The impact of the product description on purchasing intention in cross border e commerce evidence from vietnamese consumers

This studyemploys the commitmcnt-involvcmcnt theory, involvement theory and perceived trustbased on the hierarchy-of-effects model to create a three-stage model assessing howproduct cogn

VIETNAMESE CONSUMERS

Thuộc nhóm chuyên ngành : Kinh tế

TP Hồ Chí Minh, tháng 02/2024

Cross-border E-commerce (CBEC) has thrived and grown in recent years Due to the

more complicated interaction processes compared to domestic e-commerce, consumers experience more significant uncertainty Despite the rapid development and expansion

of CBEC into Vietnam, research on consumers’ intentions remains scarce This studyemploys the commitmcnt-involvcmcnt theory, involvement theory and perceived trustbased on the hierarchy-of-effects model to create a three-stage model assessing howproduct cognition affects purchase intention in CBEC Data was collected fromShopee platform users The analysis reveals that a high-quality product descriptionsignificantly positively affects product involvement, platform involvement, and

perceived trust (integrity and ability) Platform enduring involvement positivelyinfluences perceived trust (benevolence and ability), and platform situationalinvolvement affects all three indicators of perceived trust Interestingly, product

involvement does not significantly impact purchase intention, but platforminvolvement does Regarding perceived trust, ability and integrity do not significantly

affect purchase intention, while benevolence does This study highlights that althoughproduct descriptions may not directly influence purchase intention and perceived trust (benevolence), high-quality product descriptions play a vital role in attracting consumers and shaping their decisions on CBEC platforms Therefore, optimizing

descriptions can increase consumer engagement and trust

Keywords: Cross-border E-commerce, Product Descriptions, Shopee, Purchase intention.

2.2.2 The commitment-involvement theory 15

CHAPTER 3 RESEARCH MODEL AND HYPOTHESES DEVELOPMENT 18

1 Impact ofproduct cognition on behavior intention 19

3.1.1 Product description and purchase intention 193.2 Impacts of product cognition on variables ofthe affective stage 20

3.2.1 Product description and product involvement 20

3.2.2 Product description and platform involvement 213.2.3 Product description and perceived trust 22

3.3 Impacts among internal variables of the affective stage 23

3.3.1 Platform enduring involvement and perceived trust 23

3.3.2 Platform situational involvement and perceived trust 23

3.4 Impacts of variables of the affective stage on behavior intention 24

3.4.1 Product Involvement and purchase intention 24

3.4.2 Platform Involvement and purchase intention 25

3.4.3 Perceived Trust and purchase intention 26

4.1 Research Approach 27

4.3 Data collection 32

CHAPTER 5 DATA ANALYSIS AND RESULTS 34

5.1 Demographic descriptive statistics 34

5.2 Measurement model results: 35

6.2.2 Building Trust through Enduring Involvement 57

6.2.3 Focusing on Benevolence towards situational Purchase Intention 57

6.2.4 Resource Allocation for Cognitive and Affective Involvement 58

6.2.5 Considering Resource Allocation for Integrity 59

6.3 Limitations and Future Research

APPENDIX

REFERENCES

59

62 64

LIST OF FIGURE

Figure 4 Structural model andresults of PLS-SEM 46

LIST OF TABLE

Table 1 Observed variables using measurement ofresearch concepts 29

Table 3 Results of scale reliability and convergent validity analysis 37

Table 4 Results of discriminant validity analysis (Fornell & Larcker criterion) 40

Table 5 Results of discriminant validity analysis (HTMT) 41

Table 7 Collinearity statistics: variance inflation factor (VIF) result 43

Table 8 The f - Square index and the corresponding impact level 45

Table 10 R - Square and R - Square adjusted results 49

Table 11 Descriptive statistics of all measurement items 62

15 PT1 Perceived Trust Integrity

CHAPTER 1 INTRODUCTION

The rapidly advancing processes of economic integration and technological progress

have made the world smaller and more interconnected, creating a variety of opportunities for international trade and service It increases consumers’ awareness of both online purchasing and shopping destinations, which promotes the growth of CBEC According to an Accenture report in 2019, the CBEC market is developing at

twice the rate compared to the domestic e-commerce market The number of consumers using the CBEC platform is continuously increasing; by 2020, the number

of cross-border orders is expected to reach 9.3 billion (McKinsey, 2022), and the

global CBEC will be forecasted to account for 17% of the global e-commerce market

share by 2023 (Forrester, 2023) CBEC booms all over the world (Stoklosa et al.,

2018), and it is an inevitable development that no country can stand aside from

Vietnam is one of them The demand for cross-border shopping by Vietnamese consumers via e-commerce sites is growing, notwithstanding the fact that the method

of cross-border sales via e-commerce in Vietnam is still relatively new The proportion

of consumers who purchase products directly from foreign websites rises considerably from 49% in 2020 to 56% in 2021 (IDEA, 2022) Seeing the high demand and potential, Vietnamese e-commerce platforms have been developing internationalpurchasing capabilities; consumers buy goods from foreign sellers on Vietnamese e-commerce platforms, with that percentage having risen from 41% in 2020 to 57% in

