Editorial Board

A Refereed Monthly International Journal of Management

FACTORS AFFECTING ONLINE BUYING INTENTION: THE CASE OF TIKI.VN

Author

Phuong Viet Le-Hoang Ho Chi Minh City Open University, Vietnam Industrial University of Ho Chi Minh City, Vietnam

Bao Quoc Nguyen Industrial University of Ho Chi Minh City, Vietnam

Danh Xuan Luu Industrial University of Ho Chi Minh City, Vietnam

Vi Truc Ho University of Economics and Law, Vietnam Industrial University of Ho Chi Minh City, Vietnam

Nhan Trong Phan University of Economics and Law, Vietnam Industrial University of Ho Chi Minh City, Vietnam

Abstract

The purpose of this research is to explore the scale and identify factors affecting online buying intention in Ho Chi Minh City area. To conduct the research, the authors do group discussions and expert discussion and then analyzed 200 observations with six factors: perceived behavioral control, subjective norms, trust, vendor’s creativity, perceived usefulness, and perceived risk. The results of Exploratory Factor Analysis (EFA) show that there are five factors: perceived behavioral control, subjective norms, trust, vendor’s creativity and perceived usefulness that has a positive impact on online buying intention; besides, the result from the research is that perceived risk factor negatively affects the intention of online buyer in Ho Chi Minh City, Vietnam. The contribution of this study is that the authors confirm the Theory of Reasoned Action (1967), the Theory of Planned Behavior (1991), Technology Accept Model (1986) in the context of online buying. Also, from qualitative studies and relevant studies, the author has adjusted the scale and analyzed data in a new context. Based on that, the research recommends the necessary management solutions to increase online buying intention and open a new field for further research.

Keywords: Online Buying; E-Commerce; Subjective Norms; Trust; Perceived Usefulness. 

Introduction

According to the Vietnam E-Commerce Index (2018) of Vietnam E-commerce Association, the figures show the attractiveness of Vietnam's e-commerce market, according to the growth rate of e-commerce in 2017 reached over 25% and this speed can be maintained for the next three years 2018-2020. For the online retail sector, the revenue growth rate in 2017 increased by approximately 35%. It shows the enormous potential of online shopping in the Vietnamese market, especially online shopping via mobile devices. Creating a stable and qualified workforce will help businesses save a lot of costs (recruitment costs, training, ...), reduce errors in work, create a working environment trust and solidarity among members. These things help businesses operate more effectively, making customers trust in the company's services and products, and thereby creating a competitive position in the market. Besides domestic enterprises, the e-commerce market is also affected by foreign enterprises and corporations with reliable capital sources and robust development strategies. Typically, two large e-commerce sites that are backed by two large corporations of China and Singapore are Lazada Vietnam and Shopee Vietnam e-commerce site. Domestic businesses have also moved to catch up with the wave of investment from foreign investors to continue this long race. Typically Tiki Joint Stock Company, a company derived from the online retailer has shifted to the marketplace. In fact, Tiki in particular, and e-commerce enterprises in general, especially domestic enterprises, are trying to find advantages to gain more market share. However, in this race, foreign enterprises seem to be dominating due to their financial potential and business experience in e-commerce. Besides, they also thoroughly understand the consumption habits of Vietnamese. As proof, Shopee is the only platform that allows sellers and buyers to communicate with each other when shopping directly. So whatever the method is, the final destination of businesses is how to attract more and more consumers. According to Jusoh (2012) wants to attract consumers, not only traditional methods such as promotions, discounts, advertising. Enterprises need to understand consumers. Must grasp the tastes, intentions of consumers need and want to meet the needs of customers better, that is the key, the key to the success of the Enterprise. So it is the reason that the authors conduct the research "Factors affecting online buying intention: the case of tiki.vn."

