The effects of e review on online shopping intention of igen

HO CHI MINH CITY UNIVERSITY OF FOREIGN LANGUAGES — INFORMATION TECHNOLOGY FACULTY OF BUSINESS ADMINISTRATION HUFLIT GRADUATION PAPER Major: Business Administration THE EFFECTS OF E-

HO CHI MINH CITY UNIVERSITY OF FOREIGN LANGUAGES —

INFORMATION TECHNOLOGY

FACULTY OF BUSINESS ADMINISTRATION

HUFLIT

GRADUATION PAPER

Major: Business Administration

THE EFFECTS OF E-REVIEW ON ONLINE SHOPPING INTENTION OF iGEN

Student’s name: Nguyen Thi Thuy Thanh

ID Number: 18DH480328 Class: KM1803

Course: K24 Instructor: Mr Dang Quan Tri

HCMC, 07/2022

I would like to thank many people who have directly and indirectly given their advice, suggestions, and support me to complete this graduation dissertation First and foremost, I would like to thank my lecturer Mr Nguyen Thanh Luan, who is the first person

to open up and lead me into the world of scientific research Next, thank you to my supervisor, Mr Dang Quan Tn, for discovering and exploding my potential His invaluable advice and guidance throughout the various stages of my study did make me grow and glow myself a lot I also would like to thank Ho Chi Minh City University of Foreign Languages — Information Technology (HUFLIT) and the Faculty of Business Administration (FBA) for providing excellent courses for me to pursue my study Finally, I would like to thank my beloved friend, Mr Nguyen Phuoc Thanh, for always standing by my side and listening to

me whenever I feel down Thanks to his big encouragement, I was motivated to go on my own joumey and be more assertive with my decision

ADVISOR’S ASSESSMENT

DISSERTATION ASSESSMENT FORM

Name of Student: Nguyen Thi Thuy Thanh ID number: 18DH480328

Title: The effects of e-review on online shopping intention of iGen

Supervisor’s Name: Dang Quan Tri

Starting: Feb, 2022 Finishing: July, 2022

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Elmet © The dissertation is organized and Table of contents and summary Cl not met consistent with the format

Notation and list of sources in * This report is also proof-read

accordance with formal rules in carefully and effectively

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met with degree programme

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guidelines

5 USE & CITATION OF SOURCES

6 STRUCTURE OF THE ARGUMENT

7 COMPOSITION & STYLE

Other comments:

ABSTRACT

ASSESSMENT

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COMMENTS The theory used in this report is appropriate and consistent with the

early 2010, are expected to become potential consumers for this market in the future Besides, e-review has become a primary source of product information and an important influence on customers’ online shopping intention The effects of e-review on customer behavior have recently gained significant academic attention Therefore, there is a need to provide a review of such effects This paper, thus, aims to investigate customers’ online shopping intention of iGen by following an inductive and more differentiated approach By examining the link between e-review and customers’ online shopping intention through e- satisfaction, results help to gain a broader understanding of the effects of e-review on online shopping intention of iGen E-review is categorized into 3 specific variables in this study, namely valence, the quantity of e-review, and quality of e-review The conceptual framework

of this study was tested using data gathered from the online questionnaires directed to a total sample of 222 iGen located m Ho Chi Minh city and was then analyzed using the PLS-SEM approach The investigation produces three findings: (1) the valence has a positive effect on e-satisfaction, (2) e-satisfaction generally increases with the high-quality of e-review but the quantity of e-review does not necessarily affect customers’ e-satisfaction, (3) customers’ online shopping intention is strongly affected by e-satisfaction given in e-commerce platform context The research contributes novel insights into 1Gen’s online shopping behavior and provides important managerial implications that are of interest to 1Gen, online store owners, and e-commerce platforms

Keywords: e-WOM; e-review; review valence; quantity of e-review; quality of e- review, e-satisfaction; online shopping intention

LIST OF TABLES

Table 1.1 The Mapping of E-commerce in Vietnam 2c 2c 1221112112111 15x21 xke 4 Table 2.1 Previous research abouf G-TCVICW nh TH HH HH HH hư HH 12 Table 4.1 Questionnalre sÍTUCẦUT€S 5 1111211111211 111111101111 1111111 1kg kknEnEHE 2 1x1 kg 21 Table 4.2 Respondents” profiÏe c0 2211121111211 121 1221111111 18111011 1111811101110 1 khe 25 Table 4.3 Overview of measurement model quaÌ1ty c2 22 2221222122122 tre 27 Table 4.4 Outer loadings of the measurement modelÌ 5: 5 22222 2E 2+2 22 szEcscsrxcses 28 Table 4.5 Fornell-Lacker”S CTIEGTIOH - óc c2 n1 v12 1911111111111 11111101 1111111111 1kg 29 IEl) 8.6 a 30 Table 4.7 án ái .ẢẢ 31 Table 4.8 HypothesIs testing resuÏfs - 1S 1 221221112111 121 115115111111 151128111 1520111151111 kt 32

LIST OF FIGURES

EIigure 3.L Research mmode€Ì - - c1 13212511153 151 11111511111 111211 11112011 011111011 11 111 1101k rrưệt Figure 4.1 Research pTOC€SS Q10 11 112111011011 11111 11111111111 111011 11811 E111 H1 HH ru

Average Variance Extracted

Common Method Bias

e-Word of Mouth

Generation Cohort Theory

Gross domestic product

TABLE OE CONTENT

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lễ ` loi /ddiiidi 7 1.5 Sipgmficanf OÊ §Study c1 c2 1111221122 1n 1H11 1n r HH k HT Hàng 8 1.5.1 Theoretical contrIDUfIOIS 2: 2 121 11211211151111 11531111111 11 1111 111011118111 khay 8 1.5.2 Managerial contributions 0 000 cece c c2 12111212115 115111511 151151 1111111211111 1k 8

