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© 2024 by the authors; licensee Eastern Centre of Science and Education, USA 

 

Asian Business Research Journal 
Vol. 9, 31-38, 2024 
ISSN: 2576-6759 
DOI: 10.55220/25766759.173 
© 2024 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
The impact of working capital management to profitability of chemicals companies 
listed on the Vietnamese stock market 

 
Dinh The Hung1 
Ngo Anh Thu2 

 
1National Economics University, Vietnam. 
Email: hungdt@neu.edu.vn  
2The American School, Vietnam. 
Email: elenango02052007@gmail.com  
( Corresponding Author) 

 
 

 
 

Abstract 
Working capital plays an important role in ensuring business continuity. Effective management of 
working capital will directly affect the success of the business. In contrast, poor management will 
lead to a lack of financial ability, difficulty in payment, reduced sales leads to reduced profits. This 
paper examines the impact of working capital management on the profitability of chemical 
companies listed on the Vietnamese stock market. Data sources used in the analysis are from the 
financial statements of 32 chemical companies listed on Hanoi and Hochiminh City Stock 
Exchange form 2019 to 2023. Through the results of SPSS 20, the authors assessed the impact of 
working capital management to profitability and recommendations to improve working capital 
management of chemical companies listed on the stock market of Vietnam. 

 
Keywords: Chemical companies listed, Profitability, Working capital management, Working capital. 
JEL Classification: D40; F21. 

 
1. Introduction 

Working capital plays an important role in ensuring the continuity of business operations. Effective 
management of Working capital resources such as receivables, payables, inventory... will directly impact the 
success of the business. On the contrary, poor management will lead to financial shortages, making it difficult to 
pay and can cause sales to decline, leading to reduced profits. Therefore, Working capital management and profits 
are closely related to each other.  

Chemicals are an industry that plays an important role in economic development, providing input materials for 
many essential industries serving production and consumption such as fertilizers, pesticides, and detergents, paint... 
The chemical industry in the coming years is likely to increase, this shows that the importance of the chemical 
industry in Vietnam is increasingly being emphasized. Although we have not encountered many difficulties in 
capital management because the potential market is being exploited, in the long run it will be inevitable that as the 
number of businesses in the same industry increases, the market share will increase divided, the cost of 
differentiation increases. 

The article aims to understand and research how the components of Working Capital affect the profitability of 
chemical enterprises on the Vietnamese stock market. Research data was taken from the financial statements of 32 
chemical enterprises listed on the Vietnamese stock market in the period 2019-2023. From there, recommendations 
for Working Capital management to increase improve business efficiency of chemical enterprises. Thereby 
contributing to increasing profitability, while promoting the competitiveness of chemical enterprises in the market. 
 

2. Literature Review 
Deloof (2003) studied the impact of working capital management on the profits of 1,009 non-financial 

enterprises in Belgium during the period 1992-1996. The author uses the Gross Profit variable to measure 
profitability and uses variables to measure Working Capital management such as: Average collection period (ACP), 
Inventory turnover period (DIH), Average debt payment period (AP) and Cash conversion cycle (CCC). In 
addition, the author also uses control variables: Revenue size, Revenue growth, Debt ratio (DR). Research shows 
that there is an inverse relationship between gross profit and ACP and DIH. From there, the author makes 
recommendations to financial managers to improve profit levels by reducing ACP, DIH and increasing AP. 

Safi Ullah Khan et al (2005) studied the impact of Working Capital management on the profitability of listed 
enterprises in Pakistan. Relevant data are taken from the financial statements of 296 randomly selected listed 
enterprises from all major sectors of the economy except financial enterprises with 2933 observations over the 
period 1995-2004 . The linear regression research model uses dependent variables to measure business 
performance: Gross Profit (GOP) and Net Profit from Business Activities (NOP). The independent variables and 
control variables are listed the same as in Deloof's (2003) study. Research results show that ACP has a positive 
relationship; DIH, AP, CCC have negative relationships with GOP and NOP. 

mailto:hungdt@neu.edu.vn
mailto:elenango02052007@gmail.com
https://www.doi.org/10.55220/25766759.173


