Index

Abstract

Small and medium-sized enterprises (SMEs) play a crucial role in many countries’ economic growth and development, particularly in Vietnam. Vietnamese SMEs have been instrumental in creating job opportunities, especially for low-skilled laborers, and are increasingly contributing to the stability and progress of the national economy. However, the rise of globalization and the integration of economies at the international and regional levels have exposed SMEs to severe local and global competition. To sustain and develop their essential role in the face of rising competition, SMEs must innovate and adopt new technologies, with e-commerce being one of the essential solutions. The integration of e-commerce within Vietnamese Small and medium-sized enterprises (SMEs) has posed a significant challenge, making it crucial to identify the key factors that impact its implementation. A quantitative method was used to determine the four factors (Technology, Organizational, Environmental, and Management) influencing the adoption of e-commerce by Vietnamese SMEs. The study found that technology has the most significant impact on the adoption of e-commerce by Vietnamese SMEs, followed by organizational, management, and environmental factors. Technology is considered the most critical factor because e-commerce is primarily a technological solution, and SMEs need to invest in advanced technologies to keep up with their competitors.

Keywords: Adoption, Critical analysis, E-commerce, Economic integration, Globalization, SMEs, Vietnamese.

JEL Classification:D00; M15; O12.

Received: 10 January 2023/ Revised: 15 May 2023/ Accepted: 20 July 2023/ Published: 2 August 2023

Contribution/ Originality

This research identifies the critical factors that influence the adoption of e-commerce by Vietnamese SMEs. While previous research in Vietnam has highlighted the importance of e-commerce for SMEs, this study's quantitative method provides a more nuanced understanding of the specific factors that affect its adoption.

1. INTRODUCTION

E-commerce has grown enormously and has become a business trend worldwide (Kshetri & Dholakia, 2002; Nair, 2017). Applying e-commerce helps businesses achieve their goals, and E-commerce also provides a broader market for companies. E-commerce offers various information related to products, prices, models, and other utilities. In addition, e-commerce brings many advantages to SMEs, such as reducing production, management, and transaction costs and saving time (Nguyen & Dang, 2017; Rahayu & Day, 2015). Besides, e-commerce provides direct links between customers, sellers, and distributors, facilitating business through convenient information transfer, especially since e-commerce brings logistical value to customers (Kawa & Światowiec-Szczepańska, 2021; Kaynak, Tatoglu, & Kula, 2005). Its characteristics made its use exponential.

Due to technological advantage, mobile technology, and globalization, software facilities, and web page formation have become cost-effective and easy to use. E-commerce has long been recognized as a critical tool to gain information and communication technology (ICT) to enhance social and economic development (Esselaar & Miller, 2001; Gbadegeshin et al., 2019; Santoleri, 2015) and to help in the eradication of poverty (Chao, Biao, & Zhang, 2021; Kareen, Purwandari, Wilarso, & Pratama, 2018; Oreku, Mtenzi, & Ali, 2013). The government has to provide incentives and industrial partners, and the researcher should encourage SMEs to adopt new ICTs like E-commerce, which helps them be competitive in the market and more sustainable (Ashrafi & Murtaza, 2008). Small organizations (SMEs), which have small capacities in finance, capital, and market accessibility, have the facility to connect with the world of opportunity. However, environmental, technical, managerial, and organizational factors usually affect e-commerce adoption. Environmental factors that restrict SMEs from adopting e-commerce and institutionalizing it include market readiness, institutional readiness, and industry and financial institution support for e-commerce (Ahmad, Abu Bakar, Faziharudean, & Mohamad Zaki, 2015; Kabanda & Brown, 2017; Molla & Licker, 2005). Technical barriers like Internet security, legal and regulatory barriers, and limited use of Internet banking are obstacles (Nazir & Roomi, 2020; Ndyali, 2013). A web page can provide a national and international presence (Karakaya & Karakaya, 1998; Singh, Toy, & Wright, 2009). E-commerce has contributed to a remarkable shift in consumer spending from traditional physical retail in the neighborhood to online vendors (OECD, 2020). The organizations (SMEs) studied successfully competed in their traditional market segments but reported additional benefits after adopting e-commerce. It examines the perspectives of firms that have been using e-commerce for at least the previous 4-5 years and their assessments of the advantages of e-commerce on both an operational and strategic level. This would illuminate the motives for embracing e-commerce in conventional brick-and-mortar businesses and allow for re-evaluating those motivations.

