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

 

Asian Business Research Journal 
Vol. 9, 24-30, 2024 
ISSN: 2576-6759 
DOI: 10.55220/25766759.170 
© 2024 by the authors; licensee Eastern Centre of Science and Education, USA 

 
 

 

 
Determinants of business performance of listed information technology companies 
in Vietnam 

 
Dinh The Hung1  
Pham Ngoc Thuy Tram2 

 

1National Economics University, Vietnam. 
Email: hungdt@neu.edu.vn  

 
 

2Tran Dai Nghia High School for the Gifted, Vietnam. 
Email: thuytram1701.2007@gmail.com  
( Corresponding Author) 

 

 
Abstract 

The information technology (IT) industry is rapidly growing and playing a significant role in 
modern society, impacting almost every aspect of life. The operations of IT businesses have been 
increasing and developing rapidly in recent years. For a business to achieve sustainable 
development, its production and business activities must be as efficient as possible. This paper 
aims to determine the impact of various factors on business performance based on data collected 
from the financial reports of 14 listed IT companies on the Vietnamese stock market from 2019 to 
2023. The paper employs a regression model using Stata17 software, and the results show that 
two factors, Capital structure and Age of the company, have a significant impact on business 
performance; while two other factors, Fixed asset investment and Liquidity, have no significant 
impact on business performance. Based on the research findings, the authors provide several 
recommendations to help IT companies improve their business performance. 

 
Keywords: Business performance, Capital structure, Liquidity, Rate of return. 
JEL Classification: M10. 

 
1. Introduction 

The information technology (IT) industry is increasingly developing and playing a crucial role in today's 
society. IT is a prerequisite for economic and social development (Castells, 1999). The IT industry now influences 
almost all aspects of life, including economics, education, healthcare, entertainment, and many others. With the 
development of technology and the internet, the demand for IT services has increased significantly, including data 
storage, software development, and information security. The global value of the IT market in 2024 is 800 million 
US dollars and is expected to quadruple by 2030. Revenue in Vietnam's IT-Telecommunications sector has 
continuously grown over the past 10 years from 40 billion USD in 2013 to 148 billion USD in 2024. Vietnam has 
consistently been in the top 10 countries exporting IT services, with hardware and electronics exports in 2022 
reaching approximately 136 billion USD, an increase of 11.6% compared to 2021. The total number of IT 
businesses in Vietnam is around 70,000 and there are over 1500 digital technology businesses with revenue 
approaching 10 billion USD. IT labor has increased from 226,300 people in 2009 to 1,005,206 people in 2019, with 
a 4.4 - fold increase (Ministry of Information and Communications, 2023). With these developments, IT has 
transformed from a small secondary economic sector into the largest secondary economic sector in Vietnam, with 
the highest growth rate, highest labor productivity, and largest export value. Moreover, the IT industry has 
benefited significantly from the impact of the Covid-19 pandemic due to the increased demand from consumers for 
technology applications. Applications related to education and health have witnessed a surge in visits and usage as 
students and companies have had to study and meet online. 

In operations, every business aims to achieve high efficiency in business activities. To achieve this goal, 
managers need to consider which factors have a positive impact and which have a negative impact on business 
performance, thereby establishing strategies suitable for the specific characteristics of the business. 

This article focuses on studying the factors affecting the business performance of 14 IT companies listed on the 
HNX and HOSE stock exchanges from 2019 to 2023. The article uses Return on Equity (ROE) as a key measure of 
business performance. Based on previous studies, this article will present a research framework showing the 
independent variables to be included in the model and the research hypothesis. From the collected data set, the 
research team will select a suitable model for the data to conduct impact analysis. The results obtained will be the 
basis for the research team to provide recommendations for IT companies listed on the HNX and HOSE to 
improve operational efficiency. 
 
