ABSTRACT:
The cash conversion cycle (CCC) is a key indicator for assessing the efficiency of working capital management in enterprises. This study investigates the impact of the cash conversion cycle on the profitability of 25 listed textile and garment companies on the Vietnamese stock market over the period 2020–2024. Using the Generalized Least Squares (GLS) method with 125 firm-year observations, the empirical results indicate that the cash conversion cycle exerts a statistically significant negative effect on profitability, measured by return on assets (ROA) and return on equity (ROE), at the 99% confidence level. Specifically, a 1% increase in the cash conversion cycle is associated with a decrease of 0.037% in ROA and 0.064% in ROE. In addition, firm size and the current ratio are found to have a positive influence on ROA, whereas financial leverage and the structure of fixed assets negatively affect profitability. These findings highlight the critical role of efficient working capital management in enhancing the financial performance of Vietnamese textile and garment enterprises.
Keywords: cash conversion cycle, profitability, listed textile and garment companies, ROA, ROE.
1. Introduction
In the context of deepening international economic integration, Vietnam's textile and garment industry has been affirming its position as one of the country's key export industries. According to data from the Vietnam Textile and Apparel Association (VITAS), textile and garment export turnover in 2023 will reach about 40.3 billion USD, significantly contributing to the national GDP and creating jobs for millions of workers. However, Vietnamese textile and garment enterprises are facing many challenges such as fierce competition from countries in the region (Bangladesh, Cambodia), fluctuations in raw material prices, rising labor costs and pressure from increasingly stringent environmental standards. The characteristics of the textile and garment industry are large working capital turnover, long production time and heavy reliance on imported raw materials. Therefore, effective working capital management plays a key role in maintaining production and business activities and improving the profitability of enterprises. According to Lancaster and Stevens (1999), the day-to-day management of a company's assets and short-term liabilities plays an important role in improving the financial performance of a business. Among the indicators to measure the effectiveness of working capital management, the Cash Conversion Cycle (CCC) is considered one of the most widely used.
The cash conversion cycle, first introduced by Richards and Laughlin (1980), reflects the period between when a business spends cash on materials and when cash is obtained from the sale of goods. Cash Conversion Cycle is calculated as the sum of the customer collection time and inventory time minus the time to pay the supplier. According to Raheem and Qaisar (2013), Cash Conversion Cycle is the most widely used indicator to assess both the risks and returns associated with liquidity management. A shorter Cash Conversion Cycle implies reduced capital tie-up, enabling firms to redeploy funds for further operations or investment, thereby enhancing profitability.
Extant international literature predominantly documents a negative relationship between CCC and profitability (e.g., Deloof, 2003; Shin & Soenen, 1998; Karaduman et al., 2010), although some studies report contrary findings (e.g., Lyroudi & Lazaridis, 2000; Sharma & Kumar, 2011). In Vietnam, research on this topic is still limited, especially for the textile and garment industry. Previous studies have mainly focused on industries such as construction (Vo&Le, 2022), food (Tran, 2018), leather shoes (Tran and Le, 2018) or the overall study of listed enterprises (Nguyen Thi Xuan Hong, 2024). Meanwhile, the textile and garment industry with its own characteristics in terms of production cycles, inventory management and trade credit policies need to be studied more in-depth.
The 2020-2024 period is particularly salient, encompassing the severe disruptions of the COVID-19 pandemic (2020–2021) and subsequent recovery (2022–2024), alongside opportunities arising from free trade agreements such as EVFTA (effective August 2020) and CPTPP. To date, no study has examined the CCC–profitability nexus in this sector during this distinctive timeframe.
Against this backdrop, the present study provides additional empirical evidence on the impact of the cash conversion cycle on the profitability of listed textile and garment firms in Vietnam from 2020 to 2024. The findings are expected to enrich the literature on working capital management in Vietnam and offer actionable managerial implications for optimizing the CCC to improve profitability.
2. Theoretical basis and research overview
2.1. Theoretical basis
2.1.1. Cash Conversion Cycle (CCC)
a) Concept of cash conversion cycle
To measure the time, it takes a business to convert input resources into cash flow, Richards and Laughlin (1980) introduced the concept of a cash conversion cycle (CCC). This metric has become one of the most widely used indicators for evaluating the efficiency of working capital management. According to Keown et al. (2003), the cash conversion cycle represents the length of time that a company sells its inventory, collects its receivables, and pays its debts. This definition is used by many researchers and is expressed by the formula:
CCC = Customer Billing Period (ACP) + Inventory Conversion Period (INT) - Vendor Billing Period (APP)
In which:
- Customer Collection Period (ACP) = Average Balance of Customer Receivables / (Sales Revenue/365)
- Inventory Conversion Period (INT) = Average Inventory Value / (Cost of Goods Sold/365)
- Vendor Billing Period (APP) = Average Liability Balance / (Cost of Goods Sold / 365)
b) Meaning of the cash conversion cycle
According to Raheem and Qaisar (2013), CCC is the most widely used indicator to assess both the risks and returns associated with liquidity management. The CCC provides useful information for managers in finding a reasonable inventory holding period, which is reflected in the total number of days of customer debt collection in a business cycle, from the time the goods are produced to the time they are sold.
