Establishing loyalty in e-commerce- Phương pháp nghiên cứu | Đại học Ngoại ngữ - Tin học Thành phố Hồ Chí Minh
Since maintaining a pleased customer core is an appropriate objective, theconnection between loyalty building initiatives and financial outcomes seems a bitunclear.
Môn: Phương pháp nghiên cứu (123456)
Trường: Đại học Ngoại ngữ - Tin học Thành phố Hồ Chí Minh
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Phương pháp nghiên cứu (123456)
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Đại học Ngoại ngữ - Tin học Thành phố Hồ Chí Minh
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23:47 9/8/24
Phương pháp nghiên cứu khoa học
TOPIC: Establishing loyalty in e-commerce: The influence of
company and customer aspects Abstract
Since maintaining a pleased customer core is an appropriate objective, the
connection between loyalty building initiatives and financial outcomes seems a bit
unclear. The problem of how well it works in the real life in the present day digital world
of interactions among loyalty and other elements is far more complex compared to the
times gone by. Long-term achievement in internet shopping requires being aware,
recruiting, and planning for loyal customers. Due to the intricate relationship between the
organizational frameworks and customer related factors that could impact and promote e-
loyalty, it may be difficult for businesses to execute these kinds of activities successfully.
To establish the connection between e-loyalty and other elements, including business
credibility, e-satisfaction, site knowledge, reputation, congruence, perceived value, care,
choice, shopping experience, and shopping involvement, this study uses the PLS-SEM
method. The findings that 9 of the 10 theories have been confirmed by examining at these
connections. Because of the low level of customer satisfaction, e-satisfaction,
nevertheless, was not considered a key supporting factor to establishing loyalty. Thus,
becoming mindful of these factors is going to be a crucial tool for e-business to correctly
interpret the actions of their customers.
Keywords: E-commerce, E-loyalty, customers characteristic for e-loyalty, business factors in e-commerce about:blank 1/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học CHAPTER 1: INTRODUCTION
1.1. Background of study
In last few years, the development of e-commerce has proven rapid. An e-
commerce transaction starts when the seller publishes ads for goods on a online platform,
which was embraced by customers the examine the product’s norms, prices, and shipping
options, pick whichever products they want to buy, and continue to the checkout site
(Ribadu & Rahman, Citation2019). The relevance of internet shopping when assessing
the manner in which e-commerce businesses are able to employ cutting-edge
technologies to provide satisfied client value is shown through the fact that modifying
these goods to particular sells and aimed consumer groups boosts e-commerce sales
amounts and reduces the price tag for current data that buyers permission. Seeking to
determine those factors impacting consumer happiness in e-retail, including the quality of
data, perception safety, and concern about privacy (Ahmad et al., Citation2017; Rita et
al., Citation2019; Vasic et al., Citation2019), the research that is presently undertakes
research in this field. According to Szymanski and Henard’s (2001) meta analysis,
contentment contributes to fewer than 25% of the differences in how often one purchases 1.2. Research gap
Verhoef (2003) investigated the influence of happiness in combination with
additional factors on desertion and client share growth and discovered no significant
immediate impact for satisfaction, considering the fact that multiple studies indicate a
beneficial relationship between satisfaction and loyalty. The only factors that were found
to have an important beneficial immadiate effect on retention of clients were emotional
conmmitment as well as program membership. Nevertheless, when relationship age is put
into consideration, satisfaction is impacted. The approach employed for assessing loyalty
(intentions and real behavior) has a bearing on the results additionally. For example,
Seiders et al. (2005) find that consumer happiness has little impact on the frequency of
purchases but has a substantial benefical impact intentions to repurchase. It results in an
absence of applicable empirical results related to the multiple psychological and
emotional indicators of loyalty and also the significance of client, interpersonal and
market-specific variables for comprehending the connections among loyalty and other
elements (Garbarino & Johnson, 1999).
1.3. Research objectives about:blank 2/39 23:47 9/8/24
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- Make up the void through investigating the connection between other factors and e- loyalty
- Figure out which migration element has a greater affinity with the commitment of
customers in embracing to develop e-loyalty.
CHAPTER 2: LITERATURE REVIEW
2.1. Theoretical Background and Conceptualization of E-loyalty
Anderson and Srinivasan (2003) claimed that commitment to a brand was at first
mainly linked with a regular purchasing pattern. In addition, they kept that behavioural
assessment simply is inadequate of assessing or preserving loyalty as it can’t distinguish
the distinction between true devotion and counterfeit loyalty due to factors like an
absence of suitable and pertinent choices for buying. Take the situation that follows as an
example. Because of many recurring visits, a customer might appear to be dedicated just
one shop. She could be devoted, nevertheless, because she has limited options. Both
behavioural and psychological factors must be examined at when evaluating loyalty. E-
loyalty can be defined as a consumer’s positive opinion of an online shopping system as
evidenced by an increasing number of repeated purchases.
