The Effects of Working Capital Management on the Financial Performance of Commercial and Service Firms Listed on the Nairobi Securities Exchange in Kenya

Abstract

Working capital management is critical because it affects a company’s profitability, liquidity, and investment decisions, all of which have an impact on financial performance. As a result, effective and efficient working capital management is an essential component for commercial and service businesses. Given the importance of the commercial and services industries to the Kenyan economy, the goal of this research was to investigate the impact of working capital management on the financial performance of these firms, particularly those listed on the Nairobi Securities Exchange (NSE), from 2003 to 2022. Working capital management was measured using the average age of inventory, average collection period, average payment period, and cash conversion cycle, whereas financial performance was measured using return on asset, return on equity, and net operating profit margin. Using panel regression analysis, the results showed that the average inventory age, average collection period, average payment period, and cash conversion cycle were all negatively related to financial performance for NSE-listed commercial and service firms. Based on the findings, it is recommended that Kenyan commercial and service firms adopt prudent optimal working capital management practices to improve firm financial performance and maximize shareholder wealth.

1. Introduction

Working capital management (WCM), like capital structure, cost of capital, dividends, and capital budgeting, is a critical concept in financial management. WCM refers to the management of current liabilities and current assets and is extremely important because it affects a firm’s profitability and decisions, which in turn influence its financial performance (Umar and Al-Faryan 2024). As a result, to maintain stability and competitiveness, commercial and service firms (CSFs) must strike a balance between current liabilities and current assets. Appropriate and efficient working capital management is an essential component of commercial and service firms because it allows them to operate efficiently and contribute to a country’s economic development. For example, CSFs create new jobs, increase economic flexibility, provide opportunities for upward social mobility, and contribute to the country’s GDP. Kenya, for example, is dealing with a high unemployment rate, which is expected to reach 5.6% in 2023 and rise to 6.19% (1.64 million people) by the end of 2024 (World Bank 2024). To achieve long-term growth and address the country’s economic development challenges, successful CSFs must improve their performance. As a result, there is a need to better understand the relationship between WCM and the performance of CSFs.

Different industries listed on the NSE, such as manufacturing, agriculture, energy, banking and financial services, and telecommunications, have different WCM practices. For instance, to satisfy regulatory requirements and customer withdrawals, banks must maintain high levels of liquidity. Effective loan and receivables management is also essential for banks to reduce default risk and guarantee consistent revenue from interest and fees. However, since this study focuses on CSFs in the services and commercial sector, it might be challenging to examine the WCM of all sectors listed on the NSE without compromising the focus of the study. CSFs are businesses that act as intermediaries, purchasing and selling goods and services in their area of expertise. They do not engage in the synthesis of raw materials from start to finish, as do manufacturing companies that transform raw materials, but rather in commercial transactions that connect the producer with the end consumer. These companies act as intermediaries, transferring, storing, distributing, and selling goods and services. Apart from Longhorn Publishers, which has been reporting good performance or profits (Longhorn Publishers 2018), the Capital Markets Authority reports that there has been a persistent downward trend in financial performance results posted by companies in the NSE, particularly CSFs, with the majority of these firms on the verge of collapsing between 2013 and 2017, namely Atlas Development, Deacons, Eveready, Express Limited, Hutchings Biemer, Kenya Airways, and Uchumi supermarkets. Kenya Airways, for example, reported the country’s worst corporate results in history, totaling USD 258 million for 2015–2016 (Kenya Airways 2016). The central question is whether these firms experienced a challenge to manage their working capital.

Nonetheless, various researchers conducted research on the WCM and performance and reported mixed results, namely a positive relationship (Fungai 2024; Kumar et al. 2024; Šeligová 2021; Seth et al. 2020) and a negative relationship (Jaworski and Czerwonka 2024; Mandipa and Sibindi 2022; Umar and Al-Faryan 2024). A study by Fungai (2024) and Kumar et al. (2024) examined the impact of WCM on the profitability of South African industrial firms. Their findings indicate that if such businesses can reduce their investment in working capital, their profitability tends to rise. For the reasons stated earlier, the current study will focus solely on Kenyan CSFs. Second, there have been very few studies in Kenya that focus on WCM and firm performance in the commercial and services segments of NSE-listed Kenyan companies. Despite extensive research, there remains a gap due to a lack of factual evidence to justify the optimal level of working capital required by firms to influence their profitability. A lack of consensus persists regarding the optimal level of working capital and how it affects a company’s profitability (Briones et al. 2024; Deloof 2003; Sawarni et al. 2020; Ukaegbu 2014). The empirical research suggests that the direction of the relationship between working capital management and financial performance that generates shareholder value is still debated. This study aims to fill a knowledge gap by investigating the relationship between WCM and the financial performance of firms listed on the Kenya’s Nairobi Securities Exchange.