The private sector, of which the majority are SMEs, is considered the driving force of Vietnam's economy (Vu Hung, 2016). However, Vietnamese SMEs are increasingly facing many difficulties and challenges, especially the change in models and ways of production and business due to the boosting of information technology and the development of the industrial era. E-commerce plays a crucial role in fostering the growth of SMEs in Vietnam. For SMEs to thrive and flourish, they must efficiently invest and use resources. However, the application of e-commerce by SMEs is still at a low level and faces many difficulties and challenges that hinder their growth. Therefore, this paper focuses mainly on exploring the factors affecting the adoption of e-commerce by Vietnamese SMEs. On that basis, the authors propose some solutions to promote the effective application of e-commerce by small Vietnamese businesses.

2. THEORETICAL FRAMEWORK

In this context, an international perspective on E-commerce is adopted, i.e., it is about not only buying and selling on the Internet but also customer service, collaborating with business partners, and conducting business internally within the organization (Efraim, David, Jae, Merrill, & Michael, 2002). This description facilitates the identification of three distinct levels of e-commerce. (i) Intra-organizational electronic commerce, also known as e-commerce conducted within a company, primarily involves the utilization of the Internet and internal email systems. (ii) Inter-organizational business, referring to business activities carried out with other organizations, predominantly relies on the use of extranets. Lastly, the third stakeholder in the business-customer relationship is the general public, as the Internet serves as a public network accessible to all individuals. Lawrence and Brodman (2000) described e-commerce as a collection of networks using the same concept. The intranet is exclusive to the company; the extranet is also exclusive to business partners; and the Internet is a public and worldwide network available to everyone. In particular, Tornatzky, Fleischer, and Chakrabarti (1990) built the Technology, Organization, and Environment (TOE) Framework to study the adoption of technological advancement. TOE is the basic foundation for applying technical advantages such as e-commerce. Based on the TOE research framework of  Tornatzky et al. (1990), which included three factors, Van Huy, Rowe, Truex, and Huynh (2012) added the Managerial factor influencing the adoption of e-commerce. Their observation is that the adoption of e-commerce has four aspects.

  1. Technological aspect: The existing and emerging technologies relevant to the firm.
  2. Organizational aspects: Firm, size, scope, managerial structure, and internal resources.
  3. Environmental aspect: Area in which an organization conducts business, industry, competitors, and government policies.
  4. Managerial factors: Reduce costs; level of risk tolerance; attitudes towards innovation and creativity; knowledge.

3. THE BENEFITS OF E-COMMERCE

The literature shows a broad spectrum of different motivators and advantages for e-commerce. Maloff identified four areas or categories of benefit (Maloff, 1996). The first category concerns benefits from reducing external and internal communication expenses, e.g., speeding upbusiness processes andreducing administrative tasks. The second element is revenue, which might come from existing companies or new projects. The third benefit is concrete, such as lower expenses and more flexible working habits, while the fourth is intangible, such as more robust competitive positioning and customer connections. Other research discovered some of the same advantages and some new ones. For example, an analysis of three organizations (Dell, Cisco, and FedEx) noted decreased sales and marketing expenditures, lower service and support costs, and stronger customer connections (Currie, 2000). A list of 33 motivators and benefits tested in a survey was produced using factor analysis of the 33 benefits (Lederer, Mirchandani, & Sims, 1996). Moreover, they made lists of critical themes: Information, Cost savings, Competitiveness, productivity, Control, and New applications. This list was utilized by Zhuang and Lederer (2003) to quantify the commercial advantages of e-commerce in the retail sector, indicating some distinct extra benefits peculiar to that industry.

4. RESEARCH METHODOLOGY

4.1. Research Model

This study examines the adoption of e-commerce in Vietnamese SMEs, drawing on the TOE framework proposed by Tornatzky et al. (1990) and the research conducted by Van Huy et al. (2012). The  findings of this paper indicate that the adoption of e-commerce in Vietnamese SMEs is influenced by key factors such as technological, organisational, managerial, and environmental variables.

