Article Type : Research Article
Authors : Tran Ngoc Son
Keywords : Economic zone; People; Policies; Project; Position; Potential; Cronbach’s alpha; EFA; KMO; Regression
In Vietnam, the Economic zone is a driving force for growth
in the emerging economy. An economic zone is included as an area with a
separate economic space with an investment and business environment that is
particularly favourable to investors, has defined geographical boundaries, is
established under the conditions, and order and procedures specified. Besides,
the organization and management are decisive issues for this new model.
However, at present, to identify the factors affecting the organization and
management of the model of economic zones in Vietnam has not been studied
properly by domestic and foreign authors. New studies in this paper using SPSS
data analysis method will describe the relationships between the independent
variables, the organizational model and management of economic zones in Vietnam
and 05 dependent variables: People, Policies, Project, Position, Potent ion use
multiple linear regression models to help research, organization and management
of economic zones in Vietnam better.
Current
situation of economic zones in Vietnam
In the Decree No.
82/2018/ND dated May 22, 2018 of the Prime Minister of Vietnam, stipulates
that: An economic zone (EZ) is an area with defined geographical boundaries,
including many functional areas, established to realizing the objectives of
attracting investment, developing socio-economic, and protecting national
defence and security. The EZ in this study is a coastal EZ. Coastal EZ means an
EZ formed in the coastal area and in the vicinity of the coastal area. On the
basis of the provisions of Decree No. 29/2008/ND-CP, a coastal EZ is an area
with a separate economic space with a business investment environment
especially favourable for investors. Defined geographical boundary attached to
a deep-water seaport (or airport). Coastal EZ are organized into functional
areas including: non-tariff zones, tax-suspension zones, export processing
zones, industrial zones, entertainment zones, tourist resorts, urban areas,
residential areas, administrative areas. Key and other functional areas
suitable to the characteristics of each EZ:
· The
Government performs the unified state management of coastal EZ nationwide on
the basis of assigning specific tasks and powers of each ministry, branch,
provincial People's Committee and EZ Management Board health.
· To
direct the formulation and implementation of development planning’s and plans
and to promulgate policies and legal documents on EZ.
· Corporate
income tax incentives: According to Decree No. 218/2013/ND-CP, projects
investing in a coastal EZ are entitled to 10% corporate income tax rate for 15
years, tax exemption for 04 years and 50% reduction in the next 9 years.
· Import
tax incentives and personal income tax incentives: according to Decree No.
29/2008/ND-CP, Vietnamese people and foreigners working in coastal EZ are reduced
by 50%. Personal income tax for people with taxable income.
· The
land use term of an investment project in a coastal EZs is 70 years.
About the establishment
planning: since the first coastal EZ, Chu Lai Open Economic Zone, was
established in 2003, up to 2020, in Vietnam, 18 EZs have been established and
16 EZs in operation, including: 2 EZs in the Red River Delta region: Van Don
(Quang Ninh province) and Dinh Vu - Cat Hai (Hai Phong city); Economic zones in
the Central Coast region are Nghi Son (Thanh Hoa province), Southeast Nghe An
(Nghe An province), Vung Ang (Ha Tinh province), Hon La (Quang Binh province),
Chan May-Lang Co ( Thua Thien Hue province), Chu Lai (Quang Nam province), Dung
Quat (Quang Ngai province), Nhon Hoi (Binh Dinh province), Van Phong (Khanh Hoa
province), Nam Phu Yen (Phu Yen province) and Southeast Quang Tri (Quang Tri
province); 03 EZs in the South are Phu Quoc Island EZ and Nam An Thoi Island
Cluster (Kien Giang Province), Dinh An (Tra Vinh Province), Nam Can (Ca Mau)
Total land and sea surface area of 16 EZs are nearly 815 thousand hectares.