2. LITERATURE REVIW

According to Ajzen (1991), the intention is a factor that motivates an individual to be ready to perform a specific behavior, and shopping intent is no exception. So Delafrooz et al. (2011) argue that "the intention of online shopping is the ability of consumers to make purchases via the Internet." Theory of Reasoned Action (TRA) is a predictive model of behavioral intent. Between the attitude and the behavior that is partly in the middle of the connection is the intention. The intention of the individual to carry out a specific behavior is governed by the attitude towards behavior and subjective norms. Behavior towards behavior is what a positive or negative feeling or an individual's perception is when they perform a specific behavior. Subjective factors are seen as how people perceive their work and how it is appropriate to external society. In other words, subjective norms are the social effects on individual behavior. Theory of Planned Behavior (TPB) published by Ajzen in 1991 is an extended Theory of Reasoned Action. This theory was derived from the limitations of the previous theory that the will to fully control individual behavior. The central element of the Theory of Planned Behavior that TPB is to anticipate individual behavior in performing specific actions. Ajzen added the "perceived behavioral control" factor to the TRA model. Technology Accept Model (TAM) is based on TRA in building relationships between variables to explain the acceptance behavior of personal information systems. TAM is often used to model factors affecting the adoption of new technologies by users. In the TAM model, the "attitude" factor is affected by two "perceived usefulness" and "perceived ease of use" variables.

3. HYPOTHESES DEVELOPMENT

According to Ajzen (1991), perceived behavioral control is defined as an individual's perception of easy or challenging behavior. Perceived behavioral control indicates the level of control over behavior but not the result of behavior. In the context of online shopping, perceived behavioral control describes consumers' perceptions of the availability of necessary resources, knowledge, and opportunities to implement online shopping (Lin, 2007). According to Javadi et al. (2012), perceived behavioral control reflects an awareness of internal constraints as well as external constraints on behavior, such as the availability of resources. Perceived behavioral control directly affects online shopping behavior and has a close relationship with Internet purchases.

H1: Perceived behavioral control will positively affect consumers' intention of online shopping.

Theory of Reasoned Action (TRA) by Fishbein and Ajzen (1975) explaining the that human behavior has also used subjective norms and attitude factors as a factor to formulate intent and thereby create action real action. While the attitude reflects the individual's favorable or unfavorable feelings towards the conduct of an act, subjective norms capture consumer awareness of the influence of others such as family, co-workers, government, and media ... Subjective norms standards are related to purpose because people often act on their perceptions of what others think they should do. According to Taylor and Todd (1995), subjective norms tend to be more influential in the early stages of innovation implementation when users have direct experience from which to develop attitudes (Taylor, 1995). Moreover, accordingly, in this period, online retailers can influence consumer shopping behavior. (Yu, 2007)

H2: Subjective norms will positively affect consumers' intention of online shopping.

Trust is defined as "the willingness of one party to be vulnerable to the actions of another party based on the expectation that another person will take a specific action important to the person who trusts them, regardless of whether it is beyond the ability to monitor or control the other" (Mayer, 1995). According to Hoffman et al. (1999), Trust has an essential role in human behavior. When people face risks and cannot control the behavior of others, the meaning of trust is evident, especially in accepting new technologies such as e-commerce (Hoffman, 1999). Trust is also a requirement to connect the relationships of retailers and consumers in the e-commerce environment (Gefen, 2000). Lack of trust will become a barrier to e-commerce, especially in the developing period. Therefore, for e-commerce environment, where sellers and buyers do not meet directly, trust is an essential factor to attract and maintain customers (Corbitt, 2003).

H3: Trust will positively affect consumers' intention of online shopping.

Innovative products often describe newness and relevance, or they are just standard products. In the marketing context, product creation is defined as the concept of new ideas and a product with a competitive advantage (Couger & Dengate, 1992). Only creativity will be able to overcome what already exists to maintain the state of the business and promote its development. Creativity adds value to the overall consumer product experience when the market demand structure changes from product-based products to competitive value-based products in the market. (Horn et al., 2006). It shows that businesses need to be creative to maintain their competitive advantage. The definition of creativity is discussed in the online technology context, which is an essential factor to promote online buying. Although considering the shared characteristics between online and traditional purchases, the online environment is more competitive because of direct access to competitors. Therefore, the supplier's creativity, defined as creating new concepts and new products to fulfill consumer needs, has a direct impact on satisfaction and purpose of participating in the shopping group online. (Zhou et al., 2001)

H4: Vendor's creativity will positively affect consumers' intention of online shopping.

