Frontiers in Business and Economics

: Today, Shopee customer loyalty is superior than Lazada customer loyalty. Although, the Lazada entered Indonesia in 2012 it was much longer than Shopee which entered Indonesia in 2015 and had controlled the market share in Indonesia. However, the last few years have shown that Lazada has not been able to maintain the loyalty of its customers. So, it is necessary to do further research on the effect of customer loyalty and customer satisfaction. The purpose of this study is to examine the effect of website quality, trust, and ease of use on customer loyalty. Also, to determine the mediating role of customer satisfaction in the relationship of website quality, trust, and ease of use on customer loyalty. A total of 96 respondents were participated in this study. The data analysed using structural equation modelling through partial least square by assisting with statistical software, namely SmartPLS-3. The result indicated that website quality, trust and ease of use have a significant and positive effect on customer loyalty. Also, customer satisfaction mediates the relationship of website quality and trust on customer loyalty. Besides that, customer satisfaction mediates the relationship of ease of use on customer loyalty. In conclusion, this study has successfully investigated and examined the influential factor of customer loyalty and the mediating customer satisfaction in the relationship of website quality, customer trust and ease of use on customer loyalty.


Introduction
E-commerce is all buying and selling activities carried out online by businesspeople through electronic media such as television, telephone and internet (Harn et al., 2006;Harnani et al., 2020). E-commerce is different from transactions when we visit shops. People should be more confident with direct transactions to shops compared to online transactions, because in online transactions consumers cannot see the products that are sold directly (Cho, 2004;Liao & Cheung, 2001;Wolfinbarger & Gilly, 2001). However, in fact online transactions continue to increase. In 2020 the value of the Ecommerce market in Indonesia is predicted to grow by around USD $ 55 billion -USD $ 65 billion. The estimate comes from a new report released by management consulting firm McKinsey. The rapid growth of the E-commerce sector in Indonesia is supported by several factors including the increasing number of smartphone and internet users, the large population in Indonesia and strong macroeconomic growth causing purchasing power to increase, Indonesia has a population that is easy to adapt to new technologies.
In addition, the growth of E-commerce is also due to the Covid-19 pandemic which has caused an increase in the number of fulfilment needs (Aditantri et al., 2021;Settey et al., 2021;Suguna et al., 2022). The Indonesian government has implemented several policies to deal with the Covid-19 pandemic. According to (Supriatna, 2020), a policy has been implemented for the Indonesian people in March 2020 in the form of social distancing and physical distancing. This policy causes consumers to shift and change purchasing patterns from buying goods directly to buying goods through E-commerce. On the basis of the nominal data of E-commerce transactions in Indonesia in 2017-2020 that the nominal E-commerce transactions in 2017 were 42.2 Trillion, in 2018 it increased by 150.24% compared to 2017 to become 105.6 Trillion. In 2019 it increased by 94.6% compared to 2018 so that it became 205.5 Trillion. In 2020 it increased by 29.6% compared to 2019 to become 266.3 Trillion.
Although transactions through E-commerce continue to increase every year, this makes businesspeople in cyberspace have their own challenges to maintain the products they sell. This challenge is certainly not easy, because all internet users can access the virtual world wherever they are. Based on data from internet users in 2017-2020, it can be concluded that in 2017 there were 84 million internet users in Indonesia, growing by 13.3% in 2018 to 95.2 million people. In 2019, internet users in Indonesia grew 12.6% compared to 2018 to 107.2 million people. In 2020 internet users in Indonesia grew 11.01% compared to 2019 to 119 million people. Information from these sources also explains that mobile messaging is a popular online activity in Indonesia.
