Analysis of the Effect of E-Service Quality on E-Customer Satisfaction and E-Customer Loyalty on Shopee
Debi Yulina Br. Ginting1*, AMA Suyanto1, Mochamad Yudha Febrianta1
1 Faculty of Economic and Business, Telkom University, Bandung, Indonesia
*Corresponding Author: [email protected] Accepted: 15 September 2022 | Published: 1 October 2022
DOI:https://doi.org/10.55057/ajrbm.2022.4.3.35
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Abstract: In the third quarter of 2020, Shopee was the top marketplace in Indonesia, ranked first with 93.4 million users with a total daily active users (DAU) of Shopee reaching 834,520,000 million users accessed the Shopee application in August 2021. However, from the number of visiting and the number of users using the Shopee application, there is a low percentage of services provided by Shopee to its users, which is only 63%. This study aims to determine and analyze how much influence the services provided by Shopee based on the dimensions of e-service quality have on ecustomer satisfaction and e-customer loyalty to Shopee application users. Distributing online questionnaires with the help of Google Form to Shopee application users with a total of 439 respondents. In this study indicate that the dimensions of efficiency, fulfillment, privacy have a positive and significant influence on the e- customer satisfaction variable, but the system availability dimension does not have a positive and significant relationship with e-customer satisfaction. The results of this study also show that only the efficiency dimension has a positive and significant effect on the e-customer loyalty variable where the other dimensions, namely system availability, fulfillment and privacy, do not have a positive and significant relationship with e-customer loyalty. This research also shows that the e-customer satisfaction variable has a positive and significant effect on the e- customer loyalty variable.
Keywords: technology, e-commerce, e-service quality, e-customer satisfaction, e-customer loyalty
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1. Introduction
The development of e-commerce in Indonesia is always increasing, this can be used as a motivation for companies to create new innovations. One of the new innovations is to use this internet network as a medium for conducting online buying and selling transactions. Buying and selling transactions made online are commonly referred to as e-commerce. Shopee is an application that aims to make buying and selling transactions using network technology. The development of Shopee's e-commerce can be said to be developing well. According to the results of data obtained by the Compass team (Ramadhani, 2021) it can be seen that there are 5 marketplaces that are ranked at the top in Indonesia. Shopee is in first place with 93.4 million users in the second quarter of 2020, followed by Tokopedia with 86.1 million people.
In 2020 the third quarter described in (Burhan, 2020) it is said that monthly e-commerce visitors are increasing rapidly due to the Covid-19 pandemic which triggers people to shop using online media. One of the online media that is often used by the public is Shopee. In the
third quarter of 2020, Shopee ranked first, followed by Tokopedia, Bukalapak, Lazada, Blibli, etc. At the end of 2020, Shopee occupies the first position as an online media that is often used by the public. Based on the results of the report submitted to SimilarWeb in (Jemandu, 2021) it is said that in August 2021, Shopee has 26.92 million daily active users using Android smartphones in Indonesia and if calculated until 31 days In August, Shopee's daily active users (DAU) achieved a monthly visit of 834,520,000 million users who access Shopee using the application. Meanwhile, Tokopedia occupies the second position after Shopee in the number of daily active users (DAU) of 7,882,000 in the same period and if calculated until August 31, 2021, Tokopedia has an average DAU level of 244,240,000 million in that period. Shopee also ranked first in the most total visits from both websites and applications followed by Tokopedia, Lazada, Bukalapak and Blibli.
The number of total Shopee visits used by users by using websites and applications implies that the total downloads of the Shopee application have increased as according to the results of a report launched by The Map of E-Commerce in Indonesia issued by Iprice in the second quarter of 2021, it is said that Shopee occupies first position with the most total downloads than its competitors. Shopee occupies the first position in the most total downloads from the AppStore and Play Store, followed by Tokopedia in the second position and followed by other e-commerce. However, based on the results of a survey conducted by Snapcart conducted since September 2021 using an online survey method that has 1000 respondents from various genders, ages and incomes of respondents. This study distributes questionnaires throughout Indonesia. According to the results of the Snapcart survey, 87% of respondents said that Shopee was the most used e-commerce at that time (September 2021).