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CHAPTER 3 RESEARCH MODEL AND HYPOTHESES DEVELOPMENT 17

3.1 E-review 6n nổ ố ằằ 17 3.2 E-satisfaction and Online Shopping Ïntenf1ion 2 2S 1222222112 x2 re xcz 17 3.3 Research propos§aÏ - c 1221111221121 1 2111111012 111150115 xxx kh ke 18 CHAPTER 4 RESEARCH METHODOLOGY 2 112112122121 121 1211 211 tt ren 19 An nan 35 19 4.2 Sampling method - - + 1 221221112111 151 1151151111111 1501 151111111511 1511 1011111 k ng nhện 20

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4.8 Common method bias (CMMB) 2c 121111211 122112 11511 15112111111 1118111111181 1 11kg 26 4.9 Assessing the oufer measuremenft mod€ÌÏ - - + c1 112211221122 222 111511512511 cxk2 26 CHAPTER 5% DISCUSSION AND IMPLICATIƠN - 2 2c 2122112112122 rey 33

1S I6 is 6e ,äiiDủẦỤẶẠỤ 35 3.2.2 Managerial ImpÌIcation§ -: c1 221221111111 1211 2111111118111 1551111281111 35 5.3 Conclusion and ÏImIfafIO'S c1 2211211211111 111 12111111111 111 111111110111 1 1110 1 Hiệu 37 REFERENCES 2112121112121 111 121111 1Ẹ1 111151 0115111110111 11 11111 11 111111111 T 1n tt HH 39 0550905522575 33 Questionnaire (English V€TSIOT)) - c1 2112211112111 111 11118111111 111111 118111011 111181 rvky 33 Questionnaire ( Vietnamese V€TSIOT)) c2 12211212122 12 12111 11151115110 2111192111111 key 59

CHAPTER 1 INTRODUCTION 1.1 Background of the study

In the past, the practice of Word of Mouth (WOM) as the medium of reference is widely practiced by customers WOM has grown as a result of the development of the Internet, enabling people to more quickly access the thoughts, views, assessments, and experiences of others (Sen & Lerman, 2007) It consequently created more chances for acquiring and disseminating product information People no longer only utilize physical modes of communication to exchange information; rather, they also use online forums, chat rooms, blogs, online reviews, and newsgroups (Fei, 2011) However, there is now more information than ever before that may affect and aid in customer decision-making

In today's modern world, there is more advanced WOM that the customer can refer to before making a purchasing decision known as e-Word of Mouth (e- WOM) eWOM is an internet-based development in which the delivery of reviews takes place on the Internet (Belarmino & Koh, 2018) The e-WOM 1s the electronic type of WOM that has more wide coverage rather than WOM The quick rise and widespread expansion of virtual communities has given rise to a new sort of e- WOM, also known as e-review According to research, e-review is one of the most common and vital types of e- WOM (Purnawirawan et al., 2012; Sen & Lerman, 2007) Research from Nielsen revealed that 61% of customers read e-review before making a purchase decision (Nielsen, 2012) It is also estimated that 69% of customers place equal weight on e-reviews and personal recommendations (Myles Anderson, 2011)

Considering the significance of e-review, this study examines its impacts and the relationship with e-satisfaction on the online shopping intention of 1Gen

1.1.1 Online shopping overview in recent years (Covid-19)

Online shopping is increasing day by day, characterized by strong customer demand and the increasing type of available goods (Tolstoy et al., 2021) With the advancement of modern technology, the online market is growing considerably People nowadays prefer online shopping because it saves time, energy, and money Due to the blessing of the Internet that online shopping has made its debut which also affects the common citizens for online shopping (PermataBank, 2021) Online shopping is growing so fast that the global online

shopping market size nearly hit 4 trillion in 2020 It is estimated that in 2023, eCommerce retail purchases are expected to rise from 14.1% to 22% (Optinmonster, 2022)

Throughout history, disease outbreaks and pandemics have shaped politics, altered societies, affected personal relationships, and changed world paradigms (Snowden, 2019) The Coronavirus (Covid-19) pandemic has already heavily influenced the way we live As governments try to minimize the spread of the pandemic, several lockdown restrictions have been imposed that directly affect the way people and businesses operate One of the sudden changes imposed by lockdowns is a higher use of various digital technologies such as internet-based services for communicating, interacting, and working from home (De’ et al., 2020) Customers have been more inclined to change their preferences and behavioral patterns such as shifting to online shopping and alternative pickup and delivery options (De’

et al., 2020)

Due to the extraordinary containment measures, some customers for instance have had to move to online shopping, home deliveries, or cashless payment, which they never considered before (Pantano et al., 2020) The Covid-19 pandemic impacts the whole e- commerce of the world It has changed the nature of business According to research, 52%

of customers avoiding to go brick and mortar shopping and crowded areas (Khan et al., 2020) Furthermore, 36% avoiding brick and mortar shopping until they get the Covid-19 vaccine (Tibay, 2021) Covid-19 pandemic affects differently on different nature of products

It means the impact of Covid-19 on several products is very high and on some products less impact (Andrienko, 2020) People now avoid going out, keep their social distance, buy from home, and work from home The overall sale of e-commerce increases because of this virus such as Walmart’s grocery e-commerce increased by 74% (Troy, 2020)

