Identifying factors influencing on the cash flow of construction companies: Evidence from Vietnam stock exchange

* Corresponding author. 
E-mail address: anhcongtuan@gmail.com (V. C. Nguyen) 
© 2020 by the authors; licensee Growing Science, Canada 
doi: 10.5267/j.msl.2019.7.036 
Management Science Letters 10 (2020) 255–264 
Contents lists available at GrowingScience 
Management Science Letters 
homepage: www.GrowingScience.com/msl 
Identifying factors influencing on the cash flow of construction companies: Evidence from Vi-
etnam stock exchange 
Thi Tu Oanh Lea, Thi Thanh Thuy Vub and Van Cong Nguyenc* 
aAccounting Department, University of Labour and Social Affairs (ULSA), Vietnam 
bAccounting Department, University of Labour and Social Affairs (ULSA), Vietnam 
cSchool of Accounting and Auditing, The National Economics University, Vietnam 
C H R O N I C L E A B S T R A C T 
Article history: 
Received: June 9 2019 
Received in revised format: July 9 
2019 
Accepted: July 24, 2019 
Available online: 
July 26, 2019 
 Good management of cash flow will help managers control cost estimates, control plan implemen-
tation as well as control additional costs, ensuring project success. Therefore, identifying the factors 
influencing the cash flow of enterprises helps create synchronous solutions to improve the effi-
ciency of cash flow management, contributing for improving the operational efficiency of the en-
terprises. This study is based on a survey to determine the factors influencing the cash flow through 
the questionnaires and interviews of 105 construction companies listed on the Vietnam Stock Ex-
change in 2018. The study conducts descriptive statistics analysis of surveyed enterprises; check 
the Exploratory Factor Analysis (EFA) analysis conditions to determine the groups of influencing 
factors on cash flow in construction companies listed on the Vietnam Stock Exchange. At the same 
time, this study remains to check a sample T-test with a value set to 3.0 and 5% reliability and 
analysis of EFA discovery factors to select components with the highest coefficients and load com-
ponents. Based on the EFA analysis results, the study finds six main groups of factors affecting the 
cash flow of construction companies. They are: macro environment; construction period; payables 
and receivables; construction cost; retention; loan payment and tax. The study also shows that the 
effect of variables to cash flow management varied with a mean value from 0.17 to 0.518. Based 
on the research results, the authors provide some recommendations to strengthen cash flow man-
agement in construction companies listed on the Vietnam Stock Exchange. 
© 2020 by the authors; licensee Growing Science, Canada 
Keywords: 
Cash flow 
Construction companies 
Identifying factors 
Vietnam stock exchange 
1. Introduction 
Construction enterprises play an important role in the economy, reflecting the sustainable development 
of the economy and participating in most other economic sectors. Construction enterprises face many 
risks due to the large value of goods. If the inventory is high, slow debt recovery will affect liquidity, 
thereby slowing down the ongoing activities of the enterprises. Many previous studies have shown that 
poor liquidity is a fundamental factor affecting the breakdown of contracts and leading to the bankruptcy 
of construction businesses (El-Kholy, 2014). Cash flow management is one of the governance contents 
dominating the survival of a business. Cash flow management is an important activity to create the li-
quidity of a business to monitor, to analyze and to maximize the net value earned when taking the earned 
money minus the amount to be spent. In order to manage cash flow effectively, it is necessary to under-
stand the factors affecting cash flow. The research on cash flow and cash flow index has been more 
 256
focused since the beginning of 1966 in the world. Cash flow is considered as an important predictor of 
the enterprises’ financial situation. Cash flow not only plays an essential role in credit rating but also 
helps business forecasting the risk of bankruptcy. The collapse of Lehman Brothers was an alarm bell to 
businesses for ineffective cash flow management. The cash-flow problem can influence productivity and 
affect the quality of the product (Gundecha, 2018). 
