The influence of website quality on consumer’s e-Loyalty through the mediating role of e-trust and e-satisfaction: An evidence from online shopping in Vietnam

ed that “consumer WOM transmissions consist of informal communications directed at other consumers about the ownership, usage, or characteristics of particular goods and services and/or their sellers”. Bone (1992) conceptualized WOM as “a group phenomenon - an exchange of comments, thoughts, and ideas among two or more individuals in which none of the individuals represent a marketing source”. WOM is also defined as being informal and non-commercial communication and as an exchange of information between two or more individuals regarding a product or a service (Silverman, 2011). WOM is one of the critical factors changing consumer behavior. WOM can be one- way suggestions and recommendations or mutual conversations; live or recorded; in person, by email, by telephone, or by any other means of communication; one-to-one, one-to-many, or group discussion as long as they are from or among people perceived as non-commercial interest in encouraging others to a product or a service. These people can be friends, family, acquaintances or even strangers (Cheung & Thadani, 2012). Electronic word-of-mouth (eWOM) is a new form of online WOM communication in the new digital era (Yang, 2017). According to Litvin, Goldsmith, and Pan (2018), eWOM “as all informal communication via the Internet addressed to consumers and related to the use or characteristics of goods or services or the sellers thereof”. Abubakar, Ilkan, and Sahin (2016) stated that “eWOM has taken on a special importance with the emergence of online platforms, which have made it one of the most influential information sources on the Web”. eWOM could lead to shifts in consumer behavior because it enables buyers to exert on each other by allowing them to receive or share information and opinions about products or services. Besides, eWOM has a prominent advantage due to its availability to everyone who can use online platforms to share their reviews and opinions with other users. Nowadays, buyers from everywhere can be easy to leave their comments and opinions that other buyers can use to get information about products and services efficiently. Therefore, where buyers trusted WOM from their family and friends, now they could get online reviews (eWOM) for information about goods or services that they need. Furthermore, in an environment in which consumers’ trust of both organizations and advertising has been reduced, eWOM gives a way to gain a significant competitive advantage. Both of eWOM and traditional advertising can be seen as forms of advocacy; however, eWOM is perceived free of vested interest while advertising and commercial communication is information from a source having vested interest in presenting the information in a particular way 358 (Silverman, 2011). It is evident that purchasers commonly view eWOM as more trustworthy and credible than marketing communications (Yang, 2017). Concerning the factors that affect eWOM, it is believed that satisfaction has a positive relationship with the desire for customers to make recommendations and reviews for the service providers (e.g., Prayag et al., 2017; Tsao & Hsieh, 2012). Organizations tend to expect that satisfied customers will automatically spread eWOM (Lii & Lee, 2012). Within the context of online shopping, eWOM seems to occur when people are either satisfied or dissatisfied with experiencing a product or service. The satisfied mode is based on the level of the product or service performance exceeding from customers’ expectations and is probably resulted in positive eWOM, referring to pleasant experiences. While dissatisfied emotion depends on the level customer’s expectations are not met and may lead to negative eWOM, including product denigration, unpleasant experiences, negative feelings, rumor and private complaining (Dolnicar, Coltman, & Sharma, 2015; Richins, 1983). These results explained that it is crucial for organizations to minimize eWOM from customers with low levels of satisfaction with the website and to maximize eWOM from highly satisfied customers. Furthermore, some authors Serra- Cantallops et al. (2018)demonstrated that e-satisfaction is a crucial antecedent of eWOM. Therefore, within the online shopping context, the author put forward the hypothesis as follows: H9: E-satisfaction has a positive effect on the formation of positive eWOM. On the other hand, Mohan, Sivakumaran, and Sharma (2013) clarified that perceived enjoyment might influence the different aspects of consumer behavior. A higher level of perceived enjoyment predisposition could lead to higher levels of positive affect. Thus, when buyers perceive a particular online shopping as playable or enjoyable, they are likely to recommend such a website to their family, colleagues, and