DIGITAL COMMERCE SHAPE VIETNAMESE SHOPPING HABIT IN 4.0 INDUSTRY

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UNIVERSITY….
GRADUATION PROJECT
PROJECT NAME
DIGITAL COMMERCE SHAPE
VIETNAMESE SHOPPING HABIT IN 4.0 INDUSTRY
Student’s name: …

… - 2021

UNIVERSITY….
GRADUATION PROJECT
PROJECT NAME
DIGITAL COMMERCE SHAPE
VIETNAMESE SHOPPING HABIT IN 4.0 INDUSTRY
SUPERVISOR: …
STUDENT: …
STUDENT ID: …
COHORT: …
MAJOR: …

TP.HCM- 2021

v
GUARANTEE
I hereby declare that this is my personal application guide, the process of
making this thesis and the results are honest, never made public, used to defend
a degree.
Thesis author

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CONTENTS
Student’s name: ….............................................................................................. 1
TP.HCM- 2021..................................................................................................... 4
LIST OF TABLES, DIAGRAMS.....................................................................ix
CHAPTER 1: AN INTRODUCTION............................................................... 1
1.1. Background of the study............................................................... 1
1.2.Research objectives and research questions................................... 2
1.2.1.Research objective............................................................................ 3
1.2.2.Research questions ........................................................................... 4
1.3.Research methodology ...................................................................... 4
1.4. Research results................................................................................ 4
1.5. Structure of the study ...................................................................... 5
CHAPTER 2. LITERATURE REVIEW.......................................................... 6
2.1 Theoretical basis for digital commerce and people's consumption
habits......................................................................................................... 6
2.1.1. Digital commerce ............................................................................ 6
2.1.2 People's consumption habits............................................................. 7
2.1.3 Industrial Revolution 4.0.................................................................. 8
2.2. Theoretical background................................................................. 10
2.2.1 Planned behavioral theory.............................................................. 10
Shape 2.1: Expected behavioral theory model ............................................... 10
Nguồn: Ajzen (1991) .............................................................................. 10
2.2.2 The theory of sowing...................................................................... 11
2.3. Overview of previous research...................................................... 16
2.3.1. Research in the country................................................................. 16
2.3.2. Study abroad.................................................................................. 18
2.3.3. Research gap.................................................................................. 23
CHAPTER 3: RESEARCH METHODS........................................................ 26

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3.1. Research process............................................................................. 26
3.1.1. Research design............................................................................. 26
Table 3.1. Content variables in the research model....................................... 28
3.2 Methods of information collection................................................. 33
3.2.1 Methods of collecting secondary information................................ 33
3.2.2 Methods of collecting primary information................................... 33
3.3 Methods of data analysis................................................................. 34
3.3.1 Verifying the reliability of the scale............................................... 35
3.3.2 Analysis of discovery factors ......................................................... 35
3.3.3 Confirmation factor analysis .......................................................... 36
3.3.4 Linear structure model.................................................................... 37
Summary of chapter 3....................................................................................... 38
CHAPTER 4: DATA ANALYSIS AND RESEARCH FINDINGS.............. 39
4.1. Characteristics of survey samples................................................. 39
4.2. Verifying and evaluating the scale................................................ 41
4.2.1. Cronbach's Alpha analysis ............................................................ 41
4.2.2. Exploratory Factor Analysis (EFA) .............................................. 43
4.3. Multiple regression analysis .......................................................... 48
4.3.1. The correlation matrix between variables..................................... 48
Table 4.5 Correlation coefficient matrix............................................. 48
4.3.2 Results of regression analysis ........................................................ 51
Table 4.6. Regression summary table.............................................................. 52
Table 4.7. Bảng ANOVA analysis.................................................................... 52
Table 4.8. Regression results table................................................................... 54
4.3.3. Check the suitability of the model................................................. 55
4.3.4. Test hypothesis of the model......................................................... 59
Table 4.11: Summary of the impact of the factors affecting Shopping habits60
4.5 Discussion of research results......................................................... 61
SUMMARY OF CHAPTER 4 ......................................................................... 62

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CHAPTER 5: CONCLUSIONS AND POLICY IMPLICATIONS............. 63
1.1 Conclusions .................................................................................. 63
5.2 Policy implications........................................................................... 65
5.3 Limitations of the study and new directions of research............. 69
REFERENCE .................................................................................................... 70
APPENDIX ........................................................................................................ 78

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LIST OF TABLES, DIAGRAMS
Shape 2.1: Expected behavioral theory model ....................................... 10
Table 3.1. Content variables in the research model............................... 28
Table 4.1: Characteristics of survey samples ......................................... 39
Table 4.2. Cronbach's Alpha Coefficient Summary Table................... 41
Table 4.3: Summary table of EFA analysis results on quality of life
scale..................................................................................................................... 44
Table 4.4. Name the variable.................................................................... 48
Table 4.5 Correlation coefficient matrix................................................. 48
Table 4.6. Model summary table ............................................................. 52
Table 4.7. Bảng ANOVA .......................................................................... 52
Table 4.8. Regression weight table .......................................................... 54
Table 4.9. Model summary table ............................................................. 55
Table 4.10: Residuals Statisticsa
.............................................................. 56
Table 4.11: Summary of trend of factors affecting Shopping habits
(from model results).......................................................................................... 60

x

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CHAPTER 1: AN INTRODUCTION
1.1. Background of the study
Since the translation of Covid-19 has entered and complicated
developments in our country, in order to limit the need to go out and contact in
crowded places, many people have chosen to shop online (online) and since
When the Prime Minister issued Directive No. 16 on the implementation of
social isolation, the demand for online shopping has really exploded. In the past,
the items purchased online were often home appliances, electronics, clothes,
office goods ...; or snacks, regional specialties ... in recent days, online shopping
has flourished.
In addition to the traditional products mentioned above, which continue to
grow, processed food products, fresh foods, from meat, poultry, seafood, to
green vegetables ... have the increase. increase. Along with the change in buying
and selling methods, many people use payment methods by means of transfer,
electronic wallets.
Faced with the spike in demand for online shopping, many businesses are
also quickly adapting to the new situation. Promotion measures for online
shopping were quickly rolled out. Many businesses are racing on delivery time,
promoting access to consumers via social networks ... In addition to electronic
trading floors, such as: Tiki, Lazada, Shopee ...; Large enterprises such as Big C
supermarkets, Co-op mart, Vinmart, BRG ... and many other retailers have
achieved high growth in online sales. Some businesses achieved growth of up to
150% over the same period in 2019.
In May 2014, the Government approved the National E-commerce
Development Program for the period 2014-2020, with the aim of making online
shopping a popular form of consumer purchases; Payment cards are widely used
to replace cash. According to the Ministry of Industry and Trade, in recent years,

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the growth rate of e-commerce has reached about 35% / year. In 2019, revenue
from e-commerce over the total retail sales of goods nationwide reached 4.2%.
Due to cost savings, the price of goods through e-commerce is usually cheaper
than the price of goods bought and sold in normal form. Before the Covid-19
epidemic happened, experts predicted that the size of Vietnam's e-commerce
market by 2020 could reach $ 20 billion. However, online buying and selling is
only relatively popular in a part of urban residents. Most Vietnamese people still
have the habit of going to the place to see products before deciding to buy and
pay. This lack of trust stems from many unsatisfied customers with delivery
time; the product has the design, the quality is not right with the advertisement
or the error correction of the product is still delayed ...
Nowadays, owning a digital device becomes easy and inexpensive. One
person is able to own and connect more than one digital device, such as a
smartphone with a smart watch, or a tablet. Furthermore, there is no doubt to
state that the emergence and significant development of internet connection
bringing these digital devices more functional and applicable. They not only
contribute to the convenience of human life but also change the way humans
perceive using service from traditional to smart technologies from digitalisation.
Among the changes that shape today’s human life, shopping is one of significant
sectors that has seen the dramatic surge of commerce. Especially, since the
internet connection became publicly and commonly in Vietnam, the
development and innovation of digital technology has shaped a new consumer’s
shopping habits, noteworthy of the young generation. Hence, this research aims
at exploring how development of digital commerce in Vietnam, as long as the
shopping experience and habits of Vietnamese people in the new era of digital
commerce.
1.2.Research objectives and research questions

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1.2.1.Research objective
General objective
The general objectives of this research are understanding the
transformation of shopping habits of Vietnamese consumers in the 4.0 industrial
revolution as well as to identify how to maximize the impact of the usage of
digital devices on shopping.
Explain As a matter of fact, a study has questioned the significant complex
of the relationship between e-shopping and traditional shopping (Cao, Xu &
Douma 2012). Explain better or delete it, I do not understand what you want to
say.
Detail objective:
The general objective of the research can be operatively broken-down in
sub objectives like in the following list:
- Identify the theoretical basis of digital commerce and people's shopping
habits in the era of industrial revolution 4.0
- Identify factors of digital commerce affecting shopping habits of people
in the industrial age 4.0
- Determine the level of impact of each factor on Vietnamese consumers'
shopping habits in the 4.0 era
- Proposing proposed solutions to contribute to completing and developing
various forms of shopping in the digital age.
In order to achieve the research objectives, there are three research
questions raised in accordance with specific aims.