Indonesia is a country with people who like to do online shopping. There are several online shopping sites in Indonesia such as Lazada.co.id, Shopee.co.id, Tokopedia.com, Bukalapak.com, and others. Lazada.co.id is one of the most popular online shopping sites in Indonesia. Lazada.co.id is part of the Lazada group and is also a subsidiary of Rocket Internet from Germany which was founded in Indonesia in 2012. Lazada sells innovative online products in the world such as fashion, electronic products, home decor, health products to beauty products. Lazada in increasing its customer loyalty during Ramadan for the period 19 May -25 June 2017 provides digital parcel rewards in the form of "Eid gifts" which are packaged in digital form to Lazada consumers who make every three purchase transactions. The digital parcel rewards consist of free cinema tickets, Lazada vouchers, and free JOOX subscriptions. In addition, Lazada also implements the LazMall program to increase customer loyalty.
Based on List of 50 E-Commerce Websites & Applications in Indonesia in 2019 Lazada.co.id has the fourth highest number of E-Commerce visitors in each quarter in 2019. The number of Lazada visitors in the first quarter of 2019 was 52.04 million people and decreased in the second quarter of 2019 by 4.69% to 49.6 million people. Furthermore, it decreased again in the third quarter of 2019 by 43.75% to 27.9 million people and increased in the fourth quarter of 2019 by 1.43% to 28.3 million people. While in 2020 it can be seen that Lazada has the fourth highest number of E-Commerce visitors in each quarter in 2020. The number of Lazada visitors in the first quarter of 2020 was 24.4 million people and decreased by 9.84% in the second quarter of 2020 to 22 million people. Furthermore, it increased in the third quarter of 2020 by 2.73% to 22.6 million people and continued to increase in the fourth quarter of 2020 by 60.18% to 36.2 million people.
On the basis of the data on the number of E-commerce visitors in 2019 -2020 and based on the results of a survey of 30 marketplace users spread over 5 districts in Tebing Tinggi, it is stated that Shopee customer loyalty is superior to Lazada customer loyalty. Although Lazada's entry into Indonesia in 2012 was much longer than Shopee's entry into Indonesia in 2015 and had controlled the market share in Indonesia, currently Lazada's market share is not as large as Shopee's. There are many complaints faced by Lazada consumers. The results of a pre-survey on 30 marketplace users spread over 5 sub-districts in Tebing Tinggi revealed that Lazada has no discount, free shipping vouchers only occur on certain dates, for example June 6, there is no shopetter like the one on Shope. The main complaint from Lazada users is that the free shipping voucher at Lazada only occurs on certain dates. Lazada is not able to maintain the loyalty of its customers because there are many complaints faced by Lazada consumers and these complaints cannot be resolved by Lazada. Thus, the present study seeks to examine the effect of website quality, trust, and ease of use on customer loyalty. Also, to determine the mediating role of customer satisfaction in the relationship of website quality, trust, and ease of use on customer loyalty. Tandon et al., (2020) stated that website quality is the perception of the quality of the site based on the point of view of customers who make purchases via the internet. Traver & Laudon(2012), website quality is a technique to measure website quality through end user perception. Priansa(2017) defines trust is a person's belief in another person in carrying out transactions that are full of uncertainty. Haryanto & Sektiyaningsih (2021) added that trust is the experience that consumers have in using a product so that they can conclude the product used, its attributes, and benefits.

Ease of Use
Ease of use is a consumer's belief that a particular technology can solve problems easily Setiawan (2018). Meanwhile, Ella et al., (2012) stated that ease of use is the state of consumers who feel that the website makes it easy for consumers to get information . Hamdani & Zaman (2017) defines the customer satisfaction is the customer's response to the purchased product. Meanwhile, Arianto & Kurniawan (2021) stated that customer satisfaction is a condition where the customer is satisfied if the quality of the product received is as expected, and the customer is very satisfied if the quality of the product received is more than expected.

Loyalty
Customer loyalty is a customer 's commitment to make repeat purchases of a product brand and same store Sangadji (2013) . Meanwhile, Hasan (2014) added that customer loyalty is a person's commitment to buy a product because they want to have the product with the aim of satisfying their desires so that they come to the same place over and over again.

