HO CHI MINH CITY UNIVERSITY OF FOREIGN LANGUAGES — INFORMATION TECHNOLOGY FACULTY OF BUSINESS ADMINISTRATION HUFLIT GRADUATION PAPER Major: Business Administration THE EFFECTS OF
Online shopping overview in recent years (COvVId-19) oo eee cee -cc sen 1
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 intention Moreover, it is important to examine whether effectiveness also results in any new mechanisms affecting online shopping intention
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 in 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
3 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 Table
11, 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
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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, iGen has a different view of shopping and consumption than previous generations (Apptus, 2019)
More notably, according to an earlier study, 41% of iGen 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, it 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.
Online shopping and c6
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 iGen will have a substantial impact on customer sales Therefore, it is 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 iGen’s wallets.
Problem statement nh
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 is 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.
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To address these gaps and objectives above, this study will answer the two below questions:
- Is there any relationship between e-review and online shopping intention through e- satisfaction?
- Which sub-variables of e-review (valence, quantity, and quality of e-review) impact online shopping intention through e-satisfaction?
In accordance with the research objectives, the research was carried out in Ho Chi Minh City, Vietnam Ho Chi Minh City was chosen as a sampling location for a variety of reasons
To begin with, Ho Chi Minh City is one of Vietnam's most economically developed cities (Vneconomy, 2022) Second, many iGen are studying and working here (Cong Doan, 2022) 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
Theoretical Contributions T8
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.
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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
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.
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 Table 2.1 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 industry (e.g Bigné et al., 2016; Fong et al., 2017; Tan et al., 2018).
However, as existing literature focused on 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 (Casalé et al., 2015) Such research in developing countries like Vietnam is extremely limited Especially, most of the studies used ANOVA (CY Chen et al., 2011) or T-test (Casal6 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 (Flavidn 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 Liet al., 2013) However,
10 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
Table 2.1 Previous research about e-review
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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)
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)
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) investigated the impact of bloggers’ recommendations on consumers’ intents 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
RESEARCH MODEL AND HYPOTHESES DEVELOPMENT
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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:
1: Valence has a positive effect on e-satisfaction
H2: Quantity of e-review has a positive effect on e-satisfaction
H3: 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 is that customers are reusing services Similarly, Zeithaml et al (1996) emphasize that satisfaction is related to
17 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:
4: E-satisfaction has a positive effect on online shopping intention
Quality of e-review Control variables:
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 is 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
Sampling methOd - -<- + HT HH HH He ĐH 20 “ra ẽ
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 is 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 born 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
One of the typical sampling methods that can be mentioned is the 10 times rules (Rigdon et al., 2017) According to this method, the sample size of PLS-SEM studies should
“equal to the larger often times the largest number of formative indicators used to measure a single construct, or ten times the largest number of structural paths directed at a particular construct in the structural model” (Rigdon et al., 2017) In the research model of this article, there are 4 hypotheses indicating 4 paths directed at online shopping intention Thus, the minimum size required is 40 In this study, the sample size of 222 has clearly exceeded 40 (ten times the number of paths directed at online shopping intention)
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 - B), 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 (I - B) = 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
20 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 Table 4.1
VAL 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
Valence | good or bad the online product is
(VA) 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 irrelevant for evaluating the online product
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
(ON) QN3 Higher ranking and recommendation, inferring Mayzlin, 2006) that the online product has a good reputation
QN4 The number of e-review doesn't promise that the review quality is good
QL1 In online shopping, the e-reviews are clear
QL2 In online shopping, the e-reviews are
(Lin et al., 2013) e-review | QL3 In online shopping, e-reviews are helpful
(Lee et al., 2008) (QL) QL4 In online shopping, the e-reviews have sufficient reasons supporting the opinions
QLS Overall, the quality of each e-reviews is high
E- ES1 Iam satisfied with my decision to shop online
(Anderson & satisfaction | ES2 My choice to shop online was a wise one
Srinivasan, 2003) (ES) ES3 Iam happy I made my shopping online
OS1 After reading e-review, it makes me desire to buy the online product (D H Park et al.,
Online OS2 I will consider buying the online product after I Shopping | read e-reviews
Intention | OS3 | intend to try the online product discussed in the
OS4 In the future, I intend to buy the online product discussed in the e-reviews
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 4.1, 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 is 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
Literature review phase a Ƒ The The
3 Research 4 Research 5 Survey 6 Expert Panel model method instrument
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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
24 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
The data collected in this research includes 222 respondents who are iGen Table 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 1 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
Experience in