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Mathias B. Baveld (2012) researched and investigated listed enterprises in the Netherlands, with a sample of 37 
enterprises with the goal of analyzing the impact of working capital management on profitability during the 
financial crisis. The author analyzes the impact of components of Working Capital on profits through two 
representative factors: Return on total assets (ROA) and Gross profit (GOP). Working Capital management 
variables: ACP, AP, DIH, CCC; Control variables: Company size (SIZE), Debt ratio (DEBT), Growth rate 
(GROWTH), Current ratio (CR), Fixed asset structure (FATA). The results of this study indicate that in times of 
crisis, enterprises do not need to change their Working Capital management policies related to accounts payable 
and inventory, if the goal is to increase profitability. Accounts receivable need to change because during a crisis 
accounts receivable have a positive impact on the company's profits in the coming year. 

Research by Nguyen Thi Viet Thuy (2012) on the impact of working capital management on profitability and 
market value of joint stock enterprises in Vietnam. The research sample includes 173 enterprises in the period 2009 
- 2011 with 519 observations. In the research model, the author uses the dependent variables as: Market value of 
the company (Tobin Q); Return on assets (ROA); Return on invested capital (ROIC). And variables measuring 
Working Capital management: DSO, DIH, DPO, CCC; Control variables: DTA (liabilities/total assets), CLTAR 
(current liabilities/total assets), CATAR (current assets/total assets), CR. Research results show that there is a 
relationship between the component variables of Working Capital and profit indicators: AR, DIH, CCC have a 
negative relationship with ROA and ROIC. On the contrary, AP has a positive relationship. The study also shows 
that there is no notable relationship between Working Capital management and market value. 

Mhs Anas Husaria (2015) researched and investigated the relationship between Working Capital management 
and profitability of businesses in the Middle East and Western Europe. The study uses a sample of 54 listed 
enterprises in the Middle East and Western Europe. The author's purpose is to examine the effectiveness of 
Working Capital management on profits. The author included in the model the independent variables of Working 
Capital management: RTD, PTD, ITD, CCC; Controlled variables: SIZE, DEBT, GROWTH. The results of this 
study show that there is no statistical significance in the relationship between the components of Incoming 
Working Capital and the profitability of the business (the representative factor is ROA). Furthermore, managers 
should use other tools and strategies to improve business profitability than effective Working Capital management. 

Dinh Thi Hong Tham (2015) conducted in-depth research with a sample of 49 listed construction materials 
enterprises in the period 2009 - 2013 with 231 observations to evaluate the impact of Working Capital 
management on profitability. The author analyzes the relationship between Working Capital management and 
three representative indicators: ROA, ROE and TOBINQ. In addition to the familiar variables that have appeared, 
here the author adds to the model a control variable: State ownership ratio (STATE). The results show that there 
is no relationship between ACP, AP and TOBINQ; DIH and ROA, TOBINQ have a negative relationship, but have 
no relationship with ROE; CCC has a negative impact on TOBINQ. Companies should reduce payment time to 
suppliers and reduce CCC to increase sustainable competitive advantage and business profitability. 

Frederico Robles (2016) studies the impact of Working Capital management on profitability in different types 
of enterprises in the UK. The author analyzes the impact of each business cycle on Working Capital management 
on profitability, using a sample of 400 unlisted enterprises in the period 2006 - 2014. The author included in the 
model the independent variables of Working Capital management: AR, AP, DIH, CCC; Control variables: CR, 
DEBT, SALES. The results show that the impact of Working Capital management on profitability (the 
representative factor here is: ROA) is higher, specifically: There is a positive relationship between AP and ROA and 
a positive relationship between AP and ROA. negative relationship between AR, DIH, CCC and ROA. 

Vuong Duc Hoang Quan and Duong Diem Kieu (2016) on the impact of working capital management on the 
profits of enterprises listed on the Hochiminh City Stock Exchange (HOSE). With a sample of 29 enterprises in 4 
industries: pharmaceuticals, food, seafood, and steel in the period 2010 - 2014. The study's multivariate regression 
model includes 9 independent variables (Working capital variables: ACP, DPO, DIH, CCC; control variables: 
CA/TA, K, D/A, CL/TA, Ln_S) impact ROA. The results shown through analyzing the regression model 
separately for each industry is that the impact of Working Capital on profits for the 4 research industries is very 
different. 
 