The research on how working capital management affects the financial performance of CSFs listed on the Kenya’s Nairobi Securities Exchange (NSE) makes several important contributions to the body of literature. For example, it offers contextual insights into the Kenyan market, which differs from other markets regarding its economic and regulatory conditions. Policymakers, investors, and scholars with an interest in the area will find this localized understanding beneficial. The report emphasizes Kenyan CSFs’ potential for foreign investors wishing to diversify their portfolios. Additionally, the research helps investors evaluate possible investment targets’ operational efficiency and financial stability. The study tackles the subtleties of working capital management in these sectors by concentrating on CSFs. This contrasts broader research that might miss industry-specific dynamics and provide these sectors with customized strategies.

Additionally, the study broadens our understanding of the connection between financial performance and working capital management by providing empirical data. It adds to the few empirical studies conducted in the African context by using data from the NSE. Comparably, the results close the gap between theory and practice by providing managers in CSFs with useful advice to maximize working capital to boost financial performance. The study makes it possible to compare Kenyan businesses with those in other markets. Lastly, the study’s findings guide the development of policies that promote improved financial practices by informing Kenyan regulators and policymakers about the operational efficiency and financial stability of businesses in the commercial and services sectors. The study considerably adds to the knowledge of working capital management and financial performance in emerging markets by addressing these gaps and offering thorough analysis and suggestions.

Several theories have been proposed to explain WCM and firm financial performance, including the Financing Advantage Theory, Cash Conversion Cycle (CCC) Theory, Transaction Cost Theory, and Stakeholder Theory. According to Schwartz (1974), the Financing Advantage Theory postulates that managers who have a financial advantage develop an efficient receivables management strategy. This theory outlines a variety of receivables management practices, including assessing consumer credit worth, encouraging credit reimbursement in the event of nonpayment, and tracking reimbursements. The current study finds that the theory of financial advantages applies to CSFs when it comes to managing their account receivables. Richards and Laughlin (1980) introduced CCC Theory, which states that rapid cash conversion leads to proficient operating revenue management, which increases liquidity, profitability, and business value. However, the “cash conversion process” refers to the period during which real money is held in various accounts, such as receivables and inventory. Thus, a longer CCC lowers the firm’s value and reduces profitability, causing a lag in the company’s development. Firms with rising long-term expectations may fail and go bankrupt unless their liquidity is properly managed. This was one of the issues that contributed to Uchumi supermarket’s poor financial performance in Kenya. This theory promotes the use of cash management to ensure that CSFs’ financial performance improves.

According to Ferris (1981), the Transaction Cost Theory states that good payables management can reduce the transaction costs of paying bills by accumulating bills and making scheduled payments for all of them, as opposed to piecemeal payments, which increase the organization’s costs. However, when linking account payable to financial performance, the theory fails to consider the type and mode of business operation. According to the theory, management should devise a strategy for regulating and managing various companies’ inventories and payables, as well as their impact on the organization’s financial performance (Deloof 2003). This theory is based on managing and regulating payables expenditure to maximize expected revenue, which translates into profit. If the firm does not manage and regulate its payables’ expenditure well, it may fail to settle payables on time, reducing the cash available for running the business and, as a result, affecting revenue generated from sales levels and profit (Deloof 2003).

Stakeholder Theory is a view of capitalism that emphasizes the interconnected relationships between a company and its customers, suppliers, employees, investors, communities, and other stakeholders (Freeman et al. 2010). The theory contends that a company should generate value for all stakeholders, not just shareholders. Before the variable allocation is made between investors, the state, shareholders, and employees, the value typically emerges from the wealth generated by the adequate utilization of the company’s supplies. Since the 1980s, scholars all over the world have questioned the viability of focusing on shareholders’ wealth as the most fundamental goal of business. This theory lends support to the argument that improved financial performance (as measured by net profit margin) can be used to increase equity for stakeholders.

