debt to equity or by cost of funds (Gulet al., 2018). Another claim is that there is no discernible causative link existent amongst an institution’s debt and the adjusted costs of funding (Gulet al., 2018). The third claim is that a company’s dividend payout does not really influence its worth (Gulet al., 2018).

Each company conforms to a risk level, defined by Modigliani and Miller (1958). Stiglitz (1969) provided evidence against that notion, proving it to be unfounded and far from being real. Another criticism is an apparent lack of realism in theory because it ignores how income taxes and distress costs affect a firm’s capital structure (Luigi & Sorin, 2009). Considering that the theory excludes aspects that influence a firm’s worth, evaluating it is exceedingly challenging. Additionally, the theorem is unable to explain how a company’s finances operate and how the job is done (Kouki, 2011).

In their preliminary irrelevance proposition, Modigliani and Miller (1958) demonstrate that in an ideal capital market, the decisions a company makes about how to structure its capital have no effect on how much it is worth, and the WACC as a whole should stay unchanged in absence of taxes and bankruptcy costs. The MM thesis, in short, states that the underlying risk for the company’s assets determines its market value and its power of profits (Abdul Hadiet al., 2017).

While the MM irrelevance theory is technically extremely good, its assumptions particularly regarding a tax-free capital market are unrealistic and unworkable in the real world. As a result, when a tax shield is there, the firm’s value rises as debt does. This clarifies why the company benefits from employing loan capital and that it reduces its capital costs. Owing to its unrealistic and erroneous presumptions, MM theory has always been controversial among academics and has opened the door for other hypotheses.

According to this hypothesis, public corporations’ performance will not improve regardless of any type of capital structure used. This is because financial leverage carries a threat of insolvency and equity funding has tax additional costs (Breuer & Gurtler, 2008). The relationship between performance of the company and working capital, nevertheless, would not be impacted by such notion. The M&M capital structure irrelevance theory may be utilized to leverage organizational characteristics tactics of businesses, even though it is predicated on implausible assumptions. This theory supports working capital as probable factors influencing financial performance of firms since debt financing is assumed to be irrelevance in elaborating the economic success of institutions.

Empirical Review

Working capital finance, firm performance, and financial limitations were investigated in India by Altaf and Ahmad (2019). The investigation was founded on secondary financial information from the Capital line data set for 437 non-financial Indian enterprises over a 10-year period (2007–2016). The outcomes of this investigation are based on a generalized two-step technique for moments procedures. The report’s findings support the inverse U-shaped association between working capital finance and company success. Furthermore, the authors discovered that enterprises that are more likely to perform well finance a bigger percentage of their working capital with debt. This study was conducted in India, the results cannot be broadly applied in EAC because to contextual variations and disparities in economic environments.

The question of whether working capital financing requirements, financial flexibility, and the performance of SMEs in Spain are related was investigated by Soniaet al. (2015). Between 1997 and 2012, they looked into the linkages together with firm performances. They discovered that an appropriate financing plan (debt financing) helps enterprises to improve performance using a 2-step generalized method of moments estimator. Because short-term bank debts have advantages, companies with low ratios of working capital requirements from short-term debt can increase the ratio and improve performance.

Working capital's moderating effect on the variables affecting capital structure was examined by Sattar (2019) in Pakistan. Three methods are utilized while applying panel regression: pooled regression. The findings discuss important function that working capital plays. The factors like tangibility, firm size, and FP have become important indicators of financial leverage with the addition of WC as a mediator.

The question of whether WC financing requirements, financial flexibility, and the performance of SMEs in Spain are related was investigated by Soniaet al. (2015). Between 1997 and 2012, they looked into the linkages together with firm performances. They discovered that an appropriate financing plan (debt financing) helps enterprises to improve performance using a 2-step generalized method of moments estimator. Because short-term bank debts have advantages, companies with low ratios of working capital requirements from short-term debt can increase the ratio and improve performance.

Working capital management’s (WCM) effect on an organization’s financial performance (FP) Kenya’s coastal public universities was evaluated by Ombuiet al. (2024). Examined were the impacts of cash, inventories, accounts payable, receivable, and university size on financial performance. Financial controllers and managers participated in the study’s primary data collection process, which involved primary and secondary data analysis. The study’s conclusions proved that working capital management improves financial performance. The report emphasizes how crucial it is to put customized financial management plans in place for every institution, taking into account their unique requirements and size.