Technological Factors: In the context of constantly changing and developing technology, when applying e-commerce to business activities, businesses will first pay attention to the values that e-commerce brings. Next, companies are interested in the compatibility of e-commerce technology and the cost of applying e-commerce to their business activities. According to the studies by Evelina (2022); Cui, Mou, Cohen, and Liu (2019); Rahayu and Day (2015); and Oliveira and Martins (2010), the benefit of applying e-commerce to businesses is the recognition of its advantages. Thereby helping companies effectively manage resources through e-commerce and allocate resources for using e-commerce technology (Iacovou, Benbasat, & Dexter, 1995; Ordanini & Rubera, 2010; Sutanonpaiboon & Pearson, 2006). In addition, the compatibility of e-commerce technology with infrastructure, corporate culture, and processes will bring high efficiency in the application of e-commerce (Ghobakhloo, Arias-Aranda, & Benitez-Amado, 2011; Kurnia, Choudrie, Mahbubur, & Alzougool, 2015; Park & Kim, 2020). Moreover, the quality of technology applied to e-commerce will have long-term benefits, determining the competitiveness and growth of enterprises in the industry (Pontikakis, Lin, & Demirbas, 2006).

Organizational Factor: Like the technology factor, the organization is an essential factor with business characteristics that affect the application of e-commerce. These characteristics are the readiness of technology, such as infrastructure and technology resources, to help the application of e-commerce achieve high efficiency. Technology readiness is related to how the technological infrastructure, relevant systems, and technical business skills can support e-commerce adoption (Nurlinda et al., 2020; Rahayu & Day, 2015; Zhu, Kraemer, & Xu, 2006). Technological readiness refers to specialized infrastructure and human resources (Zhu & Kraemer, 2005). This prerequisite is essential for e-commerce applications and achieving a competitive advantage. The other factors that affect adoption are lack of management support, SMEs' low adoption of online banking and web portals due to organizational reluctance to change, etc. (Hussain, Shahzad, & Hassan, 2020; Zaied, 2012).

Environmental Factor: Environmental factors are external influences on enterprises' adoption of e-commerce, such as government policies, the macroeconomic environment, infrastructure, or competitive pressures in the industry (Adam & Alarifi, 2021; Anuj, Fayaz, & Kapoor, 2018; Duan, Deng, & Corbitt, 2012; Zhu, 2004; Zhu & Kraemer, 2005). Therefore, businesses are forced to apply technologies that bring many benefits and competitive advantages, such as e-commerce.

Managerial Factor: In Vietnam, like in other developing nations, the management or owner of a small business makes a strategic choice. Therefore, the company owner heavily influences the decision to use e-commerce technology. Furthermore, because SMEs are structured to function in a specific field, owners and managers play a critical role in decision-making (Awiagah, Kang, & Lim, 2016; Nguyen & Waring, 2013). Therefore, the level of risk tolerance, the attitude towards innovation, and the managers' creativity will significantly affect enterprises' adoption of e-commerce (Lestari, 2019; Van Huy et al., 2012). Besides, applying new technologies such as e-commerce will only be possible with organizational knowledge, especially managers' technology (Badamas, 2009; Xin, Miao, Chen, & Shang, 2022). Furthermore, cost reduction is essential for SMEs in the face of ever-increasing competition. Therefore, in management activities, cutting costs through valuable tools such as e-commerce has always interested SMEs managers (Sedighi, Sirang, & Azerbaijan, 2018).

This study identifies technological, organizational, environmental, and managerial factors influencing the adoption of e-commerce Figure 1.

Figure 1. Factors determinant technology result adoption.

The indicated research method involved a survey of Chief Executive Officers (CEOs) or managers among SMEs in Vietnam to investigate the impact of factors in adopting e-commerce.

Based on the research framework above, the research hypothesis is set up as follows:
Hypothesis H1: Technological factors positively affect the application of e-commerce by Vietnamese SMEs.
Hypothesis H2: Managerial has a positive influence on the application of e-commerce by Vietnamese SMEs.
Hypothesis H3: Organizational factors positively affect the application of e-commerce by Vietnamese SMEs.
Hypothesis H4: Environmental factors have a positive influence on the application of e-commerce by Vietnamese SMEs.

4.2. Questionnaire and Data Collection

The research team mainly relied on the research model constructed above to build the questionnaire for this study. First, in-depth interviews were conducted by the research team with some managers at SMEs and e-commerce experts to get a complete questionnaire. Questionnaires were designed to cover all the factors and target small-scale enterprises. Then, the questionnaire was distributed to the respondent to assess how all four factors influenced the adoption of e-commerce in the organization. In addition, however, some questionnaires were distributed personally. The survey collected data conveniently from CEOs or managers of SMEs in three cities representing three regions of North, Central, and South Vietnam.

