Factors Influence on Customers Repurchase Intention in Customer-ToCustomer E-Marketplace. The Case of Shopee Vietnam
Huynh Thi Vi Na
Nguyen Van Phuong
International University, Vietnam National University HCMC, Vietnam
Abstract
This paper empirically investigates the influence of customers’ perception on customer satisfaction and
repurchase intention towards one particular seller in customer-to-customer (C2C) e-marketplace in case of
Shopee Vietnam. By using the structural equation analysis (SEM) with a sample of 319 buyers who have
already used Shopee online shopping service. The result shows that customers’ perception of interaction and
information quality have a direct positive impact on satisfaction with the seller and online repurchase
intention, while product quality and price fairness perception only have an effect on customer satisfaction.
The result also indicates that customer satisfaction is the most significant factor that directly influences
customer repurchase intention in C2C e-marketplace. Moreover, the study also confirms the positive
influences of effective use of instant messenger and feedback-comment system on customer perceived
interactivity in the C2C platform. This study provides some practical implications for sellers and similar
operating platform to develop appropriate strategies and methods to retain customers in C2C e-marketplace.
Keywords: C2C; e-marketplace; repurchase intention; satisfaction; CMC tools; Shopee
JEL codes: M, Z33
1. Introduction
Thanks to the rapid growth of the Internet, e-commerce has quickly attracted the attention of the public as
well as the business community and researchers. Recently, along with the growth of online shopping,
Customer to Customer (C2C) e-commerce has been more and more popular in the e-commerce market. In
Vietnam, C2C e-commerce has been more active due to the entry of Shopee in 2015. If in the past, the C2C
model in Vietnam is an only personal business model that sells to individuals and most only appears on
forums and social networks. Individuals and small shops will post product information, all the remaining
procedures such as contact, payment, delivery are done manually. This method does not have much
performance and scale. Nowadays, the C2C model is becoming more professional. Although it is defined as a
marketplace, it is not similar to traditional market. Transactions are mainly conducted between buyer and
seller; all orders have been completed, automatically implemented through the electronic system. By this way,
sellers can benefit from higher sales, while buyers have more options for lower prices (Tian, Y. et al. 2015).
Founded in July 2015, Shopee is a Singapore-based mobile shopping platform. Shopee's strategy focuses

on the Southeast Asian market, including Singapore, Malaysia, Indonesia, Thailand, the Philippines, Taiwan
and Vietnam. In Vietnam, there are no promotional activities, but Shopee is ranked in top 3 online shopping
sites. Shopee develops based on the C2C model (customers to customers), the online platform service that
provides a place and opportunity for the sale of goods between the buyer and the seller. Shopee has
successfully integrated social networking like Facebook, Zalo, Twitter in their e-commerce platform in order


to enhance the effectiveness in communication between parties. Buyers and Sellers are connected, exchanged
directly through the features: Chat, Bid, Comment, Rating, Product Sharing and Tracking. These features help
buyers gather more information about the product and seller before they really feel confident for order.
Particularly, in recent times, Lazada.vn, Adayroi.vn, Tiki.vn, taking largest market share in Vietnam ecommerce market have shifted from B2C platform to C2C platform, which has shown the potential of C2C
development in Vietnam e-commerce market.
The increasing number of Vietnam e-marketplaces has broken down the entry barriers for the new
businesses in e-commerce market. The consequence is forming the intense market competition which is very
low profitability and survival rate for online businesses. It is noted that the expense for developing a new
customer in the e-marketplace is considered higher than in traditional one. But repurchased online buyers
have tendencies to spend more than they do in the first time, once buyer-seller relationship exists, profits will
grow faster and faster (Reichheld and Schefter, 2000). Therefore, in order to survive in the C2C e-marketplace
like Shopee, online sellers must have effective ways to attract previous customers to make another purchase
(Chen et al., 2017). Chiu et al. (2009) also indicated that in order to gain profits from a buyer, that buyer must
purchase at least four times at one same seller’s store. However, in e-marketplace, only half of them have
intention to repeat purchases. This leads to the following question: What factors impact online buyers’
repurchase intention to a particular seller? The main objective of this study is to examine the potential factors
affecting the decision to repurchase in C2C e-marketplace, thereby suggesting appropriate solutions to sellers
and website developers.
Numerous empirical studies have attempted to examine factors leading to online repurchase intention,
focus on defined buying factors buying (Kim et al. 2013); information systems (IS) success (Wang 2008);
customer satisfaction (Khalifa and Liu 2007); website quality (Shin et al., 2013); website identification (King et
al., 2016) and computer-mediated communication tools (Bao et al. 2017, Ou et al. 2014). However, these factors
above had not been integrated into a comprehensive model yet. This integrated approach is also lacking in