Amarjit and Charul (2012) emphasize that cash is a critical component for sustaining and growing a business. Firms that continuously adjust cash holdings toward an optimal level are likely to experience corresponding improvements in profitability and firm value (Shipe, 2015).
c) Components of the cash conversion cycle
Average Collection Time (ACP): Indicates the average number of days required to collect receivables from customers. A longer ACP implies greater capital tie-up, increased opportunity costs, and heightened credit risk.
Days of Inventory (INT): Indicates the average time needed to convert inventory into revenue. If the inventory turnover is high, businesses will accelerate the consumption of goods, proving that resources are being used effectively.
Average Payment Time (APP): Indicates how long it takes the company to pay its supplier debts. According to Okpe and Duru (2016), it is beneficial for businesses to recover customer receivables as quickly as possible, meanwhile, for the amount to be paid for the purchase of goods and services, it should be extended to the extent possible.
2.1.2. Profitability of the enterprise
a) Concept of profitability
According to Nguyen (2017), profitability in a business is a measure of the ability to generate profits from revenue, from assets or from equity in a certain period. These metrics reflect the efficiency with which resources are utilized to produce returns. According to Nur (2018), profitability can be calculated through the profitability of a business. Profit is an extremely important goal, if businesses are not profitable, it will be difficult for them to continue operating.
b) Indicators measuring profitability
Return on Total Assets (ROA): Measures the profit generated per unit of total assets employed.
ROA = Profit after tax / Average total assets
A higher ROA indicates efficient asset utilization in generating earnings. This metric is widely used in studies examining the CCC–profitability relationship (Uyar, 2009; Van et al., 2019).
Return on Equity (ROE): Assesses the return generated on shareholders' equity. This indicator shows how much after-tax profit a dong of equity generates.
ROE = Profit After Tax / Average Equity
According to Abdallah (2014), ROE is considered an indicator of profitability, reflecting management efficiency and increasing investors' demand for stocks, leading to an increase in the market value of the business.
2.1.3. The relationship between the cash conversion cycle and profitability
Yakubu et al. (2017) note that maintaining an appropriate CCC is essential for liquidity preservation, yet excessive working capital may cause firms to forgo profitable investment opportunities. Chuke et al. (2018) conclude that the CCC has a significant negative impact on ROA. When businesses can shorten inventory time, accelerate debt collection and extend payment time to suppliers, profitability will be improved. U. T. Tran (2018) asserts that the shorter the CCC, the higher the efficiency of listed enterprises, this view is supported by Roberta and Paola (2019).
2.2. Research overview
2.2.1. International research
A substantial body of international research has examined the relationship between the CCC and firm profitability, yielding largely consistent findings.
Studies documenting a negative relationship: Deloof (2003) studied businesses in Belgium and concluded that effective working capital management, as demonstrated by the shortening of CCCs, had a positive impact on profitability. Shin and Soenen (1998) studied businesses in the U.S. showing an inverse relationship between CCCs and profitability. Besides, Raheem and Qaisar (2013) confirmed a negative association between CCC and both ROA and ROE among listed manufacturing firms in Pakistan. Similar results were reported by Y. Muhammad et al. (2014) in Pakistan's cement industry, Roberta and Paola (2019) among Italian textile SMEs, and Yazdanfar and Öhman (2014) in Sweden.
Studies reporting on a positive relationship: Lyroudi and Lazaridis (2000) found a positive association between CCC and ROE in the Greek food industry. Comparable findings emerged from Sharma and Kumar (2011) and Panigrahi (2013) in India.
2.2.2. Domestic research
Research in Vietnam on this topic remains relatively scarce, though several noteworthy studies exist. Tran and Le (2018) studied the cash conversion cycle and profitability of leather footwear enterprises in Vietnam, concluding that CCC has an inverse impact on profitability with two variables that depend on ROE and ROA. Furthermore, Vo Minh Long and Le Thi Thanh Hang (2022) examined 58 listed construction firms (2014–2018) using GLS regression and reported that ACP, INT, CCC, and leverage exert negative effects on profitability. Nguyen Thi Xuan Hong(2024) analyzed 1,531 listed firms (2015–2022) and confirmed a significant inverse relationship between CCC and profitability indicators (ROA, ROE, ROI) at the 99% confidence level. Additional studies by Van et al. (2019), Tô and Nguyễn (2015), Dương and Trần (2018), and Bùi (2022) generally support a negative association between CCC and financial performance in Vietnam.
2.2.3. Research gap
From the summary of domestic and foreign studies, some research gaps:
Firstly, industry gaps: Studies in Vietnam mainly focus on industries such as construction, food, leather shoes, or overall research of listed enterprises. Meanwhile, the textile and garment industry - one of Vietnam's key export industries has not been studied.
Secondly, temporal context: The period 2020-2024 is a special period when Vietnam's textile and garment industry is strongly affected by the COVID-19 pandemic and the post-pandemic recovery process. However, no studies have evaluated the relationship between CCC and the profitability of the textile and garment industry during this period.
Thirdly, the results of the study are inconsistent: Studies have shown different results on the impact of CCCs on profitability, so more empirical evidence is needed to clarify this relationship.
2.3. Research hypothesis
Based on the theory and overview of previous studies, the study proposes the following research hypotheses:
H1: The cash conversion cycle has a negative impact on the profitability of listed textile and garment companies in Vietnam.