The emergence of special circumstances due to Covid-19 has created unique conditions where users do not have the time to go through the usual decision-making process

of technology adoption, initial use, and post-adoptive use phases as defined by (Snowden, 2019) Transitioning between various stages happens more rapidly and often under different levels of social isolation, where customers do not have the same access to information resources when making decisions (Raza et al., 2021) Therefore, a question arises about how and to what extent e-review affects customers’ online shopping mtention Moreover, it is

important to examine whether effectiveness also results in any new mechanisms affecting online shopping intention

1.1.2 Online shopping in Vietnam

It has been more than two decades since the Internet started to have been used in Vietnam When Vietnam connected to the world in 2000, the Internet users was a small figure, just 0.3% of the population 2000 However, in 2014, according to the Vietnam Internet Network Information Center, Vietnam ranks 18/20 countries with the largest number

of Internet users in the world, ranking eighth in Asia and ranking third i Southeast Asia with 31,302,752 Internet users as of Dec 2013, 35.53% of the population Moreover, Vietnam is among the countries with the most Internet users in the Asia Pacific region As of January 2021, out of its population of over 96 million people, the number of Internet users reached approximately 69 million (Statista, 2021) The number of Vietnam Internet users has increased from 1.3% of the population in 2001 to 70.3% of the population in 2020 (Datacommons, 2020) Such an advantage of the Internet is very potential for the development of e-commerce services

Developing in line with the popularity of the Internet, Vietnam's online shopping market has become more vibrant The growth rate of national e-commerce was higher than

32 percent in 2019 and is predicted to be over 30 percent this year (Vietnam E-Commerce Association, 2020) Vietnam E-Commerce Association (VECOM) also predicted that our e- commerce sales in 2019 will increase to more than US$15 trillion, up 10 percent in total GDP in 2020 (Vietnam E-Commerce Association, 2020) Obviously, online shopping is becoming more important in the Vietnam economy

In Covid-19 current situation, it is understandable when customers accept to use online shopping sites to limit exposure and cause the spread of the disease to the community, which has made e-commerce significantly grow since 2020 The Covid-19 pandemic with limited travel and crowdsourcing and therefore the level of health concerns changed customers’ buying habits and behaviors The Deloitte survey shows that the pandemic has created a powerful shift in the omnichannel shopping trend People increase in-house consumption, reduce shopping at traditional sales channels and increase the frequency of online transactions on e-commerce platforms (Deloitte, 2020) Moreover, Vietnam has an e-

commerce growth rate in the top 3 in Southeast Asia Trading activities on the e-commerce platforms are very active, furthermore as extremely fast development (Baochinhphu, 2020)

As shown in Zable 1.1, as of the first quarter of 2022, five of the ten most successful Southeast Asian e-commerce platforms operate in Vietnam — namely Shopee, Lazada, Tiki, Shein, and Sendo In Vietnam, Shopee occupies the biggest e-commerce platform with the number one spot in AppStore rank and PlayStore rank, closely followed by Lazada, and Tiki

Table 1.1 The Mapping of E-commerce in Vietnam

(Source: Iprice.vn, updated 16.05.2022)

1.1.3 iGen

iGen or Generation Z is the first generation to have grown up immersed in digital communication (Reinikainen et al., 2020) They are the generation born between 1995 and the beginning of the 2010s (Priporas et al., 2020) They are regarded as the most materialistic (Flurry & Swimberghe, 2016), seek rapid gratification, and value brands' social media connection (Vitelar, 2013) According to Apptus, some interesting statistics about iGen and their online shopping habits can be listed with 77% have taken some form of action for a cause they believe in, 23% have even boycotted a brand, 65% have purchased

something based on an influencer’s recommendations (Apptus, 2019) Moreover, 1Gen has a different view of shopping and consumption than previous generations (Apptus, 2019) More notably, according to an earlier study, 41% of 1Gen customers are impulse shoppers, followed by 34% of Millennials and 32% of Generation X (Brewis, 2020) They want the most recent products at a quicker rate Therefore, firms must build marketing strategies to meet the needs of this target demographic They have less brand loyalty and are more fashion-forward As iGen has matured alongside the Internet, 1t has become customary for individuals to seek inspiration through social media sites (Brewis, 2020) Additionally, this customer has a growing ability to influence the purchasing decisions of their peers and family members (Coray, 2019) These traits make it potentially valuable to investigate this generation's online shopping intention

1.1.4, Online shopping and iGen

iGen has been called the most critical customer group until now, and they are the first generation to spend their entire adolescence with a smartphone This has had ripple effects across many areas of their lives As teens especially, they spend their time differently from any generation before them (Angie, 2017) They are the latest to enter the workforce and have strong purchasing power According to Influencer Marketing Hub, iGen represents the largest generation of customers yet Already, the generation has accounted for $29 billion in direct spending with the number steadily increasing (Geyser, 2021) On a global scale, it is predicted that 1Gen will have a substantial impact on customer sales Therefore, it 1s crucial

to research this potentially influential generation (Wolf, 2020) However, with a bigger demand for higher-quality items and monitoring their spending more closely, brands have to try hard to earn their place in 1Gen’s wallets

1.2 Problem statement

Nowadays, online shopping is a phenomenon that is expanding quickly (Optinmonster, 2022) There are still a lot of possibilities to investigate, as seen by this industry's exponential growth Online shopping 1s actually becoming more and more popular with customers, especially iGen, thanks to how convenient it is The ubiquity of e-review has increased customer interest and desire while shopping online Reading e-review now

becomes one of the indispensable parts when customers search for some products on the Internet, indicating how vital e-review is in online shopping (J Q Zhang et al., 2010) Populations can be classified into generations depending on age, according to the Generation Cohort Theory (GCT) Previous GCT-based studies discovered considerable disparities in the customer behavior of several generations, such as Baby Boomers and iGen (Gilal et al., 2021) According to GCT, every generation that experiences identical political and social events in their formative years will eventually come to hold the same values and beliefs However, little study has been done on the relationship between online shopping intention and impulse e-reviews, which may provide some profound insights into this particular age group