The questionnaire used a Likert scale (1. Strongly disagree; 2. Disagree; 3. Normal; 4. Agree; 5. Strongly agree). The total number of questionnaires was issued to 715 SMEs, and after removing the inappropriate answers, the research team obtained 700 questionnaires.

4.3. Data Analysis

The information collected from the questionnaire will be encrypted and analyzed using SPSS 22.0 software. First, the author conducted a Cronbach’s Alpha analysis to measure reliability. At the same time, the authors used Exploratory Factor Analysis (EFA) to test the unidirectional scales. Finally, the authors use multivariate regression to estimate the influence of four factors (Technology, Organizational, Environmental, and Management) on the application of e-commerce by Vietnamese SMEs.

5. RESEARCH RESULTS

5.1. Verify the Reliability of the Scale

The reliability of the scales in this study was checked through Cronbach's Alpha coefficient. Cronbach's Alpha coefficient for four groups of factors affecting the application of e-commerce is guaranteed to be greater than 0.6 (Appendix 1). At the same time, all observed variables in these four groups have a correlated total of greater than 0.4. Therefore, all the scales in this study are satisfactory for exploratory factor analysis.

According to the findings of the EFA, the value of KMO (Kaiser-Meyer-Olkin) is 0.884> 0.6. Moreover, Bartlett's test with the Sig is 0.00 <0.05. Therefore, factor analysis is compatible with the data from the investigation. Table 1 presents the results of EFA’s factors and an evaluation of the reliability of the scale.

The value of Eigenvalues = 1.432> 1, which points out that the number of factors extracted is suitable. According to the research results, the total Variance Explained is 63.45%; therefore, the extracted factors can explain 63.45% of the observed variables.

5.2. Regression Analysis

The adjusted R Square for regression analysis is 0.673, indicating that independent variables can explain 67.3% of the dependent variable, "The application of e-commerce by Vietnamese SMEs." The value of F = 360.497 with the statistical significance Sig = 0,000 0.05 was obtained from ANOVA results. It indicates relationships between independent and dependent variables, and the model used in this study ensures consistency.

Table 2 displays the results of multiple regressions. The research findings also show that the Sig value of all variables is 0.05, indicating that all hypotheses are accepted.

Table 1. The results of EFA's factors and evaluation of the reliability of the scale.

Factors

Factor loading

1

2

3

4

OR02

0.816

 

 

 

OR05

0.793

 

 

 

OR04

0.788

 

 

 

OR01

0.773

 

 

 

OR03

0.766

 

 

 

MA03

 

0.785

 

 

MA01

 

0.769

 

 

MA04

 

0.746

 

 

MA02

 

0.728

 

 

TE02

 

 

0.811

 

TE04

 

 

0.804

 

TE01

 

 

0.781

 

TE03

 

 

0.775

 

EN06

 

 

 

0.811

EN05

 

 

 

0.793

EN01

 

 

 

0.627

EN02

 

 

 

0.626

EN03

 

 

 

0.592

The following is the regression model of factors influencing Vietnamese SMEs' e-commerce applications:

AD= 0.231 + 0.196*OR + 0.528*TE + 0.081*EN + 0.137*MA.

Table 2 . Regression analysis.

Coefficientsa

Model

Unstandardized coefficients

Standardized coefficients

T

Sig.

Collinearity statistics

B

Std. error

Beta

Tolerance

VIF

1

(Constant)

0.231

0.098

 

2.359

0.019

 

 

OR

0.196

0.021

0.229

9.441

0.000

0.796

1.256

TE

0.528

0.020

0.608

25.832

0.000

0.844

1.185

 EN

0.081

0.024

0.085

3.417

0.001

0.751

1.332

 MA

0.137

0.023

0.156

6.070

0.000

0.708

1.413

Note:

 a. Dependent variable: AD (e-commerce adoption by SMEs).

 

Predictors: (Constant), MA (Managerial), TE (Technological), OR (Organizational), EN (Environmental).
VIF: Variance inflation factor.