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1.2.2.Research questions
RQ1: What factors of digital commerce shape Vietnamese shopping habits?
RQ2: How to quantify to what extent are the factors affecting the shopping
habits of Vietnamese people in the industrial revolution 4.0 era?
RQ3: What are solutions and recommendations to promote Vietnamese
consumers' shopping habits through digital commerce?
1.3.Research methodology
The research is conducted applying a qualitative approach. Quantitative
research is intended to complement each other for the most positive and accurate
research results. Qualitative research was conducted based on group discussion
method, in-depth interviews with Vietnamese consumers used the form of
digital shopping and consulting experts to choose the necessary factors affecting
habits. Vietnamese consumers' shopping habits in the era of industrial revolution
4.0.
Quantitative research is done through face-to-face interviews or via email
to people who have purchased digital commerce in Vietnam. Data is processed
by SPSS software, by an analytical tool like Cronbach Alpha to test the
information technology level of the scale, discovery factor analysis method,
EFA, correlation coefficient analysis, process model analysis to test the
suitability of the hypothesis model.
1.4. Research results
The topic has a certain scientific and practical significance. Especially:
- In terms of science: the topic will contribute to supplementing evidence of
the benefits of digital commerce to the forms of shopping of Vietnamese
consumers, from there for the next topic to refer to.

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- In terms of implementation: The research results of the topic give the
most general view on digital commerce that shapes the buying habits of
Vietnamese consumers and can propose some suitable recommendations. related
to improving and developing digital commerce in the coming time.
1.5. Structure of the study
The topic is made with a layout of 4 chapters, specifically:
- CHAPTER 1: INTRODUCTION
- CHAPTER 2. LITERATURE REVIEW
- CHAPTER 3: MARKETING RESEARCH
- CHAPTER 4: DATA ANALYSIS AND RESEARCH RESULTS
- CHAPTER 5: CONCLUSIONS AND RECOMMENDATIONS

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CHAPTER 2. LITERATURE REVIEW
2.1 Theoretical basis for digital commerce and people's consumption habits
2.1.1. Digital commerce
Trade is the exchange of innovation, goods, services, knowledge, currency
etc. between two or more partners and can receive back some value (in money
through both) or By commodity or other services as in the barter trade (barter)
format. In this process, the seller is the one who provides the improvements, the
goods, services ... to the buyer, in return the buyer will pay the seller a
corresponding value.
Trade exists for many reasons. Its cause is the specialization and division of
labor, in which certain groups of people focus solely on production work to
provide applications for goods or services that belong to a particular field.
exchange for goods or services of another person group.
Trade also exists between regions because differences between these
regions provide comparative or absolute advantage in the production of
commercial goods or services or otherwise. The differences in the dimensions of
the area (eg population) allow an advantage in mass production to be obtained.
Therefore, trading at market prices benefits both regions. "The term Trade
should be interpreted in a broad sense to cover matters arising from any
commercial relationship with or without contract. Commercial relationships
include the following transactions. this: any commercial transaction for the
supply or exchange of goods or services; distribution agreements; trade agent or
agency, commission trust; long-term lease; construction of facilities submission;
consulting; engineering; investment; financing; banking; insurance; exploitation
or concession agreements; joint ventures of industrial or commercial
cooperation; freight or passengers by sea, air, rail or land. "

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Goods and services, also known as e-commerce, e-comm or EC, is the
purchase and sale of products or services on electronic systems such as the
Internet and computer networks. Goods and services based on a number of
technologies such as electronic money transfer, supply chain management,
Internet marketing, online transaction processes, electronic data exchanges
(EDI), management systems inventory, and automatic data collection systems.
Modern goods and services often use the World Wide Web as a must-have point
in the transaction cycle, although it can cover a wider range of technologies such
as email, mobile devices. like a phone.
Goods and services are generally seen in aspects of e-business. It also
includes the exchange of data that facilitates the financial sources and payment
aspects of business transactions.
Digital commerce is a form of organizing and carrying out commercial
activities on communication networks - computers, especially on the Internet.
With the rapid development of information technology and solutions to secure
and secure online information, most of the basic processes of commercial
activities such as transactions, marketing, advertising, negotiation, Contract
agreement, payment, delivery tracking ... can be done quickly and securely by
exchanging email on the Internet. TMSH is likely to become the primary form
of global trade in the coming years.
2.1.2 People's consumption habits
Consumer behavior (also commonly known as buying habits) is the study
of individuals, collectives or organizations and the processes they use to choose,
engage, use, and dispose of. products, services, experiences, or ideas to satisfy
the needs and effects of these processes on consumers and society. This course
covers knowledge from fields such as psychology, sociology, anthropology,
marketing and economics. Strive to understand how shoppers' decision-making,
both individually and collectively, as emotions drive buying behavior. It studies

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the characteristics of the buying individuals such as demographics or personality
and the variation in core buying behavior to understand people's wishes. This
course also tries to identify the influence of family, friends, sports, society, and
reference groups in general on customers.
Research on customer behavior is based on buying behavior of customers,
customers play 3 separate roles: users, payers and buyers. Research has shown
that it is difficult to predict customer behavior, even being expert in the field.
The marketing relationship is a valuable source of influence on customer
behavior analysis because it has an interest in rediscovering the true meaning of
marketing by reaffirming the importance of customers. goods or buyers. An
even greater importance has also been placed on customer retention, customer
relationship management, personalization, customization and one-to-one
marketing. Social function can be classified as social preference and welfare.
Each censorship method is assumed to be a social function but if Arrow's
theorem is used for social function, the welfare function is achieved. Some
features of social function are assertiveness, neutrality, and uniqueness
(Kioumarsi et al., 2009)
2.1.3 Industrial Revolution 4.0
The Fourth Industrial Revolution (Industrial Revolution 4.0) is a
combination of technologies that blurs the boundaries between the fields of
physics, digitalization and biology.
The term "Fourth Industrial Revolution" has been applied to important
technological developments several times over the past 75 years, and is for
academic discussion. The concept of Industry 4.0 or smart factory was first
launched at the Hannover Industrial Fair in the Federal Republic of Germany in
2011. Industry 4.0 aims to make manufacturing and management intelligent in
the industry. make. The introduction of Industry 4.0 in Germany has prompted

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other advanced countries such as the US, Japan, China, and India to promote the
development of similar programs to maintain their competitive advantage.
In 2013, a new keyword "Industry 4.0" (Industrie 4.0) started to emerge as
a result of a German government report referring to this phrase to refer to the
strategy of high-tech, industry computing. production without human
involvement.
At the 46th World Economic Forum (WEF), which officially opened in the
Swiss city of Davos-Klosters, with the theme "The 4th Industrial Revolution",
the President of the World Economic Forum made a statement. a new, more
extensive definition of the German Industrial 4.0 concept. Humanity is facing a
new industrial revolution that can radically change the way we live, work and
relate to each other. The scale, scope, and complexity of this transformation are
unlike anything human has ever experienced.
Specifically, this is a "term phrase for the technologies and concepts of the
organization in the value chain" associated with physical systems in virtual
spaces, the Internet of Things (IoT) and the Internet of services (IoS).
Currently, Industry 4.0 has gone out of the German project framework with
the participation of many countries and has become an important part of the
fourth industrial revolution.
The essence of the 4th Industrial Revolution is based on digital technology
and integrates all intelligent technologies to optimize production processes and
methods; emphasizing the technologies that are and will have the biggest impact
are 3D printing technology, biotechnology, new material technology,
automation technology, robotics, ...
Industry 4, or Industry 4.0, is the current trend of automation and data
exchange in manufacturing technology. It includes physical networks, the
Internet of things, and cloud computing.