In addition, the purchasing power of iGen is growing which makes the understanding

of their behavior in online shopping becomes vital Additionally, these customers are almost always connected to the Internet, which makes them especially predisposed to engaging in online shopping However, due to easy access to information needed for the analysis of the offer and comparison of the products and services, their online shopping intention is more complex and hard to predict

1.3 Research gaps, research questions, and objectives

in general Moreover, the e-review topic was conducted mainly in developed countries like the United States (Zhu & Zhang, 2010) or Spain (Casalo et al., 2015) Such research in developing countries like Vietnam is extremely limited Especially, most of the studies used ANOVA (Y Chen et al., 2011) or T-test (Casalo et al., 2015) approach There is hardly any

study that is conducted with the method using PLS-SEM It is, indeed, vital for research on the effects of e-review on online shopping intention of iGen in Vietnam with a newly developed approach using PLS-SEM

1.3.2 Objectives

Based on the problem statement in the previous section, this research aims to determine and provide a thorough understanding of online shopping intention, with the following objectives in mind:

- To determine the impact of e-review on online shopping intention throughout e- satisfaction

- To accomplish e-review and online shopping intention from iGen perspective in Vietnam

- To get a better understanding of e-review included sub-variables: valence, quantity, and quality of e-review

Data for this study were gathered from Vietnamese customers with the prior online shopping experiences The research was primarily concerned with iGen (born between 1995- 2010) who has advantages in technology and online shopping using an online questionnaire survey that can quickly reach focus groups and bring convenience and flexibility to the

participants Furthermore, the sample size of this study is 222, which fits the requirements of the analytical method

1.5 Significant of study

1.5.1 Theoretical contributions

This study illustrates the effects of e-review with three e-review characteristics by examining the theories and methods developed relating to e-review In particular, it explores the role of valence, quantity, and quality of e-review in influencing online shopping intention The first theoretical implication is that this study explores three attributes that have never been combined before This indicates that this study will contribute new knowledge to the e-review literature Furthermore, this study adds to existing knowledge by addressing the mechanism by which e-reviews influence users’ online shopping intention

1.5.2 Managerial contributions

This paper helps marketers to understand the impact of e-review on customers’ e- satisfaction and how that affects their online shopping intention This is important because the more knowledge marketers have about customers’ involvement in e-review, the more chances they will have to handle the way in which e-review affects customer online shopping intention Furthermore, the marketing department of e-commerce platforms will benefit from this study because they will have more knowledge of the crucial elements of e- review content As a result, they can expect the study's findings

1.5.3 Social contributions

By identifying how e-review affects customers’ online shopping intention, this study helps customers and readers to recognize and appreciate their role and behavior in their online shopping journey Besides, this study will investigate whether e-review has an important role in online shopping intention compared with the situation where customers do not read e-review when looking for an online product

CHAPTER 2 LITERATURE REVIEW

2.1 E-review

E-review is feedback and recommendation from experienced customers about a certain product or service on electronic commerce and online shopping sites that can persuade potential buyers to make a purchase (Khammash, 2008) Previous research has found that e-review is more persuasive than marketing content since customers have no strong interest and are thus more self-reliant and trustworthy (Do-Hyung et al., 2007; Reimer

& Benkenstein, 2016) Customers also often use this type of Internet data to compare different market options and get advice on what to buy (Floh et al., 2013)

E-review has a dual role It provides product information and then makes recommendations from a customer's perspective (Lee et al., 2008) As an informant, it provides user-oriented product information, including honest evaluations of the advantages and weaknesses of a product As a recommender, it shares opinions and experiences to help other customers judge the quality of a product (Lee et al., 2008; D H Park et al., 2007) Compared with the traditional review, e-review is fast, savable, and anonymous It also transcends time and space and can be received instantly (Hennig-Thurau et al., 2004) (D J Kim et al., 2009) suggested that in the age of shopping on the online platform where customers do not have chances to approach products or services directly, positive feedback from experienced customers can build up confident expectations and trust for products or services affecting purchasing intention (D J Kim et al., 2009) The importance of e-review has been widely documented in the existing literature (Zhu & Zhang, 2006)

The role of e-review has been investigated in several industries (see Zable 2.] for the list of selected papers across industries), showing how certain consumption practices are affected For instance, e-review seems to have an influential role in the entertainment and media industry (Godes & Mayzlin, 2004), movies (Hennig-Thurau et al., 2015), online book sales (Chevalier & Mayzlin, 2006), bath, fragrance, and beauty products (Moe et al., 2011), video gaming (Frick & Kaimann, 2017; Zhu & Zhang, 2010) and in the hospitality mdustry (e.g Bigné et al., 2016; Fong et al., 2017; Tan et al., 2018)

However, as existing literature focused ơn e-review within a specific industry such as restaurants (Smith et al., 2005), automobiles (Y Chen et al., 2011), or movies (Schlosser, 2005); the role of e-review is rooted within that industry context and their influence as such should be understood in light of said industry It is therefore important to consider the role of e-review in the whole context of online shopping, and this paper focuses on online shopping

in general Moreover, this research topic was conducted mainly in developed countries like United States (Zhu & Zhang, 2010) and Spain (Casalo et al., 2015) Such research in developing countries like Vietnam is extremely limited Especially, most of the studies used ANOVA (Y Chen et al., 2011) or T-test (Casalo et al., 2015) approach There is hardly any study that is conducted with the method using PLS-SEM It is, indeed, vital for research on the effects of e-review on online shopping intention of iGen in Vietnam with a newly developed approach using PLS-SEM

