1. Introduction
Technology has been playing a huge role in many aspects of human life. Right from the moment we wake up to the moment we go back to bed at night, technology surrounds us. Technology has changed everything, including the way human do their banking activities. Technology has a key role to play in Banking today. Internet banking has made life simpler for millions and millions of people around the world. To improve the service towards customers, banks must use Internet technology in order to serve their customers in 24 hours. The presence of the Internet facilitates the banks to do their marketing and service electronically, whereas for customers Internet banking has made it possible to do conventional banking transactions like accessing savings account anytime, keep track of their account balance, get e-statements, pay bills online, shop online, transfer funds and much more in under a few clicks and within a matter of minutes. Customers can access Internet banking whenever and wherever they want. Both government and private banks can facilitate their customer through Internet banking features. This matter is surely parallel with one of the main purposes of a bank, which is to provide the best service for customers so that the banks could achieve the loyalty of customers. Internet banking provides customers with a convenient method of conducting bank business from the comfort and security of their own home and personal computer. Customers can check account balances and review other account information any time of the day or night. Internet banking has changed the face of transactional business and affects commerce across many trades and industries. The feature benefits and easy access to Internet banking provided by a particular banking organization will be the factor that develops customer satisfaction and customer loyalty. This study aims to analyze the relationship and influence of the factors.
Some previous research shows that e-loyalty is influenced by customer’s evaluation of e-service quality as shown by Chen et al. (2013). In online transactions, e-service quality is the difference between customers’ expectations for service performance priorities the service encounters and their expectations about service performance before the service offering. A key success factor to survive in a fierce competitive e-environment is a strategy that focuses on services. A bank must deliver superior e-service experiences to its customers so that they would be loyal to the firm. To obtain high levels of customer satisfaction, high service quality is needed, which often leads to customer loyalty (e-loyalty) (Zeithaml et al., 2000).
Ghane et al. (2011) and Kaya et al. (2019) showed that e-service quality has a significant positive influence on the loyalty of customers (e-loyalty). Another result of the research is e-service quality has a significant positive influence on customer satisfaction, and e-service quality has a significant influence on e-loyalty through customer satisfaction. Meanwhile, research done by Othman et al. (2015) examined the impacts of e-service quality, e-satisfaction, and e-trust on e-loyalty in online banking. Their results showed that e-satisfaction and e-trust play major roles in building e-loyalty in online banking. They also found that e-service quality is not a predictor of e-loyalty in online banking. What these results imply is that e-service quality cannot ensure e-loyalty in online banking. E-loyalty can only be ensured when there is both e-satisfaction and e-trust. Chen et al. (2013) strengthened the hypothesis that e-service quality has a significant positive influence on e-satisfaction and e-loyalty of B2B (business to business) e-commerce users.
Parasuraman et al. (2005) identified seven dimensions of e-service quality, which consist of efficiency (easy and fast access of using the site), fulfillment (fulfill a promise regarding delivery and availability of products), system availability (appropriate technical function from a website), privacy (safety and protection of customer’s information), responsiveness (handling situations effectively), compensation (site should provide compensation when customers have problems), and contact (assistance through telephone or an online representative). According to Li et al. (2009), the e-service quality dimension is seen from two perspectives, which are the company’s perspective and the customer’s perspective. From the company’s perspective, the dimensions of e-service quality that should be considered are the ease of use (in terms of using the website), website design (website should be designed properly and attractively in terms of visual appearance), reliability (performance consistency and web reliability), system availability (appropriate technical function from website), privacy (safety and protection of customer’s information), responsiveness (handling situations effectively), and empathy (individual’s treatment and attention given to the customers through the electronic site). Meanwhile, if it is seen from the customer’s perspective, then the dimension of e-service quality that should be considered is customer experience, which is the impression of the company through testimonies and trust of the customers by giving a fast service and information that is complete, accurate and relevant.
Research done by Khan et al. (2019) and Nasution et al. (2019) showed that the improvement in e-service quality will increase customer satisfaction (e-satisfaction) and e-loyalty. Customer satisfaction is the measurement between customer’s expectations and the service or products they received from the company (Andreas, 2013). It acts as a key differentiator that enables you to attract new customers in competitive business markets. Not only is it a leading indicator used to measure customer loyalty and retention, but it also enables businesses to identify unhappy customers, reduce customer losses and negative word of the mouth whilst increasing revenue. Service quality and customer satisfaction are the factors of the success of a particular company to achieve a competitive advantage (Sawitri et al., 2013). Research done by Ghane et al. (2011) and Nasution et al. (2019) showed that e-service quality has a significant positive influence on e-satisfaction, but e-service quality does not directly influence loyalty. Laurent (2016) claimed that e-service quality has a significant positive influence on e-satisfaction, and e-service quality has a direct influence on e-loyalty.