5.2.1. Dependent Variables

The dependent variable is e-commerce adoption, which is determined by the scope of e-commerce employed by SMEs, which includes many activities throughout the value chain, such as marketing, sales, procurement, services, support, and integration of e-commerce technology (Gibbs & Kraemer, 2004; Iacovou et al., 1995).

6. ANALYSIS AND DISCUSSION

According to the study, four factors (technological, organizational, managerial, and environmental) influence Vietnamese SMEs' adoption of e-commerce. The results in Table 2 show that technical factors have the most significant impact on the application of e-commerce by Vietnamese SMEs (Beta unstandardization factor =0.528). Organizational and managerial factors have a lower influence on the e-commerce applications of Vietnamese SMEs (Beta unstandardization factor =0.196 and 0.137). Finally, the environmental factor has the most negligible impact on the application of e-commerce by Vietnamese SMEs (Beta unstandardization factor =0.081).

Four variables in the technological factor impact the adoption of e-commerce by SMEs. These are readiness of information technology infrastructure, website, perceived compatibility, cost  (other tools and technologies suitable for e-commerce), and enthusiasm for information technology human resources for the application of e-commerce. According to the study, perceived benefits positively and significantly correlate with e-commerce adoption, implying that it is a determining factor in SMEs' e-commerce adoption. It also shows that the more awareness of the owner's benefits and innovation increases, the more businesses will increase their ability to apply e-commerce to SMEs. However, in Vietnam, SMEs operate strongly in areas with modest profit margins and low technology because they do not have the advantages of scale, financial potential, and market share.

Furthermore, business owners' capacity, management experience, and relationships significantly impact enterprises' production and business activities. According to the survey results of the Vietnam E-Commerce Association (2020), SMEs have started to pay average attention to e-commerce. However, they do not understand the content, benefits, and development trends of e-commerce worldwide or in Vietnam.

On the other hand, the COVID-19 pandemic also affects the operations of SMEs and changes consumers' shopping habits. Furthermore, e-commerce consumption will become increasingly popular in the coming years. As a result, SMEs must raise awareness about the benefits of e-commerce and invest in technology platforms to implement e-commerce.

Organizational factors such as technology readiness, information technology experience, firm size, support and encouragement for employees to use e-commerce, export orientation, and market expansion are critical to the use of e-commerce by SMEs. In which technology readiness is one of the factors influencing SMEs' adoption of e-commerce technology. This finding is similar to previous research, including Azizan and Binti (2015), Mazzarol (2015), Boumediene Ramdani, Chevers, and Williams (2013), Oliveira and Martins (2010), and Iacovou et al. (1995). However, according to the survey of the Vietnam E-Commerce Association (2020), more than 70% of SMEs do not have personnel specialized in e-commerce. Additionally, only about 40% of companies have websites, despite the fact that these websites are the most resilient for organisations in the internet world and are consistently viewed as crucial channels for affirmation and value.

Except for EN04: Supporting policies from the government in promoting e-commerce development (financial, technology, infrastructure, etc.), the remaining variables (customer/supplier pressure, competitor pressure, and external support) significantly correlate with e-commerce adoption. With e-commerce's rapid growth, the Vietnamese market has seen a race between businesses investing in e-commerce technology and developing online sales channels. The appearance of international retail companies and brands makes the Vietnamese market more competitive and puts pressure on small and medium-sized businesses. As a result of competitive pressure from customers and suppliers, SMEs will be more motivated to implement e-commerce and effectively use this tool in production and trade. IT experience, knowledge, and innovation of the owner are the determinant factors influencing SMEs to adopt e-commerce in the managerial aspect. All three variables have a positive and significant correlation with e-commerce adoption. This study is consistent with previous studies such as Xin et al. (2022), Ghobakhloo and Tang (2013), Lip-Sam and Hock-Eam (2011), Ramdani, Kawalek, and Lorenzo (2009), and Thong (1999). Switching to an e-commerce business model is inevitable for small and medium enterprises. However, SMEs are often quite hesitant to expand e-commerce distribution channels, stemming from three "fears": fear of not being able to sell, fear of not being able to manage, and fear of "Spending money and only getting worse" (Ha, 2020). These concerns are that the knowledge about IT, e-commerce, and business on e-commerce platforms is quite limited due to the transformation from the traditional model to the online business model, which requires companies to change their inherent business thinking flexibly. While SMEs are still quite confused about the transformation process because they still face many difficulties and challenges.