(Sutriyanto, 2021) said that there are several factors and reasons why people prefer Shopee to be their media platform. According to SnapCart research results, the factors that make Shopee an e-commerce platform in Indonesia are shopee as a trusted e-commerce, reliable e- commerce, offering the most promotions, helping the economy for MSMEs, helping to move the wheels of the people's economy and Shopee is an e-commerce that prioritizes their services for their users. Service and trust factors have a lower percentage level which is only 63%. The low presentation of services available at Shopee can be said that the services provided by Shopee to its users have not been maximized. This is supported by the number of users who still feel they have not been served well by Shopee regarding the problems experienced by their users. Reviews relating to user disappointment by shopee for services can be seen based on the dimensions of e-service quality in the core online service proposed by Parasuraman et al, (2005) in (Hariansyah et al., 2019) namely efficiency, fulfillment, system availability and privacy. Reviews related to these dimensions can be seen in the tables below.
Table 1: Reviews of User Complaints Againts E-Service Quality
Reviewer Review Date and Review
Rate
Review Dimension
Erna Chen December 05 2021, Rate 1
Sampai kapan ini shopee tidak bisa log in. Katanya ada masalah dengan sistem Shopee. Saya sudah mengalami ini sebanyak 2 kali.
Pertama, saya gabisa lihat saldo, sekarang gabisa log in dan gabisa lihat saldo. Dipusat bantuan tertulis bahwa masalah pada sistem Shopee akan diperbaiki, tapi
Efficiency
customer service nya saja tidak bisa.
Ceniss Melinda
December 05 2021, Rate 3
Saya membeli baju dengan menggunakan metode mbanking dan saldo saya sudah terpotong.
Tetapi status pemesanan saya masih belum dibayar. Saya sudah menghubungi CS dan suruh menunggu 1X24 jam.
Namun, baju yang saya pesan akhirnya dibatalkan oleh pihak Shopee dan uang saya hilang begitu saja.
Fulfillment
Latif December 05 2021, Rate 1
Aplikasi ini terlalu lemot dan sering minta update. Kebanyakan update terus dan buang- buang kuota data internet. Setiap buka aplikasi ini harus didiemin dulu 1 menit baru lancar. Padahal sinyal kuat. Tolong ditingkatkan kecepatan sistemnya ya.
System Availability
Asry Arima December 02 2021, Rate 1
Hari ini saya sangat kecewa ketika saya membuka aplikasi shopee biasa aja kemudian saya kembali ke beranda hp saya lalu disuruh untuk masuk kembali. Namun kenapa akun saya menjadi hilang?
Saya login dengan akun yg sama tapi tidak bisa. Katanya akun dan password saya bermasalah.
Privacy
Based on Table 1, complaints given by users based on the dimensions of efficiency, fulfillment, system availability and privacy, it can be concluded that each complaint on these dimensions still has its own shortcomings experienced by its users. Complaints submitted by users based on the dimensions of e-service quality are said to be insufficient to meet user expectations for the services provided by Shopee. Meanwhile, according to (Özkan et al., 2020) it is said that if the company provides good services, then indirectly the company will have a company image and reputation. Then according to (Novianti et al., 2018) it is said that service quality is a measurement that is carried out thoroughly based on the services obtained by customers. The services provided will indirectly switch to services that will be provided online which is commonly referred to as E-Service Quality (Pudjarti et al., 2019).
The services provided by Shopee already use electronic media so that if a user complains about their service, it is likely that Shopee will respond using or electronically based. However, basically providing quality services using electronics is not easy where the many obstacles experienced will affect customer satisfaction. The e-service quality provided by the company to its users must really be considered because good e-service quality will increase satisfaction and have an impact on the formation of user loyalty for the Shopee application. Service quality and user satisfaction are important aspects for the company's success (Suprapti & Suparmi, 2020) and (Pudjarti et al., 2019).
Based on the research researched by (Sastika, 2018) it was stated that the percentage of satisfaction level that users had with the Shopee Application was only 68.62% from 100% with a total of 400 respondents. From the results of the reviews submitted, that there are still many sellers and users who say that this application is unsatisfactory. From the reviews of the Shopee
application users, it is clear that the Shopee application should be improved. The reviews that will be presented in the table below are some of the user complaints about the low level of satisfaction while using the application.
Table 2: Reviews of User Complaints Againts E-Customer Satisfaction
Reviewer Review Date and Review
Rate
Review
Riza Yulia Anggraheni
December 06 2021, Rate 1
Kalo bisa tidak diisi saya tidak isi. Saya amat sangat tidak merekomendasikan aplikasi ini. Saya ada isi shopee-pay lewat virtual account ternyata saldo saya hilang. Respon team Shopee terlalu bertele-tele dan lama. Saya amat sangat kecewa!
Rainidah Smp
December 04 2021, Rate 1
Sebenarnya saya sangat kecewa banget sama Shopee, saya ini udah member lama, udah seneng banget belanja di sini, lalu tiba-tiba kemarin sinyal saya buruk dan akun saya kok jadi berubah terganti sendiri. Poin hilang,uang di Shopee pay hilang,sekarang saya jadi member baru, disuruh dari Shopee login tapi tidak bisa-bisa, saya bener- bener kecewa banget sama Shopee.