Due to various research, the extent of consumer loyalty in online transaction
commerce differs between both people and companies (Grewal et al., 2004). Moreover,
Anderson and Srinivasan (2003) added that a deeper understanding of the components
that give rise to such modifications in consumer loyalty might help businesses to develop
techniques that maintain consumers and boost revenue. Two distinct collections of
requirements have been found to impact on online loyalty using in-depth field surveys
and primary information obtained from a customer panel. Based on the research of
Srinivasan Swaminathan, Rolph Anderson & Lei Song (2018), business credibility,
esatisfaction, and site knowledge are investigated in this study. We also look into the
reasons behind essential company factors that improve managerial awareness and
decision-making. The reputation of the e-company and its congruence with the
customer’s self-image happen to impact the business’s credibility. Consumers’ perceived
value, care, and choice are known to affect e-satisfaction. Site knowledge is influenced
by customer experience and shopping involvement.
In line with the study of Srinivasan Swaminathan, Rolph Anderson & Lei Song
(2018), this writing work is organized as follows: First, the multiple organization and
customer aspects that impact e-loyalty. Then, we will define hypotheses, address about:blank 3/39 23:47 9/8/24
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methodologies, provide the analytical data, and propose our overall findings with expert suggestions.
2.2. Business Credibility
The degree to which a person perceives a website as a trustworthy service provider
with the necessary capabilities is considered the level of an e-business's credibility. When
a consumer is uncertain that e-business will honor its agreements, it is unreasonable to
assume that the customer would be devoted to the company (Flavián et al., 2006).
Customers are more likely to recommend a trustworthy company when they feel confident doing so.
For several reasons, credibility in e-commerce is crucial. First, because the results
of online transactions are unclear, the typical consumer perceives a larger level of risk in
electronic purchases than in traditional ones (H. Wang et al., 1998a). Customers in
Internet commerce, for instance, must rely on the website's perceived trustworthiness.
Many online customers are concerned about making purchases of items that are not
physically present at the time of the transaction and letting the firm capture, utilize, and
perhaps sell their credit card and personal information (H. Wang et al., 1998a). Second,
unlike customers who buy in a real store, online shoppers are unable to hold, taste, or
smell the item before making a selection.
Hypothesis 1: Consumers are going to be more loyal to an internet-based company if
they consider that to be more trustworthy.
2.2.1. Reputation and Congruence Impact on Business Credibility:
Reputation is characterized by Doney and Cannon (1997) as "the extent to which
organizations and individuals in the sector believe that a vendor is truthful and worries
about its consumers." The veracity of sellers in the minds of clients continues to be an
aspect in their standing. A good reputation will have a positive effect on the minds of
customers and vice versa. Studies show that factors such as reputation, reliability, and
ethical behavior can influence customers' perception of a company's reputation. Another
case that affects the customer's perceived reputation of a company is "selfimage". Sirgy
(1997) defines "self-image fit" as the match between the customer's selfimage and the
product and/or supplier's image. Consumers’ desire for self-consistent, from beginning to
end uniformity could impact their choices when making purchases. When someone has
an upbeat attitude toward of themeselves, working is not needed through any conflicting
issues, so customers tend to feel that the business is trustworthy. If the homogeneity
happens to be low, the inconsistency will increase and customers feel that the business is
less trustworthy. Besides, the fit between product values, company messages, and actions about:blank 4/39 23:47 9/8/24
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can also play a role in shaping credibility. Overall, building a positive reputation along
with maintaining perfect congruence contributes to a business’s credibility. a/ Reputation
A previous study has suggested that the credibility and reputation of an
ecommerce business positively affect the perception of product quality and online
purchase intention of customers (Biswas & Biswas, 2004; Lee & Tan, 2003; Yen, 2006).
In the online shopping environment, tangible intrinsic factors are limited, and the security
of electronic transactions is a common concern among many customers. External factors
play an important role as the foundation for users to decide on the overall evaluation of
the product. There are many types of external signals online such as manufacturer name,
price, warranty, reputation, etc. In particular, the "reputation" signal is probably the most
researched topic, that is the reputation - brand of the manufacturer/retailer. When
products are manufactured or distributed by a well-known or credible enterprise,
consumers tend to be willing to buy them because they perceive the products as quality
products. In addition, the concepts of “retailer reputation”, “business credibility” and
“brand name” are closely related, all referring to a positive perception of and trust in the
business of customers (Herbig & Milewicz, 1995). The level of risk, when customers buy
products from a reputable retailer, is less than when they buy from a less famous retailer
(Hendrix et al., 1999). Relying on reputation as a cue to form product reviews among
consumers. Business reputation can improve or develop a retailer's reputation and vice
versa (Chu & Chu, 1994; Dodds et al., 1991a).