7. OBSTACLE

The adoption of e-commerce by SMEs necessitates a high level of technical and organizational expertise to ensure a smooth transition. Complex technology creates hurdles in adopting e-commerce, and this platform has unpredictable results that sometimes turn out differently than expected. 

8. CONCLUSION AND RECOMMENDATION

This study thus focuses on the factors influencing SMEs' adoption of e-commerce in Vietnam. The study employed exploratory factor analysis (EFA) and simple regression for statistical analysis, with data on Vietnamese SMEs collected via questionnaires. The questionnaires were filled out by 700 Vietnamese SMEs from the North, Central, and South regions of Vietnam. Most respondents work in the consumer, health and beauty, agriculture, services, construction, and furniture and home decor industries.According to research findings, perceived benefits, technology readiness, owner innovation, and IT experience are all factors that influence SMEs' adoption of e-commerce in Vietnam.. E-commerce has received wide acceptance due to its ease of use and extensive market access compared to traditional brick-and-mortar businesses. It has changed business procedures, abolished geographic and time barriers, accelerated business processes, changed supply chain management, and accelerated competition. It will also improve communication with customers, suppliers, and employees, opening up new opportunities for promotional mechanisms. There is statistical evidence that SMEs using e-commerce obtain intermediate benefits from it. Moreover, a strong relationship exists between commerce adoption and SMEs in Vietnam.

Funding:   This research is supported by the Vietnam National Foundation for Science and Technology Development (Grant number: 502.01-2019.15).
Institutional Review Board Statement: The Ethical Committee of the National Foundation for Science and Technology Development, Vietnamhas granted approval for this study on 25 April 2019  (Ref. No. 333/TB- NAFOSTED).
Transparency: The authors state that the manuscript is honest, truthful, and transparent, that no key aspects of the investigation have been omitted, and that any differences from the study as planned have been clarified. This study followed all writing ethics.
Data Availability Statement: The corresponding author can provide the supporting data of this study upon a reasonable request.

Competing Interests: The authors declare that they have no competing interests.

Authors’ Contributions: Giving ideas, creating research models and methodology, analyzing models, writing draft, D.T.B.; collecting data, writing draft, H.T.H.; collecting data and answers, searching documents, design tables, P.V.H.; collecting data, draw figures, B.V.C.; collecting data, checking format, D.H.H.; writing draft, making questions, correcting grammar, managing team members, responding to editors, N.H.P.D. All authors have read and agreed to the published version of the manuscript.

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APPENDIX

Appendix 1. Cronbach’s Alpha for independent variables.

Technology factor (TE)

Reliability statistics

Cronbach's alpha

N of items

0.831

4


Item-total statistics

 

Scale mean if the item deleted

Scale variance if item deleted

Corrected item-total correlation

Cronbach's alpha if the item deleted

TE01

10.53

4.052

0.656

0.787

TE02

10.52

3.947

0.681

0.776

TE03

10.56

4.090

0.647

0.791

TE04

10.57

4.131

0.648

0.791

Organizational factors (OR)

Reliability statistics

Cronbach's alpha

N of items

0.871

5


Item-total statistics

 

Scale mean if item deleted

Scale variance if item deleted

Corrected item-total correlation

Cronbach's alpha if item deleted

OR01

14.02

7.372

0.680

0.847

OR02

14.04

7.190

0.716

0.838

OR03

14.00

7.330

0.677

0.848

OR04

14.00

7.243

0.699

0.843

Environmental factor (EN)

Reliability statistics

Cronbach's Alpha

N of items

0.819

6


Item-total statistics

 

Scale means if item deleted

Scale variance if item deleted

Corrected item-total correlation

Cronbach's alpha if item deleted

EN01

17.76

9.105

0.593

0.789

EN02

17.65

8.977

0.659

0.775

EN03

17.81

8.578

0.643

0.778

EN04

17.82

8.978

0.576

0.793

EN05

17.78

9.823

0.506

0.806

EN06

17.75

9.673

0.531

0.802

Management factor (MA)

Reliability statistics

Cronbach's alpha

N of items

0.807

4


Item-total statistics

 

Scale means if item deleted

Scale variance if item deleted

Corrected item-total correlation

Cronbach's alpha if item deleted

MA01

10.64

3.611

0.666

0.738

MA02

10.77

4.019

0.592

0.774

MA03

10.47

4.061

0.654

0.745

MA04

10.51

4.270

.591

0.774

Appendix 2 shows Cronbach’s Alpha for dependent variable -application e-commerce of Vietnamese SMEs (AD).