From Table 2 it can be seen that there are user reviews on the satisfaction of using the Shopee application, in that review Riza & Rainidah gave 1 star to the Shopee application which means that the user is very dissatisfied with what they get. The average shopee user complains of their dissatisfaction related to the system and poor performance of Shopee (customer service, system). This dissatisfaction can be seen from the number of complaints and services they get.
Meanwhile, according to (Pudjarti et al., 2019) companies must really pay attention to their e- service quality because good e-service quality will increase user satisfaction so that this will also have an impact on user loyalty.
When customers feel a good transaction experience through service quality, customer loyalty will arise. With good service quality, customers will lead to reciprocal behavior that leads to loyal customers (Pudjarti et al., 2019) and(Suprapti & Suparmi, 2020). E-service quality has a good relationship to create and increase customer loyalty. Megasari (2019) in (Suprapti &
Suparmi, 2020) says that e-service quality has a positive and significant influence on e- customer loyalty. From this explanation, it can be seen that the reviews given by Shopee Application users on the Google Play Store who decide to switch from Shopee and use other applications can be seen in Table 3 below.
Table 3: Reviews of User Complaints Againts E-Customer Loyalty
Reviewer Review Date and Review
Rate
Review
Ryan Ferdianta
December 06 2021, Rate 1
Sangat tidak jelas dalam membuat gratis ongkir minimal belanja 30rb. Tapi ketika saya pesan, ongkir nya menjadi 90rb. Memang paling enak menggunakan Tokopedia. Aplikasinya jelas dan gratis ongkir
Dwi Putra December 04 2021, Rate1
Pengalaman yang buruk saat menggunakan aplikasi Shopee. Sangat payah. Lebih baik belanja di Tokopedia saja, lebih aman dan lebih cepat.
It can be concluded that, Shopee application users do not feel satisfaction and a high level of service so that it will adversely affect consumer loyalty behavior. Ryan & Dwi said that they were not satisfied with what Shopee provided and were not satisfied with the features used.
Not only expressing his frustration with Shopee, this user also gave advice to other users not to use Shopee and turn to Tokopedia. From the results of the reviews submitted by these users, Shopee should provide good electronic services so that it will lead to satisfaction and loyalty to the Shopee application. Based on the data on phenomena and problems that have been collected by researchers, the researchers intend to conduct this research more deeply to find out how the dimensions of e-service quality affect e-customer satisfaction and e-customer loyalty to Shopee application users.
2. Literature Review
2.1. E-Commerce
E-commerce is the distribution, purchase, sale and marketing of goods and services using electronic media such as the internet, television or other internet networks. Electronic funds transfer, electronic data exchange, etc. are things that involve e-commerce in it. Meanwhile, according to (Harmayani et al., 2020) the benefits that can be felt when using e-commerce are to be able to reduce the costs to be incurred for goods and services, and to build consumer satisfaction. By using e-commerce, the transaction costs incurred will be less and lower where the transaction process has a thorough nature. Where consumers can only access the internet network to the company's website that introduces their products to the internet.
(Kasmi & Candra, 2017) said that e-commerce is a technology that has developed rapidly in buying and selling goods and services using electronic networks such as the internet. E- commerce itself is one of the online media that can be used to shop online to find the needs and desires of each individual. So it can be said that e-commerce is a process of buying and selling products or services that involves two parties via the internet (commerce-net). As stated by (Alwendi, 2020) that e-commerce is a medium that is implemented on the development of the internet which aims to market products (goods or services) to all segments and places, both in physical and digital form on a national and international scale.
From the e-commerce description and explanation, it can be concluded that e-commerce is a transaction medium to make sales and purchases (goods or services) using internet technology.
With this internet technology, it can help consumers to find their needs and desires simply by accessing the e-commerce platform media to complete their needs. The costs incurred are also less and the transaction process that is owned is more thorough.
2.2. E-Service Quality
According to (Akhmadi & Martini, 2020) e-service quality is a theory derived from service quality or what is called the quality of the services provided, carried out or what occurs on the internet network. Furthermore, according to (Permana & Djatmiko, 2018)e-Service Quality can be seen based on the extent to which the website makes it easier to shop, make purchases and deliver goods that are supported by providing effective and efficient services.