When the CCC is low, the number of days of recovery of the company's cash in the business cycle decreases, the enterprise is not appropriated capital, can use that capital to continue business activities, expand investment opportunities, so profitability will increase. This hypothesis is supported by studies by Deloof (2003), Raheem and Qaisar (2013), Vo Minh Long and Le Thi Thanh Hang (2022), Nguyen Thi Xuan Hong (2024). In addition to the CCC variable, the study also included several control variables in the model.
Hypothesis H2: Firm size has a positive impact on profitability.
Larger firms benefit from economies of scale, better access to low-cost financing, and stronger bargaining power (Uyar, 2009; Van et al., 2019).
Hypothesis H3: Financial leverage has a negative impact on profitability
While debt provides tax shields, excessive leverage increases interest costs and financial distress risk (Vo Minh Long & Le Thi Thanh Hang, 2022; Van et al., 2019).
Hypothesis H4: Current ratio has a positive impact on profitability
Higher liquidity reduces liquidity risk, enabling firms to focus on core operations and seize business opportunities (Van et al., 2019).
Hypothesis H5: Fixed asset structure has a negative impact on profitability
A higher proportion of fixed assets reduces operational flexibility, increases depreciation and maintenance costs, and adversely affects profitability. This hypothesis is supported by studies by Al-Mohareb (2019), Van et al. (2019).
3. Research methodology
3.1. Research model
Based on an overview of previous studies and the hypotheses proposed in Chapter 2, the research model was developed to evaluate the effect of the cash conversion cycle on the profitability of listed textile and garment enterprises in Vietnam. The influence of the cash conversion cycle and control variables on profitability is specified through the following regression equations:
Model 1: ROA-dependent variable
Model 2: ROE-dependent variable
In which:
- ROAit: Rate of return on total assets of enterprise i in year t
- CCCit: Cash conversion cycle of enterprises i in year t
- QMit: Firm size of firm i in year t
- DBit: Financial leverage of enterprises i in year t
- CRit: Current ratio of firm i in year t
- TSCĐit: Structure of fixed assets of enterprise i in year t
- β0: Intercept
- β1 ... β4: regression coefficients of independent and control variables
- εit: Random error
3.2. Measurement of Variables
3.2.1. Dependent Variable
The profitability of a business is measured through two indicators: return on total assets (ROA) and return on equity (ROE).
ROA = Profit after tax / Average total assets
ROE = Profit After Tax / Average Equity
3.2.2. Independent variable
The Cash Conversion Cycle (CCC) is calculated according to the formula of Keown et al. (2003):
CCC = ACP + INT - APP
In which:
- ACP (Average Collection Period) = Average Accounts Receivable / (Net Sales / 365)
- ACP (Average Collection Period) = Average Accounts Receivable / (Net Sales / 365)
- APP (Average Payment Period) = Average Accounts Payable / (Cost of Goods Sold / 365)
3.2.3. Control variable
- Firm Size (QM): Measured by the natural logarithm of total assets (Ln (Total Assets)).
- Financial Leverage (DB): DB = Total Liabilities / Total Assets
- Current Solvency (CR): CR = Current Assets / Current Liabilities
- Structure of fixed assets (fixed assets): Fixed assets = Value of fixed assets / Total assets
3.3. Research data
3.3.1. Data sources
The data used in the study are secondary data, collected from the audited financial statements of textile and garment enterprises listed on the Vietnamese stock market. The main data sources are taken from:
- Vietstock Database (vietstock.vn)
- Financial statements published on the websites of businesses
- Databases of Stock Exchanges (HOSE, HNX, UPCOM)
3.3.2. Sample selection
The research sample was selected according to the following criteria:
- Selection criteria:
+ Enterprises in the textile and garment industry listed on the Vietnamese stock market
+ Enterprises with the proportion of revenue from textile and garment activities accounting for over 50% of total net revenue
+ The enterprise has full audited financial statements during the study period
+ Enterprises not subject to special control or delisted
- Exclusion criteria:
+ Businesses lack data or incomplete data
+ Enterprises with unusual financial indicators (outliers)
After applying the above criteria, the study sample included 25 listed textile and garment enterprises in the period from 2020 to 2024 (5 years), with a total of 125 observations (balanced panel data).
3.4. Analytical methods
The study used quantitative research methods with panel data. Stata software is used for data processing and analysis. The analysis process is carried out in the following steps:
Step 1: Descriptive Statistics - Summary statistics (mean, standard deviation, minimum, maximum) for all variables.
Step 2: Pearson Correlation Analysis - Examination of pairwise correlations and preliminary check for multicollinearity.
Step 3: Multi-linear testing - Using the variance magnification factor (V IF). If the VIF < 5: there is no severe multi-linear phenomenon.
Step 4: Table Data Regression - Using Pooled OLS, FEM, REM methods.
Step 5: Model Selection Verification - Use F-test, LM, Hausman testing to select the right model.
Step 6: Defect Testing - Heteroskedasticity and Autocorrelation Testing.
Step 7: GLS regression – If the model has defects, use the GLS (Generalized Least Squares) method to fix it and give the final result.
4. Research results
4.1. Descriptive statistics
The statistical results describing the variables in the study model are presented in Table 4.1. The descriptive statistics table provides an overview of the basic characteristics of 25 listed textile and garment enterprises in Vietnam in the period 2020-2024 with a total of 125 observations.
