Factors affecting the adoption of e commerce model developed for small and medium enterprises in vietnam

FACTORS AFFECTING THE ADOPTION OF E-COMMERCE MODEL
DEVELOPED FOR SMALL AND MEDIUM ENTERPRISES IN viet nam

A Dissertation
Presented to the Faculty of Graduate School
Southern Luzon State University, The Philippines and
Thai Nguyen University, Socialist Republic of Vietnam

In Partial Fulfillment
of the Requirements for the Degree
Doctor in Management

By
NGUYEN TIEN HUNG - FAT
SLSU-DBA 6A (Hanoi)

May 2013


CHAPTER 1
INTRODUCTION
1.1. Background of the Study
According to figures from the Ministry of Industry and Commerce, at present, small and
medium enterprises account for more than 85% of enterprises in Vietnam, with registered
capital of nearly 2,313,857 billion dongs (equivalent to 121 billion dollars) and 100% having
Internet access. While more and more customers are looking for new products and trade
opportunities with Southeast Asian countries, Vietnamese enterprises are also seeking new
opportunities to reach them through e-commerce. Along with maintaining domestic operations
actively, Vietnam will definitely continue to receive more attentions from importers in a near
future (Mr Vincent Wong, Senior Managing Director of Business Development and Customer
Services Department of Group Alibaba.com shared).

Năm 2012 Hiệp hội Thương mại điện tử Việt Nam (VECOM) tiến hành hoạt động
xây dựng Chỉ số Thương mại điện tử với mong muốn hỗ trợ các cơ quan, tổ chức và
doanh nghiệp nhanh chóng đánh giá được tình hình ứng dụng thương mại điện tử trên
phạm vi cả nước cũng như tại từng tỉnh, thành phố trực thuộc Trung ương. Chỉ số
Thương mại điện tử, gọi tắt là EBI (E-business Index), giúp cho các cơ quan, tổ chức và
doanh nghiệp có thể đánh giá một cách nhanh chóng mức độ ứng dụng thương mại điện tử
và so sánh sự tiến bộ giữa các năm theo từng địa phương, đồng thời hỗ trợ việc đánh giá,
so sánh giữa các địa phương với nhau dựa trên một hệ thống các chỉ số.
Như vậy, ở Việt Nam hiện tại chỉ có Hiệp hội Thương mại điện tử Việt Nam
(VECOM) đã xây dựng chỉ số ứng dụng thương mại điện tử để đánh giá mức độ ứng
dụng thương mại điện tử của các doanh nghiệp và tổ chức trong nước. Nhưng những
chỉ số này mới chỉ mang tính thống kê điều tra đưa ra mức ứng dụng thương mại điện
tử hàng năm chứ không đưa ra được những chỉ số đánh giá giúp cho các doanh nghiệp
nhận biết: năng lực của doanh nghiệp của mình có thể ứng dụng thương mại điện tử
được không; Doanh nghiệp cần phải đầu tư như thế nào, những vấn đề nào cần phải
giải quyết... để có thể áp dụng thương mại điện tử vào kinh doanh.
1.2. Statement of the Problems
This research project focuses on the adoption of e-commerce in Viet Nam SMEs and
aims to test adoption factors in e-commerce model which it built based on the models of
effective e-commerce in the world. Authors propose a model that factors are based on the
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actual situation of e-commerce in the viet nam enterprises

. Thus the research problem for

this study can be as follows:
What are the main factors, which influence the adoption of e-commerce in Viet

nam SMEs?
Thus the research problem for this study can be as follows:
H1. Capacity of firm affects e-commerce adoption
H2. Compatibility of e-commerce for the value, work practices, and technology in the
firm affects e-commerce adoption
H3. Managers influence e-commerce adoption
H4. The ease of use affects e-commerce adoption
H5. The usefulness affects e-commerce adoption
H6. Effectiveness affects e-commerce adoption

1.3. Significance of the Study
- This study can serve small and medium enterprises in Vietnam.
- This research will support the enterprises in constructing business strategies,
strengthening advertising, and improving competitive advantages in the market economy and
in the integration of Vietnam today to the world economy.
- This study can provide necessary information and support the Government‟s programs
in formulating policies and laws on e-commerce applied for businesses in Vietnam.
- Researchers can use this study as a reference for further research related to this issue.
1.4. Scope and Limitation
The study shall focus on determining the e-commerce strategy that might help small and
medium enterprises improve the production, sales and profit of the company.
- Scope: Mainly research on the small and medium enterprises (SMES) in Vietnam.
- The forms of ownership and types of enterprises: The enterprises of all forms of
ownership and business types, except for the enterprises with 100% foreign capital.
- The geographical limits: the research focuses on the enterprises in Hanoi. This
representative meet the requirement and capacity for applying e-commerce in particular and
IT in general at the highest level in Viet Nam.