Based on the background of problems and previous research’s result, this research aims to find out the role of e-satisfaction in mediating the influence of e-service quality on e-loyalty is done. This research is done in Indonesia, where college students who are using Internet banking from one of the private banks in Indonesia have been chosen as the population of this study. Their respondent is chosen because they are the millennials who have a close relationship with technology and could give responses and evaluative behavior towards the implementation of informational technology.
2. Literature Review
2.1. E-service Quality
E-service quality plays a very important role in every society, as it has become the basis for how customers interpret online banking and, in the end, how it interacts and operates with online services. E-service quality shows how a particular Internet banking service could serve and facilitate online transactions effectively and efficiently (Zeithaml, 2000; Zeithaml et al., 2000). One of the e-service quality models that is frequently used is the E-SERVQUAL model. According to Tjiptono (2008), this model identifies the principal dimensions (or components) of service quality; proposes a scale for measuring service quality (SERVQUAL), and suggests possible causes of service quality problems. The dimension forwarded by Zeithaml et al., (2000) is relevant and in overall, meets the needs to evaluate the quality of electronic service.
From the traditional model of SERVQUAL above, Zeithaml et al. (2000) succeed in developing five main dimensions of quality of service to become seven dimensions of e-SERVQUAL (Tjiptono, 2008), which consist of: (1) Efficiency: the ability of customers to access the website, looking for desired products and information related to the product, and leave the site with minimum effort. (2) Reliability: related to the website’s technical functionality, especially how far the site is provided and how it appropriately functions. (3) Fulfillment: involve the accuracy of the promise of the service, availability of products, and product delivery corresponds to the time agreed with the customer. (4) Privacy: the guarantee that the transaction data will not be given to anyone and customer’s personal information is guaranteed for its safety. (5) Responsive: the seller gives proper information to customers when a problem occurs, has a mechanism to handle quality products, and provides an online warranty. (6) Compensation: involving the purchase money, delivery cost, and cost of product handling. (7) Contact: explaining the customer’s needs through online service or telephone calls.
2.2. E-Satisfaction
Kotler and Armstrong (2008) defined customer satisfaction as the result felt by the customers when the company meets their expectations. Meanwhile, Hellier et al. (2003) defined customer satisfaction as the overall feeling of happiness and satisfaction felt by the consumers, which resulted from the ability in meeting their wants, expectation, and needs due to the service given by the company. With the development of e-commerce, the concept of customer satisfaction in an online context is called e-satisfaction. E-satisfaction can be defined as the contentment of the customer concerning his or her prior purchasing experience with a given electronic commerce firm and results in favorable responses, such as purchase as well as repurchase. The experience comes from two main factors, which are the service from the online site (waiting for the bought products), and the online site itself (interaction with the online site). Kim et al. (2009) defined e-satisfaction as the accumulation of consumer satisfaction from every purchase and experience in consuming the products or service from time to time in an online site. Consumers who are satisfied tend to do repurchase and even recommend products or services (Zeithaml, 2000; Zeithaml et al., 2000). Meanwhile, unsatisfied customers will tend to choose another company and have no desire to build a relationship with the previous company (Anderson & Srinivasan, 2003).
2.3. E-Loyalty
With the development of e-commerce, researcher grows a concept of loyalty into the context of the online environment, which is called e-loyalty. When it comes to the growth and longevity of an online business, e-loyalty refers to the act of generating and maintaining customer loyalty within the virtual marketplace Anderson and Srinivasan (2003) defined e-loyalty as the repeated satisfaction of a customer with a specific e-commerce website that keeps them coming back to purchase products or services. Besides that, e-loyalty refers to a consumer’s intention to buy from an online site or the intention to purchase in a certain online site (Hur et al., 2011). Indicators to measure the variable of e-loyalty refers to Anderson and Srinivasan (2003) as follows: (1) consumers do not have any thoughts to buy from another site, (2) consumers attempt to use the online site when there is a desire in purchasing, (3) when consumers want to purchase, this certain online site is their first choice, (4) consumers will tend to be attracted to this online site, (5) in customer’s opinion, this online site is the best for them, (6) consumers believe that this site is their favorite site.