Vietnamese online buyers researches. Moreover, most of the e-commerce studies have examined primarily on
the business-to-customer (B2C) context, largely ignoring the C2C context. In order to fulfill the gap of previous
researchers and accomplish the comprehensive model of repurchase intention; this research will explore more
relevant literature about customer loyalty and from that develops an integrated model of repurchase intention
in C2C e-marketplace.
Based on the above argument, this paper aims to answer: 1) what factors impact on customer satisfaction
and repurchase intention towards one particular seller in the C2C e-marketplace? and 2) How do computermediated communication (CMC) tools influence interaction between buyers and sellers?
2. Literature review
Repurchase Intention
Fornell (1992) defined repurchase intention as a “consumer behavioral intention” using a service provider
again in the future, based on his or her previous experiences. In the online context, repurchase intention is
defined as revisiting intention in the future of online buyers at one same shop (Kim et al., 2012). Gruen et al.
(2006) find that customer repurchase intention is used to measure the customer loyalty in C2C online context.
Enhancing loyalty from customers, sellers can gain lots of competitive benefits because loyal customers tend
to buy and spend more, search more for information and willing to give positive word-of-mouth than firsttime customers (Jiang & Rosenbllom, 2005). Thus, the identification of determinants of intention to repeat
purchasing in the C2C e-commerce is important for both sellers and practitioners in predicting customer
behavior in the future. (Kim et al. 2013; Khalifa and Liu 2007; Shin 2013; Bao et al. 2017, Ou et al. 2014).
Satisfaction with seller


Customer satisfaction is viewed as a psychological state created from post-purchase evaluation when
customers’ needs are met or exceed the pre-purchase expectations (Oliver, 1980). According to Shankar, Smith,
and Rangaswamy (2003), online customer satisfaction is considered be formed from the customer evaluation
about previous experiences including searching, buying, and using a product. Bhattacherjee (2001) and Oliver
(1980), in his Expectation Confirmation Theory, suggested that satisfied customers will form repurchase
intention; because a dissatisfied buyer is more likely to search for alternative products and move to a
competitor than a satisfied buyer. Past studies were consistent with the findings that supported the positive
relationship between satisfaction with behavioral retention (Wang, 2008; Shin et al., 2013; Hsu et al., 2014).
This study thus proposes the following hypothesis:
H1: Satisfaction with seller has a positive effect on Repurchase intention

Perceived Product Quality
In previous studies, product quality perception is defined as the way consumers evaluate or perceive about
the product’s overall performance (Chen, 2003). Keeney (1999) suggested that to survive in the online business,
it is required that sellers must maximize product quality along with minimize product cost. Patterson (1993)
figured out that perceived product quality is the most significant factor impacting customer satisfaction.
Multiple researches have similar finding that supported a correlation between perceived product quality with
customer satisfaction (Sweeney, Soutar, & Johnson 1999; Tsiotsou, R. 2006, Lin; C. C et al. 2011). In the context
of consumers’ satisfaction, Tsiotsou (2005) further showed that there is a strong relationship between product
quality perception and intention to purchase, including predicting repurchase intention. Many studies have
provided the empirical evidence to support a positive direct effect of customer perceived product quality on
purchasing behavior including intention to repeat (Bei & Chiao, 2006; Tsiotsou, R. 2006; King et al., 2016).
Based on the above arguments, this study thus proposes the following hypotheses:
H2a: Perceived product quality has a positive effect on Repurchase intention
H2b: Perceived product quality has a positive effect on Satisfaction with seller
Perceived Information Quality
DeLone & McLean (1992) defined information quality as desired characteristics of the product information.
Online information quality is the buyer’s evaluation of the quality of information about products displayed
on the seller website (McKinney et al., 2002). According to previous papers, information quality has been
measured by following subconstructs: information accuracy; timeliness; format; relevancy; completeness;
clarity and understandability; timeliness; ease of understanding personalization; and reliability (Brown &
Jayakody, 2008; DeLone & McLean, 2003; Chen et al., 2017; Wang, 2016).
When sellers provide buyers with complete and helpful information, the buyers will take less effort to
conduct additional searches for diminished information (Liang and Chen 2009). This implies that high-quality
information displayed on the seller website demonstrates the sellers’ capability and their honest interest in
customers; which will influence consumers’ satisfaction towards this seller website (Chen et al. 2017). Hence,
according to Chen et al. (2017), the more customers perceived that the accuracy, format, reliability, and
completeness of the information displayed on buyer’s website, the more they will be satisfied with the sellers.
What is more? Information quality also enables customers to make comparisons among products, enhance
transaction security as well as understand purchasing procedures (Liu & Arnett, 2000). Thus, information
quality can be a considerable factor when online shoppers revisit seller website in future intentions to make

another purchase (Shin et al., 2013; Kim and Niehm 2009). This study thus proposes the following hypotheses:
H3a: Perceived information quality has a positive effect on Repurchase intention H3b:
Perceived information quality has a positive effect on Satisfaction with the seller
Perceived Price Fairness


