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FACTORS AFFECTING THE INTENTION TO BUY ONLINE DURING
COVID-19: ELECTRONIC DEVICES IN SOUTHERN VIETNAM
Nguyen Thi Phuong Giang1,(*); Vo Thi Huynh Han2; Danh Thi Ngoc Anh3;
Nguyen Binh Phuong Duy4
1,2,3,4 INDUSTRIAL UNIVERSITY OF HCM CITY-IUH, HCMC, VIET NAM
Email: nguyenthiphuonggiang@iuh.edu.vn; 17029061.anh@student.iuh.edu.vn;
17030571.han@student.iuh.edu.vn; nguyenbinhphuongduy@iuh.edu.vn
ABSTRACT
E-commerce is an industry that has an important influence on businesses in building business
strategies. Vietnamese businesses are now promoting the development of e-commerce
transactions to create a new way of doing business and attracting more customers. The
ecommerce market brings a lot of benefits to businesses as well as convenience to customers.
However, the market of this industry has fierce competition not only between domestic
competitors, but also foreign competitors. In particular, during the raging epidemic of acute
respiratory infections of COVID-19, this competition is increasing. Knowing that, this study
focuses on exploring the influential factors such as product value, perceived risk, website
quality, trust and perceived usefulness that affect customers' online purchase intention. This
study combines both qualitative and quantitative research methods. The survey has a scale of
481 people; the survey subjects are consumers in South Vietnam. After collecting the feedback
samples, the data is analyzed using SPSS software. The results of the study show that product
value, website quality, trust and perceived usefulness positively affect the online purchase
intention. There is also a perceived risk factor that negatively affects online purchase intention.
The study also showed that the three factors that have the greatest impact on online purchase
intention are arranged in the following order: perceived usefulness, trust, and website quality.
As a result of the study, a number of governance implications have been proposed to suit
businesses in the context of the ongoing COVID-19 pandemic.
Keywords: Electronic products, Online purchase intention, Product value, Perceived usefulness,
Perceived risk, Website quality.
1 INTRODUCTION
The economy has been growing, people are increasingly busy, the time to shop as traditional is
to go to stores, supermarkets are shrinking. Meanwhile, nowadays internet access means are
increasingly popular, people can access by phone, laptop, desktop, other devices with internet
connection and anywhere (at home, at work or on outings). It is for that reason that more and
more people have formed the intention to buy online. Online shopping in the world is not new
but is gradually accounting for a very high percentage of people's shopping, especially during
the current COVID-19 pandemic. Currently, Vietnam is considered one of the fastest growing
electronics markets in Southeast
The E-Commerce market has been bringing many benefits to businesses and customers.
However, the fierce competition of the online market is also a pressure on businesses.
Therefore, to have a base, a development orientation and attract the interest of customers leads
to increased consumption through websites. Businesses need to have an understanding and
grasp what factors motivate consumers to buy. What factors affect the customer's purchasing
intention? What prevents consumers from making purchases? This research is aimed at
identifying factors affecting the intention to buy online during COVID-19: electronics in
Southern Vietnam. From there, businesses build customer research models on the basis of
premises and data analysis results. Help businesses and websites sell online in a timely manner
to understand and adjust the policies of businesses so that more and more customers can make
transactions. This research project wishes to contribute a part of data to international scientific
studies on the issue of influencing factors to the intention to buy electronic devices online. This
research of the author wants to find out suitable solutions for online business for businesses so
that businesses can apply to achieve high efficiency.
2 LITERATURE REVIEW
The theory of planned behavior _ TPB was originally studied by Martin Fishbein and Ajzen
in 1980. This is a model inherited from Theory of Reasoned Action _ TRA. TPB Theory
Planned Behavior is successfully applied in many different fields. This theory refers to factors
that affect the intended behavior and behavior of consumers, including: attitudes, subjective
norms, perceived behavioural control. In particular, behavior control perception refers to
human perception of how easy or difficult it is and the perception of controlling behavior can
vary from situation to situation and action (Ajzen (1991, p.183)). Subjective attitude and norms
are inherited from TRA theory. Attitudes to behavior and refers to the extent to which a person
has a favorable or unfavorable assessment or assessment of the behavior in question (Ajzen
1991, p.188). Subjective norms refer to the social pressure of being aware to perform or not to
perform behavior (Ajzen 1991, p.188). According to Ajzen 1991 argues that the more favorable
the attitude and subjective norms for a behavior and the greater the ability to perceive
behavioural control, the stronger the person should be the intention of an individual to perform
the behavior under consideration.