Stakeholder, Transaction Cost, and CCC Theories were all applied in this study so that a more thorough and efficient approach to financial management can be taken. Each of these theories provides a different perspective for analyzing and managing working capital. CCC Theory, for example, assists businesses in identifying operational inefficiencies, such as delays in inventory turnover or receivables collection, and implementing corrective measures to enhance their liquidity and overall financial health. Transaction Cost Theory can help businesses optimize inventory levels, negotiate better credit terms, and improve payment system efficiency to optimize working capital processes, minimize wasteful spending, and increase profitability. Lastly, companies can create working capital strategies that meet investor expectations, sustain positive relationships with suppliers, and enhance financial performance by implementing the Stakeholder Theory. Long-term success and sustainable business practices can result from this all-encompassing strategy.

Previous research has filtered the importance of WCM through various dimensions, such as the best approach to managing accounts receivable to maximize returns and the importance of optimal inventory management, among others. Although numerous studies have been conducted on the relationship between WCM and financial performance in both developed and developing countries, research on the impact of working capital management on firm profitability in Kenya is extremely limited. This is supported by existing literature (Wambia and Jagongo 2020; Ombui et al. 2024; Safari and Mwanyefa 2024; Sogomi et al. 2024; Simiyu et al. 2024) and a few student theses on the subject, some of which have been highly cited (Zhong 2022; Achuka 2022), as well as a few works published in predator journals. Furthermore, few studies have been conducted on the relationship between working capital and its impact on a firm’s financial performance, particularly in Kenyan CSFs. Several studies that investigated the link between WCM and profitability yielded conflicting results. The mixed findings necessitate additional research, particularly to determine the consistency of results before making recommendations or policy recommendations.

Studies conducted by Fungai (2024), Šeligová (2021), Seth et al. (2020), found a positive relationship between WCM and profitability, whereas Kumar et al. (2024), Mandipa and Sibindi (2022), Jaworski and Czerwonka (2024) reported a negative relationship. For example, Šeligová (2021) looked at how WCM affects the size and profitability of companies in the Czech Republic’s manufacturing, wholesale, and retail sectors. From 2009 to 2019, the data sample included 3645 manufacturing firms and 5257 retail and wholesale firms. Using the GMM and comparison approach, the results showed a statistically significant relationship between WCM and company profitability and size. The findings revealed that CCC, working capital ratio, current liabilities ratio, and current assets ratio all have a positive impact on the profitability and size of all firms studied. Kumar et al. (2024) investigated the effect of WC efficiency (WCE) on the composite financial performance (CFP) of 796 non-financial listed Indian companies between 2015/16 and 2021/22. They used fixed-effect logistic regression models to estimate the effect of CCC (accounts payable days, inventory days, and accounts receivable days) on the CFP score while controlling for four variables (age, firm size, growth, and leverage). Their findings show that CCC is inversely associated with CFPS, implying that the firms’ WCE leads to superior financial performance on a composite basis.

Jaworski and Czerwonka (2024) examined the relationship between returns and WCM by selecting 43 scientific papers from 2003 to 2018, which covered nearly 62,000 enterprises in 35 countries. Using meta-analysis and meta-regression methods, the findings show that returns and the CCC have a common, negative relationship. There was a statistically significant and negative relationship discovered between profitability and the average collection period, inventory turnover cycle, and accounts payable period. Umar and Al-Faryan (2024) investigated how WCM affects the returns of 56 listed halal food and beverage companies in Malaysia, Indonesia, Pakistan, Saudi Arabia, and the United Arab Emirates. Unbalanced panel data from 2008 to 2021 were obtained from the Bloomberg database and analyzed using the two-step generalized method of moments (GMM) technique, with the robustness of the results tested using generalized least squares regression (FGLS).

Based on the objective of the study, the following three null hypotheses were formulated:

$H_{o1}:$ There is no significant relationship between WCM represented by average age of inventory (AAI), average collection period (ACP), average payment period (APP), and cash conversion cycle (CCC) on NOPM in the listed manufacturing companies on NSE.

$H_{o2}:$ There is no significant relationship between WCM represented by average age of inventory (AAI), average collection period (ACP), average payment period (APP), and cash conversion cycle (CCC) on ROA in the listed manufacturing companies on NSE.