Zeithaml, (1988) defines the perception of the price is a monetary sacrifice that customer must take to obtain
a product. For online shopping, product price fairness perception tends to be comparisons between vendors.
Herrmann et al. (2007) stated that price fairness perception is a consumer’s assessment and associated
emotions of whether their monetary sacrifice is more than or the same as competitors. Pingjun Jiang (2005)
indicated that in uncertainty performance as online shopping, a favorable price perception tenda to play a
significant part in increasing both online satisfaction and intention to return. Peatti & Peters (1997) also stated
that when price perception matches with customers expectation, the customers will make a repeat purchase.
In contrast, if customers perceive a monetary loss on price, they will switch to another online vendor
(Keaveney, 1995). According to Fornell, et al. (1992), price perception takes an important impact on customer
satisfaction since customers tend to concern on price when evaluating product and service value. Many studies
have provided the finding that supported an influence of fairness perception on customer satisfaction and
repurchase intention towards online shopping (Jiang & Rosenbloom, 2005; Grewal et al. 2004; Martin 2009,
Suhaily, L., & Soelasih, Y. 2017). This study thus proposes the following hypotheses:
H4a: Perceived price fairness has a positive effect on Repurchase intention H4b:
Perceived price fairness has a positive effect on Satisfaction with the seller
Perceived Interactivity
Newhagen et al. (1995) suggested interactivity including two dimensions: “(1) viewers’ psychological sense
of efficacy and (2) viewers’ sense of the media system’s interactivity”. In online context, interactivity is defined
as ability for a member in the website communicate with other members (Hoffman and Novak, 1996).
Perceived high interactivity may allow consumers to communicate with other members to access information
from the website, which results to greater control of their shopping experience (Ballantine, 2005). Thus, it is
suggested that perceived interactivity is understood as an antecedent of satisfaction (Shankar & Rangaswamy,
2003; Ballantine, 2005). In C2C markets, Chen et al. (2009) found that the core competitive advantage of a C2C
platform comes from members communication. This is explained by interactivity is significant determinant

for enhancing e-commerce loyalty because it enables customer to collect more information than searching
experiences so that it increases the amount of information available to the customer (Ha et al., 2010). Thanks
to communication, buyer and seller have more chance to understand each other which will create a long-term
buyer-seller relationship in online context (Ou et al. 2014). There are many studies have demonstrated that
interactivity is the significant determinant of repurchase decisions (Song & Zinkhan, 2008; Ha et al., 2010). The
following hypotheses are suggested:
H5a: Perceived interactivity has a positive effect on Repurchase intention H5b:
Perceived interactivity has a positive effect on Satisfaction with the seller
Computer-Mediated Communication Tools
According to Kaplan and Haenlein (2010), Computer-Mediated Communication Tools (CMC) are
considered as a mediator that connects buyer and seller as well as facilitates the communication effectively. In
C2C platform, typical CMC tools consist of the feedback & comment system and instant messenger (Ou et al.
2014). Thanks to these tools, buyers and sellers have opportunities to share information, experiences and
interact with each other (Cho et al. 2005).
Instant messenger is an “online private communication channel” which can mimic traditional interactive
face-to-face communications. Thus, customers easily exchange information by attaching pictures, get advice
from buyer, adjust the order information as well as negotiate for lower price (Bao et al 2016). On Shopee,
Instant messenger tool is embedded on the website and customers use it to communicate with buyers. Instant
messenger also combines picture or photo taking feature so that pictures of products can be sent directly to
seller to check the related information. Besides, Instant messenger is similar to well-known chat tools such as
Facebook messenger, Apple I-message, by which customers can use avatars and emoticons (such as smileys,


flashing icons) in diagrams which results in enhancing a buyer-seller relationship. In sum, the effective use of
Instant messenger in C2C e-marketplaces like Shopee facilitate communication and negotiation processes in
online shopping experience, as the result, it enhances interactivity perception from customers (Bao et al, 2016,
Ou et al, 2014).
Feedback & comment system plays a role of evaluation tool that allows customers evaluate to a specific
product after purchasing and the seller is also able to respond to customer’s feedback (Pavlou & Dimoka,
2006). In C2C platform, feedback & comment system can be viewed as a “two-way communication” tool in