The technology acceptance model _ TAM was introduced by Davis (1989). TAM refers more
specifically to the prediction of the acceptability of an information system. The model aims to
anticipate the application of certain information technology and propose necessary changes to
information technology to achieve greater acceptance. Davis pointed to the influence of factors:
Perceived usefulness and perceived ease of use. In the model, the perceived usefulness is
defined as "the degree to which a person believes that using a particular system will enhance
their work performance." The perceived ease of use is understood that the use of specific
systems is easy and without difficulties (Davis 1989, p320). In Viswanath Venkatesh and Davis
2000, the perception of usefulness is a strong decisive factor for intended use (p187).
2.1 Online Purchase Intention
The intention to make an online purchase is a trend of participating in online purchases or being
willing to participate in consumer purchasing activities (Wen & Maddox, 2013). In the study
of (Nguyen et al., 2019) Online shopping intention shows the extent to which customers intend
to use e-commerce sites to shop in the future and can recommend others to do so online
shopping practice. Online purchase intention is influenced by many factors. As in Sam and
Tahir's 2009 research, Purchase intention is influenced by factors such as : Usability, website
design, Information quality, Trust, Perceived risk, Empathy. Or in the study Hemantkumar and
his associates conducted in 2020 also showed that trust, perceived risk affects the intention to
buy online, In addition, the study also showed other factors such as: perceived usefulness (PU),
perceived ease of use, perceived behavioral control, E-shopping quality and subjective norms.
2.2 Website Quality
A quality website involves many different factors such as: Website design, site speed,
content,... The design of websites plays an important role in attracting and retaining customers
(Liao and associates 2006). Website design refers to the content on the site including: text,
images, graphics, layout, sound, movement. These are identified as one of the main factors
contributing to pulling customers back (Sam and Tahir 2009). Research on website design
shows that providing richer media with a more realistic environment is more positive than the
influence with user participation (Hausman & Siekpe, 2009). Some elements of web quality,
such as information quality (Lederer, Maupin, Sena, & Zhuang, 2000; Lin & Lu, 2000) have
been verified as being related to Perceived Usefulness . Research on website design shows that
providing richer media with a more realistic environment is more positive than the influence
with user participation (Hausman & Siekpe, 2009). The quality of the content of websites can
increase consumer confidence in usefulness (Liao and associates 2006). From there the group
of authors hypothesized, following:
H1 : Website quality positively affects the intention to purchase electronic devices online. H2
: Website quality positively affects the perceived usefulness
2.3 Trust
Trust is defined as the dimension of a business relationship that determines the extent to which
each party feels they can rely on the integrity of the promise made by the other (Kolsaker &
Payne, 2002). According to Jarvenpaa and his associates (2000) distinguish online shopping
from traditional commerce and argue that trust is crucial for online trading (Chen et al, 2010).
Trust is considered an important factor in affecting the purchasing intention of customers(Sam
et al, 2009; Rasha Abu-Shamaa et al, 2015 ). In addition, trust is also believed to be weak which
can increase the level of co-awareness usefulness (Gefen et al, 2003). From previous studies,
the author gives two hypotheses as follows:
H3: Trust has a positive effect on the intention to purchase electronic online. H4:
Trust has a positive effect on the perceived usefulness.
2.4 Perceived Usefulness
Perception of usefulness is the powerful factor that determines the intended use of information
technology (Viswanath Venkatesh and Davis 2000; Zhu with associates 2012, Vijayasarathy
and associates (2004); Hemantkumar and associates 2020). As well as previous research by
Rasha Abu-Shamaa et al 2015 colleagues on the issue, this study also suggested that perceived
usefulness has a relationship with online shopping intention. It has a significant impact on
online shopping intention. Many studies later gave similar results (Peña García and associates
2020; Hemantkumar and associates 2020). Therefore, the author hypothesized as follows:
H5 : Perceived usefulness positively affects the intention of purchasing electronic devices
online.
2.5 Perceived Risk
Risk can be defined as the expectation of a defined loss of online consumers in a specific online
purchase estimate (Hasan and Rahim 2008; Leeraphong and Mardjo 2013) There are many
studies on the relationship between perceived risk and perceived usefulness. Such as
Featherman and Wells (2010) found that perceived risk significantly reduced the perceived
usefulness of paying bills online. Likewise, Li and Huang's findings (2009) show that the
perceived risk has a negative effect on the perceived usefulness when shopping online. As Li
& Huang (2009) claims that online shopping includes more uncertainty and risk than traditional
shopping. According to Dan &ctg (2005) research on student online purchasing intention has
shown that risk-taking has a negative effect on online purchasing intention. In the Study
Hemantkumar and associates 2020, also said that perceived risk is a factor in affecting
consumers' online purchasing intention. Thus, the proposed hypothesis is:
H6 : Perceived risk has a negative effect on the intention of purchasing electronic devices
online.