Hypothesis 1a: The greater the reputation of e-business, the higher the e-business's credibility. b/ Congruence
As previously stated, researchers investigated media effects through the prism of
source reliability. But several researchers have concentrated on the contextual impacts of
the media environment on advertisement efficacy. Because commercials are integrated
into editorials, programs, or other vehicles, the total media environment or context
influences consumer responses to advertisements put on the vehicle (Cho, 2003).
Congruency is important to this contextual impact. Congruency has been shown in earlier
studies in traditional media to have a favorable influence on advertising efficacy (Park
and Young, 1983). That is, the relationship of a program's, editorial's, or Web site's
content to the commercials put in it is often thought to elicit more favorable ad-related
responses. Implementing this line of research to internet advertising, studies have
revealed that users are more receptive to banner ads when the content of a website and about:blank 5/39 23:47 9/8/24
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the product category of the banner advertisement are relevant (Chu et al., 2005; Zanjani & Chan, 2011).
However, several earlier research contends that this is not always the case.
Congruency can occasionally have no impact, occasionally a bad effect, and occasionally
a good one (Tse, 1999). For example, Moore and colleagues (1998) discovered that while
the compatibility of the content of the Web site with the promoted product category
favorably influenced views toward the ad and the brand, it was adversely correlated with
ad recall. Consideration of additional configurational elements is a suitable method for
exploring the congruence effects. For instance, Shamdasani and associates (Shamdasani
et al., 2001) proposed product engagement as a modulator of congruence effects. In other
words, the congruency effect has minimal influence on customer responses to banner
advertising for low-involvement items compared to high-involvement products since
there is less correlation between the content of a Web site and the product category. In
addition, a more recent study by Wang and Calder (2009) claimed that media context
effects should be investigated in a dynamic setting since they incorporate various
contexts of the media environment. They discovered that program-ad compatibility was
influenced by the degree of intrusiveness of advertising and the transportation experience.
A banner advertisement on a Website containing ads which are continous
environment might be more informative and alluring to consumers since surrounding
information (like a banner ad) can be digested alongside one's primary job (like reading
Web site content) (Cho, 2003). Customers are more likely to process advertisements that
are relevant to the website they are visiting. Due to the restricted amount of space and
information contained in banner advertising, the advertiser's trustworthiness can be a key
indicator when assessing both the ads and the products being promoted. If the advertiser
has a high level of reputation, the material on a website that is relevant to their area of
expertise may further enhance their credibility. However, if the advertising lacks
credibility, the complementary material could instead be irrelevant or counterproductive,
having little to no influence or even harming the advertiser's reputation. Consumers react
more favorably to banner advertising that is relevant to the content of a renowned Web
site than to those that are relevant to a Web site with a lackluster reputation when it
comes to high involvement items, according to Shamdasani and colleagues (Shamdasani et al., 2001).
In other words, a suitable advertisement with the website's content may be
interpreted as the website endorsing the offered goods. In this regard, if the website is
seen as being new or non-established, the relevance may have a negligible effect on
improving the source's reputation. To evaluate the relationships between the promoted about:blank 6/39 23:47 9/8/24
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product Web site content congruency, advertiser credibility, and Web site repute,
respectively the following hypotheses are developed.
Hypothesis 1b: The more congruence of customers' self-image with e-business, the higher
the perception of the prestige of the e-business 2.3. E-satisfaction
As stated by Oliver (1981), the “summary psychological state that occurs when the
emotion surrounding unfulfilled hopes merges with a consumer's prior feelings about the
consumer experience" is what is envisioned under the term of satisfaction. Another
concept from Eugene W. Anderson and Mary W. Sullivan (1993) reported that
satisfaction is also described as an emotional customer condition that comes from an in-
depth examination of every component of the consumer relationship. In addition,
Srinivasan Swaminathan, Rolph Anderson & Lei Song also (2018) believed that, in
today’s world, e-satisfaction is referred to as a customer’s enjoyment of his or her recent
purchase transaction with a particular electronic commerce business. A discontented
customer is more willing to search for information about alternatives and to respond to
competitive advances than a delighted one. Similarly, Anderson and Srinivasan (2003)
mentioned that an unhappy customer is more inclined to disagree with attempts by his or
her present shop to build a greater connection and to take steps to reduce their
dependence on that supplier. As a consequence, Srinivasan Swaminathan, Rolph
Anderson & Lei Song (2018) clarifies that, frequently, a disappointed customer may be
eager to restructure the relationship. A certain level of satisfaction seems to be crucial
when determining loyalty. In connection with the above assessments, we hypothesize:
Hypothesis 2: The stronger an e-business perceived e-satisfaction, the better its customer loyalty a/ Perceived value
Following the research of Srinivasan Swaminathan, Rolph Anderson & Lei Song
(2018), the perceived value of goods purchased at a designated site has the potential to
affect a customer’s degree of e-satisfaction. According to Zeithaml (1988), perceived
value is “the consumer’s overall assessment of a product’s utility based on perceptions of
what is received and what is given”. Empirical research in the world of Internet literature
of high quality reveals that consumers’ perceived value leads to esatisfaction (Hsu et al.,
2013). This concept is, once again, claimed by Sakun Boon-itt (2015) that customer’s
perceptions of service value are closely linked to their comprehension of the outstanding
value that they got from a service exchange with a service provider, along with how
customer e-satisfaction represents the customer’s overall emotion derived from that about:blank 7/39 23:47 9/8/24
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value. The term perceived value originates from equity theory, which analyzes the
percentage of the consumer’s outcome/income to the service provider’s outcome/income
(Oliver & Desarbo, 1988). Bolton and Lemon (1999) also confirmed that the concept of
equity relates to the customer’s perception of what is fair, right, or deserved with the
perceived cost of the product.
