(Luận văn thạc sĩ) factors affecting live streaming shopping intention invietnam the case of fashion products

VIETNAM NATIONAL UNIVERSITY, HANOIVIETNAM – JAPAN UNIVERSITY NGUYEN PHUONG ANH FACTORS AFFECTING LIVE STREAMING SHOPPING INTENTION IN VIETNAM: THE CASE OF FASHION PRODUCTS MAJOR: BUSIN

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

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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 e-commerce 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 centered environment instead of a product-oriented shopping environment as before (Busalim 2016, Wongkitrungrueng et al., 2018) Live-streaming commerce can be seen

customer-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 live-streaming 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 live-streaming 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 streaming 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-to-date 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 streaming commerce

Live-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

Chen and Barns (2007); Winch

and Joyce (2006); Dash and Saji

(2006)]

Trust is very important in the process of decision making when consumers purchase a product in both offline and online environments

Liu (2019); Winch and Joyce

(2006); Bart et al.,(2005)]

Building trust may help develop business transactions or some responses such as purchase intention in an online environment

Dabbous et al., (2020) , Y Kim

& Peterson (2017), Chang et al.,

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 live-streaming 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 Live-streaming context by adopting S-O-R theory

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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:

Figure 3.1 Research process proposed by the author

Pilot Test Collect data

Analyze and interpret data

Review the literature and

based on previous studies

Meeting with supervisors for finalizing the plan for survey and questionnaires Conclusions and suggestions

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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

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 Live-streaming 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 Item Scales Scales reference

IQ1 I think the content provided by the

streamer is reliable (such as product, brand, and use experience) Xu et al., (2020) IQ2 The streamer provides real-time

information to meet my needs in the live stream

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IQ3 The streamer provides

in-dept./detailed information about the fashion products (materials, colors, )

Chen et al., (2020) IQ4 The streamer provides accurate

information about the fashion product that I want to purchase

IQ5 The streamer provides up-to-date

information about the fashion product in live streaming video

Interactivity

I1 When watching a live-stream, I can

exchange and share opinions with the streamer or other audiences easily

Liu (2003)

I2 When watching a live-stream, I feel

closer to the streamer

I3 I feel that streamers care my

respond in live streaming

I4 When I am watching a live-stream,

the streamer provides sufficient opportunities to talk and ask a question

I5 The streamers respond to my

question very fast

Trust

T1 I believe in the information that the

streamer provides through live streaming

Wongkitrungrueng & Assarut, (2020)

T2 The sellers in live-streaming

commerce are trustworthy

T3 I think fashion products I order

from Live streaming will be as I imagined

T4 I trust that the products I receive

will be the same as those shown on

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Live streaming

Live streaming

Shopping

Intention

LSI1 I will continue watching Live

streaming for purchasing fashion products

Venkatesh et al., (2012)

LSI2 I will consider live streaming

shopping as my first shopping choice

Sun et al., (2019) LSI3 I expect that I will purchase fashion

products through live streaming shopping in the near future

LSI4 I will recommend people around

me to watch Live-streaming for purchasing fashion products

One of the most important things in this research is that it is needed to collect enough respondents to apply Structural Equation Model (SEM) for examining primary data Hair et al., (1998, p.604) argued that sample size played a vital role in estimating and explaining the results of SEM There are common opinions among scholars that larger samples could provide more stable parameter estimates Bollen (1989), Hair et al (1998); Hulland et al., (1996) suggested that the sample size included 200 observations should be conducted as a standard to research However, Hair et al., (2014) recommended that the sample size did not have a clear principle and this sample size could be increased

if there were any technical fails in a research model

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To make sure the representation of the study, the author predicted the sample size was 200 and the actual respondents collected was 349 which was suitable with the research objectives

Secondly, the secondary data is information that collected from other investigators

in previous literatures, science journals, and articles related to research objectives and problems The secondary data in this data from credible internet sources for instance; companies‟ websites, official sources were collected and used as reference

3.4 Data analysis

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The data was assembled in form of primary data through survey The first thing, the author checked and eliminated unvalid respondents Then, this primary data were inputed into Excel to store and transfered to SPSS to make discriptive statistic At the same time, SMARTPLS 3.0 was used to evaluate mesuarments, determine the important level of factors and test hypotheses