H7 : Perceived risk has a negative effect on perceived usefulness.
2.6 Product Value
Product value represents the perceived quality of products and services by consumers (Boyer
and Hult (2006); Chen et al. 2010). It is possible that in online purchases the product value is
focused on the quality of the product. However, reasonable price and high quality are equally
important to increase the value of the product, thereby increasing the intention to buy. (Turban
et al (2006)). In this study the features that the author wanted to mention were based on the
2010 Study of Chen & associates including: features of the product that matched customer
expectations (Boyer and Hult, 2006; Brucks et al, 2000); easy-to-use products (Brucks et al,
2000); product price reflects reasonable product brand (Turban & ctg 2006). The hypothesis
given is:
H8: The value of the product has a positive effect compared to the intention of purchasing
electronic devices online.
Figure 1. Suggested author model
(Source: Synthesis Author)
3 RESEARCH METHOD
The research was carried out with two methods: qualitative research and quantitative research
[5]. Qualitative research is carried out by discussing the group through the phone. From the
results of the group discussion, adjust the scale for the last time to make the official scale and
the official questionnaire. Questionnaires for quantitative research are also established [6]. The
next step is quantitative research carried out with a total sample collection of 501 samples
through online surveys and paper surveys. At the beginning of the cleaning phase, 20 imperfect
samples were removed. Finally, the official data set used for the analysis was 481 and included
in the statistics.
In terms of gender, the percentage of men who participated was 41.8% less than the female
ratio. Meanwhile, the proportion of females accounted for 58.2%. In terms of age, with the age
of 18 - 22 having 303 customers accounting for the highest proportion with 63.0%, the age of
23 - 27 years old participating in the survey accounted for nearly 16.0% 8%, the age of 28 - 32
years old participating in the questionnaire survey accounted for about 7.5%, the age of 33 - 37
years old participated in the survey accounted for nearly 8.9% , the age of over 37 years and
older accounts for nearly 3.7%. Therefore, customers in Ho Chi Minh City have a high
percentage of youth. In terms of careers, the percentage of students surveyed accounted for
nearly 62.16% of the study samples. However, office workers also accounted for 16.01% of
other occupations; civil servants and employees account for about 4.78% of other branches;
with the profession of entrepreneur, managers participating in the survey accounted for nearly
2.29%; other professions surveyed by question accounted for nearly 14.76%. The survey was
conducted in a variety of industries to help the study get a better overview of the intention to
buy electronic devices. In terms of income, income of less than VND 3 million accounts for a
high proportion of 40.1%. This was followed by income from 3 million to 7 million, accounting
for 25.4%, income from 7 million - 15 million accounting for 25.8% and finally the income of
over 15 million accounted for the lowest 8.7% compared to other income levels.
Then the data were analyzed using SPSS software for EFA analysis and linear regression
analysis. Research linear regression analysis to test the impact of the independent variables on
the dependent variable. The linear regression model applies a supervised learning algorithm.
This algorithm first determines the output of a new input based on previously known data
(input, result) also known as data labels. The creation of an approximate reflection function to
compute the output data (Khoa et al, 2021). The linear regression model can be described
through the following equation:
𝑌
𝑡= 𝛽1+ 𝛽2𝑋𝑡2+. . . +𝛽𝑘𝑋𝑡𝑘 + 𝑢𝑡
Finally, the research team discusses the research results and offers some management
implications to improve the intention to buy electronic devices online for consumers in the
South region. The detailed observed variables will be presented in Appendix 1.
4 RESULT Cronbach's Alpha test
Ensure sufficient reliability of the coals, conducting inspections with Cronbach's Alpha. The
results showed that the reliability of all 7 scales had Cronbach's Alpha range from 0.788 - 0.895.
In many previous studies, Cronbach's Alpha was between 0.8 and close to 1, the scale was good
(Nunnally and Bernstein, 1994, quoted by Duy et al (2020)). From 0.7 to 0.8 is usable
(Peterson, 1994, quoted by Duy et al (2020)). Therefore, we conclude that the scales set out
such as PV, PR, WQ, TRU, PU, OPI are standard and statistically significant. Details are
presented in table 1.
Table 1 . Cronbach's Alpha results for 6 coal measurements
No.