In addition, previous research shows that customer e-review influence product sales

in certain industries such as books, restaurants, and technology products (e.g Chevalier & Mayzlin, 2006; Frick & Kaimann, 2017; Moe et al., 2011; L Zhang et al., 2013) E-review has also been known to influence the formation of customers’ trust, particularly the competence dimension of trust judgments (Stouthuysen et al., 2018) Moreover, e-review also influenced customers’ willingness to pay in varying degrees (Wu & Wu, 2016) and also positively influence their offline purchase intentions (Flavian et al., 2016) and online purchases such as software downloads (Frick & Kaimann, 2017) As a result, e-review plays

an increasingly significant role in customers’ online shopping decisions (Duan et al., 2008) The present literature has a limitation that there is little literature on the effect on e- review attitude and online shopping intention Researchers have already explored various variables, but little research has been conducted on the fundamental e-review features This missing piece leads to the study's e-review elements: valence, quantity, and quality It is recognized that these e-review components are crucial C M K Cheung & Thadani (2012) highlighted valence as one of the key factors influencing response The quantity of e-review

is the number of comments from reviewers about a specific product or service (Davis & Khazanchi, 2008) The quality of an e-review is based on how useful and relevant the e- review is in helping the buyer decide what to buy (Connors et al., 2011; Y Li et al., 2013)

However, none of the aforementioned factors were taken into consideration in conjunction with others In the end, it is indeed vital to understand the customer's attitude and online purchase intentions As such, this study will look at the effects of e-review in terms of valence, quantity, and quality on online shopping intention

Valence is the average rating of how customers feel about a product (Cui et al., 2012), which is usually either positive, negative, or neutral (H N Chen & Huang, 2013) E-review valence is measured in the form of numerical ratings (Hu et al., 2014; J Q Zhang et al., 2010) The effect of e-review valence can be found clearly in the study of Cheung et al (2009) when they indicated that positively valenced e-reviews highlight a product's advantages, whereas negatively valenced e-reviews emphasize its disadvantages (M Cheung

et al., 2009) For instance, five-star e-reviews often promote product sales, whereas one-star e-reviews would result in a reduction in product sales (Chevalier & Mayzlin, 2006) Prior studies found that positive e-reviews were associated with higher levels of credibility than negative e-reviews, whereas, a negative e-review has a negative effect when decreasing perceived trustworthiness (Ketelaar et al., 2015; Utz et al., 2012) In the study of (Tata et al., 2020), they also indicated that positive e-reviews, as opposed to negative, had a stronger influence on both attitude and purchase intention Mixed reviews have a neutral effect on a customer's attitude and buying intention (Tata et al., 2020) However, the theory

of negativity bias suggested that negative e-reviews have a greater influence than positive e- reviews (Lee et al., 2008; Sen & Lerman, 2007) The explanation for this conclusion is that negative e-reviews can also boost sales if reviewers properly figure out the benefits and drawbacks of products and provide enough evidence to back up their perspectives (Ghose & Ipeirotis, 2011) Indeed, e-review valence has a significant influence on shoppers’ buying behavior (Floh et al., 2013)

2.3 Quantity of e-review

In online settings, the quantity of e-review is the number of comments from reviewers about a specific product or service (Davis & Khazanchi, 2008) Since the e-review quantity possibly is a sign of how much the product is valuable and popular, customers tend to look at the e-review quantity of a product to determine whether it is worthy or not (Bataineh, 2015) Several studies demonstrate that quantity significantly correlates with customer buying intention (D H Park et al., 2007; Sutanto & Aprianingsih, 2016) Before customers decide

to buy a product about which they have little information, some awareness has to be built (Mahajan et al., 1984) The higher quantity of e-review, either positive or negative, in online communities is more likely to attract information seekers and then increase product awareness (Davis & Khazanchi, 2008)

In fact, the more e-review there are, the more likely it is that customers will be informed, which will make them more likely to shop (Liu, 2006) Since the quantity of e- review shows how many customers have bought and used the product (Chatterjee, 2001), it makes sense that the quantity of e-review gives them more confidence and makes them less worried about making mistakes or taking risks when buying products online (Buttle, 1998) Even if the quality of an e-review is low, it was found that the quantity of e-review still makes customers more likely to purchase (D H Park et al., 2007)

2.4 Quality of e-review

An empirical examination of panel data reveals that customers do not simply blindly follow e-review; rather, they read and evaluate the e-review (Duan et al., 2008) This might imply that the quality of e-review is significant A review’s quality or helpfulness is measured by its utility and relevancy in influencing the buyer's decision (Connors et al., 2011; Y Li et al., 2013) It has been demonstrated that information adoption is positively correlated with the quality of e-review (C M K Cheung et al., 2008) Additionally, numerous studies have revealed a connection between e-review quality and sales of particular products (P.-Y Chen et al., 2008; Sen & Lerman, 2007) According to Awad & Ragowsky (2008), the quality of e- WOM has a significant and favorable impact on trust in e-commerce Regarding e-review, Hsu et al (2013) mvestigated the impact of bloggers’ recommendations on consumers’ mtents and found that customers’ attitudes and intentions to shop online are positively influenced by the quality of recommendations