friends. Mihić and Kursan Milaković (2017) justified that perceived enjoyment had a positive relationship with eWOM. Based on the aforementioned discussion, the author hypothesizes that: H10: Perceived enjoyment has a positive effect on the formation of positive eWOM. Loyalty is a crucial factor in achieving organizational sustainability and success (Bulut & Karabulut, 2018). Loyalty can be related both to the period when a buyer shops online as well as after that buyer finish his or her shopping. It is indicated that loyal customers tend to make a positive recommendation to relatives and friends. They have more incentives to get new information as well as resist more negative information about the organization (Salehnia et al., 2014). Conversely, Wangenheim (2005) argued that if customers have no loyalty to the firm, they tend to switch to another alternative and probably distribute negative words of mouth about the firm to reduce their cognitive dissonances. As a consequence, loyalty can be seen as one factor effective on WOM. Besides, in the online shopping context, Salehnia et al. (2014) found that e-loyalty had a positive relationship with eWOM (see Figure 2). Based on the above discussion, the following hypothesis is proposed: H11: E-loyalty has a positive effect on the formation of positive eWOM. 2.7 The mediating role of e-trust and e-satisfaction Besides the direct impact of website quality on customers’ e-loyalty, website quality also could influence customers’ e-loyalty through e-trust and e-satisfaction. The author states that e-trust and e- satisfaction are the mediating factors on the connection between website and e-loyalty because lack of e-trust and e-satisfaction could be the main reason customers decide not to shop online or they could consider switching to another website. Moreover, some studies have shown that the direct relationships between website quality and e-loyalty (e..g., Tandon et al., 2017), website quality and e-trust, e-trust and e-loyalty (e.g., Ghalandari, 2012; Tirtayani & Sukaatmadja, 2018), website quality and e- satisfaction (e.g., Tirtayani & Sukaatmadja, 2018), e-satisfaction and e-loyalty (e.g., Safa & Solms, 2016; Taheri & Akbari, 2016). Based on the linking of the relationships mentioned above, the author state that there is a likelihood that e-trust and e-satisfaction mediate the relationships between website quality and e-loyalty. So, the following hypotheses are proposed: H. N. K. Giao et al. /Uncertain Supply Chain Management 8 (2020) 359 H12a: E-trust mediates the relationship between website quality and e-loyalty. H12b: E-trust mediates the relationship between website quality and e-loyalty. Fig. 2. An integrated model for customer’s e-loyalty (Source: the author proposes) 3. Research methodology 3.1 Procedure and sampling size The sample was selected using a nonprobability sampling with a technique-convenience sample. Target respondents of this survey were people who aged16 years old and have ever purchased on online shopping websites in Vietnam. The current study consisted mainly of two stages including qualitative and quantitative research. For qualitative research, the questionnaire was originally formulated in English and then the author translated it into the Vietnamese language with the support of English specialists. In the qualitative research, the Vietnamese version of the questionnaire was tested by an in- depth interview method in one week with ten people who have ever purchased on online shopping to ensure if they understood the questions and revised Vietnamese terms which were unclear during due to translation. Based on the comments of respondents, the survey questionnaire was modified properly. The pilot study was sent to 50 people who have ever purchased on online shopping. The participants were asked to provide advice on elements of the survey that they are confusing, recommendations on wording, overall mechanics of taking the survey online, the instructions provided, and any questions they felt uncomfortable answering. Modifications were made to the instrumentation, specifically around grammatical errors and survey logic. The modified instrument was found to be reliable due to the minimum Cronbach’s Alpha of each factor equals to 0.746 (Table 1). The individual items were deemed to be valid for the research as for each dimension the Cronbach’s alpha was above the acceptable threshold of 0.70 (Giao & Vuong, 2019). For quantitative research, after the modifications for the questionnaire, the survey was issued to all respondents who work in the Vietnamese state-owned organizations in Vietnam at the time the research was deployed by delivering mainly via the internet by Google Docs. In this way, the author sent directly the survey link to respondents’ email. In total, 650 responses were collected, but 29 questionnaires were removed because respondents