Factors affecting live streaming shopping intention invietnam the case of fashion products

VIETNAM NATIONAL UNIVERSITY, HANOI
VIETNAM – JAPAN UNIVERSITY

NGUYEN PHUONG ANH

FACTORS AFFECTING LIVE STREAMING SHOPPING
INTENTION IN VIETNAM: THE CASE OF FASHION PRODUCTS

MAJOR: BUSINESS ADMINISTRATION

RESEARCH SUPERVISORS:
Assoc. Prof. NHAM PHONG TUAN
Prof. PEIJUN GUO

HANOI, 2021


ACKNOWLEDGEMENT
The Master's thesis could be completed thanks to the strong supports of many
people who contributed with great guidance and willingness for this study.
First of all, I would like to express my sincere appreciation to my honor
supervisors Professor Peijun Guo and Assoc. Prof. Nham Phong Tuan for their advice
and kind cooperation. Their valuable recommendations with their deep and wide
knowledge have led my research in an appropriate way.
I would like to thank not only my program – Master Business Administration and
all staff in the program including Ms Huyen Huong – the assistant of MBA program,
Hino Sensei, Hanh Sensei, and Lien Sensei but YNU IPO staffs also for their help
throughout the whole period of the study. I will never forget my beloved teachers namely
Matsui Sensei, Guo Sensei, Tanabu Sensei, Morita Sensei, Inoue Sensei, Kodo Sensei,
Heller Sensei, Yang Sensei, Sakakibara-san, Mizuno-san, and other Professors from
YNU.

Last but not least, I am so grateful for my family and my friends at VJU, always
being on my side and encourage me to go to the end of this journey. Hence, It is my very
lucky to have all of you in my whole life.
Once again, thank you all.
Ha Noi, May 2021,
Nguyen Phuong Anh.

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ABSTRACT
Nowadays, most people become familiar with live streaming which is one of the
new trends in the digital era. Along with the development of Internet connection speed
and terminal configuration, streaming services will be applied more to entertainment,
online meetings and online sales. Furthermore, the combination of live streams and ecommerce could create an industry worth tens of billions of US dollars. The present
research examines some factors influencing the shopping intention to use live streaming
services in Vietnam. To this end, the study fills the research gaps by applying S-O-R
framework with important determinants including streamer attractiveness, information
quality, interactivity and trust. Using the data collected from 332 valid questionnaires, the
proposed model was empirically assessed by partial least square (PLS) SEM. The study‟s
findings suggest that the relationships between information quality, interactivity and
intention to shopping through Live streaming commerce are fully mediated by trust.
Whereas, streamer attractiveness has a significant impact on both trust and Live
streaming shopping intention.
Keywords:
Streamer attractiveness, Information quality, Interactivity, Trust, Live streaming
Shopping Intention

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TABLE OF CONTENTS

CHAPTER 1. INTRODUCTION ........................................................................................ 7
1.1. Research background .................................................................................................... 7
1.2. Research objectives ...................................................................................................... 9
1.3. Research scope ............................................................................................................. 9
1.4. Research structure......................................................................................................... 9
CHAPTER 2. LITERATURE REVIEW ........................................................................... 11
2.1. Related Definitions ..................................................................................................... 11
2.2. Research conceptual model ........................................................................................ 20
CHAPTER 3. RESEARCH METHODOLOGY ............................................................... 23
3.1. Research process......................................................................................................... 23
3.2. Questionnaire Construction ........................................................................................ 24
3.3. Sample and data collection ......................................................................................... 27
3.4. Data analysis ............................................................................................................... 28
CHAPTER 4. DATA ANALYSIS .................................................................................... 33
4.1. Measurement Model Test ........................................................................................... 33
4.2. Cronbach‟s Alpha measurement................................................................................. 36
4.3. Exploratory Factor Analysis (EFA)............................................................................ 36
4.4. Structural Equation modeling (SEM) ......................................................................... 41
CHAPTER 5. CONCLUSION .......................................................................................... 47
5.1. Discussion on Findings ............................................................................................... 47
5.2. Contribution of Research ............................................................................................ 49
5.3. Practical implication ................................................................................................... 50
5.4. Limitations and Future Research Directions .............................................................. 51
REFERENCES .................................................................................................................. 52

