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 Sta

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

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

CHAPTER II REVIEW OF RELATED LITERATURE

2.1 Internet

2.2 E-commerce

2.3 SME S

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:

Figure 2.12: Research model

Easiness

Usefulness

Effectivenes

s

E_commerce Model Adoption in SMES

Manager

Compatibility

Capacity of the firms

Ecommerce Model

Advantage

SMES‟ Readiness

to Adopt

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:

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

Quantitative research

Processing scale:

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

- Reject the variables with small EFA

Recurrent analysis:

- Build a research model

- Binary Logistic Regression

Propose for e-commerce

development in small and

medium enterprises in Vietnam

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

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

Bartlett's Test of Sphericity

Approx Chi-Square 4.302E3

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

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 0.921

Bartlett's Test of Sphericity Approx Chi-Square 4.184E3

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

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

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

Easiness

Usefulness

Effectiveness

E_commerce Model Adoption in SMES

Manager

Compatibility

Capacity of the firms

Ecommerce Model

Advantage

SMES‟ Readiness

to Adopt

Figure 4.3:Research model after factor analysis

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 df Sig

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

-2 Log likelihood

Cox & Snell

R Square

Nagelkerke R Square

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"

10

Nagelkerke‟s R2 is part of SPSS output in the „Model Summary‟ table and 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

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 Table a

Observed

Predicted Does your firm utilize e-commerce?

Percentage Correct

Step 4 Does your firm utilize

e-commerce?

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