People often use or not use an application that they believe will help them perform better, and it is perceived usefulness. Perceived usefulness is defined as the degree to which a person believes that using a particular system will improve his or her work efficiency. In the cognitive process, the emergence of perceived usefulness is considered conceptual: the user is motivated to apply an important application because the functionality of the application is made for them, and how do they get the system to perform those functions. (Davis et al., 1989)

H5: Perceived usefulness will positively affect consumers' intention of online shopping.

According to Dowling et al. (1994), perceived risk refers to consumer perceptions of the uncertainty and consequences of participating in a specific activity. Because online transactions create a lot of different risks (Fortes et al., 2016), so the classification of risk into financial risk, seller risk, privacy risk (personal information can be disclosed illegally) and Security risks (theft of credit card information) (Pavlou, 2003). Besides, according to Kim et al. (2004), perceived risk will negatively affect the intention to use the internet to find product information and online shopping.

H6: Perceived risk will negatively affect consumers' intention of online shopping.

4. METHODOLOGY

The authors use a mixed approach that includes scaling by qualitative methods and quantitative research methods to analyze the factors affecting online buying intentions in Ho Chi Minh City, Vietnam. Quantitative research method based on information collected from online shopping customers in Ho Chi Minh City. The research uses convenient sampling methods and uses five levels of Likert scale, namely strongly disagree, disagree, neutral, agree and strongly agree to be used for measurement Impact of factors affecting online buying intention. According to Hair et al. (2014) indicated that the research should ensure a minimum sample size of 5*n = 5n when using the Likert scale with five variables. To ensure the quality of the sample, the authors decided to survey a total of 200 questionnaires. In particular, this research uses an online survey through the google form tool and sends a survey link to customers who have been shopping online via Tiki e-commerce site. The results obtained 212 surveys, and after screening data, there are 200 valid questions will be used in quantitative analysis (accounting for 94.3%). The authors used descriptive statistical methods, assessing reliability through the Alpha coefficients of Cronbach, EFA method, and regression to determine factors affecting the intention of purchasing online customers.

5. ANALYSIS AND RESULTS

5.1 Data description:

The survey was conducted in 2 months from February to April 2019, and the data was analyzed in the first two weeks of April, the authors collected 200 surveys out of 212 surveys (occupied 94.3%), and the following table will describe the data:

Table-1: Data description

		Frequency	Percent
Gender	Male	88	44%
	Female	112	56%
Age	Under 18 years old	3	2%
	From 18 to under22 years old	33	17%
	From 22 to under25 years old	70	35%
	From 25 to under 30 years old	80	40%
	From 30 to under 40 years old	13	7%
	Over 40 years old	1	1%
Education background	Undergraduate high school	15	8%
	Graduated high school	18	9%
	Technical school. colleges	60	30%
	University	107	54%
	Postgraduate	0	0%
Income	No income	1	0.5%
	Under 3 million VND	20	10%
	From 3 to under7 million VND	68	34%
	From 7 to under9 million VND	75	37.5%
	From 9 to under15 million VND	32	16%
	From 15 to under20 million VND	4	2.%
	Over 20 million VND	0	0%

Regarding gender: The majority of gender is female, with 112 people accounting for 56%, while the number of male participants is 88 people, accounting for 44%, and the data show that the gender gap is not high. Regarding age: 80 people are from 25 to under 30 years old, accounting for 40% and get the highest rate, the second is 70 people who are from 22 to under 25 years old, accounting for 35%, the third is 33 people who are from 18 to under 22 and accounting for 17% and eventually the people who are over 40 years is 1 people. Based on the analysis of age, online buying customers in Ho Chi Minh City are mainly between 25 and 40 years old. Regarding education background: According to the results, the education background has a quite high disproportion. The university has the highest number of respondents, which is 107 people, accounting for 54%, while Technical school and colleges have 60 people, accounting for 30%. Besides, graduated high school has 18 people who join the survey, accounting for 9%. Undergraduate high school who answers the survey is 15, accounting for 8%. Also, finally, postgraduate has no participants. Looking into the income: From 7 to under 9 million VND, the highest number of participants is 75 people, accounting for 37.5%. Income from over 3 to under 7 million VND has 68 people, accounting for 34%. Income over 9 to under 15 million VND has 32 people, accounting for 16%. Income under 3 million VND has 20 people, accounting for 10%. Income over 15 to under 20 million VND has four people, accounting for 2%. No income has one people, accounting for 0.5% and finally, Over 20 million VND no one participated in the survey. From the percentage of the income, Online shopping customers in the Ho Chi Minh city have a stable income from 3 to 15 million VND.