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The 4th Industrial Revolution is not only about intelligent and connected
machines and systems, but also a much broader range. At the same time are
waves of further breakthroughs in fields ranging from genomic encoding to
nanotechnology, from renewable energies to quantum computing.
Industry 4.0 facilitates the creation of "smart factories" or "digital
factories". In these intelligent factories, virtual space physics systems monitor
physical processes, creating a virtual copy of the physical world. With the IoT,
these virtual spatial physical systems interact with each other and with humans
in real time, and through IoS, users will be involved in the value chain through
the use of these services.
2.2. Theoretical background
2.2.1 Planned behavioral theory
The expected behavioral theory is proposed by Ajzen (1991), based on the
rational action theory Fishbein and Ajzen (1975). This theory suggests that, for a
behavior predicted by the performance behavior tendencies, the behavioral
tendencies include motivational factors to perform that behavior. Expected
behavioral theory model is shown in Figure 2.1.
Shape 2.1: Expected behavioral theory model
Nguồn: Ajzen (1991)
Accordingly, behavioral intent is a major impact on actual behavior and is
influenced by attitudes, entertainment, and trustworthiness of digital commerce.
Attitude
Real behavior
Behavioral
intentions
Subjective standards
Behavior control

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In which, the attitude towards the behavior is described as the degree to which
an individual rated positively or negatively for the behavior when performing, if
the attitude is positive then the behavior will be performed (Ajzen, 1991 ).
Therefore, if the reviews are positive, it will contribute to increase the intentions
of buying behavior of consumers. Ajzen (1991) argued that a person's likely
behavior can be determined according to one's attitudes. Many researchers also
agree with expected behavior theory, which is described as an overall
assessment of an individual's performance of a particular behavior (Celik &
Yilmaz, 2011). Dennis et al (2009) concluded that, buying intent is positively
affected by positive attitudes.
As for Customer's Shopping Habits, there are also numerous studies that
corroborate this expected behavioral theory for Customer Annoyance. Lam and
Cathy (2006), Hsu and Huang (2012), Song et al. (2014) have demonstrated that,
attitudes towards Customer Habits influence Customer nuisance. A customer's
shopping habit is defined as the degree to which an individual evaluates
positively or negatively, the stronger the attitudes towards the Customer's
Shopping Habits will increase Customer Annoyancy (Zarrad and Debabi). ,
2015) .When positive information is transmitted electronically, the customer
will have a good attitude towards the Customer's Shopping Habits, thereby
increasing Product Discomfort (Bhattacherjee and Sanford, 2006). The customer
is an effective predictor of a customer's decision-making when it comes to a
certain customer shopping habit (Jalilvand and Samiei, 2012). Di and Di (2012),
Jalilvand and Samie (2012) establish Comment, attitude towards Customer's
shopping habits has an impact on Customer's annoyance.
2.2.2 The theory of sowing
Cultivation theory was initiated by George Gerbner in the 60s and is
constantly evolving to suit the constantly changing media environment. The
cultivating theory holds that the media tends to cultivate reception of the beliefs,
values and attitudes it describes to the audience. This shows that, the media

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helps to improve the image of the product or service in the eyes of the customer,
the customer will also show attitude towards the product or service before
attempting to buy that product or service. Cultivation theory initially only
mentioned media as television, but the continued development of society and
technology, the theory of cultivation also applied to other forms of
communication. According to Eman (2015), the mass media fields have formed
and exist in many forms such as cable, satellite, video game and most recently,
digital commercial media. Michael (2017) said that advertising products through
television is no longer the only one, companies can apply technology in
transmitting information to consumers through Digital Commerce. is a way to
enhance the company's image and increase consumer buying intent.
On the other hand, Ahmed (2010) thinks that, for the traditional
communication method without the participation of customers, while Digital
commerce is mass, so Digital commerce is a form of is selected more and more
popular. Moreover, Gong (2012) the development of smart mobile devices has
provided tools for consumer behavior customers to easily connect while on the
go for goods and services and interact directly with the on-time time. In
addition, this also creates the appeal of digital commerce.
This shows that, Digital Commerce is a popular and highly effective
communication tool. According to Nguyen and Vo (2015), digital commerce
sites are a modern form of communication that is increasingly used by young
people around the world. Moreover, Gong (2012) said that users using digital
commerce are not only the receiver of information, but also the person sharing
information on digital commerce. Therefore, Chryssoula (2017) of advertising
through Digital Commerce represented by many other people has formed and is
growing day by day. Through digital commerce, users can easily update and
share information, so that information is easily spread to recipients.

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This shows, Digital commerce is also a form of word of mouth, but it
through the medium is digital commerce. Ramdan et al (2017) said that word of
mouth has formed a completely new form of communication by applying
internet technology, this is called electronic word of mouth communication.
Besides, Machado (2015) said that Digital Commerce provides users with a new
communication platform similar to word of mouth, can create exchange quickly
and easily spread.
Goods and services are increasingly considered as super profitable
industry, bringing great revenue for the locality. Chun and Suwannee (2018)
said that revenues from goods and services and digital trade contributed greatly
to the global economic development. Therefore, for the field of Goods and
Services, Digital Trade is a practical promotional tool.
Because according to Rosemary et al (2017), the ease and convenience of
accessing information about digital commerce also greatly supports the
development of Goods and services. Using a lot of information, digital
commerce deeply affects goods and services (Filieri and McLeay, 2014; Li and
Young, 2011). According to Huang et al (2010), compared to other services or
products, Service goods often require more information processing before
making a decision, because the purchase of Goods or Services products is
considered highly interactive by the ITN. Therefore, in order to consume Goods
and Services products, individuals must leave their daily environment and move
to another geographic area (Werthner and Ricci, 2004). Therefore, consumer
behavior customers will need to seek information to make decisions about which
destination, airline or hotel (Jeng and Fesenmaier, 2002).
With Digital Commerce, consumer behavior customers can easily access
information related to goods and services destination. Digital commerce is used
by people to chat and share information about their Goods and Services,
something that was not possible before (Senthil et al., 2013). Furthermore,

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Xiang and Gretzel (2010) point out that digital commerce is an important
component in the Goods and Services sector because the majority of digital
commerce users are interested in organizing trips. In particular, the digital
commerce medium has seen an exponential increase in internet users, as users
can easily share and spread information (Fotis et al., 2012). Mohamed and
Mahamoud (2016) believe that digital commerce has become popular in Goods
and services, because consumer behavior customers can share their online
experiences in many forms. via: forum, blog, Facebook, Twitter and YouTube ...
Similarly, Hays and Buhalis (2013) also argue, the medium of Digital
Commerce is an effective way to promote communication with customers about
consumer behavior. to chat and share information about their Goods and
services (Senthil et al., 2013) .With the development of information and
communication technology, today customers consumer behavior can be
convenient in updating and sharing information on goods and services online
(Kim et al., 2014). Digital Commerce related to Goods and Services websites
have become popular, as not only for finding information on consumer behavior,
but also for personal posting of information about their travels. last name (Xiang
and Gretzel, 2010). Fotis et al (2012) said that, due to the nature of digital
commerce is shared by internet users, information about Goods and Services is
more reliable than other traditional channels ( Goods and services website,
Goods and service company).
This shows that, through digital commerce, consumer behavior customers
can freely share information about their goods and services, for potential
consumer behavior customers. is an essential source of information to prepare
for the upcoming trip. Filieri & McLeay (2013), Huang et al (2010), Xiang &
Gretzel (2010) argue that there exists a stimulus of word of mouth for consumer
decision-making processes and consumer behavior. they are of significant
importance for Goods and Services planning. Therefore, internet users can
actively participate and disseminate the timely transmission, with such

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information that consumer behavior customers can access and prepare necessary
for their trip (Syed, 2014 ). Consumer behavioral customers making decisions
and planning for goods and services have changed their habits significantly,
because in the technology age, consumer behavior customers can easily access
information from Merchants. commercial (Xiang and Gretzel, 2010). Chun and
Suwannee (2018) argue, Digital commerce is an effective tool to influence the
entertainment and the Goods and Services intent of customers, for those who
access to commercial Digital has the opportunity to access information about
Goods and services. As Sheila and Danielle (2017) argue, today's digital
commerce media are popular tools for communicating, sharing and expressing
personal opinions and they are increasingly becoming alternatives to media.
Traditional communication in the planning process for Goods and services.
More than that, information shared on digital commerce is largely
trustworthy. Mohamed and Mahamoud (2016) argue that word of mouth is often
trustworthy, Digital Commerce has its unique feature of creating and
distributing content by users. are the people who create information, circulate,
share with each other and this creates the reliability of the information (Kevin et
al., 2012). As the amount of content and engagement increases, consumer
behavior customers are more willing to accept words from companions than
marketers (Smith et al., 2005). Moreover, O'Connor (2008) confirmed that as the
number of reviews increases, the impact of false reviews is significantly reduced
when they are overshadowed by honest reviews from consumer behavior. .
Many researchers have proven that digital commerce contributes to
improving the entertainment and positive attitudes of customers, increasing
consumer annoyance. Digital Trade Media plays an important role in various
aspects of Goods and services, especially helping behavioral customers to seek
information and make decisions, promote the image of Goods. service and
customer interaction (Zeng and Gerritsen, 2014). Digital commerce plays an
important role in destination selection, entertainment, destination-loving

16
attitudes (Di and Di, 2012) .Chun and Suwannee (2018) argue that, due to
information explosion, the amount of information an individual can access
rapidly increases, especially when the medium of Digital Commerce Digital
Commerce, Entertainment, Intent Goods and Services have a close relationship
with each other.Di and Di (2012) have found that most customers behave
Consumers are using popular digital commerce to choose hotels in their
vacation. Therefore, the Digital Commerce medium plays an important role in
customer behavior planning and decision-making (Suzanne and Paulo, 2017).
2.3. Overview of previous research
2.3.1. Research in the country
According to Hoang Luong Vinh (2015), there is an article "Predicting the
habits of Vietnamese consumers through digital commerce" master thesis,
University of Economics Ho Chi Minh City. Topic clearly stated in goods and
services, but especially in the city. In Ho Chi Minh City and Hanoi, demand for
VietGap food products in supermarket chains and convenience stores is
estimated to exceed supply. The author has emphasized the impact of digital
commerce on the habits of Vietnamese consumers through the following model:
Consumption
intentions
Information
Calculation
entertainmen
t calculation
Trust
Distraction
s