 
 
 

mailto:hungdt@neu.edu.vn
mailto:thuytram1701.2007@gmail.com
https://www.doi.org/10.55220/25766759.170


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2. Research Overview 
2.1. Research on Business Performance 

Numerous theoretical and empirical studies on business performance have been conducted worldwide. 
Theoretical research is grounded in macroeconomic principles, while empirical studies primarily focus on two key 
areas: measuring business performance and examining the factors influencing it. Business performance is defined as 
the ability to utilize and manage a company's resources in various ways to develop a competitive advantage 
(Iswatia and Anshoria, 2007). It is evaluated based on three dimensions: productivity, profitability, and market 
share (Walker, 2001). There are two types of business performance: financial and non-financial performance 
(Hansen and Mowen, 2005). Financial or economic performance is often reflected in revenue growth, increased 
profits, or stock prices (Havnes and Senneseth, 2001). Common performance metrics are categorized into five 
groups: liquidity ratios, asset management ratios, debt management ratios, profitability ratios, and market value 
ratios. These ratios can be combined to create composite performance measures. For instance, combining 
profitability ratios and asset management ratios yields ROA and ROE. However, three primary measures are 
commonly used by researchers: ROA (Chu Thi Thu Thuy et al, 2015; Dam Thi Phuong Thao and Nguyen Tien 
Manh, 2017; Gjoni, 2022; Chawla and Manrai, 2019) ROE (Chu Thi Thu Thuy et al, 2015; Dam Thi Phuong Thao 
and Nguyen Tien Manh, 2017; Chawla and Manrai, 2019) and ROCE (Chawla and Manrai, 2019). In studies by 
Diaz and Pandey (2019) and Nguyen et al (2021) etc., business performance is measured by ROA. Meanwhile, ROE 
is used to measure business performance in studies by Onaolapo and Kajola (2010) and Pouraghajan et al (2012). 
Some studies utilize all three ratios: ROA, ROE, and ROS, when measuring business performance (Pham and 
Nguyen, 2018; Tran and Nguyen, 2019). However, the most suitable measure of business performance remains a 
subject of debate. 
 

2.2. Research on Factors Affecting Business Performance 
Chu Thi Thu Thuy et al (2015) found that the state capital ratio, company size, financial leverage, business 

cycle, quick payment ability, and management competence have a negative impact on financial performance.  

Pham Sơn Tùng's (2015) research on the financial performance of joint-stock companies in the real estate 
industry revealed that liquidity, size, operating time, and GDP growth positively impact the ROA, which is the 
financial performance measure used in the article. Conversely, a high fixed asset ratio and a long business cycle 
have a negative impact on ROA.  

Raghav Chawla and Rishi Manrai (2019) showed that the debt-to-equity ratio and company size have a 
negative impact on the ROA and ROE of manufacturing companies in India. 

Dam Thi Phuong Thao and Nguyen Tien Manh (2017) indicated that the fixed asset ratio has a positive impact 
on ROA but a negative impact on ROE. Additionally, the study found that operating time has a positive impact on 
both ROA and ROE.  

Studies by Ghosh, Nag, and Sirmans (2000); Berger and Bonaccorsi (2006); Gleason et al (2000); Simerly and 
Li (2000) and Liargovas and Skandalis (2008) among others, in different research contexts, have consistently 
shown that financial leverage has a significant impact on business performance.  

Miruna Florina (2020) indicated that profitability, asset utilization efficiency, and capital structure are related 
to company performance.  

Hoang Thi Thu Ha and Nguyen Thi Tuyet Mai (2023) found that capital structure, size, investment in fixed 
assets, and growth rate all impact business performance. 
 

3. Theoretical Foundation and Research model 
3.1. Theoretical Foundation 

Business performance is a concept that has garnered significant attention from numerous researchers and has 
been interpreted in various ways. Primarily, it is understood from two perspectives: the company's achievements 
related to financial activities and those related to non-financial activities (Taouab and Issor, 2019). According to 
Lebans and Euske (2006) Business performance refers to the operational results achieved by a company. It 
encompasses multiple facets of the company, from non-financial factors that provide information about flexibility or 
the degree to which company objectives are met, to financial factors, including financial indicators for assessing 
performance and Business performance. Business performance is not only a measure of quality and reflects the level 
of organization and management of a business but also serves as the foundation for a company's survival and 
growth. A company's survival and growth are determined by its reputation and influence in the market. Ultimately, 
a company's market reputation and its ability to gain customer trust are influenced by its Business performance. 
Here, Business performance cannot be simply understood as minimizing costs and maximizing profits. Rather, the 
achieved Business performance is due to the quality of the products produced and supplied by the company to its 
customers. In this paper, the authors use financial performance to measure a company's performance. 
 

