research article on working capital management

Profitability and working capital management: a meta-study in macroeconomic and institutional conditions

• Research Article
• Published: 14 March 2024
• Volume 51 , pages 123–145, ( 2024 )

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• Jacek Jaworski   ORCID: orcid.org/0000-0002-6629-3497 1 &
• Leszek Czerwonka 2  

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Working capital management (WCM) concerns decisions on the levels and turnover of the inventories, receivables, cash and current liabilities of a company. Consequently, WCM affects the profitability of an enterprise. This paper aims to determine the relationship between profitability and WCM, characterised by components of the company’s operating cycle. The research is based on meta-analysis and meta-regression methods that allow for the combination and analysis of the outcomes of individual empirical studies using statistical methods. Our final research sample consists of 43 scientific papers from 2003 to 2018. These studies covered almost 62,000 enterprises in 35 countries from 1992 to 2017. Our results indicate that there is a common, negative relationship between profitability and the cash conversion cycle (CCC). This relationship is conspicuous in various countries and in different economic contexts. A negative, statistically significant relationship was also detected between profitability and average collection period (ACP), the accounts payable period (APP) and inventory turnover cycle (ITC) as well. We also identified moderators of the diagnosed dependencies on the grounds of macroeconomic and institutional factors. The richer the economy, the weaker a negative impact of CCC on profitability. The higher the protection of creditors and debtors, the weaker the negative relationship between profitability and ITC. The opposite is applicable to inflation and ACP and APP, unemployment and CCC, ACP and APP, the availability of credit and APP and the degree of capital market development and CCC and ACP. The aforementioned macroeconomic and institutional factors cause the negative relationship between particular components of the operating cycle and profitability to deepen even further. Our research contributes to the existing knowledge by confirming that the negative relationship between profitability and all components of the operating cycle is dominant in the global economy. It also indicates that there are macroeconomic and institutional moderators of the strength and direction of these relationships.

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Source : Own elaboration based on (Brealey et al. 2016 )

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Funnel plots of standard error by partial correlation coefficient (Fisher’s z transformed) ratio for studies included in meta-analysis (after trimming and filling).

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Jaworski, J., Czerwonka, L. Profitability and working capital management: a meta-study in macroeconomic and institutional conditions. Decision 51 , 123–145 (2024). https://doi.org/10.1007/s40622-023-00372-x

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Working capital management impact on profitability: pre-pandemic and pandemic evidence from the european automotive industry.

1. Introduction

2. literature review and hypotheses development, 2.1. importance of profitability and working capital, 2.2. impact of working capital on profitability, 2.3. working capital in periods of economic crises, 3. methodology, 3.1. variables, 3.2. data sample, 3.3. research model.

• PROFITABILITY refers to the return on assets (ROA) of firm i in year t;
• WCM refers to days-AR, days-INV, days-AP, and the cash conversion cycle (CCC);
• Control Variables refer to Size (S), Sales Growth (SG), Current Ratio (CR), and firm Leverage (Lev);
• Year fixed effects are included in the model;
• εit denotes the error term.

4. Results and Discussion

4.1. descriptive statistics and variable correlation, 4.2. regression results, 4.3. practical and theoretical implications, 5. conclusions, author contributions, data availability statement, acknowledgments, conflicts of interest.

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Share and Cite

Demiraj, R.; Dsouza, S.; Abiad, M. Working Capital Management Impact on Profitability: Pre-Pandemic and Pandemic Evidence from the European Automotive Industry. Risks 2022 , 10 , 236. https://doi.org/10.3390/risks10120236

Demiraj R, Dsouza S, Abiad M. Working Capital Management Impact on Profitability: Pre-Pandemic and Pandemic Evidence from the European Automotive Industry. Risks . 2022; 10(12):236. https://doi.org/10.3390/risks10120236

Demiraj, Rezart, Suzan Dsouza, and Mohammad Abiad. 2022. "Working Capital Management Impact on Profitability: Pre-Pandemic and Pandemic Evidence from the European Automotive Industry" Risks 10, no. 12: 236. https://doi.org/10.3390/risks10120236

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• Working Capital Mgmt.
• Understanding It

Types of Working Capital

• Why Manage Capital?

Working Capital Cycle

• Limitations

The Bottom Line

• Corporate Finance
• Financial statements: Balance, income, cash flow, and equity

Working Capital Management Explained: How It Works

What Is Working Capital Management?

Working capital management is a business strategy designed to manage a company's working capital. A company's working capital refers to the capital it has left over after accounting for its current liabilities. Working capital management ensures that a company operates efficiently by monitoring and using its current assets and liabilities to their most effective use. The efficiency of working capital management can be quantified using ratio analysis .

Key Takeaways

• Working capital management requires monitoring a company's assets and liabilities to maintain sufficient cash flow to meet its short-term operating costs and short-term debt obligations.
• Managing working capital primarily revolves around managing accounts receivable, accounts payable, inventory, and cash.
• Working capital management involves tracking various ratios, including the working capital ratio, the collection ratio, and the inventory ratio.
• Working capital management can improve a company's cash flow management and earnings quality by using its resources efficiently.
• Working capital management strategies may not materialize due to market fluctuations or may sacrifice long-term successes for short-term benefits.

Investopedia / Sydney Saporito

Understanding Working Capital Management

Working capital is a key metric used to measure a company's short-term financial health and well-being. It is the difference between a company's current assets and current liabilities. As such, it is the capital that is left after accounting for its current liabilities. Working capital management is a strategy that companies use to manage their leftover cash.

Current assets include anything that can be easily converted into cash within 12 months. These are the company's highly liquid assets. Some current assets include cash, accounts receivable (AR), inventory, and short-term investments. Current liabilities are any obligations due within the following 12 months. These include accruals for operating expenses and current portions of long-term debt payments.

The primary purpose of working capital management is to enable the company to maintain sufficient cash flow to meet its short-term operating costs and short-term debt obligations. A company's working capital is made up of its current assets minus its current liabilities.

Working capital management monitors cash flow, current assets, and current liabilities using ratio analysis, such as working capital ratio , collection ratio, and inventory turnover ratio .

Working Capital Management Components

Certain balance sheet accounts are more important when considering working capital management. Though working capital often entails comparing all current assets to current liabilities, there are a few accounts that are more critical to track.

The core of working capital management is tracking cash and cash needs. This involves managing the company's cash flow by forecasting needs, monitoring cash balances, and optimizing cash flows (inflows and outflows) to ensure that the company has enough cash to meet its obligations.

Because cash is always considered a current asset, all accounts should be considered. However, companies should be mindful of restricted or time-bound deposits .

Receivables

To manage capital, companies must be mindful of their receivables. This is especially important in the short term as they wait for credit sales to be completed. This involves:

• Managing the company's credit policies
• Monitoring customer payments
• Improving collection practices

At the end of the day, having completed a sale does not matter if the company is unable to collect payment on the sale.

Account Payables

Account payables refers to one aspect of working capital management that companies can take advantage of that they often have greater control over. While other aspects of working capital management may be uncontrollable, such as selling goods or collecting receivables, companies often have a say in how they pay suppliers, what the credit terms are, and when cash outlays are made.

Companies primarily consider inventory during working capital management as it may be the most risky aspect of managing capital. When inventory is sold, a company must go to the market and rely on consumer preferences to convert inventory to cash.

If this cannot be completed quickly, the company may be forced to have its short-term resources stuck in an illiquid position. Alternatively, the company may be able to quickly sell the inventory but only with a steep price discount.

In its simplest form, working capital is the difference between current assets and current liabilities. However, different types of working capital may be important to a company to best understand its short-term needs.

• Permanent Working Capital: Permanent working capital is the amount of resources the company will always need to operate its business without interruption. This is the minimum amount of short-term resources vital to a company's operations.
• Regular Working Capital: Regular working capital is a component of permanent working capital. It is the part of the permanent working capital that is required for day-to-day operations and makes up the most important part of permanent working capital.
• Reserve Working Capital: Reserve working capital is the other component of permanent working capital. Companies may require an additional amount of working capital on hand for emergencies, seasonality , or unpredictable events.
• Fluctuating Working Capital: Companies may be interested in only knowing what their variable working capital is. For example, companies may opt to pay for inventory as it is a variable cost . However, the company may have a monthly liability relating to insurance it does not have the option to decline. Fluctuating working capital only considers the variable liabilities the company has complete control over.
• Gross Working Capital: Gross working capital is simply the total amount of current assets of a business before considering any short-term liabilities.
• Net Working Capital: Net working capital is the difference between current assets and current liabilities.

Why Manage Working Capital?

Working capital management can improve a company's cash flow management and earnings quality through the efficient use of its resources. Management of working capital includes inventory management as well as management of accounts receivable and accounts payable . 

Working capital management also involves the timing of accounts payable like paying suppliers. A company can conserve cash by choosing to stretch the payment of suppliers and to make the most of available credit or may spend cash by purchasing using cash—these choices also affect working capital management.

In addition to ensuring that the company has enough cash to cover its expenses and debt, the objectives of working capital management are to minimize the cost of money spent on working capital and maximize the return on asset investments.

Working Capital Management Ratios

Three ratios that are important in working capital management are the working capital ratio, the collection ratio, and the inventory turnover ratio.

Working Capital Ratio

The working capital ratio or current ratio is calculated by dividing current assets by current liabilities. This ratio is a key indicator of a company's financial health as it demonstrates its ability to meet its short-term financial obligations.

A working capital ratio below 1.0 often means a company may have trouble meeting its short-term obligations. That's because the company has more short-term debt than short-term assets. To pay all of its bills as they come due, the company may need to sell long-term assets or secure external financing.

Working capital ratios of 1.2 to 2.0 are considered desirable as this means the company has more current assets compared to current liabilities. However, a ratio higher than 2.0 may suggest that the company is not effectively using its assets to increase revenues. For example, a high ratio may indicate that the company has too much cash on hand and could be more efficiently utilizing that capital to invest in growth opportunities.

Collection Ratio (Days Sales Outstanding)

The collection ratio, also known as days sales outstanding (DSO) , is a measure of how efficiently a company manages its accounts receivable. The collection ratio is calculated by multiplying the number of days in the period by the average amount of outstanding accounts receivable.

This product is then divided by the total amount of net credit sales during the accounting period. To find the average amount of average receivables, companies most often simply take the average between the beginning and ending balances.

The collection ratio calculation provides the average number of days it takes a company to receive payment after a sales transaction on credit. Note that the DSO ratio does not consider cash sales. If a company's billing department is effective at collecting accounts receivable , the company will have quicker access to cash which is can deploy for growth. Meanwhile, if the company has a long outstanding period, this effectively means the company is awarding creditors with interest-free, short-term loans.

Inventory Turnover Ratio

Another important metric of working capital management is the inventory turnover ratio. To operate with maximum efficiency, a company must keep sufficient inventory on hand to meet customers' needs. However, the company also needs to strive to minimize costs and risk while avoiding unnecessary inventory stockpiles.

The inventory turnover ratio is calculated as the cost of goods sold (COGS) divided by the average balance in inventory. Again, the average balance in inventory is usually determined by taking the average of the starting and ending balances.

The ratio reveals how rapidly a company's inventory is used in sales and replaced. A relatively low ratio compared to industry peers indicates a risk that inventory levels are excessively high, meaning a company may want to consider slowing production to ease the cost of insurance, storage, security, or theft. Alternatively, a relatively high ratio may indicate inadequate inventory levels and risk to customer satisfaction.

In addition to the ratios discussed above, companies may rely on the working capital cycle when managing working capital. Working capital management helps maintain the smooth operation of the net operating cycle, also known as the cash conversion cycle (CCC) . This is the minimum amount of time required to convert net current assets and liabilities into cash. The working capital cycle is a measure of the time it takes for a company to convert its current assets into cash, or:

Working Capital Cycle in Days = Inventory Cycle + Receivable Cycle - Payable Cycle 

The working capital cycle represents the period measured in days from the time when the company pays for raw materials or inventory to the time when it receives payment for the products or services it sells. During this period, the company's resources may be tied up in obligations or pending liquidation to cash.

Inventory Cycle

The inventory cycle represents the time it takes for a company to acquire raw materials or inventory, convert them into finished goods, and store them until they are sold. During this stage, the company's cash is tied up in inventory.

Though it starts the cycle with cash on hand, the company agrees to part ways with working capital with the expectation that it will receive more working capital in the future by selling the product at a profit .

Accounts Receivable Cycle

The AR cycle represents the time it takes for a company to collect payment from its customers after it has sold goods or services. During this stage, the company's cash is tied up in accounts receivable.

Though the company can part ways with its inventory, its working capital is now tied up in accounts receivable and still does not give the company access to capital until these credit sales are received.