Factors
influencing the organization and management of EZ in Vietnam
Over the past 20 years in Vietnam, the development of coastal EZ covering 18/28 coastal provinces and cities is mainly. The organization and management of EZ have not yet relied on the scientific basis of theories of industrial territory. Awareness of factors affecting the organization and management of EZ is inadequate. Since then, there is no most common model to organize and manage. The factors that this article will mention is that considering the relationship as to form EZ (dependent variables), it is necessary to have factors (independent variables) such as People, Policies, Project, Position, Potential. This relationship is shown through survey and data analysis method using SPSS 26 Premium tool will clarify. The author of the paper has identified factors through theories of Industrial Territories and the Vietnamese government's views on the EZ and through the experiences of countries around the world such as China, South Korea, and Singapore, Indonesia, United Arab Emirates (Figure 1).
Figure 1: The factors affecting the organization and management of economic zones in Vietnam.
The research applies
and combines many methods, in which the final decision method is to analyse the
discovery factor EFA, analyse the multiple regression model to assess the
influence of the factors on the organization. And management of economic zones
in Vietnam. To use the above methods, the author used SPSS 26 Premium tool to
analyze the data. There are many conventions on sample size, such as that the
sample size must be in accordance with the formula: n ? 8m + 50 (n is the
sample size, m is the number of toxic variables model). Suggested that when
using regression analysis, the sample size needs at least 200 observations.
Meanwhile, Hair, Anderson, assumed that the minimum sample size should be 50,
preferably 100, and the observed / observed ratio is 5/1, meaning that for each
variable Observation requires a minimum of 5 observations. Accordingly, this
study has a research model with 28 questions, so the minimum sample size is 28
x 5 = 140. To achieve a minimum of 140 observations, the author sent 250
questionnaires to the representative. 216 survey forms have been received, of
which 06 questionnaires were rejected due to their invalid validity. Therefore,
the number of remaining observations for analysis is 210 votes [1-3].
Results
Descriptive
statistics of factors in the research model
With data of 210
observations. The average value of the observed variables (belonging to groups
of independent variables) ranged from 2.65 to 4.18, this reflects that the
customers all think that the factors included in the study are evaluated
concentrated in the "3: Normal" and "4: Agree" levels
(Table 1).
Test
the scale Cronbach’s alpha
Cronbach's Alpha coefficients are a statistical
test used to examine the rigor and correlation of observed variables. This
relates to two aspects: the correlation between the variables themselves and
the correlation of the scores of each variable with the scores of all variables
for each respondent. This method allows the analyst to eliminate the
inconsistent variables and limit the trash in the research model because
otherwise we cannot know the exact variation and error of the variables.
Accordingly, only variables with Corrected Item - Total Correlation coefficient
greater than 0.3 and Cronbach's Alpha coefficient of 0.6 or more are considered
acceptable and suitable for analysis in the next steps [4].
Cronbach's Alpha scale test (1st time)
Cronbach's
Alpha |
Number of
observations |
710 |
4 |
Observed variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
PEO1 |
11.24 |
7,656 |
0.600 |
0.587 |
PEO2 |
11.11 |
7,528 |
0.583 |
0.594 |
PEO3 |
11.22 |
7,438 |
0.548 |
0.614 |
PEO4 |
11.87 |
8,863 |
0.290 |
0.772 |
We see:
Test Cronbach's Alpha scale (2nd time)
Cronbach's
Alpha |
Number of
observations |
772 |
3 |
Observed
variables |
Average scale if variable
type |
Scale variance if variable
type |
Total variable correlation |
Cronbach's Alpha if variable
type |
PEO1 |
7.96 |
4,491 |
0.632 |
0.667 |
PEO2 |
7.83 |
4,484 |
0.586 |
0.715 |
PEO3 |
7.94 |
4.188 |
0.604 |
0.698 |
We see:
Cronbach's
Alpha is greater than 0.6.
POL5 observed variable has the total variable correlation coefficient is less than 0.3, so we remove this variable.