In Vietnam, the management of cash flow in enterprises has not really been properly concerned. Since 
the characteristics of the construction industry are associated with large-scale products, complex struc-
tures; long construction time; companies must spend a large amount of initial capital. Finding out the 
factors affecting cash flow is really necessary to help businesses improve liquidity and manage cash flow 
effectively. This study is carried out to identify factors influencing on the cash flow of Vietnamese con-
struction companies to find appropriate solutions to strengthen cash flow management, ensure solvency 
and security of financial security and contribute to improve business efficiency. The study is experi-
mental research on identifying and analyzing the factors impact on the cash flow in Vietnamese listed 
construction companies. The study also recognizes the factors affecting businesses' cash flow by regres-
sion analysis. Factors are divided into groups of factors that influence cash flow by analyzing the corre-
lation between them. 
To conduct this study, the authors conducted a sample and surveyed 105 construction companies listed 
on the Vietnam Stock Exchange in 2018. The survey results received 102 valid responses, accounting 
for 97.14% in the total issued questionnaire. We believe our sampling fully meets the comprehensive and 
complete aspects of the research sample to ensure that research results are reliable when they are analyzed 
and verified. The selection of our research sample is based on the following criteria: 
First, enterprise size: Selected construction enterprises include large and medium-sized enterprises,  ... sing 
SPSS 22. In Table 3, the average of all scales is greater than 3.0. The hypothesis H0 is that variables with 
an average value of 3.0 are rejected while the hypothesis H1 states that variables with a mean of 3.0 are 
acceptable. Observation from the mean value in Table 3 shows that most of the variables reached the 
mean at the consent level (the mean ranged from 3.41 to 4.2). However, 8 variables have lower average 
values (mean between 3.01 and 3.38), which are F22, F23, F51, F52, F53, F13, F14, F64. From Table 4, 
with the level of freedom of choice of 101 observations, the level of test significance of 3 variables> 0.05 
(F23, F51, F14) should not reject the hypothesis H0 (mean = 3). Based on the results in Table 3, the mean 
of these variables has the lowest value (from 3.01 to 3.14), meaning that the comments on these variables 
can reach the average level. The remaining variables are worth Sig. (2 –tailed) <0.05 so the hypothesis 
H1 is approved (all variables have a mean level greater than 3, all scales are at the average level and 
agree). 
Table 4 
One-Sample Test 
 Cod Variables 
Test Value = 3 
t df Sig. (2-tailed) 
Mean 
Difference 
95% Confidence Interval of the Difference 
Lower Upper 
1 F31 Payment duration 12.953 101 .000 1.147 .97 1.32 
2 F32 Terms of payment 10.992 101 .000 .980 .80 1.16 
3 F33 Advance payment 11.928 101 .000 .990 .83 1.15 
4 F21 Retained rate 6.239 101 .000 .520 .35 .68 
5 F22 Time of releasing retention 4.636 101 .000 .382 .22 .55 
6 F23 Limit of retention 1.084 101 .281 .088 -.07 .25 
7 F15 Loan payment 13.345 101 .000 1.010 .86 1.16 
8 F16 Withholding tax 7.074 101 .000 .578 .42 .74 
9 F11 Payment for suppliers 8.543 101 .000 .814 .62 1.00 
10 F12 Delayed payment for suppli-
ers 
5.251 101 .000 .461 .29 .63 
11 F52 Over work measurement 2.191 101 .031 .176 .02 .34 
12 F51 Under work measurement 1.519 101 .132 .137 -.04 .32 
13 F41 Cost of materials 8.594 101 .000 .696 .54 .86 
14 
F42 
Wages of labor and staff 13.313 101 .000 .990 .84 1.14 
15 F43 Plan and equipment costs 6.369 101 .000 .500 .34 .66 
16 F17 Bank Interest rate 5.041 101 .000 .520 .32 .72 
17 F13 Selling price adjustment 5.212 101 .000 .392 .24 .54 
18 F34 Work execution errors 2.524 101 .013 .216 .05 .39 
19 F14 Claims .105 101 .916 .010 -.17 .19 
20 F66 Global financial crisis 7.032 101 .000 .598 .43 .77 
21 F61 Lending interest rates of state 
banks 
7.985 101 .000 .716 .54 .89 
22 F62 Inflation 7.848 101 .000 .627 .47 .79 
23 F63 Tax policies 9.043 101 .000 .725 .57 .88 
24 F64 Political instability 2.980 101 .004 .255 .09 .42 
25 F65 Technological advances 7.120 101 .000 .549 .40 .70 
Source: Compiled by the authors based on research results 
Table 5 
KMO and Bartlett's Test 
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. 0.624 
Bartlett's Test of Sphericity 
Approx. Chi-Square 1833.298 
df 231 
Sig. 0.000 
Source: Compiled by the authors based on research results 
T.T.O. Le et al. / Management Science Letters 10 (2020) 261
Table 5 shows that the coefficient KMO = 0.624> 0.5 and Sig = 0 <0.05 indicates that the variables have 
a linear relationship with each other and factor analysis is consistent with survey data. 