Accounts Payable Cycle

The AP cycle represents the time it takes for a company to pay its suppliers for goods or services received. During this stage, the company's cash is tied up in accounts payable.

On the positive side, this represents a short-term loan from a supplier meaning the company can hold onto cash even though they have received a good. On the negative side, this creates a liability that needs to be managed.

Limitations of Working Capital Management

With strong working capital management, a company should be able to ensure it has enough capital on hand to operate and grow. However, there are downsides to the approach. Working capital management only focuses on short-term assets and liabilities. It does not address the long-term financial health of the company and may sacrifice the best long-term solution in favor of short-term benefits.

Even with the best practices in place, working capital management cannot guarantee success. The future is uncertain, and it's challenging to predict how market conditions will affect a company's working capital. Whether there are changes in macroeconomic conditions and customer behavior, or there are disruptions in the supply chain, a company's forecast of working capital may simply not materialize as expected.

While effective working capital management can help a company avoid financial difficulties, it may not necessarily lead to increased profitability. Working capital management does not inherently increase profitability, make products more desirable, or increase a company's market position. Companies still need to focus on sales growth, cost control, and other measures to improve their bottom line. As that bottom line improves, working capital management can simply enhance the company's position.

Working capital management aims at more efficient use of a company's resources by monitoring and optimizing the use of current assets and liabilities. The goal is to maintain sufficient cash flow to meet its short-term operating costs and short-term debt obligations while maximizing its profitability. Working capital management is key to the cash conversion cycle, or the amount of time a firm uses to convert working capital into usable cash.

Why Is the Current Ratio Important?

The current ratio or the working capital ratio indicates how well a firm can meet its short-term obligations. It's also a measure of liquidity . If a company has a current ratio of less than 1.0, this means that short-term debts and bills exceed current assets, which could be a signal that the company's finances may be in danger in the short run.

Why Is the Collection Ratio Important?

The collection ratio, also known as days sales outstanding, is a measure of how efficiently a company can collect on its accounts receivable. If it takes a long time to collect, it can be a signal that there will not be enough cash on hand to meet near-term obligations. Working capital management tries to improve the collection speed of receivables.

Why Is the Inventory Ratio Important?

The inventory turnover ratio shows how efficiently a company sells its inventory. A relatively low ratio compared to industry peers indicates a risk that inventory levels are excessively high, while a relatively high ratio may indicate inadequate inventory levels.

Working capital management is at the core of operating a business. Without sufficient capital on hand, a company is unable to pay its bills, process its payroll, or invest in its growth. Companies can better understand their working capital structure by analyzing liquidity ratios and ensuring their short-term cash needs are always met.

Dr. Ajay Tyagi, via Google Books. " Capital Investment and Financing for Beginners ," Page 3. Horizon Books, 2017.

Dr. Ajay Tyagi, via Google Books. " Capital Investment and Financing for Beginners ," Page 4. Horizon Books, 2017.

Dr. Ajay Tyagi, via Google Books. " Capital Investment and Financing for Beginners ," Pages 4-5. Horizon Books, 2017.

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Analyzing the Efficiency of Working Capital Management: a New Approach Based on DEA-Malmquist Technology

Ahmed mohamed habib.

1 Independent Accounting and Finance Researcher, Zagazig, Egypt

Nahia Mourad

2 College of Computer Information Technology, American University in the Emirates, Dubai, UAE

In this study, we analyze the efficiency of working capital management (WCME) for Gulf companies before and during the coronavirus crisis, then explore the influence of the coronavirus crisis on WCME. This study uses several techniques to achieve its goals, including the Malmquist index (MI), data envelopment analysis (DEA), and Tobit regression. The results demonstrate that most firms (approximately 84%) adopt a conservative strategy for their WCM. The WCME results revealed a statistical difference in the technological and pure efficiency scores for companies before and during the coronavirus crisis, while the results revealed no statistical difference in the technical, scale, and total factor productivity scores. Tobit’s results show that the coronavirus crisis had no significant influence on companies’ WCM performance. Finally, our results indicate that firms that are efficient in terms of WCM have higher sales returns and net income. The findings of this study have important implications for stakeholders to increase their awareness of companies’ WCM performance before and during a crisis. In addition, the results could have implications for trading strategies as investors and financiers seek to invest in companies with good WCM. The implications of WCM performance on social interests would cause decision-makers to use the best strategies and procedures to enhance WCM activities to improve their investments and image in the community in which it operates. We advance a novel contribution to the literature by analyzing and appraising the WCME for companies before and during the coronavirus crisis using a new approach based on DEA-Malmquist technology and then examining whether the coronavirus crisis has affected the WCME.

Introduction

Businesses strive to make the best use of their limited resources. Resource allocation theory states that firms choose the most cost-effective distribution and allocation of resources for various productive activities [ 1 , 2 ]. As a result, firms that strive for excellence manage their WC to achieve best practices. The WC term arose from corporate finance and was initially mentioned at the inception of the twentieth century [ 3 , 4 ]. The WC is one of the most confusing accounting concepts. The lack of clarity concerning the employment of WC may be excused by the fact that there is no analogous classification of WC in firms’ balance sheets. WCM appears to have been primarily disregarded in businesses, even though bad WC decisions are responsible for a considerable portion of business failures, and WCM is essential for corporate financial management because it directly affects a firm’s profitability [ 5 ]. This is more striking as a large share of past firm bankruptcies was created by ineffective or inadequate WCM [ 6 ]. As WC significantly influences a firm’s operational and financial security, the literature confirms that it is necessary to develop an optimal WC strategy for a firm [ 7 , 8 ]. The literature suggests three strategies for managing WC: conservative, moderate, and aggressive [ 8 – 10 ]. The conservative strategy is safe for a firm and provides a high level of liquidity as it keeps current assets at high levels compared to current liabilities. In contrast, the aggressive strategy keeps current assets at low levels compared to current liabilities. Finally, the moderate strategy is considered a sensible method as it aims to minimize the drawbacks of the aforementioned methods and maximize their benefits. Exploring the suitable linkages between the items of current assets and liabilities will help a firm to adopt a good WC strategy. Therefore, a firm should adopt and manage its WC strategy on a solid and secure basis to achieve best practices.

The literature on corporate finance recognizes the significance of short-term business decisions on firm profitability. WCM is a recurring topic on a global scale because it is critical to ensure a business’s optimal path. WC is essential during economic downturns because it acts as a liquidity buffer [ 11 , 12 ]. Additionally, WC practices benefit firm profitability by facilitating solid sales and income growth [ 13 , 11 ]. While inventory stockpiling protects businesses from price fluctuations, trade credit increases sales and strengthens customer relationships. In addition, short-term debt related to financing the WC has low interest rates and is unaffected by inflation [ 14 ]. In contrast, the PricewaterhouseCoopers (PwC) Global report notes that promoting WC could free up €1.3 trillion in cash, allowing for a 55% increase in capital investment [ 15 ]. Furthermore, the report identifies new calls for global publicly traded firms’ business performance over the last 5 years as capital expenses have decreased, cash has shifted to be more costly and tough to convert, and the WC has slightly improved. Firms must cultivate and enhance their WC practices to improve business performance. On the other hand, excessive investment in WC necessitates financing and, as a result, additional payments, which may produce negative consequences and sacrifices for stockholders [ 13 , 16 ]. Kieschnick et al. [ 17 ] argue that an increase in WC financing increases the likelihood of bankruptcy because it requires additional financing requirements and financing expenses.

Moreover, various components of WCM contribute significantly to its effectiveness. Firms must make critical decisions about how much stock to keep on hand as having a large inventory protects them from costly stockouts and manufacturing process interruptions. Customers who are given more credit are more likely to use and verify products before making a payment, which benefits the company [ 18 ]. According to de Almeida and Eid [ 19 ], WC is a critical component of operational cash flow and is used to calculate the free cash flow. Effective WCM reduces a firm’s reliance on external funding, frees up cash for additional investments, and increases its financial flexibility. Business administration is constantly striving to maintain optimal WC volume. Increased WC investment energizes the sales process and provides discounts to suppliers for prompt payments at low WC levels. Nonetheless, once a certain level of WC investment is reached, additional interest costs are incurred, eroding firm value [ 20 ].

Two approaches have been used to assess a firm’s efficiency in terms of WCM. The first approach for assessing the WCME is to use ratio analysis as a parametric method. For example, quick and current ratios have been used to assess a firm’s liquidity [ 21 ]. In addition, Zimon and Tarighi [ 8 ] explored the WCM strategies of small- and medium-sized firms in Poland using liquidity and turnover ratios, cash conversion cycle (CCC), and other ratios. This approach has been criticized for its inherently static nature as a parametric method [ 22 ]. The CCC proposed by Richards and Laughlin [ 23 ] was also criticized for being mathematically incorrect, failing to focus on the total amount of funds committed, and lacking differentiation in the weights assigned to each component of WC [ 24 ]. According to Goel and Sharma [ 24 ], other measurement ratios, such as weighted CCC, have calculation issues owing to a lack of relevant data. Accordingly, researchers have developed alternative methods for measuring the WCME to overcome the weaknesses of the traditional approach. DEA is one such measure that has been used to calculate WCME as a non-parametric method in previous studies [ 25 – 30 ].

The DEA approach is distinguished by its ability to capture relationships between multiple outputs and inputs [ 31 – 33 ]. Additionally, DEA is a non-parametric technique that does not require prior assumptions about the distribution form of data or its residuals, and does not require any previous knowledge of the variable weights [ 34 – 36 ]. In addition, DEA is distinguished by its powerful benchmark in assessing the efficiency of firms, as it focuses on the best practices of firms rather than traditional methods, such as ratios and regression analyses, which rely on measures of average and central tendencies as criteria for evaluation, as it benchmarks a firm’s performance with maximum relative performance or best practices [ 37 , 38 ]. Therefore, DEA is considered a powerful approach for the continuous improvement process as it provides critical benchmark information for inefficient firms in achieving the best practices [ 33 , 38 ].

Empirical evidence shows that WCM has garnered substantial interest in accounting and finance research. Considering the Gulf firms, WCM is vital to firms’ economic development. Gulf member states are monarchies with distinct legal structures, and their public corporations operate in distinct institutional, economic, and political environments [ 39 ]. To integrate with the global economy, they shifted their focus from an oil-based economy to a knowledge-based one [ 40 ]. Gulf firms outrank the Middle East and North Africa (MENA) regions but not other regions with comparable per capita income levels. Thus, inefficient employment of assets and WC impedes progress toward sustainable and equitable growth. Gulf firms should invest in balancing their assets and WC to alleviate this trend. In addition, the existing literature on WCM has rarely focused on this crucial phenomenon in Gulf firms. Therefore, more research is needed to analyze the WCME for firms operating in the Gulf and investigate the influence of the coronavirus crisis on WCM performance, which is considered a novel contribution to the literature. Therefore, this study analyzes the efficiency of WCM by integrating the data envelopment analysis approach and the Malmquist productivity index in the context of a unique Gulf setting. The objective of this study was to investigate data from 2018 to 2020. The DEA-Malmquist analysis is extended to capture the efficiency of WCM in terms of technical efficiency (effch), technological efficiency (techch), pure efficiency (pech), scale efficiency (sech), and total factor productivity (tfpch) before and during the coronavirus crisis. The efficiency of the WCM results revealed a statistical difference in the technological and pure efficiency scores before and during the coronavirus crisis. Tobit’s results show that the coronavirus crisis had no significant influence on Gulf firms’ WCM performance. The findings of this study have important implications for stakeholders to increase their awareness of companies’ WCM performance before and during a crisis. In addition, the results could have implications for trading strategies as investors and financiers seek to invest in companies with good WCM. The implications of WCM performance on social interests would cause decision-makers to use the best strategies and procedures to enhance WCM activities to improve their investments and image in the community in which it operates.