indicated that the respondents are under 15 years old and the rest (27 questionnaires) was eliminated because they were invalid (respondents just chose one option for all questions). Finally, there only 594 valid questionnaires were used for the data analysis process. Website quality E-trust Perceived enjoyment E-satisfaction E-loyalty Positive eWOM 1H 2H 3H 4H 5H 7H 8H 9H 11H 6H 10H 360 Table 1 The pilot testing summary Dimension Code Items Cronbach’s Alpha Website quality Website design WD 4 0.811 Security/privacy SE 4 0.863 Fulfillment/Reliability RE 4 0.766 Consumer service CS 4 0.851 E-trust ET 4 0.911 Perceived enjoyment PE 4 0.849 E-satisfaction ES 4 0.893 E-loyalty EL 4 0.746 Electronic word of mouth EWOM 4 0.887 Table 2 Distribution of the sample N=594 Frequency Percent Gender Female 381 64.1 Male 213 35.9 Marital status Married 375 63.1 Single 219 36.9 Age 15-25 years old 117 19.7 26-30 years old 279 47.0 31-40 years old 168 28.3 Over 40 years old 30 5.1 Education Under College 69 11.6 College 224 37.7 Bachelor 270 45.5 Postgraduate 31 5.2 Monthly income < 5 million VND 183 30.8 5-10 million VND 285 48.0 10-20 million VND 96 16.2 > 20 million VND 30 5.1 Online shopping frequently 1-3 times 272 45.8 4-5 times 185 31.1 > 5 times 137 23.1 Categories Household items 33 5.6 Books and stationery 60 10.1 Food and beverages 153 25.8 Fashion 201 33.8 Cosmetics and personal care 72 12.1 E-accessories 30 5.1 Baby products 45 7.6 Note: 1 million VND ≈ 43 USD 3.2 Instruments All constructs in the conceptual model were measured with multiple items, which were developed by previous researchers. All of the measurement scales used a five-point Likert scale including “Strongly disagree” (=1), “Disagree” (=2), “Neutral” (=3), “Agree” (=4), and “Strongly agree” (=5) to explore the opinion of the respondents. Specifically, website quality measured by sixteen items of Li et al. (2015) with four dimensions: website design (four items: e.g., “This website has effective search functions”); Security/privacy (four items: e.g., “I feel safe in my transactions at this website”); Fulfillment/Reliability (four items: e.g., “I obtain exactly the products which I ordered”); Consumer service (four items: e.g., “This company is responsive to my requests”). E-trust was measured by four items of Jin, Yong Park, and Kim (2008). A sample item for e-trust was “This company gives me a trustworthy impression”. E-satisfaction was measured by four items of Li et al. (2015). A sample item for e-satisfaction was “Overall, this website consistently meets my expectations”. Perceived enjoyment H. N. K. Giao et al. /Uncertain Supply Chain Management 8 (2020) 361 was developed by four items of Wen (2012). A sample item for perceived enjoyment was “I found my visit to this website interesting”. E-loyalty was developed by four items of Chang and Chen (2008). A sample item for e-loyalty was “I usually visit this website first when I need to shop online for this type of product/service”. Electronic word of mouth was developed by four items of Wen (2012) and Bulut and Karabulut (2018). A sample item for eWOM was “I say positive things about this website to other people”. 3.3 Partial Least Squares Regression Partial least square-structural equation modeling (PLS-SEM) was employed by the SmartPLS 3.0 software to evaluate the hypotheses in this study. PLS-SEM is a statistical analysis technique for data exploration within the quantitative research discipline used to measure the observed variables collected from instruments to determine their influence on latent or unobserved variables (Fornell & Larcker, 1981). Hair et al. (2014) proposed the use of PLS-SEM due to its effective use as an analysis tool used to support prediction models from empirical data. Vuong and Giao (2019) also advocated that PLS- SEM has the capability to calculate p-values through a bootstrapping technique if samples are independent and if the data is not required to be normally distributed. 4. The results 4.1 Reliability and Validity of Constructs Fig. 3. Measurement model Following Giao and Vuong (2019), who indicated that the composite reliability values should be 0.7 or greater to be considered reliable in a model, each variable was evaluated and charted to verify reliability. From Figure 3 and Tables 3 presented, it is clearly stated that all the variables used in this research were reliable since it obtained the Composite Reliability and Cronbach’s Alpha values more than 0.7. So, all values fall within the acceptable range to conclude good reliability. Moreover, convergent validity is the amount of variance when two or more items agree when measuring similar constructs and is calculated using the Average Variance Extracted (AVE). AVE measures the captured by a construct as a percentage (Fornell & Larcker, 1981). Convergent validity is said to be reliable when the AVE is above 0.50 (Fornell & Larcker, 1981; Hair et al., 2014). However, Fornell and Larcker (1981) stated that an AVE below 0.5 would be acceptable as long as the composite reliability is above 0.7. Table 3 showed a summary of the PLS quality of the measurement model. The mean composite reliability (CR) for all of the constructs fell well above the threshold with values ranging between 0.869 and 0.928, and AVE values were ranging between of 0.631 and 0.874. Thus, all the items in the survey instrument are now considered convergent validity. 