This research has analyzed the role of e-satisfaction in mediating the influence of e-service quality on e-loyalty. Figure 1 shows the concept of a framework that explains the relation of each variable.
Figure 1: Research Framework
Some literature reviews show that e-service quality has a significant positive influence on e-satisfaction and e-loyalty. Ludin and Cheng (2014) claimed that e-service quality and e-satisfaction have a significant positive influence on e-loyalty in online transactions in Malaysia. Likewise, Chen et al. (2013) showed that e-service quality has a significant positive influence on e-satisfaction and e-loyalty on B2B (business to business) e-commerce users. Other research also shows that improvement of e-service quality will increase e-satisfaction and e-loyalty (Nasution et al., 2019). Meanwhile, research done by Othman et al. (2015) claimed that the hypothesis that e-service quality has a significant positive influence on e-loyalty is rejected, so, e-service quality is not a predictor of e-loyalty of online banking application users. Likewise, Tan (2019) showed that e-service quality has a significant positive influence on e-satisfaction, but e-service quality has no direct influence on e-repurchase intention in an online transaction. Meanwhile, Laurent (2016) strengthened the hypothesis that e-service quality has a significant positive influence on the e-loyalty of Go-Jek customers. Other results showed that e-service quality has a significant positive influence on e-satisfaction, and also e-service quality has a significant influence on e-loyalty through e-satisfaction.
Based on the previous empirical study, the hypotheses are arranged as follows:
H1: e-service quality has a significant positive influence on e-satisfaction.
H2: e-service quality has a significant positive influence on e-loyalty.
H3: e-satisfaction has a significant positive influence on e-loyalty.
H4: e-satisfaction significantly mediates the influence of e-service quality on e-loyalty.
3. Research Method
3.1. Definition of Operational Variable and Indicator
The method used in this research is the quantitative method. Data collection is done by spreading questionnaires to every college student in a private college and also as a customer in one of the private banks in Indonesia. Based on the conceptual framework and mapping in Figure 1, variables of indicators can result. The variable of indicators is adopted from the previous research, as well as with some modifications on the indicators, so the operational variables can be defined as follows:
(1) E-service quality. It shows how a particular e-commerce site serves and facilitate online transactions, ordering, and delivery of products or service effectively and efficiently (Zeithaml et al., 2000). E-service quality is measured based on indicators adapted by Parasuraman et al. (2005). There are seven items used in this research, which are X1–X7, which consist of: “Internet banking access is very easy”, “features in Internet banking sites are technically well-functioned”, etc.
(2) E-satisfaction. It is the evaluation of customers’ emotions concerning the fulfillment of their expectations based on the online transaction experience. E-satisfaction is measured based on indicators adapted by Fang et al. (2011). There are five items used in this research, which are Z1–Z5, which consist of: “I feel satisfied in doing online transactions in the internet banking facilitation”, “doing transactions through internet banking saves time”, etc.
(3) E-loyalty. It is defined as the preference and commitment of customers towards a certain online site and doing repurchase in the online site (Srinivasan et al., 2002). E-loyalty is measured based on indicators adapted by Melinda (2017). There are five items used in this research, which are Y1–Y5, which consist of: “I am willing to recommend this internet banking service to other people”, etc.
The questionnaire is closely designed, except for the questions/statements concerning the respondent’s identity, which is a semi-opened questionnaire. For every closed questions/statement, five answer options are given in Likert scale, which consists of: strongly agree (SA) with 5 scores, agree (A) with 4 scores, Neutral (N) with 3 scores, disagree (DA) with 2 scores, and strongly disagree (SDA) with 1 score. The method in data processing is by using PLS and SmartPLS 3.0 software as the tool.
3.2. Population and Sample
The population in this research is 417 college students in one of the private colleges in Indonesia and also the Internet banking site users of one of the private banks in Indonesia. The questionnaire is given by a purposive sampling method. Criteria in purposive sampling are that the respondent needs to be a bank customer and must have an Internet banking account. The questionnaire was returned and valid for as many as 205 samples.
4. Results and Discussion
4.1. Sample Description
The population in this research is 417 college students in one of the private colleges in Indonesia. The questionnaire is given by a simple random sampling method. The questionnaires were returned and valid for as many as 205 samples. Characteristics of the respondents are seen from the gender, age, and the tools used to access the Internet banking site. It is shown in Table 1 that the highest percentage of respondents are men (63%), in the age group of 20–25 years old (45%), and the tools used to access Internet banking is dominated by smartphone (62%).