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LIST OF TABLES
Table 4.1. Cronbach‟s Alpha result 1 (Analyzed by SPSS) .............................................. 36
Table 4.2. EFA results of Stimulus Scale .......................................................................... 37
Table 4.3. EFA results for Organism scale........................................................................ 39
Table 4.4. EFA results for Organism scale........................................................................ 40
Table 4.5. Outer Loading of the constructs ....................................................................... 42
Table 4.6. Construct Reliability and Validity.................................................................... 42
Table 4.7. Correlation among Constructs and AVE square root ....................................... 43
Table 4.8. R square results ................................................................................................ 44
Table 4.9. Mean, STDEV, T-Values, P-Values ................................................................ 44

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LIST OF FIGURES

Figure 2.1. Research conceptual model ............................................................................. 22
Figure 3.1. Research process proposed by the author ....................................................... 23
Figure 4.1. Model results ................................................................................................... 46

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CHAPTER 1. INTRODUCTION
1.1. Research background
Nowadays, most people become familiar with live streaming which is one of the
new trends in the digital era. People can be easy to approach live streaming app and
broadcast many activities on this platform including gaming, singing, and selling at
anytime and anywhere. According to Brands Vietnam, Lazada and Shopee encourage

live-stream activities to increase sale volume during COVID 19. In September 2020,
viewers rise 21 times, and the number of customers who buy via Live stream increase 24
times, compared with the same period last year. Moreover, based on the prediction of the
Vietnam E-commerce Association, the rapid growth of e-commerce could maintain over
30% and the scale of this market could express 15 billion USD.
Not similar to other forms of social media, according to Lie et al (2018), they
mentioned that live streaming can be integrated by features including video content,
consumption and real-time communication. Furthermore, e-commerce has been shifted
by the innovative live streaming commerce, which is a social, hedonic, and customercentered environment instead of a product-oriented shopping environment as before
(Busalim 2016, Wongkitrungrueng et al., 2018). Live-streaming commerce can be seen
as a novel business model that provides a range of stimuli to attract consumers to
immerse in shopping.
There are many platforms that provided video streaming including Twitch,
Facebook, Youtube and Instagram. Based on the statistics of Restream, Facebook
experienced the largest live streaming website worldwide, with 2.5 billion active monthly
users in the fourth quarter of 2019. However, 70% of people prefer to watch live
streaming on Youtube, according to Vimeo in 2020. China is one of the biggest markets
for the development of live-streaming. In March 2020, China Internet Network
Information Center informed that the figure of live-streaming users in this country has
reached 560 million, rising 163 million from the end of 2018, accounting for 62 per cent

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of the total netizens. And the live streaming‟s market size in China reached 16.3 billion
U.S. dollars in 2020, which was published in the market research report of Statista.
Vietnam is also the potential market to develop live-streaming commerce. Based
on the data of Nielsen Vietnam (2019), there were 62 million active social media users
and 58 million people were mobile social media user, accounting for 64 per cent of the
total population. The average daily time viewing broadcast, streaming and video on

demand was 2 hours 31 minutes. In addition, 99 per cent of internet users watched videos
online. In one of the interviews with Tran Tuan Anh - managing director of Shopee
Vietnam which is one of the e-commerce giant, he shared that many brands and sellers
considered Shopee Live as a vital tool to meet evolving demands and promote their
product effectively. There was a 70 per cent rise in the total duration of Shopee
Livestream in April from February 2020.
It could be obvious that live-streaming commerce becomes more popular;
nevertheless, live-streaming has not received much research attention and explored fully.
Previous literature on live-streaming has mainly focused on addressing customer‟s
engagement, people‟s continuous watching intention and drivers affecting behavior of the
customer. There is not much comprehensive study investigate what factors or how
contextual cues influence live streaming customer behavior from a customer environment
interaction perspective (Wongkitrungrueng et al; 2018). Moreover, the current affecting
factors may be different from those that have been explored in past studies and the
different context.
Therefore, Live stream should be considered seriously to create attract people to
purchase products. And it is important to understand factors affecting customer‟s
intention in live streaming shopping. In the research, I would like to focus more on livestreaming shopping intention in the case of fashion products to have a detailed view of
the live-streaming shopping intention of Vietnamese consumer.