term of ratings and evaluations. In details, when customers finish a transaction, both buyers and sellers can
rate and write detailed text comments about that transaction. Shopee encourages customers to give feedback,
comments by providing a promotion that for each feedback, customers will receive 5 points value as 5 VND.
Following Ou et al.’s (2014) study, feedback & comment system allows two-way and synchronized
communication in interaction between seller and buyer for both pre-purchase and post-purchase, so that the
interactivity is influenced by feedback & comment system (Bao et al. 2016). This study thus suggests the
following hypotheses:
H6: Effective use of Instant Messenger has a positive effect on Perceived interactivity
H7: Effective use of feedback & comment system has a positive effect on Perceived interactivity
Conceptual Framework
Base on previous studies (Kim et al., 2013; King et al., 2016; Bao et al., 2016), the conceptual model is
developed to illustrate relationships among variables mentioned in the hypotheses. Figure 1 illustrates the
research framework [see Figure 1].
3. Methodology
Research methodology
This study employs a casual research design with quantitative approach. The reason why choosing this
method is that quantitative analysis is considered an appropriate method to measure the degree and extent of
attitudes, ideas, performance, and other variables (Ledgerwood & White, 2006).
This study collects data through using online surveys. Target population that is appropriate to the topic is
online customers who have already used Shopee online shopping service. Moreover, the study focuses on
collecting data form university students including International University. The reasons for this is that
students are considered as large-scale internet users as well as represent a potential segment of online
shoppers. Thus, they can reflect the behaviors of online shoppers deeply (Li et al., 2006).
Research Design and Data Collection
The questionnaire must be designed in easy, clear and easy sentences for participants to understanding. It
consists of three main parts. The first part includes screen question to classify respondents to find the right
one. The second part includes questions that determine observations of factors mentioned in conceptual
model. The final part includes questions to collect demographic information such as: gender, age, career,
income per month, how long have respondent engaged in shopping online.
The pilot test is conducted test the questionnaire effectiveness, and then make more appropriate

adjustments in terms of content and language so that respondents can easily understand as well as access the
study. The suggestions and feedback from the pilot are included in the final version of the questionnaire. The
final questionnaire is used to test larger samples for the study.
The questionnaire for observations in Table 1 [see Table 1] is designed based on previous papers and pilot
test adjustments with the Likert scale of 5 points: (1) totally disagree;

(5) totally agree.

(2) disagree; (3) neutral; (4) agree;


Collecting data activity is spread out for nearly 8 weeks (including pilot tests). The questionnaire is send
private on Shopee and Facebook through Instant Messenger system to 400 people. 330 answers were returned
to the system due to invalid addresses, and so on. 11 of the surveys are eliminated from the study due to
missing data and random, resulting in a total of 319 valid responses which are used for further analysis in this
study.
Survey response
Table 2 illustrates proportion of respondents’ demographic information [see Table 2]. The number of female
respondents is greater than number of male respondents (69.6% > 30.4%). The majority of the customers are
from 18 to 24 with 73.4 %. Students and officers are familiar with the online shopping on Shopee account for
77.7% and 11.9%, that is why most of respondents in survey earn less than 3 million VND (62.4%). Moreover,
people have more experienced and keep online shopping habit for 2 years rate 46.7% has highest value, while
number of people have under six months experiences less enjoy shopping online on Shopee account for 25.4%.
4. Results
Statistical Product and Services Solutions 20.0 (SPSS) software and Analysis of Moment Structure 22.0
(AMOS) are used to analyze responses from participants. The data analysis is conducted with several tests;
the results of these tests are presented as below:
Exploratory Factor Analysis (EFA) and Confirmatory factor analysis (CFA)
First, EFA is conducted to reduce data into a smaller set of variables and then measure the number of factor
and the factor structure as a set of variables. After finalizing the number of factors, CFA is conduced to test