Researchers additionally found a link between perceived value and
purchase/repurchase desire (Chiu et al., 2005; Dodds et al., 1991b; Parasuraman &
Grewal, 2000). As Hsin Hsin Chang, Yao-Hua Wang & Wen-Ying Yang (2009) stated in
their research, perceived value assists an e-business’s loyalty by limiting a consumer’s
need to seek out other providers of services. Customers are more likely to move to other
competitors for greater perceived value when perceived value is low failing in loyalty.
Even happy customers are hesitant to come back to an e-commerce site if they consider
they are not getting the best value for their money. They will instead seek out other
suppliers in an ongoing bid to get a better deal (R. E. Anderson & Srinivasan, 2003;
Chang, 2006). When customers think that their current selected e-business supplier offers
greater overall benefits than opponents, the most significant connection between
customer satisfaction and loyalty appears to be present there.
As a result, this study shows that customer-perceived value has an important
moderating impact on the link between consumer fulfillment and consumer loyalty. This
brings us to the third hypothesis.
Hypothesis 2a: The more perceived value a customer has, the more consistent the
connection between e-satisfaction and e-loyalty b/ Care
To be more understanding of this term, Srinivasan Swaminathan, Rolph Anderson
& Lei Song (2018) said that care indicates the level of concern that an organization
exhibits with its clients. A business’s extent of worry conveys itself by decreasing
unsuccessful attempts at customer service performance. For instance, an online retailer
that hopes for showcasing a high level of customer service might prevent downtime on its
website. Customer service also includes activities such as suitable billing and timely
delivery of goods. Service disruptions are likely to hurt customer satisfaction ratings.
In accordance with Srini S. Srinivasan, Rolph Anderson, and Kishore Ponnavolub
(2002), Care relates to e-retailer’s attention to both pre-and post-purchase user experience
activities that are aimed at assisting both immediate sales and longterm client
connections. Customer care can be seen in the e-retailer’s attention to detail in order to
ensure that there is no breakdown in service, along with the concern displayed in rapidly about:blank 8/39 23:47 9/8/24
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solving any issues that do occur. “In the physical world, if I make a customer unhappy,
they’ll tell five friends, on the internet they will tell 5000”, says Poleretzky (1999, p.76),
Likewise, an online customer has nearly instant access to rivals, making changing to a
competing supplier simple. E-retailer must subsequently take great consideration for their customers.
As perceived care shows the quality of service provided to customers, it is
considered to have a positive impact on customer satisfaction. According to Md. Anisul
Islam, Mohammad Khadem, and Ahmed Sayem (2012), care in e-satisfaction was
separated into ten dimensions: specifically tangibles, reliability, responsiveness,
communication, credibility, security, competence, courtesy, understanding, and access.
These original dimensions were subsequently updated and minimized to five dimensions:
namely tangibles, reliability, responsiveness, assurance (including competence, courtesy,
credibility, and security), and empathy (including access, communication, understanding,
and the customer) (Zeithaml & Berry, n.d.). Under these definitions, we have decided to hypothesize that:
Hypothesis 2b: The more e-business takes care of their users, the more e-satisfaction and e-loyalty customer has c/ Choice
According to Srini S. Srinivasan, Rolph Anderson, and Kishore Ponnavolub
(2002), when compared to a traditional shop, an e-retailer shop may frequently offer an
expanded selection of categories for products and a greater variety of goods within each
category. A store in a mall is limited by the availability and cost associated with floor
space, while its online counterpart is not. E-retailers can also develop connections with
other virtual vendors to provide consumers with a wider selection.
When buying things, plenty of customers do not want to deal with many sellers.
Consumers’ search expenses related to shopping between vendors grow with the number
of comparable choices, according to Bergen, Dutta, and Shugan (1996). In contrast, Srini
S. Srinivasan (2002) believed that increasing the number of available options at a single
e-retailer could dramatically minimize the opportunity cost of time alongside the real
costs of annoyance and search spent in virtual store hopping. The eretailer with the most
choices may emerge as the prevailing, top-of-mind location for one-stop shopping, engendering e-loyalty.
