2
2
2024
1714997254513_4636
107-117
https://journals.researchapt.com/index.php/stbr/article/download/25/20
https://journals.researchapt.com/index.php/stbr/article/view/25
Working Capital Management Profitability Textile Industry Financial Performance Sustainability
Published: 31 December 2024
Accepted: 8 December 2024
Received: 21 July 2024
Working capital management (WCM) gives businesses a higher level of liquidity to satisfy their short-term financial obligations, ensuring the long-term viability of the business and maximizing profitability (Habib & Dalwai, 2024; Nuta et al., 2024). Although WCM is a major challenge for enterprises, it has vital importance for the total assets of the companies because it’s more related to current liabilities and the current assets. Retaining current assets beyond a certain limit can pose a serious threat to the firm’s sustainability (Ali & Amir, 2018; Amir et al., 2021). A firm that maintains a minimal level of current assets must deal with a variety of issues and difficulties that may raise the firm's liquidity risk or make it even more difficult for the enterprise to satisfy its short-term financial obligations. Contrarily, if a firm holds a significant share of current assets, it could result in losses or lower returns on short-term investments (Nuta et al., 2024).
For greater profitability and value for the firm’s investors, an appropriate working capital policy is needed (Nguyen et al., 2020). WCM is extremely important for the overall value of the firms which includes its resources, profitability, and liquidity (Zheng et al., 2022). Therefore, when investing in current assets, businesses aim to preserve an appropriate degree of working capital investment. They do so to balance the risks and returns get out of them (Tsagem et al., 2015). The relationship between cost and the way of financing assets, as quick assets are typically used to finance liquid assets, also sheds light on the position of working capital management (Habib & Dalwai, 2024). Current assets and current liabilities are distinguished by working capital. Additionally, firm's profitability will suffer if it finances its working capital through long-term funding sources (Subramanyam, 2014).
Moreover, the constrained credit and lack of liquidity due to COVID-19 pandemic is quite similar to the period 2007 financial crises. As a result, WCM became a factor in how well industrial enterprises performed when they provided liquidity to finance their operations through short-term loans and automated financing (WCM Effective Management). Therefore, working capital plays the role of the lifeblood for the firms. It helps them to achieve their objectives by contributing endurance to its operations (Altaf, 2024). Different studies investigated the bond between profitability and WCM (Banerjee & Guha Deb, 2024). The primary value of this study, however, is that it investigates how WCM, EPS, and ROI influence the profitability of textile industries in Pakistan. According to Banerjee and Guha Deb (2024) the profitability of this study is determined by combining the four variables: operating profit margin (OPM), return on equity (ROE), return on assets (ROA), and return on capital employed (ROCE).
Compared to earlier research, this one is comprehensive in that it scrutinizes the effect of working capital management on corporate profitability from the perspectives of economic (ROA), financial (ROE), operational (OPM), and capitalistic variables (ROCE). To enhance the truthfulness of research's findings, this study absorbed all these perspectives, which will help the organizations to evaluate working capital with different dimensions (Habib & Dalwai, 2024; Nuta et al., 2024). It will also help the organizations to identify WCM inadequacies that impact their profits and provide more vigorous recommendations in this regard (OSAZEVBARU et al., 2021). Moreover, this study is aligned with the United Nations Sustainable Development Goals (SDGs), particularly SDG 8, which present the “Decent Work and Economic Growth”, as it analyzing the efficient WCM’s impact as one of the proactive and responsible measures can help to achieve economic growth and increase business’s financial success (Habib & Dalwai, 2024). Additionally, it is related to SDG 9, this agenda is “Industry, Innovation, and Infrastructure” as the textile industry is essential for both the development of industries and innovations especially in the emerging nations such as Pakistan. Furthermore, this research supports SDG 12, which is “Responsible Consumption and Production” is improved by the fact that working capital management aids in avoiding excess investment in current assets to reduce wastage of resources among businesses. With regards to profitability concerns, this research empirically explores the role of WCM in the textile sector and offers useful recommendations about OPM, ROE, ROA, and ROCE for organizations aspiring for better economic returns and the general economic sustainability of the industry (Habib & Dalwai, 2024; Nuta et al., 2024).