2021 (IDEA, 2022)

However, in a CBEC environment, interaction processes are more complex, and

consumers may experience more uncertainly (Mou et al., 2017) as a result of the geographical distance barrier between buyers and sellers, causing differences in

cultures, languages, customs, and history (Zhu ct al., 2019) Product information

cognition represents a problem in CBEC that cannot be overlooked In this situation,the product description serves as a connection between the two sides (Kim et al.,

2017) In general, when consumers conduct online product searches, a brief

description of the product is shown The description is an important indicator for the consumer in determining whether or not it is of high quality (Gabriella & Agus, 2020)and making further decisions (Resatsch et al., 2008) If consumers are unaware of a

product, they will not express any desire or interest in it, thus ensuring that no purchase will occur From the perspective of a product seller on CBEC platforms, it is

critical to understand consumer behavior during online purchasing to properly targetthem and deliver information based on their request (Demangeot & Broderick, 2007)

Given the aforementioned considerations and previously released data, the purpose of

this study is to evaluate and investigate the role of the product description on purchase intention within the CBEC platform Thus far, the success ofdomestic e-commerce has

been thoroughly researched, whereas CBEC has received less attention to our

knowledge (Mou et al., 2017) In-depth studies in this area have mainly concentrated

on B2B strategics that encourage and foster cross-border trade, yet there are many limitations for B2C, with only exploratory aspects of the relationship between satisfaction with logistics quality and price (Do et al., 2023), the formation of consumer intentions in cross-border trade (Phuong & Tung, 2021), and CBECplatform selection (Cuong & Chau, 2021) Thus, this study can be incorporated intothe research system on consumer behavioral intents in CBEC with the goal of effectively exploiting the market potential In addition, the HOE model is used in the research to systematically forecast purchase intention, reflect the entirety of the processing response procedure performed by consumers as well as systematically

forecast purchase intention on the CBEC platform (Zhu et al., 2019)

Accordingly, Shopee, with a 63% market share, is one of the typical examples inVietnam's e-commerce, the largest market share in the entire country's e-commerce sector (Momentum Works, 2023) and right in the first quarter of In 2022, the platformrecorded the most impressive monthly visitation total of 84.520.000, followed by

Lazada, with only 16.970.000 visits (Iprice, 2022) This fact supports the choice of

Shopee as the study’s case so that the performance of the CBEC may be seen and studied in the context of Vietnam

While there are no statistics on the number of Internet users by age, it is thought that young people in Vietnam make up the majority of Internet users, with older individuals

using the Internet less frequently than younger ones According to a survey published

in 2023 by We Are Social, 54.1% of social network users in Vietnam were in the

18-34 age range In addition, Shopee’s most frequent user demographic is 18-34 years

old, who account for 81.63% of all desktop usage (Similarweb, 2023) As a result, for

the purpose of ensuring study efficacy, the research objectives are Vietnamese consumers aged 18 to 34 who have previously shopped on the Shopee platform

The study investigates a variety of points of view, including both the direct and indirect impacts of the integration of the two theories, and takes into account perceived

trust to figure out whether or not product descriptions influence consumers’ purchase

intentions in the CBEC environment The potential contribution of this research would

be to provide insight for suppliers and sellers, a guide to improve product awareness,

and optimize every stage of the journey, thereby driving buying behavior of

cross-border consumers This research collected data from 353 subjects and analyzedthe data using SmartPLS software to assess the given hypotheses

The theoretical basis of the research is expanded on in the following section After

describing the connections between the constructs and hypotheses, the paper moves on

to describe the study methodology and measurement techniques Based on the study's

results and limitations, the current article examines pertinent applications and possiblefuture research paths

CHAPTER 2 LITERATURE REVIEW 2.1 Conceptual Background

2.1.1 Cross-Border E-commerce (CBEC)

Cross-border E-commerce (CBEC) is a form of transaction that takes place between

different countries or customs areas, facilitated by an e-commerce platform and

cross-border logistics (Tmogroup, 2015) This trend has become increasingly prevalent

in the modern e-commerce landscape, particularly for small and medium enterprises

(SMEs), as it offers the potential to reduce trade barriers and stimulate growth in

otherwise limited marketplaces (Terzi, 2011)

While the advent of the internet and digital technology has significantly reduced the

impact of geographical distance on trade costs, other factors have emerged assignificant in the context of CBEC These include language and cultural differences,the quality of legal institutions, and the efficiency of payment and parcel delivery

systems These factors can significantly influence the cost and feasibility of online

trade (Blum & Goldfarb, 2006; European Commission, 2012)

In essence, CBEC is a complex concept that encapsulates various aspects of

international trade, digital technology, and logistics Its definition and implications can vary across different studies, reflecting the evolving nature of e-commerce and the

diverse perspectives of researchers in this field

Consumers often make reasonable decisions and rely on factors such as product

information, prices, benefits, and promotions to evaluate the quality of a product