Meanwhile, according to David in (Akhmadi & Martini, 2020) that a company should indeed provide quality services because this can make the company superior. E-service quality is a very important right that is useful for seeing customer satisfaction with the services provided by the company. Electronic service quality can also be said as a method used to measure customer satisfaction from internet-based service providers. This measurement is carried out
to find out what consumers feel and match it with the services that have been expected by consumers based on the dimensions contained in the quality of the electronic service.
According to Zeithaml, et all (2002) in (Ulum & Muchtar, 2018) it is said that there are seven dimensions that make up the scale of core online services and online recovery services. The core scale contained in e-service quality has three main dimensions, namely efficiency, reliability, and fulfillment. This dimension is the core dimension used to measure the user's view of the quality provided by the company. This dimension will be used to correct online services that are carried out regularly where users do not experience problems when using certain sites. In addition, four other dimensions contained in e-service quality are privacy, responsiveness, compensation, and contact. These four dimensions are included in the online service recovery scale. Where this dimension plays an important role when users experience problems or when users have questions that they want to find solutions for.
Parasuraman et al, (2005) in (Hariansyah et al., 2019) say that the e-service quality model is a model developed to measure the quality of an electronic system in the form of a multi-item scale that is shared using e-commerce sites. Meanwhile, e-recovery service quality is a model that is intended to measure the quality of a service provider improvement and is centered on handling problems and research-related questions. The e-service quality model consists of efficiency, fulfillment, system availability and privacy, while the e-recovery service quality model consists of responsiveness, contact, and compensation. The explanation of the dimensions that have been conveyed by Parasuraman et al, (2005) in (Hariansyah et al., 2019).
Efficiency is a useful variable to determine the level of speed and ease of use of a site or electronic system. Fulfillment is a variable that is useful for determining the level of compliance based on an agreement on the availability of goods or products. System availability is a useful variable to determine the accuracy of functionality based on electronic systems.
Privacy is a variable that is useful for determining a level of protection and security of a site against customer personal data. Responsiveness is a useful variable to determine success in dealing with problems through the site. Compensation is a variable that is used to see the extent to which the site compensates for the problems experienced by customers. Contact is a variable that is used to see the readiness for assistance that will be distributed via telephone or online.
2.3. E-Customer Satisfaction
E-customer satisfaction is a consumer's expectation of a product or service, the level of consumer satisfaction when buying a product or service will be compared with the purchase experience and expectations expected by consumers after purchase (Ahmad et al., 2017). In the same vein, e-customer satisfaction usually leads to user satisfaction with a previous purchase experience using the website (Ulum & Muchtar, 2018). Judging by the purpose of e-service quality, it can be concluded that e-service quality has a very important role in influencing e- customer satisfaction and e-customer loyalty. Offer is a certain factor that must be provided by electronic services using shopping sites. This e-service quality offer will provide satisfaction to customers and can encourage customers to reach their level of loyalty to web-shopping sites (Khan et al., 2019).
2.4. E-Customer Loyalty
Customer loyalty on online shopping websites or what is called electronic loyalty can be seen based on product purchases that have previously been made on webstores or shopping sites and the customer intends to return. The expected result with this electronic loyalty is when users can convey things of positive value related to the company to other parties (Priowirjanto et al., 2021).
According to (Jeon & Jeong, 2017), electronic loyalty can be interpreted as a favorableuser trait for online sellers where the user will make repeat purchases. This e-loyalty is a result of customer satisfaction with service quality. If the company has loyal users, it will be very profitable for the online company seen from the increasing amount of competition. Loyal consumers will recommend their relatives or family to make a purchase. It can be concluded that electronic loyalty is an activity carried out to make repeat purchases where the user will recommend it to relatives and family. This electronic loyalty is formed by user satisfaction with quality services from the company to users.
3. Methodology
3.1. Population and Sample
The population that will be used in this study will focus on the Indonesian people who have used the Shopee application. With this number of Shopee users, the total population of this study is 834,520,000 million visits by Shopee application users in Indonesia. This study will provide questionnaires to respondents who have the following characteristics or characteristics live or domiciled in the territory of Indonesia, have used the Shopee application at least once in a certain period of time (at least once a month), and 400 respondents are users who have at least used the Shopee application within a certain period of time (at least once a month).
3.2. Data Collection
The data collection technique that will be used in this study is to use an online questionnaire, with the help of a google form and will be distributed to various social media such as Instagram, WhatsApp, Line and Telegram. The distribution of this questionnaire was carried out to assist researchers in obtaining respondents and to help researchers to shorten the time for filling out the questionnaires.
