Impact of working capital management on firm profitability: Empirical study in Vietnam

improve the disadvantages of model such 
as self-correlative, changing variance; thus, the result is reliable. 
5. Research findings and discussions 
The results in Table 2 indicated that average return on asset (ROA) is 6%. Since INV, AR, AP and CCC > 0 and vary in big 
ratio, we counted the logarithm value of INV mean 4179, AR mean 4377, AP mean 3522. The size is measured by the logarithm 
value of average total asset mean 11722. The firms with average financial gearing (LV) of 50.3%, with current ratio 2098 and 
investment ratio measured by fixed assets on total assets 19.6%. 
Table 2 
Descriptive analysis 
Variable N Mean Std. Deviation Min Max 
ROA 5259 0.067 0.067 -0.120 0.330 
INV 5259 4.179 1.489 -9.401 7.717 
AR 5259 4.377 1.017 1.234 7.572 
AP 5259 3.522 1.119 -4.816 6.629 
CCC 5259 4.795 1.151 -3.271 7.771 
SIZE 5259 11.722 0.618 10.275 13.439 
LV 5259 0.503 0.218 0.022 0.971 
CR 5259 2.098 1.930 0.388 19.032 
FAR 5259 0.196 0.188 0.000 0.953 
Source: Calculated form Stata 14.0 
In Table 3, analysis of correlation coefficient considers the close relationship between two or more variables with absolute value 
of correlation coefficient. If this coefficient is lower than 0.8, there are diversified values between two variables. The correlation 
coefficients between independent variables fluctuate from -0.46 to 0.642 (lower than 0.8), multi-collinearity problem rarely 
happens. 
Table 3 
Correlation matrix 
 ROA INV AR AP CCC SIZE LV CR FAR 
ROA 1 
INV -0.21 1 
AR -0.34 0.255 1 
AP -0.29 0.33 0.497 1 
CCC -0.25 0.642 0.705 0.266 1 
SIZE -0.07 0.14 0.101 0.166 0.107 1 
LV -0.46 0.26 0.15 0.338 0.123 0.323 1 
CR 0.307 -0.15 -0.04 -0.3 0.023 -0.19 -0.6 1 
FAR 0.057 -0.26 -0.29 -0.13 -0.38 0.035 -0.08 -0.1 1 
Source: Calculated form Stata 14.0 
 264
The regression results have indicated that INV, AR, AP, CCC and WCM had negative impacts on FP at level 1%. CCC showed 
the period of time since the firm pays for the input resource until it receives money from customers. The shorter CCC, the higher 
FP. This research finding matches with the results of Jose et al. (1996), Shin and Soenen (1998), Wang (2002), Mansoori and 
Muhammad (2012), Tauringana and Adjapong Afrifa (2013), Dang & Tran (2019), Van Thuy Thi et al. (2019) and Dang et al. 
(2018). 
Table 4 
Regression results of Model 1 
 Model 1a Model 1b Model 1c Model 1d 
INV -0.00468*** 
AR -0.0198*** 
AP -0.00913*** 
CCC -0.0135*** 
SIZE 0.00995*** 0.0115*** 0.0103*** 0.0115*** 
LV -0.135*** -0.131*** -0.132*** -0.134*** 
CR 0.00152*** 0.00187*** 0.000717 0.00220*** 
FAR -0.000969 -0.0212*** 0.000971 -0.0221*** 
_cons 0.0345** 0.0837*** 0.0432*** 0.0629*** 
N 5259 5259 5259 5259 
t statistics in brackets * p<0.1, ** p<0.05, *** p<0.01 
Source: Calculated form Stata 14.0 
Table 5 
Regression results of Model 2 
 Model 2a Model 2b Model 2c Model 2d 
INV 0.00113** 
INV2 -0.000689*** 
AR 0.0138*** 
AR2 -0.00391*** 
AP 0.00589*** 
AP2 -0.00258*** 
CCC 0.0118*** 
CCC2 -0.00292*** 
SIZE 0.0107*** 0.0119*** 0.0100*** 0.0131*** 
LV -0.134*** -0.133*** -0.127*** -0.133*** 
CR 0.00167*** 0.00188*** 0.00108** 0.00229*** 
FAR -0.00567 -0.0248*** -0.00176 -0.0262*** 
_cons 0.0239 0.0128 0.0257 -0.00496 
N 5259 5259 5259 5259 
t statistics in brackets * p<0.1, ** p<0.05, *** p<0.01 
Source: Calculated form Stata 14.0 
The results of GLS regression (Table 5), for model 2 have indicated that INV, AR, AP and CCC positively impact on FP. 