However, considering how WCM positively affects business profitability, little is known regarding the nature of WCM in the textile industry in Pakistan. Based on prior research, providing insights into the relationship between WCM and corporate performance misses a clear understanding of all aspects of WCM, including accounts payable, inventory turnover, and accounts receivable (Zheng et al., 2022). In addition, the research relationship between these WCM components and other types of profitability such as ROA, ROE, and ROCE have not been well explored especially for textile firms listed in the Pakistan Stock Exchange (PSX). This is a research gap that has implications given that appropriate implementation of WCM could reveal profitable prospects to textile firms to increase profitability, make positive cash flow, and operational efficiency. Thus, this study aims at addressing this research gap by empirically analyzing the effect of WCM on the textile firms’ profitability in Pakistan that will help the managers and policymakers to refocus their attention towards the enhancement of working capital management practices and the financial performance of textile sector in the country.
Brigham and Houston (2013) state that one of the management ideas known as "working capital management" focuses on figuring out how much cash, debt, and inventory is optimal, as well as how to finance current liabilities as cheaply as possible while still meeting the demands of the business. Brigham (2016) stated that businesses must understand the management of their short-term assets and liabilities as variations exist in managing each of its fundamental elements, and the profitability of a corporate entity is affected by the efficient management of each facet.
Accounts Receivable and firm profitability
The first step in handling receivables is adopting the firm’s credit strategy. Though, a firm needs a structure to observe and manage whether the financing requirements are met (Athar et al., 2023). Corrective actions on numerous credit rules are usually required, and the process to identify if the issue is in control is to implement a good receivable management program (Brigham & Houston, 2013). Through a statistical perspective, the researcher observed an unfavourable link between profitability and accounts receivable, suggesting that shortening the duration of days collected from creditors should result in a favourable improvement in profitability (Zheng et al., 2022). A favourable association exist between accounts receivable and the firm’s profitability (Alvarez et al., 2021; Amponsah-Kwatiah & Asiamah, 2021; Jakpar et al., 2017). Few researchers have found no discernible correlation between accounts receivable and profitability (Arnaldi et al., 2021; Sensini & Vazquez, 2021). Thus, we purpose the following hypothesis:
H1: Accounts Receivable has a positive and significant impact on firm’s profitability.
Inventory and the firm’s profitability
Inventory management stands out as the cornerstone of production management, with the firm's management bearing the responsibility of funding necessary to maintain adequate inventory levels (Ahmad et al., 2023). This is because low inventory levels can negatively affect sales, which are manufacturing companies' main source of revenue and can therefore have an impact on their profitability. According to Brigham and Houston (2013), the objective of management of inventory is to hold ample inventory on hand to continue the process of creation and to minimize the cost of maintaining stock. According to Olaoye et al. (2019) and Enow and Brijlal (2014), INV and firm's profitability are positively correlated, where inventory levels are high, which minimizes sales losses (Xu, Haris & Irfan, 2022) and lower the risk of breakage costs in the supply chain or production (Baños-Caballero et al., 2014; Deloof, 2003). According to several findings, there is an unfavourable association between INV and profitability of the firm (Arnaldi et al., 2021; Aytac et al., 2020; Högerle et al., 2020). Thus, following hypothesis is developed:
H2: Inventory has a negative and significant impact on the firm’s profitability.
Accounts Payable and the firm’s profitability
Accounts payable is made up of trade credit and accumulated costs, which when combined provide funding for continuing corporate operations (Ahmad et al., 2023). Earlier studies revealed a significant positive relationship between accounts payable and the firm's profitability (Gonçalves et al., 2018; Hsieh & Wu, 2013; Mathuva, 2015). Companies can increase their profitability by using the liquidity they have to invest in liquid assets in exchange for a gradual payment schedule from creditors (Boachie & Mensah 2022). There is a conflicting link among profitability and AP (Deloof, 2003; Enqvist et al., 2014). Thus, the following hypothesis is formulated.
H3: Accounts Payable has a negative and insignificant impact on firm’s profitability.