$H_{o3}:$ There is no significant relationship between WCM represented by average age of inventory (AAI), average collection period (ACP), average payment period (APP), and cash conversion cycle (CCC) on ROE in the listed manufacturing companies on NSE.

2. Materials and Methods

2.1. Study Population and Sample Size

The Nairobi Securities Exchange (NSE) has 59 listed companies, 13 of which are in the commercial and services segment. As a result, the study’s population consisted of all CSFs listed consecutively on the NSE between 2003 and 2022. To avoid survivorship bias, the study only included CSFs that were consistently listed at NSE during this period (Wanzala 2018; Wanzala et al. 2018). Therefore, the final sample consisted of six CSFs with 20 years of operation, yielding panel data of 120 observations.

2.2. Data Collection

The required data for the study were gathered from audited financial statements and other relevant annual reports available on the respective CSFs’ websites from 2003 to 2022. These data included the nature of the company, its size, total asset value, period of existence, county, firm background, and financial performance parameters (profitability ratios).

2.3. Variable Description

The financial performances of the firms were assessed using profitability ratios as the dependent variable. To assess the efficiency of the CSFs in generating profits, the dependent variables were measured using return on equity (ROE), return on asset (ROA), and net operating profit margin (NOPM). In line with earlier research (Mandipa and Sibindi 2022; Umar and Al-Faryan 2024), it is wise to use net operating profit margin (NOPM), return on equity (ROE), and return on investment (ROI) in the same research article, particularly when examining the impacts of working capital management on financial performance. This is because every metric offers a unique viewpoint on financial performance; therefore, it provides thorough analysis. For example, ROI measures the total effectiveness of investment, ROE measures profitability about equity, and net operating profit margin measures operational efficiency. A more thorough and sophisticated understanding of the firm’s performance is provided by utilizing all three. Furthermore, because these measures are linked, increases in the net operating profit margin brought about by effective working capital management may result in increased ROE and ROI. Understanding these relationships is helped by analyzing them collectively. A multi-metric approach also increases the research findings’ robustness so that conclusions are not predicated on a single performance indicator that could be swayed by several outside variables. Lastly, different stakeholders might give distinct metrics varying priorities. While management may be interested in net operating profit margin, shareholders may prioritize return on equity (ROE). Including all three meets the needs of a wider range of people with different interests.

The WCM was represented by four variables (CCC, ACP, AAI, and APP) that served as independent variables in the study. The study’s control variables included firm size, leverage, and current ratio. The variables are defined in

Table 1. Description of variables.