5.2 Reliability test: Cronbach’s Alpha

According to Nunnally and Bernstein (1994), for variables to be accepted, Cronbach’s Alpha if item deleted is equal or greater than 0.7 and the corrected Item - Total Correlation is equal or greater than 0.3. According to Hoang and Chu (2008), Nguyen (2011), Nguyen and Ha (2008), Hair et al. (2014), new research can accept that Cronbach’s Alpha, if item deleted, is equal or greater than 0.6. Therefore, the research does not eliminate the items, and the items satisfy the condition, so all items can be used for analyzing Exploratory Factor.

Table-2: Constructs, corrected item – total correlation and Cronbach Alpha

Items	Constructs	Corrected Item – Total Correlation	Cronbach’s Alpha if item deleted
Perceived behavioral control - Cronbach’s Alpha = 0.847
PBC1	The extent of knowledge that I feel I have in making my purchase decision is sufficient.	0.630	0.822
PBC2	The extent of self-confidence that I feel I have in making my purchase decision is sufficient.	0.656	0.816
PBC3	I do not shop online as I do not have a computer with an internet connection	0.640	0.820
PBC4	I do not shop online as I do not have a credit card	0.672	0.811
PBC5	I do not shop online because Internet speed (Web page download time) is very slow	0.679	0.809
Subjective norms - Cronbach’s Alpha = 0.859
SN1	I like to shop with my family members or friends	0.731	0.812
SN2	When I make a purchase my friend's opinion is important to me	0.692	0.826
SN3	I will have no problem with shopping online if I get to know that my friends and relatives are doing it without any problems	0.716	0.817
SN4	Sharing my experience through online product reviews will make me noticeable	0.687	0.828
Trust- Cronbach’s Alpha = 0.856
TRU1	The web retailers are trustworthy.	0.658	0.833
TRU2	The web retailers keep their announce.	0.692	0.819
TRU3	The web retailers keep their promises and commitments.	0.701	0.816
TRU4	The web retailers keep their customer’s best interests in mind.	0.747	0.796
Vendor’s creativity - Cronbach’s Alpha = 0.833
CRE1	The online group buying vendor suggests new product ideas	0.630	0.803
CRE2	The online group buying vendor often has new ideas about how to promote products	0.657	0.791
CRE3	The online group buying vendor often has a new approach to sell products	0.673	0.784
CRE4	The online group buying vendor develops new ways to meet consumer demands	0.688	0.777
Perceived usefulness - Cronbach’s Alpha = 0.843
PU1	Shopping on the internet allows me to save money	0.706	0.790
PU2	Shopping on the internet allows me to save time.	0.718	0.784
PU3	Shopping on the internet provides me access to a wide variety of products and services.	0.647	0.815
PU4	For me, it is useful to make purchases on the internet.	0.644	0.817
Perceived risk - Cronbach’s Alpha = 0.797
PR1	Providing credit card information online is safe.	0.596	0.754
PR2	Providing personal information (i.e., social security number and mother’s maiden name) online is not risky.	0.616	0.744
PR3	Providing my and phone number online is not risky.	0.630	0.736
PR4	It is not riskier to shop online for a product than to shop offline for it.	0.596	0.753
Online buying intention- Cronbach’s Alpha = 0.890
INT1	It is likely that I make purchases on the Internet in the future.	0.753	0.861
INT2	It is possible that I make purchases on the Internet in the future.	0.774	0.853
INT3	I think online shopping through e-commerce sites should be encouraged to everyone	0.775	0.853
INT4	I will recommend online shopping via e-commerce sites to friends or family	0.735	0.868

5.3 Exploratory Factor Analysis (EFA)

To reduce data, Exploratory Factor Analysis (EFA) is a technique that can do that, so it is beneficial to define groups of variables. The authors used Principal Component Analysis and Varimax rotation to make components converge in a group in exploratory factor analysis.