The motivation for the study stems from market characteristics and the economic prospects of the Gulf. Most Gulf countries experienced increased inflation during the study period, resulting in higher interest rates, influencing a firm cost of capital. The Gulf Statistics Centre recently released a report on the Gulf countries’ inflation rates, which were 3.5% in April 2021, up from 3.5% the previous year. In April 2021, Saudi Arabia had the highest inflation rate in the Gulf, at 5.3%, up from 3.1% in April 2020, followed by Kuwait (3.1%), Oman (1.6%), and Qatar (1.6%). In the United Arab Emirates and Bahrain, inflation decreased about 0.5% and 0.1%, respectively. Besides, the coronavirus epidemic, on the other hand, had a tremendous impact on the entire world, as every country, industry, and civilization were affected in some way [ 41 ]. Many activities have been restricted because of the pandemic to slow the spread of the virus. We should turn everything off to limit the negative impact. When public authorities take decisive action to address the emerging health threat of coronavirus, business leaders are faced with the challenge of channeling their WCM through the issue. Recognizing the crisis impact on the people who drive the firm’s operations is critical. That highlights the importance of a resilient leader in a fast-changing environment and working differently. Also, the author has not found any research by reviewing previous studies on WCM in the context of the coronavirus pandemic. Using MI and DEA, this study is thought to be one of the earliest attempts to analyze and appraise the WCM performance of the firms. Moreover, Gulf firms were adversely impacted by the numerous issues that arose because of the outbreak. Based on these arguments and evidence, this study investigates the following:

RQ1. Are there, on average, significant differences in firms’ WCME over the study period? RQ2. Has coronavirus crisis affected firms’ WCME over the study period?

The remainder of this paper is organized as follows. Section  2 presents a literature review and hypotheses formulation. Section  3 clarifies the data and the methodology used. Section  4 presents the empirical results. Finally, Sect.  5 presents a summary and conclusions of the study.

Literature Review and Hypotheses Formulation

WCM is a critical component of a firm’s success [ 42 , 43 ]. Furthermore, the WCM can help with risk management and increase the value of a business [ 44 ]. Furthermore, a conservative approach to WCM necessitates increased inventory and accounts receivable investment, which has the advantage of lowering supply-chain costs and price fluctuations, posing less risk to businesses [ 21 , 45 ]. Increased sales and market share generate profits [ 46 ]. Firms that take a proactive approach to WCM reduce risk exposure by reducing inventory investment and credit terms with customers [ 13 ]. Besides, a study of Indian industrial firms between 2004 and 2013 revealed continuous growth in WCME. The DEA-based approach effectively overcame the limitations associated with traditional WCME measures [ 26 , 27 ]. Furthermore, an examination of Indian industrial firms revealed a high degree of efficiency volatility among manufacturing firms, with those operating at 50 to 60% efficiency lacking liquidity management expertise [ 28 , 29 ]. According to Ukaegbu [ 47 ], there is a negative relationship between WCM and Egyptian manufacturing firm profitability. According to a study conducted in 46 countries, lowering CCC could increase business profitability and value [ 16 ]. Furthermore, publicly traded European hospitals with a low leverage ratio show that increasing WCM increases profitability [ 48 ].

Prior research in developed countries revealed various WCME and firm performance outcomes [ 49 ]. While these studies have been extensive in developed countries, they have only recently been extended to developing countries. In developing countries, the relationship between WCME and profitability has been documented using a variety of proxies. Over 10 years, Akinlo [ 50 ] investigated the relationship between WCME and non-financial sector firm profitability in Nigeria. Inventory days, average payment period accounts receivable, and WCM efficiency were all calculated by WCME. The data were analyzed using fixed effects and a pooled ordinary least squares model. Nigerian businesses’ return on assets (ROA) decreases as accounts receivable, accounts payable, and inventory turnover days increase, but increases as CCC decreases. Altaf [ 51 ] investigated the effect of WCME on the performance of the Indian hospitality sector using a two-step efficient GMM (generalized method of moments). WC financing is calculated using the short-term debt-to-working-capital ratio. The results were a non-monotonic relationship with ROA and Tobin Q. That means that a low level of short-term debt benefits the performance of the business.

Wasiuzzaman [ 49 ] calculated the WC using inventory, receivables, payables, and WC balance. According to this study, WC is negatively correlated with ROA in Malaysian manufacturing firms. The payables and hypothesized relationships were incompatible. Soukhakian and Khodakarami [ 52 ] investigated whether WCME could significantly improve the ROA and economic performance of publicly traded Iranian industrial firms. Even though CCC was negatively associated with ROA, there was no significant relationship between WCM and refined economic value added when endogeneity was considered. Wang et al. [ 46 ] investigated the corporate relationships of non-financial listed firms in Pakistan over their existence. According to the findings, an increase in WCME (as measured by the net trade cycle) decreased ROA regardless of the life cycle stage. Zimon [ 10 ] analyzed WCM in small firms in Poland using a sample of 96 commercial firms operating in the construction industry from 2015 to 2017. The results demonstrate that firms operating within purchasing groups focus on financial safety and adopt a moderate-conservative strategy. Lyngstadaas [ 53 ] investigated the link between WCM packages and financial performance using a sample of 589 firms in the USA from 2012 to 2019. The results indicate that out of the 11 effective packages in terms of WCM, six are significant. Additionally, the results confirm that the six packages systematically relate to operational and financial WC performance.

In addition, Chamberlain and Aucouturier [ 54 ] explore the influence of WCM on the performance of publicly traded companies in Europe from 2004 to 2016. The results indicate that the links between WCM, profitability, and firm value are positive and significant. This study suggests that directors should take a nuanced view of WCM’s influence on performance. Zimon [ 7 ] reviewed prior research on WCM. This study shows that higher WC levels enable firms to increase their sales volume. The study concludes that directors should base their WCM strategies on high sales volumes to enhance firms’ WCM efficiency, profitability, and financial security. Aldubhani et al. [ 55 ] explored the linkage between WCM policies and profitability of manufacturing firms in Qatar from 2015 to 2019. The results reveal that firms with a shorter duration of receivables and CCC, and a longer duration of accounts payable and inventory turnover are more profitable. Jaworski and Czerwonka [ 56 ] explored the linkage between WCM measures using a sample of 326 Polish firms from 1998 to 2016. The results revealed a significant nonlinear linkage between working capital, liquidity, and profitability. Mazanec [ 57 ] explored the influence of WCM on a firm’s performance using 3828 transport firms in the Visegrad Group in the European Union in 2019. The results indicate that cash ratio affects firm performance in all models, excluding the Polish and Czech models. In addition, small firms are at a disadvantage in terms of WCM compared to medium-sized firms in Slovakia and the Czech Republic. Zimon and Tarighi [ 8 ] examined the influence of the COVID-19 crisis on WCM using a sample of 61 Polish firms from 2015 to 2020. The results demonstrate that firms manage a moderately conservative strategy for their WCM. Additionally, the results indicated that the COVID-19 crisis did not significantly alter firms’ WCM strategies. Tarkom [ 58 ] investigates the influence of the COVID-19 crisis on firms’ WCM using a sample of 2542 US-publicly traded US firms from 2019 to 2021. The results show that firms with more investment options and government incentives operate at lower levels during cash-conversion cycles. Additionally, the results demonstrated a significant negative influence of COVID-19 on WCM. This finding suggests that the influence can be mitigated by increasing government incentives and investment opportunities. Struwig and Watson [ 59 ] critically examined the WCM research conducted during the COVID-19 crisis in South Africa. The study concludes that during a crisis, the WC examination focuses on workforce safety and demand volatility. This suggests that effective cash management and digital transformation shifts are necessary to relieve undesirable changes in supply chains. Based on these arguments and evidence, this study hypothesizes the following:

H 1 . On average, there were significant differences in firms’ WCME over the study period. H 2 . The coronavirus crisis has affected the firms’ WCME over the study period.

Data and Methodology

The sample size included 459 publicly traded companies in the following industries: communication services, consumer discretionary, consumer staples, energy, health care, industrials, materials, real estate, and utilities. These companies are located in Oman, Qatar, Saudi Arabia, Kuwait, Bahrain, and the United Arab Emirates. According to Pastor and Ruiz [ 60 ] and Portela et al. [ 61 ], negative data values would limit the capacity of the DEA model to perform the analysis. As a result, 273 firms were excluded due to negative values in some cases and a lack of data in others. As a result, the final decision-making units (DMUs) are 186 firms. The primary data sources were based on the annual reports of the selected firms. These firms’ annual reports were obtained from the standard and poor’s DataStream, the platform of Mubasher-info, and firms’ websites.

Among the numerous approaches available for assessing DMU efficiency scores, the DEA approach was chosen to evaluate the efficiency of the firms under study because of its unique characteristics. First, as Mourad et al. [ 31 ], Shahwan and Habib [ 32 ], and Tone [ 33 ] argue, DEA is a versatile and powerful technique for capturing the relationship between specific outputs and inputs. Furthermore, DEA can provide critical information for continuous improvement, assisting inefficient DMUs in achieving best practices. Second, like Cooper et al. [ 37 ] and Habib and Shahwan [ 38 ] argued, DEA stands out as a benchmark technique that focuses on the best practices of DMUs rather than traditional methods that rely on measures of central tendencies. Finally, as demonstrated by Habib and Kayani [ 36 ], Mourad et al. [ 31 ] and Tuskan and Stojanovic [ 35 ], DEA distinguishes itself as a non-parametric technique that does not require prior assumptions about the distribution form of data (or its residuals). Furthermore, DEA does not require any previous knowledge of the variable weights.

To calculate efficiency using DEA, we require a set of inputs and outputs pertinent to the analysis’s primary objective [ 36 , 37 , 62 ]. DMUs are expected to provide outputs based on their possible inputs related to the primary objective under analysis. According to prior research, e.g., Gill and Biger [ 25 ], Goel and Sharma ( 24 , 26 , 27 , and Seth et al. [ 30 ], the inputs for calculating the WCME should include those items that account for a significant portion of WC investments. Additionally, each firm invests in WC to maintain consistency and increase sales. Thus, firms that generate more sales while supporting the same WC can be considered more efficient. As a result, net sales should be chosen as an output variable. Almost all prior research has overlooked the significance of net income as a by-product of WCM. A business that generates a higher net income while investing the same WC is more efficient. Following a review of the prior literature, the current DEA-WCME model used inventory, accounts receivable, accounts payable, and cost of goods sold as inputs and net sales and net income as outputs. Finally, the radial Malmquist DEA model is obtained by solving the next linear optimization problem:

where x in s (resp. y rn s ) is the value of the i -th input (resp. r -th output) of the n -th DMU observed in period s , the λ n 1 ≤ n ≤ N are the weights corresponding to the DMUs. The DMU is considered relatively efficient in period s measured by frontier technology t if δ s X n t , Y n t = 1 ; otherwise it is inefficient. It should be noted that, e n 1 = 1 δ 1 ( X n 1 , Y n 1 ) (resp. e n 2 = 1 δ 2 ( X n 2 , Y n 2 ) ) is the constant return to scale (CCR) efficiency score for the n -th DMU in the first (resp. second) period.

Following the evaluation of the firms’ WCME using the DEA approach, the current study used the Tobit regression analysis to identify the potential statistical effect of the coronavirus on firms’ WCME. This model is a valuable tool for assessing the relationships between variables when the dependent variable contains censored data or has a range constraint [ 38 ],Verbeek 2008). The equation represents the Tobit linear regression relationship:

where  e i represents each firm’s WCME; v 1 is the coronavirus as an independent variable defined by a dummy variable. To put it another way, if the time is related to the time before the coronavirus crisis, this indicator variable equals 1, and if it is associated with the time before the coronavirus crisis, it equals 0. Furthermore, to improve the accuracy of the analyses, the study used various control variables such as size, age, and leverage. Thus, v 2 represents the firm size as defined by the natural logarithm of total assets; v 3 represents the firm age as defined by the natural logarithm of firm age from the start of the activity until the end of the current year; v 4 represents firm leverage as defined by dividing a firm’s total liabilities by shareholders’ equity; v 5 refers to the communication services sector; v 6 refers to the consumer discretionary sector; v 7 refers to the consumer staples sector; v 8 refers to the energy sector; v 9 refers to the health care sector; v 10 refers to the industrials sector; v 11 refers to the materials sector; v 12 refers to the real estate sector. β 0 is a constant; β i represents the Tobit regression coefficients; and ε i are known by the Gaussian noises or errors.

Results and Discussion

Results of the efficiency model.

Table ​ Table1, 1 , panel A, shows the Malmquist index summary for the top ten DMUs under analysis (tfpch > 1) over the study period (2018–2020) in terms of WCME changes. In terms of improvement, the KWSE:HUMANSOFT achieved the best results (2.331), followed by the SASE:9510 (2.100), the DSM:NLCS (1.960), and so on. Table ​ Table1, 1 , panel B, displays the Malmquist index summary for all DMUs under consideration during the study period (2018–2020) regarding WCME changes. According to the Malmquist index summary, technological efficiency or frontier-shift (techch) was the primary source of the increasing efficiency of the total factor productivity index of the DMUs under study, rather than technical efficiency or catch-up changes (effch). In terms of improvement (tfpch > 1), 100 DMUs out of 186 under investigation achieved the best results (tfpch > 1). Only 86 DMUs appeared to be inefficient, and they should reconsider operating processes and improve performance through necessary corrective actions to achieve best practices and improve overall factor productivity.