3. Theoretical Basis of Working Capital and Working Capital Management 
3.1. Working Capital 

To ensure that the production and business process is conducted regularly and continuously, enterprises are 
required to have a certain amount of current assets. Therefore, to form current assets, enterprises must advance a 
certain amount of monetary capital to invest in that asset. This amount of capital is called the Working Capital of 
the enterprises.  

According to the Corporate Finance Textbook (Academy of Finance, 2014): “A business's working capital is the 
entire amount of advance money that the enterprises spends to invest in forming frequently needed current assets 
for production and business activities of the enterprise. In other words, Working Capital is the monetary 
expression of current assets in an enterprise”. 

Working capital is a financial measure that represents the current liquidity of a enterprise, measures the 
financial strength of a enterprise, and it plays an important role in maximizing the wealth of shareholders. . 
However, it needs to be financed and may entail other operating costs, such as credit losses on accounts receivable, 
storage costs, and logistics costs for inventory. Along with tangible and intangible assets, Working Capital is also 
a part of operating capital. If the amount of Working Capital is not guaranteed, it will lead to a shortage and 
difficulty in daily business operations.  
Working capital is determined as follows: Working capital = Current assets – Short-term liabilities 

Current assets here are specifically understood as: cash, cash equivalents; short-term receivables; inventory; 
other current assets. Short-term debts are debts with a term of 1 year or less (payables to suppliers, due debts to 
financial institutions). If current assets are less than current liabilities, the business will lack Working Capital, also 
known as a Working Capital deficit. 
 



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3.2. Working Capital Management 
Decisions related to Working Capital and short-term finance are called Working Capital management. In other 

words, Working Capital management includes all aspects of current assets and short-term liabilities. The focus of 
Working Capital management is to optimize the levels of inventory, accounts receivable, cash and other current 
assets held by the business enterprise at a point in time. It shows the relationship between a business's current 
assets and current liabilities. The goal of Working Capital management is to ensure that a business can continue its 
operations and has sufficient cash flow to meet both short-term debt due and upcoming expenses. 

Working Capital Management needs to answer several important questions that affect a company's 
sustainability and shape its financial strategy, both in the short and long term: How much cash and inventory 
should we hold? Inventory on hand? Should the credit period be extended to customers? Is the same case with the 
payment period to suppliers? Is it necessary to mobilize short-term finance from any sources and what is the debt 
repayment plan? 
 

4. Research Methods 
4.1. Research Sample 

To determine the impact of Working Capital management on the profitability of chemical enterprises, the 
authors collected secondary data taken from the annual financial reports of listed chemical enterprises on the 
Vietnamese stock market. The research sample includes 32 enterprises out of a total of 107 joint stock enterprises 
operating in the chemical manufacturing industry, including 16 enterprises listed on HNX; 16 businesses listed on 
HOSE. The study period is 5 years (2019 - 2023) with a total of 160 observations. 
 
4.2. Building Models and Research Hypotheses 
4.2.1. Research Framework 

Based on an overview of previous studies, the authors selected the variables to measure Working Capital 
management as: Average collection period (ACP), Inventory turnover period (DIH), Debt average payment period 
(DPO), Cash conversion cycle (CCC) to consider the impact of these variables on the dependent variable which is 
the profitability of the enterprise. 

The chemical industry in Vietnam is in the process of development, investment in building production lines, 
factories, and warehouses is top priority, fixed assets are increasing rapidly, leading to an increase in loans finance. 
The question here is whether enterprises can balance working capital to meet business needs? How does working 
capital management impact profitability on total assets because chemical enterprises have to use many assets in 
their operations? For this reason, the authors decided to choose the variable Return on Assets (ROA) as the 
variable reflecting profitability. Furthermore, the chemical industry still has many potential markets, so the 
authors ignore the variable Return on Equity (ROE). The authors decided to include in the analysis control 
variables: Current Ratio (CR), Debt Ratio (DR) and Enterprise Size (SIZE). Below is a Figure showing the impact 
of variables on profitability: 
 

 
Figure1. Model to study the impact of factors on working capital. 