Table 4.1. Statistical results describing the study variables
Source: Analysis results from Stata software
Regarding profitability, the sampled textile and garment firms reported a mean Return on Assets (ROA) of 6.57%, implying that each 100 units of currency in assets yielded 6.57 units of net income. The ROA exhibited a broad spectrum, spanning from -8.70% to 24.43% with a standard deviation of 5.13%, which underscores significant disparities in asset management efficiency among the observed entities. Negative returns observed in certain firms highlight the operational hurdles encountered, particularly amidst the COVID-19 pandemic (2020-2021).
Furthermore, the average Return on Equity (ROE) stood at 15.64%. The wider range of ROE (-18.11% to 53.16%) and a higher volatility (standard deviation of 10.80%) signify a pronounced divergence in shareholder wealth creation. These substantial fluctuations in ROE reflect sharp stratification within the industry, distinguishing high-performing, well-governed corporations from those struggling with structural or financial constraints.
The average CCC of textile and garment enterprises is 89.39 days, which means that on average, it takes about 3 months for businesses to convert from spending cash to buying raw materials to collecting cash from sales. This figure is quite high compared to some other industries, reflecting the characteristics of the textile and garment industry where the production process is long, the inventory is large, and the debt collection time is often long due to the payment characteristics of export customers. However, the very high standard deviation (58.85 days) shows significant differences in working capital management between enterprises. The shortest CCC is 14.11 days (demonstrating highly efficient cash flow management), while the longest reaches 283.21 days (almost 9.5 months), reflecting severe capital tie-up in some cases such as (export garments often have longer production and delivery times than domestic garments), the market served, the credit policy of the business, and the ability to negotiate with customers and suppliers.
Firm size (QM), measured as the natural logarithm of total assets, averages 26.56, with a wide range (2.40 to 56.45) and standard deviation of 9.08, indicating substantial diversity in firm scale within the sample. This diversity allows research to explore the effect of scale on the relationship between the cash conversion cycle and profitability.
The average current solvency of the businesses in the sample is 1.53 times, which means that for every 1 dong of short-term debt, the enterprise has 1.53 dong of short-term assets to secure payment. This average indicates that textile and garment businesses generally maintain solvency at a safe level (normally CR > 1 is considered safe). However, this index has quite large volatility with a standard deviation of 0.74, ranging from 0.72 to 7.10 times. The smallest value of 0.72 indicates that there are businesses in the sample that face short-term payment pressure when short-term assets are insufficient to cover short-term liabilities, while the highest value of 7.10 times reflects having businesses that maintain excess liquidity, which may not be effective because the capital is not used for profit.
The average financial leverage of enterprises in the sample is 40.54%, i.e. debt accounts for about 40% of total assets, indicating that textile and garment enterprises tend to use debt at an average level. This index ranges greatly from 0.21% to 98.42%, reflecting very different capital financing strategies among businesses in the industry. Some businesses use almost no debt (DB = 0.21%), mainly relying on equity to finance business activities, while some businesses use very high financial leverage with debt accounting for nearly 98% of total assets, showing significant financial risks. This difference can depend on the stage of the business's development, access to capital, and business strategy.
The average fixed asset structure of textile and garment enterprises in the sample is 24.56%, meaning that fixed assets account for about 1/4 of total assets. This figure reflects the characteristics of the textile and garment industry as an industry that employs a lot of labor and working capital, while investment in fixed assets such as machinery and factories accounts for a lower proportion than heavy industries. This rate ranges from 5.39% to 62.46%, indicating differences in business models and the level of automation between businesses. Businesses with a low fixed asset ratio are usually outsourcing or employing a lot of manual labor, while businesses with high rates have often invested heavily in automation machinery and modern workshops.
4.2. Correlation Matrix
To examine the linear relationship between variables in the model and to preliminarily examine the multilinear phenomenon, the study performed Pearson correlation analysis. The results of the analysis are presented in Table 4.2.
Table 4.2. Pearson correlation matrix
Notes: * p<0.05; ** p<0.01; p<0.001
Source: Analysis results from Stata software
The Pearson correlation matrix (Table 4.2) provides preliminary insights into the relationships among variables and allows for an initial assessment of multicollinearity.
The results show that CCC is inversely correlated with both ROA and ROE. Specifically, the correlation coefficient between CCC and ROA was -0.3238 with a statistical significance of p < 0.05, indicating a statistically significant inverse relationship between these two variables. Similarly, CCC is also inversely correlated with ROE with a coefficient of -0.2868 (p < 0.05). This provides preliminary support for Hypothesis H1: a shorter cash conversion cycle is associated with higher profitability. This is completely in line with the theory of working capital management, when businesses can shorten the transition time from cash to inventory, from inventory to receivables and finally to cash, capital will not be occupied for long and can be used more effectively to generate profits.
In terms of the correlation between the two dependent variables, ROA and ROE have a very strong positive correlation with a coefficient of 0.8402 at a statistically significant p < 0.001. This result is completely reasonable and consistent with financial theory because both ROA and ROE are indicators that measure the profitability of a business, differing only in the denominator (total assets and equity). When a business is able to generate high returns from assets, it often also generates high returns for shareholders. However, an imperfect correlation (other than 1) shows that there are still differences due to the capital structure of different businesses.