(Gentry et al., 2006) Toulmin (1958) claimed that information sources that lead tostrong statements in purchase intention with the framework and well-organized content

arc likely to be more appealing and arc seen as more credible The persuasive power of quality in product description has been discovered in a variety of contexts

2.1.3 Perceived Trust

The presence of perceived trust significantly influences alleviating the uncertaintiesthat consumers typically encounter while going through the online shopping process.According to Lemire et al.‘s (2008) evaluation of the trustworthiness of onlineretailers' product descriptions, this study considered perceived trust as a composite construct comprising specific dimensions, especially the integrity, benevolence, and

ability of the product supplier (McKnight et al., 2002) On a CBEC platform,

consumers will engage in buying behavior and increase their perceived trust in a product seller ifthey believe that the seller has integrity, benevolence, and ability

Investigating the perceived trust on CBEC platforms, benevolence is the aim for companies to conduct themselves in a way that benefits their consumers whilebehaving with an egotistical economic goal (Mayer Ct al., 1995) Integrity expresses consumer expectations that sellers will perform their commitments ethically,

consistently, and in fulfillment of accepted standards and principles (McKnight, 2002).According to Mayer el al (1995), the ability is a collection of capabilities, competencies, and behavioral traits that enable a seller to impact the process of providing a product

2 1.4 Purchase Intention in CBEC

Following a consumer's overall assessment of a product, certain exchange behaviorsare referred to as buying interest This is a perception based on how someone feels

about a certain object As a result, opinions about items or altitudes towards brands,

along with the stimulation of outside stimuli, help to shape consumer purchasing

desires The propensity of clients to make online transactions is refeiTed to as buy interest in CBEC (Lu, 2016) Purchase intentions are formed under the assumption of a

pending transaction and, consequently, are often considered an important indicator of actual purchases (Chang Ct al., 1994)

2.2 Theoretical Background

2.2.1 The involvement theory'

The involvement theory introduced by Zaichkowsky (1986) relates to an individual's perceived relevance regarding an object according to instinctual needs, values, and

interests Zaichkowsky (1986) posits that involvement acts as a motivating force andthat ego involvement is utilized to forecast individuals' attitudes When individuals arestimulated or find themselves in a particular situation, they will establish a connection

between the stimulus and themselves, leading to heightened interest (Sherif & Cantril,1947) Numerous researchers have since investigated the validity ofthe participation

hypothesis through empirical investigations in the framework of traditionale-commerce For example, studies by Herrero & San Martin (2012) and Huang et al

(2010) contribute to the involvement theory, indicating that the amount of engagementhas a substantial impact on how consumers perceive information about the product(Zhu et al., 2019)

Consumers select products on CBEC platforms depending on their engagement and assess whether rational or emotional aspects are most crucial in the decision-making process and the development of behavioral intentions, claim Zhu et al (2019) Thus,

we distinguish between cognitive involvement and affective involvement when

describing product involvement Affective involvement describes how relevant an item

is to consumers based on their feelings, moods, and thoughts, whereas cognitiveinvolvement relates to how consumers assess the item's usefulness and functionality

(Drossos et al., 2014) So reasoning and factual knowledge are related to cognitive

involvement, but emotions and mood slates are related to affective involvement

2.2.2 The commitment-involvement theory ’

According to Zhu et al (2019), the commitment-involvement theory is an integration

of both the involvement theory and commitment theory It posits that when individuals perceive a connection or relevance to a stimulus or activity, they develop interest and commitment toward it (Sherif & Cantril, 1947; Hornback, 1971) Mou et al (2020) argue that platform involvement can be defined as an individual's perception of thesignificance of a platform stimulus or situation When consumers purchase on an e-commerce website, their level of platform involvement is reflected in how they

evaluate the importance of factors such as user interface, payment system reliability,

consumer service quality, etc Consumers’ level of involvement and value interaction

on that platform will be high if they consider these factors important and significantly

affect their buying experience

According to the commitment-involvement theory, Zhu et al (2019) categorize platform involvement into two types: platform enduring involvement and platformsituational involvement Platform enduring involvement signifies a continued interest

or sustained concern with the platform (Keusch, 2015), while platform situational

involvement pertains to temporary feelings evoked by specific platform situations (Havitz & Mannell, 2005) Enduring involvement is characterized by a consumer'slong-term interest in a platform This form of involvement is often associated with a

deep psychological bond between the consumer and the platform, which is cultivated

over time through repeated interactions and experiences (Beatty et al., 1988) Situational involvement, on the other hand, indicates a temporary interest in a

platform This form of involvement is often triggered by specific situations or events

and is not as enduring as enduring involvement (Zaichkowsky, 1985)