Cash Conversion Cycle and the firm’s profitability
The time it takes to acquire, produce, and warehouse raw materials is related to managing the cash conversion cycle (Kayani et al., 2025). Based on the type of goods and nature of the work, the inventory is then traded for earnings or debtor invoices are converted into cash (Kim et al., 2025). Numerous studies examining the association between WCM and profitability determined an unfavourable link between profitability of firm and CCC, demonstrating that lessening in the collective cash conversion cycle will become an improvement in profitability of firm (Arnaldi et al., 2021; Enow & Brijlal, 2014; Enqvist et al., 2014; Usman et al., 2017). Nonetheless, a small number of studies (Alvarez et al., 2021; Amponsah-Kwatiah & Asiamah, 2021), find a positive correlation between profitability and the conversion cash cycle. Profitability and cash conversion cycle had no statistically meaningful association (Jakpar et al., 2017; Osazevbaru et al., 2021; Rey-Ares et al., 2021). Thus, we suggested the following hypothesis:
H4: Cash Conversion Cycle has an insignificant impact on firm’s profitability.
Population and Sample
The study focuses on ten textile firms listed on the Pakistan Stock Exchange (PSX) between 2018 and 2023. These firms were selected based on their consistent listing during this period and their dominant presence in the textile industry in Pakistan. The selection criterion emphasized the availability of comprehensive data for the chosen firms, ensuring robust analysis, as their annual reports were available for the last six years. The data were collected through content analysis of these annual reports, which provided essential financial data for the analysis.
Data Collection
Data for this study were collected from the publicly available annual reports of the selected textile firms, accessible on the Pakistan Stock Exchange website. These reports provided financial data, including key working capital metrics and profitability indicators, which were essential for the analysis. The period under review, from 2018 to 2023, captures both stable and volatile economic phases, providing a comprehensive basis for understanding the effects of working capital management on profitability during different market conditions. The financial data extraction was conducted through content analysis, focusing on the firms' balance sheets, income statements, and related financial notes.
Variables and Measurements
The study examines several key variables related to working capital management and profitability. Independent variables include Average Collection Period (AR), Inventory Turnover (INV), Average Payment Period (AP), and Cash Conversion Cycle (CCC). These variables represent different aspects of working capital management, such as how efficiently a firm collects receivables, manages inventory, pays suppliers, and converts working capital into cash flow. Dependent variables are profitability indicators: Return on Assets (ROA), Return on Equity (ROE), Return on Capital Employed (ROCE), and Operating Profit Margin (OPM). These indicators measure the overall profitability and operational efficiency of the firm. Control variables include Firm Size (f_size), Debt Ratio (DR), and Sales Growth (SG), which account for other factors influencing profitability. The sources for the measurement of these variables are available in Table 1.
Data Analysis
To analyse the relationships between working capital management and profitability, various statistical techniques were employed. The study first utilized descriptive statistics, including the mean, maximum, minimum, and standard deviation, to summarize the data for the independent, dependent, and control variables. This provided a basic understanding of the data distribution. The Shapiro-Wilk W test was applied to check for normality in the data, ensuring the validity of subsequent tests. The analysis indicated no heteroscedasticity, with a p-value of 0.001, confirming the appropriateness of further regression analysis.
The study employed an Ordinary Least Squares (OLS) regression model to test the hypotheses and examine the cause-and-effect relationships between working capital management and profitability. The variance-inflation factor (VIF) was used to check for multicollinearity among the independent variables. To account for any individual firm-specific effects, the Hausman test was performed, leading to the selection of the fixed effect model for this study. The regression models used to assess the impact of working capital management on profitability are outlined below
Model
1: ROA = 
SG)
+β7(DR) + e
Model
2: ROE =
SG)
+β7(DR) + e
Model
3: ROCE =
SG)
+β7(DR) + e
Model
4: OPM =
SG)
+β7(DR) + e
The debate above served as the foundation for the development of the conceptual model below in Figure 1.