Specifically, if INV, AR, AP and CCC lengthen to one unit (count by logarithm value), FP will correspondingly increase 
0.00113, 0.0238, 0.00589, 0.0118 with statistical significance of 1%. Meanwhile, variables INV2, AR2, AP2 and CCC2 
negatively impacts on FP. The signs of coefficient β1 (β1>0) and β2 (β2<0) imply the relationship between INV, AR, AP and 
CCC with FP is reverse parabolic relationship. Max value of INV, AR, AP and CCC (curving point) valued at –β1/2β2. It does 
mean that when at first lengthening INV, AR, AP and CCC will increase FP, however, when exceeding the optimized level (at 
curving point), it will cause negative impacts on FP. When the working capital is lower than the value at curving point, increasing 
working capital contributes to increase FP. This could be explained since expanding credit sales will encourage business 
(Brennan et al., 1988; Peterson & Rajan, 1997), encourage customer to buy more goods or service at low demand period (Emery, 
1984) and allow the buyer to assess the quality of the products or service before payment (Smith, 1987). However, that does not 
mean continually increase working capital will create continual increase FP, when the working capital exceeds optimize level 
(curving point), it will have negative impacts on FP. This could be explained since keep too much inventories will increase the 
expenses of stock, security and insurance (Kim & Chung, 1990). Besides, maintaining high working capital will lead to external 
capital expense, the firm needs to bear more interest rate (Kieschnick et al., 2013) and higher credit risks. Moreover, keeping 
high level of working capital means that the firm may lose many other projects for lack of money. From the regression results 
of Table 5, the research findings indicate that variables control impacts on FP. SIZE, CR positively influence on FP with 
statistical significance 1%, this also matched with researches by Dang et al. (2018) and Ha et al. (2019). Meanwhile, variables 
LV and FATA negatively influence on FP with statistical significance 1%, matching with research of Dang et al. (2019). 
N. T. T. Phuong and D. N. Hung /Accounting 6 (2020) 265
6. Conclusion and recommendations 
6.1. Conclusion 
Our research has studied the relationship between WCM and FP of non-financial firms listed on Vietnam stock exchanges in 
the period 2009-2018. Our significant contribution consists in using GLS model to control the correlation and unchanged error 
which could seriously impacts on the previous research in Vietnam. The above results ordinarily answer the questions. Firstly, 
the study has indicated the relationship between WCM and FP which was not linear relationship as in many previous researches. 
The relationship between them is non-linear (parabolic relationship). It does mean that there is an optimized level of working 
capital which balance benefit, expense and maximizes the firm performance. The lower (or higher) the working capital in 
comparison with the optimized level, the less profit the firm gets, matching with previous research of Bano Caballero et al. 
(2010, 2014). When the working capital is lower than optimized level, it is suitable for the firm to invest more in working capital 
to increase turnover. Business opportunities open with increasing credit sales for customers, increasing more inventories to 
sustain the product price and keep the production line. Investing more in working capital is similar to reducing payment for 
suppliers, collecting more payment discount; at the same time helping the firm reduce input expense and having good 
relationship with suppliers. However, when the working capital exceeds the optimized level, it would negatively influence on 
FP because of increasing expense such as: stocking, insurance, securities and expenses for mobilizing external capital. Keeping 
too high working capital is similar to increasing interest rate, credit risk and bankruptcy, and at the same time losing other 
investment opportunities of participating in other projects. Therefore, the firm managers should efficiently manage working 
capital close to optimized level to avoid the negative influence of it. 
6.2. Recommendations 
Our findings are interesting for firm managers. Firstly, they should focus on working capital management, trying to keep the 
optimize level of working capital, avoiding negative impact on the firm performance. Besides, they could increase FP by 
optimizing INV, AR, AP and CCC. Besides, it is significant to manage working capital for better firm performance. On one 
side, good WCM will increase liquidity and positive influence on the corporate finance. On the other side, good WCM will 
increase other sponsorship since the credit organization will assess balance sheet of the firm to decide to invest in the corporate 
business. However, the impacts of WCM on FP also depend on characteristics of each firm as above analysis, i.e. internal 
investment, sponsor expenses when mobilizing external capital, ability to approach capital market and financial crisis of the 
firm. Therefore, the firm managers should define the financial situation of the company to efficiently control working capital. 
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