One question that emerges in this context is what qualifies an e-review A valuable e- review, according to Schindler & Bickart (2005), has a moderate amount of positive words; nevertheless, too many positive statements may cast doubts on the reviewer's motivations They discovered that a qualified e-review is related to the number of statements, the usage of

a positive style, and the number of descriptive statements Additionally, it has been

discovered that a two-sided or balanced e-review is more beneficial than a one-sided e- review (Cheema & Papatla, 2010; Sen, 2008) Similarly, a lengthy e-review is regarded as being more qualified than a brief one (Baek et al., 2012; Mudambi & Schuff, 2010; Sen, 2008) The laboratory study by M Li et al.'s (2013) demonstrates that the quality of e-review

is influenced by source credibility, review content, and the use of indirect expressions Additionally, their data show that the substance of an e-review, regardless of its source, significantly influences how useful it is According to Mudambi and Schuffs (2010) analysis

of e-review quality from the viewpoint of product type, the depth and severity of the e- review have varying effects on the quality of e-review depending on the type of product For experience products as opposed to search products, a moderate rating is more useful The influence of e-review depth on the quality of e-review, however, is greater for search goods than for experience products (Mudambi & Schuff, 2010)

The e-review quality can be described as the convincing power of comments rooted

in an informational message (Bhattacherjee & Sanford, 2006) When customers are looking for information, the quality of information possibly will impact customers' acceptance of it regarding e-review channels (Cheung et al., 2009) In the view of Lin et al (2013), when an e-review is fair, comprehensible, and rational with the other opinions, will definitely have a positive influence on purchase intention Similarly, according to Cheung et al (2008), customer choice and buying decisions can be built on certain criteria that meet their needs For that reason, the extent to which provided information is helpful, clear, and easy to understand could be an essential request to determine 1Gen's perception of information quality as an element for assessing their probable online shopping intention

2.5 E-satisfaction

In the virtual environment, Anderson & Srinivasan (2003) defined e-satisfaction as

“the contentment of the customer with respect to his or her prior purchasing experience with

a given electronic commerce firm” In this study, however, e-satisfaction refers to the summary psychological state resultmg when the emotion surrounding disconfirmed expectations is coupled with a customer’s prior feelings about the online customer experience (R.L Oliver, 1997) The expectation-disconfirmation theory proposes that

customers compare their expectations before buying products and services with the actual perceptions of how the products and services performed (Richard L Oliver, 1980) Expectation-disconfirmation theory also explains the formation of customer satisfaction and dissatisfaction in the online environment (I Park et al., 2015) The market signal theory and expectation-disconfirmation theory explain the various sources of satisfiers and dissatisfiers and the formation mechanism of customer satisfaction and dissatisfaction, which provides the theoretical foundation of this study Customer satisfaction and dissatisfaction also have

an affective nature, which shows the psychological state, expressed as mood or emotion, derived from consumption (Bowen & Clarke, 2002) Both the cognitive and affective images affect e-satisfaction, as shown in the cognitive-affective model of customer satisfaction (del Bosque & San Martin, 2008) Indeed, Do-Hyung et al (2007) found that e-satisfaction would be positively affected by website design, while Keblis & Chen (2006) pointed out that the high e-satisfaction in Amazon.com results from its high-quality services relating to the customers, payment system and security, communication, comprehensive and relevant information, and website design It is, still, a question of whether e-satisfaction is affected by e-review in the online shopping context

2.6 Online shopping intention

Ajzen (1991) stated that people's intentions are thought to show how close they are willing to get to certain behavior and how many times they are trying to do that behavior

He et al (2008) found that the biggest problem with the growth of electronic commerce is that people do not want to buy things online According to the theory of planned behavior (TPB) used with Thai customers, the likelihood of an individual's intention to purchase online was most likely to be influenced by their perception of behavioral control and subjective norm, which is the culmination of their social environment's attitudes (Orapin, 2009) To establish and sustain a positive relationship with customers, an e-commerce website needs to comprehend the customers' purchase behavior (E Kim & Hong, 2010) Jamil and Mat (2011) suggested that the intention to shop online can have a favorable impact

on the actual online purchases Thus, Limayem et al (2000) notified researchers to look into the intention, assuming that behavior will automatically follow the intention

CHAPTER 3 RESEARCH MODEL AND HYPOTHESES

DEVELOPMENT

3.1 E-review and E-satisfaction

Most of the current studies only study the relationship of e-review with customer satisfaction In the hotel industry, fifteen factors from the e-review, which were classified into six categories, influence customer satisfaction (H Li et al., 2013) Also, e-review reflects the determinants of customer overall satisfaction with hotels (Xu, 2020) There is an asymmetric effect between e-reviews and determinants of customer satisfaction, in which not all positive or negative textual factors mined from e-review significantly influence their overall satisfaction (Xu, 2020) E-review is proven to have an impact on customer satisfaction with the hotel (Ban et al., 2019) However, it is still limited in the number of studies on e-reviews and e-satisfaction in the context of online shopping For that reason, this study will delve into the problem by subdividing the e-review into valence, the quantity

of e-review, and quality of e-review and discovering the relationship of each variable with the e-satisfaction in the online shopping context

Thus, the hypotheses are as follows:

HI: Valence has a positive effect on e-satisfaction

2: Quantity of e-review has a positive effect on e-satisfaction

3: Quality of e-review has a positive effect on e-satisfaction

3.2, E-satisfaction and Online Shopping Intention

Following the model presented by Zeithaml et al (1996), e-satisfaction can be perceived by means such as intent to acquire, word-of-mouth, loyalty, complaining, sensitivity behavior, and perceived price level High quality of service as perceived by the customer often results in positive e-satisfaction