DEA-Malmquist index summary of firm means

All Malmquist index averages are geometric means

effch technical efficiency change, techch technological change, pech pure technical efficiency change, sech scale efficiency change, tfpch total factor productivity (TFP) change

The DEA-Malmquist index summary of annual means in terms of WCME changes over the study period is shown in Table ​ Table2, 2 , panel A. The Malmquist index increased by about 1.002 (0.2%) from the base year in the first period (2018–2019) before the coronavirus crisis. This increase is the result of an increase in technological efficiency or frontier-shift changes (techch) of about 1.083 (8.3%) multiplied by a decrease in technical efficiency or catch-up changes (effch) of about 0.926. (7.4%). Similarly, the situation has not changed significantly during the crisis; the Malmquist index for the second period (2019–2020) increased by about 1.034 (3.4%), with this increase attributed to the rise in technological efficiency changes of about 1.135 (13.5%) multiplied by a decrease in technical efficiency changes of about 0.911. (8.9%). Over the study period, the Malmquist index increased by about 1.018 (1.8%), the technological efficiency increased by approximately 1.108 (10.8%), and the technical efficiency decreased by about 0.918 (8.2%).

DEA-Malmquist index summary of annual means

* p  < 0.1; ** p  < 0.05

Table ​ Table2, 2 , panel B, shows a complementary statistical test for confirming significant differences in firm efficiency scores regarding WCM over the study period using Wilcoxon tests (via IBM-SPSS ver26). The results showed no statistical difference in technical efficiency scores at a 5% significance level before and during the coronavirus crisis. Similarly, at a 5% significance level, there was no statistical difference in scale efficiency scores and total factor productivity scores. As a result, we retain the null hypothesis that the median of differences between effch (before the crisis) and effch (during the crisis) equals 0; sech (before the crisis) and sech (during the crisis) equal 0; tfpch (before the crisis) and tfpch (during the crisis) equal 0. Furthermore, the results revealed a statistical difference in technological efficiency scores and pure efficiency scores at a 5% significance level before and during the crisis. As a result, we reject the null hypothesis that the median of differences between techch (before the crisis) and techch (during the crisis) equals 0; pech (before the crisis) and pech (during the crisis) equals 0. All previous results indicate that H1 is partially supported.

Results of the Tobit Regression Model

Following the evaluation of the firms’ WCM performance using the DEA approach, it is helpful to identify some of the factors that affect WCM performance. In this section, the following factors are investigated for their impact on performance: the coronavirus crisis, size, age, leverage, and sector classification.

Tobit regression analysis was used to investigate factors influencing WCM performance using Stata/MP ver16. Table ​ Table3 3 depicts the effect of the variables under investigation on the WCM performance of the firms over the study period. Table ​ Table3 3 shows that firm size and sector (Sec1, the communication services sector; Sec2, the consumer discretionary sector) have a significant favorable influence at the 0.10 significance level or less. Furthermore, at the 0.10 significance level or less, the leverage and the industry sector (whether Sec5, the health care sector; Sec7, the materials sector) negatively influence.

The results of Tobit regression

* p  < 0.1; ** p  < 0.05; *** p  < 0.01

The current study’s findings revealed that the coronavirus crisis had no significant influence on WCM performance. As a result, the H2 hypothesis is unsupported. This findings are consistent with Zimon and Tarighi [ 8 ] study as they reveal that the COVID-19 crisis did not significantly alter firms’ WCM strategies. In contrast, the findings are inconsistent with Tarkom [ 58 ] study, as they demonstrate a significant negative influence of the COVID-19 crisis on WCM. In contrast, the findings revealed that firm size and leverage significantly impact WCM performance. Moreover, the results showed that the sector category (whether Sec1, the communication services sector, Sec2, the consumer discretionary sector; Sec5, the health care sector; Sec7, the materials sector) have a significant influence on the WCM performance at the same time the sector category (whether Sec3, the consumer staples sector; Sec4, the energy sector; Sec6, the industrials sector; Sec8, the real estate sector) have no significant influence on the WCM performance.

Sensitivity Analysis and Model Validation

Internal and external validity can be used to analyze findings. Internal validity investigates whether the methods utilized to change the results are valid, whereas external validity explores whether could generalize the results away from the present data [ 63 , 38 , 64 ]. Sensitivity examinations are helpful for both types of evaluations. Thus, the internal validity is appraised by utilizing various variables’ combinations. Table ​ Table4, 4 , panel A, presents the results of sequentially removing different variables used from the basic model. The current study adopted the Mann–Whitney U test to examine the efficiency scores of the modified DEA-WCME models to the original efficiency scores via the basic DEA-WCME model to verify if the removal of variable occurred a significant difference in the relative efficiency scores. Besides, the correlations of Spearman rank were computed as well.

Sensitivity analysis and model validation

It is exposed in Table ​ Table4, 4 , panel A, that the accounts payable removal significantly decreased the model’s efficiency distinction by diminishing the average of firms’ efficiency scores of 0.61 to 0.51 and the rate of the efficient DMUs of 12.9 to 9.1%. Similarly, removing either input accounts receivable, cost of goods sold, or inventory significantly influenced the model results concerning the efficiency score distribution and the rate of the efficient DMUs. Moreover, the high correlations of Spearman ranks suggest that the firms’ rankings were not significantly altered through the efficiency models. It is not surprising that removing either input impacted the model results because they blend various resource kinds. Therefore, excluding each would occur significant information removal.

Finally, the current study used the consistency of the results over time to assess the external validity of the firms’ efficiency model. The firms’ efficiency model was re-applied utilizing 2018 data in this analysis and then matched the relative efficiency scores to the 2019 and 2020 results (Table ​ (Table4, 4 , panel B). The Mann–Whitney U test revealed no statistically significant variance in the efficiency score distribution for the study years 2018–2019 ( p  = 0.497), 2019–2020 ( p  = 0.944), and 2018–2020 ( p  = 0.684). The Kruskal–Wallis H test revealed no statistically significant variation in the efficiency score distribution over the study ( p  = 0.814). The correlation of Spearman rank between each year was also highly significant. As a result, the general distribution of efficiency scores and the rate of the efficient DMUs not appear to change significantly from period to period, and the firms ranked as efficient remain mostly harmonious from period to period.

Summary and Conclusion

Empirical evidence shows that WCM has garnered substantial interest in accounting and finance research. Tewolde [ 5 ] shows that inadequate WC decisions are responsible for a considerable portion of business failures, and that WCM affects a firm’s profitability. This is striking because an ineffective WCM strategy creates a large share of past firm insolvencies [ 6 ]. As WC significantly influences a firm’s operational and financial security, the literature confirms that it is necessary to develop a good strategy for a firm’s WCM [ 7 , 8 ]. Drawing on this, there are increasing concerns regarding the coronavirus crisis toward firms that adopt WCM strategies, which may harm their performance and value. Using a unique Gulf setting, this study analyzes the efficiency of WCM before and during the coronavirus crisis using an integration between the data envelopment analysis approach and the Malmquist productivity index, and then explores the influence of the crisis on WCME using Tobit regression. To the best of our knowledge, the current study is the first to develop and apply the data envelopment analysis methodology using the Malmquist productivity index to evaluate WCME. Besides, the authors advanced a novel contribution to the literature by examining whether the coronavirus crisis has affected the WCM for firms under investigation. This study is essential for regulators, management, and investors to increase their awareness of firms’ WCM performance before and during a crisis. In addition, it provides insight into how the coronavirus crisis affects firms’ WCM, which is likely to strengthen firms’ financial policy and improve their strategies. These findings are consistent with Zimon and Tarighi [ 8 ] study as they reveal that the COVID-19 crisis did not significantly alter firms’ WCM strategies. In contrast, the findings are inconsistent with Tarkom [ 58 ] study, as they demonstrate a significant negative influence of the COVID-19 crisis on WCM.

The results show that 157 firms (approximately 84%) adopt a conservative strategy as a safe strategy for their WCM, while 29 firms have adopted an aggressive strategy, suggesting that most firms strive to provide a high level of liquidity and maintain current assets at high levels compared to current liabilities. In addition, the results of the DEA-Malmquist analysis revealed that the annual means of WCME increased by approximately 0.2% before the coronavirus crisis due to technological efficiency or frontier-shift changes. The results did not change significantly during the coronavirus crisis, with only a 3.4% increase due to technological efficiency or frontier-shift changes. Furthermore, at the 5% significance level, the Wilcoxon test revealed no statistical difference in the efficiency scores of technical and scale efficiency, and total factor productivity before and during the coronavirus crisis. In contrast to previous findings, the results revealed a statistical difference in technological efficiency and pure efficiency scores at a 5% significance level. In addition, the current study’s findings showed that the coronavirus crisis and firm age have no significant influence on WCM performance. By contrast, the findings reveal that firm size and leverage substantially impact WCM performance. Furthermore, the results indicate that sector category (communication services, consumer discretionary, healthcare, and materials) significantly influences WCM performance. Finally, our results indicate that firms that are efficient in terms of WCM have higher sales returns and net income, as the sales and net income averages of firms with relative efficiency in terms of WCM are approximately 11 and 30 times higher, respectively, than inefficient firms in terms of WCM.

Given the study findings, decision-makers and WC managers of firms should develop the necessary means and schemes to ensure the best practices of WCME and address the inefficiency aspects in terms of technical efficiency and scale efficiency to ensure that a firm operates efficiently, which would likely positively reflect on the firm and the confidence of many stakeholders. These findings highlight the need to disclose WCM practices within traditional firm reports or integrated reporting, where conventional statements alone would be insufficient to appraise firm performance, especially given the current ecosystem’s rapid and consecutive development. The findings would also pique the interest of decision-makers and WC managers, who could use the DEA methodology to investigate and identify weaknesses in firm performance, and then take significant actions to optimize performance and achieve best practices.

This study has some limitations. This study focuses on 186 firms (558 firm-year observations) in the Gulf Cooperation Council (GCC), and the findings are limited to the period 2018–2020. Based on the findings of the sensitivity analysis and model validation, the findings can be generalized to other firms in GCC and Middle Eastern countries, and future research may include all non-financial sector firms for broader applicability. Managerial ability, intellectual capital, real earnings management, ESG criteria, and the likelihood of financial distress are also important elements of financial policy that are not considered in this study but can be investigated in future studies. Despite these limitations, our study contributes to the literature by providing empirical evidence that most firms adopt conservative WCM strategies. Additionally, the WCME results revealed a statistical difference in firms’ technological and pure efficiency scores before and during the coronavirus crisis. The study also shows that the coronavirus crisis had no significant influence on firms’ WCM performance. Finally, this study may have implications for many stakeholders, including decision-makers, WCM managers, financiers, investors, financial consultants, researchers, and others, in increasing their awareness of firms’ WCM performance before and during a crisis. In addition, the results could have implications for trading strategies as investors and financiers seek to invest in companies with good WCM. The implications of WCM performance on social interests would cause decision-makers to use the best strategies and procedures to enhance WCM activities to improve their investments and image in the community in which it operates.

Author Contribution

The first author conceived the project and planning; fundamental analysis; the framework and statistical models; collected data and analyzed it; wrote the abstract, introduction, literature review and hypotheses formulation, data and methodology, results and analyses, and conclusions and implications; reviewed and edited the manuscript; responding to coming reviewers’ comments. The second author conceived the project and planning; results and analyses; reviewed the manuscript.

Declarations

The authors declare no competing interests.

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Ahmed Mohamed Habib, Email: moc.oohay@bibahdemha_rd .

Nahia Mourad, Email: [email protected] .

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Home > Books > Accounting and Finance Innovations

Impact of Working Capital Management on Profitability: A Case Study of Trading Companies

Submitted: 27 April 2021 Reviewed: 13 August 2021 Published: 21 September 2021

DOI: 10.5772/intechopen.99912

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The success of any business depends on its profitability, liquidity, and solvency. Liquidity plays an important role in the successful running of a business. Many prior studies have been conducted to measure the relationship between working capital and profitability. The results showed that the high investment in inventories and receivables is associated with lower financial performance. They found a negative relationship between Return on Assets and Inventory turnover and Cash conversion cycle the present study is designed to know the direct impact of working capital on profitability by choosing the days of collection, days of payment, days inventory converts to sales and finally the cash conversion cycle. This study examines the association between the profitability and working capital using the data of 15 US trading companies for the period of 2015 to 2019. The key points in this study are firstly there exists a negative relationship between the profitability and the average collection period, the lower the average collection period higher will be the profitability, indicating that a decrease in the number of days a firm receives payment from sales affects the profitability of the firm positively. Secondly there is a highly significant positive relationship between average payment period and profitability. This implies that the longer a firm makes the payment to its creditors, the more profitable it is. Thirdly the cash conversion cycle decreases it will lead to an increase in profitability of the firm, and managers can create a positive value for the shareholders which indicates that it has been maintained. The regression analysis showed the value for the R-squared in the model is 0.584, i.e., 58.4% of the variation in the dependent variable Net Profitability is explained by the independent variables.