Regarding the correlation between independent variables, the results showed that the correlation coefficients between independent pairs of variables were all low or medium, with no pairs of variables having a correlation coefficient exceeding 0.8. Specifically, CCC is positively correlated with QM (r = 0.4112, p < 0.05), indicating that large-scale businesses tend to have longer cash conversion cycles. This can be explained by the fact that large enterprises often have large production scales, large inventories, and longer production and delivery times. CCC is also positively correlated with CR (r = 0.4031, p < 0.05), reflecting that highly solvent businesses often maintain large levels of short-term assets including inventory and receivables, resulting in longer CCCs. The correlation between CCC and fixed assets is also positive (r = 0.2426, p < 0.05), indicating that businesses that invest heavily in fixed assets tend to have longer cash conversion cycles.
Enterprise size (QM) is positively correlated with DB (r = 0.4079, p < 0.05), reflecting the trend of large enterprises often using higher financial leverage. This is in line with the fact that large enterprises often have better access to loans and can negotiate more preferential interest rates. QM also has a positive correlation with fixed assets (r = 0.2697, p < 0.05), indicating that large enterprises often invest more in machinery, equipment and factories. In contrast, QM has a positive correlation with CR (r = 0.2191, p < 0.05), reflecting that large enterprises are better able to maintain liquidity.
Financial leverage (DB) is inversely correlated with CR (r = -0.2406, p < 0.05), which is completely reasonable because when a business uses a lot of debt, the ratio between short-term assets and short-term liabilities decreases. DB also has a positive correlation with fixed assets (r = 0.3790, p < 0.05), indicating that businesses that invest heavily in fixed assets often have to use debt to finance this large investment.
It is worth noting that the structure of fixed assets (fixed assets) is inversely correlated with both ROA (r = -0.2580, p < 0.05) and ROE (r = -0.2443, p < 0.05), indicating that businesses with a high proportion of fixed assets tend to have lower profitability. This can be explained by the fact that large investments in fixed assets increase depreciation and maintenance costs and reduce the flexibility of a business.
In general, the correlation coefficients between the independent variables are low or medium (as high as 0.4112), indicating that the likelihood of a serious multi-linear phenomenon is low. However, to confirm more certainly, the study continues to carry out VIF inspection in the next part.
4.3. Multicollinearity Test
Multilinear phenomena occur when independent variables in the model are highly correlated with each other, which can skew regression results and reduce the reliability of the estimation coefficients. To test this phenomenon more accurately, the study used the Variance Inflation Factor (VIF). The inspection results are presented in Table 4.3.
Table 4.3. VIF inspection results
Source: Analysis results from Stata software
The results of the VIF test showed that all the independent variables in the model had a VIF coefficient of less than 5, with the highest value being DB (VIF = 1.53) and the lowest being the fixed asset (VIF = 1.23). The average VIF coefficient of the model is 1.42, which is a very low number. According to the criteria of Hair et al. (2021), a VIF of less than 5 indicates that the model is not affected by severe multilateral phenomenon. Some literature even gives a tighter threshold of VIF < 10, but with an average VIF result of only 1.42 in this study, it can be confirmed that the model does not have a multi-community problem at all.
The VIF values of each variable are very low, indicating that each independent variable carries separate information and does not overlap the information with other variables. Specifically, DB has the highest VIF (1.53) but is still at a very safe level, showing that although DB is correlated with some other variables such as QM and fixed assets, this level of correlation is not strong enough to cause multi-linear phenomenon. Similarly, QM has a VIF of 1.50, CCC has a VIF of 1.43, CR has a VIF of 1.40, and a fixed asset has a VIF of 1.23, all of which suggest that these variables are independent of each other and can be included in the same model without causing multi-community problems.
This result confirms that the independent variables in the study model can be used simultaneously to explain the variability of the dependent variable without concern for multicolonial phenomena. Therefore, the regression coefficients obtained from the model will be reliable and can be used to make governance conclusions and recommendations.
4.4. Regression results
The study carried out regression by three methods: Pooled OLS (the smallest square of the sum), FEM (fixed impact model) and REM (random impact model). Statistical tests are then carried out to select the most suitable model. The results of the F-test show that the FEM model is more suitable than Pooled OLS with p-value = 0.0004 < 0.05 for the ROA model and p-value = 0.0000 < 0.05 for the ROE model. Next, the Hausman test was performed to choose between FEM and REM, the result showed that p-value = 0.8269 > 0.05 for the ROA model, thus choosing REM, while p-value = 0.0000 < 0.05 for the ROE model, thus choosing FEM.
After selecting a suitable model, the study examines the defects of the model including variance, heteroskedasticity and autocorrelation. The test results showed that both models had a variable error with a p-value = 0.0000 < 0.05. Therefore, to overcome this defect and ensure the reliability of the results, the study used the GLS (Generalized Least Squares) method to re-estimate the model. The GLS method has been used by many previous studies such as Vo Minh Long and Le Thi Thanh Hang (2022), Nguyen Thi Xuan Hong (2024) to overcome the phenomenon of variance of variance errors and self-correlation in table data.
The final GLS regression results are presented in Table 4.4 for both models with the dependent variables being ROA and ROE.
