Research on online purchase intention of customers in Ho Chi Minh based on TPB theory
Ngo Cao Hoai Linh/ MICA 2018 Proceedings
International Conference on Marketing in the Connected Age (MICA-2018), October 6th, 2018
Danang City, Vietnam
Research on Online Purchase Intention of Customers in Ho
Chi Minh Based on TPB Theory
Ngo Cao Hoai Linha*
aIndustrial University of Ho Chi Minh City, 12 Nguyen Van Bao Street, Ho Chi Minh City, Vietnam
A B S T R A C T
This research was carried out to analyze factors influencing the online purchase intention of customers in Ho
Chi Minh City. Through the combination of qualitative research and quantitative research, the author
conducted a survey of 226 customers who intended to engage in online purchase in Ho Chi Minh City. As can
be seen from the research results, there were 5 groups of factors influencing the customers' online purchase
intention, namely: (1) Perceived usefulness; (2) Perceived ease of use; (3) Perceived risk; (4) Subjective norm;
and (5) Price expectation. This is an important basis for enterprises in Ho Chi Minh City to develop directions
and strategies for the purpose of positively influencing on the customers' online purchase intention by
applying the extended theory of planned behavior (abbreviated TPB).
Keywords: TPB theory; Online purchase intention; Perceived risk; Customers in Ho Chi Minh City.
1. Introduction
The development of 4.0 technology revolution and digital technology on these days brought great impact on
e-commerce, making it growing strongly beyond the border. Ho Chi Minh City gets the same situations as e-
commerce is becoming more and more convenient, making customers' purchase behavior change rapidly,
requiring enterprises and retailers to strive to meet this trend. With the launch of a series of e-commerce websites
such as Thegioididong, Lazada, Tiki, Shopee, Sendo, Adayroi, etc., online purchase is no stranger to Vietnamese
consumers, especially young people. In Ho Chi Minh City, the e-commerce market continues to expand with a
wide range of new models. Ho Chi Minh City's enterprises have also quickly caught up with new trends and
made investments in e-commerce websites in a professional way with the aims of building a version used on
mobile devices such as smartphone, tablet; etc., to serve production and business activities in the network
environment effectively. According to Ho Chi Minh City Department of Industry and Trade, there are about
127,100 active websites, including 8,910 e-commerce websites in the city registered with the Ministry of
Industry and Trade, namely: 819 e-commerce websites on sales of goods and 391 e-commerce website on
provision of services. Such large numbers of websites in Ho Chi Minh City do help enterprises maximize their
value chain and effectively exploit this resource. However, this requires enterprises accurately grasping a core
issue which is to understand factors that affect the consumers' online purchase intention because the purchase
intention is seen as a factor that have a decisive influence on consumer behavior. The research of consumers'
online purchase intention was conducted based on various theories such as: Theory of Reasoned Action (TRA)
model, Theory of Planned behavior (TPB), Technology Acceptance Model (TAM) Among these theories, the
Theory of Planned Behavior (TPB) has been widely used in lots of studies and applied successfully as a
theoretical framework for predicting purchase intention and behavior. TPB was developed by Ajzen (1991) on
* Corresponding author. E-mail address: ngocaohoailinh.iuh@gmail.com
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Ngo Cao Hoai Linh/ MICA 2018 Proceedings
the basis of Theory of Reasoned Action (TRA) of Fishery and Ajzen (1975) by adding the "behavioral
awareness" factor to the TRA. Although the TPB model is more optimistic than the TRA model in predicting
purchase behavior, it still has shortcomings that only provide three certain factors to explain purchase behavior
while there are many other factors in fact. From the above issues, the paper focuses on research and solutions to
apply TPB to explore online purchase intention of customers in Ho Chi Minh City.