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CHAPTER II
REVIEW OF RELATED LITERATURE
2.1. Internet
2.2. E-commerce
2.3. SMES
2.4. E-Commerce Models.
2.5. Theory of Reasoned Action (TRA)
2.6. Technology Acceptance Model
2.7. Grandon and Perason's Model
2.8. Innovations Diffusion Theory (IDT)
2.9. Model of Factors Influencing Electronic Commerce Adoption and Diffusion in
Small- & Medium-sized Enterprises
2.10. Model for Assessing E-commerce Success in SMEs
2.11. Conceptual Framework
The author offers a theoretical model suitable for the model B2C e-commerce as
follows:

Usefulness
Easiness
Effectivenes
s

Manager
E_commerce
Model
Adoption in
SMES

Capacity of

the firms
Compatibility

SMES‟ Readiness
to Adopt

Ecommerce Model
Advantage
Figure 2.12: Research model

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CHAPTER III
RESEARCH METHODOLOGY

3.1. Research Design
This research is aiming at investigating an e-commerce adoption model in
VietNam SMEs based on the . In order to meet the objective, the research methodology
which is undertaken is as follow:

Basis of theory

Scale

Quantitative research
Recurrent analysis:
- Build a research model.

Processing scale:

- Binary Logistic Regression

- Calculate the Cronbach Alpha to
test the degree of close
correlation between the question
items.

Propose
for
e-commerce
development in small and
medium enterprises in Vietnam.

- Reject the variables with small
EFA.

3.2. Determination of sample size
The number of respondents are 200 enterprises in total.
3.3. Sampling design and techniques
- interview people working in small and medium enterprises in Hanoi.
- Data collection tool was a survey questionnaire. The attitude towards the acceptance
of e-commerce application is evaluated by the 5-point Likert scale, distributed from 1
(Strongly Agree disagree) to 5 (Strongly Agree agree).
3.4. Research instrument
To process data collected from the survey questionnaires, SPSS version 16.0 is used to
define the factors affecting the trend of acceptance of e-commerce application.

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3.5. Data processing method

- Factor Analysis is used to determine what the most important criteria are?
- After Factor Analysis, Test the factors with Cronbach Alpha (the Cronbach alpha
coefficient >=0.6 is used and corrected iTerm - Total correlation must be greater than 0.3)
- After determining the most important criteria, logistic analysis helps to build a
prediction equation of the adoption of e-commerce at enterprises.

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CHAPTER IV
PRESENTATION, ANALYSIS AND INTERPRETATION OF
RESULTS

This chapter presents the analysis and interpretation of the resuslt. To present the
sequence of findings of the study, the discussions were arranged according to the stated
problems:
4.1. Respondents’ profile
4.2. Factors afecting the adoption of e-commerce
In order to analyze the factors of ecommerce model advantage and SMES‟ readiness
to adopt ecommerce, a Factor Analysis was conducted using SPSS 16.0.
Factor analysis was performed with 28 variations of ecommerce model advantage and
SMES‟ readiness to adopt ecommerce in Vietnam. Through the analysis, the variables are at
the request of the model is: KMO coefficient values (Kaiser-Meyer-Olkin) greater than 0.5,
the Fator loading greater than 0.5. Analysis method is chosen to be Principal components
analysis with varimax rotation.The results are as follows:
From Factor Analysis, two tables were chosen for the analysis. The first table is called
“KMO and Bartlett‟s test”, which presents the adequacy of the sampling for each variable.

According to Table 4.4, the result of KMO for Capacity of the firm is 0.85, a satisfactory
result:
Table 4.4: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling
Adequacy.
Bartlett's Test of
Sphericity

Approx. Chi-Square
df
Sig.

0.922
4.302E3
378
0.000

The second table is Rotated Component Matrix which reports the factor loadings for
each variable on the components or factors after rotation. The factor analysis used principal
components in order to extract the maximum variance from the items. To minimize the
number of items that have high loadings on any given factor, a varimax rotation was utilized.
The Rotated Component Matrix in Table 4.5 shows that contrary to the original
model, the items of Effectiveness were loaded into five components, which means that
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Effectiveness is divided into five factors. In Table 4.5 there is 1 indicator to be rejected from
the list (Improve customer service) because this item have loading smaller than 0.5.
After removing one indicator we perform factor analysis with the remaining 27
variables. The analytical results are as follows:

In the Table 4.6 (KMO and Bartlett‟s test), the result of KMO for Capacity of the firm is
0.921, a satisfactory result.

