Building scale factors in the business environment affect asset investment of Son La’s enterprises
BUILDING SCALE FACTORS IN THE BUSINESS ENVIRONMENT
AFFECT ASSET INVESTMENT OF SON LA’S ENTERPRISES
MSc. Dao Thi Van Anh
Department of Economics, Tay Bac University, Son La, Vietnam
Abstract
Asset investment is a period which decides business results of enterprises. In
addition, the business environment is also important for enterprises. In order to
accurately determine the impact of factors in the business environment on the assets
investment, especially the qualitative factors, the development of a measure of factors
with measurement indicators is really needed. By combining qualitative and
quantitative methods with 107 questionnaires in Son La, Vietnam, the author
develops a scale that measures three groups of qualitative factors in the business
environment that influence the asset investment of Enterprises, include:
infrastructure and technology factors, local incentive factors and socio-cultural
factors of the locality.
Keywords: Enterprise, factor, investment decision, scale.
1. Introduction
In recent years, the average production and business efficiency of enterprises
in Son La province has been decreasing, the average return on asset has decreased
from 1.31% in 2014 to 0.66% in 2017 and the average return on equity decreased
from 3.42% in 2014 to 2.01% in 2017. This situation leads to concerns about
rationalization and effectiveness of asset investment in enterprises. Therefore,
clarifying the factors affecting asset investment of these enterprises is necessary in
the current conditions.
Previous studies have confirmed that there are many factors affecting the
overall investment of businesses (Gill et al., 2012; Erkaningrum, 2013; Piotr
Bialowolski and Dorota Weziak-Bialowolska, 2014; Le Bao Lam and Le Van Huong,
2010). However, the research results had not uniformity, or yet to be shown
dimension and tendency the impact of these factors. Meanwhile, in-depth research on
the factors affecting the asset investment of enterprises is still very limited, especially
the research on testing the relationship between qualitative factors in the business
environment with asset investment of enterprises. Therefore, the scale of qualitative
factors in the business environment influences asset investment of enterprises in
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Vietnam in general and enterprises in Son La province - one of the Northwest
provinces receives many preferential policies development of the state in particular -
has not yet been built. This is a blank that needs further study.
In addition, the business environment has important implications for businesses,
it will create favorable conditions for enterprises operating easier if businesses know how
to utilize the advantages due to this environment brings, but also may be the constraints
impede development if businesses do not adapt to this environment. To determine
exactly the impact of factors in the business environment on asset investment of
enterprises, especially qualitative factors, the construction of the scale of factors with
measurement indicators is really needed. This is considered a prerequisite stage but it is
decisive in the process of determining the impact of qualitative factors in the business
environment to invest assets of enterprises.
Based on that theory and practice, the author builds scales for qualitative factors in
the business environment affecting the asset investment of enterprises in Son La province.
2. Database and research methods
The study was conducted with a combination of quantitative and qualitative
research methods. In particular, qualitative research was conducted with in-depth
interview techniques and quantitative research conducted with Cronbach’s Alpha
reliability assessment techniques and explore factor analysis (EFA). Specifically:
2.1. In-depth interview techniques
The in-depth interview technique was carried out in the process of finalizing
the preliminary scale, after defining a temporary scale through desk research.
Conducting in-depth interviews with 10 enterprises with the main purpose is to
modify the scale of style, to avoid the situation that the interviewees understand
incorrectly, inadequately, misunderstand the question or the questions are not
consistent with the actual situation of enterprises in Son La province. On that basis,
the questionnaire was completed and conducted on a large scale.
The in-depth interview process was conducted with the director or deputy
director of the enterprise. Due to time constraints and depending on the coordination
of interviewees, the interview process is conducted in two forms: direct and telephone
exchanges. Interview time lasts from 30 minutes to 1 hour. The interview process was
conducted by the author and recorded.
2.2. Cronbach’s Alpha reliability analysis
Primary data collected through survey questionnaires were analyzed with
techniques to assess the reliability of Cronbach's Alpha by SPSS software 20.
Analysis of Cronbach's Alpha to check Cronbach's Alpha coefficients and variables
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with correlation coefficient with the total variable is small will be deleted. According
to Joseph F. Hair et al. (1998), the Cronbach Alpha coefficient from 0.6 and above is
a qualified measurement scale, above 0.8 to nearly 1, which shows a very good
measurement scale. At the same time, indicators to measure the same research
concept must also be closely correlated. If the indicator has a total correlation
coefficient of greater than 0.3, it is considered satisfactory (in case it is less than 0.3,
it must be removed from the scale).