Table 4.4. GLS regression results
*Notes: *p < 0.1; **p < 0.05; **p < 0.01. The value in parentheses is z-statistic
Source: Analysis results from Stata software
The GLS regression results showed that both models were statistically significant with a Prob > chi2 = 0.0000, allowing to refute the hypothesis that there is no relationship between independent and dependent variables. Both models are statistically significant with Wald chi2(5) being 45.66 and 56.44 respectively (Prob > chi2 = 0.0000 < 0.01), which allows to refute the hypothesis that there is no relationship between independent and dependent variables. While the R-squared coefficient is not too high, it is a reasonable level in studies of corporate finance, especially when using table data and focusing on some specific factors such as the cash conversion cycle.
For Model 1 with the dependent variable ROA, the results showed that the cash conversion cycle (CCC) had an inverse impact on ROA with a coefficient of β = -0.0370412 and a very high level of statistical significance (p < 0.01, z = -5.39). This means that when the cash conversion cycle increases by 1 day, the ROA will decrease by 0.037%. In other words, if a textile and garment enterprise shortens the cash conversion cycle from 89 days (average) to 80 days (9 days down), the ROA will increase by about 0.33% (9 x 0.037%). With this result, the H1 hypothesis is accepted, affirming that the cash conversion cycle has an inverse impact on the profitability of textile and garment enterprises listed in Vietnam.
The size of the enterprise (QM) had the same impact on ROA with a coefficient of β = 0.1150595 and a statistical significance of p < 0.01 (z = 2.72). This result shows that when the size of the business (measured in total asset logarithms) increased by 1%, ROA increased by 0.115%. This reflects the advantages of large enterprises in taking advantage of economies of scale, better negotiation ability with suppliers and customers, as well as access to capital at lower costs. Therefore, the H2 hypothesis is acceptable for ROA.
Current solvency (CR) also had a positive impact on ROA with a factor of β = 2.241215 and a statistical significance of p < 0.01 (z = 3.56). This result shows that when the current solvency increases by 1 time, ROA increases by 2.24%. This reflects that maintaining good solvency helps businesses reduce liquidity risks, avoid costs arising from lack of capital, and take advantage of business opportunities when there is an unexpected payment need. The H4 hypothesis is acceptable for ROA.
Financial leverage (DB) has a coefficient of β = 0.0263275 but is not statistically significant (p = 0.211 > 0.05, z = 1.25). This shows that in the study sample, financial leverage does not have a significant effect on the ROA of textile and garment enterprises. This result can be explained by the fact that the benefits from the tax shield and the cost of using debt are balanced, or because businesses are using debt at a reasonable level. Therefore, the H3 hypothesis is not acceptable for ROA.
The structure of fixed assets (fixed assets) has an inverse impact on ROA with a coefficient of β = -0.087282 and a statistical significance of p < 0.01 (z = -3.18). This result shows that when the proportion of fixed assets in total assets increased by 1%, ROA decreased by 0.087%. This reflects that over-investing in fixed assets can reduce business flexibility, increase depreciation and maintenance costs, which in turn negatively affect profitability. The H5 hypothesis is acceptable for ROA.
For Model 2 with the dependent variable ROE, the cash conversion cycle (CCC) continues to show an inverse effect with a coefficient of β = -0.0637242 and a very high level of statistical significance (p < 0.01, z = -5.05). This result shows that when the cash conversion cycle increases by 1 day, ROE will decrease by 0.064%, the level of impact is stronger than ROA. This means that shortening the cash conversion cycle not only increases asset efficiency, but also increases shareholder returns more significantly. The H1 hypothesis is strongly accepted for both profitability indicators.
However, the size of the enterprise (QM) gives different results for ROE. Although there is a coefficient β = -0.1037158 (negative), it is not statistically significant (p = 0.191 > 0.05, z = -1.31). This shows that the size of the business does not significantly affect the ability to generate profits for shareholders. This result can be explained by the fact that large enterprises, although they have the advantage of economies of scale, also have a more complex capital structure and higher management costs. The H2 hypothesis is not acceptable for ROE.
Current solvency (CR) is also not statistically significant for ROE with a factor of β = 1.117124 (p = 0.307 > 0.05, z = 1.02). This suggests that maintaining high solvency does not necessarily lead to higher returns for shareholders. The H4 hypothesis is not acceptable for ROE.
A noteworthy finding is that financial leverage (DB) exerts a positive influence on ROE, yielding a coefficient of $\beta = 0.129$ with high statistical significance ($p < 0.01$, $z = 3.63$). Although this contradicts the initial H3 hypothesis, the results indicate that a 1% increase in the debt-to-total assets ratio leads to a 0.129% rise in ROE. This phenomenon aligns with capital structure theory, suggesting that the advantages gained from the tax shield and the leverage effect outweigh the potential costs of financial distress. When firms employ debt, equity is relatively reduced while net income is partially protected by tax-deductible interest expenses, thereby enhancing ROE. These empirical results demonstrate that the sampled textile and garment enterprises are utilizing debt efficiently, maintaining leverage within a threshold that avoids the adverse effects of over-indebtedness.
The structure of fixed assets (fixed assets) has an inverse impact on ROE with a coefficient of β = -0.1254361 and a statistical significance of p < 0.05 (z = -2.12). This result shows that when the proportion of fixed assets increased by 1%, ROE decreased by 0.125%, the impact was stronger than ROA. This further confirms that overinvesting in fixed assets not only reduces asset efficiency, but also reduces returns for shareholders. The H5 hypothesis is acceptable for both models.