362 Table 3 Summary of PLS Quality Construct Indicator Indicator loading Cronbach’s Alpha Composite Reliability (CR) AVE R 2 Website design WD1 0.832 0.813 0.878 0.645 WD2 0.870 WD3 0.812 WD4 0.684 Security/ privacy SE1 0.835 0.881 0.918 0.736 SE2 0.865 SE3 0.863 SE4 0.868 Fulfillment/ Reliability RE1 0.787 0.862 0.907 0.709 RE2 0.840 RE3 0.858 RE4 0.879 Consumer service CS1 0.827 0.874 0.874 0.874 CS2 0.836 CS3 0.895 CS4 0.848 CS1 0.827 E-trust ET1 0.816 0.857 0.903 0.700 0.289 ET2 0.811 ET3 0.885 ET4 0.832 Perceived enjoyment PE1 0.856 0.890 0.924 0.753 0.304 PE2 0.916 PE3 0.886 PE4 0.811 E-satisfaction ES1 0.893 0.895 0.928 0.763 0.423 ES2 0.889 ES3 0.916 ES4 0.792 E-loyalty EL1 0.854 0.798 0.869 0.631 0.401 EL2 0.900 EL3 0.815 EL4 0.566 Electronic word of mouth EWOM1 0.868 0.889 0.923 0.751 0.518 EWOM2 0.893 EWOM3 0.879 EWOM4 0.825 In order to determine item discriminate validity, the factors should be examined and analyzed to ensure that items load on constructs they were intended to load, do not load on constructs they were not designed to load (Giao & Vuong, 2019). Table 4 identifies the item cross-loadings for this research. Hair et al. (2014) stated that if the load of the items on other constructs, the item is said to not measure the construct appropriately and continuing to use the item in analysis can alter results and interpretation of the data. According to Table 3, because all constructs did not load on any construct, it was not removed from the measurement model, as discriminate validity was acceptable. Besides, discriminant validity can be shown through the correlation matrix. The square root of a construct’s AVE value should be greater than the squared correlation with any other construct “since a construct shares more variance with its associated indicators than it does with any other construct” (Hair et al., 2014). The table above (Tables 4) was the correlation matrices of the constructs with the diagonal values. Each construct square root of their AVE values was indeed greater than the squared correlation with any other construct. Therefore, discriminant validity has been established for the constructs. H. N. K. Giao et al. /Uncertain Supply Chain Management 8 (2020) 363 Table 4 Correlations of constructs CS EL ES ET EWOM PE RE SE WD CS 0.852 EL 0.524 0.794 ES 0.507 0.534 0.874 ET 0.517 0.546 0.606 0.836 EWOM 0.534 0.581 0.657 0.606 0.867 PE 0.520 0.602 0.416 0.514 0.484 0.868 RE 0.467 0.323 0.391 0.411 0.383 0.359 0.842 SE 0.484 0.387 0.334 0.396 0.315 0.492 0.522 0.858 WD 0.598 0.413 0.456 0.408 0.440 0.403 0.537 0.658 0.803 4.2 Structural Model 4.2.1 Multicollinearity Hair et al. (2014) recommended that indicators that indicate the presence of multicollinearity is a problem, as the indicator has the possibility of inflating bootstrap standard errors, thus increasing the probability of failing to detect that an effect is present in the research. They also proposed the Variance Inflation Factor (VIF) indicator to measure multicollinearity issues. The VIF should be less than a 5.00 tolerance level (Giao & Vuong, 2019). In this study, the maximum inner VIF of constructs was 1.850. As a result, the collinearity of the constructs was not a concern (Table 5). Table 5 The result of multicollinearity Construct Inner VIF Values Website quality PE ET ES EL Website design 1.000 Security/ Privacy 1.000 Fulfillment/Reliability 1.000 Consumer service 1.000 E-trust 1.000 Perceived enjoyment 1.000 E-satisfaction 1.645 1.555 1.590 E-loyalty 1.539 1.766 1.728 Electronic word of mouth 1.599 1.428 1.850 4.2.2 Hypotheses Testing Based on what was discovered in the PLS-SEM estimates (Fig. 4 and Table 6), the results of the hypotheses were indicated as the following: Hypothesis 1: the result showed that website quality had a positive and significant relationship with e- trust, (p-value = 0.000 and beta coefficient = 0.537). This was supported by the previous research of Tirtayani and Sukaatmadja (2018). The result indicated that the higher website quality, the greater is the possibility that buyers will trust in online vendors. Thus, hypothesis 1 was supported. Hypothesis 2: the result showed that website quality had a positive and significant relationship with perceived enjoyment (p-value = 0.000 and beta coefficient = 0.552) which means that consumers who had a good perception of website quality tended to show a higher level of perceived enjoyment. This was supported by the previous investigation of Juyeon Kim et al. (2013). Thus, hypothesis 2 was supported. 