2.3. Exploratory factor analysis - EFA
The process of exploratory factor analysis - EFA is also implemented by SPSS
software 20. According to Kim and Mueller (1978) and Kaiser (1974), the assessment
of Cronbach Alpha reliability was done before analyzing the exploratory factor to
eliminate garbage variables because garbage variables could create fake factors when
analyzing exploratory factors. The tool used to analyze exploratory factors includes
factor loading and extract variance. Specifically:
In theory, the KMO coefficient (Kaiser-Meyer-Olkin) is used to consider the
appropriateness of factor analysis. KMO values must be at least 0.5 (0.5 ≤ KMO ≤ 1)
as a sufficient condition for factor analysis to be appropriate.
To review the correlation between the indicators of factors that can be used to
test the Bartlett (Bartlett's test of sphericity). If the test results show no statistical
significance: sig Bartlett’s Test ≥ 0.05, it means that the indicators do not correlate
with each other, so factor analysis for the variables under consideration should not be
applied. The case of Bartlett test results is statistically significant: sig Bartlett’s Test
<0.05, means that the indicators are correlated with each other in the factor.
Eigenvalue value is a criterion for determining the number of factors in EFA
analysis. With this criterion, only those elements with Eigenvalue ≥ 1 are retained in
the analysis model. To test the appropriateness of the model, use the indicator Total
variance Explained. If the total variance explained ≥ 50% indicates that the EFA
model is appropriate. If the variation is 100%, then this value represents how much
of the extracted factor is extracted and how much is lost in the indicator.
In addition, the relationship shows the correlation between the indicators with
the factors expressed by Factor Loading. The higher the coefficient, the greater the
correlation between the indicator and the larger factor and vice versa.
3. Range of research
In-depth interview techniques were carried out with 10 enterprises in Son La city
by convenient sampling method. Among them, 10 enterprises belong to different types
of enterprises, have forms of ownership and operate in different business sectors.
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According to Hair et al. (2006), the minimum sample size to use exploratory
factor analysis should be 100 and the ratio of observations to the number of factors is
5: 1, each factor should be at least 5 observations. The survey was developed with 13
elements corresponding to 3 groups of factors, whereby the minimum sample size
was determined to be: 13 x 5 = 65 observations.
The study was carried out from April to June 2018 with 300 business managers
(Director or Deputy Director of Enterprises) in Son La province according to the
method of random sampling non probability. The results obtained were 107
questionnaires which were larger than the minimum sample size (65 observations).
Thus, the sample size is suitable.
4. Research results
4.1. Results of desk research and in-depth interviews
When studying at the table, the author has formed a preliminary survey to serve
the in-depth interview process. At the end of the in-depth interview process, the survey
questionnaire was completed with specific questions, consistent with Vietnamese
semantics and the reality of enterprises in Son La province. Specifically:
4.1.1. Infrastructure and technology factor
This group of factors includes a scale reflecting both the factors of
infrastructure and technology developed from the questionnaire in the study of Galan
et al. (2007), Tatoglu et al (1998), Loree et al (1995) have adjusted and supplemented
to match the characteristics of enterprises in Son La province. The scale details are
presented in Table 1.
Table 1: Scale of infrastructure and technology factor
Sign
Description of the scale
Note
The development of infrastructure services
(transport,
electricity,
water
and
HT1
telecommunications) meet the needs of enterprises'
Galan et al.
(2007),
assets investment
Ability to access modern technology and Tatoglu et al
machinery/equipment to meet the needs of (1998), Loree
HT2
HT3
enterprises' assets investment
et al. (1995)
Ability to access production and business premises is
suitable for enterprises' assets investment demand
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4.1.2. Factors of local preferential policies
Inheriting the scale in the study of Ulgado (1996), Tatoglu (1998), Yongqiang
Gao (2011), but omitting some factors to ensure compliance with the situation in
Vietnam and the Northwest sub-region. For example, the author does not use the
"state support for profit sharing policy" scale to apply to research. Because in
Vietnam, the state only intervenes and regulates the dividend distribution policy of
state enterprises. Meanwhile, according to the provisions of the Law on Enterprises
in 2014, the number of state-owned enterprises in these localities has become less and
less. The dividend distribution policy of other types of businesses is not affected, so
using this scale will not be feasible.