Table 1: Descriptive Sample Information
4.2. Results for Validity Test and Research Indicator Reliability
Stages of measuring the testing model involve convergent validity and discriminant validity test. The value of Cronbach’s alpha and composite reliability is needed in testing for construction reliability. PLS analysis results could be used to test for research hypothesis if all indicators in the PLS model have met the requirements of convergent validity, discriminant validity, and reliability test.
Figure 2: Model Fit
4.3. Convergent Validity Testing
Convergent validity states that tests having the same or similar constructs should be highly correlated. Factor loadings are correlation coefficients between observed variables and latent common factors A convergent validity test is done by seeing the value of the loading factor of each indicator towards the construct. In most references, a factor weighing from at least 0.5 is considered to have validity that is strong enough to explain the latent construct (Chin, 1998; Ghozali, 2014; Hair et al., 2010). In this research, the minimum limit of loading factor that is accepted is 0.5, with the condition of AVE score for every construct > 0.5 (Ghozali, 2014). Based on the data processing result in SmartPLS 3.0, all indicators have a loading factor value above 0.5. Therefore, the convergent validity of this research model has met the requirements. Loading factors, Cronbach’s alpha, composite reliability, and AVE in every construct can be seen in Table 2 below:
Table 2: Items Loadings, Cronbach’s Alpha, and Composite Reliability
4.4. Discriminant Validity Test
Discriminant validity is done to ensure that every concept of each latent variable is in contrast with the other latent variables. Discriminant validity refers to the extent to which factors are distinct and uncorrelated. The rule is that variables should relate more strongly to their own factor than to another factor. A model has a good discriminant validity if the square root of the AVE of each latent variable is greater than the correlation coefficients between that latent variable and other latent variables in the measurement model, then the model satisfies the discriminant validity criterion (Ghozali, 2014). The discriminant validity can be evaluated by using cross-loading of indicator or Fornell and Larcker (1981) criterion as shown in Table 3. Moreover, collinearity evaluation is done to discover whether there is collinearity in the model. To find out about collinearity, VIF estimation from every construct is required. If the VIF score is higher than 5, then the model will show collinearity (Hair et al., 2014). As shown in Table 5, all VIF score is less than 5 means so that the model has no collinearity.
Table 3: Discriminant Validity
Table 5: R Square Value
The discriminant validity test result shown in Table 3 above indicates all constructs have a square root value of AVE above the correlation value with the other latent construct (through Fornell-Larcker Criterion). Likewise, the cross-loading value of all items from another indicator is mentioned in Table 4, so it can be concluded that a model has met the discriminant validity (Fornell & Larcker, 1981).
Table 4: Collinearity Statistics (VIF)
4.5. Construct Reliability Test
Construct reliability can be assessed from the value of Cronbach’s alpha and composite reliability from each construct. The value of composite reliability and Cronbach’s alpha is suggested to be more than 0.7 (Ghozali, 2014). Reliability test results in Table 2 above show that all construct has composite reliability value and Cronbach’s alpha value higher than 0.7 (>0.7). In conclusion, all construct has met the reliability that is required.
4.6. Hypothesis Examination
The hypothesis test in PLS is also denoted as an inner model test. This test covers a significance test that has a direct and indirect impact as well as how large is the measurement of the exogenous variable impact on the endogenous variable. To discover the influence of e-service quality on e-satisfaction and e-loyalty, it needs a direct impact test. The direct impact test is done by using the t-statistic test in an analysis model called Partial Least Squared (PLS) with the help of SmartPLS 3.0 software. With the bootstrapping technique, R square value and significance test value can be obtained as shown in Table 5 below:
4.7. Discussion
Discussion of the hypothesis is done based on the analysis result shown in Table 6, where the result is obtained as follows:
Table 6: Hypothesis Testing
First, based on the first hypothesis (H1), Table 6 shows that the significance of two sides of the T-test for the e-service quality variable has a value of 0.000 that is smaller than 0.05 with a positive regression coefficient of 0.496. This explains that H1 is accepted, which means that e-service quality has a significant positive influence on e-satisfaction. This positive influence means that the increase in e-service quality would increase the e-satisfaction of college students as Internet banking users in Indonesia.