5.3.1 Independent variables

The results show that Eigenvalue = 1,067> 1 (representing the variation part explained by each factor), then the five factors that draw out the most meaningful summary information. The total variance extracted (Total Variance Explained) in Component number six and Cumulative% columns have cumulative variance values of 67.437%> 50% satisfying the standard variance explained. Observed variables explain conclusion 67.437% of changes in factors. Test results Bartlett’s test is worth Sig. <0.05 should reject the hypothesis H0. Conclusion, the observed variables are correlated with each other in each group of factors. KMO value (Kaiser-Meyer-Olkin) = 0.855 satisfying 0.5≤KMO≤1. So independent factor analysis is consistent with actual data. The rotated matrix in EFA show that the factor loadings of the observed variables are met when factor analysis is a Factor loading factor greater than 0.55 and the number of factors generated by the analysis is six factors by the following table:

Table-3: Rotated matrix

	1	2	3	4	5	6
PBC4	0.760
PBC1	0.749
PBC2	0.714
PBC5	0.708
PBC3	0.680
PU1		0.814
PU2		0.813
PU3		0.764
PU4		0.752
CRE4			0.836
CRE3			0.826
CRE2			0.801
CRE1			0.776
SN4				0.775
SN3				0.729
SN2				0.711
SN1				0.701
TRU4					0.800
TRU3					0.794
TRU2					0.671
TRU1					0.613
PR3						0.799
PR2						0.792
PR1						0.780
PR4						0.768
Cronbach’sAlpha	0.847	0.843	0.833	0.859	0.856	0.797
Eigenvalues	1.067
Total Variance Explained	67.437%
Sig.	0.000
KMO	0.855

5.3.2 Dependent variable:

The results show that KMO is 0.843 and can make sure the requirement 0.5 <.KMO <1. Test results Bartlett’s test is worth Sig. <0.05 should reject the hypothesis H0; the observed variables are correlated with each other in the group of dependent factors. Eigenvalues = 3.013> 1 represents the variable section explained by the dependent variable, the most informative summary factor. Total variance extracted (Total Variance Explained) in Component row number one and Cumulative% column has cumulative variance values of 75.337%> 50% that meet the permitted criteria. Conclusion 75.337% change of factor explained by observed variables (a component of factor). The rotated matrix in EFA show that the factor loadings of the observed variables are met when factor analysis is a Factor loading factor higher than 0.55, The number of factors created is one factor, and no observed variables are excluded.

Table-4: Dependent variable, and testing

	1
INT3	0.878
INT2	0.877
INT1	0.864
INT4	0.852
Cronbach’s Alpha	0.890
Eigenvalues	3.013
Total Variance Explained	75.337%
Sig.	0.000
KMO	0.843

5.4 Pearson correlation

A Pearson correlation is a number between -1 and 1 that indicates the extent to which two variables are linearly related. The Pearson correlation is also known as the "product moment correlation coefficient" or only "correlation." The results of Pearson analysis shows the independent variables perceived behavioral control, subjective norms, trust, vendor's creativity, and perceived usefulness that has a positive impact on online buying intention. The sig of its less than 0.05 and the correlation coefficients of the variables are positive. In particular, the most significant factor affecting the variable "Intention" is the subjective norms factor (r = 0.750). The factor with the lowest correlation to the "Intention" is the vendor's creativity (r = 0.251). Perceived risk is not correlated with "Intention" because the Sig coefficient of Perceived risk = 0.482 > 0.05. The intention of online buying is correlated with five independent variables (perceived behavioral control, subjective norms, trust, vendor's creativity, and perceived usefulness). So the variables are eligible for regression analysis, and perceived risk variable is not eligible for regression analysis. The research eliminates Perceived risk because the majority of online buyers in Vietnam now use cash on delivery (COD). Meaning that online payment is not high; customers have not realized the risk of information security. Therefore, this factor has not been correctly and thoroughly assessed by consumers.