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Source: Hoang Luong Vinh (2015)
According to the research results, the exact size of the Vietnamese organic
food market has not been determined, but this market is still very small.
However, there are signs that this market is showing positive growth signals.
Studies have shown that if supply increases, many Vietnamese consumers are
willing to pay high prices for safe organic food products.
Author Vu Anh Dung (2012), "Assessment of perception and consumer
behavior of Hanoi people through digital commerce", National Economics
University.
Through the thesis, the author emphasizes that consumption is considered
the consumption trend of humanity when the environment becomes a major
disturbance of many countries around the world. As consumers pay more
attention to the environment, they attach more importance to eco-friendly
buying. The number of people willing to pay more for eco-friendly products
recently suggests that the market for eco-friendly products is expanding.
Especially through digital commerce, sellers can know consumer perception and
behavior of consumers. Some of the factors influencing digital commerce on
perception and consumer behavior include:
Entertainment
Novelty

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Source: Vu Anh Dung (2012)
Sustainable consumption or consumption is a concept introduced after the
trend of production and consumption relying mainly on natural resources has
resulted in forest depletion, water disputes, and inactive fisheries. crime, the
gradual loss of biodiversity, more and more plants and animals disappear ... In
order to achieve sustainable development, consumption is also relatively popular
in Vietnam. This is evident in the Green Growth Strategy 2011 - 2020 and
Vietnam's vision to 2050 with a focus on the following goals: Greening
production; Reduce the intensity of greenhouse gas emissions per unit of GDP
and increase the rate of using renewable energy; Greening lifestyles and
sustainable consumption.
2.3.2. Study abroad
In the world, there are many studies on the influence of communication
through ITN. Typically, the work "The impact of digital commerce on eco-

19
friendly behavior" by Jessica Lynn Schuett (2011). In addition, there are authors
studying the effects of digital commerce on the environmental protection
behavior of the public (Roshandel Arbatani, T.1, Labafi, S.1, Robati, M., 2016).
. In addition, Wei Han, Scott McCabe, Yi Wang & Alain Yee Loong Chong
(2017) have researched whether the content created by users on the ITN really
affects the promotion of friendly behavior. with the environment in the tourism
industry or not.
There have been a number of previous studies related to the effects of
digital commerce on consumer behavior. According to Ajzen's planning
behavioral theory [1] approach, studies have shown that the elements of digital
commerce that influence consumer intent and behavior include informativeness,
interpretability intelligence, trust, distractions, social-interaction. The model is
outlined as follows:
Consumer behavior
Social
interaction
Distraction
Entertainment
calculation
Information
calculation
Trust

20
Source: Ajzen [1]
On the basis of developing the theory of planning behavior in the context of
consumer research, the authors Paul et al [12] add a variable of concern to the
environment and Lee [9] add the knowledge variable. environment, or Wang's
study [18] suggests that perceptions of consumer value also have a positive
effect on consumer behavior.
Authors such as Chen and Chang [4], Gleim et al [5], give approaches from
the demographic variables, information, entertainment, trustworthiness,
interactivity - social demographics. learn. These studies show that the
availability of digital commerce affects consumer behavior, confidence in Goods
and Services, and promotes the marketing mix for Goods and Services, which
positively affects intentions and acts of purchasing Goods and services. The
model is as follows:
Consumer behavior
Social
interaction
Trust
Entertainment
calcultion
Number of
users
Information
calcultion

21
Source: Chen and Chang [4], Gleim et al [5]
In addition, Chen and Chang [4] approach to the cultural and
psychological factors of digital commerce users that affect the shopping habits
of young consumers. The study results show that collectivism has indirect
effects on purchasing intent through the intermediate variables such as
environmental concern and attitudes towards green buying behavior. The model
is shown as follows:
Source: Chen and Chang [4]
Meysam et al (2012), Mohammad et al (2013) conducted a study with the
aim of examining the impact of Digital Commerce on customer shopping habits
in Iran. Through the analytical method is a linear structure model, research has
Consumer behavior
Social
interaction
Trust
User
psychology
User culture
Social
Information

22
shown that digital commerce positively affects customer attitudes and increases
their consumer distractions.
In addition, Devi et al (2019) conducted a study to examine the impact of
electronic word of mouth on Instagram digital commerce on the form of Coffee
Goods and Services in Bali. Research results show that electronic word of
mouth on Instagram digital commerce has a positive impact on the
entertainment and annoyance of customers. Moreover, the entertainment also
contributes to increase the annoyance of customers. On the other hand, Rangga
et al (2016) is also one of the research proving that electronic word of mouth has
a positive impact on the entertainment, shopping habits of customers, and
consumption intentions. Consumers' entertainment and buying habits also have a
positive impact on customer annoyance.
Afny (2017) has also demonstrated that electronic word of mouth
positively affects customer attitudes and consumption intentions. Client attitudes
also increased their Disturbance with Marine Goods and Services in Yogyakarta.
Another study from Ramdan et al (2017) also said that electronic word of mouth
contributes to increased entertainment, customer attitudes and consumption
intentions. Customer entertainment and attitudes will positively impact
Customer annoyance.
Finally, based on the Digital Commerce (SMM) environment approach,
Maoyan et al. [10] analyze the key factors of attitudes to digital commerce that
influence consumer buying intent. That is, the external factors and the
perception of the internal factors. The results show that Attitude marketing to
digital commerce stimulates external factors and then influences the internal
cognitive factors of consumer behavior, which ultimately influences intent.
consumer purchases.

23
2.3.3. Research gap
In fact, although there has been a lot of research on the influencing factors
of digital commerce influencing consumer behavior, there are not many solid
theoretical explanations of why consumers buy or not buying goods and services
and many people, despite their environmental support, fail to perform the
behavior.
The relative lack of knowledge that makes many consumers focus solely on
environmental protection rather than purchasing Goods and Services represents
a significant gap in theory.
Previous studies on the relationship between consumer intentions and
behavior with digital commerce show that there is usually a causal relationship
between consumer intent and behavior (intentions have positive effects.
However, these studies did not show consistent results. In other words, there is
still a gap from the attitude / intention to the behavior. Thus, the impact of
intention on consumer behavior in different circumstances is different, possibly
due to the impact of a number of other factors. These factors can strengthen or
weaken the relationship between consumer intent and behavior.
Although there have been a few studies related to the factors influencing
digital commerce on consumer behavior, studies have only shown a few factors
that influence or show a number of factors. influence, but not really studied in
depth on these factors.
With the above theoretical gaps, this topic focuses on studying the factors
of digital commerce affecting consumer behavior of Vietnamese people. The
research results are expected to contribute to the government and especially
production and business enterprises the measures to promote the consumption of
goods and services in Vietnam through promoting consumer products. actual
behavior instead of just intended or having a good attitude towards consumption.

24

25
Summary of chapter 2
In this chapter, the thesis systematizes some basic issues about consumer
behavior, green products, digital commerce. These are the concepts, nature and
factor of indicators. Mention the problems that need to be resolved. Elements of
digital commerce like informativeness, entertainment, social interaction,
trustworthiness, and distraction. These factors have a direct or indirect influence
on shopping habits. The author gives hypotheses that need research, analytical
framework of the thesis. This chapter 2 has helped us generalize the whole
theoretical basis that is the foundation for the content development in the next
chapters of the whole thesis.

26
CHAPTER 3: RESEARCH METHODS
3.1. Research process
3.1.1. Research design
The research process of the thesis is done through 2 processes with 8 steps
as follows:
* Preliminary study (5 steps)
- Step 1: Identify the research problem
The first step when doing research of the thesis is to identify the problem
that needs to be done in the research.
- Step 2: Document review
Once the problem of research has been identified, the author will search
and collect relevant research documents. From there, the summary of the
previous research results to refine and inherit the study.
- Step 3: Design research model and scale
Also through the review of previous studies, research models and scales for
concepts in research models are proposed based on inheritance from previous
studies.
- Step 4: Interview the expert
From the proposed scale, the author will interview experts who are teachers
of the University ... and Director of the Ministry of Information and
Communication in Vietnam to consider the appropriateness of the author scales.
offer.
- Step 5: Complete the scale
The author synthesizes the opinions of experts, on that basis, completes the
scale for the concepts in the research model.

