DOI QR코드

DOI QR Code

Customer Perception of E-Service Quality: An Empirical Study in Indonesia

  • 투고 : 2021.02.20
  • 심사 : 2021.05.02
  • 발행 : 2021.06.30

초록

E-service quality has received attention from customer perception, especially online shopping customers. In the context of online shopping, e-service quality is believed to make an enormous contribution to customer perception. This study examines the effect of e-service quality (web design, responsiveness, reliability, trust, and personalization) on customer perception. A total of 278 respondents from South Sulawesi, North Sulawesi, Maluku, North Maluku, and Papua were involved and selected by purposive sampling method. The validity was tested using factor analysis with a loading factor value of ≥0, the reliability was tested by a Cronbach's Alpha value ≥0.6, and hypotheses were tested by regression analysis. The results show that e-service quality (web design, responsiveness, reliability, trust, and personalization) has a positive effect on customer perception. This study summarizes new findings regarding the positive effect of e-service quality (website design, responsiveness, reliability, trust, personalization) on customer perception as well as providing recommendations for future research. This research underlines that women dominate shopping more than men and that Generation Z dominates online shopping. In addition, the research provides new evidence that earnings do not determine how much online purchases will made by consumers as the majority of our respondents are students who are active purchasers.

키워드

1. Introduction

The Internet has become part of the lifestyle of most people in the world (Harcar & Yucelt, 2012). This can be seen from the level of Internet usage, which has rapidly increased in the last two decades (Trivedi & Trivedi, 2018). Indonesia becomes the second-highest Internet user country in the Asia Pacific region for online shopping (Statista, 2018a; Statista, 2018b; Statista, 2018c; Kalia, 2016; Kalia et al., 2016; Kalia et al., 2017).

The Internet has changed the way of doing business as today’s consumers have more ways to access information about an organization’s products and services (Law & Leung, 2000; Muafi et al., 2021; Wattimena & Sin, 2020; Warrier et al., 2021). One of the businesses for which the potential is limitless using the Internet is online shopping (e-commerce) (Guo & Barnes, 2009; Ha et al., 2021). This potential incorporates good service quality since it is one of the main aspects of business success (Tshin et al., 2014). Service quality is defined as a comparison between expectations and reality (Parasuraman et al., 1988). It results in a good consumer experience and leads to greater customer loyalty with higher returns (Tan & Goh, 2017; Dam & Dam, 2021; Putra et al., 2021).

Hapsawati, (2019) argued that service quality carries new customers and maintains their loyalty. In an online shopping environment, e-service quality is understood as customer perceptions and judgments regarding the excellence and quality of delivery (Lee & Lin, 2005). Customer perceptions of the service they received are influenced by several components and determined based on their experience (Rakhmawati et al., 2013). However, the challenge to determine the effect of e-service quality on customer perception in the context of online shopping is the novelty of this study. Most studies discuss consumer perceptions based on the e-service quality variable, rather than each variable of customer perception and e-service quality separately (e.g., Kim & Kim, 2020; Setiawan et al., 2020; Suhartanto et al., 2019).

This research was conducted with online shoppers of Shopee and Tokopedia in Indonesia spread over the areas of South Sulawesi, North Sulawesi, Maluku, North Maluku, and Papua. This research breaks new academic grounds in this field.

2. Literature Review

2.1. Web Design

A website is a design process with a more effective approach used in a limited manner and developed based on customer perceptions (Katz et al., 1991; Weinberg, 2000). The successful rise of the Internet has made web-based systems an important medium in connecting service providers or products with their customers (Taherdoost, 2019). Customers’ perceptions of the websites change when the information they are looking for is not available and they will move to other websites providing the information they need (Gao, 2005). This means that online shopping must be able to present their website visually attractive, have an easy-to-understand interface for consumers, and a fast and easy completion process (Parasuraman et al., 1988; Kim & Lee, 2002). When this has been fulfilled by online shopping, it will attract the attention of consumers by itself (Ganguly et al., 2010).

2.2. Responsiveness

Responsiveness refers to the speed and accuracy of responding to consumer requests and transactions (Zeithaml et al., 2002; Hu et al., 2012; Ayo et al., 2016). It concurrently fulfills the commitment of the online shopping party, both listening and providing clear information to consumers (Zemblytė, 2015). Besides, post-purchase inconveniences still occur frequently. Online shopping responds to this by providing support and after-sales service to respond to customer problems, which are part of the quality of the process that can only be felt (Bauer et al., 2006). Consumers expect online shopping parties to respond to their complaints promptly as quick responses help consumers solve their problems and make decisions in a timely manner (Wolfinbarger & Gilly, 2003; Liao & Cheung, 2002). A quick response to consumer demand is likely to increase perceived comfort and reduce uncertainty (Gummerus et al., 2004). Thus, high responsiveness can form positive consumer perceptions.