Variables	Symbol	Description	Measurement
Return on assets	$ROA$	$ROA$is a profitability ratio that provides how much profit a company can generate from its assets	${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}$
Return on equity	$ROE$	$ROE$ is the measure of a company’s annual return (net income) divided by the value of its total shareholders’ equity, expressed as a percentage	${\displaystyle \frac{\mathrm{N}\mathrm{e}\mathrm{t}\mathrm{i}\mathrm{n}\mathrm{c}\mathrm{o}\mathrm{m}\mathrm{e}}{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y}}}$
Net operating profit margin	$NOPM$	$NOPM$is a ratio of earnings before interest and tax divided by total revenue.	${\displaystyle \frac{\mathrm{E}\mathrm{B}\mathrm{I}\mathrm{T}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{r}\mathrm{e}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{u}\mathrm{e}}}$
Average age of inventory (AAI)	$\lambda$	The AAI is the average number of days it takes for a firm to sell off inventory.	$\left[{\displaystyle \frac{\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{g}\mathrm{o}\mathrm{o}\mathrm{d}\mathrm{s}}{\mathrm{s}\mathrm{o}\mathrm{l}\mathrm{d}\mathrm{i}\mathrm{n}\mathrm{v}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{o}\mathrm{r}\mathrm{y}}}\right]\times 365\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$
Average collection period (ACP)	$\psi$	ACP refers to the amount of time it takes for a business to receive payments owed by its clients in terms of accounts receivable.	$\left[{\displaystyle \frac{\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{r}\mathrm{e}\mathrm{c}\mathrm{e}\mathrm{i}\mathrm{v}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}}{\mathrm{N}\mathrm{e}\mathrm{t}\mathrm{s}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}}}\right]\times 365\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$
Average payment period (APP)	$\delta$	The APP is the average period taken by a company to pay off their dues against the purchases made on a credit basis from the supplier.	$AAI+ACP=APP$
Cash conversion cycle (CCC)	$\varpi$	CCC is the average time difference between paying suppliers and recouping the amount invested in inventory and debtors.	$\left[{\displaystyle \frac{\mathrm{A}\mathrm{c}\mathrm{c}\mathrm{o}\mathrm{u}\mathrm{n}\mathrm{t}\mathrm{s}\mathrm{p}\mathrm{a}\mathrm{y}\mathrm{a}\mathrm{b}\mathrm{l}\mathrm{e}}{\mathrm{C}\mathrm{o}\mathrm{s}\mathrm{t}\mathrm{o}\mathrm{f}\mathrm{s}\mathrm{a}\mathrm{l}\mathrm{e}\mathrm{s}}}\right]\times 365\mathrm{d}\mathrm{a}\mathrm{y}\mathrm{s}$
Firm size (SIZE)	$\vartheta$	Firm size is the logarithm of the total assets of any given firm. It is assumed that as the company grows, its sales also increase.	Logarithm of total assets
Current ratio (CR)	$\theta$	The current ratio is a liquidity measure that indicates whether a firm has current assets to cover its short-term financial obligations. It is a ratio of current assets to current liabilities.	${\displaystyle \frac{\mathrm{C}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}{\mathrm{c}\mathrm{u}\mathrm{r}\mathrm{r}\mathrm{e}\mathrm{n}\mathrm{t}\mathrm{l}\mathrm{i}\mathrm{a}\mathrm{b}\mathrm{i}\mathrm{l}\mathrm{i}\mathrm{t}\mathrm{i}\mathrm{e}\mathrm{s}}}$
Leverage (LEV)	$\eta$	Leverage is considered a firm’s risk, and it is measured as the amount of debt a firm uses to finance assets.	$1-\left[{\displaystyle \frac{\mathrm{E}\mathrm{q}\mathrm{u}\mathrm{i}\mathrm{t}\mathrm{y}}{\mathrm{T}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}\mathrm{a}\mathrm{s}\mathrm{s}\mathrm{e}\mathrm{t}\mathrm{s}}}\right]$

Source: Authors’ compilation.

.

2.4. Diagnostic Tests

Diagnostic tests were performed to ensure that the estimated models were robust and well specified, including unit root tests, Hausman specification tests, Variance Inflation Factor (VIF) tests for multicollinearity, and the Breusch–Pagan Lagrange multiplier (LM) test. The unit root test indicated that the data were stationary at the first difference; the Hausman specification test indicated that the fixed-effect estimator was superior to the random-effect estimator; the VIF indicated that there was no multicollinearity in the dataset; and the Breusch–Pagan Lagrange multiplier (LM) test revealed that there was no serial correlation in the data. To avoid digression and focus on the study’s objective, diagnostic tests are not included in this paper but are available upon request.

2.5. Model Specification

Consistent with Jaworski and Czerwonka (2024), Mandipa and Sibindi (2022), and Ngari and Kamau (2022), the relationship between WCM and financial performance was estimated using a panel regression model. Given the three dependent variables (return on assets, return on equity, and net operating profit margin), the estimation models are as follows:

$$Model1:ROE={\alpha }_o+{\lambda }_1{\alpha }_1+{\psi }_1{\alpha }_2+{\delta }_1{\alpha }_3+{\eta }_1{\alpha }_4+{\vartheta }_1{\alpha }_5+{\theta }_1{\alpha }_6+{\varpi }_1{\alpha }_7+{\epsilon }_{it}$$

(1)

$$Model2:ROA={\beta }_o+{\lambda }_2{\beta }_1+{\psi }_2{\beta }_2+{\delta }_2{\beta }_3+{\eta }_2{\beta }_4+{\vartheta }_2{\beta }_5+{\theta }_2{\beta }_6++{\varpi }_2{\beta }_7{\epsilon }_{it}$$

(2)

$$Model3:NOPM={\varphi }_o+{\lambda }_1{\varphi }_1+{\psi }_2{\varphi }_2+{\delta }_2{\varphi }_3+{\eta }_2{\varphi }_4+{\vartheta }_2{\varphi }_5+{\theta }_2{\varphi }_6++{\varpi }_2{\varphi }_7{\epsilon }_{it}$$