27
* Official research (3 steps)
- Step 6: Official survey
When the survey is adjusted, the author will conduct official interviews
with consumers in Vietnam with a sample size of 300.
- Step 7: Analyze
After the survey process, the author will enter valid survey forms and
analyze the relationship between informativeness, entertainment,
trustworthiness, annoyance, and social interaction. Association up the shopping
habits of consumers in Vietnam. The analytical methods used in this step
include: scale reliability test, discovery factor analysis, confirmation factor
analysis, linear structure model.
- Step 8: Discuss and propose governance implications
From the analysis results, the author will discuss and propose governance
implications to enhance people's shopping activities for destinations in Vietnam
through Digital Commerce.
3.1.2. Build scale
The scales used to analyze the factors are the scales used in previous
studies, translated into Vietnamese and adjusted to suit the research context. The
Likert scale with 5 levels from 1 completely disagree to 5 fully agreeing was
used in this study. The scale for the observed variables in the study is designed
according to the 5-level Likert scale:
1 - Totally disagree
2 - Disagree
3 - Neutral
4 - Agree
5 - Totally agree

28
On the other hand, the scale for the concepts in the research model,
including: information, entertainment, trustworthiness, distraction, social
interaction, and shopping habits are mainly designed by the author. redundant
from previous studies. However, During the process of researching and
referencing the studies done before, the author found that in Vietnam there is no
research on considering the impact of digital commerce on shopping habits.
Therefore, this is a very new research direction in Vietnam. Therefore, the
author will also consult with experts who are some teachers / teachers of the
Faculty of Economics - University…. and directors of the Ministry of
Information and Communications of provinces and cities in Vietnam. The expert
consultation process will be carried out after the outline report.
Specific scales of the variables are shown in Table 3.1.
Table 3.1. Content variables in the research model
CONCEPT SYMBOL
OBSERVED
VARIABLES
MEASURE SOURCE
INFORMATION
COMPUTER
INF1 Channel updated
information
promptly
Likert 1-5 Blanco et
al. (2010),
Hoang
Luong
Vinh et al.
(2013),
Tsang et
al.
(2014),
Ashmawy
INF2 Provide essential
and useful
information
channel

29
INF3 Provide more
information than
other advertising
channels
(2014)
INF4 Indicates the
brand that is being
searched
ENTERTAINMENT ENT1 Advertising
through ITN is
very interesting
al. (2010),
Hoang
Luong
Vinh et al.
(2013),
Tsang et
al. (2014),
Ashmawy
(2014)
ENT2 Advertising
through ITN is
entertaining
ENT3 Advertising
through ITN is
much more
interesting than
traditional
advertising
ENT4 The content of the
ads via ITN is
very fun
RELIABILITY CRE1 Use as reference Likert 1-5 Yaakop et

30
source for
shopping
al. (2012),
Bamoriya
et al.
(2012),
Hoang
Luong
Vinh et al.
(2013),
Tsang et
al. (2014),
Ashmawy
(2014)
CRE2 Advertising
through ITN is
trustworthy
CRE3 The choice to
accept or cancel
the ad at any time
is important
CRE4 More reliable than
traditional
advertising
CRE5 ITN should be
used for
advertising
INTERACTION -
SOCIAL
INT1 Receive
advertising
information
quickly
Likert 1-5 Radder et
al. (2010),
Yaakop et
al. (2012),
Hoang
Luong
Vinh et al.
(2013),
Ashmawy
INT2 Receive
information
regularly

31
INT3 Makes product
purchase and
access easier
(2014)
INT4 Like to advertise
through ITN
because of its
interactivity
INT5 Facilitates two-
way
communication
INT6 Like to be
interested and
shared about the
product / service
that is being used
INT7 Know what
products / services
people are
interested in
buying and using
INT8 Know which
products / services
are suitable for
your personal

32
characteristics
INTERFERENCE IRR1 Feeling bothered
when receiving
advertisements via
ITN
Likert 1-5 Tsang et
al. (2004),
Hoang
Luong
Vinh et al.
(2013),
Ashmawy
(2014)
IRR2 The content is
often annoying
IRR3 Ads through ITN
are annoying
Purchasing habits of
Vietnamese people
in industrial
revolution 4.0
TQMS1 Use ITN ads for
future purchases
al. (2010),
Noor et al.
(2013), Ho
(2013),
Seyed et
al. (2013),
Yan
(2014)
TQMS2 Look at the
products
advertised on the
ITN first
TQMS3 The richer the
content of
advertisements on
the ITN, the
higher the buying
intent
TQMS4 Comments shared
on ITN can trigger
buying intent

33
Source: Synthesized from studies
3.2 Methods of information collection
3.2.1 Methods of collecting secondary information
Secondary information used in the study includes: shopping situation of
people in Vietnam (revenue, purchases, business establishments, restaurants and
hotels); The shopping situation of people in Vietnam is collected from the
General Statistics Office, the Ministry of Information and Communication in
Vietnam.
3.2.2 Methods of collecting primary information
Determination of sampling is very important for the research process. This
work usually includes the following steps: identification of survey object,
method of sampling, determination of sample size.
a. Respondents
In order to identify the audience to be surveyed, first of all, it is necessary
to define the population of the study. According to Hair et al. (2003), the overall
study included all subjects involved in the study. The author's research focuses
on the relationship between Digital Commerce, Trust, Entertainment and Green
product destination shopping habits in Vietnam of customers and people in
Vietnam. . Therefore, the overall population of this study is customers and
people living in Vietnam. However, research has limited financial resources,
time, and accessibility to survey subjects. Therefore, the study focuses on
TQMS5 Effective
advertising can
influence buying
intent

34
surveying customers and people living in Hai Chau district, Cam Le district,
Thanh Khe district, Lien Chieu district, Ngu Hanh Son district, Son Tra district,
Hoa Vang district, Hoang Sa district.
b. Phương pháp chọn mẫu
The sampling method of this study is the non-probability sampling method:
- A convenient method is the method that the interviewer approaches any
respondents who are willing to participate in the questionnaire response (Cooper
and Chindler, 2006).
- Germination method is the method in which the interviewer selects other
survey subjects for the research sample based on the recommendation of the
initial survey subjects (Nguyen Dinh Tho, 2013). In other words, this method
allows interviewers to take advantage of social relationships to get a large
number of respondents.
The author's research selected the non-probability sampling method
because with this sampling method, it allows the author to achieve a large
sample size in the condition of limited financial resources and time.
c. Mẫu nghiên cứu
The topic uses the non-probability sampling method. In multiple linear
regression analysis, according to Tabachnick & Fidell (1991), to achieve the
best results, the sample size must satisfy the formula n ≥ 8m + 50. Where: n is
the size sample size - m is the number of independent variables of the model.
Accordingly, in this topic, there are 26 independent variables, so the minimum
sample size is 8 x 26 +50 = 258. After removing some invalid samples, at the
same time to serve research and answer purposes. To meet the sample size
requirements, the author chooses a sample size of 300.
3.3 Methods of data analysis

35
The data analysis methods used in the research include: scale reliability
testing, discovery factor analysis, confirmation factor analysis, linear structure
model.
3.3.1 Verifying the reliability of the scale
Testing the reliability of the scale through Cronbach's Alpha coefficient is a
necessary step to consider the validity and internal consistency of the designed
questionnaire (Hoang Trong and Chu Nguyen Mong Ngoc, 2008). . Cronbach's
Alpha coefficients have been considered by many researchers, Nunnally (1978),
Peterson (1994), Slater (1995), George and Mallery (2003) have shown, when
performing a reliability test, it is necessary to pay attention to The inspection
number is as follows:
- Cronbach's Alpha coefficients of the scale need to have a value greater
than 0.6 for the scale to be reliable;
- The total variable correlation coefficient of each observed variable should
be greater than 0.3. If the value is less than 0.3, it should be removed from the
scale;
- Cronbach's Alpha coefficients, if the variable type of each observed
variable needs to be less than the Cronbach's Alpha coefficients of the whole
scale, if the value is larger, the observed variable will be excluded from the
scale.
3.3.2 Analysis of discovery factors
According to Hair et al. (1998), exploratory factor analysis is a statistical
method, used to extract a set of many observable variables related to each other,
into a smaller set of variables called factor. Accordingly, Hair et al (1998) also
mentioned that when performing discovery factor analysis, it is necessary to pay
attention to the following issues:
- Test KMO to consider the suitability of research model, KMO value
needs to be in the range of 0.5 - 1 to meet suitable conditions;

36
- Bartlett's test to consider general thinking, Barlett's test values need to be
less than 0.05 to match;
- Total variance extracted shows the ability to explain the variation of the
research model, the value needs to be greater than 50% to match;
- Eigenvalues value represents the group of factors extracted;
- Factor load factor is an indicator that ensures the practical significance of
discovery factor analysis, factor load factor> 0.35 is considered to be the
minimum and sample size should be at least 350. ; The factor load factor> 0.5
reaches the minimum level if the sample size belongs to about 100 - 350
observations; If the sample size is below 100 observations, the factor load
factor> 0.75 is considered to be of practical significance.
3.3.3 Confirmation factor analysis
According to Hair et al (2010), confirmation factor analysis is a method
used to test the suitability of research data with theoretical models. Factors
analysis confirms the need to perform tests such as convergent value,
discriminant value, unidirectionality (Steenkamp and Van, 1991).
- Check the unidirection to consider the suitability of the research data with
the theoretical model. Some parameters are used to check the unidirection of
data: Chi-square (CMIN), Chi-square for adjusting degrees of freedom (CMIN /
df), index of compatibility for comparison (CFI), index Tucker and Lewis (TLI),
RMSEA index. The model is considered appropriate with market data when the
P-value of the Chi-square test is <0.05; GFI, TFI and CFI have value> 0.9;
CMIN / df <2; RMSEA <0.08 (Hu and Bentley, 1999);
- The convergence value is to consider the convergence of the scales, the
scale achieves the convergence when the normalized weights of the scales are>
0.5 and statistically significant (Anderson and Gebring, 1988);