 

4.2.2. The Content Describes the Variables, Measurement Methods and Research Hypotheses 
a. Dependent Variable:  

Return on Assets (ROA): This indicator shows the efficiency of using assets in business activities, it shows how 
much profit each dong of assets used brings. The higher the ratio shows the more efficient the enterprises is 
operating. Case studies: Wang (2002); Padachi et al (2006); Garcia-Teruel & Martinez-Solano (2007); Samiloglu & 
Demirgunes (2008); Nazir & Afza (2009) and Shama & Kumar (2011)... used this ratio as a variable to study the 
impact of Working Capital management. 

 
Calculation formula: ROA = Profit after tax/Total Assets 

 

 
 



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b. Independent Variable 

• Average collection period (ACP): is an index indicating the average number of days to collect a enterprise's 
receivables. If this ratio is low, the company only needs a few days to recover money from customers and vice 
versa. Therefore, a negative sign is expected between ACP and ROA. Studies on this variable include: Deloof 
(2003), Mkhululi Ncube (2011) and Jião Serrasqueiro (2014)... have shown that it has an impact on the Return 
on Assets. 

Calculation formula: Average collection period = (Receivables / Budget) * 365 
Research hypothesis: Ho1: There is a relationship between ACP and ROA. 

• Inventory turnover period (DIH): This indicator reflects the number of days to perform an inventory 
turnover during the year. The lower the inventory turnover period, the better the enterprise is. Expectations 
for the relationship between DIH and ROA are inverse. Previous studies: Deloof (2003); Padachi et al. (2006); 
Mhd Anas Husaria (2015) and Nguyen Thi Viet Thuy (2012)... have shown that there is an impact on the 
ROA. 

Calculation formula: Inventory turnover period = (Inventory / Cost price) * 365 
Research hypothesis: Ho2: There is a relationship between DIH and ROA. 

• Average payables payment period (DPO): This index shows the average number of days it takes a enterprises 
to pay the seller. A high coefficient shows a good relationship between the enterprise and the seller. A low 
coefficient shows that enterprises have to pay sellers in a short time, leading to an imbalance in working 
capital management. The expected sign in the relationship between DPO and ROA is negative. Previous 
studies: Frederico Robles (2016); Mhd Anas Husaria (2015); Dinh Thi Hong Tham (2015)... have shown an 
impact on the ROA. 

Calculation formula: Liabilities payment period = (Average liabilities/Cost price) *365 
Research hypothesis: Ho3: There is a relationship between DPO and ROA. 

• Cash conversion cycle (CCC): This index measures the time it takes to invest in Working Capital until cash is 
recovered from sales revenue. The higher this index shows the time capital resources are invested in high 
working capital, leading to scarcity of payment resources. If this number is small, it can be assessed as good 
working capital management ability. The expected sign for the relationship between CCC and ROA is 
negative. Previous studies: Safi Ullah Khan et al (2005); Mathias B. Baveld (2012) and Jião Serrasqueiro 
(2014),... have shown an impact on the ROA. 

Calculation formula: CCC = ACP + DIH – DPO 
Research hypothesis: Ho4: There is a relationship between CCC and ROA. 
c. Control variable 

• Current ratio (CR): This index shows the solvency of a enterprise in the short term, reflecting whether the 
enterprise has enough ability to pay short-term debts with short-term assets. This index is greater than 1, 
indicating good short-term solvency of the enterprise. On the contrary, if this index is less than 1, it shows 
that the enterprise cannot ensure its solvency. Previous studies: Frederico Robles (2016) and Mathias B. 
Baveld (2012) 

Calculation formula: Current ratio = Current assets / Short-term liabilities 

• Debt ratio (DR): This ratio shows what percentage of a business's assets are from debt. If a business has a 
low ratio, it has a high ability to repay debt; on the contrary, a high ratio is an alarming point in managing 
the enterprise's business capital because of the higher level of risk. Previous studies: Nguyen Thi Viet Thuy 
(2012), Vuong Duc Hoang Quan and Duong Diem Kieu (2016)... 

Calculation formula: Debt Ratio = Total debt/Total assets 

• Enterprise size (SIZE): This index shows the scale of the enterprise. Larger scale shows that business 
operations are favorable, increasing revenue, thereby increasing profits. This variable has been studied by 
Dinh Thi Hong Tham (2015). 