364 Fig. 4. Structural Model Hypothesis 3: the result showed that website quality had a positive and significant relationship with e- satisfaction (p-value = 0.000 and beta coefficient = 0.259) which means that consumers who had a good perception of website quality tended to show a higher level of e-satisfaction. This was supported by the previous study of Polites et al. (2012). Thus, hypothesis 3 was supported. Table 6 Hypothesis Testing Results Hypothesis Dependency Path Standard T-Statistics P-Values Conclusion WQ → WD 0.857 0.012 71.499 0.000 WQ → SE 0.824 0.013 62.736 0.000 WQ → RE 0.768 0.021 36.052 0.000 WQ → CS 0.795 0.017 46.233 0.000 H1 WQ → ET 0.537 0.029 18.524 0.000 Supported H2 WQ → PE 0.552 0.031 18.061 0.000 Supported H3 WQ → ES 0.259 0.038 6.746 0.000 Supported H4 ET → ES 0.443 0.042 10.421 0.000 Supported H5 PE → ES 0.046 0.041 1.108 0.268 Not Supported H6 WQ → EL 0.240 0.049 4.867 0.000 Supported H7 ES → EL 0.248 0.053 4.715 0.000 Supported H8 ET → EL 0.267 0.045 5.918 0.000 Supported H9 ES → eWOM 0.466 0.034 13.823 0.000 Supported H10 PE → eWOM 0.141 0.048 2.899 0.004 Supported H11 EL → eWOM 0.248 0.045 5.450 0.000 Supported Hypothesis 4: the result showed that e-trust had a positive and significant relationship with e- satisfaction (p-value = 0.000 and beta coefficient = 0.443) which means that consumers who had a high e-trust tended to show a higher level of e-satisfaction. This was supported by the previous examination of Taheri and Akbari (2016). Thus, hypothesis 4 was supported. Hypothesis 5: the result showed that perceived enjoyment didn’t have a significant relationship with e- satisfaction (beta coefficient = 0.046). Besides, perceived enjoyment showed a positive relationship with e-satisfaction which means that consumers who had a good perception of enjoyment tended to show a higher level of e-satisfaction. However, this relationship was not statistically significant (p- value = 0.268), which means that there is a high potential that this relationship may occur purely by chance. Thus, hypothesis 5 was not supported. H. N. K. Giao et al. /Uncertain Supply Chain Management 8 (2020) 365 Hypothesis 6: the result showed that website quality had a positive and significant relationship with e- loyalty (p-value = 0.000 and beta coefficient = 0.240). This was supported by previous studies of Tirtayani and Sukaatmadja (2018), Tandon et al. (2017). When the perceived risk is low, consumers are more willing to continue to repurchase at the website. Online vendors need to focus on the online store to safely and promptly deliver the ordered product as promised, especially ensure the consumer's security. Thus, hypothesis 6 was supported. Hypothesis 7: the result showed that e-satisfaction had a positive and significant relationship with e- loyalty (p-value = 0.000 and beta coefficient = 0.248) which means that consumers who had a high e- satisfaction tended to show a higher level of e-loyalty. This was supported by previous researches of Taheri and Akbari (2016), Safa and Solms (2016). Thus, hypothesis 7 was supported. Hypothesis 8: the result showed that e-trust had a positive and significant relationship with e-loyalty (p-value = 0.000 and beta coefficient = 0.267) which means that consumers who had a good perception of e-trust tended to show a higher level of e-loyalty. This was supported by the previous analysis of Safa and Solms (2016). Thus, hypothesis 8 was supported. Hypothesis 9: the result showed that e-satisfaction had a positive and significant relationship with eWOM (p-value = 0.000 and beta coefficient = 0.466) which means that consumers who had a good perception of e-satisfaction tended to show a higher level of eWOM. This was supported by the previous investigation of Dolnicar et al. (2015). Thus, hypothesis 8 was supported. Hypothesis 10: the result showed that perceived enjoyment had a positive and significant relationship with eWOM (p-value = 0.000 and beta coefficient = 0.141) which means that consumers who had a good perception of enjoyment tended to show a higher level of eWOM. This was supported by the previous study of Mihić and Kursan Milaković (2017). Thus, hypothesis 10 was supported. Hypothesis 11: the result showed that e-loyalty had a positive and significant relationship with eWOM (p-value = 0.000 and beta coefficient = 0.248) which means that consumers who had a high e-loyalty tended to show a higher level of eWOM. This was supported by the previous examination of Salehnia et al. (2014). Thus, hypothesis 11 was supported. Table 7 The mediating role of e-trust and e-satisfaction Hypothesis 12a: Based on Table 7, e-trust mediated the relationship between website quality and e- loyalty due to some following reasons: first, the