2.2.3 Hierarchy of Effects model (HOE)

The HOE model is a response hierarchy model that analyzes consumers, evaluateshow consumers respond to information, and systematically demonstrates the

progression from consumers' ignorance regarding a product to their actual buying

behavior (Smith et al., 2008) including three stages: cognition, affective and behavior

Consumers understand or interpret details about products in the cognitive stage, andthey also associate products and services with themselves (Barry & Howard, 1990) The effect stage could show the consumers' fervent preferences, including their desire,belief, and so on (Barry & Howard, 1990) Finally, in the action stage, consumers'

desire to make a purchase is piqued, and their strongly held preference prompts them

to make a decision on the products in question (Zhu el al., 2019)

Similarly, this study is to operationalize a logical procedure with three stages (sec inFigure 1), which sequence corresponds to how consumers process and respond in the

setting of CBEC

Figure 1 Conceptual Framework

According to Barry & Howard (1990), cognition is defined as a mental process that expresses itself in the form of consumer opinions, convictions, or product knowledge.Initially, consumers are unaware of the product on a CBEC platform In this situation,

a particular product description may pique consumers' interest, and sustain it for

enough time to develop the link between the product description and consumer's

product cognition (Smith el al., 2008) Since this link is made, the consumer becomes

conscious of the product and takes it into account when making a decision (Smith &

Swinyard, 1988) Wc refer to this stage as known as product cognition The team

research, therefore, proposes product description as an analytical variable at theproduct cognitive stage in the HOE model

In the affective stage, consumer attitudes and feelings for products and platforms areprogressively fostered, and consumers develop a positive view and a level of

preference for them (BaiTy & Howard, 1990) The consumer’s comprehension ofthe

product description gradually improves, on the one hand, leading to a higher level of perceived relevance to the consumer based on individual perceptions or emotions

(Mou et al., 2020) On the other hand, consumers get more engaged with the platform

where these products are displayed (Celsi & Olson, 1988), and as a result, they give their feelings greater consideration and devote more time to digesting or responding on

this platform Consumers can steadily build their trust in the product sellers in the

involvement process (Zhu et al., 2019) Thus, during the affective stage, we suggest product involvement, platform involvement, and perceived trust as analytical variables

The conation stage shows the intention to conduct an action (e.g., buy) or the activity

itself (Egan, 2007) on the CBEC platform The team research refers to this stage asknown as behavior intention After the affective phase, purchasing desire of consumers

is stimulated to elicit potential consumer actions We recommend purchase intention asthe analytical variable during the behavior intention stage

CHAPTER 3 RESEARCH MODEL AND HYPOTHESES DEVELOPMENT

The research team proposed a model with three stages in order to assess the both relationship direct and indirect between product description and purchasing intention within a CBEC context depending on the HOE model, the involvement and commitment-involvement theory, along with possible mediating impacts of perceived trust

Figure 2 Research Model

3.1 Impact of product cognition on behavior intention

3 1.1 Product description and purchase intention

An online product description serves as a means of showcasing a product’s

functionality and performance (Yao & Shao, 2021) as well as an essential variable that

sellers utilize to assist consumers in their product evaluation and purchasing choices(Khare & Rakesh, 2011) All of these factors of product descriptions impact potential consumers' purchasing decisions (Benlian et al., 2012) When consumers buy a product online, Park et al (2005) claim that the product description may elicit an

emotional reaction A high-quality product description fulfills or surpasses the

consumer's expectations (Kahn et al., 2002) This can lead to increased confidence inpurchasing decisions Alternatively put, product description plays a crucial role inreducing consumers’ doubts and uncertainties regarding the product The more detailed

the product description, the more positive the psychological response from consumers

approaching the product As a result, consumers become more inclined to participate

in various psychological activities associated with the product, such as making a purchase

Hl: Product description positively affects purchase intention.

3.2 Impacts of product cognition on variables of the affective stage

3.2.1 Product description and product involvement

According to Kitchen et al (2014), a person's information processing can go either a

"central route" or a "peripheral route” In “central route” circumstances, consumers will use extra cognitive effort to analyze the problem (Mou Ct al., 2019) Consumersare more likely to use the "central route" when the product description's quality is higher and tend to concentrate more on viewing product information (Nkwocha et al.,

2005)

A high-quality product description not only enhances the product's image but alsopositively impacts consumers' emotions about it, unlike an inadequate productdescription (Krishna & Ahluwalia, 2008) Al this moment, consumers hold a positive psychological concern for the product In addition, the involvement theory slates that

an effective product description can somewhat raise a product's potential worth and

strengthen its interaction with consumers This encourages consumer involvement inthe products and moves them from a state of disinterest to one of cognitivesclf-idcntification and successful full investment (Huang Ct al., 2010) Based on the explanation above, we propose the following hypothesis:

H2a: Product description positively affects cognitive involvement.

H2b: Product description positively affects affective involvement.