whether the measurement model fits with the data from survey, which EFA technique is not meant to measure.
At the first round of EFA test [see Table 3a], variables whose factor loading are greater than 0.5 grouped
into eight factors as expected in the proposed theoretical model. Item RI2 have a positive value of loading
value less than the recommended 0.5, while Item PP3 and PQ5 have a positive value of loading value minus
crossing value is less than 0.3, which does not satisfy with the threshold of EFA test (Hair et al., 2010). Thus
RI2, PP3, and PQ5 are deleted. After PQ5, RI2 and PP3 are dropped, the new adjusted EFA test has remain
items grouped into eight factors as expected in the proposed theoretical model [see Table 3b]. The value
loadings and cross-loadings of the remaining items are displayed in Table 3b. All items have factor loading
greater than 0.5 as well as the value of loading value minus crossing value greater than 0.3, which is consistent
with criteria for Exploratory Factor Analysis (Hair et al., 2010).
In order to evaluate model fit in CFA, the following indexes are applied: Chi-square/df ratio, Comparative
Fit Index (CFI), Standardized Root Mean Square Residual (SRMR), Residual Mean Square Error of
Approximation (RMSEA) and p of Close Fit (PCLOSE)(Gaskin, J. & Lim, J. 2016). In the CFA model fit results,
almost goodness-of-fit indices of measurement model are very good: CMIN/df= 1.914 (≤3); SRMR= 0.059
(<0.08), RMSEA= 0.054 (≤0.06) and PClose= 0.154 (>0.05). However, CFI of measurement model did not meet
the required value of 0.95 with the value of 0.930 but still in acceptable scale (Gaskin, J. & Lim, J., 2016).
Therefore, the low value of CFI does not distort the results of the study.
Reliability and validity
To test the reliability and validity, three indexes are used: (1) Cronbach’s alpha; (2) composite reliability
(CR) and (3) average variance extracted (AVE)
Composite Reliability (CR) and Cronbach’s Alpha are two important tests to evaluate the internal
consistency reliability. The rules for Cronbach’s Alpha and Composite Reliability as follow: the overall
Cronback’s Alpha > 0.6, Item-total correlation > 0.3., Composite reliability > 0.70 (Nunnally and Bernstein,
1994; Chin, 1998). Tables 4 presents the Cronbach’s Alpha and Composite Reliability (CR) of all items of all


factors after running SPSS 20.0 software. The table presents that Cronbach’s Alpha is significant showing that
most items are effectively measuring the same factor. All items which are over 0.3 satisfy the Item-total
correlation. Moreover, the CR results obviously satisfy the conditions of the test with the values are greater
0.7. Therefore, the internal consistency reliability is confirmed with confidence.

Average Variance Extracted (AVE) is the important tests in testing the construct validity and discriminant
validity to guarantee the fitness of measured model. The thresholds for this value are: the AVE value must
greater than 0.5, the correlations between two constructs are all smaller than the square root of the construct’s
AVE (Fornell and Larcker, 1981; Hair et al., 2014). In the first test (results are showed in the table 4), there is a
Convergent Validity concern in terms of factor “Effective use of Feedback & comment system” (FB) because
the AVE less than 0,5, which does not satisfy the conditions of the test. To solve the Convergent Validity
problem, the item FB4 is eliminated in order to improve the value of AVE. After adjustment, the results of
AVE value are showed in the table 5. All the value obviously satisfy the validity measure thresholds with the
AVE values are greater 0.5, and the square root of AVE values are larger than the inter-construct correlations;
Hence, the results the validity test satisfy to move the next steps.
Testing the hypotheses and discussion:
Structural Equation Modeling (SEM) test is used to test the hypothesized relationships in the proposed
conceptual model. Table 6 shows the results of SEM by AMOS 22.0 software.
The effects of perception factors on customer satisfaction with seller (H2b, H3b, H4b, H5b):
Based on SEM’s results [see Table 6], all these hypotheses are supported because their p-values are less
than 0.05. This implies that all factors of perception including Product quality (PQ), Information quality (IQ),
Price Fairness (PP) and Interactivity (IT) have significant positive impacts on satisfaction with seller (SA) with
their standardized regression weight of the direction are 0.293, 0.314, 0.222 and 0.134. This finding is similar
to the argument of Ballantine’s (2005), Lin C. C et al.’s (2011) and Kim et al.’s (2013) studies which highlighted
that consumer satisfaction is significantly impacted by the quality of product, information interaction and
price fairness. This shows that even though the current study examines in Vietnam C2C context instead of B2C
platform, and it still find that Vietnamese buyers in both e-commerce platform consider perceptions of Product
quality, Information quality, Price Fairness and Interactivity are important determinants in building online
customer satisfaction.
The effects of perception factors on customer repurchase intention (H1, H2a, H3a, H4a, H5a):
Based on SEM’s results [see Table 6], only three hypotheses are H1; H3a and H5a are supported because
their p-values are less than 0.05. In details, there are only three out of 5 factors namely Satisfaction with seller
(SA), Perceived Information quality (IQ), and Perceived Interactivity (IT) have directly significant influences
on customer repurchase intention (RI) with their standardized regression weight of the direction are 0.605,
0.194 and 0.204. This proves that Satisfaction with seller (SA) is the factor has the most influence on repurchase

intention (RI). The result of this study is appropriate with Hsu et al.’s (2013), Wang (2008)’s, Kim and Niehm’s
(2009) and Ha et al.’s (2010) findings suggesting a positive influence of customer satisfaction, perceived
information quality and perceived interactivity to repurchase intentions. Thus, seller in e-marketplace should
develop appropriate business strategies and methods to meet customers’ expectation, especially improve the
quality of information and interaction to maintain long term relationship with customers. This can be achieved
by developing the right mix of website content and services, especially after-sale service. In details, sellers
should proactively display important information such as a pop-up discount, hot deal or FAQs (frequently
asked questions) on their front sites. Ensuring information quality by providing relevant, sufficient, accurate,
and up-to-date information makes comparisons among alternative sellers easier for customers, so that they
are likely to remain their satisfaction and loyalty to that seller.