In addition, based on the status of investment promotion in the locality,
especially preferential policies on land and tax for newly invested enterprises in the
locality. The author adds two new scales: state/local support for access to production
and business premises and taxes.
Table 2: Scale of local preferential policies factor
Sign
Description of the scale
Note
The level of state / local support / incentives for Decision No. 08/2013
CS1
access to production and business premises
/ QD-UBND of the
Provincial People's
The level of state / local support / preferential tax
Committee was issued
on June 3, 2013
CS2
CS3
CS4
CS5
CS6
(CIT, excise tax, agricultural land use tax, ...)
The level of state / local support / incentives for
on-site labor training
The level of support / incentives of the state /
locality on the selling price of output products, the
purchase price of inputs
Ulgado
(1996),
(1998),
Gao
Tatoglu
Yongqiang
(2011)
Strict / strict level of state / local regulations on
enterprises' asset investment activities
The compatibility between the socio-economic
development objectives of the state / the locality and
the orientation of asset investment in enterprises
4.1.3. Factor of local cultural and social
This group of factors represents issues surrounding people's attitudes and
levels of social and cultural development in localities, which are inherited from the
scales used in previous research by Hofstede (1980), Galan et al. (2007) omitted some
factors to ensure compliance with the local situation.
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In addition, in view of economics, if the living standard is lower, the
demand of people will be more limited, then enterprises may have to narrow the
scale of production and business, which is synonymous with limiting asset
investment and vice versa. Therefore, the author adds a scale related to the living
standard / income of the local people. Besides, a characteristic of Son La province
is that ethnic minorities live mainly on Thai, H'mong and Dao ethnic groups. Each
ethnic group has different customs, like the H'mong people who live in high
mountains with small and low houses while the Thai people live in tall and wide
stilt houses. Therefore, they do not have much demand for construction, especially
large civil works.
In addition, ethnic groups such as Thai and H'mong have a custom of living
in villages with high community. With the distribution of such residential areas, it
also affects the construction of product distribution systems of enterprises. In
addition, the practice of sedentarization and settlement of ethnic minorities in high
hills with small production methods, self-sufficiency by agriculture and forestry
is essential. So consumer demand for luxury goods and services is very limited.
Accordingly, local consumer goods are still essential goods. This situation also
affects the orientation of providing products of enterprises, especially the
determination of strategic products of enterprises in this market. On that basis, the
study adds a scale of customs and practices of local people. The scale details are
presented in Table 3.
Table 3: Scale of local cultural and social factor
Sign
Description of the scale
Note
VH1
The level of local socio-economic development
Galan et al (2007),
Hofstede (1980)
Attitude of local people to the expansion of
investment assets of enterprises
VH2
VH3
The customs and practices of local people
Annual report on
socio-economic
situation of the
province
VH4
Living standard / income of local people
4.2. Results of Cronbach’s Alpha reliability assessment
The research results were carried out with 107 enterprises in Son La province,
showing that basically, the measures are Cronbach’s Alpha coefficient> 0.7; KMO
coefficients, Bartlett testing, Eigenvalue values and total variance were all met (above
0.3). The results of scale reliability analysis are presented in Table 4.
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Table 4: Results of scale reliability analysis
Scale Mean
if Item
Scale
Corrected
Item-Total Alpha if Item
Item Deleted Correlation Deleted
Cronbach’s
Sign
Variance if
Deleted
Infrastructure and technology factor(Cronbach’s Alpha = 0.802)
HT1
HT2
HT3
7.90
8.10
8.09
2.772
2.886
2.482
.607
.681
.665
.772
.703
.713
Factors of local preferential policies (Cronbach’s Alpha = 0.797)
CS1
CS2
CS3
CS4
CS5
CS6
18.51
18.28
18.88
18.74
18.87
18.78
10.045
9.713
.625
.639
.655
.535
.492
.375
.748
.743
.739
.769
.779
.807
9.428
10.591
11.341
11.138
Factor of local cultural and social (Cronbach’s Alpha = 0.803)
VH1
VH2
VH3
VH4
18.51
18.28
18.88
18.74
10.045
9.713
.625
.639
.655
.535
.748
.743
.739
.769
9.428
10.591
4.3. Result of exploratory factor analysis (EFA)
The results of the KMO coefficient and Barlett test are presented in Table 5,
showing that the KMO coefficient = 0.856> 0.5 and the critical value of the Barlett
test is 0.000 which is satisfactory, it also means the indicators in the model is
correlated with each other. Thus, the sample size is suitable and large enough to
implement factor analysis techniques.