results in Table 6 revealed that the p-value for the direct path WQ→EL was 0.000; QW→ET was 0.000; ET→EL was 0.000 which were statistically significant (p<0.05). Second, the p-value of the indirect effect (WQ→ET→EL) was 0.000 (Table 7) which was statistically significant as well. Hence, the mediating role of e-trust has existed (Giao & Vuong, 2019). Therefore, hypothesis 12a was supported and this mediation was partial. Hypothesis 12b: Based on Table 7, e-satisfaction mediated the relationship between website quality and e-loyalty due to some following reasons: first, the results in Table 6 revealed that the p-value for the direct path WQ→EL was 0.000; WQ→ES was 0.000; ES→EL was 0.000 which were statistically significant (p<0.05). Second, the p-value of the indirect effect (WQ→ES→EL) was 0.000 (Table 7) which was statistically significant as well. Hence, the mediating role of e-trust has existed (Giao & Vuong, 2019). Therefore, hypothesis 12b was supported and this mediation was partial. 4.2.3 Model fit of PLS model Hair et al. (2014) suggested that a high R2 value of the dependent construct could be well predicted in Relationship Direct effect Indirect effect Total effect Mediating effect Conclusion WQ→ET→EL 0.240*** 0.143*** 0.512*** Partial Mediation Supported WQ→ES→EL 0.064*** Partial Mediation Supported WQ→ET→ES→EL 0.059*** Note: ***=p<0.001 366 the PLS path model. The R2 value for e-loyalty (0.401) indicates that 40.1% of the total variation of the endogenous construct e-loyalty may be explained by the exogenous constructs such as website quality, e-trust, and e-satisfaction. The R2 value for an electronic word of mouth (0.518) indicates that 51.8% of the total variation of the endogenous construct (eWOM) may be explained by the exogenous constructs such as perceived enjoyment, e-satisfaction, and e-loyalty. Additionally, Giao and Vuong (2019) recommended that R2 values and the effect for endogenous latent variables could be estimated as 0.02 (weak), 0.13 (moderate), and 0.26 (large). In this study, R2 coefficients for e-loyalty and eWOM were greater than 0.26 (40.1%, 51.8%, respectively). Consequently, the PLS model of this research demonstrated the good model-data fit. 5. Conclusion The main objective of the research was to examine the relationship between website quality, e-trust, e- satisfaction, e-loyalty, and electronic word of the mouth thoroughly. Hence, an integrated model for customer’s e-loyalty was proposed in an online shopping context in Vietnam. This study achieved some results like the following: First, measurement scales in this study were adapted from some prior researches and were employed to measure in the Viet Nam market. This study could be a useful reference for future research related to behavioral intention in an online shopping context. Seconds, this study also showed that individual users’ intention to be a positive word of mouth in online websites was mainly motivated by e-trust, e-satisfaction, website quality, perceived enjoyment, and e-loyalty. Moreover, the results indicated that website quality also indirectly impacted on e-loyalty through e- trust and e-satisfaction. Third, this study was consistent with prior researches about consumers’ e- loyalty in online shopping, and this relationship is also confirmed its meaning in Vietnam online shopping market. The relationship between e-trust and e-satisfaction often changes from one study to another. It remains unclear whether consumers are satisfied because they trust online shopping, or if they report improved trust because they are satisfied with internet shopping. In this study, when measuring this relationship in an online shopping context, e-trust was found that it has a strong impact on e-satisfaction. Furthermore, e-trust was also a significant motivator on customers’ e-loyalty. Fourth, this study also confirmed the relationship between website quality and other factors which was examined not much in previous studies. Website quality is a vast concept and multi-dimensional construct. Many researchers have tied to propose different measurement scales to measure this concept. In this study, four constructs of website quality (website design, security/privacy, fulfillment/reliability, consumer service) were used to measure customer cognition to the quality of online shopping websites. Results of the study specified that website design and security/privacy had a stronger impact than fulfillment/reliability and consumer service on user perception of website quality. 