The effect of different factors on investment decision of enterprises in industrial parts

factors includes 4 observed variables and the author named the group “Labor”: The labor factor 
consists of 4 observed variables: TC13: Labor meets the requirements of quantity; TC16: Labor meets the requirements of 
quality; TC17: Labor can acquire and use technology well; TC18: Labor has discipline, meeting investor requirements. 
The authors have set the criteria group 2 denoted by FAC2 with the equation: 
FAC2 = 0.812 TC13 + 0.851 TC16 + 0.804 TC17 + 0.81 TC18 
(3) The third group of factors includes four observed variables and the author named the group “Local advantages”, where: 
TC5: The location of the industrial park is convenient for investors; TC8: Investment and Trade Promotion Centre provides 
good support for investors; TC14: Good product consumption market; TC15: Advantages of supporting industries compared to 
other localities. 
The authors set the criterion group 3 symbols denoted by FAC3 with the equation: 
FAC3 = 0.797 TC5 + 0.792 TC8 + 0.800 TC14 + 0.816 TC15 
(4) The fourth group of factors consists of four observed variables and the author named the group “The infrastructure”. 
The “infrastructure” group consists of four observed variables: TC1: Convenient transportation system inside and outside the 
industrial park; TC2: The electrical system in the industrial park is stable; TC3: Lighting system in the industrial park is stable 
and TC4: Technical infrastructure system for secured industrial parks (internet, post, telephone network) 
The authors set the criteria group 4 denoted as FAC4 with equations: 
FAC4 = 0.872 TC1 + 0.668 TC2 + 0.777 TC3 + 0.809 TC4 
After performing exploratory factor analysis (EFA), the study uses Binary Logistic regression to analyze the influence of factors 
on the attraction of enterprises' investment capital into industrial parks. Which: 
Dependent variable (Y): Investment decision of the enterprise in industrial zones; Y gets a value of 1 when deciding to invest 
and a value of 0 when not investing. 
Independent variable: 
+ Institutional factors are also studied in the very popular study in Vietnam by the Vietnam Chamber of Commerce and Industry 
(VCCI) it is a study of the provincial competitiveness index PCI. Special economic zones are allowed to apply specific economic 
policies and economic management institutions, not providing budgets but creating mechanisms, these include more open 
incentives than other countries for foreign investors, along with high autonomy coupled with doing well in operation 
management of special economic zone (Aggarwal, 2007). 
+ Labour (FAC2): Human resources are considered as one of the important factors affecting FDI enterprises' attraction 
(Dunning, 1997). Investors will select the area, meeting the requirements of quantity, quality and price of labor. Labor quality 
is a competitive advantage for investors in high technology content areas, using modern technology (Gilmore et al., 2003). This 
factor was also confirmed to be an important influence in the research of Nguyen Dinh Tho (2009). 
+ Local advantages (FAC3): In the study of factors affecting the decision to invest in Vietnamese industrial parks by Mai Van 
Nam and Nguyen Thanh Vu (2010), the authors mentioned 6 factors and the case study results are typical industrial zones of 
Tien Giang province that have identified three groups of factors affecting the investment decisions of enterprises; namely 
infrastructure, human resources and location of the industrial park where the location factor of the industrial park most strongly 
influences investment decisions. The geographical location of the economic zone is also mentioned as a key to the success of 
economic zones in the comprehensive study of UNIDO (2015). 
 594
+ Infrastructure (FAC4): Infrastructure plays a very important influence on the production and business efficiency of enterprises. 
This is also a top concern of investors when making investment decisions (Le Hoang Ba Huyen, 2015; Mai Van Nam &Nguyen 
Thanh Vu, 2010). Good infrastructure is one of the important factors that help reduce the business costs of investors (Gilmore 
et al., 2003). Outdated infrastructure will increase incoming investment costs and create barriers to investment activities. The 
theory of Durning (1997), the theory of local marketing by Kotler et al. (2002) as well as the experiments of Nguyen Dinh Tho 
(2008) and Le Hoang Ba Huyen (2015) have proved the importance of this factor. 
+) Factors that belong to within enterprises: The scales used are the Gender of enterprise leadership (GT), Qualifications of 
enterprise managers (HV); Management experience (KN); Firm size (QM) and uptime of the enterprise (TG), these are factors 
related to the personal opinion of the top managers in the investment. From the perspective of behavioral financiers, factors such 
as diagnosis, bias, effect framework, representation, sense of loss, risk aversion, etc... Influence and change investor decisions 
(Pavabutr P, 2002). However, studies carried by Waweru et al. (2008) show that investment decision making is also influenced 
by psychological, emotional and behavioral factors. Specifically, investment decisions are influenced by individual investor 
desires, goals, prejudices, and emotions. The study of Sandberg & Hoper (1987) also complements this view. 
To perform the regression, firstly, research is performed to test the suitability of the model: 
Table 5 
Omnibus Tests of Model Coefficients 
 Chi-square df Sig. 