Table 5: KMO coefficient and Barlett test results
KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy. .856
Approx. Chi-Square
Bartlett's Test of Sphericitydf
Sig.
579.649
78
.000
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The resultspresentedin Table6showthat theindicatorsensuretheteamconverges.
The total number of Rotation sums of squared loadings of 63.405%> 50% means that the
use of factors representing 13 indicators can explain up to 63.405% of explanations of all
indicators. Initial Eigenvalues = 1,197 in the third factor and starting less than 1 from the
4th factor shows that the indicators in the model converge to 3 factors.
Table 6: Total variance explained
Total Variance Explained
Co
mp
one
nt
Initial Eigenvalues
Extraction Sums of Squared
Loadings
Rotation Sums of Squared
Loadings
Total
% of Cumulativ Total
Variance e %
% of Cumulativ Total
Variance e %
% of
Cumulative
%
Variance
1
5.458 41.982 41.982 5.458 41.982 41.982 2.912 22.403 22.403
1.588 12.215 54.197 1.588 12.215 54.197 2.687 20.666 43.069
2
3
4
5
6
7
8
9
1.197 9.208
63.405 1.197 9.208
69.250
63.405 2.644 20.336 63.405
.760
.709
.591
.568
.472
.444
5.845
5.456
4.545
4.371
3.633
3.417
2.740
2.542
2.182
1.865
74.706
79.251
83.622
87.254
90.671
10 .356
11 .330
12 .284
13 .243
93.411
95.953
98.135
100.000
Extraction Method: Principal Component Analysis.
With 3 scales and 13 indicators obtained after Cronbach’s Alpha verification,
EFA factor analysis is performed. In the process of analyzing the exploratory factor,
the author chooses factor loading factor of 0.5, so the indicators with load factor less
than 0.5 will be disqualified, so they are not displayed on the results table. Using
Varimax Produce rotation method to rotate the original elements to minimize the
number of indicators with large coefficients in the same factor. The Varimax rotating
factor matrix shown in Table 7 shows that the load factor of CS5 and CS6 indicators
is less than 0.5, so the remaining indicators are above 0.5.
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Table 7: Factor rotation matrix
Rotated Component Matrixa
Component
1
2
3
VH3
VH4
VH2
VH1
CS6
HT2
HT3
HT1
CS5
CS2
CS3
CS4
CS1
.829
.828
.668
.572
.881
.789
.676
.822
.782
.763
.635
Extraction Method: Principal Component Analysis.
Rotation Method: Varimax with Kaiser Normalization.
a. Rotation converged in 5 iterations.
5. Conclusion
Thus, from 3 groups of factors with 13 initial indicators, after analyzing the
factor of discovery, EFA has formed 3 groups of factors with 11 indicators to explain
63.405% of enterprises' asset investment, and 36,595 % investment of enterprises'
assets explained by other factors. The groups of factors include: (1) Infrastructure and
technology, (2) local preferential policies and (3) local socio-culture. They are all
groups of qualitative factors in the business environment that can affect the asset
investment of enterprises in Son La province. Details of the scale and indicators of
completion are shown in Table 8. Research results are really necessary to serve the
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process of finding qualitative factors affecting the asset investment of enterprises by
quantitative research method.
Table 8: Scale of factors in the business environment affecting the asset
investment of enterprises in Son La province
No
I
Sign
Scale / indicator
Infrastructure and technology factor
1
HT1
The development of infrastructure services (transport,
electricity, water and telecommunications) meet the needs of
enterprises' assets investment
2
3
HT2
HT3
Ability to access modern technology and machinery/equipment
to meet the needs of enterprises' assets investment
Ability to access production and business premises is suitable
for enterprises' assets investment demand
II
Factors of local preferential policies
1
CS1
The level of state / local support / incentives for access to
production and business premises
2
CS2
The level of state / local support / preferential tax (CIT, excise
tax, agricultural land use tax, ...)
3
4
CS3
CS4
The level of state / local support / incentives for on-site labor training
The level of support / incentives of the state / locality on the
selling price of output products, the purchase price of inputs
III
1
Factor of local cultural and social
VH1
VH2
The level of local socio-economic development
2
Attitude of local people to the expansion of investment assets of
enterprises
3
4
VH3
VH4
The customs and practices of local people
Living standard / income of local people
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