3.2. Research Model  
There have been numerous studies worldwide, including in Vietnam, on the factors influencing business 

performance. Zeitun and Tian (2014) demonstrated that the debt ratio has the strongest negative impact, while 
growth in total assets, size, and tax rate have a positive impact on ROA. Moreover, the study indicated that 
significant investments in fixed assets do not necessarily yield high returns. Additionally, other factors such as 
industry and the macroeconomic environment also have a considerable impact on this factor. In the study by 
Onaolapo and Kajola (2010) the debt ratio and the fixed asset ratio have a negative impact on both ROA and ROE, 
while asset turnover has a positive impact on these ratios. 

Vo Van Can (2017) when investigating foreign-invested seafood enterprises in Khanh Hoa during the period 
2011-2015, indicated that the growth rate of assets, revenue, total assets, and the structure of fixed assets have an 
impact on ROA but not on ROE. The research by Diaz and Tin (2017) showed that asset size has a positive impact 
on financial leverage, leading to increased debt and consequently, a decrease in business performance. 



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The number of years a company has been operating increases business experience and will affect business 
performance. However, previous studies have yielded different results. Loderer et al (2009) indicated that the 
longer the company's tenure, the better the business performance. However, studies by Liu et al (2014) and 
Marinova et al (2016) showed that company tenure has a negative impact on the business performance of 
companies. 

Solvency plays a very important role in a company's financial situation, as demonstrated by several studies by 
Almajali et al (2012); Maleya and Muturi (2013); Amalendu (2010); Liargovas and Skandalis (2008) and Khalifa and 
Zurina (2013), etc. Low and prolonged solvency often indicates financial risk and the possibility of bankruptcy. 
However, if a company maintains excessively high solvency, it can also have a negative impact on financial 
performance. Specifically, when solvency is high, it means that the company either invests too much in current 
assets such as cash, inventory, accounts receivable, or uses too little short-term debt to finance current assets (not 
taking advantage of the low cost of short-term sources or non-interest-bearing sources), thus reducing profits. 

Asset structure affects a company's business performance. When the proportion of fixed assets is large, the 
company has the opportunity to mortgage these assets to access external capital more easily. Previous studies have 
shown that a company with a higher proportion of fixed assets to total assets often uses more debt or has an asset 
structure that is proportional to the debt ratio. However, for businesses in industries that require large investments 
in fixed assets, it entails a significant risk due to the impact of fixed costs and the very low liquidity of fixed assets, 
making it difficult for businesses to change direction. Meanwhile, the research of Zeitun and Tian (2007) and 
Onaolapo and Kajola (2010) showed that the proportion of fixed assets has a negative impact on business 
performance. The research of Do Duong Thanh Ngoc (2010) indicated that the ratio of fixed assets to total assets 
has no impact on business performance. 

According to the result above, the paper proposes a research model as the Figure 1. There are 4 following 
hypothesis: 

1. Capital structure has negative correlation with business performance. 
2. Investment on fixed asset has positive correlation with business performance. 
3. Liquidity has positive correlation with business performance. 
4. Age of the company has positive correlation with business performance. 

 

 
Figure 1.  
Research model. 

 
Variables in the model are measured as Table 1. 

 
Table 1. Description of variables in the model. 

Variable group Symbol Calculation Reference 

Dependent variables 

Business performance ROE Profit after tax/ 
Owner’s equity 

Salim and Yadav (2012); Hoang Cam Trang 
and Vo Van Nhi (2014); Dam Thi Phuong 
Thao and Nguyen Tien Manh (2017) and 
Chu Thi Thu Thuy et al (2015) 

Independent variables 
Capital structure CAP Short-term debt/ 

Owner’s equity 
Gjoni and colleagues (2022); Chawla and 
Manrai (2019), Pham Son Tung (2015) and 
Hoang Thi Thu Ha and Nguyen Thi Tuyet 
Mai (2023) 

Investment on Fixed assets ASS Fixed assets/Total 
assets 
  

Dam Thi Phuong Thao and Nguyen Tien 
Manh (2017); Pham Son Tung (2015) and 
Hoang Thi Thu Ha and Nguyen Thi Tuyet 
Mai (2023) 

Liquidity LIQ Total long-term 
assets/ Long-term 
debt 

Gjoni and colleagues (2022); Chawla and 
Manrai (2019) and Pham Son Tung (2015) 

AGE YEAR Year of financial 
statement - year of 
establishment + 1 

Loderer et al (2009); Nagy (2009); Onaolapo 
and Kajola (2010) and Pham Son Tung 
(2015) 

 

3.3. Research Model 
Business Performance = ß0 + ß1*CAP + ß2*ASS + ß3*LIQ + ß4*YEAR 



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Including:  

• CAP: Capital structure. 