Second, based on the second hypothesis (H2), Table 6 shows that the significance of two sides of the T-test for the e-service quality variable has a value of 0.040 that is smaller than 0.05 with a positive regression coefficient of 0.249. This shows that the second hypothesis (H2) is accepted, which means that e-service quality has a significant positive influence on e-loyalty. This positive influence means that the increase in e-service quality would increase the e-loyalty of college students as Internet banking users in Indonesia.
Third, based on the third hypothesis (H3), Table 6 shows that the significance of two sides of the T-test for the e-service quality variable has a value of 0.000 that is smaller than 0.05 with a positive regression coefficient of 0.506. This shows that the third hypothesis (H3) is accepted, which means that e-satisfaction has a significant positive influence on e-loyalty. This positive influence means that the increase in e-satisfaction would increase the e-loyalty of college students as internet banking users in Indonesia.
Fourth, based on the fourth hypothesis (H4), the mediation test result from t-statistics has a value of 3.708. Therefore T-statistics value is larger than 1.96, hence, the fourth hypothesis (H4) is accepted. This means that e-satisfaction significantly mediates the influence of e-service quality on e-loyalty. This result indicates the importance of the role of e-satisfaction towards e-service quality would be impactful to the e-loyalty of college students as Internet banking users in Indonesia.
5. Conclusion
The conclusion of this research according to its data analysis result and discussion above shows that: (1) e-service quality has a significant positive influence on e-satisfaction. With the increase in e-service quality of Internet banking, there will be an increase in consumer e-satisfaction. (2) e-service quality has a significant positive influence on e-loyalty. With the increase in service quality of Internet banking, there will be an increase in consumer e-loyalty. (3) e-satisfaction has a significant positive influence on e-loyalty. With an increase in consumer e-satisfaction, there will be an increase in consumer e-loyalty. (4) finally, this research concludes that the role of e-satisfaction significantly mediates the influence of e-service quality on e-loyalty. This result indicates that that the importance of the role of e-satisfaction towards e-service quality would be impactful to the e-loyalty of college students as Internet banking users in Indonesia.
This research has some limitations. Firstly, this research analyzed the influence of e-service quality on e-loyalty both directly or indirectly through the e-satisfaction variable. This is because there are maybe some other variables (like motivation, belief, company branding, e-CRM, etc.) that influence e-loyalty. The author recommends further studies to discover, explore, and analyze the next research. Second, this research is done in a unit of analysis of college students, and may not be generalized to other industries. Therefore, it is highly advisable to do further research regarding this topic in other industries or even adding to all regions or other countries and show a comparison between private and public organizations.
References
- Akob, M., Yantahin, M., Ilyas, G. B., Hala, Y., & Putra, A. H. P. K. (2021). The element of Marketing: SERVQUAL toward patient loyalty in the private hospital sector. (2021). The Journal of Asian Finance, Economics, and Business, 8(1), 419-430. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.419
- Anderson, R. E., & Srinivasan, S. S. (2003). E‐satisfaction and e‐loyalty: A contingency framework. Psychology & Marketing, 20(2), 123-138. https://doi.org/10.1002/mar.10063
- Andreas, J. (2013). The influence of brand credibility on word of mouth through customer satisfaction and customer loyalty at Giant Hypermarket in Surabaya. Jurnal Ilmiah Mahasiswa Manajemen, 1(4), 1-8. https://core.ac.uk/download/pdf/298889864.pdf
- Ardi, A., Djati, S. P., Bernarto, I., Sudibjo, N., Yulianeu, A., Nanda, H. A., & Nanda, K. A. (2020a). The Relationship Between Digital Transformational Leadership Styles and Knowledge-Based Empowering Interaction for Increasing Organisational Innovativeness. International Journal of Innovation, Creativity and Change, 11(3), 259-277.
- Ardi, A., Djati, S. P., Bernarto, I., Sudibjo, N., Yulianeu, A., Nanda, H. A., & Nanda, K. A. (2020b). The Secret to Enhancing Innovativeness in the Digital Industry. International Journal of Innovation, Creativity and Change, 12(12), 225-243.