Table 6 
Total Variance Explained 
Initial Eigenvalues Extraction Sums of Squared Loadings Rotation Sums of Squared Loadings 
Total % of Vari-
ance 
Cumulative % Total % of Vari-
ance 
Cumulative % Total % of Vari-
ance 
Cumulative % 
7.915 35.978 35.978 7.915 35.978 35.978 3.461 15.730 15.730 
2.439 11.088 47.066 2.439 11.088 47.066 3.396 15.437 31.167 
2.265 10.295 57.360 2.265 10.295 57.360 3.372 15.328 46.495 
1.723 7.832 65.192 1.723 7.832 65.192 2.645 12.024 58.519 
1.470 6.683 71.875 1.470 6.683 71.875 2.415 10.979 69.499 
1.245 5.661 77.536 1.245 5.661 77.536 1.768 8.037 77.536 
.797 3.622 81.158 
.716 3.253 84.411 
.558 2.537 86.948 
.512 2.325 89.273 
.427 1.941 91.214 
.419 1.905 93.119 
.296 1.346 94.465 
.260 1.181 95.646 
.236 1.074 96.720 
.172 .780 97.500 
.157 .714 98.214 
.127 .578 98.793 
.097 .439 99.232 
.093 .422 99.654 
.054 .247 99.902 
.022 .098 100.000 
Extraction Method: Principal Component Analysis. 
Source: Compiled by the authors based on research results 
As can be seen from Table 6, there are 6 groups of factors with Initial Eigenvalues > 1 affecting the cash 
flow management of Vietnamese listed construction enterprises. The total variance extracted is 77,536%, 
showing that the factors can explain 77,536% for the influence on the management of cash flow of listed 
construction companies. 
Table 7 
Rotated Component Matrixa 
Cod Variables Component 1 2 3 4 5 6 
F61 Lending interest rates of state banks .871 
F63 Tax policies .822 
F66 Global financial crisis .690 
F62 Inflation .661 
F64 Political instability .549 .620 
F65 Technological advances .805 
F52 Over work measurement .759 
F53 Work execution errors .740 
F13 Selling price adjustment .657 
F31 Payment duration .776 
F11 Payment for suppliers .758 
F32 Terms of payment .752 
F33 Advance payment .724 
F12 Delayed payment for suppliers .661 
F43 Plan and equipment costs .842 
F41 Cost of materials .809 
F42 Wages of labor and staff .704 
F21 Retained rate .857 
F22 Time of releasing retention .894 
F15 Loan payment .817 
F16 Withholding tax .674 
 Extraction Method: Principal Component Analysis. 
 Rotation Method: Varimax with Kaiser Normalization. 
 a. Rotation converged in 6 iterations. 
Source: Compiled by the authors based on research results 
 262
Table 7 shows that 6 groups of factors are formed with the change of scale compared with the original 
plan. Specifically: 
Group 1: Consisting of 4 observed variables: F61, F63, F66, F62, to keep the same group of Macro 
environment as originally expected. 
Group 2: Includes 5 observed variables: 2 variables from Macro environment group (F64, F65), turn F13 
from “Financial risk” group and 2 initial variables of “During construction” group (F52, F53). Therefore, 
these variables are grouped “During construction”. 
Group 3: Consisting of 5 variables, grouped by 3 variables F31, F32, F33 of “Receivables” group and 2 
variables of “Financial risk” group F11, F12. This group is renamed “Payables and Receivables”. 
Group 4: The “Construction cost” group consists of 3 unchanged variables. 
Group 5: The “Retention” group has 2 original variables. Limit of retention is eliminated due to low 
factor loading factor (less than 0.5). 
Group 6: Includes two variables of the group “Financial risk” is “Payment” and “Withholding tax”. The 
new group is named “Payment of principal and tax”. 