Table 4.6: KMO and Bartlett's Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Bartlett's Test of Sphericity

Approx. Chi-Square
df
Sig.

0.921
4.184E3
351
0.000

The Rotated Component Matrix in Table 4.7 shows that contrary to the original
model, the items of Effectiveness were loaded into five components, which means that
Effectiveness is divided into five factors. Table 4.7 also shows that all items have loading
greater than 0.5 and loaded stronger on their associated factors than on others, and there isn‟t
any indicator to be rejected from the list.
After performing factor analysis of 27 variables as above, we have 5 factors are drawn:
- Capacity of the firm
- Compatibility
- Easiness
- Usefulness
- Effectiveness

Adjust the research model
Through the above analysis shows that from 27 variables to measure the factors

affecting the adoption of e-commerce model developed for small and medium enterprises in
Viet Nam have been a change in content.
Thus, the research model after factor analysis results are adjusted as follows (Figure
2.1) with the assumptions of the model are:
H1. Capacity of firm affects e-commerce adoption
H2. The ease of use affects e-commerce adoption
H3. The usefulness affects e-commerce adoption
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H4. Effectiveness affects e-commerce adoption
H5. Compatibility of e-commerce for the value, work practices, and technology in the
firm affects e-commerce adoption

Manager
Usefulness

Easiness

E_commerce
Model
Adoption in
SMES

Effectiveness

Capacity of the
firms
Compatibility

SMES‟ Readiness
to Adopt

Ecommerce Model
Advantage
Figure 4.3:Research model
after factor analysis

Test the factors with Cronbach Alpha
After performing factors analysis, the factors were drawn. Perform testing by Cronbach
Alpha for each factors to measure a set of questions in each section were drawn factors to
link together or not. Many researchers agree that the Cronbach alpha coefficient >=0.6 is
used and corrected iTerm - Total correlation must be greater than 0.3 (Mong Hoang Trong
and Nguyen Ngoc Chu, 2005).
5 factors were achieved Cronbach alpha coefficient and corrected iTerm - Total
correlation as necessary level, to ensure the conditions for inclusion in the next model
analysis.
4.4. Logistic Regression
Logistic regression is used to predict a categorical (usually dichotomous) variable from
a set of predictor variables. With a categorical dependent variable, discriminant function
analysis is usually employed if all of the predictors are continuous and nicely distributed; logit
analysis is usually employed if all of the predictors are categorical; and logistic regression is
often chosen if the predictor variables are a mix of continuous and categorical variables and/or

8


if they are not nicely distributed (logistic regression makes no assumptions about the
distributions of the predictor variables).
Model chi-square. The overall significance is tested using what SPSS calls the Model

Chi square, which is derived from the likelihood of observing the actual data under the
assumption that the model that has been fitted is accurate. There are two hypotheses to test in
relation to the overall fit of the model:
H0 The model is a good fitting model.
H1 The model is not a good fitting model (i.e. the predictors have a significant effect).
Table 4.18: Omnibus Tests of Model
Coefficients
Chi-square
Step 4 Step

df

Sig.

8.819

1

0.003

Block

201.993

4

0.000

Model

201.993

4

0.000

Reject the null hypothesis H0:

capacity of the firm = easeness = usefulness =

compatibility 0 because the p-values is less than 0.05.
Table 4.19: Model Summary
Step
4

-2 Log
likelihood
71.333c

Cox & Snell Nagelkerke R
R Square
Square
0.636

0.853

The value of -2 Log likelihood in step 4 equals to 71.333 and it has the lowest value in 4
step.
The results of Table 4.18: Omnibus Tests of Model Coefficients and Table 4.19:
Model Summary, we see that -2 Log likelihood = 71,333 is not high, acceptable because only

0:28 of the Chi-square (71.333 /201.993).

According to the example in the book "Data Analysis with SPSS research" (Hoang
Trong and Nguyen Mong Ngoc Chu, 2005), value of "-2 log likelihood" is only 0.5 value of
"Chi-square".

Therefore, it demonstrates the pretty good relevance of overall model.
9


Nagelkerke‟s R2 is part of SPSS output in the „Model Summary‟ table and is the mostreported of the R-squared estimates. In our case it is 0.853, indicating a moderately strong
relationship of 85.3% between the predictors and the prediction.
Classification Table. Rather than using a goodness-of-fit statistic, we often want to look at the
proportion of cases we have managed to classify correctly. For this we need to look at the
classification table printed out by SPSS, which tells us how many of the cases where the
observed values of the dependent variable were 1 or 0 respectively have been correctly
predicted. In the Classification Table (Table 4.20), the columns are the two predicted values
of the dependent, while the rows are the two observed (actual) values of the dependent. In a
perfect model, all cases will be on the diagonal and the overall percent correct will be 100%.
In this study, 91.2% were correctly classified for the not utilize e-commerce group and 87.2%
for the utilize e-commerce group. Overall 89.5% were correctly classified.