(3)

where $ROA$ is the return on assets, $ROE$ is the return on equity, and $NOPM$ represents the net operating profit margins. In addition, $\lambda$ is the average age of inventory; $\psi$ is the average collection period; $\delta$ is the average payment period; $\eta$ is the leverage; $\vartheta$ is the firm size; $\theta$ is the current ratio; $\varpi$ is the cash conversion cycle; and ${\epsilon }_i$ is the error term. The regression coefficients are as follows: ${\alpha }_o,{\alpha }_1,{\alpha }_2,\dots {\alpha }_6$ for Model 1; ${\beta }_1,{\beta }_2,\dots {\beta }_6$ for Model 2; and ${\varphi }_1,{\varphi }_2,\dots {\varphi }_6$ for Model 3. ${\alpha }_o$ is a constant for Model 1, ${\beta }_o$ is a constant for Model 2; and ${\varphi }_o$ is a constant for Model 3.

3. Results

3.1. Descriptive Statistics

Table 2. Summary of descriptive statistics.

Variable	Mean	Median	Standard Deviation	Minimum	Maximum
Return on assets	0.092	0.095	0.428	−0.510	0.432
Return on equity	0.284	0.260	0.872	−1.358	2.264
Net operating profit margin	0.073	0.051	0.750	−0.816	620.00
Average collection period	78.26	40.85	1.946	5.000	571.00
Average age of inventory	35.92	42.69	1.361	0.000	105.47
Cash conversion cycle	70.71	30.52	15.675	−62.470	598.00
Average payment period	30.64	29.79	0.592	3.000	101.00
Current ratio	2.02	1.05	1.38	0.510	5.940
Leverage	0.549	0.527	0.947	0.084	0.842

Source: Authors’ computation.

summarizes the descriptive statistics for the study variables, including the minimum and maximum values, median, mean, and standard deviation.

The average return on assets for Kenyan commercial and service firms listed on the NSE was 9.20 percent, implying that every shilling invested in assets produced KShs. 9.20 in earnings. The commercial and service firms averaged a return on equity of 28.40%, demonstrating efficient use of shareholders’ capital. Furthermore, the commercial and service firms in this study averaged a net operating profit margin of 7.30%. According to these findings, commercial and service firms achieved a higher return on equity than both the return on assets and the net operating profit margin, which are profitability measures. This suggests that management in commercial and service firms used shareholders’ equity to generate income in an effective and resourceful manner. This result supports Stakeholders’ Theory that firms exist to maximize shareholder wealth. The Stakeholders’ Theory supports the argument that improved financial performance (measured by net profit margin) can be used to increase stakeholder equity. The findings revealed that commercial and service firms took 78.26 days on average to collect payment from debtors. This means that commercial and service firms had to wait more than a month on average to collect payment for credit sales. The average inventory age, which measured how long it took to sell the goods recorded by commercial and service firms, was 35.92 days, with no minimum days and a maximum of 105.47 days.

On average, commercial and service firms had a cash conversion cycle of 150.71 days, with minimum and maximum values ranging from −62.47 days to 598 days. This implies that the cash conversion cycle was highly variable, possibly due to differences in credit policies between commercial and service firms. This five-month CCC is extremely long on average, and according to Cash Conversion Cycle Theory, it reduces the firm’s value and profitability, causing a lag in the company’s development. As previously stated, this was one of the issues that contributed to Uchumi supermarket’s poor financial performance in Kenya. Commercial and service firms reported an average payment period of 30.64 days, with a median value of 29.79 days. What stands out is that the mean and median values were nearly identical, indicating a symmetric data distribution. The average payment period recorded by commercial and service firms was 3 days at its lowest, while it reached its highest of 101 days.

3.2. Empirical Findings

Table 3. Panel regression results for the three models.