The variables related damage rest with coefficients relatively important variables total are greater than 0.3
Check Cronbach's Alpha scale (1st time)
Cronbach's
Alpha |
Number of
observations |
801 |
5 |
Observed variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
POL1 |
14.90 |
15,660 |
0.636 |
0.746 |
POL2 |
14.97 |
14,985 |
0.678 |
0.731 |
POL3 |
14.90 |
16,684 |
0.564 |
0.769 |
POL4 |
14.81 |
15,045 |
0.791 |
0.699 |
POL5 |
16.32 |
19,005 |
0.296 |
0.847 |
We see:
Cronbach's
Alpha is greater than 0.6.
POL5 observed variable has the total variable correlation coefficient is less than 0.3, so we remove this variable.
The variables related damage rest with coefficients relatively important variables total are greater than 0.3Test Cronbach's Alpha scale (2nd time)
Cronbach's Alpha |
Number of observations |
.847 |
4 |
Observed
variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
POL1 |
12.25 |
11,728 |
0.595 |
0.845 |
POL2 |
12.32 |
10,515 |
0.726 |
0.788 |
POL3 |
12.25 |
11,852 |
0.625 |
0.831 |
POL4 |
12.16 |
10,841 |
0.808 |
0.756 |
We see:
Cronbach's Alpha is greater than 0.6
The variables relating police are there coefficient relatively important variables total are greater than 0.3.
Cronbach's Alpha |
Number of
observations |
.859 |
5 |
Observed
variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
POS1 |
16.47 |
13,131 |
0.741 |
0.813 |
POS2 |
16.61 |
13,033 |
0.605 |
0.851 |
POS3 |
16.44 |
13,740 |
0.617 |
0.844 |
POS4 |
16.49 |
13,524 |
0.673 |
0.830 |
POS5 |
16.45 |
13,053 |
0.756 |
0.809 |
Cronbach's Alpha is greater than 0.6.
The variables relating police are there coefficient relatively important variables total are greater than 0.3.
Because of this, the variation observed is put in to distribution volume in steps to follow
Cronbach's Alpha |
Number of
observations |
.897 |
5 |
Observed
variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
PRO1 |
15.79 |
15,825 |
0.813 |
0.859 |
PRO2 |
15.75 |
16.218 |
0.752 |
0.872 |
PRO3 |
15.85 |
17,074 |
0.645 |
0.895 |
PRO4 |
15.90 |
15,468 |
0.719 |
0.882 |
PRO5 |
15.80 |
15,788 |
0.808 |
0.860 |
We see:
Cronbach's Alpha is greater than 0.6.
The variables relating police are there coefficient relatively important variables total are greater than 0.3.
Cronbach's Alpha |
Number of
observations |
0.747 |
6 |
Observed
variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
POT1 |
17.89 |
23.208 |
0.361 |
0.744 |
POT2 |
18.27 |
22,744 |
0.385 |
0.738 |
POT3 |
17.84 |
20,854 |
0.557 |
0.691 |
POT4 |
17.67 |
22,185 |
0.440 |
0.724 |
POT5 |
17.85 |
20,117 |
0.623 |
0.671 |
POT6 |
17.81 |
20,917 |
0.557 |
0.691 |
We see:
Cronbach's Alpha is greater than 0.6
The variables relating police are there coefficient relatively important variables total are greater than 0.3
Because of this, the variation observed is put in to distribution volume in steps to follow.
MODEL of organization and management of economic zones in Vietnam (OZ)
Cronbach's Alpha |
Number of
observations |
0.626 |
3 |
Observed
variables |
Average
scale if variable type |
Scale
variance if variable type |
Total variable correlation |
Cronbach's
Alpha if variable type |
OZ1 |
8.37 |
1,066 |
0.444 |
0.525 |
OZ2 |
7.83 |
1,368 |
0.431 |
0.538 |
OZ3 |
8.39 |
1,272 |
0.443 |
0.518 |
We see:
·
Cronbach's
Alpha is greater than 0.6.
·
The
variables related damage rest with coefficients relatively important variables
total are greater than 0.3.