• ASS: Investment on fixed assets. 

• LIQ: Liquidity. 

• YEAR: Age of company. 
 

3.4. Research Methodology 
The data used in this study was collected from the financial reports of 14 IT companies listed on the 

Vietnamese stock market from 2019 to 2023, available on https://finance.vietstock.vn/. This panel data consists of 
70 observations. Therefore, to analyze the factors affecting business performance, this paper uses panel data 
regression models, including the fixed effects model (FEM) and the random effects model (REM). 
 

Table 2. Descriptive statistics of variables in the model. 

Summarize ROE CAP ASS LIQ YEAR, separator (5)  

Variable  Obs. Mean Std. dev. Min. Max. 

ROE  70 1.469726 60.18256 0.0004986 286.0935 
CAP  70 0.4890842 0.2316762 0.000269 1.423553 
ASS  70 .0722663 0.0655542 0.0004066 0.2208568 
LIQ  70 15.43383 23.77294 0 111.7877 
YEAR 70 19.21429 4.892742 8 28 

 

4. Research Results  
4.1. Descriptive Statistics  

Table 2 shows that during the period 2019-2023, the average return on equity (ROE) of the companies was 
1.469726, meaning that for every dollar of equity, the company earned 1.469726 dollars in after-tax profit. The 
highest and lowest ROE ratios of the companies were 286.0935 and 0.0004986, respectively. The average capital 
structure (CAP) of the companies during this period was 0.4890842 times. The average liquidity (LIQ) of the 
companies was 15.43383. The average fixed asset investment (ASS) was 0.0722663 and the average age of the 
companies was 19.21429 years. 
 

4.2. Correlation Analysis  
Table 3 describes the correlation relationships among the variables in the research model, including the 

dependent variable ROE and the remaining 4 independent variables. The analysis results will show the correlation 
between the independent variables and the dependent variable, aiming to eliminate variables that may lead to 
multicollinearity before running the regression model.  
 

Table 3. Correlation matrix of variables. 

  ROE CAP ASS LIQ YEAR 

ROE 1.0000     

CAP -0.4424 1.0000    

ASS 0.0984 0.2803 1.0000   

LIQ -0.0106 0.2868 0.2079 1.0000  

YEAR -0.3822 0.0889 -0.1029 -0.0332 1.0000 

 
According to the results in Table 3, the correlation coefficients between pairs of independent variables in the 

model are all less than 0.8, indicating a low probability of multicollinearity among the independent variables when 
included in the model. Therefore, it can be concluded that the model does not suffer from serious multicollinearity.  

On the other hand, to obtain more concrete evidence of whether multicollinearity exists among the collected 
variables, we examined the Variance Inflation Factor (VIF) in the regression model. The results of the 
multicollinearity test are presented in Table 4. 
 

Table 4. Results of multicollinearity test with variance inflation factor (VIF). 

Variable VIF 1/VIF 

ROE 1.55 1.25 
CAP 1.55 1.24 
ASS 1.18 1.09 
LIQ 1.12 1.06 
YEAR 1.18 1.09 
Mean VIF 1.32 

 

 

The multicollinearity test results using Stata 17 software, as presented in Table 4, show that the average VIF 
is 1.32, and no independent variable has a VIF value exceeding 10. Therefore, there is no evidence of 
multicollinearity based on the Variance Inflation Factor (VIF) criterion for the variables tested for linear 
relationships. 
 

4.3. Regression Results 
4.3.1. Regression Estimation Using POLS, FEM, and REM Models  

Table 5 presents the regression results of factors affecting firm performance, according to two models: FEM 
and REM.  
 
 

https://finance.vietstock.vn/
https://finance.vietstock.vn/


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Table 5. Summary of regression results from FEM and REM models with the test results to compare the FEM and REM models 
(Hausman test), the REM model is the optimal model suitable for the study. 