Table 8 
Component Score Coefficient Matrix 
Cod Variables Component 1 2 3 4 5 6 
F61 Lending interest rates of state banks .337 -.096 -.130 -.075 .032 .190 
F63 Tax policies .287 .015 -.087 -.062 -.020 .026 
F62 Inflation .214 -.035 -.048 .084 -.032 -.045 
F66 Global financial crisis .202 .036 .075 -.042 .036 -.328 
F65 Technological advances .022 .337 -.050 -.146 -.054 -.106 
F52 Over work measurement -.112 .250 .010 .037 .000 .004 
F53 Work execution errors -.023 .246 -.076 -.039 .049 .076 
F13 Selling price adjustment -.013 .198 .015 -.018 .111 -.090 
F64 Political instability .123 .170 .007 -.050 -.126 .021 
F31 Payment duration .013 -.121 .316 -.068 -.128 .069 
F32 Terms of payment -.142 .036 .299 -.058 .054 -.081 
F11 Payment for suppliers -.080 .080 .271 -.037 -.088 .005 
F33 Advance payment -.031 -.004 .271 -.121 .198 -.223 
F12 Delayed payment for suppliers -.023 -.036 .209 .109 -.187 .103 
F43 Plan and equipment costs -.143 .027 -.105 .412 -.012 -.001 
F41 Cost of materials .006 -.081 -.016 .381 -.012 -.106 
F42 Wages of labor and staff .123 -.134 -.070 .338 -.031 -.023 
F22 Time of releasing retention -.027 .050 -.117 -.043 .446 -.071 
F21 Retained rate -.013 -.045 -.015 -.028 .390 -.018 
F16 Withholding tax -.066 .120 -.073 -.018 -.015 .399 
F15 Loan payment .027 -.059 .027 -.100 -.050 .518 
Extraction Method: Principal Component Analysis. 
 Rotation Method: Varimax with Kaiser Normalization. 
 Component Scores. 
Source: Compiled by the authors based on research results 
Table 8, variables with the highest factor loading and component score coefficient from the rotated com-
ponent matrix and component score matrix were selected significantly. 
In the group of factor 1, “Macro environment”, observed variables F61 “Lending interest rates of state 
banks” has the strongest impact (0.337) followed by “Tax policies” (0.287). Variable “Global financial 
crisis” has the weakest impact (0.202). 
In the group of factor 2 “During construction” and “Technological advances” have the strongest impacts 
(0.337). The variable with the least impact is “Political instability” (0.170). 
In the group of factor 3 “Payables and Receivables”, had quite similar effects, of which, the highest 
impact is “Payment duration” and the lowest is “Delayed payment for suppliers”. 
Group of “Construction cost” has variables with high impact levels, “Plan and equipment costs” has the 
greatest impact, followed by “Cost of materials”. 
The “Retention” group also has a high weight, “Time of releasing retention” is higher than “Retained 
rate”. 
T.T.O. Le et al. / Management Science Letters 10 (2020) 263
The group of “Principal loan and tax payment” has the highest weighting factor, “Loan Payment” is 
higher than “Withholding tax”. 
5. Conclusions and recommendations 
The construction companies listed on the stock exchange of Vietnam largely are formed through the 
process of equitization of state-owned enterprises, so the management in general, as well as the effective 
control and use of cash flow, are not flexible and responsive to the decision of the administrator. It is 
necessary to identify the factors affecting cash flow management for construction companies listed on 
the Vietnam stock exchange. Research results have pointed out 6 groups of factors affecting the cash 
flow of listed construction companies on Vietnam's stock exchange, namely: Macro environment; During 
construction; Payables and Receivables; Construction cost; Retention; Loan payment and tax. In addi-
tion, the research has also determined the weight of each variable in each factor to the cash flow of 
construction companies listed on the Vietnam stock exchange. The determination of 6 groups of factors 
influencing cash flow management will be a useful information channel for managers of construction 
companies listed on the Vietnamese stock exchange in making decisions. In addition, the weighting ef-
fects of each element in the factor will be suggestions for administrators in choosing the priority for each 
of these factors. This result can also be applied more widely for Vietnamese construction enterprises. 
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