4.5. Discussion of results
The results of the study have provided important evidence on the effect of the cash conversion cycle on the profitability of textile and garment enterprises listed in Vietnam in the period 2020-2024. This section will discuss the implications of the results in more depth and compare them with previous studies to further clarify the contributions of this study.
The most important outcome of the study was that the cash conversion cycle had an inverse impact on both ROA and ROE with 99% confidence. Specifically, when CCC increased for 1 day, ROA decreased by 0.037% and ROE decreased by 0.064%. This means that textile and garment businesses have a shorter cash conversion cycle, the higher the profitability. This result is completely consistent with the working capital management theory and is supported by the majority of previous empirical studies in the world as well as in Vietnam.
When compared to the study by Vo Minh Long and Le Thi Thanh Hang (2022) on the construction industry in Vietnam, the results of this study show similarity in the direction of impact but with differences in extent. While the study by Vo and Le (2022) used a sample of 58 construction enterprises in the period 2014-2018 and also concluded that CCC has the opposite impact on ROA and ROE, the current study with 25 textile and garment enterprises in the period 2020-2024 shows that the impact of CCC on ROE (β = -0.064) is stronger than ROA (β = -0.037). This can be explained by the peculiarities of the textile and garment industry with a faster working capital turnover and more reliance on cash flow management than the construction industry. Moreover, the 2020-2024 study period includes the COVID-19 pandemic period, when effective working capital management became extremely important for businesses to survive and recover.
When placing this result in the context of Nguyen Thi Xuan Hong's (2024) study of 1,531 listed enterprises in Vietnam in the period 2015-2022, the inverse relationship between CCC and profitability is not only true for a specific industry but a general rule for Vietnamese businesses. However, the current study delves into a specific industry (textiles) and provides more detailed insights into how the cash conversion cycle affects profitability in the industry's specific context. In particular, the textile and garment industry with the characteristics of made-to-order production, long production time and heavy dependence on imported raw materials, effective CCC management is particularly important.
Another notable comparison is with Roberta and Paola's (2019) study of small and medium-sized textile companies in Italy. Both studies focused on the textile industry, and both claimed that CCC had a negative impact on profitability. This shows that regardless of whether in the developed market (Italy) or the emerging market (Vietnam), with different economic conditions and business environment, effective management of the cash conversion cycle is still a key factor determining the profitability of textile and garment enterprises. However, research in Vietnam must also consider specific factors such as export policies, free trade agreements (EVFTA, CPTPP) and the impact of the COVID-19 pandemic.
In terms of control variables, the results of the study show that the size of the enterprise has a positive impact on ROA but is not statistically significant for ROE. This result is partly consistent with research by Uyar (2009) and Van et al. (2019), which argue that large enterprises have an advantage in using assets more efficiently due to economies of scale, better negotiation power, and access to capital at lower costs. However, the fact that scale does not affect ROE may be because large enterprises often have more complex capital structures and higher management costs, thus not necessarily generating higher returns for shareholders per dollar of equity.
Current solvency has a positive impact on ROA but is not statistically significant for ROE. The ROA results are in line with the research of Van et al. (2019), which shows that maintaining good solvency helps businesses reduce liquidity risks, avoid costs arising from lack of capital, and take advantage of business opportunities. However, maintaining too much liquidity can also be a sign of not using capital efficiently, thus not generating higher returns for shareholders.
A notable and somewhat unexpected result is that financial leverage does not affect ROA but has a positive impact on ROE. This result is contrary to the original H3 hypothesis but is consistent with Modigliani and Miller's theory of capital structure when considering the tax factor. When businesses use debt, tax-deductible loan interest creates a tax shield, and the financial leverage effect increases ROE when the return on assets is higher than the cost of using debt. This result shows that the textile and garment enterprises in the sample are using debt effectively and have not reached the level of debt that is too high, causing financial exhaustion costs. This is in contrast to the results of Vo and Le (2022) in the construction industry, where financial leverage has the opposite impact on profitability, possibly because construction projects often have higher risks and higher capital costs.
Finally, the fixed asset structure has an inverse impact on both ROA and ROE, which is consistent with the H5 hypothesis and previous studies by Al-Mohareb (2019) and Van et al. (2019). This result shows that textile and garment enterprises investing too much in fixed assets can reduce business flexibility, increase depreciation and maintenance costs, which in turn negatively affect profitability. This is especially true in the context of Vietnam's textile and garment industry today, when the trend of digital transformation and automation is taking place rapidly, large investments in machinery and equipment need to be carefully considered to ensure efficiency.
Overall, the results of the study have provided strong evidence of the inverse impact of the cash conversion cycle on profitability in Vietnam's textile and garment industry. This is an important contribution to the theoretical basis of working capital management in Vietnam, especially in the context that the textile and garment industry is facing many challenges from international competition, fluctuations in raw material prices and pressure from environmental standards.
5. Conclusions and recommendations
5.1. Conclusion
This study investigates the impact of the Cash Conversion Cycle (CCC) on the profitability of 25 listed textile and garment enterprises in Vietnam from 2020 to 2024. Employing the Generalized Least Squares (GLS) estimation technique with 125 observations, the empirical results reveal a significant negative correlation between the CCC and both ROA and ROE at the 1% significance level. Specifically, a one-day increase in the CCC is associated with a 0.037% decrease in ROA and a 0.064% decline in ROE. These findings corroborate the hypothesis that shortening the cash conversion cycle enhances corporate profitability within the sector. Regarding control variables, firm size and solvency exert a positive influence on ROA, while financial leverage demonstrates a positive impact exclusively on ROE, suggesting effective debt utilization. Conversely, the fixed asset structure is found to adversely affect both profitability metrics. These results align with previous literature and offer robust empirical evidence concerning the critical role of working capital management in the Vietnamese textile and garment industry.