Materials and Methods
The population in this study were all Lazada customers in Tebing Tinggi City. In this study, it is not known the size of the population to be studied, so the sample size used in this study uses the Cochran formula, so the number of samples in this study can be calculated as follows: 96 respondents. Accidental sampling is a method of determining the sample by taking respondents who happen to meet with the researcher. and can be used as a sample if the person met is in accordance with the researcher's criteria. The primary data in this study were obtained based on the results of the questionnaires filled out by the respondents. Secondary data in this study were obtained based on previous research, literature review, electronic media (internet), and journals. The data analysed using structural equation modelling through partial least square by assisting with statistical software, namely SmartPLS-3.

Results and Discussion
The measurement model (outer model) is confirmatory factor analysis (CFA) by testing the validity and reliability of latent constructs. The following are the results of the evaluation of the outer model in this study. This study uses the help of Smart PLS 3.0 software to test the validity and reliability of the research instrument. To test the validity of a data, convergent validity can be used by looking at the loading factor value and discriminant validity by looking at the cross-loading value. Convergent validity of the measurement model with the reflective indicator model is assessed based on the correlation between the item score / component score and the construct score calculated by PLS. The reflective measure is said to be high if it has a correlation of more than 0.70 with the construct to be measured. However, Hartono & Abdillah (2014) for early stage research with the development of a measurement scale the loading value of 0.5 to 0.6 is considered sufficient. In this study using a loading factor of 0.60 with an algorithm calculation on Smart PLS 3.0, the following results of testing the convergent validity measurement model using the loading factor can be seen in Table 1  0.764 Table 1 shows the result of Loading Factor. This study indicates that all loading factor values have crossed the limit of 0.6 so that it can be concluded that each indicator in this study is valid. Therefore, these indicators can be used to measure research variables. Discriminant validity compares the Average Variance Extracted (AVE) value of each construct with the correlations between other constructs in the model. If the AVE root value of each construct is greater than the correlation value between constructs and other constructs in the model, then it is said to have a good discriminant validity value. The discriminant validity measurement model using cross loading, which can be seen in Table 2 below:  Table 2 displays all cross-loading values of each targeted indicator have a higher correlation with each variable compared to other variables. It can be concluded that all of these indicators are valid as a whole. An instrument can be said to be reliable by looking at the value of Cronbach Alpha greater than 0.60. Composite Reliability is greater than 0.7 and Average Variance Extracted (AVE) is greater than 0.5. The following are the results of reliability calculations through Cronbach Alpha, Composite Reliability and Average Variance Extracted (AVE), which can be seen in Table 3 below:  Table 3 captures the Cronbach Alpha value of each variable is > 0.6. The Composite Reliability value of each variable is > 0.70, and the Average Variance Extracted (AVE) value of each variable is > 0.5. From the above calculation results, all indicators are reliable in measuring the latent variables. The evaluation of the inner model can be seen from several indicators which include the coefficient of determination (R2), Predictive Relevance (Q2) and the Goodness of Fit Index (GoF) (Hussein, 2015). The results of the structural model displayed by Smart PLS 3.0 in this study are as follows: In assessing the model with PLS, it begins by looking at the R-square for each dependent latent variable. The results of the calculation of r2 in this study are as follows:  Table 4 shows the R square value of the Customer Satisfaction variable (Z) is 0.631 which means that Customer Satisfaction (Z) is influenced by Website Quality (X1) , Trust (X2) and Ease of Use (X3) of 63 ,1% or in other words the contribution of Website Quality (X1), Trust (X2) and Ease of Use (X3) is 63.1% while the remaining 36.9% is the contribution of other variables not discussed in this study. The results of r2 of the Customer Loyalty variable (Y) of 0.719 which means that Customer Loyalty (Y) is influenced by Website Quality (X1), Trust (X2), Ease