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1.2. Research objectives
Derived from the context described above, this present research aims to contribute
prior studies on identifying factors affecting live-streaming shopping intention in
Vietnam. The case of fashion products.
In particular, the present study examines the proposed framework from previous
theoretical studies to understand determinants on consumer‟s live streaming purchasing
intention based on SOR theory.

Based on the objective, I formulated two questions to conduct the research:
- What factors affect Live-streaming purchasing intention of consumers?
- How do these factors impact consumer shopping intention?
1.3. Research scope
Content scope: Factors affecting live streaming shopping intention in Vietnam.
The case of fashion products.
Place scope: all the locations in Vietnam.
Time scope: October 2020 to May 2021.
1.4. Research structure
The study has 5 chapters, including:
The first chapter introduces the research background which is the circumstance as
motivation for this research to be conducted.
The second chapter is on the literature review. This chapter presents related
definitions, gives an overview of previous studies on consumers‟ live streaming
purchasing intention, literature gap, which are the foundation for developing hypotheses.
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The third chapter is about research methodology, design and procedure, pilot test,
survey adjustment, variables measurement, data collection and analysis method.
The fourth chapter is data analysis and results.
The final chapter is a discussion on findings, limitations of this research, the
recommendation for future studies and implications if there is any.

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CHAPTER 2. LITERATURE REVIEW
2.1. Related Definitions
2.1.1. Live-streaming

Today, the concept of live-streaming has emerged as the latest trend in the
economy. Live-stream selling is expected to be a key competency that both individual
sellers and e-commerce players need to develop in the very near future because of its
distinguishes. In the research of Smith et al (2013), they found that Live video streaming
service was different from other social media types by the appearance of broadcasters or
streamers. In the context of China, Lisa Magloff (2020) described Taobao‟s live
commerce as a place where hosts would try on different clothes or items and
communicate with users through live chats. With the same idea, the study of both Bründl
et al (2017) and Deshpande & Hwang (2001) mentioned live-streaming commerce as
synchronous communication which was explained as viewers would observe customers
observe a seller‟s behaviors including verbal and nonverbal and their identity.
Furthermore, while a live stream allowed streamers to interact with many customers at
the same time, those customers could respond through write communication. Thus, live
streaming commerce refers to the combination of live streaming video and e-commerce
to sell products in the streaming (Wang, 2017). It is related to real-time social interaction
(including real-time video and text-based chat channels) (Cai & Wohn, 2019).
In another view from Singh et al (2020), the authors showed that streaming
services were also known as an entertainment alternative to the traditional model of
broadcasting services because of their better quality and variety of contents. In livestreaming platforms, users create their own content such as game playing, cooking,
painting, singing, and eating (Recktenwald, 2017) to interact with their followers, which
facilitates the rising of the emerging entertainment industry in terms of beauty vlogging
or videogames. A time killer is the commendation of Rhea Liu & Dannie Li (2016) about
the role of live-streaming in their report. Viewers spend their free time looking for
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companionship and interaction in a form that is closest to real-life communication and
forms a relaxing escape from daily stress.
In the research of Apiradee et al (2020), they mentioned that there are three kinds
of channels that Live streaming commerce can take place including live streaming