		Dependent Variables
		Constant	AAI	ACP	APP	CCC	LEV	SIZE	CR
Model 1	Pooled-OLS ROA	−0.413 * (−1.24)	−0.018 ** (−3.19)	−0.148 *** (−0.35)	−0.019 * (−4.08)	−0.317 * (−0.06)	−0.059 (−8.63)	0.033 *** (−7.31)	−0.745 *** (−5.62)
	Random-Effects ROA	−0.554 (−1.93)	−0.007 ** (−2.36)	−0.147 ** (−0.42)	−0.022 ** (−1.62)	−0.310 ** (−5.09)	0.053 (−5.94)	0.030 * (−1.09)	−0.745 * (−4.02)
	Fixed-Effects ROA	0.449 (−0.84)	−0.018 ** (−6.17)	−0.148 ** (−4.21)	−0.021 ** (−2.72)	−0.316 ** (−4.11)	0.156 (−8.37)	0.032 (−5.53)	0.789 (−2.46)
Model 2	Pooled-OLS ROE	−0.352 *** (−3.58)	−0.014 ** (−1.64)	−0.077 ** (−4.18)	−0.028 ** (−4.28)	−0.173 ** (−1.06)	0.418 (−4.08)	0.996 *** (−4.28)	0.122 (−5.08)
	Random-Effects ROE	−0.345 (−2.11)	−0.004 ** (−0.67)	−0.072 ** (−0.67)	−0.025 ** (−2.09)	−0.174 ** (−7.26)	−0.417 (−1.34)	0.996 ** (−1.87)	0.121 (−1.36)
	Fixed-Effects ROE	−0.351 (−5.14)	−0.005 *** (−3.86)	−0.077 *** (−1.56)	−0.028 ** (−1.82)	−0.173 ** (−3.17)	−0.417 (−3.60)	0.995 (−2.48)	0.122 (−1.41)
Model 3	Pooled-OLS NOPM	−0.274 (−1.24)	−0.115 *** (−6.24)	−0.108 *** (−4.03)	−0.103 ** (−1.35)	−0.825 ** (−4.59)	−0.184 (−0.44)	0.351 ** (−2.42)	−0.189 (−3.37)
	Random-Effects NOPM	−0.178 (−0.59)	−0.114 *** (−6.37)	−0.106 *** (−4.71)	−0.102 ** (−1.27)	−0.812 ** (−1.44)	0.179 (−2.08)	0.356 (−1.08)	−0.195 (−2.42)
	Fixed-Effects NOPM	0.269 (−1.09)	−0.115 *** (−6.21)	−0.106 *** (−4.80)	−0.102 ** (−1.25)	−0.825 ** (−2.63)	0.183 (−2.45)	0.354 (−0.93)	0.188 (−1.36)

Note: * Significant at the 10% level; ** Significant at the 5% level, *** Significant at the 1% level. Source: Results estimates.

shows the summary results of the panel regression model analysis for the three dependent variables.

Table 3 shows the panel regression results for the three models; return on asset (ROA) is the dependent variable for Model 1, return on equity (ROE) is the dependent variable for Model 2, and net operating profit margin (NOPM) is the dependent variable in Model 3. The test results for Model 1 show that return on asset is negatively correlated with all four working capital management variables: average age of inventory (AAI), average collection period (ACP), average payment period (APP), and cash conversion cycle (CCC). According to the FE estimator, a 1% increase in average inventory age, average collection period, average payment period, and cash conversion cycle resulted in a 0.02%, 0.15%, 0.02%, and 0.32% decrease in NOPM, respectively, all of which were statistically significant at the 5% level.

The test results for Model 2 show that return on equity is negatively related to all four working capital management variables: average age of inventory, average collection period, average payment period, and cash conversion cycle. The results show that the relationship between return on equity and average inventory age, as well as return on equity and average collection period, is statistically significant at the 1% level. Furthermore, the results show that the relationship between return on equity and average payment period, as well as return on equity and cash conversion cycle, is statistically significant at the 5% level.

The test results for Model 3 show a negative relationship between net operating profit margin and average inventory age, which is statistically significant at the 1% level. Similarly, the results show a negative relationship between the net operating profit margin and average collection period, which is statistically significant at the 1% level. Net operating profit margin is also negatively related to both average payment period and cash conversion cycle. The FE estimator shows that a 1% increase in average payment period and cash conversion cycle resulted in a 0.10% and 0.83% decrease in net operating profit margin, respectively, all of which are statistically significant at the 5% level of significance.