37
- Distinguishing value to consider the difference between the concepts in
the research model, that is, the correlation coefficient between concepts on the
overall scale is different with 1 and statistically significant with the coefficient.
P <0.05;
- The reliability of the scale is tested through such values as: composite
reliability factor (pc); Total variance extracted PVC (Variance extracted);
Cronbach's Alpha coefficients. The scale achieves reliability when the pc value>
0.5; pvc> 0.5 (Hair et al., 2010) and Cronbach's Alpha coefficients> 0.6
(Anderson and Gebring, 1988).
3.3.4 Linear structure model
Linear structure model is performed to test the hypotheses in research
model. Linear structure model is like affirmative factor analysis, but linear
structure model is an extension, able to analyze complex relationships between
components in research model. Therefore, when performing data analysis in the
SEM linear structure model, it is also necessary to consider the suitability
through unidirectional tests. Besides, it is necessary to consider some other
values such as: P-value to determine the statistical significance of the research
hypothesis; standardized weight values to determine the impact between the
factors; R2 value to test the interpretability of the research model.

38
Summary of chapter 3
In this chapter 3, the thesis has presented the main contents from the
overview of the research area, research process, research samples. From the
theoretical issues in the program combined with the local characteristics, the
thesis specifically stated the research method of the topic. The main contents of
the research method such as: research process, sampling methods, data sources
used as well as analytical methods and statistical data processing for the thesis
are mentioned by the author.

39
CHAPTER 4: DATA ANALYSIS AND RESEARCH FINDINGS
In this chapter we show the basic characteristics of the data collected, and the
statistical procedures conducted to answer to the research questions formulated.
4.1. Characteristics of survey samples
With a sample size of at least 300 Vietnamese people, the author sent
questionnaires through direct interviews and email. The total number of
questionnaires collected was 342 boards. After filtering and discarding 42
unsatisfactory tables, the author was given 300 valid tables for analysis. After
entering data into SPSS software, the author encoded data and conducted sample
statistics, the results are as follows:
Table 4.1: Characteristics of survey samples
Classification criteria Amount
Account for
density in the
sample (%)
By gender 300 100
Male 187 62.3
Female 113 37.7
By age group 300 100
- Under 30 years old 60 20.0
- From 31 to 50 years old 77 25.7

40
Classification criteria Amount
Account for
density in the
sample (%)
- From 51 to 60 years old 74 24.7
- Over age 60 89 29.7
According to income / month 300 100
- Under 5 million 166 55.3
- 5 million to 10 million 90 30.0
- 10 million to 15 million 28 9.3
- 15 million or more 16 5.3
According to education level 300 100
Graduate 27 9.0
University 89 29.7
College, intermediate, profession 162 54.0
High school or lower 22 7.3
(Source: From results of processing survey data with SPSS)
The data appears to be well differentiated:
- By gender: Among the 300 surveyed Vietnamese people, 187 people are
male, accounting for 62.3% and female is 113 people, accounting for 37.7%.

41
- By age group: In terms of age, it can be seen that the survey subjects are
concentrated in many different ages, but mainly in the 2 age groups that are
from 31 to 50 years old, accounting for 25.7% and the age over 60 accounts for
29.7%.
- According to monthly income: In general, Vietnamese people belong to
all working classes, so the income level is also quite plentiful at different levels,
accounting for most of the income level of 3-5 million / month, accounting for
55.3 % and the income of 5 - 10 million / month accounts for 30%, showing that
Digital commerce of the population here is still at an average level.
- By educational level: In general, the population here has a fairly high
educational level, the part of the population with vocational training,
intermediate and college qualifications accounts for the largest proportion of
54.0%, the part of the population. with high school graduation or lower,
accounting for the smallest rate of 7.3%.
4.2. Verifying and evaluating the scale
4.2.1. Cronbach's Alpha analysis
The Likert’s 5 point scale used in measuring the answers are tested with
respect to their reliability using the Cronbach's Alpha method. The testing
results reported show that all scales are reliable (Cronbach's Alpha> 0.6).
The results of Cronbach's Alpha's 1 analysis are summarized as follows:
Table 4.2. Cronbach's Alpha Coefficient Summary Table
STT
The scale
Number of
observed variables
Cronbach's Alpha
coefficients
1 Information 3 or 4? 0.951

42
(IFN)
2 Entertainment
(ENT)
5 or 4? 0.961
3 Credibility
(CRE)
4 or 5? 0.944
4 Distractions
(IRR)
3 0.933
5 Interoperability – social
(INT)
3 or 8? 0.921
Source: Data analyzed by SPSS
- Component "Information" includes 4 observed variables IFN1, IFN2,
IFN3, IFN4 all achieve reliability due to the total variable correlation
coefficient> 0.3 (correlation coefficients are respectively 0.833, 0.845, 0.874,
0.848,).
- Component "Entertainment" includes 4 observed variables ENT1, ENT2,
ENT3, ENT4, all achieve reliability due to the total variable correlation
coefficient> 0.3 (total variable correlation coefficients are respectively 0.853,
0.883, 0.889, 0.895.).
- Component "Confidence" or “Credibility”? Choose only one in the
table and in the text includes 5 variables CRE1, CRE2, CRE3, CRE4, CRE5
all achieve reliability due to the total variable correlation coefficients> 0.3,
(correlation coefficients are respectively 0.870, 0.887, 0.855, 0.882, 0.879).
- Component "Disturbance" “Distraction” or Disturbance? Choose only
one in the table and in the text including 3 observed variables IRR1, IRR2,

43
IRR3 all achieve reliability due to the total variable correlation coefficients> 0.3
(correlation coefficients are respectively 0.806, 0.868, 0.840).
- Component "Interoperability" includes 8 observed variables INT1, INT2,
INT3, INT4, INT5, INT6, INT7, INT8 all achieve reliability due to the total
variable correlation coefficient> 0.3 (correlation coefficients are respectively
0.846, 0.888, 0.810, 0.856, 0.883, 0.820 0.848, 0.828).
Therefore, the scales of the factors are reliable, and we can proceed with
the next step of the analysis, to analyze the discovery factor Exploratory Factor
Analysis (EFA).
4.2.2. Exploratory Factor Analysis (EFA)
4.2.2.1. EFA analysis of independent variables
Analysis results Cronbach's Alpha has identified the reliability of the 5
independent variables included in the analysis of discovery factors EFA. The
independent variable "Informationality" has 4 observed variables, the variable
"Entertainment" has 4 observed variables, "Confidence" has 5 observed
variables, "Disturbance" has 3 observed variables, Social - interaction "has 8
observed variables. Thus, the total number of observed variables included in the
analysis is 21 variables.
In the following statistical analysis we considered two type of test: KMO
test has been used to measure the sampling adequacy, to assess the
appropriateness of using the factor analysis on the data collected; The Bartlett
test is a test for the absence of correlation between the variables considered in
the analysis.
The specific results are as follows:
KMO and Bartlett's tests in factor analysis show that KMO coefficient =
0.917 (> 0.5) shows suitable data for factor analysis.