5.2. Recommendations
Based on the results of the study, several recommendations are proposed to help textile and garment enterprises shorten the cash conversion cycle and improve profitability.
Firstly, shorten inventory times by improving inventory management processes through ordering and supply optimization. Businesses should integrate an automated inventory management system, using supply chain management software to track and forecast demand more accurately. Implementing customer demand forecasting and proper production planning will help avoid excessive inventory or shortages. At the same time, it is necessary to monitor and identify items that are slow to be consumed in order to take timely measures to avoid appropriating capital.
Secondly, accelerate the collection of receivables through the establishment of a clear and effective collection process. Businesses need to send invoices in a timely manner, closely monitor the list of receivables and remind customers to pay on time. Applying discount policies for early payment or providing flexible payment methods such as online payments and automatic transfers will encourage customers to pay faster. For large and important customers, businesses need to have a separate credit policy based on payment history and reliability.
Thirdly, optimize accounts payable management by negotiating to extend payment deadlines reasonably without affecting the cooperative relationship. Businesses can negotiate flexible payment terms such as installments or installment payments to reduce cash flow pressure. However, it is necessary to determine the order of priority in payments to ensure that important relationships are not affected by payment delays.
Fourthly, large enterprises should take advantage of scale to invest in modern technology and management systems to improve asset efficiency. At the same time, maintain solvency at a reasonable level to reduce liquidity risks but avoid excessive liquidity reserves that cause waste of capital.
Finally, carefully consider decisions to invest in fixed assets, ensuring these investments are truly effective and do not reduce business flexibility. Businesses should carefully evaluate the rate of return from fixed asset investment projects before implementing.
5.3. Limitations and direction of further research
This study has some limitations that need to be overcome in subsequent studies.
Firstly, the research sample only includes 25 listed textile and garment enterprises, which do not represent the entire textile and garment industry in Vietnam, especially unlisted enterprises. Follow-up research may expand the sample or comparative study between listed and unlisted enterprises.
Secondly, the study did not analyze the components of the cash conversion cycle (ACP, INT, APP) to determine which components have the strongest effect on profitability. Later studies can delve into the analysis of each of these components to provide more specific recommendations.
Thirdly, the 2020-2024 study period includes the COVID-19 pandemic period, but the study has not separately analyzed the impact of the pandemic on the relationship between CCCs and profitability. Further research can compare the period before, during, and after the pandemic to better see this impact.
Fourthly, the study can be extended to other industries or compared across industries to determine whether the relationship between cash conversion cycles and profitability differs between industries. Finally, the following studies may further consider factors such as corporate culture, governance capacity, and digitalization technologies in influencing the effectiveness of the administration of the cash transition cycle.
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TÁC ĐỘNG CỦA CHU KỲ CHUYỂN ĐỔI TIỀN MẶT
ĐẾN KHẢ NĂNG SINH LỜI CỦA CÁC DOANH NGHIỆP DỆT MAY NIÊM YẾT
TẠI VIỆT NAM (2020-2024)
• ĐỖ THU HƯƠNG
Viện Ngân hàng Tài chính, Đại học Kinh tế Quốc dân
TÓM TẮT:
Chu kỳ chuyển đổi tiền mặt (Cash Conversion Cycle – CCC) là một chỉ tiêu quan trọng dùng để đánh giá hiệu quả quản trị vốn lưu động trong doanh nghiệp. Nghiên cứu này phân tích tác động của chu kỳ chuyển đổi tiền mặt đến khả năng sinh lợi của 25 doanh nghiệp dệt may niêm yết trên thị trường chứng khoán Việt Nam trong giai đoạn 2020–2024. Với 125 quan sát từ 05 doanh nghiệp và áp dụng phương pháp Bình phương nhỏ nhất tổng quát (GLS), kết quả thực nghiệm cho thấy chu kỳ chuyển đổi tiền mặt có tác động âm và có ý nghĩa thống kê đến khả năng sinh lợi, được đo lường bằng tỷ suất sinh lợi trên tổng tài sản (ROA) và tỷ suất sinh lợi trên vốn chủ sở hữu (ROE), ở mức ý nghĩa 99%. Cụ thể, khi chu kỳ chuyển đổi tiền mặt tăng 1% thì ROA giảm 0,037% và ROE giảm 0,064%. Bên cạnh đó, quy mô doanh nghiệp và hệ số thanh toán hiện hành có tác động tích cực đến ROA, trong khi đòn bẩy tài chính và cơ cấu tài sản cố định ảnh hưởng tiêu cực đến khả năng sinh lợi. Các kết quả này khẳng định vai trò then chốt của việc quản trị vốn lưu động hiệu quả trong việc nâng cao hiệu quả tài chính của các doanh nghiệp dệt may Việt Nam.
Từ khoá: chu kỳ chuyển đổi tiền mặt, khả năng sinh lời, doanh nghiệp dệt may niêm yết, ROA, ROE.