4. Discussion

The findings for Model 1 in Table 3 are consistent with Jaworski and Czerwonka (2024), Mandipa and Sibindi (2022), and Ngari and Kamau (2022), but not with Briones et al. (2024), Fungai (2024), and Šeligová (2021). For example, Ngari and Kamau (2022) discovered a negative relationship between average payables period and return on assets, indicating that longer payables periods had the potential to reduce a firm’s earnings if the cost of financing purchases was greater than the benefits. Nonetheless, the finding for Model 2 in Table 2 is consistent with Briones et al. (2024), Kumar et al. (2024), Mandipa and Sibindi (2022), Seth et al. (2020), and Panigrahi (2024). For example, Briones et al. (2024) discovered that return on equity has a negative relationship with the cash conversion cycle (CCC), a negative, statistically significant relationship with the average collection period (ACP), and a negative, statistically significant relationship with the accounts payable period (APP). These findings were consistent with Cash Conversion Cycle (CCC) Theory, which advocates for efficient account payable management to reduce the risk of bankruptcy and financial distress, thereby increasing a firm’s value. Lastly, the finding for Model 3 in Table 3 supports the findings of Mandipa and Sibindi (2022), who discovered a significant negative relationship between the average collection period and the net operating profit margin. This implies that early collection from customers boosts a company’s performance by providing cash flow to support both operational and financing activities. Overall, the findings of this study revealed that the following: (i) the average age of inventory was negatively related to working capital management; (ii) the average collection period was negatively related to working capital management; (iii) the average payment period was negatively related to working capital management; and (iv) the cash conversion cycle was negatively related to working capital management. The findings of this study also show that the relationship between working capital management and financial performance is statistically significant at the 5% level. This result is consistent with Jaworski and Czerwonka (2024), Mandipa and Sibindi (2022), and Umar and Al-Faryan (2024). Mandipa and Sibindi (2022) used a fixed-effects estimator to investigate the relationship between financial performance and WCM retail firms listed on South Africa’s JSE; the findings indicated that financial performance was negatively related to WCM. Jaworski and Czerwonka (2024) discovered a statistically significant and negative correlation between profitability and cash conversion cycle, profitability and average collection period, and profitability and accounts payable period.

5. Theoretical and Managerial Implications of the Study

The study has broad theoretical and managerial ramifications. The study offers empirical evidence from the Kenyan market, which theoretically advances existing financial theories like CCC Theory, Transaction Cost Theory, and Stakeholder Theory. Within the framework of the Kenyan market and the CSF sector, this study validates established WCM theories. This aids in comprehending the limitations and applicability of these theories in various regulatory and economic contexts. The study contributes to the literature by emphasizing sector-specific dynamics in working capital management through its focus on CSFs. It offers a sophisticated perspective on the working capital management and operations of these companies. The study’s conclusions can be applied by managers to create working capital management plans that are more successful. Managers can optimize working capital components, such as inventory, receivables, and payables, by making educated decisions based on their understanding of the relationship between these components and financial performance. In a similar vein, the study offers useful insights into how enhancing profitability, liquidity, and general financial health can be achieved through efficient working capital management. Metrics like NOPM, ROE, and ROI can be enhanced by managers putting suggested practices into practice. The study’s conclusions can help with decision-making and strategic planning. Based on their working capital policies, managers can more accurately predict financial results and adjust to align with long-term financial objectives. Through an awareness of how working capital management affects financial performance, managers are better equipped to recognize and reduce risks related to operational effectiveness and liquidity. This may result in business operations that are more flexible and resilient. Finally, the study provides information that can help regulatory agencies and legislators create policies that will enhance the financial stability and operational effectiveness of businesses in the services and commercial sectors.

6. Conclusions

This paper looked at how working capital management affects the financial performance of commercial and service firms listed on the Kenya’s Nairobi Securities Exchange. The study’s population included all 13 commercial and service firms that were listed consecutively on the NSE between 2003 and 2022, resulting in a final sample of six commercial and service firms. Using panel regression analysis, the results revealed that the following: (i) the average age of inventory was negatively related to working capital management; (ii) the average collection period was negatively related to working capital management; (iii) the average payment period was negatively related to working capital management; and (iv) the cash conversion cycle was negatively related. As a result, it was determined that working capital management is negatively correlated with financial performance for commercial and service firms listed on the Nairobi Securities Exchange. The study’s practical implications are that firm decision makers should consider the importance of working capital management strategies in order to improve firm performance and financial sustainability. This study has a limited scope and did not consider the COVID-19 epidemic and the Russia–Ukraine war on the performance of CSFs. Future research could investigate all the firms listed on the Nairobi Securities Exchange, as well as commercial and service firms listed on all East African stock exchanges. Similarly, future studies could also include the COVID-19 epidemic and the Russia–Ukraine war variables in their WCM models.