3.2.2 Product description and platform involvement

According to Ferns & Walls (2012), enduring involvement refers to a continuous

interest in a product A greater degree of enduring involvement will arise from thisresonance’s gradual alteration of consumers’ ideas, feelings, and psychologies

regarding the platform Consumers may develop a positive attitude toward a CBECplatform after reading a high-quality product description on it, strengthening their

relationship with it and increasing the platform's capacity for enduring involvement

(Mou et al., 2020)

Giving consumers highly relevant product descriptions might improve their situationalinvolvement on online platforms (Hsia et al., 2020) Additionally, Mou et al (2020) noted that an excellent product description on CBEC platforms can improve thepurchase experience for consumers Duc to the enjoyable nature of the purchasing

experience, consumers' hedonic gains will be increased in this instance As a result,

situational involvement may rise to a higher level

rhe commitment-involvement theory claims: high-quality product descriptions on a

CBEC platform can lead to an attitude of optimism and strengthen the relationship

between consumers and the platform, consequently enhancing enduring involvement;

Additionally, the product descriptions on the platform can influence situationalinvolvement by motivating a particular buying environment On this platform,

consumers may be placed in a particular buying circumstance by reading high-quality product descriptions We hence hypothesize

H3a: Product description positively affects enduring involvement.

H3b: Product description positively affects situational involvement.

3.2.3 Product description and perceived trust

Sunnafrank (1986) uncertainty reduction theory suggests that the duration and depth of communication during initial interactions can effectively decrease uncertainty and

foster trust between parlies This theory is particularly relevant in the context of

product descriptions in e-commerce Racherla et al (2012) emphasized the significantrole of product description quality in affecting consumers' evaluations of a product's

attributes

Detailed and accurate product descriptions make consumers feel cared for and belter informed about the product, creating a sense of benevolence from the seller This alsodemonstrates the seller's care and respect for consumers, enhancing their trust (Kim et al., 2017; Mavlanova & Benbunan-Fich, 2010)

When product descriptions are honest and unambiguous, the seller exhibits integrity in

their business practices This generates trust from consumers, who believe they arc not being deceived and that the product they purchase will meet their expectations (Kim et al., 2017; Mavlanova & Benbunan-Fich, 2010)

A high-quality product description not only provides information about the product but

also demonstrates the seller's ability to understand its product This creates trust from

consumers, who believe the seller has the ability to provide good products and services

(Kim ct al., 2017; Mavlanova & Bcnbunan-Fich, 2010).Thcrcforc, we hypothesize

that:

H4a: Product description positively affects benevolence.

H4b: Product description positively affects integrity.

H4c: Product description positively affects ability.

3.3 Impacts among internal variables of the affective stage

3.3.1 Platform enduring involvement and perceived trust

According to the Social Judgment Theory, individuals with higher levels of enduringinvolvement are capable of assessing a broader range of outcomes and are lesssusceptible to persuasion (Sherif et al., 1965; Hong, 2015) This suggests that consumers with higher enduring involvement are less likely to be influenced by other

platforms and tend to have stronger trust in their chosen platform and its products

Furthermore, enduring involvement can lead to a stronger perception of benevolence,

integrity, and ability This is because consumers with a long-term interest in a platformare more likely to develop trust in the platform's intentions, honesty, and competence (Hsia et al., 2020)

This is further supported by studies such as Điyang & Agus (2020), who found a

positive connection between enduring involvement and trust Based on the abovediscussion and the supporting literature, we propose the following hypotheses:

H5a: Enduring involvement positively affects benevolence.

H5b: Enduring involvement positively affects integrity.

H5c: Enduring involvement positively affects ability.

3.3.2 Platform situational involvement and perceived trust

According to Mou el al (2020), situational involvement can be enhanced by providing

consumers with highly relevant product descriptions, which can improve theirpurchase experience on online platforms This enjoyable purchasing experience can increase consumers' hedonic gains, leading to a higher level of trust in the platform's benevolence, integrity, and ability

Moreover, research findings by Hsia et al (2020) suggest that providing consumers with highly relevant product descriptions can improve their situational involvement on

online platforms This, in turn, can lead to a higher level of perceived trust Based on

the above discussion and the supporting literature, we propose the followinghypotheses:

H6a: Situational involvement positively affects benevolence.

H6b: Situational involvement positively affects integrity.

H6c: Situational involvement positively affects ability.

3.4 Impacts of variables of the affective stage on behavior intention

3.4.1 Product Involvement and purchase intention

According to Wang et al (2009), product involvement significantly affects consumer

behavior Especially in the context of online shopping, the level of involvement with

products may impact consumer attitudes and behaviors (Bian & Moutinho, 2011)

Consumers who are highly involved with the product are more likely to perceive it positively, giving rise to a greater feeling of personal pleasure and excitement As a result, this increased product involvement is related to an increased degree of purchaseintention According to the involvement theory developed by Drossos el al (2014), a substantial level of product involvement can affect consumers' altitudes about products, fostering the development of consumers' attitudes and behaviors

The concept of product involvement is categorized into two types: cognitiveinvolvement and affective involvement Product cognitive involvement is reflected in

consumers' engagement with a product's functional and utilitarian characteristics,

resulting in them seeking relevant knowledge while formulating their intentions about

a particular product (Drossos et al., 2014) On the other hand, affective involvement

greatly influences consumers' emotional connection to the product (Drossos et al.,

2014) Similar to cognitive involvement, a positive psychological response resulting

from this type of involvement can also contribute to the formation of purchaseintentions (Drossos et al., 2014)

H7a: Cognitive involvement positively affects purchase intention.