 
 

4.4. Regression Test 
 

Table 6. 

xtgls ROE CAP ASS LIQ YEAR Cross-sectional time-series RE GLS regression with AR (1) 
disturbances Group variable: firm 
R-sq 
Within = 0.0993 
Between = 0.4049 
Overall = 0.3301  
Corr (u_i, Xb) = 0 (Assumed) 

Number of obs = 70 
Number of groups = 14 
Time periods = 5 
Wald chi2 (5) = 13.43 
Prob > chi2 = 0,0197  

ROE  Coef. Std. err. z P > | z | [95% conf. interval] 

CAP -80.38026*** 27.8843 -2.88 0.004 -135.0325 -25.72802 
ASS 36.93763 113.4643 0.33 0.745 -185.4483 259.3236 
LIQ 0.0981144 0.02022982 0.48 0.628 -0.2983828 0.4946115 
YEAR -3.940736** 1.81835 -2.17 0.030 -7.504636 -0.3768355 
_cons 124.9064*** 40.22018 3.11 0.002 46.07631 203.7365 
rho_ar 0.31035076 (Estimated autocorrelation coefficient) 
sigma_u 32.561633  

sigma_e 31.422708  

rho_fov 0.51779442 (Fraction of variance due to u_i) 
Theta 0.50082622  

Note:  Standard errors in parentheses *** p<0.01, ** p<0.05, * p<0.1. 

 
The research team employed the RE GLS regression method, and the results indicated that the variables 

Capital Structure (CAP) and Age of company (YEAR) were statistically significant (supporting hypotheses H1 and 
H4), while Fixed Asset Investment (ASS) and Liquidity (LIQ) were not statistically significant. The final 
regression model is as follows: 

ROEit = 124.9064 – 80.38026*CAPit – 3.940736*YEARit + i + Uit  
The regression model reveals the following: 

The variable Capital Structure (CAP) has a coefficient of -80.38026, with a p-value of 0.004 < 0.05, indicating a 
negative impact on business performance (ROE). This means that as a company's capital structure increases, its 
business performance decreases. This result is consistent with the findings of Rohaya et al. (2010); Gatsi et al. 
(2013); Pitulice et al. (2018), and others. 

The variable Liquidity (LIQ) has a coefficient of 0.0981144, with a p-value of 0.628 > 0.05. This result suggests 
that there is no significant relationship between liquidity and business performance of the company. 

The variable Fixed Asset Investment (ASS) has a coefficient of 36.93763, with a p-value of 0.745 > 0.05. This 
result suggests that there is no significant relationship between fixed asset investment and business performance of 
the company. 

The variable Age of company (YEAR) has a coefficient of -3.940736, with a p-value of 0.030 < 0.05. This result 
suggests that there is a negative relationship between company operating time and business performance. This 
result is also consistent with the findings of Pouraghajan et al. (2012). 
 

5. Conclusion and Recommendations 
The research findings indicate that two factors: Capital Structure and Age of company, significantly impact the 

business performance of IT companies listed on Vietnamese stock exchanges from 2019 to 2023. Conversely, Fixed 
Asset Investment and Liquidity do not significantly affect the business performance of IT companies listed on 
Vietnamese stock exchanges. Based on the research results, to increase business performance, enterprises need to 
improve several financial indicators, specifically: 



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Firstly, building a reasonable capital structure: This is one of the most important tasks. If IT companies can 
establish a reasonable capital structure, they can significantly reduce the risk of bankruptcy and increase the 
efficiency of capital use in business operations. The efficiency of asset use and the debt ratio have an inverse 
relationship, so to increase the efficiency of asset use, companies should limit investment in assets using short-term 
debt, encourage the use of long-term debt; and simultaneously use other alternative sources of capital such as 
retained earnings; mobilize capital from issuing shares, funds, etc. 

Secondly, enhancing market reputation and sustainable development orientation. Enterprises need to actively 
update information, train professional skills to improve knowledge, grasp market trends in order to adjust 
production and business accordingly to improve product quality. Companies need to build a reputation for 
management capacity, operational skills, financial capacity as well as business acumen. 

Thirdly, the government should review the framework of fiscal and monetary policies to reduce inflation, 
stabilize the exchange rate, reduce interest rates, and prevent unhealthy interest rate competition. Closely monitor 
the stock market, ensure the safety and stability of the banking system; stabilize prices of important commodities, 
and prevent monopolistic price increases. 