Calculation formula: Enterprise size = Ln (Total assets) 
 
4.3. Build A Research Model 

The authors use a multivariate regression research model to study the impact of Working Capital 
management on profitability. The authors built 2 separate research regression models because the CCC variable is 
formed from 3 variables ACP, DIH, DPO (CCC = ACP + DIH – DPO) so that when putting data through analysis 
software variables were not removed from the model for multicollinearity reasons. In addition, analyzing the 
impact on the two models helps managers make decisions and policies that will not be too misleading. Businesses 
can adjust the ACP, DIH, and DPO indexes, but the CCC index remains at the allowable level. 
Model 1:  

ROA = β01 + β11.ACP + β21.DIH + β31.DPO + β41.CR + β51.DR + β61.SIZE + ε1 
Model 2: 

ROA = β02 + β12.CCC + β22.CR + β32.DR + β42.SIZE + ε2 
 

5. Analyze and Discuss Research Results 
5.1. Statistical Analysis Describes the Variables 

The authors put the data set collected from 32 chemical enterprises in the period 2019 – 2023 with a total of 
160 observations into SPSS analysis software to run descriptive statistics and obtain results as shown in the table 
below: 

 
 
 
 

 



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Table 1. Descriptive statistics of variables in the regression model. 

Descriptive statistics 

 N Min. Max. Average Standard deviation 

ROA 160 -0.046 0.266 0.110 0.057 
ACP 160 1.605 539.056 64.340 71.994 
DIH 160 3.992 308.291 99.687 56.082 
DPO 160 6.451 382.536 139.218 68.022 
CCC 160 -260.371 241.751 24.809 79.149 
CR 160 0.512 6.897 2.246 1.181 
DR 160 0.109 0.758 0.407 0.161 
SIZE 160 10.888 16.622 13.430 1.286 
Valid N (Listwise) 160     

 
From the data table above, it can be seen that:  

• Average collection period (ACP) has an average of 64,340 days with a standard deviation of 71,994 days. The 
above average value is appropriate because normally invoices for sales of goods and services will have a 
payment term of 15 to 60 days.  

• The average Inventory turnover period (DIH) is 99,687 days with a standard deviation of 56,082 days. The 
above average period corresponds to one quarter (3 months), which is a suitable period for chemical-related 
products. 

• The average Debt payment period (DPO) is 139,218 days on average, ranging from 6,451 days to 382,536 
days. Fast or slow payment will depend on the financial situation and management policy of the business.  

• The average Cash Conversion Cycle (CCC) is 24,809 days, meaning the time period from investment in 
Working Capital to the time of cash recovery from sales revenue is short. This also demonstrates good 
Working Capital management ability, because this index is calculated from the 3 aforementioned indexes: 
ACP, DIH and DPO. 

• The average Current ratio (CR) is 2.246, this index is greater than 1, showing that the short-term solvency 
of businesses is in good condition.  

• The average Debt ratio (DR) is 0.407 (40.7%), in the capital structure of the enterprise, 40.7% is borrowed 
capital and 59.3% is total assets. This means that businesses can still utilize their assets to invest in other 
areas to earn higher profits.  

• The average Enterprise size (SIZE) is 13,430, suitable for enterprises in developing countries like Vietnam. 
 
5.2. Analyze Correlations Between Variables 

The correlation coefficient between variables shows the relationship between variables. We will evaluate the 
correlation through the Pearson coefficient (r) with a significance level of 5% (sig≤0.05).  
 

Table 2. Correlation coefficients between variables in the model. 

Correlation coefficient 

 ROA ACP DIH DPO CCC CR DR SIZE 

ROA 1 -0.313** -0.022 -0.410** 0.052 0.297** -0.466** 0.133 
ACP  1 -0.068 0.493** 0.437** 0.101 -0.109 0.041 
DIH   1 0.172* 0.499** 0.221** -0.250** -0.009 
DPO    1 -0.289** -0.342** 0.307** 0.143 
CCC     1 0.543** -0.540** -0.091 
CR      1 -0.818** 0.214** 
DR       1 -0.122 

SIZE        1 
Note:  **. Correlation is significant at the 0.01 level (2-tailed). 

*. Correlation is significant at the 0.05 level (2-tailed). 
 

 
From the table above, we see that ROA is positively correlated with CCC, CR, and SIZE, but this relationship 

is not statistically significant at the 5% level, because CCC has P-value = 0.51 and SIZE has P-value. = 0.09; As for 
CR, the correlation is statistically significant at the 1% level. ROA is negatively correlated with ACP, DPO, and 
DR with statistical significance at the 1% level; with DIH is not statistically significant at the 5% level, because P-
value = 0.78. 