On the other hand, the direct relationship between perceived product quality (PQ) with repurchase
intention (RI) is insignificant since the hypothesis H2a was not supported (p-value = 0.828 > 0.05) [see Table
6]. This result is contradicted with previous research conducted by Tsiotsou, R. (2006); King et al., (2016) that
stated product quality perception takes an impact on repurchase intention. The difference in result between
this study and antecedent studies may be due to the fact that online product quality is not good as reality as
be displayed by the seller. Since the business operations between B2C and C2C platform are not the same,
most of vendors in C2C e-commerce are small entrepreneurs without any business certification. If in Lazada
or Adayroi, sellers who want to sell the goods must take a series of procedures and prove the origin of each
type of product. But seller in Shopee is extremely easy, they just provide simply photo, product information
without any Certification of business. So one of the downsides when shopping on the Shopee is very easy to
have fake, since the goods here are not censored quality input. The leads to a consequence that buyers do not
satisfy and trust on any sellers in Shopee. They easily switch to alternative shops that offer similar products
they want to purchase. As a result, product quality is not an important factor for customer repurchase intention
in C2C e-marketplace.
Hypothesis H4a, predicting a direct positive relationship between the price fairness perception (PP) and
the repurchase intention (RI), is statistically not significant (p-value = 0.227 > 0.05) [see Table 6]. The results
shows that the price fairness perception has no influence on repurchase intention. This finding is not consistent
with previous studies of Grewal et al. (2004); Martin (2009); Suhaily, L., & Soelasih, Y. (2017) that suggest a

positive influence of perceived price fairness to repurchase intentions. The possible explanation is due to the
economical price in online shopping. Most of Shopee’s customers stated that the purchase on Shopee is
cheaper than other websites. There are a huge of discount or sale events conducted everyday, which are joined
by lots of sellers with variety of product types. This leads to a intense competition among Shopee sellers, thus
mostly sellers offer the same low price for the same product. Hence, Shopee customers do not concern if a
buyer in Shopee provided low price or even they will switch to purchase in another seller that have similar
product since they believe that seller will offer better competitive price than previous one. This explains why
customers have low repurchase intention towards one seller on Shopee in term of price
The effects of computer-mediated communication (CMC) tools on interaction between buyers and sellers (H6, H7):
Two hypotheses H6 and H7 are supported because of significant p-value [see Table 6]. Results show that
CMC tools have direct effect on interactivity perceived by Shopee buyers. This finding is consistent with Ou
et al.’s (2014) and H-Bao et al.’s (2016) research that CMC tools in e-marketplace facilitates the communication
and interaction between buyer and seller. Effective communications provide good condition for the buyers to
get more knowledge about the product they are going to buy, as well as for the seller to understand their
product and concern form customers, then build a strong seller-consumer relationship in e-marketplace
context. In this manner, this current study recommends to other online marketplaces, such as Lazada and
Shendo, could consider applying CMC tools (e.g., IM, and feedback-comment system) into their platforms to
help gain similar strategic benefits.
According to the outcomes analyzed above, this leads to the result for final research model [see Figure 2].
5. Conclusions
In order to understanding the mechanism of consumers’ repurchase intention in Vietnamese C2C emarketplace in case of Shopee, this study empirically examines the effects of customers’ perceptions on
customer satisfaction and repurchase intention towards one seller. By integrating these in one model, this
study has provided a key finding of the important effects of perceived information quality, interaction quality
and satisfaction with seller (including satisfied product quality, information quality, price fairness and
interactivity) on customer repurchase intention. With these findings, this study provides directions for online


seller in e-marketplace to identify what attributes need to focus to enhance buyer repurchase intention.
Moreover, this study plays a role as an evidence proving why CMC tools are critical and must have for
enterprises which have been developing online marketplaces models not just in Vietnam, but in SoutheastAsian markets as well.