Fourthly, regularly organize training courses on professional skills and disseminate legal documents to 
enterprises. Regularly update knowledge for accountants through in-depth seminars on international accounting 
standards to integrate with global trends. 

By using a quantitative method in the regression model through data analysis of 14 listed IT companies on 
HNX and HOSE from 2019 to 2023, the research team has demonstrated the correlation between influencing 
factors and business performance of the company. The research results show that there are two factors affecting 
business performance: Capital Structure and Operating Time. Among them, the most influential factor is Capital 
Structure. Based on the research results, we have also made some recommendations to government agencies and 
IT enterprises to improve the business performance of IT companies listed on HNX and HOSE through measures 
affecting the capital structure. 

 

References 
Abbasi A, Malik QA. (2015). Firms' size moderating financial performance in groing firms: An empirical evidence from Pakistan. International 

Journal of Economics and Financial Issues; 5(2):334-339. 
Abor J. (2008). Determinants of the capital structure of Ghanaian firms. AERC Research. African Economic Research Consortium, Nairobi.;176. 
Akakpo VKA. (2009). Principles, concepts and practice of taxation. 3rd ed, Accra: BlackMask Ltd. 
Al-Qadi AS, Khanji IM. (2018). Relationship between liquidity and profitability: An empirical study of trade service sector in Jordan. Research 

Journal of Finance and Accounting. 9(7):153-157. 
Amalendu, B. (2010). Financial performance of indian pharmaceutical industry: A case study. Asian Journal of Management Research. pp. 427-

451. 
Amidu M. (2007). Determinants of capital structure of banks in Ghana: An empirical approach. Baltic Journal of Management. 2(1):67-79. 

https://doi.org/10.1108/17465260710720255. 
Chu Thi Thu Thuy, Nguyen Thi Huyen, Nguyen Thi Quyen. (2015). The factors affecting firms' financial perfomance: The case of non-

financial companies listed on HoChiMinh stock exchange, Economics& Development Journal, 215:59-66 
Davidsson P, Steffens P, Fitzsimmons JR. (2009). Growing profitable or growing from profits: Putting the horse in front of the cart?. Journal 

of Business Venturing; 24(4):388-406.  
Diaz, J. F., and Pandey, R. (2019), Factors affecting return on assets of US technology and financial corporations. Jurnal Manajemen Dan 

Kewirausahaan, 21(2), 134-144, https://doi. org/10.9744/jmk.21.2.134-144.  
Diaz, J. F. T., and Tin, T. T. (2017), Determinants of banks’ capital structure: Evidence from Vietnamese commercial banks. Asian Journal of 

Finance and Accounting, 9(1), 261-284. https://doi.org/10.5296/ajfa.v9i1.11150. 
Durrah O, Abdul Aziz Abdul Rahman AAA, Jamil S.A, Nour Aldeen Ghafeer NA. (2016). Exploring the relationship between liquidity ratios 

and indicators of financial performance: An analytical study on food industrial companies listed in Amman Bursa. International 
Journal of Economics and Financial Issues.;6(2):435-441. 

Gatsi JG, Gadzo SG, Kportorgbi HK (2013). The effect of corporate income tax on financial performance of listed manufacturing firms in 
Ghana. Research Journal of Finance and Accounting.;4(15):118-124. 

Hằng V.T.T. (2015). Research on factors affecting business performance of construction enterprises listed on the Vietnam Stock Exchange, 
Master's Thesis in Business Administration. Danang University 

Hansen, R., and Mowen, M. (2005), Management accounting, Singapore: South-Western, Cornerstones of Cost Accounting. Issues in Accounting 
Education, 25(4), 790-791.  

