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 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 quan, tổ chức doanh nghiệp nhanh chóng đánh giá tình hình ứng dụng thương mại điện tử phạm vi nước tỉnh, thành phố trực thuộc Trung ương Chỉ số Thương mại điện tử, gọi tắt EBI (E-business Index), giúp cho quan, tổ chức doanh nghiệp đánh giá cách nhanh chóng mức độ ứng dụng thương mại điện tử so sánh tiến năm theo địa phương, đồng thời hỗ trợ việc đánh giá, so sánh địa phương với dựa hệ thống số Như vậy, Việt Nam có Hiệp hội Thương mại điện tử Việt Nam (VECOM) xây dựng số ứng dụng thương mại điện tử để đánh giá mức độ ứng dụng thương mại điện tử doanh nghiệp tổ chức nước Nhưng số mang tính thống kê điều tra đưa mức ứng dụng thương mại điện tử hàng năm không đưa số đánh giá giúp cho doanh nghiệp nhận biết: lực doanh nghiệp ứng dụng thương mại điện tử không; Doanh nghiệp cần phải đầu tư nào, vấn đề cần phải giải để 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 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 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 (Strongly Agree disagree) to (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 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 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 Effectiveness is divided into five factors In Table 4.5 there is 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 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 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) 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 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 Step df Sig 8.819 0.003 Block 201.993 0.000 Model 201.993 0.000 Reject the null hypothesis H0: capacity of the firm = easeness = usefulness = compatibility because the p-values is less than 0.05 Table 4.19: Model Summary Step -2 Log likelihood 71.333c Cox & Snell Nagelkerke R R Square Square 0.636 0.853 The value of -2 Log likelihood in step equals to 71.333 and it has the lowest value in 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 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 or 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 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) 10 Step 4d H1 4.801 0.829 33.525 0.000 121.578 H2 1.051 0.366 8.238 0.004 2.862 H3 1.022 0.368 7.714 0.004 2.779 H5 2.560 0.524 23,858 0.000 12.936 Constant -1.690 0.438 14.900 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 = H0: easeness = H0: usefulness = H0: compatibility = 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 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)  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 11 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 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, 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 = 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 equals to 71.333 and it has the lowest value in 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 12 (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: capacity of the firm easeness usefulness 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 13 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 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; 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