• net profitability
• trading companies
• working capital management
• average collection period
• average payment period
• inventory turnover days
• cash conversion cycle

Author Information

Rafathunnisa syeda *.

• Northeastern Illinois University, Chicago, IL, USA

*Address all correspondence to: [email protected]

1. Introduction

An attempt has been made in this empirical study to know the impact of working capital management on profitability, both the factors are important concerns of management. If working capital is not managed perfectly it will reduce the liquidity of the company and ultimately effects profitability.

The working capital should be maintained at a desired level depending upon the size of the firm, excessive working capital leads to the unnecessary accumulation of inventories causing losses and wastages. The large debtors indicate the defective credit policy which might lead to bad debts. On the other hand, with the inadequate working capital, the firm will not be in a position to pay short-term liabilities. The firm may not be able to pay its day-to-day expenses which creates inefficiencies and reduces profits.

The success of any business depends on its profitability, liquidity, and solvency. Liquidity plays an important role in the successful running of a business. The crucial functions of financial managers to ensure the liquidity of a firm, that it must be in a position to meet its short-term obligation without which it cannot survive. The working capital which consists of current assets and current liabilities which measure the liquidity has been chosen as the main independent variable to study its relationship with the profitability. The collection period, payment period, inventory days and cash conversion cycle has been used as a measure of working capital.

Many prior studies have been conducted to measure the relationship between working capital and profitability as examined by Azhar [ 1 ]. The impact of liquidity and management efficiency on the profitability of select power sectors using different ratios as independent variables, where debtor turnover ratio, collection efficiency, and interest coverage showed a significant impact. Rathiranee and Sangeetha [ 2 ] examine the impact of working capital on financial performance in select trading firms where the regression analysis results showed that the high investment in inventories and receivables is associated with lower financial performance i.e., Return on Assets (ROA). They found a negative relationship between Return on Assets and Inventory turnover and Cash conversion cycle for the trading firms listed in Colombo Stock Exchange. Mansoori and Muhammad [ 3 ] have studied the same picture with the evidence from Singapore found that Management performance would be improved by managing working capital efficiently. Their results demonstrate that firm’s profitability is increased by decreasing in receivable conversion period and inventory conversion period. Saradhadevi found in her study that there exists a highly significant negative relationship between the profitability and cash conversion cycle and a highly significant positive relationship between the time it takes the firm to pay its (Average payment period) which implies the longer a firm takes to pay its creditors the more profitable it is.

Keeping in view the above scenario the present study is designed to know the direct impact of working capital on profitability by choosing the days of collection, days of payment, days inventory converts to sales and finally the cash conversion cycle.

Many studies have been conducted for manufacturing companies, cement and textile companies, oil and gas companies only a few have been focused on trading companies. Hence the present study has its focus on working capital management and its impact on profitability in relation to trading sector.

2. Review of literature

Working capital management and profitability [ 4 ]: This study aims to find out the impact of working capital management on profitability. Return on assets, Current ratio, debt to equity ratio, operating profit to debt ratio, and inventory turnover ratios of the firms are the variables that are used in this study carried out for electrical equipment firms listed on Karachi stock exchange for a period of six years i.e. 2007–2012. Regression analysis was applied to the data. Normality and linearity test was also applied. Results showed significant positive results. T-test is applied to see for individual variable significance, it tells that each variable is significant. It is concluded that working capital management has positive significant impact on profitability of the firms.

The relationship between working capital management and profitability [ 5 ]: A sample of 67 companies is used for a period of ten years (2007–2016). Quantitative method using multiple linear regression and pooled data set is used for analysis. The study investigates the relationship between working capital management and profitability in non-financial companies listed in the Saudi Stock Exchange. The results indicate a positive relationship between working capital management and profitability. The results indicate a weak linear relationship between WCM and profitability, indicating that no single constant practice or strategy would suit every company, managers should identify the optimal level of working capital that suits their company’s situation. The results showed a statistically positive relationship between WCM, measured by CR, RCP, APP, INP, and profitability; however, there was a weak linear relationship.

Working capital management and firms’ profitability: Dynamic panel data analysis of manufactured firms [ 6 ]: This paper examines the impact of working capital management on firm’s profitability performance of manufacturing firms by using not only static models such as ordinary least square (OLS), fixed and random effects but also dynamic models difference generalized method of moments (GMM) and system generalized method of moments (SGMM) over the period from 2007 to 2018. The results show that inventory conversion period (ICP) and payable deferral period (PDP) have a positive relationship with return on asset while the cash conversion cycle (CCC) has a negative effect on return on assets.

Working capital management and profitability: Empirical evidence [ 7 ]: Empirical findings suggest that granting longer extensions to customers does not affect profitability. The results of the other variables showed a negative relationship with the profitability of the companies, suggesting that the investment in inventories and the obtaining of extensions from suppliers determine additional costs that negatively impact profitability. This paper examines the working capital management policies in 105 manufacturing companies in the Czech Republic for five years, from 2014 to 2018.

The relationship between working capital management and profitability: A case study of cement industry in Pakistan [ 8 ]: Ikram ul Haqq, Muhammad Sohail, Khalid Zaman and Zaheer Alam examines the effect of working capital on profitability for the period of six years from 2004 to 2009 by using the data of fourteen companies in the cement industry. The ratios relating to capital management have been selected and computed for the study. The main objective of the study was to find whether financial ratios affect the performance of the firm in the special context of cement industry in Pakistan. They found that the ROI is negatively correlated with the current assets to sales ratios and cash turnover ratio while ROI is positively correlated with the current ratio, liquid ratio current assets to total assets ratio, debtors turnover ratio, inventory turnover ratio, and credit turnover ratio.

Relationship between inventory management and profitability: An empirical analysis of Indian cement companies [ 9 ]: Dr. Ashok Kumar Panigrahi has discussed the importance of inventory management practices of Indian Cement Companies and their impact on working capital efficiency over a period of ten years from 2001 to 2010. The study uses Regression analysis. The findings indicate that there exists a significant negative linear relationship between inventory conversion period and profitability. It was also found that when profitability increases with the decrease in the financial debt ratio. Further, it showed a positive relationship between profitability and firm size, as the profitability increases with an increase in firm size. Lastly, the relationship between the current ratio and profitability was negative.

Effects of working capital management on profitability: The case for selected companies in Istanbul stock exchange (2005–2008) [ 10 ]: The study was carried out by Hasan Ajan Karaduman, Halil Emre Akbas, Arzu Ozsozgun, and Salih Durer with the aim to provide some empirical evidence on the effects of working capital management on profitability for a sample of 140 selected companies listed in the Istanbul Stock Exchange (ISE) for the period of 2005–2008. The return on assets of the sample companies increases with a decrease in the number of days accounts receivable, accounts payable, and a number of days of inventory. Also, the reduction in the cash conversion cycle results in higher returns on assets. Furthermore, the results of control variables like the size have a positive effect on profitability while the debt ratio negatively affects the profitability.

Working capital management in indian oil and gas industry—A case study of Reliance Industries Ltd. [ 11 ]: Sankar Thappa has used liquidity ratios to assess the significance of working capital for a period of ten years 2004–2013.The analysis of liquidity ratios, liquidity position, item-wise analysis of components of gross working capital and liquidity ranking have been done. The results showed that the coefficient of correlation between the profitability ratio compared to the current ratio, working capital turnover ratio, and inventory turnover ratio indicates the low degree of positive correlation whereas the coefficient of correlation between profitability ratio compared to the quick ratio (liquid ratio) and absolute liquid ratio indicates that there is a low degree of negative correlation. The overall working capital position is not very much satisfactory.

Relationship between working capital management and firm profitability manufacturing sector of Pakistan [ 12 ]: Muhammad Safdar Sial and Aqsa Chaudry measure the relationship between working capital management and firm profitability in the manufacturing sector with a sample of 100 firms covering a period of ten years from 1999 to 2008. The coefficient of size was positive which means that the bigger the size have more profitability as compared to firms of smaller size. The debt ratio has been used for leverage which showed a significant negative relationship with Return on Asset which means increase in leverage adversely affect on return on assets. The results show that there is a strong negative relationship between variables of working capital management and profitability of the firm which means as the cash conversion cycle increases it will lead to a decrease in profitability of the firm.

Effect of working capital management on profitability by Asif Iqbala and Zhuquan Wang [ 13 ]: They found a diverging effect of working capital management on the profitability of manufacturing firms of Pakistan. They suggest that “paying full attention to the cash conversion cycle” has enormous effect on working capital. Minimizing the inventory level frees the capital for other use.

Relationship between working capital management and profitability by Puteri Shahirah Binti Ghazal [ 14 ]: This paper is an evidence from the UAE market focusing on real estates and construction companies from the Abu Dhabi market. The finding of this study presented that there is a negative relationship between cash conversion cycle and profitability; longer the CCC, the profitability decreases. Another finding showed that the amount of payable days is negatively related to profitability.

The effect of working capital management on profitability [ 15 ]: A sample of three (3) manufacturing companies listed on the Dar es Salaam Stock Exchange (DSE) is used for a period of ten years (2002–2012). They found negative relationship between liquidity and profitability showing that as liquidity decreases, the profitability increases, average collection period and profitability indicating that a decrease in the number of days a firm receives payment from sales affects the profitability of the firm positively.

To analyze relationship between working capital management and profitability [ 16 ]: This paper basically analysis the relationship between working capital and profitability of the Indian IT Company (TCS). This Study shows negative relationship of inventory turnover ratio with ROA excluding and including Revaluation which shows that with the inventory turnover the firm should increase its return on assets. And also study shows negative relationship of debtor turnover ratio with Return on Capital Employed.

3. Research question

The main objective of any business is to earn profit and manage the funds efficiently and effectively which has direct impact on profits. So, working capital is the major constituent to measure liquidity. This study examines the association between the profitability and working capital using the data of 15 US trading companies for the period of 2015 to 2019.

4. Hypotheses development

Working capital is an important issue during financial decision making. The crucial part in managing working capital is required to maintain its liquidity in day-to-day operation for the smooth running of business and meeting its obligations in time. Thus, working capital is selected as one of the independent variables to know that how it effects profitability.

H1: There is a significant relationship between Working Capital Management and profitability.

H2: Working capital management has strong impact on profitability.

Keeping in view the above studies the following objectives have been outlined for the present study.

5. Objectives of the study

To study the relationship between profitability and working capital management.

To examine the impact of average collection period, average payment period, inventory turnover days and cash conversion cycle on profitability.

The choice of the variables for the present study is influenced by the previous studies on working capital management. They include dependent, independent and some control variables. The profitability in terms of Return on assets, Gross profit ratio, Operating profit and Net income are taken as dependent variable in previous studies.

The dependent variable is the one which is affected during the experiment, for the present study profitability is taken as dependent variable i.e., in terms of Net Income. The independent variable is the one which effects the dependent variable. Average collection period, cash conversion cycle, average payment period, inventory turnover ratio, current ratio, liquid ratio, etc. were taken as independent variables in previous studies. For this study the independent variables are the average collection period, average payment period, inventory turnover days and cash conversion cycle. The study aims at to find out the association between the variables through different statistical analysis ( Table 1 ).

Showing the key research variables.

The following equation is used to estimate the impact of working capital on profitability.

NI (it) = profitability of the firms at time 5 years, i = 15 firms.

β0 = the intercept of an equation.

β = coefficients of independent variables.

T = time 5 years 2015–2019.

Average collection period ACP.

Average payment period APP.

Inventory turnover days ITD.

Cash conversion cycle CCC.

In the above general equation, the Profitability is the dependent variable, and it is influenced by the independent variables i.e., ACP, APP, ITD and CCC.

7. Sample and data collection

The main source of data is the S&P Capital IQ website. Many studies have been conducted to examine the relationship between the financial performance and working capital management. These studies have been done relating to the cement companies, trading companies, manufacturing companies, pharmaceutical companies and only a few have been carried out about trading companies. So for the present study sample is taken from trading companies.