Table 4.20: Classification Tablea
Predicted
Does your firm utilize
e-commerce?
Observed

no

Step 4 Does your firm utilize no
e-commerce?
yes

yes

Percentage Correct

104

10

91,2

11

75

87,2

Overall Percentage

89,5

a. The cut value is ,500

Variables in the Equation. The Variables in the Equation table (Table 4.21) have several
important elements. The Wald statistic and associated probabilities provide an index of the
significance of each predictor in the equation. The Wald statistic has a chi-square distribution.
The simplest way to assess Wald is to take the p-values, and if less than 0.05, reject

the null hypothesis as the variable does make a significant contribution. In this case, we see
that All four factors are p-value less than 0.05. Such factors
H1 - Capacity of firm affects e-commerce adoption;
H2 - The ease of use affects e-commerce adoption;
H3 - The usefulness affects e-commerce adoption;
H5 - Compatibility of e-commerce for the value, work practices, and technology in the
firm affects e-commerce adoption,
affects predictions.
Table 4.21: Variables in the Equation
B

S.E.

Wald

df

Sig.

Exp(B)
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Step 4d H1

4.801

0.829

33.525

1

0.000

121.578

H2

1.051

0.366

8.238

1

0.004

2.862

H3

1.022

0.368

7.714

1

0.004

2.779

H5

2.560

0.524

23,858

1

0.000

12.936

Constant

-1.690

0.438

14.900

1

0.000

0.185

Because H1, H2, H3, H5 have p-value smaller than 0.05, we reject the null hypothesis:
H0: capacity of the firm = 0
H0: easeness = 0
H0: usefulness = 0
H0: compatibility = 0
Thus, the regression coefficient is found to be significant and the model are good for
use.
The Exp(B) column in Table 4.21 presents the extent to which raising the corresponding
measure by one unit influences the odds ratio. We can interpret EXP(B) in terms of the
change in odds. If the value exceeds 1 then the odds of an outcome occurring increase; if the
figure is less than 1, any increase in the predictor leads to a drop in the odds of the outcome
occurring.
The „B‟ values are the logistic coefficients that can be used to create a predictive
equation (similar to the b values in linear regression).
We have an equation from the regression coefficient:

 P(Y  1) 
Loge 
  - 1.690  4.801 H1  1.051 H2  1.022 H3  2.560 H5
 P(Y  0) 
Change is equivalent to:

E(Y/X) 

e (4.801 H1  1.051H2  1.022H3  2.560H5 - 1.690)
1  e (4.801 H1  1.051H2  1.022H3  2.560H5 - 1.690)

We can explain the meaning of the Regression coefficients Binary Logistic are
follows:Capacity of the firm, Easines, Usefulnes and Compatibility increase the likelihood of
adoption of e-commerce applications in business and production of SMEs. In particular, the
impact of the capacity of the firm is the largest, followed by the Compatibility.

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CHAPTER V
SUMMARY, CONCLUSIONS AND RECOMMENDATIONS

5.1. Summary of the findings
The Factors Analysis were executed to graft groups and eliminate the items in factors,
we achieved the following results.
In the factors analysis of SMEs‟ readiness to adopt, we removed the Improve customer
service item, which had components extracted lower than 0.50. Thus, after factor analysis, the
model "Research model" only 5 factors affecting the development of electronic commerce in
small and medium-sized enterprises of Vietnam (Capacity of firm affects e-commerce
adoption; The ease of use affects e-commerce adoption; The usefulness affects e-commerce
adoption; Effectiveness affects e-commerce adoption; Compatibility of e-commerce for the
value, work practices, and technology in the firm affects e-commerce adoption).
After the factor analysis, we perform tests using Cronbach Alpha for each factor. From
testing this way, we can consider the items of factors have been linked or not.
The results of the analysis, all these factors have Cronbach Alpha coefficients greater
than 0.6. Therefore, 5 factors were achieved Cronbach Alpha coefficients and corrected
iTerm - Total correlation as necessary,ensure conditions for the next model analysis.
In the following, we proceed with logistic regression analysis, which is used for
predicting the outcome of e-commerce adoption based on predictor variables.
In Model chi-square: Reject the null hypothesis capacity of the firm = easeness =
usefulness = compatibility = 0 because the p-values is less than 0.05. The model is a good