The scale
Number of
observed variables
Coefficient
Cronbach's Alpha
1
Product Value (hereafter PV)
4
0.866
2
Perceived Risk (hereafter PR)
4
0.788
3
Website Quality (hereafter WQ)
3
0.895
4
Trust (hereafter TRU)
4
0.827
5
Perceived Usefulness (hereafter PU)
3
0.892
6
Online Purchase Intention
(hereafter OPI)
4
0.86
Explore factor analysis (EFA)
The study will perform EFA analysis, to ensure the value of the components in the suggestive
element and discover other factors. This step will be performed EFA is conducted using the
factor analysis performed using the "Principal Component" with the rotation "Varimax" as an
extraction factor ((Anderson et al., 1998) extracted Nesha et al. (2018)). The results of the
analysis are presented specifically in (Appendix 2)
After analyzing EFA for 5 independent variables, the result obtained a KMO value equal to
0.89 (conditions greater than 0.5 and less than 1) and Sig =0.00 < 0.05 showed that the data
was suitable for conducting factor analysis. The extracted variance is 72.417% (>50%) with
this result showing that 5 extracted factors explain 72,417% of the fluctuations of the observed
data. The Eigenvalues system is 1,008, the extraction system has good information
significance. All observed variables have a load > 0.5, so observation variables measure the
concept we need to measure. The rotation component matrix shows the convergence of
observation variables into factor groups. We see that all factors converge the right focus as
stated in the summary of the scale.
EFA analysis for the dependent variable KMO value of 0.819 (conditions greater than 0.5 and
less than 1), and Sig =0.00 < 0.05 show that the data is suitable for factor analysis. Extracted
variance reached 70.384% (>50%) with this result showing that the 5 extracted factors explain
70.384% of the fluctuations of the observed data. The Eigenvalues system is 2,815 >1 ,
theExtracted variance has good information significance.
So after analyzing the EFA discovery factor, the results showed that 5 independent factors were
analyzed, in accordance with the theoretical basis and the model set out. Therefore the original
research model will be retained as figure 1
Regression analysis
With the hypothetical model, the authors conduct regression analysis in turn according to the
following steps:
Firstly, perform linear regression for the model to test the impact of independent variables
(website quality, trust, perceived usefulness, perceived risk and product value). with the
dependent variable (intention to purchase electronic devices online).
Second, performing a revoicing to verify the impact of three independent variables is the
quality of the website quality, trust, and perceived risk with the dependent variable being the
perceived usefulness.
1st regression analysis
To test the research hypothesis on the link between independent factors (PV, PR, WQ, TRU,
PU) with dependent variables (OPI) - The intention to buy electronic devices online.
Performing linear regression obtained the results and presented in the tables below: Table 2:
Model summary of factors that affect your intention to buy electronic devices online
Model R R² Radj² Std. Error
of the Estimate
Durbi
n-
Watso
n
1 .816a 0.666 0.663 0.35814
1.992
a Predictors: (Constant) PV, PR, WQ, TRU, PU
Based on the table of results 2, we can give the following analysis:
The coefficient of determination R² indicates how much (%) of the dependent variable is
explained by the independent variable. Table 2, the regression results show that the coefficient
of determination R² = 0.666 (≠0) ie 66.6% of this index means that the variation of OPI variable
is explained by the variation of 5 factors (PV, PR, WQ, TRU, PU), the remaining 33.4%
belongs to other random factors and errors. In this model, using the Radj² index (R correction
square) = 0.663 (66.3%) this index can help determine the model's fit more accurately and
safely. The value of Durbin - Watson is 1,992 in the interval (1,3) from which it can be inferred
that the data has no first-order correlation phenomenon, the data meets the requirements.
Table 3: ANOVA factors that affect the quality of relationships intended to purchase
electronic devices online
Sum of Squares
df
Mean Square
F
Sig.
Regression
121.705
5
24.341
189.775
.000b
Residual
60.925
475
0.128
Total
182.63
480
b Predictors: (Constant) PV, PR, WQ, TRU, PU
Table 3 shows F = 189,775 and significant Sig. = 0.000 (sig. ≤ 0.05), which means that the
recall model is consistent with the collected data and the included variables have statistical
significance with 5%.
Table 4 Regression weight table of factors affecting the intention to purchase electronic
devices online
B Std. β t Sig. Collinearity Hypotheses Result (Constan
Error standard Statistics
t) chemical
Tolerance VIF
1.047
0.116
9.005
0
PV
0.151
0.024
0.205
6.365
0
0.679
1.472
H8
Accept
PR
-0.046
0.02
-0.06
-2.272
0.024
0.991
1.009
H6
Accept
WQ
0.151
0.026
0.208
5.775
0
0.539
1.855
H1
Accept
TRU
0.228
0.031
0.272
7.336
0
0.512
1.951
H3
Accept
PU
0.243
0.026
0.323
9.171
0
0.566
1.765
H5
Accept
As presented in table 4.10, the VIF ranges from 1,009 - 1855 < 2. Therefore, we can conclude
that the multi-mutation phenomenon in this model is small. The Sig. of all PV, PR, WQ, TRU,
PU variables is less than 0.05, so it is concluded that the above variables affect the intention to
buy electronic devices online. Pv, WQ, TRU, PU variables have the same impact on variables
depending on the intention to buy electronic devices online due to having a positive Beta
system. Pr variables have an inverse impact on variables depending on the intention to buy
electronic devices online due to having a positive Beta system.