Srinivasan Swaminathan, Rolph Anderson & Lei Song (2018) emphasized that the
depth and breadth of product lines supplied are commonly referred to as choice. It
involves up-selling and cross-selling chances. More choices enhance the possibility that about:blank 9/39 23:47 9/8/24
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buyers will discover what they are searching for. When only a few choices or types are
accessible, fewer customers will probably have their particular requirements met.
Furthermore, the amount of choices available raises a site’s perceived value. Customers’
feelings of satisfaction can be determined by this greater level of perception. As an outcome, we put forward:
Hypothesis 2c: The more variable choices of products an e-business make, the more e-
satisfaction and e-loyalty the customer have 2.4. Site knowledge
Business knowledge, as defined by Rao and Monroe (1988, p. 255), is “the
quantity of right data regarding alternatives to the item in question which is kept in mind
and the consumer’s opinions regarding the item they own understanding”. For the
purposes of this study, site knowledge is the degree to which users can recall in their
minds the goals, features, and navigation of a website. The benefits of site knowledge in
boosting e-loyalty are numerous. When a customer is more accustomed and at ease with a
website, they are more likely to feel positive about it.
Additionally, informed consumers don't need to waste time and effort looking for
more information because they are confident in their choiceIn other words, a consumer is
more likely to visit a website that they are already familiar with than to conduct a new
search each time a new purchase is being contemplated. Prior knowledge about the
desired product can help consumers focus their search because they can use it as an
advanced starting point when looking for products(Coupey et al., 1998). Frequent use of
the same e-store reduces the search effort required and encourages customers to buy
again. Only if customers comprehend how e-commerce operates will they develop loyalty
to it. Clients who have prior knowledge already have standards; clients who do not yet
have such standards are only starting to do so. Customers who are unaware of a specific
website are more likely to be uninterested in it and less likely to be loyal to it than customers who are.
Hypothesis 3: The more knowledgeable a customer has in e-business, the higher eloyalty they have.
a/ Shopping experience:
It is assumed that a person's prior interactions with a website will enhance their
understanding of it. How familiar a customer is with a website can be characterized by
their website experience. Customers learn valuable information about a website's
structure, features, offerings, and navigational style when they browse it. Customers can about:blank 10/39 23:47 9/8/24
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surf a website more quickly and confidently while learning about its contents by
becoming familiar with its subtleties. The effectiveness of knowledge programs is also
enhanced by increased familiarity with a company or product (Gommans et al., 2001).
Research on shopping experience has focused on associations with consumer
satisfaction, with less emphasis on consumer behavior variables(Brun et al., 2017; Waqas
et al., 2021). Online retailers strive to achieve consumer behavioral and attitudinal
loyalty. Although building loyalty is necessary to successfully manage customer
experience and sentiment, few studies have included both variables in explaining
customer loyalty, suggesting a gap in online content knowledge. The main contribution of
this study is the relationship between different experience dimensions and consumer
attitudes and behavioral loyalty. Based on the results of this study, retailers can identify
improvements to the shopping experience needed to attract loyal customers and
differentiate themselves from the competition.
Hypothesis 3a: The greater the individual’s experience with the e-business, the higher the knowledge.
b/ Shopping Involvement
According to Rothschild (1984), involvement is characterized as an irrational state
of energy, arousal, or interest. The degree of consumer involvement in multiple facets of
the consumption process that relate to items, advertisements, and purchases is referred to
in the context of consumer behavior (Broderick & Mueller, 1999). Josiam’s (2010)
introduction to the engagement construct as an important psychographic element in
consumer behavior offers greater backing for the importance of involvement theory in the
examination of purchasing habits. Involvement was recognized by (Foxall & Castro,
2011) for its impact on the growth of views, customer satisfaction, and brand loyalty.
When low-involvement products are taken into account, this outcome is less likely
because they lack much meaning for customers. Consumer reactions to promotional
media, attitudes and behaviors towards specific behaviors, and methods of decision-
making can all be affected by a consumer’s level of involvement (Kinley et al., 2010).
In particular, there is proof in the literature that involvement has a significant
impact on how customers perceive advertising as they frequently alter the number of
advertising messages they get and analyze according to their involvement level (Laurent
& Kapferer, n.d.). The ability to process information affects whether mindful and open
consumers are to the information they receive. Education mind, product experience,
relevant expertise, and message difficulty all influence a person's ability for
understanding and deciphering information (MacInnis & Jaworski, 1989; Petty & about:blank 11/39 23:47 9/8/24
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Cacioppo, 1990). However, experience and expertise provide the background for
recognizing a piece of information’s strong or weak points. The capacity to argue for or
against a piece of information (rather than just taking it quietly) is another manner in
which that knowledge can affect how a customer receives information. Internet
knowledge, or the degree of knowledge with the medium, can be gained through online
surfing or from additional sources ( such as technical manuals). However, consistent
Internet use is likely to give rise to Internet knowledge if the user undertakes intentional
or unintentional experiments and pays attention to the results of those trials. As a result, it
only makes sense to assume that using the internet will result in a person understanding more about it.