·
Because
of this, the variation observed is put in to distribution volume in steps to
follow.
Explore factor analysis EFA
·
After
analysing the reliability of the scale, the next step to determine the set of
variables needed for the research problem, we continue to use the exploratory
factor analysis method to consider the convergence level. Of the observed
variables for each component and the distinguishing value between the factors.
After factor analysis, only groups of factors that satisfy the conditions can
participate in the regression part of the next analysis.
The important statistical parameters in
factor analysis include
·
KMO
(Kaiser - Meyer - Olkin measure of sampling adequacy) index is an indicator
used to consider the adequacy of factor analysis. The KMO index must be large
enough (> 0.5), then factor analysis is appropriate, and if it is less than
0.5, factor analysis is likely not suitable for the data.
· Eigenvalue index represents the amount of variation explained by the factor. Only factors with Eigenvalue greater than 1 are retained in the analytical model, factors with Eigenvalue less than 1 will be excluded from the model. Variance Explained Criteria: the total extracted variance must be greater than 50%. Factor loadings is a single correlation coefficient between variables and factors. The larger this coefficient indicates the more closely related the variables and factors are related. With a sample of about 200, the accepted factor load factor is greater than 0.55, variables with factor load coefficients less than 0.55 will be excluded from the model. Bartlett test to test the correlation between observed variables and the population, the analysis only has significance when sig. have a value less than 5% [1].
|
Result |
Compare | |||||||||
|
Observed variables |
Factor |
| ||||||||
1 |
2 |
3 |
4 |
5 | |||||||
PRO1 |
.887 |
|
|
|
| ||||||
PRO5 |
.886 |
|
|
|
| ||||||
PRO2 |
.828 |
|
|
|
| ||||||
PRO4 |
.802 |
|
|
|
| ||||||
PRO3 |
.748 |
|
|
|
| ||||||
POS5 |
|
.856 |
|
|
| ||||||
POS1 |
|
.849 |
|
|
| ||||||
POS4 |
|
.802 |
|
|
| ||||||
POS3 |
|
.746 |
|
|
| ||||||
POS2 |
|
.731 |
|
|
| ||||||
POL4 |
|
|
.905 |
|
| ||||||
POL2 |
|
|
.851 |
|
| ||||||
POL3 |
|
|
.779 |
|
| ||||||
POL1 |
|
|
.765 |
|
| ||||||
POT5 |
|
|
|
.782 |
| ||||||
POT3 |
|
|
|
.748 |
| ||||||
POT6 |
|
|
|
.693 |
| ||||||
POT4 |
|
|
|
.588 |
| ||||||
POT2 |
|
|
|
.573 |
| ||||||
POT1 |
|
|
|
|
| ||||||
PEO1 |
|
|
|
|
.831 | ||||||
PEO2 |
|
|
|
|
.821 | ||||||
PEO3 |
|
|
|
|
.813 | ||||||
Factors to evaluate |
|
|
| ||||||||
KMO coefficient |
0 .755 |
0.5 <0 .755 <1 |
| ||||||||
Sig value. in the Bartlett test |
0.000 |
0.000 < 5% |
| ||||||||
Citation variance |
63,135 % |
63.135 %> 50% |
| ||||||||
Eigenvalue value |
1,943 |
1.943 > 1 |
|
The results of the
discovery factor analysis have extracted 05 components. Statistical indicators
ensure the conformity. However, b ien related damage can POT1 coefficient load
factor (factor loadings) is less than 0.55, so we type this observed variable to
analyse the discovery factor EFA (2nd time).