3.4.1 Cronbach‟s Alpha & Explore factor analysis

The research has tested Cronbach‟s Alpha measurement Testing the reliability of Cronbach‟s Alpha measurement reflects the close correlation between observed variables

in the same factor It shows in the observed variables of a factor, which variable has contributed to the measurement of the concept of a factor, which variable does not The requirement for Cronbach‟s Alpha Coefficient is larger than 0.6 and Corrected Item – Total Correlation has to greater than 0.3 [Hafiz and Shaari, (2013)]

Finally, Explore factor analysis (EFA) is an important step in quantitative analysis using SPSS EFA considers the relationship between variables in all different groups (factors) to detect observed variables that loading multiple factors or observed variables which is arranged wrongly to factors in the beginning There are some criterials in analyzing EFA:

- Kaiser-Meyer-Olkin (KMO) is the is an indicator used to consider the appropriateness of EFA The value of KMO must be higher than 0.5 (0.5 ≤ KMO

≤ 1) If this number is less than 0.5, factor analysis is likely not suitable for collected data

- Bartlett‟s test of sphericity is used to examine observed variables have correlation

or not The condition of Bartlett‟s test is that Sig Bartlett‟s Test < 0.05

- Total Variance Explained is greater than 50% determined that EFA is suitable Considering the 100% variation, this value shows how much% of the factors are extracted and how much% of the observed variables are lost [(Hair et al., (2006)]

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- Factor Loading indicates the correlation relationship between observed variables with factors The higher the factor loading, the greater the correlation between that observed variable with the factor and vice versa Based on Hair et al., (2009), factor loading is 0.5 with the sample size ranges from 120 to 350 and 0.3 with the sample size is higher than 350 [(Hair et al., (2006)]

3.4.2 Structural Equation Modeling (SEM)

Over 20 years, many scholars have applied structural equation modeling (SEM) in their research as the second generation data analysis techniques According to Byrne (2011), SEM consists of a set of multivariate techniques that are confirmatory rather than exploratory in testing whether models fit data SEM allows exploring multiple relationships at the same time, which multivariate regression might not solve Or in other words, SEM examines “a series of interrelated dependence relationships among measured variables and latent constructs, as well as between several latent constructs” (Hair et al., 2014) There are two parts in SEM including measurement and structural part In the measurement model, the study could define and validate the reliability and validity of the constructs [Hair et al., (2011)] And estimate the statistical significance for path coefficients and level of significance of hypotheses in a structural model Only when the measurement model achieves an acceptable fit, the structural relationship would be specified

The evaluation of measurement model: PLS (PLS Algorithms) in SMARTPLS was used to examine the reliability of measurement In this terms, it is needed to focus on some main indicators such as outer loading, reliability, convergent validity and discriminant validity

Meaning Requirment

Outer loading The degree of association ≥ 0.708: quality observed

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between the observed variable with the latent variable

variables [Hair et al., (2016)]

& Yi, (1988)]

Average Variance

Extracted - AVE

Convergent validity Average Variance Extracted AVE

≥ 0.5 [Hock & Ringle, (2010)]

of a particular latent variable from the concept

of another latent variable [Henseler et al., (2009)]

- SQRTAVE > Inter-construct Correlations

- HTMT ≤ 0.85 [Kline, (2015)]

The evaluation of structural model: to test for the relationship between the concepts, the impact, and the intensity of the independent variables on the dependent variable through the intermediate variable The evaluation criteria are as follows:

- R-square value: is an indicator to measure the level that the data fit with the model (the explanation ability of the model) Henseler et al., (2009) described R-square

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values of 0.67, 0.33, and 0.19 in the PLS path models as strong, medium, and weak, respectively

- Path Coefficient: The estimated values for path relationships in the structural model should be evaluated in terms of the sign, magnitude, and significance It could be interpreted as standardized beta coefficients of ordinary least squares regressions Bootstrapping should be used to determine the confidence intervals of the path coefficients and statistical inference [(Henseler et al., 2009)]

- T-statistics are generated to assess the significant level of the measurement model and structural model T-statistics is greater than 1.96 which indicates a statistical significance of hypotheses tested [Hair et al., (2012)]

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