- Aziz, T., Marwat, J., Mustafa, S., Kumar, V., & Haddad, L. (2021) Reassessment of volatility transmission among South-Asian equity markets. The Journal of Asian Finance, Economics, and Business, 8(1), 587-597. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.587
- Buldan, H., Edy, S., Sriyana, J., & Johirin, A. (2021), The role of Islamic business ethics and market condition on organizational performance. The Journal of Asian Finance, Economics, and Business, 8(1), 781-790. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.781
- Chen, J. V., Chen, Y., & Capistrano, E. P. S. (2013). Process quality and collaboration quality on B2B e‐commerce. Industrial Management & Data Systems, 113(6), 908-926. https://doi.org/10.1108/IMDS-10-2012-0368
- Chin, W. (1998). The partial least squares approach to structural equation modeling. In G. A. Marcoulides (Ed.), E-modern methods for business research. Mahwah, NJ: Lawrence Erlbaum Associates Publisher.
- Fang, Y., Chiu, C., & Wang, E. T. G. (2011). Understanding customers' satisfaction and repurchase intentions: An integration of IS success model, trust, and justice. Internet Research, 21(4), 479-503. https://doi.org/10.1108/10662241111158335.
- Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18(1), 39. https://doi.org/10.2307/3151312
- Ghane, S., Fathian, M., & Gholamian, M. R. (2011). The full relationship among e-satisfaction, e-trust, e-service quality, and e-loyalty: The case of Iran e-banking. Journal of Theoretical and Applied Information Technology, 33(1), 1-6. http://www.jatit.org/volumes/Vol33No1/1Vol33No1.pdf
- Ghozali, I. (2014). Structural equation modeling, metode alternatif dengan partial least square (PLS) (4th ed.). Diponegoro, Indonesia: Badan Penerbit Universitas Diponegoro.
- Hair, J. F., Black, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate data analysis (7th ed.). London, UK: Prentice-Hall.
- Hair, J. F., Hult, G. T., Ringle, C. M., & Sarstedt, M. (2014). A primer partial least squares structural equation modeling (PLS-SEM). Thousand Oaks, CA: Sage Publications.
- Hellier, P. K., Geursen, G. M., Carr, R. A., & Rickard, J. A. (2003). Customer repurchase intention. European Journal of Marketing, 37(11/12), 1762-1800. https://doi.org/10.1108/03090560310495456.
- Hur, Y., Ko, Y. J., & Valacich, J. (2011). A structural model of the relationships between sports website quality, e-satisfaction, and e-loyalty. Journal of Sport Management, 25(5), 458-473. https://doi.org/10.1123/jsm.25.5.458
- Kaya, B., Behravesh, E., Abubakar, A. M., Kaya, O. S., & Orus, C. (2019). The moderating role of website familiarity in the relationships between e-service quality, e-satisfaction, and e-loyalty. Journal of Internet Commerce, 18(4), 369-394. https://doi.org/10.1080/15332861.2019.1668658
- Khan, M. A., Zubair, S. S., & Malik, M. (2019). An assessment of e-service quality, e-satisfaction, and e-loyalty. South Asian Journal of Business Studies, 8(3), 283-302. https://doi.org/10.1108/SAJBS-01-2019-0016
- Kim, J., Jin, B., & Swinney, J. L. (2009). The role of retail quality, e-satisfaction, and e-trust in the online loyalty development process. Journal of Retailing and Consumer Services, 16(4), 239-247. http://doi.org/10.1.1.877.3271 https://doi.org/10.1.1.877.3271
- Kotler, P., & Armstrong, G. (2008). Marketing principles. Jakarta: Erlangga.
- Laurent, F. (2016). The effect of e-service quality on Go-jek customer loyalty through customer satisfaction. Agora, 4(2), 95-100. https://www.neliti.com/publications/56779/pengaruhe-service-quality-terhadap-loyalitas-pelanggan-go-jekmelalui-kepuasan
- Li, H., Liu, Y., & Suomi, R. (2009). Measurement of e-service quality: An empirical study in online travel service, 19, 1-13. https://core.ac.uk/download/pdf/301356349.pdf
- Ludin, I. H. B. H., & Cheng, B. L. (2014). Factors influencing customer satisfaction and e-loyalty: Online shopping environment among the young adults. Management Dynamics in the Knowledge Economy, 2(3), 462. http://managementdynamics.ro/index.php/journal/article/view/71/57
- Melinda, M. (2017). The effect of E-service quality on Go-Jek customer e-loyalty through e-satisfaction in the Go-ride category. Agora, 5(1), 17-25. https://www.neliti.com/publications/56797/pengaruh-e-service-quality-terhadap-e-loyalty-pelanggan-gojek-melalui-e-satisfa.