6. Managerial implications This research made essential contributions to online shopping research. The results of this research offered some significant implications for marketers who prepared strategic plans and implemented tools to enhance the performance of their e-business as well: First, this study could help e-sellers to fully understand the crucial factors that determine the customers’ behavioral intention, which could help e-sellers to update their managerial and IT strategies and increases profits. This result highlights the importance of website quality, e-trust, e-satisfaction, and perceived enjoyment in predicting the e-loyalty and eWOM to use online websites. Second, online vendors should provide good online website quality to retain existed customers. Online websites need to differentiate more and more, especially focus factors influence feeling or experiences customer have while engaging with the websites. Managers should commit to maintain system operation well and make the website easy and quick to be used. When shopping online, one of the problems which customers afraid of is the loss of personal data and perceived risk in security. Hence, provide a secure system, and a secure payment mechanism is very necessary for online shopping. Besides that, online vendors also must ensure reliability. Customers usually pay attention to websites that provide more information with highly reliable and accurate. Invest in fulfillment/reliability will H. N. K. Giao et al. /Uncertain Supply Chain Management 8 (2020) 367 increase the quality of the website and could attract more new customers in the competitive market. Third, this study indicates that e-trust as a predictor as well as a factor influencing on e-loyalty directly and indirectly. Thus, managers who ran websites should pay attention to enhance the level of e-trust of consumers. In modem society, although organizations have enthusiastically used the internet as a critical sales and marketing tool for their goods and services, many buyers have not trusted in e- commerce security. They are reluctant to release their personal information to a website, especially in Vietnam where institutions and infrastructure conducive to trust have not been well developed. Hence, play a high priority on increasing customers’ e-trust becomes more necessary to motivate customers’ repurchasing behavior in the Vietnam market. Fourth, this research also confirmed the role of e-satisfaction in predicting customers’ e-loyalty. Having satisfied customers is an antidote for online websites. Customers will discontinue using an online website if they are not satisfied with it, even if it is useful or well designed. Conversely, they will repurchase products or services of online websites when they feel satisfied with it. Thus, in order to retain an existing customer, online vendors should devote themselves to make customers feel satisfied with their provided products and services. They need to improve their performance to adjust to customer expectations, as well as increasing customer e-trust and e-loyalty to online websites. Fifth, this analysis showed that perceived enjoyment is an important consequence of website quality, and it has a positive relationship with eWOM. Attributes such as fun, interesting, entertainment, and enjoyable are the areas in which online vendors could work on in order to take advantage of customers’ attitudes towards online shopping, therefore increase their loyalty to shop online. Online sellers should pay attention to improve their website quality to evoke positive emotions from buyers. As a result, because a high perceived website quality tends to raise the repurchase intention and eWOM for further. Finally, increasing website quality, not only customers’ e-trust, e-satisfaction, perceived enjoyment, and e-loyalty are strengthened, but also customers’ positive electronic word of mouth is strongly advised. It will motivate customers to say positive things about online vendors, recommend and even encourage the other people using that website. This helps to create competitive advantages for online vendors in maintaining their customers, and even thanks to the existing customers for attracting new customers. Online vendors should take some actions such as doing surveys to understand buyers how well buyers satisfied with their website, their expectations about services, their comments or complaints, etc. so that vendors could give feedback on time to improve buyers’ satisfaction; these surveys would be done yearly. Besides, online vendors should provide online service with more competitive prices and enhance product quality to make buyers satisfy so that they can give positive word-of-mouth communications among them. 7. Limitations and recommendations for future research This research offered some valuable insights into online shopping studies. However, there are various limitations of this study and recommendation to the future research will also be discussed. First, empirical research was conducted only in Viet Nam. Thus, data results mainly reflected in customer behaviors in Vietnam. The author recommended replicating the study in different nations to get an international sample. 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