The present study aims at to provide some empirical evidence of impact of working capital management on profitability for a sample of 15 trading companies for the period of five years from 2015 to 2019. These companies are randomly selected from all listed companies in the New York Stock Exchange (NYSE). The sample companies includes: 1) Applied Industrial Technologies Inc. (AIT), 2) DXP Enterprises Inc., 3) Eco Shift Power Corp. (ECOP), 4) EVI Industries Corp., 5) General Finance Corp. (GFN), 6) Gypsum Management and Supply Corp. (GMS), 7) W.W. Grainger (GWW), 8) H&E Equipment Inc. (HEES), 9) HD Supply Inc. (HDS) 10) Houston Wire and Cable Company (HWCC), 11) Huttig Building Products Inc. (HBP) 12) Kaman Corporation (KAMN), 13) MRC Global Inc., 14) MSC Industrial Direct Co. Inc. (MSM), 15) ProShares Ultra Health Care (RXL).

The Net Profitability is taken as the dependent variable and the average collection period (ACP), average payment period (APP), inventory days converted to sales (ITD) ad cash conversion cycle (CCC) are considered as independent variables.

The analysis of data is done using descriptive statistics, correlation matrix and regression analysis.

The Net profitability for these companies ranges from −7.308 to 31.895 with a mean of 3.637 and standard deviation 5.85 which shows high variance.

ACP ranges between 18.57 and 133.28 days with an average of 51 days and standard deviation of 16.88 signifying very high variability across the companies.

The APP ranges between 9.6 and 79.69 with an average of 36.76 and standard deviation of 14.62. The minimum time taken to make the payment is 9 days which is unusual.

The ITD ranges between 30.62 and 139.53 with an average of 71.24 and standard deviation of 26.05. The maximum time taken to convert inventory into sales is 139 days which is a very large time period to convert inventory into sales.

The CCC ranges between 18.02 and 193.18 with an average of 85.85 and standard deviation of 36.63. The maximum time taken for cash conversion cycle is 193 days which is a large time taken to convert cash.

Descriptive statistics of 15 companies for the years from 2014 to 2019.

9. Correlation analysis

Correlation analysis is used to measure the degree of association between different variables under consideration. Correlation matrix of all variables included in the analysis is presented in Table 3 which is calculated based on data of 15 trading companies for a period of five years 2015 and 2019.

Correlation matrix of 15 companies for the year 2015 and 2019.

An attempt has been made to find the relationship between profitability and WC, for this purpose Pearson’s coefficient of correlation analysis is done. As indicated in the above studies there exist a negative correlation between the profitability and the collection period, the lower the average collection period higher will be the profitability. The correlation between average payment period and profitability is 0.127 which shows a positive relationship if there is an increase in payment period it leads to an increase in profitability. There exist a negative correlation between profitability and the cash conversion cycle is −0.271 which indicates an increase in collection period leads to increase in CCC and vice versa and ultimately effects profitability The correlation between inventory conversion days and profitability is positive which is beyond expectation. There exists a negative correlation between cash conversion cycle and average payment period.

It is recommended that the companies should avoid an increasing cash conversion cycle because it means that the business is becoming more operating inefficient, locking more and more cash into its processes. They should maintain a lowest cash conversion cycle compared to their peers, or at least a decreasing one. A decreasing CCC represents a more efficient company that converts its inventories faster as well as gets paid faster and probably is paying its suppliers later thus, holding cash for more time ( Table 4 ).

Regression results of 15 companies for the year 2015 to 2019.

10. Regression analysis

The regression analysis showed the value for the R-squared in the model is 0.584, i.e., 58.4% of the variation in the dependent variable (NI) is explained by the independent variables working capital of the model, which is represented by CCC, APP, ACP, and ITD and 42% is affected by other factors.

11. Summary and conclusions

The study is carried out for a sample of 15 trading companies for the period of five years from 2015 to 2019. These companies are randomly selected from all listed companies in the New York Stock Exchange (NYSE).

This study examined the relationship between Net Profitability and several variables of working capital management such as average collection period, average inventory turnover in days, average payment period and cash conversion cycle. The results showed that there exist a negative relationship between the profitability and the average collection period, the lower the average collection period higher will be the profitability. The correlation between average payment period and profitability is 0.127 which shows a positive relationship if there is an increase in payment period it leads to an increase in profitability. It is found that the cash conversion cycle decreases it will lead to an increase in profitability of the firm, and managers can create a positive value for the shareholders which indicates that it has been maintained.

The results of this study show that there is a strong relationship between the working capital and profitability of the firms. It means if the financial managers keep an eye on the liquidity it will lead to profitability. So, it is recommended that Companies should always maintain a sound collection policy and it is further suggested that managers can create value for their shareholders by reducing the number of days accounts receivable, increasing the number of days accounts payable and inventories to a reasonable minimum.

The hypotheses is accepted for working capital management that it has strong impact on profitability. There is a significance relationship between Working Capital Management and profitability. The study has examined the impact in terms of average collection period, average payment period, inventory turnover days and cash conversion cycle on profitability.

Furthermore, On the basis of the above analysis the results can be further strengthened if the firms manage their working capital in more efficient ways. Management of Working capital means the management of current assets and current liabilities. If these firms efficiently manage their cash, accounts receivables, accounts payables, and inventories, this will ultimately increase profitability of these companies.

12. Limitations of the study

The study is carried out for a period of five years only i.e., 2015 to 2019.

The study is based on secondary data collected from the website of S&P Capital IQ.

The study is carried out about 15 Trading companies.

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The impact of working capital management on credit rating

• Ala’a Adden Abuhommous   ORCID: orcid.org/0000-0003-3914-4266 1 ,
• Ahmad Salim Alsaraireh 1 &
• Huthaifa Alqaralleh 2  

Financial Innovation volume  8 , Article number:  72 ( 2022 ) Cite this article

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Metrics details

This study investigates the possible nonlinear relationship between working capital and credit rating. Furthermore, it examines the relationship between the three components of working capital (inventory, accounts receivable, and accounts payable) and a firm’s credit rating. Employing data for U.S listed firms for the period between 1985 and 2017, the results of our ordered probit model show a nonlinear relationship between working capital and its components and credit rating. Finally, we find that the deviation from the optimal working capital adversely affects the credit rating. The results of this study are of significant importance for policy makers, managers, decision makers, and credit-rating agencies, as they help highlight the importance of working capital management for a firm’s credit rating.

Introduction

In light of the 2008 financial crisis, credit-rating scores (CRSs) released by credit-rating agencies have grown in importance (Hung et al. 2017 ). Credit-rating agencies play a vital role in capital markets by reducing the moral hazard problem. In addition, credit ratings help investors assess the creditworthiness of issuers and the financial securities issued by them (Lee et al. 2021 ). Furthermore, credit ratings are used as a benchmark based on which investors manage their portfolios. Finally, they play an important monitoring role, as they may require firms with rating deteriorations to take corrective actions to minimize these deteriorations (Huan and Mohamed 2021 ). Additionally, CRSs have become a crucial tool that helps investors in their investment decision-making process, as they help investors identify risky assets, price their credit, and allocate their capital more efficiently (Amato and Furfine 2004 ). Investors are highly concerned about borrowers’ ability to fulfil their obligations (Haspolat 2015 ). Moreover, Bauer and Esqueda ( 2017 ) point out that banks rely on credit rating scales to compensate for information deficits when making loan decisions. Thus, a considerable amount of literature has been published on the impact of factors such as firm-specific characteristics [Return on Assets (ROA), size, and liquidity], corporate social responsibility (CSR), and operational leanness on the corporate credit rating (Attig et al. 2013 ; Hung et al. 2013 ; Bendig et al. 2017 ; Dong et al. 2021 ). However, to our knowledge, no previous study has investigated the impact of working capital management (WCM) on a firm’s credit rating. A key aspect of a WCM decision is its impact on a firm’s risk, return, and valuation (Smith 1980 ). Many researchers have attempted to investigate the impact of WCM on firms’ financial performance (e.g., Aktas et al. 2015 ; Jose et al. 1996 ; Shin and Soenen 1998 ; Deloof 2003 ; García-Teruel and Martínez-Solano 2007 ). These studies have mainly focused on the impact of WCM on a firm’s profitability performance. The interesting point here is the consensus that maintaining a high level of net working capital (NWC) reduces a firm’s risk and profitability.

The results of these previous studies led us to take the research on this topic a step further and investigate the impact of WCM on a firm’s credit rating. Understanding this relationship is of high importance for corporate managers in their quest for external financing, especially after the 2008 financial crisis (Hung et al. 2013 ).

One would expect that maintaining a high level of working capital would enhance a company’s credit rating since it reduces risk. However, is this always the case? Besides reducing risk, previous studies have shown that holding a high level of working capital reduces profits. This particular observation raises a major concern about the effect of low profits on a company’s ability to cover interest payments, especially since a company’s reliance on external financing would increase as the level of working capital increases (Kieschnick et al. 2013 ). This concern may indicate that maintaining a high level of working capital may enhance a company’s credit rating for a certain period but become harmful thereafter. This argument suggests a concave relationship between WCM and credit rating. Furthermore, this concave relationship postulates that firms could have an optimal working capital ratio to reduce risks and improve credit ratings. Therefore, we expect a positive or negative deviation from the optimal working capital to have an adverse effect on the evaluation of a firm’s risk and ultimately impact the firm’s credit rating.

To this end, this study distinguishes itself from previous studies in the following aspects: first, it examines the possible concave relationship between WCM and credit rating; second, it conducts a deeper analysis by investigating the impact of three important components of working capital, namely inventory (INV), accounts receivable (AR), and accounts payable (AP), on a firm’s credit rating; and finally, it investigates the impact of deviation from the optimal working capital on a firm’s credit rating. This study attempts to answer the following questions: (1) Does WCM and its components affect the credit rating? (2) If so, what is the nature of this relationship? Finally, (3) does deviation from optimal working capital affect a firm’s credit rating?

Center for research in security prices

Utilizing annual panel data of U.S. listed firms from Wharton Research Data Services (WRDS) merged with Center for Research in Security Prices (CRSP)/Compustat files for the period between 1985 and 2017, we find evidence to draw the following conclusions. First, there exists a positive relationship between WCM and credit rating. Second, we find that high investments in working capital have an inverse impact on CRSs; thus, our results support the non-linear relationship between WCM and credit rating. Third, there exists a nonlinear relationship between the components of working capital (inventory and accounts receivable) and CRSs, while accounts payable have a negative relationship with credit scoring. Finally, the results show that firms have an optimal level of working capital and deviation from this target harms their credit rating.

The rest of this paper is organized as follows: “ Literature review and hypotheses development ” section discusses previous studies on WCM and credit ratings; “ Methodology ” section presents the study methodology and research design; “ Data and descriptive statistics ” section presents the data and descriptive statistics sample and data resources; “ Empirical results ” section presents the empirical results; and “ Concluding remarks ” section concludes the paper.

Literature review and hypotheses development

In this section, we discuss the relevant literature on WCM and credit ratings, in addition to the development of the research hypotheses.

Working-capital management

According to Lewellen et al. ( 1980 ), decisions regarding working capital have no impact on a firm’s value in a perfect capital market. However, because of the nonexistence of a perfect capital market and the presence of an optimal level of each component of working capital, such as accounts receivable (Nadiri 1969 ; Emery 1984 ), inventories (Ouyang et al. 2005 ), and accounts payable (Nadiri 1969 ; Abuhommous 2017 ), one would expect firms to have a target or optimal level of working capital. Aktas et al. ( 2015 ) provide evidence for the presence of an optimal level of working capital.

The WCM concept pertains to how firms manage their current assets and liabilities, and this policy comprises two elements: (1) the level of investment in current assets and (2) the means of financing current assets. When selecting the most suitable policy, firms try to obtain an optimal level of working capital, depending on the trade-off between risk and return.

Focusing on the second element, firms may adopt one of three working capital strategies, namely that of a conservative, hedging, or aggressive strategy. In the conservative approach, firms try to maintain high levels of working capital (high investment in working capital), as they rely more on long-term financing compared with short-term financing. This strategy decreases both the risk and return of the firm due to the higher need for expensive external financing, which harms the firm’s profitability. In this regard, Baños-Caballero et al. ( 2014 ) state that a low level of working capital enhances a firm’s performance because of the lower need for expensive external financing. Furthermore, an increase in working capital may result in an increase in the opportunity cost of cash locked-up in accounts receivable and inventories (Tauringana and Afrifa 2013 ). However, a firm may adopt an aggressive working capital strategy by using more short-term sources of funds to finance its investments, which indicates low investment in working capital. By adopting such a strategy, both risk and returns increase. Finally, in the hedging strategy (matching strategy), the temporary amount of short-term assets is met with short-term financing, and the permanent amount of short-term assets is financed by long-term financing resources; thus, the investment in working capital may increase or decrease according to the firm’s activity.