An investigation into the connection between working capital management, institutional outcomes and monetary limitations in India was conducted by Altaf and Ahmad (2019). It was also investigated how monetary limitations affected the relationship between working capital and institutional outcomes. The intended audience consisted of 437 Indian non-financial companies. For the years 2007 through 2016, Capital line data was the source of secondary data that was used. The study failed to demonstrate how working capital influences the relationship between institutional outcomes and debt financing.

Conceptual Framework

Fig. 1 presents the conceptual framework describing how the concepts as well as the variables being studied are related (Ravitch & Riggan, 2012). “From the review of literature, it was hypothesized that working capital intervenes the relationship between debt financing and financial performance Fig. 1 shows this hypothesized relationship.

Fig. 1. Conceptual framework.

Methodology

Data

The research utilized secondary annual information in the form of listed East African companies' annual audited financial statements between 2011 and 2020. Debt financing data pertained to the value of long-term amortizing loan and fixed income debt. Financial performance data pertained to net income and total assets. The information was gathered from the listed financial companies in East Africa's annual financial reports. The reports were extracted from the NSE database, USE database, RSE database and DSE database and companies’ websites. The data collected were in absolute figures and in millions. This data enabled the researcher to calculate the ratios for analysis.

Data Analysis

Descriptive statistics were utilized to assess the data collected on debt financing, working capital, capital structure, and financial performance (mean, standard deviation, skewness, and kurtosis). Regression analysis was used to test the proposed relationships (basic regression analysis, multiple regression analysis, stepwise regression analysis). To analyze how the predictors affect the financial performance of the banks in the various East African Community countries Kenya, Uganda, Tanzania, and Rwanda comparative analysis was conducted. A comparative analysis examines and contrasts the links between data or processes. This provided context for the study and facilitated the understanding of how the relationships between the various data sets differed and overlapped. The analysis was based on ratios calculated from the absolute figures of data gathered from East African listed companies’ annual reports.

Results and Discussion

Descriptive Results

The study examined the significant factors present in each of the countries analyzed. It employed descriptive statistical techniques, including means, standard deviation, and coefficient of variation. The mean, representing the central tendency, identified the most typical value within the set of scored employed in the estimating process. The standard deviation quantified the extent to which the values deviate from the mean. Additionally, the coefficient of variation assessed the variability of responses from each surveyed country as shown in Table I.

Table I. Descriptive Results

Indicators	Countries
	Kenya			Tanzania			Uganda			Rwanda
	Mean value	Standard deviation value	CV	Mean value	Standard deviation value	CV	Mean value	Standard deviation value	CV	Mean value	Standard deviation value	CV
Debt/Asset ratio	0.275	0.490	1.780	0.216	0.204	0.944	0.215	0.183	0.849	0.183	0.111	0.605
Debt/equity ratio	−0.007	44.599	−6422.925	0.611	4.859	7.950	0.590	0.625	1.059	0.551	0.358	0.649
Current ratio	3.199	4.929	1.541	1.554	1.016	0.654	2.228	1.495	0.671	3.476	7.337	2.111
Quick ratio	2.648	3.552	1.342	0.948	0.745	0.786	1.522	1.100	0.723	0.406	0.268	0.659
Firm size	15.909	2.657	0.167	15.948	3.259	0.204	17.349	1.807	0.104	18.004	0.727	0.040
Dividend payout	3.330	3.560	1.069	1.590	1.490	0.937	1.758	0.931	0.530	1.360	1.406	1.034
Management efficiency	0.726	0.692	0.953	0.979	0.429	0.439	0.840	0.315	0.375	0.787	0.207	0.263
ROA	0.005	0.307	58.860	0.104	0.182	1.755	0.066	0.080	1.227	0.073	0.089	1.218
EPS	−35.720	6062.751	−169.729	0.010	0.012	1.241	0.126	0.294	2.332	0.180	0.222	1.238

The finding based on mean indicated that Kenya had the highest average debt ratio, quick ratio. Firm size and dividend payout in comparison to Rwanda, Uganda, and Tanzania. Tanzania possessed the highest ROA, efficiency of management, and debt/equity ratio. In terms of earnings on each share and current ratio, Rwanda led the pack. In terms of variability, the findings indicated that Kenya had high variability in most of the indicators used in the analysis, that is, very high values of coefficient of variation (CV).