3.3. Validity and Reliability Test
In this study, the validity test will use the SmartPLS software which will then be analyzed further by testing the convergent validity and discriminant validity models. Where the convergent validity is divided into two tests, namely the loading factor test and the average variance extracted (AVE) test. The discriminant validity test is divided into two tests, namely the cross loading test and the Fornell-larcker criteria. In this study, the reliability test will use the smartPLS software which will be analyzed further by looking at the value of Cronbach's alpha and composite reliability.
3.4. Data Analysis Technique
From the data that has been obtained from respondents' responses to the questionnaire given, the results will be processed with smartPLS software and the results will be analyzed by researchers who will serve to facilitate the delivery of information from the results obtained.
This study initially tested normality using the SEM method with the help of AMOS software.
However, when the authors conducted a normality test using the AMOS software, the researchers got results where the data were not normally distributed in a multivariate manner.
Where it can be seen that the main requirement for using AMOS software is that the data must be normally distributed. Thus, the data processing method in this study will be changed using smartPLS software. It can be seen that using this smartPLS software does not have the condition that the data must be normally distributed. This statement is supported by (Syahrir et al., 2021) which says that PLS is a method that does not require that the data must have a multivariate normal distribution. As previously stated, this research changed the data processing method which initially used CB-SEM and then changed to VB-SEM using PLS or
(partial least square) tools. The method in this PLS uses a bootstrapping technique or what is commonly called random doubling. The assumption that occurs in this method uses a different type of statistic from AMOS where the type of statistic in PLS is non-parametric while AMOS itself uses parametric statistics. In addition, the approach that occurs in SEM-PLS involves many independent variables by predicting the dependent variable (Syahrir et al., 2021). PLS (partial least square) itself has a goal, namely to estimate something with coefficient-oriented properties. The goodness of fit test in this method says that the GOF test offers relatively less than SEM-AMOS (Musyaffi et al., 2021).
In this study, validity testing consists of testing convergent validity and testing discriminant validity. To test convergent validity, the researcher conducted a loading factor and Average Variance Extracted (AVE) test, while for discriminant validity testing, the researcher conducted a Cross Loading and Fornell-Lacker Criterium test. Then in conducting reliability testing, the examiner tested Composite Reliability and Cronbach's Alpha. In this study, validity testing consisted of testing convergent validity and testing discriminant validity. To test convergent validity, the researcher conducted a loading factor and Average Variance Extracted (AVE) test, while for discriminant validity testing, the researcher conducted a Cross Loading and Fornell-Lacker Criterium test. Then in conducting reliability testing, the examiner tested Composite Reliability and Cronbach's Alpha. In testing the structural model or what is commonly called the inner model, it can be used to see the relationship that occurs between the constructs that occur, the significance of the value and the value of the R-Square in the research model (Wahyuni, 2022) (Ghozali, 2021). By doing this inner model testing can see the relationship that occurs between latent variables by looking at the results obtained from the path coefficient and the level of significance. In testing this structural model, it can be done with several approaches, namely path coefficient, R-Square, Q-Square, F2 (effect size). To find out whether the model is feasible to use, a full model feasibility test will be carried out by looking at the results of the calculation of goodness of fit statistics. If the goodness of fit obtained gets a good value, the model is accepted and if the results obtained are not good, then the model is rejected. From the results to be obtained, it can be seen that if the full model value has a good GOF value, it can be said that the resulting structural model can be analyzed further (Susanto, 2020).
4. Discussion and Conclusion
4.1. Data Characteristic
Researchers can collect as many as 439 respondents where the respondents are the criteria of the researcher's sampling with the sampling technique that is non-probability sampling purposive sampling type. The characteristics of this study are Shopee users who live in North Sumatra and West Java and are dominated by female users with an age range of 21-25 years with education levels, namely SD-SMA who have a job as a student with an income level of Rp <500,000 - IDR 1,000,000. In addition, in this study, Shopee users are dominated by users who have a daily level of internet use, only spending 3-4 hours/day with the level of doing shopping activities > 1 time a month using the Shopee application as a transaction medium.
4.2. Research Result
The following is a test of the outer model I that has been running by the researcher.
Figure 1: Outer Model I
Based on the results of the outer model I output in Figure 1 it can be seen that there are values that are between the lines on the path diagram are the numbers generated to analyze the outer model I test. Because there are several indicators that are not valid so that this will have an impact on further testing, therefore the researcher will test the outer model II.
Figure 2: Outer Model II
In the outer model II test that has been carried out by the researcher, it can be seen that when the researcher removes the invalid indicator and retests it, the researcher obtains a good loading factor value from each indicator and its latent variable. From the results of the outer model II test, it can be seen that the loading factor value on the efficiency variable, the EFF5 indicator gets the highest value from other efficiency indicators. Then on the system availability variable, the SA4 indicator gets the highest value from other system availability indicators. In the fulfillment variable, the FF3 indicator gets the highest value from other fulfillment indicators.