ACP is positively correlated with the DPO and CCC at the 1% significance level and increasing ACP will 
increase DPO and CCC; Other variables are not statistically significant because P-value>0.05. 

DIH is positively correlated with DPO and CR at the 5% significance level; with CCC at the 1% significance 
level; Increasing the number of days of inventory will increase DPO and CCC. DIH is negatively correlated with 
the DR at the 1% significance level.  

DPO is positively correlated with ACP, DR at the 1% significance level, with DIH at the 5% level, and with 
SIZE at the 10% significance level; Increasing DPO will increase ACP and DIH. DPO is negatively correlated with 
CCC and CR at the 1% level; CCC decreases with increasing DPO. 

CCC is positively correlated with ACP, DIH, CR at the 1% level; Increased CCC increases ACP and DIH. CCC 
is negatively correlated with DPO, DR at the 1% level, with SIZE at the 10% level. 

CR is positively correlated with ACP, DIH, CCC, SIZE with the corresponding P-value: 0.20; 0.01; 0.00; 0.01. 
CR is negatively correlated with DPO, DR with P-value respectively: 0.00; 0.00. 

DR is positively correlated with DPO at the 1% level. DR is negatively correlated with ACP, DIH, CCC, CR, 
SIZE with the corresponding P-value: 0.17; 0.00; 0.00; 0.00; 0.13. 

SIZE is positively correlated with ACP, DPO, and CR with P-values of: 0.60; 0.07; 0.01. SIZE is negatively 
correlated with DIH, CCC, DR with P-value respectively: 0.91; 0.25; 0.13. 



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5.3. Analyze Regression Models 
The authors conducted regression analysis according to the two proposed models to test the relationship.  

 

5.3.1. Model 1 
ROA = β01 + β11.ACP + β21.DIH + β31.DPO + β51.CR + β61.DR + β71.SIZE + ε1 

 
Table 3. Results of running model 1. 

Model summaryb 

Model R R squared 
Adjusted 
R squared 

Random errors Durbin-Watson 

1 0.650a 0.423 0.400 0.0443935 1.105 
Note: a. Predictors: (Constant), SIZE, DIH, ACP, DR, DPO, CR 

b. Dependent variable: ROA 

 
Table 4. Model 1 regression results. 

Coefficientsa 

Model 

Unstandardized 
regression coefficient 

Standardized 
regression 
coefficient 

T Sig. 

Collinearity 
Statistics 

Β Std. error Beta Tolerance VIF 

1 

(Constant) 0.211 0.044  4.744 0.000   

ACP 0.000 0.000 -0.304 -3.760 0.000 0.577 1.732 

DIH 0.000 0.000 -0.129 -1.812 0.072 0.748 1.337 

DPO 0.000 0.000 -0.145 -1.586 0.115 0.449 2.229 

CR -0.016 0.006 -0.328 -2.855 0.005 0.286 3.491 

DR -0.262 0.039 -0.737 -6.746 0.000 0.316 3.161 

SIZE 0.006 0.003 0.145 2.184 0.030 0.852 1.173 
Note: a. Dependent Variable: ROA 

 
From Table 3, we see that the Durbin Watson index is 1.105 (1<d<3), so there is no autocorrelation 

phenomenon in model 1. The model can explain 40% of the variation in ROA (due to The adjusted R squared is 
0.400) so the 6 independent and control variables influence 40% of the change in the dependent variable, the 
remaining 60% is due to other factors and random errors. 

From Table 4, the VIF values of the variables ACP, DIH, DPO, CR, DR, SIZE are all less than 10. In addition, 
the research data does not contain questionnaires using the Likert scale, so in regression model 1 Multicollinearity 
does not occur. With a significance level of 5%, corresponding to Sig values. of the variables in the model ACP, 
DIH, DPO, CR, DR, SIZE are: 0.000; 0.072 (>0.05); 0.115 (>0.05); 0.005; 0.000; 0.030. From there, we eliminate 
from the regression model the variables with Sig. >5% are: DIH, DPO. Based on the unstandardized regression 

coefficient (β) in the table above, we can rewrite regression model 1 in unstandardized form as follows: 
ROA = 0.211 + 0.000 ACP – 0.016 CR – 0.262 DR + 0.006 SIZE 

The β coefficient of the ACP variable has a small value, the DIH and DPO indices are eliminated because they 
are not meaningful at the 5% significance level. In the above equation, the variables retain their original units. The 
unstandardized regression equation has more mathematical meaning than economic meaning as it only reflects the 
change in the dependent variable when each independent variable changes under the condition that the remaining 
independent variables must be fixed. 