Several attempts to investigate the impact of WCM on a firm’s profitability (e.g., Jose et al. 1996 ; Shin and Soenen 1998 ; Deloof 2003 ; García-Teruel and Martínez-Solano 2007 ) suggest that there exists a linear relationship between a firm’s investment in working capital and its profitability. The findings of such studies indicate that the lower the investment in working capital, the higher the profitability. Mohamad and Saad ( 2010 ) find a negative impact of working capital elements, such as cash conversion cycles (CCCs), current ratios, current-asset-to-total-asset ratios, current-liabilities-to-total-asset ratios, and debt-to-asset ratios, on firm performance, suggesting the importance of WCM in enhancing performance at both the accounting and market levels.

Kieschnick et al. ( 2013 ) investigate the relationship between net operating WCM and firm value. They find that for average firms, every additional dollar held in cash is worth more for shareholders than investing that dollar in net operating working capital. They add that, for the average firm, investing more in trade credit would add more value for shareholders than investing in inventory, which indicates the high importance of trade credit as part of WCM for shareholder wealth. In a more recent study, Aktas et al. ( 2015 ) document a nonlinear relationship between excess NWC and stock performance, finding that this relationship is negative for firms with positive excess NWC and positive for firms with negative excess NWC. These findings further support the idea of an optimal level of NWC, and firms that reach that level increase their stock value.

Another stream of research has focused on the impact of WCM on firm risk. For instance, maintaining a high working capital might lead a firm to rely more on long-term financing, which would result in a higher interest cost. Moreover, high working capital increases a firm’s opportunity cost (Kieschnick et al. 2013 ). On the other hand, adopting excessively aggressive WCM may increase the risk of stockouts, input price fluctuations, and supply costs (e.g., Blinder and Maccini 1991 ; Fazzari and Petersen 1993 ; Corsten and Gruen 2004 ). Therefore, the negative relationship between NWC and firm performance may be due to an increase in firm risk following a decrease in NWC.

• Credit rating

A firm’s credit rating is a statistic that summarizes a firm’s creditworthiness by considering several elements of the firm’s financial characteristics, such as debt ratio, priority and maturity structure of the firm’s debt, and the volatility of the firm’s cash flows (Bali and Hovakimian 2009 ).

The corporate credit rating has grown in importance especially after the 2008 financial crisis (Hung et al. 2013 ). Therefore, Amato and Furfine ( 2004 ) assert the important role of credit rating analysis in financing and investment decisions, such as in pricing credit, determining risky assets, and asset allocation. Furthermore, investors are highly concerned about borrowers’ ability to fulfil their obligations (Haspolat 2015 ). In this regard, Bauer and Esqueda ( 2017 ) point out the importance of credit rating scales in helping banks overcome information deficits when making loan decisions.

A considerable amount of the literature has been published on firms’ credit ratings. These studies can be classified into two streams. The first stream includes studies on the factors that influence the credit rating. The second concerns the impact of the credit rating on a firm’s decision-making. Attig et al. ( 2013 ) study the impact of CSR on a firm’s credit rating and find that firms with good social performance are rated relatively high. They also conclude that CSR investments can reduce financing costs owing to high credit ratings. Hung et al. ( 2013 ) offer evidence on the effect of firm-specific characteristics on the credit rating and find that ROA, size, Earnings Before Interest, Taxes, Depreciation, and Amortization (EBITDA), and interest coverage are positively related to credit rating. However, the debt ratio and ratio of cash to current liabilities are negatively related to credit rating. In a large longitudinal study, Bendig et al. ( 2017 ) investigate the impact of operational leanness [relative inventory leanness and relative property, plant, and equipment (PPE) leanness] on a firm’s credit rating and find a concave positive relationship between inventory leanness and credit rating and a negative and concave relationship between PPE leanness and credit rating.

Turning to the impact of a firm’s credit rating on decision-making, Kisgen ( 2006 ) examine the relationship between credit ratings and capital structure decisions. He finds a negative relationship between a change in credit rating status and reliance on debt. As firms near a change in credit rating status (upgrade or downgrade), their reliance on debt decreases. Another study finds that firms pursue real earnings management activities when they have an upcoming credit rating change; however, just prior to the change, they reduce their discretionary accruals. Moreover, the study concludes that real activities management and credit rating upgrades are positively related, whereas there is no significant relationship between real activities management and credit rating downgrades (Kim et al. 2013 ).

Credit rating and WCM

Since 1941, the Standard & Poor’s (S&P) rating agency has assigned its CRSs based on two broad categories of risk: financial risk and business risk. In the business risk category, S&P is concerned with the firm’s ability to generate sufficient cash flow to cover operating revenues. However, in the financial risk category, the focus is on the firm’s ability to manage its financial leverage and debt.

As mentioned earlier, maintaining high levels of working capital reduces a firm’s risk and returns. To this point, one would expect that firms with high levels of working capital would have higher CRSs because of the low risk of such firms. However, considering the above two risk categories may change the rules. For instance, Baños-Caballero et al. ( 2012 ) suggest that excessive investment in working capital may negatively impact a firm’s operating performance, consequently reducing the firm’s cash inflows (Deloof 2003 ; García Teruel and Martínez-Solano 2007 ; Shin and Soenen 1998 ). Such studies conclude that reducing the CCC and inventory amount would lead to higher operating performance. Thus, over time, firms that suffer a decrease in their operating performance due to their high levels of working capital would be less able to repay interest payments. Kieschnick et al. ( 2013 ) suggest that these payments would be high for firms with high levels of working capital due to their high reliance on external financing, which would lead to higher bankruptcy costs according to Kieschnick et al. ( 2013 ). This may result in lower CRSs for such firms. Shin and Soenen ( 1998 ) report a good example on this point: They mention that despite the similarity of two firms in capital structure, namely Kmart and Walmart, Kmart faced higher financial troubles than Walmart due to its high NWC relative to sales, which led Kmart to close 110 stores in 1994, and in 2002 the firm filed for Chapter 11 bankruptcy protection. This example supports the argument that the relationship between working capital level and credit rating is concave. To this end, one may expect that neither over-investment in working capital nor an aggressive working capital policy would be favorable for a firm’s credit rating. This is because increasing investment in working capital is preferable for credit rating (due to its role in reducing risk) to some point, after which the rating starts to drop (due to its negative impact on cash flows). Consequently, we hypothesize a concave relationship between WCM and credit rating.

Furthermore, the above discussion leads to the conception that firms should efficiently manage their working capital by maintaining it at an optimal level. According to Gill et al. ( 2019 ), efficient WCM positively impacts the bond quality rating. Therefore, we conjecture that holding working capital on target enhances a firm’s credit rating.

Methodology

In “ Estimation framework ” section, we develop a regression model to examine whether WCM affects credit rating decisions. In “ The concave relationship between NWC and credit rating ” and “ Components of working capital and credit rating ” sections, we expand our analysis to examine the nonlinear relationship between the working capital level and its components (i.e., accounts receivable, inventory, and accounts payable) and credit rating. In the final section of the analysis, we attempt to determine the optimal working capital level and examine whether deviation from the optimal working capital adversely affects the credit rating score.

Estimation framework

In this section, we augment the previous models of Ashbaugh-Skaife et al. ( 2006 ), Alissa et al. ( 2013 ), Attig et al. ( 2013 ), Oikonomou et al. ( 2014 ), and Bendig et al. ( 2017 ) by including our main independent variable (WCM) in the credit rating model. We use an ordered probit regression, where we add the working capital proxy to the model, as follows:

where RATING is the S&P credit rating for firm i at time t . We consider the following to be determinants of credit rating: NWC ratio, log of assets, firm’s leverage, interest coverage ratio, whether the firm has losses (indicated by a dummy variable), whether the firm has fixed assets in its asset structure (measured by capital intensity), whether the firm has subordinate loans, and whether the firm’s external auditor is considered as one of the “big four” auditing firms (indicated by a dummy variable). We present the definitions of these variables in Table 2 . We also include IndustryEffects to control for differences across industries and YearEffects to control for the time-specific effect, which captures economic factors that affect all firms in the same year but vary over time, and ε i,t is the error term.

Key variables measurement

In this section, we present the key variables included in the estimated model, including their definitions and measurement.

Dependent variable (credit rating)

Credit ratings are based on opinions about credit risk and are considered a quantified forward-looking assessment of the debt issuer’s creditworthiness, which measures firm’s ability to meet its financial commitment in terms of time and fullness. They can also be used to measure the likelihood of default and can thus be assigned to individual debt issues or, as an overall credit rating, to corporations, governments, and municipalities, or to a sovereign government (Standard and Poor’s 2022 ). Following the literature (e.g., Kisgen 2009 , 2006 ; Hovakimian and Li 2009 ; Alissa et al. 2013 ) we use Standard & Poor’s Long-Term Domestic Issuer Credit Rating (RATING), a long-term scale for firms according to their overall creditworthiness, in which the rating range is assigned from “AAA” for an extremely strong obligor (highest rating) to “D” for an obligor in default (lowest rating). Consistent with Attig et al. ( 2013 ) and Bendig et al. ( 2017 ), we transform the credit rating to an ordinal scale for the purpose of our regression. Thus, we assign eight values starting from one for a “CC” rating to eight for an “AAA” rating (see Table 1 ). As a robustness check, we follow Alissa et al. ( 2013 ) and Bendig et al. ( 2017 ), who measure credit rating based on 20 micro rating classes. We calculate MICRORATING and transform credit rating to an ordinal scale with 20 categories, ranging from the value of 1 for the “CC” class to 20 for the “AAA” class. Footnote 1 Table 1 reports the number of firm-year observations for each credit-rating category. The number of firms per year ranges from 98 to 5265 (Table 2 ).

Main independent variable (WCM)

Our main independent variable that measures a firm’s WCM investment is based on the literature (e.g., Shin and Soenen 1998 ; Aktas et al. 2015 ; Kieschnick et al. 2013 ). Following these works, we use the net operating working capital to sales ratio (NWC); the dependent variable is measured as (INV) plus (AR) minus (AP), all divided by total sales. Furthermore, as a robustness check, we follow Baños-Caballero et al. ( 2012 ) and measure working capital policy as (inventory/cost of sales) × 365 + (accounts receivable/sales) × 365 − (accounts payable/cost of sales) × 365; we call this the CCC.

Control variables and firms’ characteristics

A firm’s characteristics are included in the credit rating regression model, based on the literature (e.g., Ashbaugh-Skaife et al. 2006 ; Kisgen 2006 ; Bendig et al. 2017 ; Alissa et al. 2013 ; Attig et al. 2013 ). With respect to firm size (SIZE), we expect large firms to have more leverage because of the high volume of information available about the firm; usually, large firms tend to have less asymmetric information in the market. We use the natural logarithm of total assets to measure size. Therefore, larger firms are expected to have higher credit ratings than smaller firms. Firm’s leverage (LEV) has an inverse relationship with credit rating, since firms with high leverage are more likely to suffer from financial crises and bankruptcy probability increases. We use the ratio of long-term debt-to-total-assets as an indicator of a firm’s leverage. Interest coverage ratio (COVERAGE) is used as a proxy for a firm’s default risk, which demonstrates a firm’s ability to pay its debt interest; the more able the firm is to pay its debt interest, the more likely the firm will receive a higher credit rating. This ratio is calculated by dividing the operating income before depreciation by interest expenses. A firm’s losses (LOSS) are an indicator of the firm’s likelihood of default; unprofitable firms tend to have a high probability of bankruptcy and therefore have a lower credit rating. Firm’s capital intensity (CAP_INTEN) is included as a control variable because firms with high capital intensity present a lower risk for debt providers; thus, firms with high capital intensity are expected to have a higher credit rating. This variable is measured using the ratio of property, plant, and equipment to total assets. Subordinate debt (SUBORD) is included as a control variable to capture the differences in firms’ debt structure; firms with a debt structure that includes subordinated debt are considered riskier and are expected to have a lower credit rating. Bendig et al. ( 2017 ), Ashbaugh-Skaife et al. ( 2006 ), and Alali et al. ( 2012 ) find an inverse relationship between subordinate debt and credit rating. We measure subordinate debt using a dummy variable that takes the value of one if the firm has subordinate debt and zero otherwise. We also include external auditors to control for their role in monitoring a firm’s actions (Alissa et al. 2013 ); BIG4 is the variable used as a proxy for corporate governance, which reduces opportunistic managerial behavior (Bhandari and Golden 2021 ).