platforms incorporating commercial activities (ex Tiktok); e-commerce sites,
marketplaces (e.g. Shopee) or mobile apps integrating live streaming features; social
networking sites (SNSs) that add live-streaming features (e.g. Facebook Live) to facilitate
selling. Based on this, my research will focus on e-commerce marketplaces and social
media networks that have the live-streaming function to study about purchasing intention
of viewers in the context of fashionable products.
2.1.2. Stimulus-Organisim-Respone (SOR) Model
Mehrabian and Russell (1974) invented the S (stimulus)-O (Organism)-R
(Response) model which proposed that various environmental stimuli surrounding
individuals have an impact on individual differences in emotional experience,
consequently, influence their approach behaviors. SOR model has remained the most
popular theoretical approach to retail settings in different areas including the decision to
buy (Demangeot and Broderick, 2016), impulse buying (Chan et al., 2017), self-service
(J.-H. Kim & Park, 2019) and numerous SOR based research studied in the marketing
context showed the relationship between emotional response and consumer response in
terms of intention, purchase, consultation and return (Choi et al., 2011; Li et al., 2011).
Recently, many scholars have applied SOR framework to explore online consumer
behavior such as consumers‟ trust and online re-purchase intention (B. Zhu et al., 2019),
online atmosphere affecting consumer online behavior, consumers‟ interaction and
communication to online stores. Furthermore, in terms of live streaming commerce
research context, S-O-R framework demonstrated its appropriation through a range of
existing studies. Animesh et al. (2011) and Zhang et al. (2014) adopted S-O-R to show
different categories of environmental stimuli in e-commerce including the content of

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website, streamer attractiveness, the high quality of product information and social
stimuli (social influence). The S-O-R framework helps us examine the emotional and
cognitive states of viewers or consumers relied on environmental cues and its abilities to

impact on resulting behavior. According to Yang&Lee (2018), the services of streaming
are different from other technologies due to their great convenience and exclusiveness.
Thus, using other models like TAM and UTAUT is not enough to understand the
intention of viewers. It is needed a framework that could help explore the relationship
between factors - contextual cues and the emotion or cognition decision processes of
customers; then how these factors and making decisions impact the intention of
customers to buy products, which could be answered by the S-O-R theory.
Mei Teh Goi et al. (2014), Daunt and Harris (2012), Lin (2004), and Wong et al.
(2012) indicated that stimulus directly influence customers‟ response.
Three major determinants of the S-O-R model usually demonstrate in a variety of
dimensions; nevertheless, within the live streaming context, these elements are specified
in this study as follow:
-

Stimulus (S), which is a trigger that arouses consumers (Chan et al., 2017) consits
of streamer aattractiveness, information quality and social interaction. Streamers
are one of the necessary keys in the live streaming context. A streamer is a person
who creates critical contents, delivers useful information related to selling
products or the content and real-time social interaction with the consumer during
the broadcasting time.

-

Organism (O), which is an internal evaluation of consumers (Chan et al., 2017):
live-streaming trust.

-

Response (R), which is an outcome of consumers‟ reaction(s) toward the online
shopping stimuli and their internal evaluations (Chan et al., 2017): live-streaming

shopping intention.

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2.1.3. Streamer Attractiveness
There have many studies indicating the effect of attractiveness on tangible benefits.
It could be the relationship between physical attractiveness and job offer [Michael et
al.(2009)], future success [Vicki et al. (1992)] and high-status social groups [Anne et al.
(2011)]. Moreover, interpersonally attractive individuals could be considered credible
source and more likely to receive a positive evaluation. Wohn et al., (2018) stated that
characteristics related to streamers including interpersonal attractiveness and physical
attractiveness were associated with the viewer‟s emotional support which referred to the
giving of affection, encouragement, or caring. Furthermore, Xu et al., (2020) mentioned
streamers as “endorsers” of the product or brand in live streaming commerce. On the
other hand, Baker and Churchill (1977) suggested that attractive endorsers are more
successful in adjusting consumer‟s attitudes and beliefs in a product. With the same idea,
Frevert & Walker, 2014 revealed that beautiful people are believed to be more popular
and more highly evaluated than less attractive people.
In addition, Fang et al., (2020) mentioned in their research about physical
attractiveness stereotype which demonstrated that attractive individuals highly are linked
with good personalities such as warm, kind, trustworthy and sociable. From two
experiments, Zhao et al., (2015) demonstrated facial attractiveness could affect an
individual‟s implicit and explicit trusting behavior. They also suggested implications for
managers of businesses that consumers who have opportunities to see images as they
perceive as beautiful, meet sellers they perceive as attractive which make them feel
comfortable and be in good mood may have a higher level of trust toward the products
being sold and have a greater purchase intention, compared with people who are not
encountering these pleasant and attractive experiences.
Nevertheless, not many studies test streamer attractiveness in the context of Livestreaming commerce. Supposed that the streamer attractiveness is crucial yet

underexplored in the viewer‟s or consumer‟s trust research, and studying the impact of