Havnes, P.-A., and Senneseth, K. (2001), A panel study of firm growth among SMEs in networks. Small Business Economics, 16(4), 293-302.  
Irawan A, Faturohman T. A. (2015). Study of liquidity and profitability relationship: Evidence from Indonesian capital market. Proceedings of 

31st The IIER International Conference, Bangkok, Thailand, 2nd.;64. 
Iswatia, s., and Anshoria, M. (2007), The influence of intellectual capital to financial performance at insurance companies in Jakarta stock exchange 

(JSE), Proceedings of the 13th Asia Pacific Management Conference, Melbourne, Australia, 1393-1399.  
Khai N.Q. (2015). Factors affecting the performance of listed enterprises in Vietnam, Master's Thesis in economics. Ho Chi Minh City 

University of Economics 
Makman G, Gartner W. (2003) Is extraordinary growth profiable? A study of Inc. 500 high-growth companies. Entrepreneurship: Theory and 

Practice.;27(1):65-75. 
Nguyen, V. H., Nguyen, T. T. C., Nguyen, V. T., and Do, D. T. (2021), Internal factors affecting firm performance: a case study in Vietnam, 

The Journal ofAsian Finance, Economics and Business, 8(5), 303-314, https://doi.org/10.13106/Jafeb.2021.vol8.no5.0303.  
Ngoc D.D.T. (2011). Financial factors affecting the business performance of construction enterprises listed on the Vietnamese stock market, 

Master's Thesis in economics. Ho Chi Minh City University of Economics  
Nhut Q.M. (2014). Analysis of factors affecting the performance of real estate enterprises listed on the Vietnamese stock exchange. Journal of 

Science, Can Tho University; 33:65-71. 
Onaolapo, A. A., and Kajola, s. o. (2010), Capital structure and firm performance: Evidence from Nigeria, European Journal of Economics, 

Finance and Administrative Sciences, 25(1), 70-82.  
Park K. (2011). Inter- relationship between firm growth and profitability. International Journal of Hospitality Management; 30(4):1027-1035. 

https://doi.org/10.1016/j.ijhm.2011.03.009. 
Pham, T. T., and Nguyen, T. N. A. (2018), Discussing the concept of business performance to determine criteria for evaluating business 

performance of enterprises, Industry and Trade Magazine, 7, 288-293.  

Phượng H.T.T. (2016). Research on factors affecting the performance of listed companies. Journal of Finance;47-50. 

Pitulice IC, Ștefănescu A, Mînzu VG. (2018). The impact of income tax over financial performance of companies listed on the bucharest stock 
exchange. Accounting and Management Information Systems.;17(4):626-640. https://doi.org/10.24818/jamis.2018.04006. 

Pouraghajan A, Malekian E, Emamgholipour M, Lotfollahpour V, Bagheri MM. (2012). The relationship between capital structure and firm 
performance evaluation measures: Evidence from the Tehran stock exchange. International Journal of Business and 
Commerce.;1(9):166-18 



Asian Business Research Journal, 2024, 9: 24-30 

30 
© 2024 by the authors; licensee Eastern Centre of Science and Education, USA 

 

 

Raymond A. Ezejiofor, Moses C. Olise, John-Akamelu Racheal C. (2017). Comparative Analysis on investment decision of telecommunication 
and banking industries in Nigeria. Journal of Finance and Economics; 5(2):65-75. https://doi.org/ 10.12691/jfe-5-2-4. 

Rohaya MN, NurSyazwani MF, Nor'Azam M. (2010). Corporate tax planning: a study on corporate effective tax rates of Malaysian listed 
companies. International Journal of Trade, Economics and Finance;1(2):1-5. https://doi.org/10.7763/ijtef.2010.v1.34. 

Tran M.D., Dang N.H., Vu T.T.V., Hoang T.V.H., Pham Q.T. (2019). Determinants influencing financial performance of listed firms: 
Quantile regression approach. Asian Economic and Financial Review; 9(1):78-90. 
https://doi.org/10.18488/journal.aefr.2019.91.78.90. 

Tran, T. T. H., and Nguyen, T. K. H. (2019), Analyzing the financial performance of commercial firms in Hanoi, Asia-Pacific Economic 
Review, 540(1), 70-72.  

Vo, V. C. (2017), Factors affect performance of the FDI seafood firms in Khanh Hoa province, Journal of Banking Technology, 139, 86-97.  
Walker, D. C. (2001), Exploring the human capital contribution to productivity, profitability, and the market evaluation ofthefirm, Webster 

University.  
Zeitun, R., and Tian, G. G. (2014), Capital structure and corporate performance: Evidence from Jordan, Australasian Accounting Business & 

Finance Journal, http://dx.doi.org/10.2139/ ssm.2496174.  
 

 

 