of Use (X3) and Customer Satisfaction (Z) of 71.9% or with In other words, the contribution of Website Quality (X1), Trust (X2), Ease of Use (X3) and Customer Satisfaction (Z) is 71.9% while the remaining 28.1% is the contribution of other variables not discussed in this study. Calculation of goodness of fit can be used to determine the amount of contribution given by exogenous variables to endogenous variables. The GoF value in PLS analysis can be calculated using Q -square predictive relevance (Q2). The following are the results of the calculation of the Goodness of Fit Model in this study: Q 2 = 1 -(1 -r12) (1 -r22); Q 2 = 1 -(1 -0.631) (1 -0.719); Q 2 = 0.896 The value of Q -square predictive relevance (Q2) is 0.896 or 89.6%. This is able to show that the diversity of Customer Loyalty variables (Y) can be explained by the overall model of 89.6% or it can also be interpreted that the contribution of Website Quality (X1), Trust (X2), Ease of Use (X3) and Customer Satisfaction (Z) to Customer Loyalty (Y) as a whole is 89.6%, while the remaining 10.4% is the contribution of variables that are not discussed in this study. The results of the outer model carried out, all the hypotheses tested have met the requirements, so that they can be used as an analytical model in this study. Hypothesis testing in this study uses 5% alpha, which means the t-statistic value 1.960 or the probability value level of significance (α = 5%). The 0.05 limit means that the magnitude of the chance of deviation is only 5% and the remaining 95% is indicated to be able to accept the hypothesis.
Hypothesis testing in this study is divided into two parts, namely direct influence testing and indirect influence testing (mediation). The direct effect test will use bootstrapping on the Smart PLS 3.0 software, while the indirect effect test will use t-statistics on the indirect effect. Direct effect testing is used to explain hypotheses 1,2,3,4,5,6 and 7 through path coefficients. The value of path coefficients can be seen through the value of t-statistics which must be greater than t-table, namely 1.96, which means that there is an influence of exogenous variables on endogenous variables in each predetermined hypothesis. The t-statistic value 1.96 or probability value level of significance (α = 5%), has the conclusion that the hypothesis is accepted, that is, there is a significant effect between the variables being tested. Table 5 describes the results of direct hypothesis testing by bootstrapping on Smart PLS 3.0 software. The following is an explanation of each hypothesis in this study.  Table 5 displays the result of hypothesis testing (direct effect). The result showed that website quality, trust and ease of use have a significant and positive effect on customer loyalty. The coefficient regression of website quality is 0.368, standard deviation is 0.120 with t-stat is 3.066 and significant at the level of 1 percent. It means that increase 1 percent website quality it will be increased the customer loyalty as much as 36.8 percent. For the variable trust, this study obtained the coefficient regression is 0.355, standard deviation is 0.112, t-stat is 3.179 and significant at the level 1 percent. It means that increase 1 percent customer trust it will be increased the customer loyalty as much as 35.5 percent. Also, Ease of Use coefficient regression is 0.208, standard deviation is 0.089, t-stat is 2.322 and significant at the level 5 percent. It means that increase 1 percent the ease of use variable it will be increased the customer loyalty as much as 20.8 percent. In addition, the results of the indirect effect test using Smart PLS 3.0 are as Table 6 below:  Table 6 displays the result of mediating analysis. The result indicated that customer satisfaction mediates the relationship of website quality and trust on customer loyalty. Besides that, customer satisfaction mediates the relationship of ease of use on customer loyalty.

Conclusion
In conclusion, this study has successfully investigated and examined the influential factor of customer loyalty and the mediating customer satisfaction in the relationship of website quality, customer trust and ease of use on customer loyalty. This study found that website quality, trust and ease of use have a significant and positive effect on customer loyalty. Also, customer satisfaction mediates the relationship of website quality and trust on customer loyalty. Besides that, customer satisfaction mediates the relationship of ease of use on customer loyalty. Thus, Lazada needs to pay more attention to aspects of the website, trust and ease of use. Lazada website need to add the latest and more innovative features, so that Lazada can continue to survive and compete with other marketplaces that continue to grow.