H7h: Affective involvement positively affects purchase intention.

3.4.2 Platform Involvement and purchase intention

Previous studies have found that involvement influences factors such as brand

attitudes, purchase intention, advertising attitudes, and online shopping behaviors

(Bosnjak et al., 2007; Huang et al., 2010; Yang, 2012) According to Zaichkowsky

(1986), the leading indicators of involvement are personal factors, stimulus factors,

and situational factors (i.e., brand familiarity) Bosnjak et al (2007) studied online

purchase intention and found that effective involvement was a significant indicator of

online purchase intention Platform involvement is likely to be experience-based and

remains unchanged; however, the situation changes (Patanasiri et al., 2018)

Additionally, Huang (2012) suggested that involvement is classified as a motivation

state that affects the extent and focus of the attention of consumers as well as overt behaviors, i.e., shopping and consumption activities Specifically, for the context of

online shopping or online retailing, Jiang Ct al (2010) noted that effective involvement

is associated with emotional, hedonism, and is derived from value-expressive oraffective motives

Regarding the two types of platform involvement in this study, sustained engagement

with platform products may be defined as the degree to which a platform is ingrained

in and motivated by a person's value structure (Ferns & Walls, 2012) When platform enduring involvement is strong, consumers feel psychologically more secure when

making decisions depending on product knowledge because they tend to be more

certain in their ability based on their knowledge (Alba & Hutchinson, 1987), or their

previous purchasing experience (Laaksonen, 1994) On the other hand, to some extent,

increased situational participation can minimize customers' uncertainties and lowertheir expenses and social hazards throughout the purchase process (Zhu et al., 2019).According to the commitment-involvement theory, high platform involvement

encourages consumers to dedicate more time and energy to the platform, which increases their propensity to engage in a range of purchase intentions and behavior (Im

& Ha, 2011) Asa result, we aim to verify both of the hypotheses that follow:

H8a: Enduring involvement positively affects purchase intention.

H8h: Situational involvement positively affects purchase intention.

3.4.3 Perceived Trust and purchase intention

According to Gefen et al (2003), taking part in online shopping necessitates consumers dealing with the social complexities involved with sellers' opportunistictendencies The perceived trust of consumers in product sellers affects their

dependence on their information and behaviors (Hajli et al., 2017) According toresearch on online shopping by Lim et al (2006) and Faqih (2013), consumers' trust

has a positive and considerable influence on their intention to purchase online Mayer

et al (1995) and McKnight (2002) posit that the trust of an individual is measured by perceived trust including benevolence, integrity, and ability These characteristics have

been mentioned as significant indicators that strengthen consumers' reliance, reduce

uncertainty during transactions, and foster longer-term relationships with online

product sellers (Suh & Han, 2003; Beldad el al., 2010; Blut el al., 2015) This

demonstrates that perceived trust will increase their engagement in "behavior related totrust" in online shopping, such as purchasing Therefore, we believe that perceived

trust will have a comparable favorable impact on purchase intention

H9a: Benevolence positively affects purchase intention.

H9b: Integrity positively affects purchase intention.

H9c: Ability positively affects purchase intention.

CHAPTER 4 METHODOLOGY 4.1 Research Approach

The authors have reached research questions and data collection by using quantitative

methods with the objective of focusing on the impact of the items listed in the model

on consumers’ purchasing intentions In particular, this type of research method is

suitable for our research topic because of the following reasons:

First, this method of research relates to the work of measuring variables, reviewing

and investigating relationships between variables to explore patterns, correlations, or

causal relationships between variables (Leavy, n.d.) In evaluating the influence of

product description on consumers purchasing intention, this objective is perfectly

aligned with the topic of this study

Second, the measurability of the method is a crucial point to help determine the reliability and suitability of the relationship in the research topic (Bryman & Cramer,

2012) When applying the quantitative method, the researcher will have a clearer view

of delecting the people's differences in a trait that is mentioned and studied (Bryman &

Cramer, 2012)

Finally, objectivity is also an appropriate value of quantitative research methods, reflected in transparency in research procedures (Bryman & Cramer, 2012) As a result, the authors are free to expand upon earlier research without being concerned

about any particular bias or expectations from the earlier researcher

4.2 Research design

Figure 3 Research process

The 5-lcvcl Likert scale, with the scale descriptions "Strongly Disagree," "Disagree,"