Figure 1. Research Model of the Study (Source: Authors Own Work)
Table 1. Variables of the study
Variable | Variable Name | Calculation Method | Reference |
1. Dependent Variable | Operating profit Margin Return on Assets Return on Capital Employed Return on equity | Earnings before interest and tax (EBIT)/Sales EBIT/Total assets EBIT/Capital employed Net profit/Total equity | Khan and Choudhary (2020) Alvarez et al. (2021) Al Dalayeen (2017) Alvarez et al. (2021) |
2. Independent Variable | Accounts receivable
Inventory
Accounts Payable
Cash conversion cycle | (Average of accounts receivable/Sales) *365 (Average inventory/Cost of goods sold) * 365 (Average of accounts payable/Cost of goods sold) *365 (AR + INV – AP) | Jayarathne (2014)
Jayarathne (2014)
Jayarathne (2014)
Jayarathne (2014) |
3. Control Variable | Firm size
Sale growth
Debt Ratio | Natural logarithm of total assets (LogA) = ln (asset) (Current year sales -Previous year sales)/ Previous year sales
(Total liabilities/Total assets) | Laghari and Chengang (2019)
Jayarathne (2014)
Laghari and Chengang (2019)
|
(Source: Authors Own Work)
The findings of the descriptive statistics analysis obtained ROA that ranged from its best value of 59 to its lowest value of 1. The ROE ratio that is the lowest is 1, and the ROE ratio that is the greatest is 59. The ROCE ratio can range from a minimum of 1 all the way up to a high of 59. The OPM ratio can range from a minimum of 1 all the way up to a high of 50. The CCC varied from 1 at the lowest to 60 at the highest point. With an average of 24.5 and a standard deviation of 16.426; AR ranges from a minimum of 1 to a maximum of 52. The AP with the lowest value is 1, and the one with the greatest value is 60. INVs have a maximum value of 52 and a minimum value of 1.
Table 2. Descriptive Statistics Results
Variables | Observations | Mean | Std. Dev | Min | Max |
F_SIZE | 60 | 14.850 | 1.038 | 13 | 17 |
DR | 60 | 0.4693 | 0.253 | 0.0524 | 0.987 |
ROA | 60 | 29.983 | 17.029 | 1 | 59 |
ROCE | 60 | 30.266 | 17.154 | 1 | 59 |
ROE | 60 | 29.833 | 17.078 | 1 | 59 |
OPM | 60 | 24.650 | 15.117 | 1 | 50 |
CCC | 60 | 30.50 | 17.464 | 1 | 60 |
AR | 60 | 24.50 | 16.426 | 1 | 52 |
AP | 60 | 30.5 | 17.464 | 1 | 60 |
INV | 60 | 27.25 | 14.942 | 1 | 52 |
SG | 60 | 30.5 | 17.464 | 1 | 60 |
(Source: Authors Own Work)
The presence of multicollinearity demonstrates the interdependence of the independent variables. Typically, variance influence factor (VIF) and tolerance are used to measure it. The variance influence factor value, which is less than 5, indicates that there is no association between the independent variables. The results clearly show that the independent variables have no multicollinearity. The table 3 showed that the value of VIF for each variable is less than 5, it means that there is no multicollinearity in independent variables.
Table 3. Variance Inflation Factor (VIF)
Variable | VIF |
AR | 1.62 |
AP | 1.76 |
INV | 1.46 |
F_SIZE | 1.28 |
SG | 1.29 |
DR | 1.13 |
Mean VIF | 1.42 |
(Source: Authors Own Work)
OLS Regression Analysis Results
Findings of Model 1
Table 3 represents results of OLS regression analysis. The findings depict that when ROA increases by 1 unit, then AR increases by .568 units and SG increases by .023 units. This indicates a positive association between ROA and AR and ROA and SG. Similarly, when ROA increases by 1 unit, there will a decline in AP, INV and DR by .214 units, by .247 units and by 38.74 units respectively, it means AP, INV and DR are inversely related to each other. The WCM independent variables and additional control factors/variables support a 46% variance in ROA, according to the R2 value of 0.4657. Consequently, an alternative is accepted and the null hypothesis of the study is disproved. These results are reliable with the results of Singhania and Mehta (2017), Arnaldi et al. (2021), Vukovic and Jaksic (2019) and Nastiti et al. (2019), but contradictory to the results of Sarwat et al. (2017). Moreover, Model 1 also indicates statistically meaningful connection between the working capital management represented by AR, AP, CCC and INV on ROA with F-sig (0.0001).
Results of Model 2
The results depict that when ROE increases by 1 unit, then AR increases by .344 units and SG increases by .117 units, this shows a positive correlation between ROE and AR and ROE and SG. Similarly, when ROE increases by 1 unit, there will be a decrease in AP, INV and DR by .067 units, by .419 units and by .54 units respectively, which indicates an adverse association between ROE and AP, ROE and INV and ROE and DR. A 35% variance in ROE is supported by the WCM independent variables and other control/constant variables, based on the 0.3563 R2 value. Moreover, with F-sig (0.0025), Model 2 likewise shows a significant correlation between ROE and working capital management, as indicated by AR, AP, CCC, and INV. These outcomes are coherent with the outcomes of Amponsah-Kwatiah and Asiamah (2021), Alvarez et al. (2021), and Gorondutse et al. (2017), but contradictory to the outcomes of Rey-Ares et al. (2021).