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streamer attractiveness on consumer‟s trust is an important and necessary step to reveal
customer‟s‟ purchasing intention in the Live-streaming industry. Based on the foregoing
arguments, it is reasonable to believe that streamers considered attractive by consumers
will generate trust more easily. Accordingly, the first working hypothesis is proposed:
H1. Streamer attractiveness is positively associated with trust.
NGUYEN, Nhu-Ty (2021) demonstrated that a celebrity‟s physical attractiveness
have positive impacts on young Vietnamese consumers‟ purchasing intention. With the
same idea, Juulia Karaila (2021) showed the important role of the attractiveness of social
media influencers, which positively impact purchase intention.
H2. Streamer attractiveness is positively associated with Live streaming
shopping intention.
2.1.4. Information Quality
In the online shopping context, consumers make their decisions mainly based on
the information including pictures, images, video clips or product contents provided
electronically by online stores or online sellers because those customers can not touch or
feel actual products. Information presented by online sellers should be relevant and
helpful in forecasting the quality and utility of a product or service (Wolfinbarger and
Gilly, 2001). With the same ideas, Wang and Strong (1996) and Zhang et al., (2000)
showed information that satisfies consumer‟s information needs were normally up-todate information in presenting products and services, sufficient in making a choice,
consistent in representing and formatting the content, and easy to understand.
The role of information quality was depicted by the argument of Peterson et
al.,(1997) that higher quality information available online would lead to better buying
decisions and a high level of consumer satisfaction. This was also developed by Park and
Kim (2003) that both product and service information quality significantly affected
information satisfaction. That is, product information quality and service information

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quality are critical features in searching and purchasing in terms of reducing the
transaction cost,

perceived risk, enhance confidence and increase the customer‟s

shopping experience [Gao et al., (2012); Nicolaou et al., 2013]. In 2015, Shotfarm
Product Information Report claimed that product information quality was a vital factor in
the success of online sales. This is because there were about 78% of consumers said the
quality of product content is a key point when making purchase decisions. Furthermore,
One in four consumers said they have abandoned a purchase because of poor product
information. Thus, the discussion about the salient factors affecting consumer purchase
intention in live-streaming commerce in detail and in e-commerce in general needs to
consider information quality.
Cyr (2008) found that trust was impacted by information design which defined as
information accuracy of products on e-commerce websites. In live-streaming commerce,
consumers could be perceived high-quality information thanks to multiple cues such as
images, review comments, sounds (seller‟s voices), detailed product presentations and
real-time interaction that help consumers see how products work vividly in live-stream
videos. As a result, the information quality could influence viewers to update or adjust
their understanding of products (Xu et al., 2020), which could raise the trust of viewers.
H3. Information quality is positively associated with trust.
There was many scholars mentioned the relationship of information quality and
purchase intention in their studies. Chiu, Hsieh and Kao (2005) suggest that information
quality is related to the behavioral intention of customers in terms of intention to use the
website to purchase, intention to recommend it to other people), what was also verified
by Kim and Niehm (2009). Furthermore, to support for these oppinions, G. S. Milan et al.,
(2016) identified information quality as an antecedents of purchase intention. Thus, the