Appendix 2. Cronbach’s Alpha for Dependent variable -Application e-commerce of Vietnamese SMEs (AD).

Reliability statistics

Cronbach's alpha

N of items

0.868

5


Item-total statistics

 

Scale mean if item deleted

Scale variance if item deleted

Corrected item-total correlation

Cronbach's alpha if item deleted

AD01

14.18

5.427

0.663

0.847

AD02

14.16

5.316

0.689

0.841

AD03

14.18

5.356

0.675

0.844

AD04

14.17

5.179

0.713

0.834

AD05

14.17

5.271

0.713

0.835

Appendix 3 shows EFA for independent variables.

Appendix 3. EFA for Independent variables.

First EFA

KMO and Bartlett's test

Kaiser-Meyer-Olkin measure of sampling adequacy

0.884

Bartlett's test of sphericity

Approx. Chi-Square

5590.744

df

171

Sig.

0.000


Total variance explained

Component

Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

1

5.948

31.307

31.307

5.948

31.307

31.307

3.342

17.589

17.589

2

2.604

13.704

45.011

2.604

13.704

45.011

3.046

16.029

33.618

3

1.890

9.949

54.960

1.890

9.949

54.960

2.790

14.684

48.302

4

1.432

7.539

62.499

1.432

7.539

62.499

2.697

14.197

62.499

5

0.871

4.583

67.082

 

 

 

 

 

 

6

0.769

4.047

71.129

 

 

 

 

 

 

7

0.529

2.782

73.911

 

 

 

 

 

 

8

0.518

2.726

76.637

 

 

 

 

 

 

9

0.494

2.602

79.239

 

 

 

 

 

 

10

0.472

2.483

81.722

 

 

 

 

 

 

11

0.466

2.451

84.173

 

 

 

 

 

 

12

0.444

2.337

86.510

 

 

 

 

 

 

13

0.425

2.239

88.749

 

 

 

 

 

 

14

0.403

2.122

90.872

 

 

 

 

 

 

15

0.389

2.045

92.916

 

 

 

 

 

 

16

0.375

1.973

94.890

 

 

 

 

 

 

17

0.360

1.897

96.787

 

 

 

 

 

 

18

0.329

1.730

98.517

 

 

 

 

 

 

19

0.282

1.483

100.000

 

 

 

 

 

 

Note:

Extraction method: Principal component analysis.


Rotated component matrixa

 

Component

1

2

3

4

OR02

0.815

 

 

 

OR05

0.793

 

 

 

OR04

0.788

 

 

 

OR01

0.772

 

 

 

OR03

0.766

 

 

 

MA03

 

0.776

 

 

MA01

 

0.767

 

 

MA04

 

0.738

 

 

MA02

 

0.725

 

 

EN06

 

 

0.802

 

EN05

 

 

0.778

 

EN01

 

 

0.628

 

EN02

 

 

0.625

 

EN03

 

 

0.609

 

EN04

 

 

 

 

TE02

 

 

 

0.811

TE04

 

 

 

0.804

TE01

 

 

 

0.781

TE03

 

 

 

0.774

Note:

Extraction Method: Principal component analysis.
Rotation Method: Varimax with kaiser normalization.
a. Rotation converged in 5 iterations.

Second EFA

KMO and Bartlett's test

Kaiser-Meyer-Olkin measure of sampling adequacy

0.874

Bartlett's test of sphericity

Approx. chi-square

5200.168

df

153

Sig.

0.000


Total variance explained

Component

Initial eigenvalues

Extraction sums of squared loadings

Rotation sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

1

5.608

31.154

31.154

5.608

31.154

31.154

3.334

18.520

18.520

2

2.491

13.836

44.990

2.491

13.836

44.990

2.834

15.745

34.265

3

1.890

10.502

55.492

1.890

10.502

55.492

2.694

14.966

49.231

4

1.432

7.958

63.450

1.432

7.958

63.450

2.559

14.219

63.450

5

0.871

4.838

68.287

 

 

 

 

 

 

6

0.702

3.902

72.189

 

 

 

 

 

 

7

0.520

2.888

75.077

 

 

 

 

 

 

8

0.495

2.747

77.825

 

 

 

 

 

 