If considered at the 10% significance level, the variables DIH and DPO also have the same impact on 

profitability (βDIH = 0.000; βDPO = 0.000) with an insignificant level of influence. 
Model 1, after regression analysis, has one remaining variable, ACP, which has a relationship with ROA at the 

1% significance level. ACP affects ROA in the same direction, meaning that under the condition that other 

variables do not change, increasing the average number of days of collection will increase profitability. The β 
coefficient of ACP according to the table above is 0.000. When ACP increases by 1 day, ROA increases by 0.000 (or 
0.0%). Thus, it can be said that the impact of the number of days of collection does not have too great an impact on 
profitability.  

If we consider the standardized regression coefficient (βeta), we have the following standardized regression 
equation: 

ROA = -0.304 ACP – 0.328 CR – 0.737 DR + 0.145 SIZE 
In the standardized regression equation, the variables have been regressed to the same unit. Furthermore, the 

standardized regression model is more economic than mathematical. Here, the impact of the variables on ROA is 
listed in descending order as: DR (0.737); CR (0.328); ACP (0.304); SIZE (0.145).  
 

Table 5. Results of running model 2. 

Model summaryb 

Model R R squared 
Adjusted 
R squared 

Random errors Durbin-Watson 

1 0.535a 0.286 0.268 0.0490483 1.058 
Note: a. Predictors: (Constant), SIZE, CCC, DR, CR 

b. Dependent variable: ROA 

 
 
 
 
 
 



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Table 6. Model 2 regression results. 

Coefficientsa 

Model 

Unstandardized regression 
coefficient 

Standardized regression 
coefficient t Sig. 

Β Std. error 
Beta 

1 

(Constant) 0.208 0.049 4.263 0.000 
CCC 0.000 0.000 -0.239 -2.816 0.005 
CR -0.010 0.006 -0.203 -1.633 0.104 
DR -0.268 0.043 -0.753 -6.253 0.000 
SIZE 0.003 0.003 0.063 0.873 0.384 

Note: a. Dependent variable: ROA. 

5.3.2. Model 2 
From Table 5, it shows that the Durbin Watson index is 1.058 (1<d<3), so there is no autocorrelation 

phenomenon in model 2. The model can explain 26.8% of the variation in ROA (due to the adjusted R-squared 
index of 0.268), that is, with 4 independent variables, the included control affects 26.8% of the change in the 
dependent variable, the remaining 73.2% is due to other variables. outside the model and random error. 

From Table 6, the VIF values of the variables CCC, CR, DR, SIZE are all less than 10, so multicollinearity 
does not occur in regression model 2. With a significance level of 5%, corresponding to Sig values. of the variables 
in the model CCC, CR, DR, SIZE are: 0.005; 0.104 (>0.05); 0.000; 0.384 (>0.05). From there, we eliminate from the 
regression model the variables with Sig. >5% are: CR, SIZE. We have an unstandardized regression equation 
rewritten as follows: 

ROA = 0.208 + 0.000 CCC – 0.268 DR 
Through the equation, it shows that the CCC variable has a positive impact on ROA at the 1% significance 

level, but with a small impact on profitability. βCCC = 0.000, meaning that under the condition that other variables 
do not change, when increasing CCC by 1 day, ROA increases by 0.000 (or 0.0%). In addition, the DR variable has 

a negative impact on ROA, βDR = -0.268, meaning that under the condition that other variables do not change, 
when DR increases by 1 unit, ROA decreases by 0.268 (or 26.8%). . The impact of DR on ROA is quite large, 
requiring timely adjustments. 