Table 5: Pearson correlation

		INT	PBC	SN	TRU	CRE	PU	PR
INT	Pearson Correlation	1	.602**	.750**	.625**	.251**	.499**	.050
	Sig. (2-tailed)		.000	.000	.000	.000	.000	.482
	N	200	200	200	200	200	200	200
PBC	Pearson Correlation	.602**	1	.628**	.610**	.136	.379**	.005
	Sig. (2-tailed)	.000		.000	.000	.055	.000	.948
	N	200	200	200	200	200	200	200
SN	Pearson Correlation	.750**	.628**	1	.632**	.191**	.430**	.032
	Sig. (2-tailed)	.000	.000		.000	.007	.000	.654
	N	200	200	200	200	200	200	200
TRU	Pearson Correlation	.625**	.610**	.632**	1	.142*	.449**	.109
	Sig. (2-tailed)	.000	.000	.000		.045	.000	.124
	N	200	200	200	200	200	200	200
CRE	Pearson Correlation	.251**	.136	.191**	.142*	1	.167*	.047
	Sig. (2-tailed)	.000	.055	.007	.045		.018	.507
	N	200	200	200	200	200	200	200
PU	Pearson Correlation	.499**	.379**	.430**	.449**	.167*	1	.120
	Sig. (2-tailed)	.000	.000	.000	.000	.018		.090
	N	200	200	200	200	200	200	200
PR	Pearson Correlation	.050	.005	.032	.109	.047	.120	1
	Sig. (2-tailed)	.482	.948	.654	.124	.507	.090
	N	200	200	200	200	200	200	200

5.5 Regression

Regression analysis finds out what is the factors that affect the intention of online buying customers and measure the affecting levels of these factors. Before doing the regression analysis, the authors do compute the mean value of these factors. Whereas:
INT:Online buying intention (INT1, INT2, INT3, INT4)
PBC: Perceived behavioral control (PBC1, PBC2, PBC3, PBC4, PBC5)
SN: Subjective norms (SN1, SN2, SN3, SN4)
TRU:Trust (TRU1, TRU2, TRU3, TRU4)
CRE:Vendor’s creativity (CRE1, CRE2, CRE3, CRE4)
PU:Perceived usefulness (PU1, IPU2, PU3, PU4)
The following formula can describe regression analysis model in this research:
INT = β0 + β1*PBC + β2*SN + β3*TRU + β4*CRE + β5*PU

Whereas INT is the dependent variable, and it can measure the intention of online buying customers in Ho Chi Minh City, and PBC, SN, TRU, CRE, and PU are independent variables which can measure perceived behavioral control, subjective norms, trust, vendor’s creativity, and perceived usefulness.

Table 6: Regression results

Model		Unstandardized Coefficients		Standardized Coefficients	t	Sig.	Collinearity
		Beta	Sd. Error	Beta			VIF
1	(Constant)	-0.398	0.286		-1.391	0.166
	PBC	0.162	0.074	0.130	2.179	0.031	1.908
	SN	0.520	0.066	0.485	7.853	0.000	2.052
	TRU	0.164	0.064	0.157	2.558	0.011	2.018
	CRE	0.096	0.046	0.093	2.104	0.037	1.048
	PU	0.167	0.053	0.156	3.127	0.002	1.340
R2				0.641
Adjusted R2				0.630
Sig.				0.000
Durbin Watson				1.879