9

0.472

2.625

80.450

 

 

 

 

 

 

10

0.466

2.589

83.038

 

 

 

 

 

 

11

0.450

2.499

85.538

 

 

 

 

 

 

12

0.426

2.365

87.902

 

 

 

 

 

 

13

0.423

2.350

90.252

 

 

 

 

 

 

14

0.396

2.200

92.452

 

 

 

 

 

 

15

0.377

2.093

94.545

 

 

 

 

 

 

16

0.364

2.020

96.565

 

 

 

 

 

 

17

0.334

1.856

98.421

 

 

 

 

 

 

18

0.284

1.579

100.000

 

 

 

 

 

 

Note: Extraction method: Principal component analysis.

Rotated component matrixa

 

Component

1

2

3

4

OR02

0.816

 

 

 

OR05

0.793

 

 

 

OR04

0.788

 

 

 

OR01

0.773

 

 

 

OR03

0.766

 

 

 

MA03

 

0.785

 

 

MA01

 

0.769

 

 

MA04

 

0.746

 

 

MA02

 

0.728

 

 

TE02

 

 

0.811

 

TE04

 

 

0.804

 

TE01

 

 

0.781

 

TE03

 

 

0.775

 

EN06

 

 

 

0.811

EN05

 

 

 

0.793

EN01

 

 

 

0.627

EN02

 

 

 

0.626

EN03

 

 

 

0.592

Note:

Extraction method: Principal component analysis.
Rotation Method: Varimax with kaiser normalization.
a. Rotation converged in 5 iterations.

Appendix 4 expresses EFA for dependent variable.

Appendix 4. EFA for Dependent variable.

KMO and Bartlett's test

Kaiser-Meyer-Olkin measure of sampling adequacy.

0.876

Bartlett's Test of Sphericity

Approx. chi-square

1526.075

df

10

Sig.

0.000

Total variance explained

Component

Initial eigenvalues

Extraction sums of squared loadings

Total

% of variance

Cumulative %

Total

% of variance

Cumulative %

1

3.272

65.437

65.437

3.272

65.437

65.437

2

0.484

9.671

75.108

 

 

 

3

0.460

9.201

84.309

 

 

 

4

0.409

8.178

92.487

 

 

 

5

0.376

7.513

100.000

 

 

 

Note:

Extraction method: Principal component analysis.


Component matrix a

 

Component

 

1

AD05

0.826

AD04

0.826

AD02

0.808

AD03

0.797

AD01

0.787

Note:

Extraction method: Principal component analysis.
a. 1 components extracted.

Appendix 5 describes correlations.

Appendix 5. Correlations.

Correlations

 

AD

TC

CN

MT

QL

AD

Pearson correlation

1

0.524**

0.750**

0.360**

0.435**

Sig. (2-tailed)

 

0.000

0.000

0.000

0.000

N

700

700

700

700

700

OR

Pearson correlation

0.524**

1

0.358**

0.264**

0.348**

Sig. (2-tailed)

0.000

 

0.000

0.000

0.000

N

700

700

700

700

700

TE

Pearson correlation

0.750**

0.358**

1

0.228**

0.260**

Sig. (2-tailed)

0.000

0.000

 

0.000

0.000

N

700

700

700

700

700

EN

Pearson correlation

0.360**

0.264**

0.228**

1

0.482**

Sig. (2-tailed)

0.000

0.000

0.000

 

0.000

N

700

700

700

700

700

MA

Pearson correlation

0.435**

0.348**

0.260**

0.482**

1

Sig. (2-tailed)

0.000

0.000

0.000

0.000

 

N

700

700

700

700

700

Note: **. Correlation is significant at the 0.01 level (2-tailed).

Appendix 6 presents ANOVA and regression.

Appendix 6. ANOVA and regression.

ANOVAa

Model

Sum of squares

df

Mean square

F

Sig.

1

Regression

151.528

4

37.882

360.497

0.000b

Residual

73.032

695

0.105

Total

224.560

699

Note:

a. Dependent Variable: AD
b. Predictors: (Constant), MA, TE, OR, EN

Model summaryb

Model

R

R square

Adjusted R square

Std. error of the estimate

Durbin-Watson

1

0.821a

0.675

0.673

0.3242

2.075

Note:

a. Predictors: (Constant), MA, TE, OR, EN
b. Dependent Variable: AD.

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