Hypothesis 3b: The more customers get involved in the business’s electronic commerce
platform, the higher their e-loyalty gets in e-business 2.5. Research Model
Based on the conceptual and theoretical observations above, a framework of e-
loyalty and other aspects that affect it was developed. The proposed constructs and
hypotheses below have mutual impacts on the customer’s loyalty to e-business.
Figure 2.1 Research model about:blank 12/39 23:47 9/8/24
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CHAPTER 3: RESEARCH METHODOLOGY 3.1. Research design
Information and data collected directly for routine research
purposes are referred to as primary data (Kothari, 2004). Using the
main data enables invesigators to gather data that is relevant for their
study objectives. The questions researchers ask in this study are aimed
at obtaining Internet-friendly information. context of purchased
consideration. as a result, to author gathers information and feedback
through her online questionnaire. A variety of methods can be used to
collect primary serveys, including focus groups, interviews, phone
interviews, an online serveys. among all alternatives, the authors
chose an online questionnaire survey. this is because online serveys
can reach my focus groups quickly and we're right participants with
ease and flexibility. Also, here are her three main research methods:
essay, observations, questionnaires. Questionnairres are often used,
especially for collecting data from a broad sample of a community
rather than focusing on one person. Moreover, the survey is incredibly
easy to conduct, saves a lot of time, and provides a lot of data quickly
(Kelley et al, 2003). Addtionally, it is fairly accurate and reliable,
making it useful for attitude and behavioral studies. Based on these
descriptions, this survey can be used for data collection in this study. 3.2. Sampling method
The research’s sample was picked via methods of non-probability sampling which
included decision-making. When implementing subjective sampling, investigators select
samples for an assignment according to their knowledge of the topic in an approach that
guarantees every participant had a unique combination of features (Taherdoost, 2016).
When a participant is asked whether or not they are familiar with utilizing a specific item
or doing specific duties, it can be used (Alchemer, 2018). Random sampling is allowed
for this research because the participants must be belonging to the iGen generation (those
who were born between 1995 and 2010) with smartphones and Internet access. In present
study, subjective non-probability sampling was utilized. 3.3. Sample size
One of the typical methods of sampling that may be stated is the ten-fold rule
(Rigdon et al., 2017). The size of the sample of PLS-SEM research studies ought to,
based on this technique, “corresponding to the greater frequently the largest number of about:blank 13/39 23:47 9/8/24
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initial indicators utilized to assess one element, or ten time largest number of structually
related routes directed at a particular construct in the structual model” (Rigdon et al.,
2017). The study framework used in this paper includes three hypotheses, every one of
which indicates an alternate route lead to an intent to make purchases. Therefore, 30 is
the recommended minimum size. The amount of participants for this research was 400,
which is 10 times the total amount of routes contributing to an intent to shop online.
3.4. Questionnaire design
The survey was split into two parts, with demographic information and critical
inquiries about regarding online feedback, consumer satisfaction and buying intent. A
pilot study was carried out with 30 online consumers who bought online before the main
investigation in order to improve the reliability and accuracy of the survey. Before it was
officially released, a few tiny modifications were implemented for the original survey. To
facilitate the quick and exact submission of the survey those polled, it was next converted
into Vietnamese. The questionnare has been revised from previous research. The 23
reliable scales were found in the literature and used to contruct the survey questionnaire.
A seven-point Likert scale is employed for grading, with 1 representing the most
disagreement and 7 the indicating the most agreement. The measurement components and
resources described in Table 3.1. are shown in the table below.
Table 3.1. Questionnaire structures Constructs Items References
EL1: I seldom consider switching to another (Swaminathan et Web site al., 2018)
.EL2: As long as the present service E-loyalty (EL)
continues, I doubt that I would switch Web site
EL3: When I need to make a purchase, this Web site is my first choice
BC1: This website or e-commerce features
(Low-quality products – High-quality products)
BC2: This website or platform is (Not at all Business Credibility
good at selling - Very good at selling) (BC)
BC3: This website or platform sells (Overall
inferior products - Overall superior products)
BC4: This website or platform is (Not
trustworthy at all - Very trustworthy) Reputation (RP)
RP1: This Website or platform is known to about:blank 14/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học
be concerned about its customers
RP2: This website or Platform is known to
be concerned about its customers
RP3: This Website or platform has a poor reputation in the market Congruence (CG)
CG1: The average customer of this Website
or platform is very much like me
ES1: I am pleased that i decided to make an E-satisfaction (ES)
arrangement via this site or network.