Step 1 Step 119.826 9 .000 
Block 119.826 9 .000 
Model 119.826 9 .000 
Source: Analysis results from the author's research data 
According to the results of testing the suitability of the model, with Sig coefficient <0.05, the correlation between the dependent 
variable and the independent variables in the model is statistically significant with confidence intervals above 99%. 
Table 6 
Model Summary 
Step -2 Log likelihood Cox & Snell R Square Adjusted-R Square 
1 221.463a .320 .480 
Source: Analysis results from the author's research data 
The level of explanation of the model, with the R2 coefficient = 0.48, indicates that about 48% of the variation of the dependent 
variable is explained by the independent variables in the model, the rest is due to other factors. 
Table 7 
Classification Table 
Observed 
Predicted 
 Y 
Percentage Correct .0000000000000000 1.0000000000000000 
Step 1 Y .0000000000000000 224 13 94.5 
1.0000000000000000 29 45 60.8 
Overall Percentage 86.5 
Source: Analysis results from the author's research data 
The results of the forecast accuracy table show that in a total of 237 cases forecasted without investment, the accurate forecast 
model was 224 cases, with an accurate prediction rate of 94.5%. As for 74 cases with investment decisions, which the model 
accurately predicts 45 cases, the correct prediction rate is about 60.8%. From that, calculating the correct prediction rate of the 
model is 86.5%. 
Research findings show that: 
+ Local institutions (FAC1): Measuring the influence of enterprises' investment decisions on industrial parks, the author's research 
results show that, the coefficient of variable B = 1.544 shows the positive relationship between the suitability of local institutions and 
investment decisions, this finding also supports the research of Knack and Keefer (1995), Barro and Sala-i-Martin (1998), which are 
local institution affirmed to have a positive influence on investment decisions. 
Q.N. Vu et al. /Accounting 6 (2020) 595
+ Labor (FAC2): Labor is both a factor to attract and effective use of capital attraction. Because when the labor level meets the 
requirements, high management capacity, it will create high productivity. Also, investors will reduce training costs and reduce 
training time, so the progress and efficiency of the projects will meet the set goals. The author's research results show that the 
coefficient B = 1.214, these result supports the views of previous studies when labor has a positive relationship with the 
investment decision. 
+ Local advantages (FAC3): Reflecting the level of influence of local advantages on investors' investment decisions. The more 
local, the higher the comparative advantage and the more investors choose. The author's research results show that the coefficient 
of variable B = 0.691 shows the positive relationship between local advantages and investment decisions, the findings also 
support the views of other authors (e.g. Durning, 1997; Mai Van Nam & Nguyen Thanh Vu , 2010; UNIDO, 2015). 
+ Infrastructure (FAC4): Reflecting the degree of influence of local infrastructure on investors' investment decisions. 
Infrastructure is the basis for attracting capital and it is also a factor to promote investment activities that happen quickly and 
have a decisive influence on production and business efficiency. This is the top concern of investors before making a decision. 
The author's findings show that the coefficient B = 0.46, meaning that the current infrastructure factors and investment decisions 
of investors are positive together. This result supports the view of author Durning (1997), the theory of local marketing by Kotler 
et al. (2002) as well as the experiments of Nguyen Dinh Tho (2009), Le Hoang Ba Huyen (2015) when suppose that, poor 
quality infrastructure system will negatively affect investors' investment decisions. 
Table 8 
Variables in the Equation 
 B S.E. Wald Df Sig. Exp(B) 
Step 1a GT -.123 .407 .091 1 .763 .885 
HV -.435 .176 6.069 1 .014 .647 
KM .008 .054 .022 1 .882 1.008 
QM -.177 .275 .414 1 .520 .838 
TG -.311 .080 15.063 1 .000 .733 
FAC1 1.544 .246 39.278 1 .000 4.683 
FAC2 1.214 .230 27.863 1 .000 3.368 
FAC3 .691 .172 16.165 1 .000 1.996 
FAC4 .460 .183 6.302 1 .012 1.585 
Constant .772 1.224 .397 1 .528 2.163 
Source: Analysis results from the author's research data 
5. Conclusion 
The findings show that to be able to attract investment capital of enterprises in industrial parks, industrial parks in particular and 
countries have to ensure the following: 
Firstly, to ensure human resources for the development of industrial parks since human resource is a decisive factor in the 
operation of any enterprise, the enterprise cannot perform well if its human resources do not meet the quality and quantity 
requirements. 
Secondly, completing preferential policies and supporting investment for enterprises when investing in industrial parks, this is 
a factor that creates a competitive advantage for the industrial parks themselves in the problem of attracting investment capital 
compared to industrial parks of other countries. 
Thirdly, improving the capacity of supporting industry to promote support for enterprises in the industrial park when putting 
into operation 
Fourthly, strengthening investment promotion activities-solving the problem of supporting other services for enterprises, these 
activities will help promote better industrial parks to the enterprises. 
Fifthly, synchronously developing infrastructure inside and outside the fence of industrial parks, creating favorable conditions 
for enterprises 
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