fourth research hypothesis emerges:
H4. Information quality is positively associated with Live streaming shopping
intention.
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2.1.5. Interactivity
In the context of Live-streaming commerce, Interactivity is a key characteristic,
which fosters viewers‟ attitudes and behaviors in communications and transactions. There
are many different perspectives in literature defined about interactivity; however, in the
study, I agree that interactivity refers to the degree to which interactions occur in mutual
communication between two parties (Kang et al., 2021; Bonner, 2010; Lee, 2005).
Interactivity is shown at a high-quality communication in live-streaming compared
with other e-commerce forms because live-stream shopping platforms are considered as a
unique form of social media that help users to interact with streamers as well as with
other viewers (Zhao et al., 2018). In other words, viewers could share their thoughts and
messages in real-time; while, streamers would react, respond and feedback immediately
to audiences‟ requirements/questions/comments by talking in the live-stream or
performing certain activities. Similarly, users might interact with co-viewers by chatting,
following and debating other‟s comments. This allows viewers to be perceived the useful
information and the care of streamers about what they expect and act, which can motivate
and enhance trust on sellers or streamers of participants in a live-streaming video.
Many authors asserted interactivity is conceptualized as a stimulus (Kang et al.,
2021; Sheng & Joginapelly, 2011; Fortin & Dholakia, 2005) in various aspects. Specially,
in online commerce, Sheng & Joginapelly (2011) demonstrated as a vital atmospheric cue,
interactivity can stimulate consumers‟ cognitive and emotional states and subsequently
affect their behavioral response. According to previous studies like Bao et al., (2016) and
Teo et al., (2003) they also proved that interactivity has a close relationship with positive
attitudes including trust and satisfaction. Furthermore, other scholars confirmed
interactivity is positively affected to trust (Leong et al., 2020; Kim and Park, 2013) in

social commerce and Mohd Suki (2011) discussed the similar findings in terms of mobile
Internet. Hence, a hypothesis is proposed:
H5. Interactivity is positively associated with trust.
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Fang (2012) indicated that online interactivity acted as a helpful and
complementary means to acquire additional information in processing decision making in
online environments. In investigating the determinants of interactivity, Song and Zinkhan
(2008) found evidence that interactivity has a positive impact on perceptions of site
effectiveness (e.g., purchase intention).
H6. Interactivity is positively associated with Live streaming shopping
intention.
2.1.6. Trust in live-streaming commerce
Trust refers to a catalyst for economic activities and a mechanism to better
understand “interpersonal behaviors and economic exchange” [Pavlou (2003)]. Many
scholars in previous studies confirmed trust is very important in the process of decision
making when consumers purchase a product in both offline and online environments
[Chen and Barns (2007); Winch and Joyce (2006); Dash and Saji (2006)]. Trust could be
more serious in the online environment because of the presence of uncertainty.
Customers normally perceived risks related to product, financial risk and concern for
security or privacy during online shopping. Thus, building trust may help customer
reduce risky perception [Pavlou and Xue (2007)] and promote, develop business
transactions or some responses such as purchase intention in an online environment [Liu
(2019); Winch and Joyce (2006); Bart et al.,(2005)].
Zhang et al., (2014) mentioned that organism is the internal state of consumers
related to affective and cognitive reactions defined as “the psychological process that
occurs in the individual‟s mind when interacting with the stimulus” [Eroglu et al., (2001)]
such as experience, evaluation and perception. Lewis & Weigert (1985) and McAllister
(1995) found out that trust was multidimensional combining affective and cognitive

dimensions. Furthermore, as an emotional and cognitive response, trust can, in turn,
affect people‟s value judgments and ultimate behaviors. Therefore, in live streaming
commerce, trust is the emotional state that consumers consider whether online
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communities are honest with consumers. As a result, trust is a factor of organisms (O) for
consumers.
Various researchers revealed the critical role of trust in shopping online and
consumer buying behavior is a key outcome of trust [Xu et al., (2016)], which in turn can
significantly impact on purchasing intentions [Hajli (2013)]. Additionally,Y. Kim &
Peterson (2017) conducted a Meta analysis to explore that trust shows significant
relationships with selected determinants (e.g., perceived privacy, perceived service
quality) and consequences namely (loyalty, repeat purchase intention) in the online
context. Meanwhile, Dabbous et al., (2020) examined the association between factors
including online social interactions, consumers‟ engagement on social networks,
perceived economic benefit, online brand awareness and online purchase intention was
fully mediated through trust. And looking back to the past, Chang et al., (2015) also
demonstrated the consumer‟s purchasing intention in the hotel industry was affected
significantly by perceived trust, playing a mediating role in the relationship between
website quality and purchase intention.
Agree with the above studies, in this study, trust is identified as the essential
organism and I predict trust toward streamers attractiveness, information quality and
interactivity will boost consumer‟s intention to purchase fashion products (clothes,
accessories, bags, shoes…) through Live-streaming commerce. Therefore, the following
hypothesis is proposed:
H7. Trust is positively influence consumers ’purchase intention using Livestreaming commerce.
2.1.7. Purchasing intention
Purchasing intention could be understood as a reflection of consumer‟s behavioral
outcomes [Yadav et al.,(2013); Liu (2018)], refers to the combination of customer‟s