Hypothesis Testing

The goal of the investigation was establishing the influence of working capital on the association amongst debt financing and financial performance among listed non-financial institutions in East Africa. Operationalization of working capital was done by current ratio and quick ratio. A corresponding hypothesis: H₀₂: Working capital possessed no significant intervening impact towards the association amongst debt financing and financial performance among listed non-financial firms in East Africa Community and sub-hypotheses: H_02a: Current ratio possessed no substantial intervening effect towards the link between debt financing and financial performance among listed non-financial firms in East Africa Community. The findings are shown in Table II.

Table II. Current Ratio on Debt/Asset Ratio (Kenya)

Summary of the model summary
Model	R	R square	Adjusted R square	Std. Error of the estimate	Change statistics
					R square change	F change	df1	df2	Sig. F change
1	0.045a	0.002	0.000	4.92232	0.002	0.886	1	444	0.347
ANOVA a
Model		Sum of squares	Df	Mean square	F	Sig.
1	Regression	21.464	1	21.464	0.886	0.347b
	Residual	10757.780	444	24.229
	Total	10779.243	445
Coefficients a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	95.0% Confidence interval for B
		B	Std. error	Beta			Lower bound	Upper bound
1	(Constant)	3.035	0.267		11.360	0.000	2.510	3.560
	Debt to asset ratio	0.447	0.475	0.045	0.941	0.347	−0.486	1.381

In step two, current ratio was regressed on debt/asset ratio, and the outcomes pointed to the 0.2% current ratio variation to the changes in debt to assets ratio. The model was insignificant in overall (p = 0.347). Further individual debt/asset ratio was insignificant (β = 0.447, p = 0.347). The condition in step two for mediation was not met, thus analysis stopped. This implied that the hypothesis that the current ratio possessed no major intervening influence towards the association amongst debt financing and financial performance in Kenya was supported as shown in Table III.

Table III. Current Ratio on Debt/Asset Ratio (Rwanda)

Summary of the model
Model	R	R square	Adjusted R square	Std. Error of the estimate	Change statistics
					R square change	F change	df1	df2	Sig. F change
1	0.404a	0.163	0.079	0.33500	0.163	1.950	1	10	0.193
ANOVA a
Model		Sum of squares	Df	Mean square	F	Sig.
1	Regression	0.219	1	0.219	1.950	0.193b
	Residual	1.122	10	0.112
	Total	1.341	11
Coefficients a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	95.0% Confidence interval for B
		B	Std. error	Beta			Lower bound	Upper bound
1	(Constant)	0.574	0.193		2.979	0.014	0.145	1.004
	Debt to asset ratio	1.271	0.910	0.404	1.396	0.193	−0.757	3.298

In step two, current ratio was regressed on debt/asset ratio, with the outcomes pointing to the 16.3% of the variation in current ratio being due to debt/assets ratio. The model was insignificant in overall (p-value = 0.193 > 0.05). Further individually debt/asset ratio was insignificant (β = 1.271, p-value = 0.193 > 0.05). Condition in step two for mediation was not met; thus analysis stopped. This implied that the hypothesis that current ratio possessed no major mediating influence towards the association amongst debt financing and financial performance among listed non-financial companies in East Africa Community (Rwanda) was supported as shown in Table IV.

Table IV. Current Ratio on Debt/Asset Ratio (Tanzania)

Summary of the model
Model	R	R square	Adjusted R square	Standard error of the estimate	Change statistics
					R square change	F change	df1	df2	Sig. F change
1	0.470a	0.221	0.211	0.16142	0.221	22.973	1	81	0.000
ANOVA a
Model		Sum of squares	Df	Mean square	F	Sig.
1	Regression	0.599	1	0.599	22.973	0.000b
	Residual	2.111	81	0.026
	Total	2.709	82
Coefficients a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. error	Beta			Lower bound	Upper bound
1	(Constant)	0.194	0.026		7.494	0.000	0.143	0.246
	Debt/asset ratio	−0.420	0.088	−0.470	−4.793	0.000	−0.594	−0.246

In step one ROA was regressed on debt/asset ratio, with the findings pointing to the 22.1% of the financial performance (ROA) variation being due to adjustments in debt/assets ratio. The model had overall significance with (p-value = 0.000 < 0.05). Individually debt/asset ratio was significant (β = −0.420, p-value = 0.000 < 0.05). As shown in Table V condition in step one for mediation was met; thus, analysis proceeded to step two.