From the results of the regression model show that the five variables such as PBC, SN, TRU, CRE, and PU, have a significant statistic because the sig of them is less than 0.05. As a result, these variables affect the intention of online buying customers. The adjusted R2 value is 0.630, and it means that 63% of the intention of online buying is from 5 factors and 37% of that is from the factors which are outside of the model. The sig value is 0.000, and it is less than 0.05, so the research model is fit, and the variables which use in the model have a significant statistic. Besides, Durbin – Watson is 2.125, and as a result, there is no autocorrelation between the residuals in the model. What is more, variance inflation factors (VIF) are too small, and these point out that there is no multicollinearity in this model, so all of the independent variables do not correlate together. The multiple regression model by standardized coefficients can be identified: INT = 0.130*PBC + 0.485*SN + 0.157*TRU + 0.093*CRE + 0.156*PU

5.6 Hypothesis testing:

Table 7: Hypothesis testing

Hypothesis	Content	Result
H1	Perceived behavioral control will positively affect consumers’ intention of online shopping.	Accepted
H2	Subjective norms will positively affect consumers’ intention of online shopping.	Accepted
H3	Trust will positively affect consumers’ intention of online shopping.	Accepted
H4	Vendor’s creativity will positively affect consumers’ intention of online shopping.	Accepted
H5	Perceived usefulness will positively affect consumers’ intention of online shopping.	Accepted
H6	The perceived risk will negatively affect consumers’ intention of online shopping.	Rejected

6. CONCLUSION, MANAGERIAL IMPLICATION AND LIMITATIONS

Research results have shown that five factors are Perceived behavioral control, Subjective norms, Trust, Vendor's creativity, Perceived usefulness that affects online shopping intention of customers in Ho Chi Minh City. The results of the research also show that factors affecting online shopping intention of customers in Ho Chi Minh City, in turn, arrange in the following order: Subjective norms (SN), Trust (TRU), Perceived usefulness (PU), Perceived behavioral control (PBC), Vendor's creativity (CRE). Regarding subjective norms, research results show that this factor has a positive influence on the intention of purchasing customers. It is also the most influential factor in customers' online shopping intention in Ho Chi Minh City, so it should be considered first to improve. According to Javadi et al. (2012), accepting this hypothesis means that the opinions of friends and colleagues will affect consumers' online buying behavior more. Online businesses need to pay more attention to the impact on the social community; the more business opportunities will increase. Businesses can use marketing tools to deliver messages: using online shopping will help users become a smart and modern consumer. Besides, depending on the target of each customer, online businesses can link to websites, social networks like Facebook, Zalo, Instagram ... Many targeted customers can create social links that will help customers easily share information about products, enhance word of mouth for business products. The groups of factors that Perceived usefulness, Trust, Perceived behavioral control, and Vendor's creativity is the next factors that positively affect the intention of purchasing customers. Customers always want to use an online shopping service will help have a better shopping experience than traditional shopping. It through advantages such as saving time, money, access to a variety of choices of goods, customer trust with suppliers, creativity in both products, ways to promote that product to both how to reach customers, meet demand of customers and ultimately the elements of themselves such as shopping experience or their own self-confidence in online shopping knowledge. To meet this desire of customers, online business enterprises need to use advances in technology to diversify product portfolio, focusing on building after-sales services, increasing convenience. Help customers reduce time, and money, Simplify procurement processes and forms of payment so that customers can easily make purchases. Finally, businesses need to convey messages to increase customer confidence in the business through service and product commitments. This research has brought some results and contributions to the factors affecting the intention of online buying customers in Ho Chi Minh City. However, this study still has two significant limitations. First, many researchers study the research on online buying intention, but with a separate research topic about the intention of online buying customers in Ho Chi Minh City is quite a few, not even available in Vietnam. The topic only surveyed in the Crucial southern area, so it does still not reflect the intention of online buying customers in Vietnam. Secondly, the method which implemented in the research is the convenient sampling method, the number of initial survey questionnaires is 212, and when conducting the analysis, this research use only 200 survey questionnaires. In the future, when ensuring the conditions of time and finance, the authors will conduct surveys within each province and city across the country to find out the factors affecting the intention of online buying customers; at the same time, it is possible to compare satisfaction among different regions across the country.