interest in a product or a brand and the posibility of buying these iteams [Lloyd and Luk

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(2010)]. Furthermore, in Live-streaming context, the defination of purchase intention is
the customer‟s intention to buy products or service from sellers through live streaming
shopping [Ajzen (1991); Lu et al., (2016)]. According to Balakrishnan, Dahnil, and Yi
(2014), there are three statements involved in purchase intention consiting of the
customer willing to consider the buying actions, the buying intention in the future and the
repurchase intention.
In the SOR framework, the final decisions and outcomes of consumers based on
cognitive and affective are called responses. As mentioned above, purchase intention is a
vital behavioral outcome and has been discussed widely in the existed literature. At the
same time, some studies of Live-streaming commerce [(Sun et al., (2019); Yang, (2021);
W. Yang et al., (2021)] have regarded purchase intention as the response in the SOR
model because they think it can express consumer‟s choice. Moreover, Everard and
Galletta (2005), Kang and Johnson (2013), Kim and Park (2013) demonstrated the
perception of trust impacted heavily on users‟ intentions to purchase in both offline and
online environment. Therefore, in this research, purchase intention will be considered as
the response in the research model, which represents the final decisions of consumers
based on building trust.
2.2. Research conceptual model
Research model is depicted and developed based on the explaination of the
relationship between the identified dimensions (Figure 2.1). SOR should be applied in the
research because the framework has been used widely in various psychology researches
to study consumer behaviors. Furthermore, many recent scholars approached SOR theory
in their research and gain deeper knowledge in Live-streaming context. Therefore, the
research model relied on SOR theory posits that three stimulus including streamer
attractiveness, information quality and interactivity affect trust, resulting in purchasing

intention in Live-streaming commerce. I also determine that trust mediate the relationship
between three stimuli and the response.

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Research gap:
A number of studies have addressed consumer behavior in the context of Live
streaming commerce ( Xu et al, 2020; Chen et al, 2020; Sun et al, 2019; Venkatesh et al,
2012; Liu, 2003;). Nevertheless, these studies approach mainly the China culture where
the development of Live streaming has been very modern. Meanwhile, the problem in
Live streaming shopping intention of Vietnam, in particular, is different and there have
been not many Vietnamese studies researched this field. Some Vietnamese articles
researched Live streaming as a business model for the teaching or education sector,
others mentioned consumer‟s buying intention in Live streaming but it focused on only
Facebook platforms.
In addition, Trust is very important in both online and offline environment which
was confirmed by various studies. Moreover, some scholars demonstrated that trust is a
factor of organisms.
Authors

Contents

Chen and Barns (2007); Winch Trust is very important in the process of decision making
and Joyce (2006); Dash and Saji when consumers purchase a product in both offline and
(2006)]
online environments
Liu (2019); Winch and Joyce Building trust may help develop business transactions or
(2006); Bart et al.,(2005)]
some responses such as purchase intention in an online

environment
Dabbous et al., (2020) , Y. Kim Show the significant mediating role of trust with online
& Peterson (2017), Chang et al., purchase intention
(2015)
B Zhu et al., (2019), Linlin Zhu Demonstrate that trust is a factor of organisms (O)
et al., (2020)