"Normal," "Agree," and "Strongly Agree," is the one that has been used in this study Respondents are asked to rate how much they agree or disagree with a series of comments made regarding a certain topic on the scale The Likert scale is also one of

the most precise tools for determining people's preferences, attitudes, and views regarding particular behaviors (Hair cl al., 2010; Leung, 2011)

This study uses a Likert scale to evaluate how Vietnamese consumers' attitudes toward purchase intention are measured through an online survey Besides, Hair et al (2010) argue that there is no absolute rule about which level of scale is the best choice

However, the number of levels on the rating scale should typically not exceed 5, as this

will make it challenging for respondents to make a decision The author claims that the

greater the number of levels the study employs, the greater the change in the data,

which is a crucial factor in data analysis As a result, the research paper’s use of a

5-level scale will make it simpler and more consistent for respondents to make their

decisions, especially minimizing the possibility ofresearch data being so

There are 5 research concepts used in this study, which are built on the theoretical

foundations from previous studies which are respectively:

Table I Observed variables using measurement of research concepts

The products’ descriptions were easy to understand

rhe products' descriptions are objective

The products* descriptions are credible

The products' descriptions are clear

Park et al

(2007)

2 Product Involvement

CU (Cognitive involvement) Shopping on Shopee is a very

important decision

Drossos et al.(2014)

CI2 (Cognitive involvement) Shopping on Shopee requires

thoughtful consideration

C13 (Cognitive involvement) There is a lot to lose if you select

the wrong products on Shopee

CM (Cognitive involvement) Shopee shopping is not primarily

logical or objective (*)

CI5 (Cognitive involvement) Shopping on Shopcc is based

mainly on functional facts (Search, browse, view, etc.)

All (Affective involvement) Shopping on Shopee expresses Drossos et al

SH (Situational involvement) Shopping activities on Shopee Mou et al

caught my attention (2020); Zhu et

SI2 (Situational involvement) I really enjoy the shopping

process on Shopee

(Situational involvement) Shopping on Shopee is an

al (2019);Havitz &

Mannell (2005)

SI3

activity that suits my current needs

SI4 (Situational involvement) Shopping on Shopee gives an

overview of my needs

SI5 (Situational involvement) I will feel disturbed if shopping

activities on Shopee are found to be not good

Ell (Enduring involvement) I enjoy shopping on Shopee even

if I don’ buy anything

Fems & Walls (2012)

EI2 (Enduring involvement) I consider shopping on Shopee to

be a relevant part of my life

EI3

(Enduring involvement) I am interested in shopping on

Shopee rather than other e-commerce platforms

4 Perceived Trust (PT)

PTB1 (Benevolence) I expect this Shopee’s product seller to have

good intentions toward me

Buttner &Goritz (2008)

PTB2 (Benevolence) I expect this Shopee's product seller puts

consumers' interests before their own

PTB3

(Benevolence) Ifproblems arise, I expect Shopee’s product

seller to be ready and willing to assist and support me

PTI1 (Integrity) I am happy with the standards by which

Shopee’s product seller is operating

Biittner &Goritz (2008)

PTI2 (Integrity) Shopee’s product seller operates scrupulously

PTI3 (Integrity) I can believe the statements of Shopee's product

seller

PTA2

PTA3

(Ability) Shopee’s product seller is very competent

(Ability) Shopee’s product seller is able to fully satisfy its consumers

(Ability) One can expect good advice from Shopee’s

product seller

Buttner &Goritz (2008)

5 Purchase Intention (PI)

PIl

PI2

PI3

I am interested in buying products on Shopee

In the future, I would buy products from Shopee

I am inclined to purchase products on Shopee in the future

Mou et al.(2020); Zhu et

al (2019); Hsu

Ct al (2016)

4.3 Data collection

4.3 1 Data sampling and collection

In order to ensure the representativeness of the data, this study uses a random survey method to collect samples The survey of this study is for Vietnamese consumers whoare individuals aged 18 to 34, currently residing and working in Viet Nam The data were collected over a week through an online survey using Google Forms According

to (Raykov & Widaman, 1995), SEM requires a large sample size based on the theory

of sample distribution Furthermore, Hair el al (2017) argued that a minimum sample

size of 300 is required to conduct SEM, and a good sample size should be 500 ormore In this study, 336 forms from respondents who had passed the filter stage wereused as the official sample size, ensuring compliance with the mentioned sample size requirements

4.3.2 Data Analysis Technique

We used a quantitative research method through the data obtained from the survey questionnaire Excel 2019 software was employed to filler and encode the raw data from the survey For the reversed scale (CI4: Cognitive Involvement - Shopee

shopping is not primarily logical or objective), we conducted data encoding and

altered the measurement direction of the scale (where value 1 would be transformed

into 5, value 2 into 4, and value 3 would be retained unchanged) to ensure that theindices are not disrupted during the model validation process due to the presence of a scale in the opposite direction compared to other measurement scales