2. Theoretical framework and research model
2.1 Theoretical framework
E-commerce, or EC is the purchase and sale of products or services on electronic systems such as the Internet
and computer networks. According to Monsuvve, Dellaert and K. D. Ruyter (2004): Online purchase is defined
as the behavior of consumers in purchase through online stores or websites that use online purchases. Abu Bakar
(2005): E-commerce is the exchange, purchase and sale of goods and services through wireless devices such as
mobile phones, personal digital assistant (PDAs).
As stated by Philip Kotler (2003): Consumer purchase behavior is the act of consumers in relation to the
procurement and consumption of products / services, including information search, review, purchase and post-
purchase behavior. According to Liang and Lai, (2000): Online purchase behavior refers to the process of
purchase of any product or service on the Internet, including five steps similar to traditional purchase behavior.
Theory of Planned Behavior Model (TPB) is one of the most influential theories used in the research of
human behavior, developed by Ajzen from the Theory of Reasoned Action (TRA).
TPB was accepted and used widely in the studies with the aim guessing the consuming intention and specific
behavior of individuals. Hansen and et al (2004) was tested in both 2 models TRA and TPB, the result showed
that the TPB model explained customer’s behavior better than the TRA model. Furthermore, in the research
background at Viet Nam, some studies proved TPB more suitable in guessing the online purchase intention of
consumers (Thang, 2016) . According to TPB, the motive or intention of consumption is considered the primary
motivator of consumer behavior driven by three basic factors: Attitudes, subjective norms, and perceived
behavioral control.
Besides 3 factors above, in the online purchase background, the study of Jarvenpaa and et al (2000), Lee and
et al (2001), Winch and et al (2006), Pavlou and et al (2003) also concerned with the price expectation and the
perceived risk is one of the most influent factor to consumer’s online purchase intention. In there, the attitude
“Evaluation of a individual about the result from performing a behavior” (Ajzen,1991); The subjective standard
is the individual’s awareness about stress of society to perform or not perform a behavior (Ajzen, 1991); The
behavior control awareness is the feeling of a individual being easy or difficult to perform (Ajzen, 1991); The
perceived risk mention to the awareness of customers about the uncertainty and the consequences of joining a
specific activity (Dowling & ctg, 1994); The price expectation is the evaluation of consumers about what they
have to exchange with the cost they have to spend out (Pavlou and et al, 2003). Through the theoretical summary
process, the author suggest that the research model includes 5 independent variables: (1) Perceived usefulness;
(2) Perceived ease of use; (3) Perceived risk; (4) Subjective norm; and (5) Price expectation và 1 biến phụ thuộc
“Online purchase intention”.
2.2 Research model
Perceived usefulness
Perceived ease of use
Online purchase
intention
Perceived risk
Subjective norm
Price expectation.
Fig. 1. Proposed research model
Research hypothesis:
H1: "Perceived usefulness" has a positive correlation with the online purchase intention (+).
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Ngo Cao Hoai Linh/ MICA 2018 Proceedings
H2: "Perceived ease of use" has a positive correlation with the online purchase intention (+).
H3: "Perceived risk" has a positive correlation with the intention to buy online (+).
H4: "Subjective norm" has a positive correlation with the intention to buy online (+).
H5: "Price expectation" has a positive correlation with the intention to buy online (+).
Table 1. Summary on the scale
Factor
Encoding
Observed variable
Online purchase saves you time
HI1
HI2
Online purchase saves you money
Perceived
usefulness
(HI)
Online purchase helps you find information easily
Online purchase provides lots of products to choose from
Online purchase makes it easy to search information about products at the website
Online purchase makes it easy to compare features among products
Online purchase makes it easy to order with a simple process
Online purchase makes it easy to pay when ordering
Personal information is protected at maximum level
Products meet the requirements of quality
HI3
HI4
SD1
SD2
SD3
SD5
RR1
RR2
RR3
RR4
RR5
RR6
Perceived
ease of use
(SD)
Safety when making online purchase payment is ensured
Customers always enjoy the warranty policy
Perceived
risk (RR)
Delivery is always on time
There is not any cost incurred when returning products
Consultation from feedback of customers who have engaged in online purchase is
available
CQ1
Subjective
norm
Consultation from feedback of family is available
Consultation from feedback of friends is available
Online purchase makes it easy to compare prices with the same products
Price of products upon online purchase includes type of fees involved
Online purchase is cheap
CQ2
CQ3
GC1
GC2
GC3
YD1
YD2
YD3
(CQ)
Price
expectatio
n (GC)
I have an online purchase intention in the near future
I will engage in online purchase more frequently than traditional purchase
I will introduce online purchase to others
Online
purchase
intention
(YD)
3. Research methods
The author used qualitative method for preliminary research and uses quantitative method for formal research
to provide comments, assessments, analysis as well as practical solutions.