Limitations and recommendation for future research
Besides the above conclusion, this study has some limitations that open up research opportunities for future
researchers. Firstly, this research focuses on a single e-commerce platform Shopee.vn, which is just a new-bee
of Vietnam e-commerce industry not widely perceived as dominating Vietnam e-marketplace. Future research
can extend the subject of this study to other e-commerce platforms such as Lazada.vn and Shendo.vn to better
understand customer behaviors in Vietnam e-marketplace.
Secondly, product categories likely to be an influential factor on repeat purchase intention. In term of
different products, consumers’ online behaviors are not the same. Further researchers can extend the
investigation by examining this research model in different products categories.
Finally, this study empirically examines the effects of perception factors on repurchase intention instead of
actual repurchases. In E-commerce-based studies of buyer behavior, it is noted that behavior intentions
significantly correlate with actual behavior (Ou et al. 2014). Future researchers should further extend this
research model with actual repurchases as the dependent variable.
Acknowledgement:
It is acknowledged that this work is not supported by any finding organization.
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Tables and figures
Table 1– Measurement Scale
Construct


Item

Measurement

References

Product
Quality (PQ)

PQ1

Products provided by this seller have good quality

PQ2

Products provided by this seller are dependable

Sullivan & Kim
(2018),
King et al., (2016)

PQ3

Products provided by this seller are well packaged

PQ4

Products provided by this seller have Certificate of
Origin*


PQ5

Products provided by this seller have the quality as my
expectation

IQ1

This seller provides sufficient information on features
and quality of products

IQ2

This seller provides precise information about products

IQ3

This seller provides information about products with the
reality image

IQ4

This seller provides me with up-to-date information
about products

PP1

Products provided by this seller have reasonable price

PP2


Products provided by this seller are cheaper than other
sellers

PP3

Products provided by
corresponding to the price

PP4

Products from this shop have price suit my income

IT1

This seller facilitates two-way communication between
him/herself and visitors.

IT2

This seller gives visitors the opportunity to talk to
him/her

IT3

This seller responded to my questions in a professional
and enthusiastic way*

IT4


This seller responded to my questions very quickly

SS1

I was pleased that online purchase experience from this
seller meet my expectation

SS2

I intend to recommend this seller to people around me

SS3

I think that purchasing products from this seller is a good
idea

SS4

I am satisfied with the overall purchase experience of
purchasing products from this seller

RI1

I intend to place an order from this seller instead of from
any others

Information
quality (IQ)

Price Fairness

(PP)

Interactivity
(IT)

Satisfaction
with Seller (SA)

Repurchase
Intention (RI)

this

seller

have

Wang (2008), Chen
et al., (2017)

Kim et al.
(2013)
King at al. (2016)

quality

Bao et al, (2016)

Kim et al. (2013)


Chen et al. (2017)


Effective use of
Instant
Messenger
(IM)

Effective Use of
Feedback
&
Comment
System (FB)

RI2

I predict that I would consider buying products from this
seller in the future

RI3

I will continue to buy more from this seller in the future

RI4

I will buy similar products from this seller again

IM1

I feel that Shopee’s instant messenger functions as an

effective communication channel for me to communicate
with this seller

IM2

I have used Shopee’s instant messenger mechanism to
verify information with this seller

IM3

I believe that Shopee’s instant messenger mechanism has
facilitated the direct communication and negotiation
between this seller and me.

IM4

I have great dialogues with this seller in Shopee’s instant
messenger mechanism

FB1

I feel confident that Shopee’s feedback and comment
mechanism provides accurate information about this
seller’s reputation

FB2

A considerable amount of useful feedback information
about the transaction history of this seller is available
through Shopee’s feedback and comment mechanism.


Sullivan
(2018)

&

Kim

Bao et al, (2016)

Bao et al, (2016)
FB3

I believe that that the feedback and comment mechanism
on Shopee is effective for buyers to know about this seller.

FB4

I believe that the feedback and comment mechanism on
Shopee is reliable and dependable so as to help me
evaluate this seller.

*Note: developed by pilot test adjustments

Table 2: Demographic details of respondents

Gender

Female
Male


Percent
69.6
30.4

Age

< 18
18 - 24
> 24

7.8
73.4
18.8

Occupation

Worker
Student
Others
Officer

.3
77.7
10.0
11.9

Income

< 3.000.000 VND/month

3.000.000 - 8.000.000 VND/month
> 8.000.000 VND/month

62.4
24.5
13.2

< 2 years
< 6 months
> 2 years

27.9
25.4
46.7

Experience
online

of

shopping


Table 3a- SPSS item factor loadings and cross loadings (first round)

IM2
IM3
IM1
IM4
PQ3

PQ2
PQ1
PQ4
PQ5
SA4
SA2
SA3
SA1
IT1
IT4
IT3
IT2
RI4
RI3
RI1
RI2
IQ1
IQ2
IQ4
IQ3
FB1
FB2
FB3
FB4
PP2
PP1
PP4
PP3

Factor

1
.892
.845
.772
.756

2

3

4

.769
.752
.725
.705
.519

5

6

7

8

.207
.867
.772
.743

.719
.784
.727
.699
.694
.797
.757
.688
.311
.207
-.264

.210
.218

.844
.768
.628
.579
.814
.731
.625
.580
.790
.752
.678
.488

.233


Product quality (PQ), Information quality (IQ), Price Fairness (PP) and Interactivity (IT), satisfaction with seller (SA), repurchase
intention (RI), Effective use of Feedback System (FB), Effective Use of Instant Messenger (IM)