From the analysis results, we have the regression model as:
OPI = 0.205PV - 0.06 PR + 0.208WQ + 0.272TRU + 0.323OPI
2nd regression analysis
The results of the second regression, to test the research hypothesis about the relationship
between the independent factors, the independent variable (WQ, TRU, PR) and the dependent
variable PU, the results are detailed in the Appendix. 3:
Regression results show that F = 116,098 and Sig. = 0.000 (sig. ≤ 0.05), which means that the
regression model fits the collected data, and the included variables have statistical significance
with 5%. As the results presented in Appendix 3, the highest VIF index VIF = 1,578 < 2.
Therefore, we can conclude that the phenomenon of multicollinearity in this model is small.
Sig. of the variables WQ, TRU are all less than 0.05, so it can be concluded that the above
variables have an influence on PU. The variable PR has sig. = 0.098 > 0.05, so the PR variable
does not affect the PU variable. β standardized of the variables WQ = 0.368, TRU = 0.353 that
shows that both variables are positive numbers. So, these two variables have the same positive
effect on the dependent variable PU.
Test the hypotheses of the model
The proposed hypothesis consists of 8 hypotheses. Based on Table 4, the hypotheses (H1, H3,
H5, H6, H8) all have Sig.<0.05, so it can be concluded that the hypotheses H1, H3, H5, H6,
H8 are accepted. The remaining hypotheses H2, H4 and H7 are tested in the second regression,
detailed in Appendix 3, showing that Sig. of the variables WQ(H2) and TRU(H4) are equal to
0.00 from which the conclusion is that the hypothesis H2 and H4 are accepted. The variable
PR has Sig. = 0.098 > 0.05 from which the corresponding hypothesis H7 is rejected.
5 DISCUSSION
Based on the results of the analysis above, the research team set out the topic of discussing
factors affecting the intention to buy online during COVID-19: electronics in Southern
Vietnam.
According to the linear regression equation after analysis, we can conclude that consumers'
intention to buy electronic devices online in the South region, Vietnam is influenced by 5 main
factors: product value, perceived risk, website quality, trust, and perceived usefulness. In
which, the perceived risk factor has a negative impact on the intention to buy electronic devices
online in the South region, Vietnam, the higher this factor, the lower the intention to buy
electronic devices online. For example, when there are too many risks on a product such as
quality risk, information security risk, etc. The higher the risk, the more it can disrupt the
procurement process of electrical equipment customers online.
The factors of product value, website quality, trust and perceived usefulness have a positive
impact on customers' intention to buy electronic devices online. The higher these factors, the
higher the consumer's intention to buy electronics online. In particular, the perceived usefulness
has the greatest impact, the second is the trust factor, the third is the website quality and finally
the product value. As a result, the linear revoicing equation accurately reflects the correlation
between independent variables and dependent variables in the research model.
The results achieved by the study are consistent with previous studies. The Hemantkumar P.
Bulsara and Pratiksinh S. Vaghela 2020 study found that perceived usefulness is the most
influential factor in purchasing consumer electronics online. This study suggests that online
retailers and online platform developers should focus more on the user experience.
It is also thought that trust is related to the element of online purchase intention. In the previous
study (Sam et al 2009, Leeraphong and Mardjo 2013 and Heijden and Associates 2003) gave
similar results. These studies believe that trust has a positive effect on the purchasing intention
of customers, so this factor needs to be considered and needs to improve consumer confidence
in the service and products of the store itself.
Perceived risk is a factor that has a negative impact on the intention to buy electronic devices.
This discovery is consistent with past studies (e.g., Peña-García and associates (2020),
Hemantkumar and Pratiksinh 2020, …) In addition, " Mitchell (1999) argues that consumers
tend to avoid mistakes rather than trying to maximize the utility in purchasing decisions.
Another study results show that the risk of being aware is not related to perception of
usefulness. This result is similar to those found in some past studies (e.g., Kamarulzaman, 2007;
Li / Huang, 2009)" quotes Peña-García and associates (2020).
In addition, variables such as trust and web quality have an effect on perceived usefulness. The
impact of web quality significantly affects users who are aware of usefulness in accordance
with studies by Heijden and associates (2003) and Liao and associates (2006). Trust can
increase the level of perceived usefulness, the results of which are implied by the study of
Gefen and et al, 2003 and Liao et al (2006).