ES2: It was a right choice to purchase goods from this online store.
PV1: Products purchased at this Web site are Perceived Value (PV
(Very poor value for money - Very good value for money) (Srinivasan et al.,
CR1: The items I’ve purchased before from 2002)
this website have consistently been arrived Care (CR) on time.
CR2: I believe that this website or platform takes good care
CH2: This website doesn’t satisfy a majority (Swaminathan et Choice (CH) of my online shopping needs al., 2018)
CH3: The choice of products on this website is limited.
SK1: I think that I’m qualified enough to offer feedback on this site.
SK2: I would have to acquire little to Site knowledge (SK)
nothing if I decided to purchase goods from
this Website so as to arrive at a well informed decision.
SK3: I can get around this site very easily and find the goods I need. (Swaminathan et
SE1: How often do you use this Website or al., 2018) platform ? Shopping experience
SE2: How familiar are you with this Website (SE) ?
SE3: How much attention have you paid to this Website ?
SI1: When I buy on the Internet, I feel Shopping Involvement (Uninvolved – Involved) (SI)
SI2: When I shopping online, I feel (No energy – Energy) about:blank 15/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học about:blank 16/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học
3.5. Methods for analyzing data
The Partial Least Square (PLS) was used for analyzing and processing the data.
The fundamental demands for measuring scale, the number of samples, and residue
dispersion make the PLS tackle acceptable (Monecke & Leisch, 2012). Hare et al.,
(2014) say that ever since the 2000s, as the number of people has increased, here has
been additional papers of research using PLS-SEM. PLS-SEM works better than CB-
SEM in the following conditions, especially when it compared to market study for
strategic leadership, computer systems supervisors, company behavior, and pleasure
analysis: (1) Prevents problems related to small numbers of samples and unpredictable
processing times. (2) It is capable of estimating complex research models, notably
structural models that include many intermediary, the beginning and visible factors; (3)
Compatibla with prediction-oriented research (Sarstedt et al., 2014); In this research,
we apply PLS-SEM to determine the impact of e-reviews on customers' online
purchase intentions. Respondents were asked how they feekl about the measure using a
7-point Likert scale ranging from (1) Strongest disagreement to (7) Strongest agreement.
3.6. Assessing the outer measurement model
Before testing each hypothesis in the internal framework (structual model) an
assessment of the outside simulation (measurement model) has to be worked. As a
component of the aforementioned assessment, both the convergence and discriminatory
validity of the measurement framework, in addition to its calidity and dependability of it
(Cronbach’s Alpha and combined reliability), are studied. Table 3.3. shows that the
Cronbach’s Alpha value for internal dependability, an artificial consistency the metric
system, are all higher than the recommended cutoff value of the 0.70 (Hair, et al., 2016).
The findings in the identical table, nevertheless, additionally indicate that the composite
reliability scores are larger than the correct amount of 0.70 from Hair, et al., 2016.
The analysis of multiple things which are logically comparable can be referred in
as possessing “convergent validity”. For the purpose of to figure out the reability of
convergence, Hair et al., (2016) suggest employing sample average deviation (AVE), and
converge is considered fair if the value of AVE is bigger than 0.50. All the AVEs are of
significance and exceed 0.50, as indicated by Table 3.3. On the other hand, it has also
been suggested by (Hair et al., 2016) that an outside charging level could be used to
confirm the precisenss of convergence. As an outcome, when the ouside value is bigger
than 0.70, the truthfulness of convergence is verified. Table 3.4’s results render it very
clear that every value are higher than 0.70. about:blank 17/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học
Table 3.2 An account of the determining model quality Average Composite Composite Cronbach's Variance Reliability Reliability Alpha Extracted (rho_a) (rho_c) (AVE) BC 0.780 0.789 0.858 0.602 CH 0.877 0.897 0.941 0.889 CR 0.734 0.746 0.882 0.789 EL 0.768 0.779 0.866 0.683 ES 0.815 0.815 0.915 0.844 RP 0.711 0.747 0.837 0.633 SE 0.877 0.878 0.924 0.802 SI 0.708 0.709 0.873 0.774 SK 0.723 0.730 0.843 0.642
Table 3.3 Outer loadings of the measurement model BC CG CH CR EL ES PV RP SE SI SK BC1 0.757 BC2 0.722 BC3 0.834 BC4 0.786 CG 1.00 1 0 CH 0.953 2 CH 0.933 3 CR2 0.869 CR3 0.906 EL1 0.836 EL2 0.786 EL3 0.855 ES1 0.916 ES2 0.921 PV1 1.000 RP1 0.775 RP2 0.880 RP3 0.724 SE1 0.888 SE2 0.906 about:blank 18/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học SE3 0.893 SI1 0.874 SI2 0.885 SK1 0.817 SK2 0.778 SK3 0.807
The proper questions have to depend substantially on the idea under consideration
while carrying little on addditional constructs, based on validity discrimination.