44
The Chi - square statistic of the Bartlett test reaches 14427,001 with a
significance level of 0.000, so the observed variables are correlated with each
other on the overall scale.
The extracted variance of 81.10% shows that the 9 drawn factors explain
81.10% of the variation of the data, so the drawn scales are acceptable.
It is optimal to stop considering additional factors when extracting the
factors at factor 9 shows the eigenvalue = 1.176
All observed variables have the appropriate factor loading coefficient
(factor loading)> 0.3. The results of the EFA analysis are summarized as
follows:
Table 4.3: Summary table of EFA analysis results on quality of life scale
STT
Out Factor
1 2 3 4 5
1 ENT5 .817
2 ENT3 .780
3 ENT4 .763
4 ENT2 .750
5 IFN4 .741
6 IFN1 .825
7 IFN3 .807

45
8 IFN2 .793
9 CRE4 .715
10 CRE3 .691
11 CRE2 .679
12 CRE1 .679
13 CRE5 .718
13 IRR2 .892
14 IRR3 .884
15 IRR1 .871
16 INT3 .715
17 INT2 .691
18 INT1 .679
INT4 .718
INT6 .681
INT5 .695
INT8 .785
INT7 .891

46
KMO: 0.917
Bartlett’s (Sig.): 0.000
Phương sai trích: 81,10%
Source: Data analyzed by SPSS of the author
The results show that Cronbach's Alpha coefficients of the two scales are
greater than 0.6 and the overall correlation coefficient is greater than 0.3, so the
new scale is suitable for the next test.
4.2.2.2. EFA analysis of dependent variable
The author conducts factor analysis for the dependent variable "Shopping
Habit". This variable is measured by 5 observed variables TQMS1, TQMS2,
TQMS3, TQMS4, TQMS5. The results are summarized as follows:
STT Out Factor
1 TQMS1 0.934
2 TQMS2 0.916
3 TQMS3 0.916
4 TQMS4 0.911
5 TQMS5 0.910
Cronbach’s Alpha 0.960
KMO 0.866
Bartlett’s (Sig.) 0.000

47
Citation variance 85.539%
The specific analysis results are as follows:
KMO and Bartlett testing in factor analysis shows that KMO coefficient =
0.866 (> 0.5) shows suitable data for factor analysis.
Bartlett's Chi – square statistic of Bartlett's test reaches 2157.758 with
significance 0.000, that is satisfactory.
The variance extracted from 83.539%> 0.3 meets the requirements
All observed variables have the appropriate factor loading> 0.3.
In general, the appropriateness of the exploratory factor analysis (EFA) is a
good condition in order to proceed to the next step of analysis. We will then run
a regression analysis with shopping habits as dependent variable explained on
the basis of the independent variables found so far.

48
4.3. Multiple regression analysis
To conduct a regression analysis, a factor value is required. According to
Nguyen Dinh Tho (2011), it is not advisable to use the factor values directly
generated by EFA analysis in the regression, but the best method is to use the
sum or the average of the observed variables. Therefore, the mean of the
measurements on the variables has been used. At the same time, the author also
proceeds to name the new variables:
Table 4.4. Name of the variables
(names have to be short and clear. You used different names in the table and in the text.
Furthermore, do not report the word calculation. Add a third column with an English name for
them)
STT Factor Variable name
1 Information calculation Tinhthongtin
2 Entertainment calculation Tinhgiaitri
3 Credibility Sutincay
4 Distractions Suphiennhieu
5 Interoperability - Social (INT) Tinhtuongtacxahoi
6 Shopping habits Thoiquenmuasam
4.3.1. The correlation matrix between variables
Before conducting a regression analysis, it is worth considering the
correlation among the variables involved through the study of the correlation
matrix. All the variables are included in the correlation analysis, both the
independents and the dependent one.
We report below the results relative to the correlation matrix.
Table 4.5 Correlation coefficient matrix

49
(the table is wrongly presented, please correct it)
A correlation matrix has all 1 on the diagonal and correlation among different variables
elsewhere. Here we see the 1 in the wrong cells.
The number have to be presented in a clear format (see below theone I suggest you to use)
The name of the variables should be reported in English as well
The number of 300 observation is redundant and it doesn’t help to read the data, therefore it is
good to take it out
The table should be easy to read, this is not!
Thoique
nmuasa
m
Tinhtho
ngtin
Tinhgiait
ri
Sutincay
Suphienn
hieu
Tinhtuon
gtacxaho
i
Thoique
nmuasa
m
Pearson
Correlati
on
1 ,651**
,672**
,768**
,672**
,651**
Sig. (1-
tailed)
,000 ,000 ,000 ,000 ,000
N 300 300 300 300 300 300
Tinhthon
gtin
Pearson
Correlati
on
,672**
,639**
1 ,676**
1 ,639**
Sig. (1-
tailed)
,000 ,000 ,000 ,000
N 300 300 300 300 300 300
Tinhgiait
ri
Pearson
Correlati
on
,768**
,684**
,676**
1 ,676**
,684**
Sig. (1-
tailed)
,000 ,000 ,000 ,000 ,000

50
N 300 300 300 300 300 300
Sutincay Pearson
Correlati
on
,425**
,204**
,434**
,354**
,434**
,204**
Sig. (1-
tailed)
,000 ,000 ,000 ,000 ,000 ,000
N 300 300 300 300 300 300
Suphienn
hieu
Pearson
Correlati
on
,672**
,639**
1 ,676**
1 ,639**
Sig. (1-
tailed)
,000 ,000 ,000 ,000
N 300 300 300 300 300 300
Tinhtuon
gtacxahoi
Pearson
Correlati
on
,768**
,684**
,676**
1 ,676**
,684**
Sig. (1-
tailed)
,000 ,000 ,000 ,000 ,000
N 300 300 300 300 300 300
Better to not write it, it is not a source
The table has to be presented in this way

51
From the calculated correlations between the independent variables
(Tinhthongtin, Tinhgiaitri, Sutincay, Suphiennhieu, Tinhtuongtacxahoi) and the
dependent variable (Thoiquenmuasam), we see that the variables considered as
independent in the regression show a quite good correlation with the dependent
variable Thoiquenmuasam, constituting a positive starting point for a regression
analysis. However, the quite high (0.684) correlation coefficient between
Thongtin and Sutincay variables suggests us to consider a possible problem of
multicollinearity among the regressors.
4.3.2 Results of regression analysis
The independent variables (Tinhthongtin, Tinhgiaitri, Sutincay,
Tinhtuongtacxahoi) and the dependent variable (Thoiquenmuasam) are included
in the regression in order to estimate the coefficients of the linear equation, and
to run test of hypothesis. We will test, for any independent variable, that its
coefficient is statistically different from zero and, in that case, we will consider
Thoiquenmuasam Tinhthongtin Tinhgiaitri Sutincay Suphiennhieu Tinhtuongtacxahoi
Pearson
Correlation
1 0,651** 0,672** 0,768** 0,672** 0,651**
Sig. (1-tailed) 0,000 0,000 0,000 0,000 0,000
Pearson
Correlation
0,672** 0,639** 1 0,676** 1 0,639**
Sig. (1-tailed) 0,000 0,000 0,000 0,000
Pearson
Correlation
0,768** 0,684** 0,676** 1 0,676** 0,684**
Sig. (1-tailed) 0,000 0,000 0,000 0,000 0,000
Pearson
Correlation
0,425** 0,204** 0,434** 0,354** 0,434** 0,204**
Sig. (1-tailed) 0,000 0,000 0,000 0,000 0,000 0,000
Pearson
Correlation
0,672** 0,639** 1 0,676** 1 0,639**
Sig. (1-tailed) 0,000 0,000 0,000 0,000
Pearson
Correlation
0,768** 0,684** 0,676** 1 0,676** 0,684**
Sig. (1-tailed) 0,000 0,000 0,000 0,000 0,000
Thoique
nmuasa
m
Tinhthon
gtin
Tinhgiait
ri
Sutincay
Suphien
nhieu
Tinhtuon
gtacxaho
i

52
the sign of the coefficient. The sign and the magnitude of the regression
coefficients inform us about the nature of the relationship between that
independent variable and the dependent one. For example, we expect to find a
negative value for the variable, being negative its impact on shopping habits.
When evaluating a multiple linear regression models, we consider the value
of the determination coefficient R2 (R-squared), to understand which percentage
of the total variability of the dependent variable is explained by the set of
regressors. The R2 adjusted show the determination coefficient net of the effect
of the number of regressor considered. Based on our estimates we get a
coefficient of determination R2 bigger then 0.7, showing an overall good
explanation of the shopping habits.
Table 4.6. Regression summary table
Paradigm R R2
Radj2
Predicted standard
deviation
1 .854a
.729 .721 .38567
Dependent variable: Thoiquenmuasam
Table 4.7. ANOVA analysis
Paradigm
Sum of
squares Df
Average
squared
F Sig.
1
Regression
116.00
7
9 12.890 86.658 .000b
The remainder 43.135 290 .149

53
total
159.14
2
299
Source: Data analyzed by author's SPSS, Enter method
The results of regression analysis shows that R2 is 0.729, so the research
model offers an overall good explanation. At the same time, adjusted R2 is
smaller than R2, using this coefficient to evaluate the suitability of the model
will be safer and more accurate. Corrected coefficient R2 is equal to 0.721, so
the model explains 72.1% of the impact of the independent variables on the
dependent variable (Thoiquenmuasam). The remaining 27.9% is due to the
impact of other factors not included in the model.
The F-test is a test relative to the hypothesis of the absence of regression,
implying all the coefficients of the regression equation not statistically different
form zero. F value is 86.658, at the satisfactory significance level (Sig. = 0.000
<0.05), so the regression model is suitable and the results are valuable.
In addition, the criteria for measuring multicollinearity phenomena with
VIF variance magnification coefficients of independent variables in the model
are:
IFN (2,645), ENT (2,528), CRE (2,901), IRR (1,333). , INT (1,236)
showing that the multicollinearity among the independent variables is negligible
and the variables in the model can be analyzed together in the regression.