Data and descriptive statistics

This section presents the details of the selection criteria and sample descriptive statistics. Our sample is drawn from the population of U.S. listed firms, and to serve our study aim, we select firms that have a credit rating. Thus, we exclude any firms with missing values for credit rating or working capital. Consistent with previous studies (e.g., Hovakimian and Li 2009 ; Attig et al. 2013 ; Bendig et al. 2017 ), this study utilizes annual panel data of listed firms from WRDS merged with CRSP/Compustat files for the period between 1985 and 2017. Footnote 2 Following the literature, we exclude firms with a standard industry classification (SIC) code between 6000 and 6799. Thus, we exclude all firms operating in the financial sectors. All firms should have positive total assets and net sales, because these variables are used to deflate other variables, and the results may not be consistent when they have negative or zero values. We also only consider observations without missing values. These criteria yield 43,183 firm-year observations.

Summary statistics

Table 3 presents the descriptive statistics for the explanatory and control variables. The NWC cycle has a mean of 56.94 days, while the median is 47.70 days. The financial leverage to total assets ratio is on average 33.3% (the median is 29.7%). For unreported data, the number of firm-year observations in which the external auditor ranks among the “big four” (BIG4) accounting firms is 31,603. The total number of firm-year observations for reported loss is 9,955.

Table 4 shows the correlation matrix between the variables of interest; the table shows that the correlation is not very high, the maximum value between LOSS and COVERAGE is 33.3%. Therefore, we can conclude that the multicollinearity problem is not a serious concern.

Empirical results

This section focuses on our regression on the effect of the operating working capital policy on a company’s credit rating. We examine the main hypothesis of this study, namely, that a conservative working capital policy is associated with a high credit rating. A number of different estimates are calculated using our proposed model. This enables more robust results by controlling for firm and industry effects.

Table 5 presents the credit rating regression; the dependent variable is the firm credit rating class of Standard & Poor’s domestic long-term issuer rating (RATING). The variable of interest is NWC. Columns 1 and 2 show the regression results for an ordered probit for NWC and the control variables. In column 2, we add year and industry effects, while in columns 3 and 4, we use MICRORATING as a proxy for credit rating. The relationship between NWC and credit rating is positive and statistically significant in all models in the columns; a P value of < 0.01 is found in both ordered probit regressions. These results do not change when we include the year and industry effects, as shown in column 2. Thus, the results support the hypothesis that investment in working capital (conservative working capital policy) enhances the probability of a firm having a good credit rating. In columns 5, 6, 7, and 8, we use the CCC as a proxy for the dependent variable; the results are also qualitatively similar. Our results support the findings of Blinder and Maccini ( 1991 ), Corsten and Gruen ( 2004 ), and Baños-Caballero et al. ( 2012 ), who find that a firm’s risk increases with an aggressive working capital policy because of the loss of sales due to possible stock-outs, which reduces market share and creates interruptions in the production process, or a loss of customers due to an aggressive accounts receivable strategy. The signs of the control variables are consistent with those in prior research. We find a positive and statistically significant relationship between credit rating and size (SIZE), due to the lower asymmetric information of larger firms; interest coverage ratio (COVERAGE), which indicates that firms with a higher ability to pay their debt interest are less likely to default; capital intensity (CAP_INTEN), which indicates that firms with a higher capital intensity present lower risk since these firms can use their fixed assets as collateral; and the BIG4 coefficient, showing that firms that reduce managerial opportunistic behavior have a better credit rating. However, credit rating has a negative relationship with leverage (LEV), losses (LOSS), and subordinate debt (SUBORD), implying that firms with a high leverage ratio, unprofitable firms, and firms with subordinated debt have a higher probability of receiving a lower credit score.

As a robustness check, we control for the firm-specific effect by using a random-effect ordered probit regression; the results are qualitatively similar (for brevity, the results are not included).

The concave relationship between NWC and credit rating

This section examines whether high levels of working capital decrease the credit rating. As postulated by Soenen ( 1993 ) and Baños-Caballero et al. ( 2012 ), high investment in working capital might lead firms to bankruptcy since a high level of inventory incurs costs such as rent, insurance, and security. A high level of accounts receivable is associated with a high probability of a customer default. Specifically, we expect a U-shaped relationship between a firm’s credit rating and investment in working capital. Thus, we examine the nonlinear relationship by including the square of NWC into Eq. ( 1 ). Table 6 presents the results. The results postulate that the coefficient of NWC is positive and its square (NWC 2 ) is negative and statistically significant ( P value < 0.01), and both coefficients are statistically significant. This confirms that an overly conservative working capital policy (high investment in working capital) increases the probability of bankruptcy, which adversely affects a firm’s credit rating. Thus, our results show that when working capital is below the optimal level, the benefits from low production disruption and stimulating sales enhance the credit rating of firms. On the other hand, high investment in working capital might suggest a high risk of uncollectibility and impose high financing costs for these receivables; furthermore, high inventory investment is subject to the risk of obsolescence, spoilage, and greater financing and holding costs.

Components of working capital and credit rating

In the previous section, an NWC proxy is used to examine the relationship between the operating working capital policy and credit rating. Therefore, the positive relationship between NWC and credit rating is due to the management policy of the NWC components. A useful exercise is to examine the relationship between each component of working capital and credit rating. Thus, in Table 7 , we examine the relationship between (AR), (INV), and (AP) and rating. Consistent with our prediction, the results in Table 7 show a positive and significant relationship between credit rating and (INV) and (AR), showing that higher levels of investment in inventory and accounts receivable will increase the probability of a firm receiving a higher credit score. However, the negative and significant impact of (AP) on credit rating indicates that increasing the level of accounts payable on a firm’s balance sheet will increase the likelihood of this firm receiving a lower credit rating. Furthermore, we examine the U-shaped relationship between the components of working capital and credit rating. The coefficients of AR 2 and INV 2 are negative and significant. In addition, AP 2 is negative but not significant. These results indicate that when AR and INV are below the target level, the influence on credit rating is positive. Conversely, above-optimal investment in the components of working capital has a negative relationship with credit rating.

Deviation from the optimal working capital level and credit rating

The U-shaped relationship between working capital level and credit rating is confirmed in Table 8 , due to the quadratic relationship between NWC and credit rating. In this section, we extend our regression and attempt to determine whether deviation from the optimal working capital inversely affects the credit rating; the cost of holding an amount of working capital lower than the target (such as stock sold out and losing on credit sales) may send a signal of the firm’s riskiness. In addition, a firm’s bankruptcy cost may increase as the firm increases its investment in working capital. In the first step, we examine the relationship between deviation from the optimal working capital and credit rating. In the next stage, we examine whether the deviation on the upper and lower sides of the optimal NWC adversely affects the firm’s credit rating; the deviation from NWC is interacted with a dummy variable that is equal to one if the deviation is above the optimal deviation. Thus, in the second stage, we examine whether these deviations (i.e., negative or positive) from the target working capital adversely affect the credit rating. We estimate the optimal working capital using the following equation:

Equation ( 2 )—optimal NWC

Equation ( 3 )—deviation from optimal target

To calculate the optimal NWC, we follow the model of Baños-Caballero et al. ( 2014 ), where \(CASH_{it}\) is cash flow, and is calculated by the ratio of depreciation plus net income to total assets; \(LEV_{it}\) is the firm’s leverage, and is calculated by total debt to total assets; \(GROWTH_{it}\) is measured by the percentage change in total revenue; \(TANG_{it}\) is the firm’s investment in fixed assets, and is calculated by the ratio of net fixed assets to total assets; \(PROF_{it}\) is the firm’s profitability, and is measured by earnings before interest and taxes to total assets; and \(SIZE_{it}\) is the firm’s size, and is calculated by the natural logarithm of total assets.

We use the residual from Eq. ( 2 ) and replace the NWC variable with it. The next step is to use the regression residual as a proxy for deviation from the optimal working capital (Tong 2008 ). The residual value can be positive or negative. Therefore, we use the absolute value as a proxy for deviation. We use Eq. ( 1 ) and replace NWC with the absolute value of the residual deviation. We expect the credit rating to be adversely affected as the NWC of the firm deviates from the optimal NWC level; thus, our expectation is \(\delta_{1} < 0\) in Eq. ( 3 ).

The results in Table 8 from estimating Eq. ( 3 ) are based on replacing the variables NWC and NWC 2 in Eq. ( 1 ) with the absolute residual from Eq. ( 2 ). Consistent with our expectations, the findings in Table 8 show that the coefficient of DEV is negative and statistically significant. This confirms that there is a point at which working capital has a positive relationship with credit rating, and moving from this point adversely affects this relationship. Since the results from Eq. ( 3 ) do not indicate whether deviations on both sides have an adverse effect on credit rating, we include in Eq. ( 4 ) a new variable (interaction term), which takes the value of one if the deviation is positive and zero if the deviation is negative. In Eq. ( 4 ), our main interest is to measure how the coefficients of DEV(δ 1 ) and DEV + DEV* above (δ 1  + δ 2 ) affect credit rating. Thus, we expect δ 1  < 0 and δ 1  + δ 2  < 0; this enables us to examine the negative effect of both positive and negative deviation from the optimal working capital on credit rating.

The interaction term DEV* above is defined as a dummy variable that takes the value of one for a positive residual and zero for a negative residual from the estimation in Eq. ( 2 ), in which (δ 1  + δ 2 ) represents the influence of an above-optimal working capital investment level on credit rating. If the deviation from the optimal working capital negatively affects the firm’s credit rating, we expect the value of δ 1 ˂0, and (δ 1  + δ 2 )˂0 if the deviation both above and below optimal working capital have an adverse impact on the firm’s credit rating.

Column 2 of Table 8 shows the results from the interaction term; the results show that the deviation coefficient (DEV) is negative and statistically significant at a conventional level, and the interaction term DEV* above is also statistically significant at the 1% level and positively related to credit rating. As mentioned by Tong ( 2008 ), the interaction term DEV* above may have a positive value because the positive and negative residuals may offset each other. However, the main concern here is that the sum of (δ 1  + δ 2 ) would be lower than zero, and the results, as predicted, are lower than zero; in column 2, (δ 1  + δ 2 ) is (− 0.01 + 0.002) =  − 0.008. We also conduct a likelihood-ratio test on the null hypothesis that the sum of the estimates of DEV and DEV* above is zero. The test rejects the null hypothesis at a conventional level, which supports that the deviation on both sides of the optimal working capital has a negative effect on credit rating. A likelihood-ratio test ratio for the joint significance levels of (δ 1  + δ 2 ) is lower than zero and statistically significant at the 1% level.

Finally, credit rating may have an impact on a firm’s ability to buy goods on credit and finance their accounts receivable using low-cost external financing. Thus, using the same approach as Attig et al. ( 2013 ) and Bendig et al. ( 2017 ), we test for any potential endogeneity bias caused by reverse causality. Thus, we repeat the regression model in Table 5 using the lagged values of NWC and CCC. The results are qualitatively similar, in which the coefficients of the lagged value of NWC and CCC are positive and statistically significant at a conventional level (the results are available from the authors upon request), which suggests that the endogeneity problem may have no impact on our results.

Concluding remarks

WCM is considered to be one of the most important factors in a firm’s success or failure. Therefore, in this study, we investigate whether a firm’s WCM can affect the perceived riskiness of external evaluators, such as the credit-rating agency S&P. The rationale for this relationship is that inappropriate WCM may increase a firm’s riskiness through under- or over-investment in working capital components. Based on U.S. panel data of 43,141 firm-year observations from 1985 to 2017, we find evidence of a relationship between WCM and credit rating. In particular, we find that this relationship is concave, in which firms have an optimal working capital level that balances costs and benefits to reduce firms’ riskiness; hence, credit is improved. Further, we find that the concave relationship is applicable to working capital components (inventory, accounts receivable, and accounts payable) and that deviation from the optimal working capital level may decrease a firm’s credit rating score.

This study aims to fill the gap in the existing literature by offering for the first time direct evidence of a relationship between WCM and credit rating. Based on theory and our empirical evidence, this study will help policy makers, managers, decision makers, and credit-rating agencies recognize that WCM can affect a firm’s riskiness, which in turn is reflected in its credit rating. This study highlights the advantages and disadvantages of over- and under-investment in working capital and its relationship with credit rating. However, a limitation of this study that should be taken into consideration is that we were unable to test the exact cost of over- and under-investment in working capital, which could have helped us understand the relationship more accurately.

Availability of data and materials

The data supporting the findings of this study are available from the corresponding author upon reasonable request. The data are available at https://wrds-www.wharton.upenn.edu/

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Abuhommous, A.A., Alsaraireh, A.S. & Alqaralleh, H. The impact of working capital management on credit rating. Financ Innov 8 , 72 (2022). https://doi.org/10.1186/s40854-022-00376-z

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Received : 20 November 2021

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DOI : https://doi.org/10.1186/s40854-022-00376-z

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