|
|
Compare |
| |||||||||||||
|
Observed variables |
Factor |
|
|
| |||||||||||
1 |
2 |
3 |
4 |
5 |
|
| ||||||||||
PRO1 |
.888 |
|
|
|
|
|
| |||||||||
PRO5 |
.887 |
|
|
|
|
|
| |||||||||
PRO2 |
.829 |
|
|
|
|
|
| |||||||||
PRO4 |
.805 |
|
|
|
|
|
| |||||||||
PRO3 |
.747 |
|
|
|
|
Result |
| |||||||||
POS5 |
|
.858 |
|
|
|
|
|
| ||||||||
POS1 |
|
.850 |
|
|
|
|
| |||||||||
POS4 |
|
801 |
|
|
|
|
| |||||||||
|
|
|
|
|
|
|
| |||||||||
POS3 |
|
.745 |
|
|
|
|
| |||||||||
POS2 |
|
.730 |
|
|
|
|
| |||||||||
POL4 |
|
|
.906 |
|
|
|
| |||||||||
|
POL2 |
|
|
.851 |
|
|
| |||||||||
|
|
| ||||||||||||||
|
POL3 |
|
|
.779 |
|
|
|
|
| |||||||
POL1 |
|
|
.765 |
|
|
| ||||||||||
POT5 |
|
|
|
.802 |
|
| ||||||||||
POT3 |
|
|
|
.779 |
|
| ||||||||||
POT6 |
|
|
|
.715 |
|
| ||||||||||
POT2 |
|
|
|
.594 |
|
| ||||||||||
POT4 |
|
|
|
.553 |
|
| ||||||||||
PEO1 |
|
|
|
|
.830 |
| ||||||||||
PEO2 |
|
|
|
|
.820 |
| ||||||||||
PEO3 |
|
|
|
|
.814 |
| ||||||||||
Factors to evaluate |
| |||||||||||||||
KMO coefficient |
0 .756 |
0.5 <0 .756 <1 | ||||||||||||||
Sig value. in the Bartlett test |
0.000 |
0.000 < 5% | ||||||||||||||
Citation variance |
65,115 % |
65.115 %> 50% | ||||||||||||||
Eigenvalue value |
1,881 |
1.881 > 1 |
The results of the discovery factor analysis extracted 05 components. The statistical indicators to ensure conformity, c evil observers variable coefficient load factor (factor loadings) is greater than 0. 55. Do it, exploring factor analysis is said to be compatible with the data collected.
The results of factor
analysis to discover the dependent variable EFA
Factors to evaluate |
Result |
Compare |
KMO coefficient |
0 .650 |
0.5 <0 .650 <1 |
Sig value. in the Bartlett test |
0.000 |
0.000 < 5% |
Citation variance |
57.521 % |
57.521 %> 50% |
Eigenvalue value |
1,726 |
1.726 > 1 |
|
Factor |
first | |
OZ1 |
0.763 |
OZ3 |
0.761 |
OZ2 |
0.751 |
The results of the
discovery factor analysis extracted 01 component. The statistical indicators to
ensure conformity, c evil observers variable coefficient load factor (factor
loadings) is greater than 0.55. Do it, exploring factor analysis is said to be
compatible with the data collected.
Cronbach's Alpha |
Number of observations |
0.744 |
5 |
Observed variables |
Average scale if variable
type |
Scale variance if variable
type |
Total variable correlation |
Cronbach's Alpha if
variable type |
POT2 |
14.70 |
16,921 |
0.387 |
0.743 |
POT3 |
14.26 |
15,170 |
0.576 |
0.673 |
POT4 |
14.09 |
16,963 |
0.390 |
0.742 |
POT5 |
14.28 |
14,670 |
0.630 |
0.652 |
POT6 |
14.24 |
15,292 |
0.569 |
0.676 |
We see:
Cronbach's Alpha is greater than 0.6.
The variables relating police are there coefficient relatively important variables total are greater than 0.3.
Because of this, the variation observed is put
in to distribution volume in steps to follow.
Regression analysis
will determine the relationship between the dependent variable and the
independent variables. Regression analysis model will describe the form of the
relationship and thereby help us predict the level of the dependent variable
when knowing in advance the values of the independent variables. When running
the regression, it is necessary to pay attention to the following parameters
[6-9]:
Beta coefficients: Standardized regression coefficients allow direct
comparison between coefficients based on their explanatory relationship with
the dependent variable.
