Factors Influence on Customers Repurchase Intention in Customer-To-Customer E-Marketplace. The Case of Shopee Vietnam

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 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 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 417 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 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 computermediated communication (CMC) tools influence interaction between buyers and sellers? 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 418 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 419 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, 420 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 points value as 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 illustrates the research framework [see Figure 1] 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 [see Table 1] is designed based on previous papers and pilot test adjustments with the Likert scale of points: (1) totally disagree; (2) disagree; (3) neutral; (4) agree; (5) totally agree 421 Collecting data activity is spread out for nearly 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 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 million VND (62.4%) Moreover, people have more experienced and keep online shopping habit for 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% 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 (0.05) 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 presents the Cronbach’s Alpha and Composite Reliability (CR) of all items of all 422 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 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 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 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 423 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 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 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] 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 424 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 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model and synthesis of evidence The Journal of marketing, 2-22 426 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 this 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 (PP) Fairness Interactivity (IT) Satisfaction with Seller (SA) Repurchase Intention (RI) 427 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 Age Occupation Income Experience online of Female Male < 18 18 - 24 > 24 Worker Student Others Officer < 3.000.000 VND/month 3.000.000 - 8.000.000 VND/month > 8.000.000 VND/month < years shopping < months > years 428 Percent 69.6 30.4 7.8 73.4 18.8 77.7 10.0 11.9 62.4 24.5 13.2 27.9 25.4 46.7 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 892 845 772 756 769 752 725 705 519 207 867 772 743 719 784 727 699 694 797 757 688 311 207 -.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 SA4 SA2 SA3 SA1 IM2 IM3 IM1 IM4 IT1 IT4 IT3 862 771 751 728 896 846 772 757 801 742 714 429 IT2 IQ1 IQ2 IQ4 IQ3 PQ3 PQ2 PQ1 PQ4 FB1 FB2 FB3 FB4 RI4 RI1 RI3 PP2 PP1 PP4 697 212 856 773 630 598 -.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 0.849 694 430 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 IM IT FB IQ PQ CR 0.851 0.854 0.891 0.848 0.764 0.824 0.799 AVE 0.655 0.745 0.672 0.584 0.527 0.549 0.501 MSV 0.561 0.561 0.376 0.376 0.097 0.334 0.304 MaxR(H) 0.852 0.878 0.893 0.857 0.803 0.874 0.820 RI 0.809 0.749 0.393 0.486 0.301 0.566 0.414 PP 0.750 0.502 0.187 0.761 0.366 SA IT FB IQ 0.192 0.578 0.551 0.311 0.384 0.306 0.250 0.079 0.530 0.433 0.344 0.253 0.300 PQ PP 0.863 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) 431 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 432