6 CONCLUSION
From the results of the analysis and discussion of the study, the authors team came to
conclusions. The impact of factors on the purchasing intention of customers is arranged in the
following order: perceived usefulness, trust, website quality, perceived risk and product value.
In particular, the perceived risk factor has a negative impact on the online purchase intention
of customers. On the basis of the results of the study "propose solutions suitable for online
business for businesses". From there, set out business methods and marketing strategies as well
as other activities to meet the needs of customers. This helps to boost business efficiency,
increase revenue for businesses and dominate the market.
In order to make it more convenient for businesses to develop business strategies for company
development, the research team has given administrative implications on the factors of
perceived usefulness, trust and website quality.
Perceived usefulness is the factor that most influences consumers' intention to purchase
electronic products online. This implies that online entrepreneurs should focus on the customer
experience more. Because the survey results of this study show that customers want to
experience good customer care and can communicate with sellers as quickly as possible, they
want to save more time in shopping. Managers need to come up with business strategies,
allocate rich products and focus on providing true, accurate, detailed and timely information
about products that meet the needs of customers. In addition, the quality of the site also affects
the perception of usefulness. Therefore, the quality of service of the online platform is also a
factor that needs attention.
The unpredictable nature of internet infrastructure is growing, consumers are concerned that
hackers or third parties will threaten their financial secrets or disclose personal information
(Hoffman, Novak, & Peralta, 1999; Pavlou, 2003 quoted in Liao and associates (2006).
Therefore, electronics providers need to enhance consumers' online purchase intentions by
enhancing their trust. Specify and provide rules and regulations for online trading. From there,
it is possible to make customers more confident and reliable in online goods. In addition,
increased customer care before and after purchase, is essential in the e-commerce environment.
According to the survey, responding to online questions is a problem that customers evaluate
as the factor that has not yet achieved the highest scale in this e-commerce environment.
Through the results of the analysis, the research team pointed out the quality factor of the
website quality influential in the intention to buy electronic devices online in Ho Chi Minh
City. This study also shows that in order to develop a consumer-oriented e-commerce business,
administrators need to focus on the following: Focusing on the quality of information about
electronic products on online stores. The content of the information on the website must be
accurate, complete, clear and reliable. Online stores should focus on the necessary information
for customers to refer and choose products accordingly from Tran Anh Vu, Doan Minh Thang
and Doan Thi Mai (2020). In particular, administrators need to improve the quality and speed
up website access so that customers can shop easily, avoiding the case of not being able to
connect to reduce the intention to buy or interrupt the shopping needs of customers. The
adjustment of the business strategy of the business/company according to the above results to
meet the needs of each customer.
Although in the course of implementation there have been many attempts to complete the study,
like with some other studies, this study also has some limitations. Due to limited time spent
doing the research, the team did not analyze the variables in depth. The research by the authors
has only recently tested consumers' intention to buy electronic devices online in the South
region, Vietnam, so the level of generalization may not be high. Therefore, the team proposes
to expand the scope of research in many different provinces and the next study should compare
which key factors affect the online purchase intention of electronic devices between countries.
Finally, the authors suggest that future studies should further explore other factors or attributes
that may influence the intention to purchase electronic devices online.
REFERENCES
Z. Dörnyei, Research Methods in Applied Linguistics: Quantitative, Qualitative and Mixed
Methods (Oxford Applied Linguistics). Oxford; New York, NY: Oxford University
Press, 2018.
Bui Thanh Khoa, Nguyen Minh Ly, Vo Thi Thao Nguyen, Nguyen Thi Trang Oanh, Bui Thanh
Long (April 2021). The impact of Social Media Marketing on the Travel Intention of Z
Travelers. Conference: 2021 IEEE International IOT, Electronics and Mechatronics
Conference (IEMTRONICS)At: Toronto, Canada
Hausman, A.V & Siekpe, J. E. (2009). The effect of web interface features on consumer online
purchase intentions. Journal of Business Research, 62, 5-13.
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An
integrated model. MIS Quarterly, 27(1), 51–90.
Zhu, Dauw-Song, Thomson Chih-Te Lin, and You-Chung Hsu. "Using the technology
acceptance model to evaluate user attitude and intention of use for online games." Total
Quality Management & Business Excellence 23.7-8 (2012): 965-980.
Vijayasarathy, Leo R. "Predicting customer intention to use on-line shopping: the case for an
augmented technology acceptance model." Information & management 41.6 (2004):
747762.
Venkatesh, Viswanath, and Fred D. Davis. "A theoretical extension of the technology
acceptance model: Four longitudinal field studies." Management science 46.2 (2000):
186-204.