If we consider the standardized regression coefficient (βeta), we have the following standardized regression 
equation: 

ROA = -0.239 CCC – 0.203 CR – 0.753 DR + 0.063 SIZE 
 
5.4. Discuss Research Results 

The study found a positive relationship between the ACP variable and ROA. Therefore, accept the first 
hypothesis (Ho1: There is a relationship between ACP and ROA). Analytical data extracted from the software 
shows that the impact here is insignificant. This means that when increasing the number of receivable days by 1 
unit, the profit does not increase much. However, based on this result, we can make the conclusion that: increasing 
the number of receivable days means increasing the payment term. customers, this policy helps build lasting 
relationships with partners, increasing revenue means increasing profits. This conclusion is similar to the studies of 
Safi Ullah Khan and Colleagues (2005) and Mathias B. Baveld (2012).  

The second hypothesis (Ho2: There is a relationship between DIH and ROA) is rejected. Because the DIH 
variable was removed from the regression model (sig.>0.05). This result is similar to the study of Mhd Anas 
Husaria (2015).  

The third hypothesis (Ho3: There is a relationship between DPO and ROA) is rejected for the same reason as 
the DIH variable in the second hypothesis. This result is similar to the research of Mkhululi Ncube (2011) and 
Mhd. Anas Husaria (2015). 

The study also found a positive relationship between CCC and the ROA variable. Therefore, we accept the 
fourth hypothesis (Ho4: There is a relationship between CCC and ROA). The impact of CCC on profits is 
insignificant, similar to the results obtained from hypothesis 1. The increase (decrease) in time from investment in 
inputs to recovery of money from sales revenue will affect profit increase (decrease) but very little. The results of 
this study are not similar to any of the studies mentioned by the author. 
 

6. Propose Recommendations 
Research results show that the impact of Working Capital management on the profitability of chemical 

enterprises listed on the Vietnamese stock market is not large. The analyzed data is from a period when the 
economy is stable and the chemical industry in Vietnam is still in a strong development cycle, so there is no clear 
impact on working capital management on profitability. However, with the goal of maximizing profits and benefits 
for the business, changes should still be applied, even if they have a small impact on the overall goal. The authors 
make some recommendations as follows: 

• Increase Average collection days (ACP), to increase business profitability. However, it is necessary to adjust 
to a reasonable and controllable level. That means, enterprises create conditions for customers to extend 
payment time but to an acceptable level. 

• Increase the number of days of the Cash Conversion Cycle (CCC). Increasing this index has many adjustment 
options when: CCC = ACP + DIH – DPO. Changing one of the variables in the formula will not only affect 
the CCC variable but also directly affect the internal margins. It is recommended that when businesses want 
to influence the CCC variable to increase profitability, they should be careful with the component variables. 
However, this result may change in the long term, when enterprises must make trade-offs and choose the 
balance between elements of Working Capital. Therefore, enterprises will need more specific and practical 
preparation measures. 

The above work is only temporary and lacks much basis, so to better prepare for long-term situations. 
Enterprises need to take the following preparation measures: 



Asian Business Research Journal, 2024, 9: 31-38 

38 
© 2024 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

• Send management staff to in-depth training and knowledge preparation; 

• Set hypothetical situations related to negative changes in working capital management, and find solutions; 

• Strictly implementing the terms signed in the sales contract will create good habits for businesses. 

• Need to direct the development of methods to calculate the need for Working Capital, on that basis to 
compare and evaluate the effectiveness of each unit and synthesize the Working Capital needs of the entire 
enterprise. 

• Build an effective information system and regularly analyze and evaluate the efficiency of using working 
capital of the enterprise. Take into account the implementation of an appropriate business management 
system - synchronous ERP in the enterprise, thereby providing timely information for the process of 
regularly evaluating the effectiveness of Working Capital use. 

 
7. Conclusion 

The study analyzed and evaluated the impact of Working Capital management on the profitability of chemical 
enterprises listed on the Vietnamese stock market. The authors have made their recommendations objectively. The 
contributions of the research will help Working Capital managers in the chemical industry make correct, beneficial 
decisions that optimize the organization's goals. However, the study cannot avoid shortcomings such as: the 
number of research samples is small (32 chemical enterprises with a total number of observations of 160). 
Furthermore, listed chemical enterprises only account for a small portion of the total number of enterprises in the 
industry, so the research results are still limited and need to expand the research sample to all enterprises in the 
industry. 

 

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