The preliminary research was conducted by qualitative method with group discussion and interview to gather
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opinions from a number of customers. It was aimed at adjusting and adding factors that influence the online
purchase intention to the scale
The formal research was conducted by quantitative method, using direct interview technique through
questionnaires from 02/2018 to 04/2018 to collect information from customers who intended to make online
purchase in Ho Chi Minh City. According to Hair and et al (2006) the sample size must be at least ≥ m x 5,
where m is the number of observed variables. So, with 24 variables observed in this study, the sample size must
be at least ≥ 120. However, to ensure high reliability, the author conducted survey of 250 subjects from
customers who intended to engage in online purchase in Ho Chi Minh City. All data collected from the
questionnaire was encrypted and processed by SPSS 20.0 software. These variables must have a coefficint of
correlation greater than 0.4 and a Cronbach's Alpha coefficient greater than 0.7 in order to ensure the reliability
of the scale. The exploratory factor analysis (EFA) was used to abbreviate and summarize the data. Each
observed variable was calculated as a factor called Factor Loading (> 0.5), which was used to classify the factors.
After classification, the author considered to determine that KMO (Kaiser-Mayer-Olkin) coefficient must be
about [0.5; 1 and Bartlett's test was statistically significant (Sig <0.05) so that the observed variables were
correlated in the overall. After measuring the scale using the Cronbach's Alpha coefficient and EFA, the author
conducted a multiple regression analysis to test the model.
4. Research results and findings
4.1 The characteristics of the research sample
In order to collect research data, the author conducted to survey 250 candidates. After collecting the
questionnaires and summarizing, there were a total of 241 questionnaires collected, including 15 invalid and 226
valid ones. Chi tiết bảng phân bổ mẫu điều tra thể hiện ở bảng 2.
Table 2. The characteristics of the research sample
Category
Quantity
% ratio
Sex
Male
135
91
59.73
40.27
100
Female
Total
226
Age
25 - 30 old
31 - 35 old
36-40 old
Trên 41 old
Total
110
64
48.67
28.32
13.27
9.74
30
22
226
100.0
Education
High school
College
University
Graduate
Total
54
62
23.89
27.43
33.19
15.49
100.0
75
35
226
4.2 Results of measurement scale
The reliability test showed that the Cronbach's alpha coefficients of scales were greater than 0.7 (the lowest
coefficient was Perceived usefulness scale with α = 0.716). The correlation coefficient for total variables was
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greater than 0.4. Therefore, there were not any observed variables excluded and all observed variables were
sufficiently reliable (see Table 3).
Table 3. Reliability test
Cronbach’s
Encoding
Factor
Alpha coefficient
0,716
HI
SD
RR
CQ
GC
YD
Perceived usefulness
Perceived ease of use
Perceived risk
0,898
0,819
Subjective norm
0,743
Price expectation.
Online purchase intention
0,743
0,828
4.3 Exploratory Factor Analysis – EFA
4.3.1 The independent variables
Bartlett test (table 4) with significance level sig. = 0.000 less than 0.05; coefficient KMO = 0.823. This result
showed that the observed variables in the whole are correlated.
Table 4. KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Sig.
0.823
0.000
Table 5. Exploratory factor analysis
Factor
Variable
Symbol
.