Therefore, these components are simple to differentiate from the onesfound in different
systems. Multiple methods were used to evaluate the discriminatory reliability, namely
the Fornell-Larcker’s criterion , the crossloadings and the (Claes & David, 1981)
"Heterotrait-Monotrait" ratio (HTMT) (Henseler et al., 2015).
The standard Fornell-Larcker's criteria, which stating that the square root of AVE
is higher than the sum of correlation coefficients, was first came to light in Table 3.5’s
results. The cross-loadings are additionally examined, and the results in Table 4.6
demonstrate that each loadings have a important burden for its specific constructs while
having a weak load to irrelevant ones. Finally, the recently proposed HTMT ratio below
the 0.90 threshold has also been taken into consideration (Henseler et al., 2015). Actually,
Table 3.7 illustrates that every number satisfy the requiremenyt of less than 0.90. Based
on these results, the validity of a discriminant was discovered.
Table 3.4 Fornell-Lacker’s criterion BC CG CH CR EL ES PV RP SE SI SK BC 0.77 6 CG 0.51 1.000 9 CH 0.29 0.241 0.943 1 CR 0.56 0.446 0.354 0.888 8 EL 0.57 0.302 0.453 0.481 0.826 2 ES 0.59 0.539 0.267 0.620 0.445 0.919 4 PV 0.47 0.507 0.195 0.486 0.293 0.516 1.000 9 RP 0.66 0.437 0.234 0.490 0.363 0.546 0.393 0.795 3 about:blank 19/39 23:47 9/8/24
Phương pháp nghiên cứu khoa học SE 0.50 0.461 0.180 0.528 0.329 0.603 0.446 0.416 0.896 8 SI 0.53 0.415 0.187 0.537 0.381 0.575 0.427 0.408 0.676 0.880 3 SK 0.57 0.502 0.410 0.636 0.539 0.667 0.472 0.469 0.625 0.633 0.801 7
Table 3.5 Cross-loadings BC CG CH CR EL ES PV RP SE SI SK 0.75 0.41 0.35 0.56 0.68 0.49 0.36 0.42 0.41 0.43 0.53 BC1 7 0 6 1 4 2 1 3 7 1 2 0.72 0.32 0.16 0.38 0.41 0.35 0.29 0.34 0.36 0.45 0.38 BC2 2 0 7 6 4 3 8 0 1 4 8 0.83 0.43 0.25 0.44 0.35 0.52 0.40 0.65 0.39 0.39 0.47 BC3 4 0 4 4 9 0 7 3 2 7 2 0.78 0.43 0.09 0.34 0.28 0.45 0.41 0.62 0.40 0.38 0.37 BC4 6 6 2 3 1 4 0 1 1 0 5 CG 0.51 1.00 0.24 0.44 0.30 0.53 0.50 0.43 0.46 0.41 0.50 1 9 0 1 6 2 9 7 7 1 5 2 CH 0.29 0.25 0.95 0.34 0.45 0.27 0.18 0.24 0.19 0.17 0.41 2 7 0 3 8 2 2 5 1 9 2 6 CH 0.24 0.20 0.93 0.31 0.40 0.22 0.18 0.19 0.13 0.18 0.35 3 9 1 3 6 0 8 3 7 6 1 2 0.49 0.31 0.29 0.86 0.46 0.50 0.40 0.38 0.44 0.49 0.52 CR2 6 0 9 9 8 5 4 6 5 6 9 0.51 0.47 0.32 0.90 0.39 0.59 0.45 0.47 0.49 0.46 0.59 CR3 3 1 7 6 4 1 6 8 0 2 8 0.44 0.27 0.36 0.83 0.34 0.26 0.25 0.28 0.28 0.40 EL1 0.411 4 0 5 6 9 7 5 4 6 8 0.40 0.19 0.51 0.39 0.78 0.32 0.19 0.30 0.21 0.24 0.44 EL2 7 4 4 9 6 4 2 0 6 5 1 0.55 0.28 0.23 0.42 0.85 0.42 0.26 0.33 0.30 0.39 0.48 EL3 1 0 3 6 5 1 2 7 9 7 1 0.57 0.53 0.20 0.57 0.39 0.91 0.45 0.51 0.58 0.53 0.63 ES1 2 0 2 6 5 6 8 0 8 3 2 0.52 0.46 0.28 0.56 0.42 0.92 0.49 0.49 0.52 0.52 0.59 ES2 1 2 7 3 2 1 0 3 2 4 3 0.47 0.50 0.19 0.48 0.29 0.51 1.00 0.39 0.44 0.42 0.47 PV1 9 7 5 6 3 6 0 3 6 7 2 RP1 0.51 0.28 0.03 0.34 0.27 0.43 0.28 0.77 0.30 0.34 0.29 about:blank 20/39