Results of Model 3
The findings show that with the 1 unit increase in ROCE, there will be growth in AR by .345 units, and SG increases by .022 units, this indicates a positive association between ROCE and AR and ROCE and SG. However, Model 3 indicates a statistically insignificant association between ROCE and AP with prob>0.05. Similarly, when ROCE increases by 1 unit, there will be a decline in INV by .100 units, and DR by 46.76 units which indicates an inverse association between ROCE and INV, and ROCE and DR. These findings are comparable to the outcomes of Pais and Gama (2015), Nguyen et al. (2020), and Arnaldi et al. (2021), but contradictory to the outcomes of Cristian and Raisa (2017) and Chowdhury et al. (2018). The value of R2 is 0.2527, indicating that the WCM’s independent variables and other control variables support a 25% variation in ROCE. The outcome is the dismissal of the study's null hypothesis (H0) and the affirmation of its alternative hypothesis (HA). This outcome is comparable to the results of Al Dalayeen (2017), Högerle et al. (2020), and OSAZEVBARU et al. (2021). Moreover, Model 3 also indicates a statistically substantial connection between the working capital management represented by AR, AP, CCC and INV on ROCE with F-sig (0.0374).
Results of Model 4
The findings indicate that an increase of one unit in OPM will result in a 0.60 unit rise in AR and a. 009 unit increase in SG which indicates that there is a favourable correlation between OPM and AR and OPM and SG. However, Model 4 indicates a statistically insignificant relationship between OPM and AP with prob>0.05. Similarly, when the OPM increases by 1 unit, there will be a decrease in INV by .11 units, and DR by 32.13 units which means there is a negative correlation between OPM and INV, and OPM and DR. The value of R2 is 0.4840, indicating that WCM’s independent variables and other control variables support a 48% variation in OPM. Moreover, Model 4 also indicates a statistically significant connection among the working capital management represented by AR, AP, CCC and INV on OPM with F-sig (0.0000).
Table 4. The Results of OLS Regression
Variables | Model 1 ROA | Model 2 ROE | Model 3 ROCE | Model 4 OPM |
Intercept | 19.419 | -67.727 | -81.747 | 3.979 |
AR | .5682*** | .3447*** | .3454** | .6095*** |
AP | -.2148 | -.0674 | .0714 | -.0981 |
INV | -.2474** | -.4195*** | -.1006 | -.1109 |
CCC | -.0610 | -.0993 | -.1788 | -.0745 |
SG | .0231 | .11704 | .0225 | .0944 |
F_SIZE | 1.846 | 8.377 | 8.442 | 1.612 |
DR | -38.742** | -54.069*** | -46.769* | -32.1316* |
R2 | 0.465 | 0.356 | 0.2527 | 0.484 |
F-statistic | 6.39 | 4.06 | 4.48 | 6.88 |
F-sig | 0.0001*** | 0.0025*** | 0.0374** | 0.0000*** |
(Source: Authors Own Work). Notes: p < 0.05 indicates significance at the 5% level; p < 0.01 at the 1% level; p < 0.001 at the 0.1% level.
The following table shows the results of heteroscedasticity, where the p value is 0.00102<0.05 which means there is no heteroscedasticity in our data.
Table 5. The Result of Heteroscedasticity
Variables | Observations’ | W | V | Z | Prob>z |
e | 60 | 0.92301 | 4.185 | 3.086 | 0.00102 |
(Source: Authors Own Work)
The current study examines how WCM affects a firm's profitability. Therefore, the first hypothesis of this research indicated that accounts receivable has a substantial influence on the entity’s profitability. Hence the outcome of the present study proved this hypothesis true. These findings are coherent with results of Singhania and Mehta (2017), Vuković and Jakšić (2019), Nastiti et al. (2019), and Arnaldi et al. (2021), but inconsistent with the results of Sarwat et al. (2017). The second hypothesis of this research showed that stock overall has a considerable impact on firm’s profitability. Hence, the outcome of the current study proved this hypothesis true as well. Our findings are coherent with outcomes of Pais and Gama (2015), Nguyen et al. (2020), and Arnaldi et al. (2021) but inconsistent with the results of Cristian and Raisa (2017), and Chowdhury et al. (2018). The third hypothesis of this study indicated that accounts payable has an insignificant influence on the firm’s profitability. Hence, this hypothesis is rejected, and the outcomes of this study are consistent results are comparable to the outcomes of Gill et al. (2011), Alvarez et al. (2021), Gorondutse et al. (2017), Amponsah-Kwatiah and Asiamah (2021), but conflict to the results of Rey-Ares et al. (2021). The study's final hypothesis stated that the cash conversion cycle has a negative effect on firm's profitability. Hence this hypothesis is rejected as well, and the findings of our study are very similar to the results of Al Dalayeen (2017), Högerle et al. (2020).