H. H. Hasan and S. A. Rahim, ―Factors affecting online purchasing behavior,‖ Jurnal
Komunikasi, Malaysian Jurnal of Communication, vol. 24, pp. 1- 19, 2008
Wen, GR, & Maddox, SL (2013). Influence factors on users' online shopping in China. Asia
Business Magazine, 7 (3), 214-230
Nguyen Thu Ha, Tran Trong Vu Long, Pham Thanh Thuy, Le Thi Tu Anh .Online Shopping
Intention and Behavior of Customers: Study of the Extended Unified Theory of
Acceptance and Use of Technology, Trust and Long Tail Effect .VNU Journal of Science:
Economics and Business, Vol. 35, No. 1 (2019) 112-120
Ajzen, I. (1991), "The theory of Planned Behavior " Organization behaviour and Human
decision processes 50: 179 - 211.
Hemantkumar P. Bulsara, Pratiksinh S. Vaghela. Online Shopping Intention for Consumer
Electronics Products: A Literature Review and Conceptual Model. E-Commerce for
Future & Trends. 2020; 7(1): 24–32p.
Sam, M. and Tahir, H. (2009) Website Quality and Consumer Online Purchase Intention of Air
Ticket. International Journal of Basic & Applied Sciences IJBAS, 9.
Liao, C., Palvia, P., and Lin, H-N. (2006) ―The Roles of Habit and Web Site Quality in E-
Commerce.‖ International Journal of Information Management. 26(6), 469-483
Hausman, A.V & Siekpe, J. E. (2009). The effect of web interface features on consumer online
purchase intentions. Journal of Business Research, 62, 5-13.
Lederer, A. L., Maupin, D. J., Sena, M. P., & Zhuang, Y. (2000). The technology acceptance
model and the World Wide Web. Decision Support Systems, 29(3), 269–282.
Lin, J. C., & Lu, H. (2000). Towards an understanding of the behavioral intention to use a web
site. International Journal of Information Management, 20(3), 197208.
Rasha Abu-Shamaa và cộng sự (2015), Factors Influencing the Intention to Buy From Online
Stores: An Empirical Study in Jordan,Proceedings of the 8th IEEE GCC Conference and
Exhibition, Muscat, Oman, 1-4 February, 2015
Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An
integrated model. MIS Quarterly, 27(1), 51–90.
A. Leeraphong and A. Mardjo, “Trust and Risk in Purchase Intention through Online Social
Network: A Focus Group Study of Facebook in Thailand”.Journal of Economics,
Business and Management, Vol. 1, No. 4, November 2013
Featherman, M. S., & Wells, J. D. (2010). The intangibility of e-services: Effects on perceived
risk and acceptance. ACM SIGMIS Database, 41(2), 110–131.
Li, Y., & Huang, J. (2009). Applying theory of perceived risk and technology acceptance model
in the online shopping channel. World Academy of Science, Engineering and
Technology, 53, 919–925.
Turban E, King D, Lee JK, Viehland D. Electronic commerce 2006: a managerial perspective.
Prentice Hall; 2006.
Duy, B. P. N., Bình, L. C. & Giang, N. T. P., 2020. Factors Affecting Students’ Motivation for
Learning at the Industrial University of Ho Chi Minh City. Proceedings of the Future
Technologies Conference (FTC) 2020, Volume 2, pp. 239-262.
Nesha, A.U., Rashed, M.S., & Raihan, T. (2018). Identifying the Factors that Influence Online
Shopping Intentions and Practices: A Case Study on Chittagong Metropolitan City. The
Comilla University Journal of Business Studies. Bangladesh. 5(1), 157-171.
H. Van der Heijden, T. Verhagen & M. Creemers, “Understanding online purchase intentions:
Contributions from technology and trust perspectives”, March 2003_European Journal
of Information Systems 12(1):41-48
Featherman & Pavlou P A. Predicting e-services adoption: a perceived risk facets perspective.
International Journal of Human-Computer Studies. 2003;59(4):451-74.
Trần Anh Vũ, Đoàn Minh Thắng và Đoàn Thị Mai, Customer’s Behavior on Intention to
Purchase on Online Shopping in Vietnam. Journal of Business and Management
Sciences, 2020, Vol. 8, No. 3, 85-88.
Peña García et al., (2020), Purchase intention and purchase behavior online: A cross-cultural
approach . Heliyon Volume 6, Issue 6, June 2020, e04284
H. Van der Heijden, T. Verhagen & M. Creemers, “Understanding online purchase intentions:
Contributions from technology and trust perspectives”, March 2003_European Journal of
Information Systems 12(1):41-48
Chen, Y.-H., I.-C. Hsu, et al. (2010), "Website attributes that increase consumer purchase
intention: A conjoint analysis." Journal of Business Research 63.