- Mulyadi, Sudibjo, N., & Bernarto, I. (2017). The Effect of Perceived Organizational Support, Work Engagement, and Job Satisfaction on Teacher's Performance at Xyz Middle and High School. International Journal of Economic Research, 14(13), 7-19.
- Nasution, H., Fauzi, A., & Rini, E. S. (2019). The effect of e-service quality on e-loyalty through e-satisfaction on students of Ovo application users at the Faculty of Economics and Business, University of North Sumatra, Indonesia. European Journal of Management and Marketing Studies, 4(1), 146-163. https://oapub.org/soc/index.php/EJMMS/article/view/603.
- Othman, A. S., Chowdhury, I. A., Bo, Y., Omar, A. R. C., & Osman, L. H. (2015). Key drivers of customer loyalty in online banking. Annals of Management Science, 4(1), 89. https://digitalscholarship.tnstate.edu/ams/vol4/iss1/5/ https://doi.org/10.24048/ams4.no1.2015-89
- Parasuraman, A., Zeithaml, V. A., & Malhotra, A. (2005). ESQUAL: A multiple-item scale for assessing electronic service quality. Journal of Service Research, 7(3), 213-233. https://doi.org/10.1177/1094670504271156
- Pramono, R., Sondakh, L.W., Bernarto, I., Juliana, J., & Purwanto, A. (2021). Determinants of the Small and Medium Enterprises Progress: A Case Study of SME Entrepreneurs in Manado, Indonesia. The Journal of Asian Finance, Economics, and Business, 8(1), 881-889. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.881
- Sawitri, N. P., Yasa, N. N. K., & Jawas, A. (2013). The effect of service quality on customer satisfaction and loyalty in tegal sari accommodation in Ubud. Matrik: Jurnal Manajemen, Strategi Bisnis Dan Kewirausahaan, 7(3), 1-8. https://ojs.unud.ac.id/index.php/jmbk/article/view/6070.
- Sha, N., Ismail, M., & Mohammed, Y. (2021). Behavioral investor types and financial market players in Oman. (2021). The Journal of Asian Finance, Economics, and Business, 8(1), 285-294. https://doi.org/10.13106/JAFEB.2021.VOL8.NO1.285
- Slamet, M. U. A. G., Asdiana,, Abdillah, A., Abduloh,, Fahlevi, M., Ali, R., Evanirosa,, Mufid, A., Purwanto, A., Faricha, F., Khairullah, & Zumaro, A. (2020) Islamic Leadership Model for Indonesian Millennial Teachers Performance in Pharmacy Schools. Systematic Reviews in Pharmacy, 11(8), 374-382. https://doi.org/10.31838/srp.2020.8.55
- Suheny, E., Arum, M., Wandi, D., Rahmat, A., kurnianingsih, A., Haerani, A., Dasmaran, V., Taryanto, Adha, S. & Purwanto, A. (2020) Develop Leadership Style Model for Indonesian SMEs Leaders During Covid-19 Pandemic. Systematic Reviews in Pharmacy, 11(8), 576-586. https://doi.org/10.31838/srp.2020.8.82
- Srinivasan, S. S., Anderson, R., & Ponnavolu, K. (2002). Customer loyalty in e-commerce: An exploration of its antecedents and consequences. Journal of Retailing, 78(1), 41-50. https://doi.org/10.1016/S0022-4359(01)00065-3
- Tan, Y. (2019). The effect of service quality on repurchase intention with customer satisfaction as an intervening variable at PT Bank Central Asia Tbk: A case study of KCU Darmo Surabaya Jurnal Strategi Pemasaran, 6(1), 10. http://publication.petra.ac.id/index.php/manajemen-pemasaran/article/view/8103
- Tjiptono, F. (2008). Service management mewujudkan layanan prima. Yogyakarta: Andi.
- Zeithaml, V. A. (2000). Service quality, profitability, and the economic worth of customers: What we know and what we need to learn. Journal of the Academy of Marketing Science, 28(1), 67-85. https://doi.org/10.1177/0092070300281007
- Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2000). A conceptual framework for understanding e-service quality: implications for future research and managerial practice. Cambridge, MA: Marketing Science Institute.
Cited by
- The Role of Business Capabilities in Supporting Organization Agility and Performance During the COVID-19 Pandemic: An Empirical Study in Indonesia vol.8, pp.5, 2021, https://doi.org/10.13106/jafeb.2021.vol8.no5.0897