In contrast, the low quality of the service tends to result in negative e-satisfaction An experienced customer of online shopping will have a significant influence on customers who intend to buy in the future for online shopping (Jayawardhena et al., 2007) Therefore, research indicates that the more positive a customer experience is, the more likely it 1s that customers are reusing services Similarly, Zeithaml et al (1996) emphasize that satisfaction

is related to a customer who decides to stay or leave a brand or company From the comments discussed, it can be understood that the quality of online products influences e- satisfaction, and therefore e-satisfaction influences online shopping intention Consequently, the hypothesis is as follows:

HẠ: E-satisfaction has a positive effect on online shopping intention

-Income

CHAPTER 4 RESEARCH METHODOLOGY

4.1 Research design

This study used the descriptive research design It is a theory-based design method that is created by gathering, analyzing, and presenting collected data This allows a researcher to provide insights into the why and how of research Descriptive design helps others better understand the need for the research

In this research, the quantitative data analysis approach was adopted, which is for cases where statistical conclusions to collect actionable insights are essential Numbers provide a better perspective to make critical business decisions Quantitative research methods are necessary for the growth of any organization Insights drawn from hard numerical data and analysis prove to be highly effective when making decisions related to the future of the business Especially, this approach is chosen in this study to test the relationship among variables

Primary data refers to information and data that are gathered directly for the typical purposes of any research (C.R.Katharin, 2004) An advantage of using primary data 1s that researchers are collecting information for the specific purposes of their study In this study, the questions the researchers ask are tailored to elicit the data that will be suitable for the online shopping study context Thus, the author will have data and responses via online questionnaires The primary one can be gathered through a variety of techniques, including focus groups, interviews, telephone interviews, and online questionnaires The author chose the online questionnaire survey from among the available options since it can rapidly reach focus groups and offer convenience and flexibility to the participants

Additionally, there are three main research techniques: experimental, observation, and questionnaire In particular, the questionnaire is typically used to collect primary data (Kumar Ranjit, 2019) It is a tool for gathering data from a large sample of the population rather than concentrating on a specific individual Furthermore, the survey is very simple, time-saving, and generates a huge number of data in a short amount of time (Kelley et al., 2003) Also, it is comparatively precise and dependable, making it appropriate for investigations on attitudes and behaviors The survey can then be used to collect data for this study based on these justifications

4.2 Sampling method

The study's sample was chosen using non-probability judgemental sampling techniques In judgmental sampling, the researchers choose samples based on their experience to complete a task in a way that makes sure all of the members have similar traits (Taherdoost, 2016) It 1s applied when the respondent was asked if they have experience using a certain product or doing some actions (Alchemer, 2018) Judgmental sampling is suitable for this study because the respondents are required to be iGen (who were bom between 1995 - 2010) and need to have smartphones that allow them to access the Internet Thus, non-probability sampling with judgmental is chosen in this study

Another popular sampling method can be G-Power G-Power 3 (Erdfelder et al., 1996) was also referred to in the calculation of the sample size for the present research G- Power 3 analysis includes a function of the required power level (1 - 8), the prespecified significance level, and the population effect size to be detected with probability 1 — B The required sample size based on G-Power 3 analysis for the present study is 108 with the F-test being selected for the Linear Multiple Regression statistical test (Effect size, f2 = 0.15, Probability of error, a = 0.05, Power level (1 - 8) = 0.9 and number of predictors = 4) The total sample usable for data analysis comprised 222 responses, which exceeds the minimum sample size required

4.4 Questionnaire design

The questionnaire was separated into two portions, including demographic questions and key questions relating to e-review, e-satisfaction, and online shopping intention To

increase the reliability and validity of the questionnaire, a pilot test with 30 individuals who had experience buying online was undertaken prior to the main study Before it was officially distributed, a few minor adjustments were made to the original questionnaire The questionnaire was then translated into Vietnamese so that responders could easily and appropriately complete it The questionnaire is adapted from previous studies The survey questionnaire was prepared based on validated and reliable measurement scales found in the literature All items were measured on a seven-point Likert scale ranging from (1) strongly disagree to (7) strongly agree The following table presents the measurement elements and sources, as shown in Zable 4.1

Table 4.3 Questionnaire structures

VAI Please indicate the degree to which you would

consider a negative e-review about a product

(containing some negative information about its

performance) to be relevant or irrelevant for evaluating

the online product

VA2 Please rate the degree to which a negative e-

review about a product (containing some negative

information about its performance) is indicative of how

good or bad the online product is

VA3 Please indicate the degree to which a negative e-

review about a product (containing some negative

information about its performance) would be useful to

you for evaluating the online product

VA4 Please indicate the degree to which you would

consider a positive e-review about a product (containing

some positive information about its performance) to be

relevant or nrelevant for evaluating the online product

References

(Stefanov, 2014)

VAS Please rate the degree to which a positive e-

review about a product (containing some positive

information about its performance) is indicative of how

good or bad the online product is

VA6 Please indicate the degree to which a positive e-

review about a product (containing some positive

information about its performance) would be useful to

you for evaluating the online product

QN1 The number of e-reviews is large, inferring that

the online product is popular

QN2 Highly ranking and recommendations, inferring

that the online product has good sales

QN3 Higher ranking and recommendation, inferring

that the online product has a good reputation

QN4 The number of e-review doesn't promise that the

review quality is good

QLI1 In online shopping, the e-reviews are clear

QL2 In online shopping, the e-reviews are

understandable

QL3 In online shopping, e-reviews are helpful

QL4 In online shopping, the e-reviews have sufficient

reasons supporting the opinions

QLS Overall, the quality of each e-reviews is high

ES1 Iam satisfied with my decision to shop online

ES2 My choice to shop online was a wise one

ES3 Tam happy I made my shopping online

OS1 After reading e-review, it makes me desire to buy

the online product

OS2 I will consider buying the online product after I

read e-reviews

OS3 I intend to try the online product discussed in the

€-T€VICWS

OS4 In the future, I intend to buy the online product

discussed in the e-reviews

(Chevalier & Mayzlin, 2006)