0,864
0,773
0,744
0,648
0,632
0,626
RR3
RR4
RR2
RR5
RR1
SD2
SD3
SD4
SD1
HI1
Perceived risk
0,842
0,759
0,746
0,691
Perceived ease of
use
0,787
0,726
0,697
Perceived
usefulness
HI3
HI4
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0,658
HI2
CQ1
CQ3
CQ2
G1
0,855
0,845
0,813
Subjective norm
Price expectation.
0,772
0,770
0,652
1,136
5,680
64,073
G2
G3
Eigenvalue
5,903
29,514
29,514
2,061
10,306
39,820
1,972
9,861
1,742
8,712
Variance extracted (%)
Total variance extracted (%)
49,681
58,393
Exploratory Factor Analysis (EFA) results showed that the total variance extracted was 64,073% which was
greater than 50%, which means that 64,073% of the model was explained by extracted factors while the
remaining 35.927% will be explained by other factors. As Eigenvalue was greater than 1, it was retained. As can
be seen from the results obtained, the Exploratory Factor Analysis (EFA) model was consistent with the data,
compiled into five groups of factors and can be used for multiple regression analysis.
4.3.2 The dependent variables
Bartlett test (table 6) with significance level sig. = 0.000 less than 0.05; coefficient KMO = 0.749. This result
showed that the observed variables in the whole are correlated.
Table 6. KMO and Bartlett’s Test
Kaiser-Meyer-Olkin Measure of Sampling Adequacy.
Sig.
0.749
0.000
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Table 7. Exploratory factor analysis
Variable
Symbol
Factor
YD2
YD1
YD3
0,867
0,877
Online purchase
intention
0,811
Eigenvalue
2,177
Variance extracted (%)
Total variance extracted (%)
72,581%
72,581%
Exploratory Factor Analysis (EFA) results showed that the total variance extracted was 72,581% which was
greater than 50%, which means that 72,581% of the model was explained by extracted factors while the
remaining 27,419% will be explained by other factors. As Eigenvalue was greater than 1, it was retained. As can
be seen from the results obtained, the Exploratory Factor Analysis (EFA) model was consistent with the data,
compiled into one groups of factors and can be used for multiple regression analysis.
4.4 Multiple linear regression analysis
Table 8. Multiple linear regression analysis results
Variable
Perceived risk (X1)
Normalized Beta
0,214
T
Significance level
0,000
3,769
5,184
3,212
3,768
3,651
Perceived ease of use (X2)
0,273
0,000
Perceived usefulness (X3)
Subjective norm (X4)
0,175
0,000
0,189
0,000
Price expectation (X5)
F – Value
0,207
0,000
46,410
0,513
R2 – Value
Adjuster R2 – value
Durbin-Watson
0,502
1,975
The results of regression analysis presented in Table 5 showed that R2 = 0.513, which means that the linear
regression model has an appropriate level of 51.3%. Adjustment R2 =0,502; which means that 50.2% variation of
the dependent variable "Online purchase intention" was explained by five independent factors of the model. The
remaining 49.8% were explained by other variables outside the model.
The value F = 46,410 and the sig value <0.05 showed that the regression model was consistent with the data
collected and variables were statistically significant at the significance level of 5%. The variables included in the
model have a linear relationship with the dependent variable, so the hypotheses H1, H2, H3, H4 and H5 were
accepted. The linear regression model of factors is presented as follows:
Y = 0.273*X2 + 0.214*X1 + 0.207*X5 + 0.189*X4 + 0.75*X3
(In which: Y: Online purchase intention of customers in Ho Chi Minh City; X1: Perceived usefulness; X2:
Perceived ease of use; X3: Perceived risk; X4: Subjective norm; X5: Price expectation)
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5. Conclusions and recommendations
Thus, after analyzing Pearson's correlation coefficient and multivariate regression analysis, the research model
was consistent with five factors influencing consumers' online purchase intention in Ho Chi Minh City ranked
according to the normalized beta coefficient as follows:
Table 9. The extent to which factors influence consumers' online purchase intention in Ho Chi Minh City
Variable
Perceived ease of use (X2)
Perceived risk (X1)
Regression coefficient
0,273
0,214
0,207
0,189
0,175
Price expectation (X5)
Subjective norm (X4)
Perceived usefulness (X3)
According to the analysis results, the above five factors have made influence and positively correlated with
customers' online purchase intention in Ho Chi Minh City, depending on the level of influence: (1) Perceived
ease of use; (2) Perceived risk; (3) Price expectation; (4) Subjective norm; (5) Perceived usefulness This is an
important basis for enterprises in Ho Chi Minh City to develop the right directions and strategies in applying the
extended TPB to the online purchase model.