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However, there is a lack of rigorous research in the prior literature. Some of this
unexplored trust appears to be lacking in the practice of Live streaming context. In recent
research, many authors applied SOR model to explore customer behaviors in livestreaming context but they did not mention trust in their study.
Thus, to bridge the gap, in my research, I would like to find whether trust is an
internal state, which consequently impact on customer‟s purchasing intention in Livestreaming context by adopting S-O-R theory.
Response (R)

Stimulus (S)

Organism (O)

H2

Streamer
Attractiveness

Information
Quality

H3

Trust

H7

Live-streaming
Shopping Intention

Interactivity

H4
H6
Figure 2.1. Research conceptual
model

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CHAPTER 3. RESEARCH METHODOLOGY
The aim of this chapter is to determine and demonstrate suitable research methods
approached for this study. It exhibits accurate instruments of arranging questionnaire
survey to target samples to make reliable and valid data collection.
3.1. Research process
This research was conducted following the steps shown in the figure below:
Conclusions and suggestions

Review the literature and
related researches

Identify research model,

hypotheses and
methodology

Identify research
population,sample, scale
and measurements

Plan to do survey and
develop questionnaire
based on previous studies

Analyze and interpret data

Collect data

Pilot Test

Meeting with supervisors
for finalizing the plan for
survey and questionnaires

Figure 3.1. Research process proposed by the author
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3.2. Questionnaire Construction
The instruments for constructs in this research were developed from previous
literature to ensure validity, which includes the following five constructs: streamer
attractiveness, information quality, interactivity, trust and purchase intention. All items
were measured with a five-point Likert scale ranging from “strongly disagree” (1) to

“strongly agree” (5). The Likert scale was developed by Rensis (1932) and mostly used
by many scholars in survey research. Likert scale takes advantages in simple and easy to
use. Moreover, the data conducted by the scale is highly valued [Neuman (2000)]. Hence,
the author decided to apply the analysis way for creating questionnaire survey.
1

2

3

4

5

Totally Disagree

Disagree

Consider/Normal

Agree

Totally Agree

There were two parts categorized in the survey. The first part had covered general
questions about responses such as gender, age, education background, frequency of Livestreaming commerce, the duration of watching, experience of shopping in Live-streaming
commerce and others. The second part consisted of 22 closed questions to examine the
significance of factors concerning streamer attractiveness, quality information,
interactivity and trust in Live-streaming shopping intentions.
Information quality was examined using five dimensions of information. These

include accuracy, timeliness, adequacy, completeness and credibility (Chen et al., (2020)).
Using Chen et al., (2020) five questions on information quality respondents indicated
their level of trust on a seven point Likert type scale ranging from (1) not timely (accurate,
adequate, etc.) to (5) very timely (accurate, adequate, etc.).

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The level of consumer trust was measured using Wongkitrungrueng & Assarut,
(2020). This measure reflects the reliability of the buyer to the streamers in Live

streaming shopping.
The question now concerns what is the relationship of this information quality to
trust. Again, if trust is composed of the two parts, expectations and concern, it should be
anticipated that greater information quality should lead to higher levels of trust. When
consumers receive information that is timely, accurate, adequate, complete, and credible,
it indicates the streamers are showing their professional performances and consumers will
appreciate the professional skills and expertise of those streamers; this indicates a level of
clearly denoted expectations. In addition, by fully communicating this necessary
information, the behaviors and actions (purchase products) by the buyer confirm that they
do, indeed, desire for the streamers to perform well in servicing their needs. Thus, both
dimensions of the trusting relationship are satisfied.
Constructs

Streamer
Attractiveness

Item

Scales

Scales reference

SA1

I think that the live stream streamer
is talented.

SA2

I think the live streaming style of
streamer is enjoyable.

SA3

I think that the streamer has an
interesting personality.

SA4

I think the streamer
appealing appearance.

IQ1

I think the content provided by the
streamer is reliable (such as
product,
brand,
and

use
experience).

Xu et al., (2020)

Information
quality
IQ2

has

an

Xu et al., (2020)

The streamer provides real-time
information to meet my needs in
the live stream.

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