(Lin et al., 2013) (Lee et al., 2008)

(Anderson & Srinivasan, 2003)

(D H Park et al., 2007)

4.5 Research process

Overall, at the beginning of the research process, the research idea or topic of the study was determined first, and then the extensive literature review showed the identified research problems as well as research gaps After that, as shown in Figure 4./, grounded on the hypothesis development, the research model was developed before determining the research method Next, the instrument was developed, and the validation was deployed after

An expert panel was performed to assess the face validity and content validity of the instrument The content validity index was used as the measure for content validity The instrument development was followed by a pilot test which was conducted to examine the reliability of the instrument using Cronbach's alpha A pilot test 1s an official evaluation of the questionnaire with a limited number of participants (Malhotra, 2007), so it can help the researchers to make amendments to minimize uncertain and undesirable problems (Zikmund, 2003) In the next stage, the data was gathered by questionnaires before it was coded and then analyzed to test the hypothesis Following this, the findings from the data analysis were explained and discussed The result of the discussion brought theoretical and practical contributions In the end, the shortcomings or limitations of the study as well as potential research directions were presented

Figure 4.4 Research process

4.6 Data analysis techniques

For data processing and analysis, the Partial Least Square (PLS) was used The PLS approach was appropriate because it made minimal demands concerning measurement scales, sample size, and residual distributions (Monecke & Leisch, 2012) According to Hair

et al., (2014), since the 2000s, the number of studies using PLS-SEM published increased by increasing number of people Especially in the fields of strategic management, information system management, organizational behavior, and marketing research on satisfaction analysis because PLS-SEM has advantages over CB-SEM in the following situations: ( 1)

avoid problems related to small sample size, non-standard non-delivery data; (2) it is possible to estimate complex research models with many intermediate variables, underlying and observation variables, especially structural models; (3) suitable for predictive orientation studies (Henseler et al., 2014) This study applies PLS-SEM to identify the impact of e- review on customers’ online shopping intention The Likert scale with seven-point response options ranging from “(1) Strongly Disagree” to “(7) Strongly Agree” was used to collect information from respondents on how they perceive measurement items

4.7 Profile of respondents

The data collected in this research includes 222 respondents who are iGen Zable 4.2 illustrates that 76.58 percent of the participants are female, while males make up 23.42 percent of the total There is 10.81 percent of people who have less than | year of online shopping experience, while 26.13 percent of participants shop online 1-4 years, and 26.13 percent of respondents shop for over 4 years The majority of respondents spend less than VND1,000,000 on average monthly on online shopping with 73.4234 percent, followed by VND1,000,000 - VND2,999,999 with 22.0720 percent While 3.6036 percent of respondents spent VND3,000,000 - VND6,999,999, only 0.9010 percent of respondents spent more than VND7,000,000 per month on average

Table 4.4 Respondents’ profile Demographic characteristic Frequenc Percentage

y

Gender Female 170 76.58%

Male 52 23.42% Experience in <l year 24 10.81% online shopping _!-4 years 140 63.06%

Over 4 years 38 26.13% Average monthly < VND 1,000,000 163 73.4234% spending VND 1,000,000 - VND 2,999,999 49 22.0720%

VND 3,000,000 - VND 6,999,999 8 3.6036%

> VND 7,000,000 2 0.9010%

4.8 Common method bias (CMB)

Due to the data for the exogenous and endogenous variables being gathered from a single source, common method bias has a possibility to emerge To address this issue, both procedural and statistical procedures were used in this research data analysis (Leong et al., 2018; Teck Soon & Sharifah, 2017) Firstly, for procedural-wise, there was insurance that respondents’ identities remained anonymous, and the responses would not be judged as true

or false, thus they only needed to answer candidly to all the questions Secondly, on the statistical aspect, the correlation coefficients in Table 4.3 are less than 0.90 indicating there

is no CMB problem (Lai & Hitchcock, 2017) Therefore, the insignificant effect of CMB on the results has been indeed verified

4.9, Assessing the outer measurement model

The evaluation of the outer model (measurement model) should be validated before testing the hypotheses in the mner model (structural model) which examines the measurement model's reliability (Cronbach's Alpha and composite reliability) and validity (convergent and discriminant validity) is part of the evaluation process

For the internal consistency reliability, a tool that measures the construct reliability, Table 4.3 depicts that the Cronbach's Alpha values are all higher than the recommended threshold value of 0.70 (Hair Jr et al., 2016) On the other hand, the results in the same table also demonstrate that the composite reliability values are higher than the required value of 0.70 of Hair Jr et al (2016) Therefore, the internal consistency reliability in terms of Cronbach's Alpha and composite reliability 1s verified and all constructs have significant reliability

The term "convergent validity” refers to the assessment of many conceptually similar items In accordance with Hair Jr et al (2016), the average variance extracted (AVE) is recommended to assess the convergent validity, in which the value of AVE should be greater than 0.50 before it is asserted to be validated Indeed, Zable 4.3 shows that all AVEs are substantial and larger than 0.50 On the other aspect, it is also suggested by (Hair Jr et al., 2016) that convergent validity could be confirmed by the value of outer loadings Accordingly, if the value of outer loadings is higher than 0.70, the convergent validity would