Based on the results obtained, the author proposed solutions as follows:
In term of the perceived ease of use: As the perceived ease of use is considered to be most strongly
correlated with the online purchase intention, it is the factor that needs to be improved and developed properly,
specifically:
(1) Enterprises should pay attention to website design, fanpage, etc., with simple, impressive, easy to find
design, (2) It is necessary to arrange the list of easy-to-find goods, and place any goods that customers have
bought or seen and related items on the head, (3) The ordering process should be simple, with few operations,
and the payment method should be diverse so that customers have many choices.
In term of the perceived risk: As the perceived risk is considered to be the second strongly correlated factor
with the online purchase intention, it is the factor that needs to be improved and developed properly, specifically:
(1) Online shopping service providers should have consumer protection policies that regulate the law governing
online purchase, specifically regulating conditions for online trading, as well as measures to control this problem,
(2) It is crucial to apply appropriate forms of payment to online purchasers, ensure maximum safety for
customers during online payment, (3) The warranty and return policy must clearly state customers' interests when
returning the products, as well as any provisions of goods exchange, customers' interests.
In term of the price expectation: As the price expectation is considered to be the third strongly correlated
factor with the online purchase intention, it is the factor that needs to be improved and developed properly,
specifically: (1) Providers should refer to prices of other websites to find out prices offered to their customers,
which helps reduce the price difference among enterprises and improve the fair competition in the market. (2)
The price offered must be appropriate to the products and acceptable to the customers so that it is possible to
adjust the price offered in accordance with the current situation of providers and procurement situation of the
market to be able to create profit for the providers and attract potential customers.
In term of the subjective norm: As the subjective norm is considered to be the fourth strongly correlated
factor with the online purchase intention, it is the factor that needs to be improved and developed properly,
specifically: (1) Providers should actively advertise their websites on the mass media as well as other media so
that customers know the website from a variety of sources, (2) It is crucial to publish many articles about the
providers to make customers approach and know the providers from various sources.
In term of the perceived usefulness: As the perceived usefulness is considered to be the fifth strongly
correlated factor with the online purchase intention, it is the factor that needs to be improved and developed
properly, specifically: (1) It is necessary to maximize the time by placing favorite items on the front page so that
customers can select and buy quickly (2) Product information should be provided in detail to help purchasers
save time and money.
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6. Limitations and further research
Through this research, the author presents factors of the model influencing the online purchase intention.
However, there are still some certain limitations as follows:
The very first limitation is about the scope of research. As the research was conducted through the survey of
customers in Ho Chi Minh City, research results may not be applicable to other localities. Therefore, it is
possible to expand the research scope as well as survey for different regions in Vietnam. This helps policy
makers and business managers capture e-commerce development in order to continue to develop strategies in line
with the development.
Secondly, in term of factors influencing the online purchase intention, there were only five factors influencing
the online purchase intention provided, namely (1) perceived ease of use; (2) perceived risk; (3) price
expectation; (4) subjective norm; (5) perceived usefulness based on the extended TPB model, while there are
many other factors that may also influence the online purchase intention. Therefore, further research can be
extended to new research models such as: Theory of Reasoned Action (TRA), Technology Acceptance Model
(TAM).
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