References

Ajzen, I. (1985). From intentions to actions: A theory of planned behavior. Action control. Springer, Berlin, Heidelberg, 11-39.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211. 
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: a comparison of two theoretical models. Management science, 35(8), 982-1003.
Davis, F. D. (1993). User acceptance of information technology: system characteristics, user perceptions and behavioral impacts. International journal of man-machine studies, 38(3), 475-487. 
Couger, J. D., & Dengate, G. (1992). Measurement of creativity of IS products. Paper presented at the Proceedings of the Twenty-Fifth Hawaii International Conference on System Sciences.
Corbitt, B. J., Thanasankit, T., & Yi, H. (2003). Trust and e-commerce: a study of consumer perceptions. Electronic commerce research and applications, 2(3), 203-215.
Delafrooz, N., Paim, L. H., & Khatibi, A. (2011). A research modeling to understand online shopping intention. Australian Journal of Basic and Applied Sciences, 5(5), 70-77. 
Dowling, G. R., & Staelin, R. (1994). A model of perceived risk and intended risk-handling activity. Journal of consumer research, 21(1), 119-134. 
Fortes, N., & Rita, P. (2016). Privacy concerns and online purchasing behaviour: Towards an integrated model. European Research on Management and Business Economics, 22(3), 167-176. 
Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 1(1), 8.
Hair, F.J., Back, C.W., Babin, J.B., and Anderson, E.R., (2014), Multivariate Data Analysis, London, Pearson.
Hoang, T. and Chu,N.M.N, (2008), Analysis of research data with SPSS,Hong Duc Publishing House, Ho Chi Minh city.
Hoffman, D. L., Novak, T. P., & Peralta, M. (1999). Building consumer trust online. Communications of the ACM, 42(4), 80-85. 
Horn, D., & Salvendy, G. (2006). Consumer‐based assessment of product creativity: A review and reappraisal. Human factors and ergonomics in manufacturing & service industries, 16(2), 155-175. 
Javadi, M. H. M., Dolatabadi, H. R., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A. R. (2012). An analysis of factors affecting on online shopping behavior of consumers. International Journal of Marketing Studies, 4(5), 81. 
Jusoh, Z. M., & Ling, G. H. (2012). Factors influencing consumers’ attitude towards e-commerce purchases through online shopping. International Journal of Humanities and Social Science, 2(4), 223-230. 
Kim, J., Lee, H., & Kim, H. (2004). Factors affecting online search intention and online purchase intention. 
Li, Y.-H., & Huang, J.-W. (2009). Applying theory of perceived risk and technology acceptance model in the online shopping channel. World Academy of Science, Engineering and Technology, 53(1), 919-925. 
Lin, H.-F. (2007). Predicting consumer intentions to shop online: An empirical test of competing theories. Electronic Commerce Research and Applications, 6(4), 433-442. 
Mayer, R. C., Davis, J. H., & Schoorman, F. D. (1995). An integrative model of organizational trust. Academy of management review, 20(3), 709-734. 
Nguyen, Đ.T., (2011), Methods of scientific research in business, Lao Dong-Social Publishing House.
Nguyen, M.T. and Ha, T.Q., (2008) , Research data processing with SPSS for Windows, Industry University Publishing House, Ho Chi Minh city.
Nunnally, C.& Bernstein, I.H. (1994). Psychometric theory, New York, McGraw-Hill Company.
Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International journal of electronic commerce, 7(3), 101-134. 
Pavlou, P. A., & Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior. MIS quarterly, 115-143. 
Taylor, S., & Todd, P. (1995). Assessing IT usage: The role of prior experience. MIS quarterly, 561-570. 
Viet Nam E-commerce Index. (2018). Viet Nam E-commerce Association, 3-5.
Yu, T.-K., & Wu, G.-S. (2007). Determinants of internet shopping behavior: An application of reasoned behaviour theory. International Journal of Management, 24(4), 744. 
Zhou, J., & George, J. M. (2001). When job dissatisfaction leads to creativity:                        Encouraging the expression of voice. Academy of Management journal, 44(4), 682-696.
Pavlou, P. A. and Fygenson, M. (2006). Understanding and predicting electronic commerce adoption: An extension of the theory of planned behavior.