Table 3b- SPSS item factor loadings and cross loadings (after deleting RI2, PP3, PQ5)
Factor
1
SA4
SA2
SA3
SA1
IM2
IM3
IM1
IM4
IT1
IT4
IT3

2

3

.862
.771
.751
.728
.896
.846
.772
.757

.801
.742
.714

4

5

6

7

8


IT2
IQ1
IQ2
IQ4
IQ3
PQ3
PQ2
PQ1
PQ4
FB1
FB2
FB3
FB4
RI4
RI1

RI3
PP2
PP1
PP4

.212

.697
.856
.773
.630
.598

-.255
.758
.746
.687
.648
.816
.716
.635
.576
.744
.707
.696
.785
.726
.624

Product quality (PQ), Information quality (IQ), Price Fairness (PP) and Interactivity (IT), satisfaction with seller (SA), repurchase

intention (RI), Effective use of Feedback System (FB), Effective Use of Instant Messenger (IM)

Table 4- Cronbach’s Alpha, composite reliability and average variance extracted
Factors
Perceived Product quality
PQ1
PQ2
PQ3
PQ4
Perceived Information quality
IQ1
IQ2
IQ3
IQ4
Perceived Price Fairness
PP1
PP2
PP4
Perceived Interactivity
IT1
IT2
IT3
IT4
Satisfaction with seller
SA1
SA2
SA3
SA4
Repurchase intention
RI1


Cronbach’s
Alpha
.799

Corrected ItemTotal Correlation

CR

AVE

0.799

0.501

0.824

0.549

0.750

0.502

0.848

0.584

0.854

0.746


0.851

0.655

.659
.581
.631
.591
.815
.718
.701
.615
.525
.745
.613
.554
.559
0.848
.712
.66
.726
0.65
0.896
.766
.676
.821
.828
0.849
.694



RI3
RI4
Effective use of Instant Messenger
IM1
IM2
IM3
IM4
Effective
use of Feedback
&
Comment System
FB1
FB2
FB3
FB4

.736
.726
0.889

0.891

0.672

0.767

0.461


.749
.753
.788
.751
0.78
.638
.597
.564
.542

Table 5- Correlation Matrix and AVEs (after deleting FB4)
RI
SA

CR
0.851
0.854

AVE
0.655
0.745

MSV
0.561
0.561

MaxR(H)
0.852
0.878


RI
0.809
0.749

SA

IT

FB

IQ

0.863

IM
IT
FB

0.891
0.848
0.764

0.672
0.584
0.527

0.376
0.376
0.097


0.893
0.857
0.803

0.393
0.486
0.301

0.192

0.311

IQ
PQ

0.824
0.799

0.549
0.501

0.334
0.304

0.874
0.820

0.566
0.414


0.578
0.551

0.384
0.306

0.250
0.079

0.530

PP

0.750

0.502

0.187

0.761

0.366

0.433

0.344

0.253

0.300


PQ

PP

0.267

Product quality (PQ), Information quality (IQ), Price Fairness (PP) and Interactivity (IT), satisfaction with seller (SA),
repurchase intention (RI), Effective use of Feedback System (FB), Effective Use of Instant Messenger (IM)

Table 6- Summary of Hypotheses Results
Hypothesis

Relationships

Regression
weights

P-value

Supported?

H6

IT <--- IM

.578

***


Yes

H7

IT <--- FB

.161

.007

Yes

H2b

SA <--- PQ

.293

***

Yes

H3b

SA <--- IQ

.314

***


Yes

H4b

SA <--- PP

.222

***

Yes

H5b

SA <--- IT

.134

.010

Yes

H5a

RI <--- IT

.204

***


Yes

H4a

RI <--- PP

.013

.828

No

H3a

RI <--- IQ

.194

.003

Yes

H2a

RI <--- PQ

-.079

.227


No

H1

RI <--- SA

.605

***

Yes

Product quality (PQ), Information quality (IQ), Price Fairness (PP) and Interactivity (IT), satisfaction with seller (SA), repurchase
intention (RI), Effective use of Feedback System (FB), Effective Use of Instant Messenger (IM)


Figure 1- The conceptual model

Note: Dotted line demonstrates that the relationship is not significant at 0.05 level.

Figure 2: Final conceptual model results