fitting model.
In the Model Summary, The value of -2 Log likelihood in step 4 equals to 71.333 and it
has the lowest value in 4 step. It‟s not hight. Therefore, it demonstrates the pretty good
relevance of overall model. Nagelkerke‟s R2 is the most-reported of the R-squared estimates.
In our case, it is 0.853, indicating a moderately strong relationship of 85.3% between the
predictors and the prediction.
In this study, 91.2% were correctly classified for the non e-commerce utilizing group
and 87.2% for the e-commerce utilizing group. Overall 89.5% were correctly classified.
The Variables in the Equation table (Table 4.21). The Wald statistic and associated
probabilities provide an index of the significance of each predictor in the equation. In
this case, we see that Capacity of firm contributed significantly to the prediction
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(p=0.000 and (B=4.801), Compatibility contributed significantly to the prediction
(p=0.000 and B=2.56); Ease of use and Usefulness also have certain influence to the
analysis results.
We have an equation from the regression coefficient:

 P(Y  1) 
Loge 
  - 1.690  4.801 H1  1.051 H2  1.022 H3  2.560 H5
 P(Y  0) 
H1 - Capacity of firm affects e-commerce adoption;
H2 - The ease of use affects e-commerce adoption;
H3 - The usefulness affects e-commerce adoption;
H5 - Compatibility of e-commerce for the value, work practices, and technology in the
firm affects e-commerce adoption.
5.2. Conclusion
Throughout this research, we have studied the factors that have influenced the

development of electronic commerce researchers in the world through the development model
of their e-commerce and based on the index information commerce in Vietnam (EBI INDEX).
Based on that, we have built these factors affect the development of e-commerce in
small and medium enterprises in Vietnam. from those factors, we attempted to build a model
of e-commerce adoption for SMEs Vietnam
Implications of this research study can be divided into implications for managers and
implications for government. In this research we have proposed and validated a predictive
model for managers that suggest four determinant factors for e- commerce adoption in
vietnam SMEs. These factors, according to the rank order of importance, are:
1. capacity of the firm
2. easeness
3. usefulness
4. compatibility
Therefore, managers who wish to adopt e-commerce should consider these factors in
making their decision about adoption and try to improve those factors that are within their
control.
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By increasing financial resources for adopting, implementing and supporting ecommerce, and providing more technological infrastructure within their organization,
improving computer-related

skills..., we will achieve the organizational readiness for

adopting e-commerce.
In addition, the adoption of e-commerce are affected by consistency of e-commerce
with preferred work practices, business requirements in the organization, value of
organization and culture organization. therefore. Construction business environment, work
environment and enterprise culture fit with e-commerce is very important in the strategic
application of e-commerce and business operations of the enterprise.

the adoption of e-commerce are affected by the managers who can decide e-commerce
applications in the enterprise. Thus, interventions toward changing managers‟ perceptions
about e-commerce can be devised in order to increase the adoption/utilization of ecommerce by SMEs.
This is also an important implication for government in order to improve e-commerce
utilization among Vietnam organizations.

5.3. Recommendations
Based on the above finding results, this research yields some following
recommendations for the development of electronic commerce in small and medium
enterprises in Vietnam.
Although the impact of the development of information technology in Vietnam and the
world is very strong,Vietnam enterprises gradually applied ecommerce in the business
activities of enterprises but most of the small and medium Vietnam enterprises has not really
invested in electronic commerce due to limited knowledge or do not know to investment in ecommerce development in enterprises of their own.
For the State:
- Investment in development of information technology infrastructure: Upgrade
communication system with broadband internet connection and stability; reinforce
telecommunications service providers to ensure good service quality.
- Propagation and dissemination of knowledge of e-commerce: Disseminate knowledge
about e-commerce in the public media; universal knowledge of information technology and e14


commerce in general education.
- Support for small and medium-sized enterprises: State build policies support for
enterprise e-commerce applications; To organize seminars, training ecommerce knowledge to
small and medium enterprises.
- Build the e-commerce legal system fully and fit.
For SMEs
- Enterprise development strategy consistent with e-commerce: Focus on staff training
to improve

qualifications, skills using computers and the internet; Have organizational

management strategic, product distribution strategies consistent with e-commerce; Have
business leaders understanding and enthusiasm for the development of electronic commerce.
- Investment in information technology infrastructure: Build and upgrade the LAN
system and enterprise' website; The enterprise has developed e-commerce should be
encouraged to transfer technology and experience to other businesses. This helps to develop ecommerce transactions and operation of electronic commerce more professional.
.

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Nẵng” Master‟s thesis
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