Fred D. Davis (1989) Perceived Usefulness, Perceived Ease of Use, and User Acceptance of
Information Technology . Source: MIS Quarterly, Vol. 13, No. 3 (Sep., 1989), pp. 319340
Brucks M, Zeithaml VA, Naylor G. Price and brand name as indicator of quality dimensions
of consumer durables. J Acad Mark Sci 2000;25(2):139–53.
Boyer KK, Hult GTM. Customer behavioral intentions for online purchases: an examination of
fulfillment method and customer experience level. J Oper Manag 2006;24(2):124–47
Kolsaker A. Payne C. (2002). Engendering trust in e-commerce: A study of gunder-based
concerns. Marketing Intelligence & Planning, 20/4 206- 214
APPENDIX
Appendix 1: Summary of observed variables
Factor
Scale
Code
Source
Product
Value
The features of the product meet your requirements.
PV1
Chen & ctg (2010)
The online store sells good quality products.
PV2
Online store selling products at reasonable prices.
PV3
The online store sells products of clear origin and guaranteed
genuine.
PV4
Perceived
Risk
You find it difficult to accurately assess product quality when
shopping online.
PR1
Hemantkumar P.
Bulsara, Pratiksinh
S. Vaghela (2020);
Bui Thanh Trang
(2013)
You find it very difficult to compare the quality of similar products
when shopping online.
PR2
Personal information such as your address, email, phone number
may be disclosed to others.
PR3
You find your shopping habits and process easy to track when
shopping online.
PR4
Website
Quality
Product information and images are detailed and clear.
WQ1
Hemantkumar P.
Bulsara, Pratiksinh
S. Vaghela (2020)
Chen & ctg (2010)
The online store has a user-friendly interface designed to be easy to
use.
WQ2
The website has a fast loading speed to help you find the exact
product in a short time.
WQ3
Peña García et al.,
(2020)
Trust
The online store has a solid confirmation message when you close
the purchase.
TRU1
Chen & ctg (2010)
Y. Hwang, and D. J.
Kim (2007)
The website's online reply service meets your requirements.
TRU2
Hemantkumar P.
Bulsara, Pratiksinh
S. Vaghela (2020)
Chen & ctg (2010);
Y. Hwang, and D. J.
Kim (2007)
The terms of the transaction (including payment, shipping,
warranty, return, etc.) are detailed and clear by the online store.
TRU3
The online store is known by many to be reputable and trustworthy.
TRU4
Perceived
Usefulness
An online store that offers a wide range of electronic products
information.
PU1
Hemantkumar P.
Bulsara, Pratiksinh
S. Vaghela (2020)
Chen & ctg (2010)
Peña García et al.,
(2020)
Online store and you can easily exchange information back and
forth.
PU2
Buying online saves customers shopping time.
PU3
Online
Purchase
Intention
It is likely that you will purchase electronic products through an
online store.
OPI1
Peña García et al.,
(2020); Hausnam &
Siepe (2009)
You will definitely buy electronic products through online stores.
OPI2
You will introduce other customers to buy electronic devices
online.
OPI3
You will continue to purchase electronic products through the
online store.
OPI4
Hypothesis: 6; Number of variables observed: 22
Appendix 2
Table a. EFA for 5 independent variables
Rotated Component Matrix a
Component
1
2 3 4 5
PV2
0.868
PV1
0.825
PV3
0.771
PV4
0.706
TRU2
0.778
TRU3
0.735
TRU4
0.712
TRU1
0.569
PR3
0.791
PR4
0.76
PR2
0.758
PR1
0.712
PU3
0.859
PU2
0.823
PU1
0.74
WQ2
0.847
WQ1
0.791
WQ3
0.786
Cronbach 0.866
alpha times 2
0.827
0.788
0.892
0.895
KMO
0.89
Bartlett’s (sig.)
0.00
Method wrong deduction
Eigenvalue
72.417%
1.008
Table b. EFA for OPI dependent variable
Component Matrixa
Component
1
OPI2
0.863
OPI4
0.855
OPI3
0.819
OPI1
0.818
KMO
0.819
Bartlett’s (sig.)
0.00
Method wrong deduction 70.384%
Eigenvalue 2.815
Appendix 3
Coefficientsa
Model
Collinearity Statistics
B
Std. Error Beta
t
Sig.
Tolerance VIF Result
1 (Constant)
0.956
0.194
4.94
0
WQ
0.356
0.042
0.368
8.426
0 0.635
1.575 accept
TRU
0.395
0.049
0.353
8.074
0 0.634
1.578 accept
PR
0.058
0.035
0.058
1.659
0.098 0.996
1.004 refute
a Dependent Variable: OPI