This research aimed to see how WCM policies affected the fiscal execution of textile enterprises listed on the PSX. The statistics were picked from annual reports of 10 textile industries registered on the PSX between 2018 and 2023. Descriptive statistics, VIF, and the OLS regression analysis were applied to evaluate the data. The research's experimental outcomes showed that: (1) AR has a substantial optimistic influence on ROA, demonstrating that having more inventory levels boosts corporate profitability demonstrating that firms will be profitable if they gather money from clients sooner; (2) INV has a substantial adverse influence on ROA, and AR has significant positive influence on ROA indicating that having more inventory levels boosts corporate profitability; and (3) AP has an adverse influence on ROA, ROE, OPM and ROCE, and (4) CCC substantially lowers ROA, ROE, ROCE and OPM, demonstrating that the firm can raise profitability through dropping the cash conversion cycle. Working capital management is a skill that managers need to possess because it is critical to the operation and prosperity of businesses. The optimal management/administration of working capital will furnish the requisite liquidity essential for guaranteed financing (efficient administration of WCM elements), a facet of paramount importance now due to the COVID-19 crisis' impact on credit availability and liquidity. Automatic financing is a method of financing a business' operations. Future studies should look into how spontaneous funding impacts a firm's productivity and profitability across numerous industrial sectors.
Research Implications
This research contributes to the literature on WCM and profitability, with a particular focus on the textile sector in Pakistan. The study emphasizes the positive relationship between AR and profitability, suggesting that efficient AR management, such as strengthening credit policies and improving collection processes, can significantly enhance firm performance and cash flow. Moreover, the detrimental effects of INV and CCC on profitability highlight the need for better inventory management, with practices like Just-In-Time (JIT) inventory systems that help reduce unprofitable stock and lower holding costs. For example, companies can align their production schedules with market demand to avoid excess inventory, thereby improving profitability. The research also extends the theoretical understanding of WCM by considering various profitability indicators (ROA, ROE, OPM, ROCE) and demonstrating how WCM strategies influence these financial outcomes. Importantly, the study aligns with SDGs, specifically SDG 8 (Decent Work and Economic Growth) and SDG 12 (Responsible Consumption and Production). For instance, improving WCM in the textile sector not only boosts profitability but also contributes to responsible business practices that ensure long-term sustainability. Firms that efficiently manage WCM while minimizing waste and maximizing operational efficiency can help achieve SDG 12 by reducing overproduction and consumption, thus supporting sustainable practices in the industry. Furthermore, the study underscores the importance of WCM policies during times of financial crisis, such as the COVID-19 pandemic, when firms struggle with limited liquidity and access to credit. This aligns with SDG 9 (Industry, Innovation, and Infrastructure), as firms that effectively manage their WCM are better positioned to survive financial downturns, sustain operations, and continue contributing to economic growth. Therefore, policymakers and industry leaders should consider developing guidelines and support systems that improve WCM practices, ensuring both profitability and sustainable development in the textile sector.
Limitations of the Study
This present study has few limitations which are important to note down: Firstly, there is a limitation in the number of textile companies covered in this study; as the sample frame is only restricted to 10 textile firms listed at PSX, which might not have the generalizable results of other industries or countries. Second, the study focuses on the period between 2018 and 2023, and hence the results do not incorporate long-term changes as well as the changes in economic conditions that may occur after the specified period. Third, to ensure simplicity in making causal attributions, the author confines the analysis to four WCM components relative to their effect on profitability without considering other possible influence such as macroeconomic factors, financial policies and industry trends. Finally, it is based on only secondary data collected from annual reports of organisations which might have some inconsistencies and reporting biases. Therefore, the future research could follow up on those limitations by including a larger number of organizations; collecting the data for a longer period and including more variables that could paint the picture of the WCM and profitability in a richer way.
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