Then on the privacy variable, the PR1 indicator gets the highest value from other privacy indicators. Then on the e-customer satisfaction variable, the ECS3 indicator gets the highest
score from other e-customer satisfaction indicators. And on the e-customer loyalty variable, the ECL3 indicator gets the highest value from other e-customer loyalty indicators.
The next test is the AVE (average variance extracted) test. Where in the convergent validity test, this AVE test is one of the tests that can be useful to see whether the convergent validity is said to be valid or invalid. AVE itself has a minimum value where the predictor value will be said to be valid if the AVE value is > 0.5 (Hardisman, 2021). The following is the result of the AVE (average variance extracted) value that has been carried out by the researcher.
Table 4: Average Variance Extracted (AVE) Score Results
Variable
AVE (Average Variance Extracted)
Value
Critical Value Evaluation Model
Efficiency 0,537 0,5 Valid
System Availability 0,597 0,5 Valid
Fulfillment 0,715 0,5 Valid
Privacy 0,703 0,5 Valid
E-Customer Satisfaction 0,779 0,5 Valid
E-Customer Loyalty 0,742 0,5 Valid
Based on Table 4 above, it can be seen that the results of convergent validity based on the AVE (average variance extracted) value show that all variables obtain values that are declared valid or the AVE (average variance extracted) value is greater than 0.05 so it can be concluded that all constructs are declared valid.
The next step is discriminant validity, which is an activity used to assess the validity of a predictor by comparing its relationship with other variables. In this discriminant validity the indicator used is cross loading. In other words, the predictor will be said to be valid if the cross loading value is > 0.7 or if the predictor cross loading value has a value greater than the value of the latent variable itself compared to the value possessed by other variables (Hardisman, 2021). The following is the result of the cross loading that has been carried out by researchers.
Table 5: Cross Loading Result Score E-Customer
Loyalty
E-Customer
Satisfaction Efficiency Fulfillment Privacy System Availability
ECL1 0.812 0.640 0.514 0.512 0.484 0.495
ECL2 0.878 0.606 0.547 0.512 0.438 0.454
ECL3 0.879 0.609 0.579 0.520 0.498 0.510
ECL4 0.874 0.701 0.575 0.507 0.466 0.495
ECS2 0.645 0.881 0.586 0.556 0.593 0.560
ECS3 0.661 0.889 0.575 0.612 0.539 0.543
ECS4 0.665 0.878 0.601 0.552 0.506 0.512
EFF2 0.441 0.451 0.695 0.368 0.403 0.420
EFF3 0.570 0.537 0.784 0.549 0.454 0.494
EFF4 0.305 0.375 0.628 0.388 0.353 0.618
EFF5 0.523 0.561 0.811 0.465 0.433 0.597
FF1 0.485 0.549 0.544 0.817 0.556 0.520
FF2 0.486 0.512 0.493 0.862 0.498 0.502
FF3 0.491 0.536 0.483 0.866 0.485 0.554
FF4 0.546 0.593 0.537 0.836 0.628 0.597
PR1 0.447 0.522 0.519 0.572 0.852 0.583
PR3 0.468 0.505 0.444 0.468 0.850 0.510
PR4 0.463 0.529 0.451 0.579 0.814 0.497
SA1 0.524 0.551 0.561 0.562 0.508 0.710
SA2 0.438 0.453 0.571 0.449 0.502 0.793
SA3 0.308 0.341 0.519 0.434 0.424 0.778
SA4 0.423 0.478 0.556 0.505 0.487 0.805
Based on the results of cross loading in Table 5, it can be seen that all indicators have a higher value for their own constructs compared to other constructs so that the value of cross loading can be said to be valid.
The Fornell-Larckel criterion test is a method used to compare the square root value of the AVE of each construct with the correlation of other constructs. (Hardisman, 2021) said that in the Fornell-Larckel criterion test there is a standard value where the standard value is that each latent variable must have a higher value than other variables. Where in each indicator block has a greater value with its own indicator than with other indicator blocks. The following is the Fornell-Larckel criterion that has been carried out by the researcher.