54
Table 4.8. Regression results table
the numbers of this table are not correct please check with the excel file attached tp the email. I
have changed them accordingly; format correctly the table replacing the comma with a point
Paradigm
B
Standard
deviation
β
standardized Tstatistic Sig.
Multi-collinear statistics
T -
acceptability
VIF -
Variation
magnification
factor
1
(Constant) -0,300 0,238 -2,098 0,037
Tinhthongtin 0,366 0,050 0,167 7.32 0,001 0,396 2,528
Sutincay 0,172 0,051 0,366 3.37 0,000 0,345 2,901
Suphiennhieu -0,161 0,040 -0,143 -4.03 0,000 0,750 1,333
Tinhgiaitri 0.153 0.030 0.135 5.10 0,000 0,345 2,528
Tinhsanco 0.142 0.020 0.124 7.10 0,000 0,750 2,901
Dependent variable: Thoiquenmuasam
Source: Results of regression analysis by SPSS of the author
Regarding the multicollinearity test, we can see from the table 4.8 above
that the VIF is less than 10, and according to Hoang Trong, Chu Nguyen Mong
Ngoc, it can be concluded that there is no multicollinearity phenomenon
occurring in the regression model and the variables in the model can be
considered all together.

55
4.3.3. Check the suitability of the model
Durbin-Watson (DW) test
The DW test can be considered as a test assessing the overall correct
specification of the regression. A value near to 2 is optimal and it indicates that
both the equation has no problem with serial correlation and the model is overall
correctly specified. According to the results table 4.9
Table 4.9. Model summary table
Paradi
gm R R2
Radj2
Predicted
standard
deviation
Durbin-
Watson
1 ,854a
,729 ,721 ,38567 1,876
The value of DW = 1.876 is a pretty nice result, indicating an overall good
specification of the regression model.
Furthermore, (The statistical significance level is 95%, k = 9 (9
independent variables) and N> 200 (N = 300 observations), so we choose N =
200 in the) ALL THIS TEXT HAS NO STATISTICAL MEANING EITHER
ELIMINATE OF EXPLAIN. BUT WHT NOW BECOMES 200? THIS IS NOT
CONSISTENT! Durbin-Watson Statistic lookup table.
We have dL = 1,675 and dU = 1,863
So d = 1,876> dU = 1,863
We have 4 - dU = 4 - 1,863 = 2,137
Because: dU = 1,863 <d = 1,876 <4 - dU = 2,137

56
According to Hoang Trong and Chu Nguyen Mong Ngoc, it can be
concluded that the hypothesis does not have first-order series correlation.
Test for equality of variance (constant)
The author used the Spearman test method to determine if the overall
variance between the variable changes does not change:
Hypothesis H0: The correlation coefficient of the population is 0
If the test results do not reject H0 => conclude that the variance of error
(residual) does not change. Thus, the implementation of regression according to
OLS method is acceptable.
In the table we have Sig. of each pair of variables> 0.05 => accept H0 =>
Variance of unchanged error => regression model is reached. (see table below)
Standardized residual distribution test:
We have a table of SPSS data results:
Table 4.10: Residuals Statisticsa
Minimum Maximum Mean
SCRE.
Deviation N
Predicted Value 1,9114 5,1871 3,2733 ,62288 300
SCRE. Predicted
Value
-2,187 3,072 ,000 1,000 300
Standard Error of
Predicted Value
,033 ,132 ,068 ,017 300

57
Adjusted Predicted
Value
1,9013 5,2042 3,2740 ,62224 300
Residual -1,31414 ,93920 ,00000 ,37982 300
SCRE. Residual -3,407 2,435 ,000 ,985 300
Stud. Residual -3,484 2,529 -,001 1,003 300
Deleted Residual -1,37368 1,01314 -,00064 ,39431 300
Stud. Deleted
Residual
-3,553 2,553 -,002 1,007 300
Mahal. Distance 1,200 33,947 8,970 5,109 300
Cook's Distance ,000 ,055 ,004 ,007 300
Centered Leverage
Value
,004 ,114 ,030 ,017 300
Dependent Variable: Thoiquenmuasam
The table above shows the mean of the Mean = 0 balance and the standard
deviation of the SCRE residual. Deviation = 0.985 is almost equal to 1.
According to Hoang Trong and Chu Nguyen Mong Ngoc, it can be concluded
that the normal distribution hypothesis is not violated. (see picture below)

58

59
4.3.4. Test hypothesis of the model
The results reported so far show that the variables considered in the
analysis, Tinhthongtin, Tinhgiaitri, Sutincay, Suphiennhieu, Tinhsanco. jointly
explain the shopping habits of the Vietnamese consumers. Four out of the five
variables Tinhthongtin, Tinhgiaitri, Sutincay, Tinhsanco, exert a positive effect
on the dependent variable, Thoiquenmuasam, due to the positive Beta
coefficient. The variable Sutincay has the strongest impact, with the larger Beta
coefficient of 0.375. the variable Suphiennhieu, exerts a negative effect on the
dependent variable as expected, since its beta is negative. Thus, in general, it can
be concluded that if the people's assessment of informativeness, entertainment,
trustworthiness, increases, the increasing shopping habits (considering the
change of one variable, the others assume constant). If distraction increases, the
shopping habits decrease. All the sign and the hypothesis are respected.
Thus, the regression equation of the model showing the relationship
between the factors affecting Shopping habits is:
Y = - 0.300 + 0.366 * CRE + 0.172 * IFN - 0.161 * IRR + 0.153*ENT + 0.142*INT
From the regression equation shows that Shopping habits have a
relationship with the factors: ENT, CRE, IRR, IFN, INT.
The highest level of influence on shopping habits is Confidence factor
(CRE has B = 0.366, impact in the same direction), information factor (INF has
B = 0.172, impact in the same direction). The distraction factor (IRR has B = -
0.161, opposite effect), entertainment factor has the same effect, ENT has B =
0.153), interactive factor of ITN acts in the same direction (INT has B = 0.142).

60
Table 4.11: Summary of the impact of the factors affecting Shopping habits
Factors Credibility
Distractions
Entertainment
calculation
Information
calculation
Interoperability - social
Impact
+ - + + +
Explain the meaning of the research results
The information factor increased to 1 unit, the consumption intention
increased by 0.172 units, which means that people will shop more from the
people.
When entertainment is increased to 1 unit, people will shop from people
more than 0.153 units, meaning that when entertainment is fully satisfied, people
will shop more from people. Therefore, the entertainment factor is one of the
very important factors that need to be considered when evaluating shopping
habits. Accordingly, if an advertisement via ITN brings entertainment to
consumers as much as possible, their shopping intentions will increase.
It can be seen that the basic and mandatory requirement for advertising
programs and activities is the ability to provide information and entertain
viewers. Only when consumers perceive these two values can they create a
positive attitude toward advertising. Entertainment is not only measured by
activities that bring pleasure and comfort to consumers, but it is also shown in
the content that the advertisement is conveying, the simplicity, ease of
understanding and originality. is a way to help users not feel bored when
receiving advertising information.

61
When social interaction increases by 1 unit, people will buy 0.153 more
units from the people. Social interaction is demonstrated by the fact that users
can use various types of text, images, videos and links to follow and share new
products with other users. At the same time, highly interactive messages to
social communities, from influential individuals, will have a strong impact on
consumers' attitudes as well as behavior.
When the distraction increases to 1 unit then people will buy 0.161 units
less.
Thus, in order to improve the shopping habits in Vietnam, there will be a
need to improve the factor Information per capita (IFN), Entertainment (ENT),
Confidence (CRE), the interactivity of the ITN of green products. (INT) and
Distraction Reduction (IRR) of digital commerce.
4.5 Discussion of research results
Through the use of analytical and data processing methods, the author has
synthesized the impact of the identified factors related to the digital commerce
on shopping habits: Information, Entertainment, Reliability. Social - interaction
has a positive impact on shopping habits; the factor distraction has a negative
opposite impact on shopping habits. More in particular, a change in one point in
credibility has a double effect on shopping habit compared to all the other
variables considered. This is an important result, useful to propose practical and
meaningful solutions for competent authorities in Vietnam in order to take
measures and enhancing the impact of digital commerce on shopping habits.

62
SUMMARY OF CHAPTER 4
In Chapter 4, the results of analysis and research of the 5 factors affecting
the digital shopping intentions are considered. The level of satisfaction in the
life of Vietnamese people are considered to be affected by: distractions,
Credibility, behavior for the destination. The results of regression analysis
through SPSS20.0 software showed that there are 3 mentioned factors affecting
the satisfaction level of Vietnamese people; in which: the factor `` reliability ''
has the highest impact on shopping habits and the factor of distraction. The
Independent Sample T - test is used through which shows no difference between
men and women, no difference in age in the level of life satisfaction of
Vietnamese people. Independent Sample T test - test again shows the difference
in qualifications and income to the level of people's satisfaction in life. These
results help the author make solutions in Chapter 5 more effectively.

DIGITAL COMMERCE SHAPE VIETNAMESE SHOPPING HABIT IN 4.0 INDUSTRY

DIGITAL COMMERCE SHAPE VIETNAMESE SHOPPING HABIT IN 4.0 INDUSTRY

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