Coefficient R2: Evaluating the volatility of the
dependent variable explained by the predictor or independent variable. This
factor can vary from 0 to 1.
ANOVA test: To check the suitability of the model with the
original data set. If the significance level of the test is <0.05, we can
conclude that the regression model is consistent with the data set.
Based on the adjusted model after the discovery factor analysis, we have a multiple linear regression model as follows:
|
Regression coefficient is
not standardized |
Standardized regression
coefficients |
t |
Sig. |
Multi-collinear
statistics |
|||
B |
Standard error |
Beta |
Tolerance coefficient |
VIF |
||||
|
Constant |
-.008 |
.174 |
|
-.046 |
0.964 |
|
|
|
PRO |
.149 |
.019 |
.288 |
7,732 |
.000 |
.902 |
1,109 |
|
POS |
.307 |
.021 |
.540 |
14,874 |
.000 |
.953 |
1,049 |
|
POL |
.243 |
.017 |
.520 |
14,498 |
.000 |
.976 |
1,025 |
|
POT |
.093 |
.020 |
.175 |
4,683 |
.000 |
.898 |
1,114 |
|
PEO |
.234 |
.018 |
.455 |
12,747 |
.000 |
.984 |
1,016 |
Based on the table
above we see:
Testing
the suitability of the model
·
Testing
the multicollinearity phenomenon: The variance magnification factor (VIF) of
all independent variables is less than 10, so the multicollinearity phenomenon
in the model is assessed as not serious [6, 11-15].
·
The
results of ANOVA test with significance level sig = 0.000 showed that the built
multiple linear regression model was consistent with the data set and used
[16-18].
Evaluate
the level of explanation by the independent variables in the model
R2
coefficient (R Square) = 0 .744, which means that 74.4 % variation in financial
results will be explained by the factors that are independent variables that
were selected in the model. Research model results show that all independent
variables have statistically significant effects (due to Sig. <5%).
So, the normalized
regression equation (see column Beta):
The degree of impact of
the independent variables on the dependent variable in the order of strong to
weak is as follows (based on Beta coefficient) [19-21]:
POS (Beta = 0.540) -> POL (Beta = 0.520)
-> PEO (Beta = 0.455) -> PRO (Beta = 0.288) -> POT (Beta = 0.175)
Based on the
standardized regression coefficients of the statistically significant variables
(column Beta), the larger the regression coefficient, the stronger the impact
on the dependent variable will be. From the regression equation, we see that to
organize and manage economic zones in Vietnam, the Position factor is the most
important factor. It means that in order to form an economic zone in Vietnam,
we first consider which Position is of primary concern, near seaports,
airports, and convenient transportation systems. Benefit or not. Next, consider
the Policies of the Government and localities that attract investment in the
Economic Zone or not. There are People operating well. Are national and
international key Projects being attracted. And finally there is the promotion
of Potential advantages on the spot or not. These are the criteria for
organizing and managing economic zones in Vietnam [23,24].
In summary, through
research on the basis of survey and analysis of data using SPSS tools and the
results give us a standardized regression equation can confirm that in Vietnam
or in the world countries are trending forming a concentrated industrial space
to create the spread of an affected neighbourhood for mutual development. A
concentrated industrial space that in Vietnam is often called an economic zone
or other countries called a special economic zone formed for development
management is affected by factors in order such as: Position 1, Policies 2,
People 3, Project 4, and Potential 5 that managers need to pay attention to
organize and manage. How these five factors have a causal relationship will be
studied in the author's later paper.
During the research
process, the author would like to thank enterprises of 16 economic zones in
Vietnam, Economic Zone Policy Makers in Vietnam, Managers of Economic Zones in
Vietnam, Experts specializing in research on the economic zone in Vietnam have
facilitated the survey by questionnaires, in-depth interviews and provided data
in primary and secondary formats with sufficient evidence for research.