Table 6: Fornell Larckel Criterion Result Score E-Customer
Loyalty
E-Customer Satisfaction
Efficie ncy
Fulfill
ment Privacy
System Availab ility E-Customer
Loyalty 0.861
E-Customer
Satisfaction 0.744 0.883
Efficiency 0.644 0.665 0.733
Fulfillment 0.596 0.650 0.610 0.846
Privacy 0.548 0.619 0.562 0.644 0.839
System
Availability 0.568 0.610 0.722 0.645 0.632 0,772
Based on Table 6, it can be seen that the Fornell Larckel criterion has good results and models where the square root value of each construct is greater than the other constructs. It can be seen that the value of e-customer loyalty has the highest correlation with its own variable than with other variables, namely 0.861. Then the e-customer satisfaction variable has the highest correlation to its own variable than other variables, namely 0.883. Then the efficiency has the highest correlation to its own variable than other variables, namely 0.733. Then fulfillment has the highest correlation to its own variable than other variables, which is 0.846. Then, privacy has the highest correlation with its own variables, other variables, namely 0.839. Followed by system availability has the highest correlation to its own variable than other variables, namely 0.772.
Furthermore, this reliability test can be assessed based on the value of Cronbach's alpha and the value of Composite reliability. Where the indicator will be said to be reliable if the value of Cronbach's alpha or Composite reliability where the minimum value is > 0.7 (Hardisman, 2021; Indrawati, 2017). The following is the value obtained by researchers when conducting reliability testing.
Table 7: Fornell Larckel Criterion Result Score
Variable Cronbach's Alpha Composite Reliability Critical Value
Efficiency 0.713 0.822 0,7
System Availability 0.778 0.855 0,7
Fulfillment 0.867 0.909 0,7
Privacy 0.789 0.877 0,7
E-Customer Satisfaction 0.858 0.914 0,7
E-Customer Loyalty 0.883 0.920 0,7
Based on Table 7, it can be seen that the value of each variable has a Cronbach's alpha and Composite reliability value of more than 0.7, this indicates that the latent construct or variable has good and high reliability.
Furthermore, the inner model test which is carried out using SmartPLS can use the r-square test which is also a goodness-fit model test. Where the r-square value used for each latent variable will show the predictive power of the structural model. The R-square value is divided into 3, namely strong, moderate and weak. Where the strong value is > 0.75, the moderate value is 0.50 to 0.75 and the weak value is 0.25 to 0.50 (Wahyuni, 2022). The following are the results of the R-Square that have been carried out by researchers.
Table 8: R-Square Result Score
Variable R-Square Description
E-Customer Satisfaction 0,603 Moderat
E-Customer Loyalty 0,569 Moderat
From Table 8, it can be concluded that the e-customer satisfaction variable has a value of 0.569 which means that 56.9% of the e-customer satisfaction variable can be explained by the dimensions of efficiency, system availability, fulfillment and privacy. While the other 41.3%
were influenced by dimensions or other factors not mentioned in this study. Then the e- customer loyalty variable has a value of 0.603 which means that as much as 60.3% can be explained by the dimensions of efficiency, system availability, fulfillment, privacy and can be explained by the e-customer satisfaction variable. While the other 39.7% were influenced by other factors not mentioned in this study.
In this Q-Square test there are two criteria where if the Q-Square predictive relevance is between 0 – 1 then the value of the measurement model can be said to be strong > 0. However, if a measurement model result is close to 0 or < 0, then the Q value -Square predictive relevance can be said to be weak (Wahyuni, 2022). The following are the results of the Q-Square that has been carried out by researchers.
Table 8: Q-Square Result Score
Variable SSO SSE Q² (=1-SSE/SSO)
Efficiency 1.756.000 1.756.000 -
System Availability 1.756.000 1.756.000 -
Fulfillment 1.756.000 1.756.000 -
Privacy 1.317.000 1.317.000 -
E-Customer Loyalty 1.756.000 987.879 0.437
E-Customer Satisfaction 1.317.000 744.858 0.434
Based on Table 8 it can be seen that the Q-Square results on the e-customer loyalty variable get a value of 0.437 where the value passes the Q-Square criteria itself, which is > 0, and it can be said that the e-customer loyalty variable has a strong value and is included in the Q-Square predictive relevance. Then it can be seen that the Q-Square results on the e-customer satisfaction variable get a value of 0.434 where the value passes the Q-Square criteria itself, which is > 0, and it can be said that the e-customer loyalty variable has a strong value and is included in the Q-Square. Square predictive relevance.
In the F-Square test there are three categories, namely weak, moderate and strong. The weak category has a value of 0.02-0.14; moderate has a value of 0.15-0.35 and strong has a value>
0.35 (Wahyuni, 2022). The following is the F-Square value that has been carried out by researchers.