SEM was used to examine the proposed model SEM is regarded as a modern andwidely used data analysis method, employed by numerous researchers worldwide totest research models in various fields The hypothesis testing and research model

examination using SEM have advantages over traditional methods like multiple regression, as it not only accounts for measurement errors but also allows the

integration of latent concepts with their measurements into a theoretical modelsimultaneously (Hulland Ct al., 1996)

From a technical perspective, there are two approaches to implementing SEM:

Covariance-Based SEM (CB-SEM) and Partial Least Squares SEM (PLS-SEM) Concerning data requirements, CB-SEM requires data to follow a normal distribution

or have a large sample size Therefore, PLS-SEM becomes a more attractive option in

this case, but it still necessitates considering how to handle outliers in the data.According to Xiao Ct al (2019), PLS-SEM is more flexible regarding data requirements, model complexity, and the specification of relationships to validate themodel compared to the CB-SEM technique

All statistical indicators and criteria in the measurement model arc conducted using

PLS-SEM through the SmartPLS software, which includes automated calculations This is also an advantage of PLS-SEM, as the integrated statistical indicators in the software help researchers save time by providing the necessary statistical indices For

the aforementioned reasons, we opt to use PLS-SEM in conjunction with the

Smart-PLS 4 software to examine the SEM model

CHAPTER 5 DATA ANALYSIS AND RESULTS 5.1 Demographic descriptive statistics

We conducted an online survey with 353 people and received 336 valid responses The collected data was encoded and categorized for further analysis The statistical results

are displayed in the table below:

Table 2 Respondent Demographics

Sample Information Frequency Percentage (%)

Source: Authors analysis survey data

According to the respondent's profile, the majority of CBEC Shopee users are female,

accounting for 65.8% Respondents between the ages of 18 and 24 made up 87.2% of

CBEC Shopee users Moreover, respondents who are studying and living in big cities

in Vietnam: Ho Chi Minh City, Da Nang, Ha Noi have 40.2%, 22.6% and 16.4% respectively The remaining 20.8% came from other provinces and cities Concerning their experience in Shopee's CBEC, 310 respondents have participated in CBECshopping through Shopee, accounting for 92.3% The remaining 7.7% have never shopped CBEC on Shopee since they feel that the product descriptions are not persuasive enough

The descriptive statistics of each observed variable is shown in the Appendix part

5.2 Measurement model results:

To test the measurement scales, we assess scale reliability, convergent validity, and

discriminant validity since it is the most important measurement model metrics for

PLS-SEM (Hair el al., 2017) We use Cronbach's Alpha and composite reliability (CR)

to evaluate scale reliability Convergent validity is examined by analyzing outer loadings and average variance extracted (AVE) from the measures Finally, the Fornelland Larcker criteria (as known as the Square root of the AVE (SQRTAVE)) and

Heterotrail-Monotrait Ratio (HTMT) are chosen to assess the discriminant validity

5.2.1 Scale reliability

According to Hair et al (2017), it is advisable to consider and report both Cronbach’s

Alpha and CR (as true reliability usually lies between Cronbach’s Alpha - representing

the lower bound and the CR - representing the upper bound)

The Cronbach's Alpha and CR varies between 0 and 1, with higher values indicating higher levels of reliability With a reliable scale, the obtained Cronbach’s Alpha should

be greater than or equal to 0.7 (Nunnally, 1978; Hair et al., 2009; DeVellis, 2012) In

terms of CR, Hair et al (2017) argued that in more advanced stages of research, CR values between 0.70 and 0.90 can be regarded as satisfactory Values above 0.90

(definitely above 0.95) arc not desirable because that means all the indicator variables

are measuring the same phenomenon and are therefore not likely to be a valid measure

of the construct Furthermore, CR values lower than 0.60 suggest inadequate internal consistency reliability

All Cronbach's Alpha and CR values (as shown in Table 3) of this model are wellabove the critical threshold of 0.70 It can be observed that the measurement modelmeets the requirement for reliability

loadings below 0.4 should be removed from the model When the values fall between

0.4 and below 0.7, the decision to keep or remove them depends on the researcher's

evaluation along with other indices like CR and AVE of that factor If both CR andAVE meet the recommended thresholds, observed variables with outer loadingsbetween 0.4 and below 0.7 may still be considered for retention in the research model.Regarding the aspect of evaluating the AVE, Hock & Ringlc (2010) proposed that a measurement scale achieves convergence ifAVE is 0.5 or higher This threshold of 0.5(50%) implies that the average latent construct accounts for at least 50% of the variance in each observed variable

Table 3 has shown that all outer loadings of the reflective constructs are above the

threshold value of0.70, which suggests sufficient levels of indicator reliability (except CI3: 0.651 and PD3: 0.678) Since all the AVE values arc higher than 0.5 (Hock &

Ringle, 2010) and CR met the recommended thresholds, we have decided not to

remove CI3 and PD3 (based on the arguments of Hair et al., 2017)

Table 3 Results of scale reliability and convergent validity analysis.

Composite Reliability

(CR)

Average

Variance Extracted