Table V. Current Ratio on Debt/Asset Ratio (Uganda)

Summary of the model
Model	R	R square	Adjusted R square	Standard error of the estimate	Change statistics
					R square change	F change	df1	df2	Sig. F change
1	0.164a	0.027	0.003	1.49239	0.027	1.129	1	41	0.294
ANOVA a
Model		Sum of squares	Df	Mean square	F	Sig.
1	Regression	2.514	1	2.514	1.129	0.294b
	Residual	91.316	41	2.227
	Total	93.831	42
Coefficients a
Model		Unstandardized coefficients		Standardized coefficients	t	Sig.	95.0% Confidence Interval for B
		B	Std. error	Beta			Lower bound	Upper bound
1	(Constant)	2.516	0.354		7.108	0.000	1.801	3.231
	Debt/asset ratio	−1.338	1.259	−0.164	−1.063	0.294	−3.880	1.205

In step two, current ratio was regressed on debt/asset ratio, with the outcomes pointing to the 2.7% of the current ratio variation being due to adjustments in debt/asset ratio. The model had no significance in overall with (p-value = 0.294 > 0.05). Further individually debt/asset ratio was insignificant (β = −1.338, p-value = 0.294 > 0.05). Condition in step two for mediation was not met, thus analysis stopped.

Conclusion and Recommendations

It is recommended that the listed non-financial firms in the East African community seek out tactics that boost their assets in light of the study’s conclusions. The study's findings demonstrated that non-financial firms in the East Africa Community would considerably increase their working capital financing as their firm size (total assets) increased. Big businesses should be less likely to borrow money because they should be more financially stable and have more investments. Because there are fewer current obligations as a result of the reduced borrowing, working capital is higher because there are more current assets than current liabilities.

The findings indicated that working capital financing by non-financial firms in East Africa Community would increase significantly with an increase in asset tangibility. Businesses that are more tangibly attached to their assets borrow less. The assets can be invested in by the businesses, expanding their revenue base. Investing in assets may serve as the cornerstone for eventually growing the revenue base. Higher retained earnings, which result in less borrowing, are implied by higher revenue.

Limitations

The primary goal of the research was to determine how listed non-financial enterprises in the East Africa Community's financial performance relates to debt-to-financing. The study did, however, have a few drawbacks. The study employed an explanatory research designs approach method, and data was gathered from listed East African companies’ annual audited financials between 2011 and 2020. This may have biased the conclusions, as the study was conducted within a specific collection of countries with unique characteristics. This contextual limitation was mitigated by a broad approach of incorporating all listed non-financial enterprises in the East Africa Community. The study concentrated on three characteristics in particular. However, FDI and Foreign Portfolio Investments between these states is likely to be influenced by a number of other factors, some of which are domestic and include economic growth and the macroeconomic climate in place. Others, on the other hand, are external and include governmental meddling and the performance of international businesses. However, the study concentrated on the endogenous factors that are controllable within the listed non-financial enterprises in the East Africa Community

Suggestions for Further Research

In the investigation, debt formed the independent component, financial performance was the dependent component, and working capital served as mediating variables, respectively. This is anticipated to offer greater insights into the dynamic features of debt financing and financial performance of listed non-financial firms in the East Africa Community, despite the fact that it is expensive, complex, and time-consuming.

Future studies ought to concentrate on states that are not members of the East Africa Community. By doing so, it will be possible to ascertain whether the study's conclusions apply to other parts of Africa. Future studies ought to categorize states based on their geographical locations, including the Common Market for Eastern and Southern Africa (COMESA), among other classification schemes.

Conflict of Interest

Conflict of Interest: The authors declare that they do not have any conflict of interest.