Table 9: Effect Size Result Score
Path Diagram Effect Size Rating F-Square
(EFF) -> ECS 0.118 Lemah
(EFF) -> ECL 0.045 Lemah
(SA) -> ECS 0.002 Lemah
(SA) -> ECL 0.000 Lemah
(FF) -> ECS 0.075 Lemah
(FF) -> ECL 0.012 Lemah
(PR) -> ECS 0.058 Lemah
(PR) -> ECL 0.002 Lemah
(ECS) -> ECL 0,270 Lemah
Based on Table 9, shows that the effect size on the dimensions of efficiency, fulfillment, privacy and system availability has a relationship with a weak category on the variable e- customer satisfaction. And the dimensions of efficiency, fulfillment, privacy and system availability have a weak category relation to the e-customer satisfaction variable. Then the e- customer satisfaction variable has a relationship with a small category on the e-customer loyalty variable. Where it can be concluded that in all dimensions and variables have a weak relationship to the variables as well as variables to other variables.
The results of the goodness of fit test are obtained from the square root of the product of the average variance extracted with the average R-squares. GOF itself has a value limit between 0 to 1, with a classification of values 0.1 (small GoF), 0.25 (medium GoF) and 0.36 (large GoF) (Haryono, 2016). The following is the result of the calculation of goodness of fit that has been obtained by the researcher.
Table 10: Goodness of Fit Result Score
Variable AVE R-Square
Efficiency 0,537 -
System Availability 0,597 -
Fulfillment 0,715 -
Privacy 0,703 -
E-Customer Satisfaction 0,779 0,603
E-Customer Loyalty 0,742 0,569
Average 0,678 0,586
𝐺𝑂𝐹 = √𝐶𝑜𝑚̅̅̅̅̅̅ 𝑥 𝑅̅̅̅̅ 2
𝐺𝑂𝐹 = √0,678̅̅̅̅̅̅̅𝑥 0,586̅̅̅̅̅̅̅
𝐺𝑂𝐹 = 0.630
Based on the calculation results of the goodness of fit (GOF) test above, it can be concluded that this study obtained a fit value of 0.630. Where according to the category that has been submitted by (Haryono, 2016) that the value that has been obtained is included in the large category.
4.3. Conclusion
In this study, respondents who use the Shopee Application are dominated by respondents who live in North Sumatra who are female with an age range of 21-25 years with employment status as a student/student who has an average income of <Rp 500,000-Rp 1,000. 000. Efficiency in the Shopee application has a positive and significant influence on e-customer satisfaction. This means that the existence of efficiency will lead to its own satisfaction for its users. The Shopee application has been running efficiently so that it has provided a good shopping experience and Shopee has provided convenience and speed to its users in accessing, using or transacting using the Shopee application. Where with this efficiency it will lead to satisfaction for Shopee application users. System Availability on the Shopee application does not have a positive and significant effect on e-customer satisfaction. This means that the availability of the system does not guarantee consumer satisfaction in using the Shopee application to make transactions or just for use. Fulfillment in the Shopee application has a positive and significant influence on e- customer satisfaction. The shopee application has provided very good agreement accuracy to its users regarding services and the availability of goods/products. This means that the fulfillment can provide satisfaction that can be obtained by its users. Privacy on the Shopee application has a positive and significant influence on e-customer satisfaction. This means that Shopee has succeeded in maintaining security and protecting the personal data of its users.
Where it can be said that Shopee has shown that the security of its users' personal data will be guaranteed for the sake of paying attention to user satisfaction.
Efficiency in the Shopee application has a positive and significant effect on e-customer loyalty.
The shopee application has been running efficiently so that it has provided a good shopping experience and Shopee has provided convenience and speed to its users in accessing, using or transacting using the Shopee application. Where the higher the level of efficiency provided by Shopee for the application, the loyalty of the user to Shopee will be formed. System Availability on the Shopee application does not have a positive and significant effect on e- customer loyalty. This means that the existence of a system availability on the Shopee application will only increase customer satisfaction but does not guarantee the loyalty of users to continue using the Shopee application in conducting a transaction. Fulfillment on the Shopee application does not have a positive and significant effect on e-customer loyalty. This means that the fulfillment provided by Shopee does not guarantee that it will lead to a user's loyalty to the Shopee application in conducting a transaction. Privacy on the Shopee application does not have a positive and significant effect on e-customer loyalty. This means that the privacy protection provided by Shopee to its users only gives satisfaction to its users where privacy protection does not guarantee the emergence of user loyalty to Shopee.
E-Customer Satisfaction on the Shopee application has a positive and significant effect on e- customer loyalty. This means that the higher the level of user satisfaction when using the
Shopee application, the more loyal the users will be. Where Shopee has built and generated high satisfaction so that it has an impact on loyalty.
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