2.3. Reliability

Reliability deals with the ability to perform the promised services accurately, reliably, and punctually (Parasuraman et al., 1988). It covers the ability to solve problems, blemished transactions with adequate security (Lee & Lin, 2005; Chang et al., 2016). Reliability is a major factor that motivates consumers to purchase. The reliability of products available online encourages consumers to make online purchases (Rishi, 2010). For this reason, every online shopping must be able to run its business at certain times, have a sincere intention to provide solutions to the problems faced by consumers, and avoid mistakes, whether intentional or unintentional (Parasuraman et al., 1988; Kim & Lee, 2002). This certainly has an impact on consumer perceptions of being comfortable when shopping. Therefore, one aspect of the Internet as a shopping platform is the reliability system that guarantees further examination of its convenience (Jun et al., 2004; Kim & Park, 2012; Udo et al., 2010; Yang & Fang, 2004).

2.4. Trust

Trust is the level of risk when involved in a relationship to get the expected results (Tandiono et al., 2020). Casaló et al., (2010) defined trust as an attitude to use social media toward the privacy and security issues. Trust in online shopping must be able to maintain the confidentiality of consumer data, not occur fraud related to financial transactions, must be able to protect online financial transactions, have a relatively low risk, be able to carry out obligations as an online shopping party, be highly competent in the field of e-commerce and have a good reputation (Parasuraman et al., 1988; Yang & Jun, 2002; Fang et al., 2011; Djojo et al., 2015). It has a direct influence on attitudes since perceptions are developed in the online shopping experience (Pavlou & Fygenson, 2006). It also has an impact on the decision to return to shopping in the future (Wu et al., 2008; Zheng et al., 2019). Trust also influences online shopping behavior (Hong & Cho, 2011). Teoh et al., (2013) contended that trust has an effect on customer perception in the context of online payment systems.

2.5. Personalization

According to Vesanen, (2007), there is no universally- accepted definition of personalization. It reflects the extent to which information is tailored to meet the needs of individual users and becomes an important determinant of positive experiences (Bilgihan et al., 2016). As positive experiences form a better consumer perception, online shopping (e-commerce) attempts to provide relevance by offering interesting content or products as a solution to consumer needs (Riegger et al., 2021). This includes targeted e-mails for new consumers, providing alternative recommendations for products and services based on consumer preferences, and providing a home or free space for consumers (Parasuraman et al., 1988; Yang & Jun, 2002; Fang et al., 2011; Djojo et al., 2015). Implicit relevance incorporates context specificity, which is important for personalization to deliver the right content to the right people and at the right time (Tam & Ho, 2006). Personalization generates greater attention to offerings by creating self-associations, providing a good match with customer preferences, and encouraging increased elaboration of relevant information. These efforts are more likely to succeed in positively influencing purchasing decisions, by creating joy, gratitude, or customer satisfaction (Bock et al., 2016). Anderson and Liem (2020) stated that there are varies factors that influence customers to buy products and service and experience when buy products and service is one of significant factor.

2.6. Customer Perception

In the context of online shopping, consumer perceptions come from previous experiences (Tandiono et al., 2020). As perception is the source of subjective norms (Pudaruth & Nursing, 2017), consumers’ perceptions determine the success of e-service quality (Cristobal et al., 2007). The success of electronic services is largely determined by consumer perceptions of the online shopping system compared to offline methods, including the effectiveness and efficiency of services and the time required by consumers when shopping online compared to offline and consumer perceptions regarding the appearance of a friendly design and being able to attract the desire to shop online compared offline (Teoh et al., 2013; Fang et al., 2011). It is largely determined by the application of e-service quality such as web design, responsiveness, reliability, and trust (Lee & Lin, 2005) as well as personalization (Bues et al., 2017).

Based on the description above, this study illustrates the research model as in Figure 1 and proposes the following hypotheses:

Figure 1: Research Model

H1: Web design has a positive effect on customer perception.

H2: Responsiveness has a positive effect on customer perception.

H3: Reliability has a positive perception.

H4: Trust has a positive effect on customer perception. H5: Personalization has a positive effect on customer perception.

3. Methodology

The population of this study is online shoppers of Shopee and Tokopedia. The sample was taken by purposive probability sampling method fulfilling the criterion of having made an online purchase from Shopee and or Tokopedia. The questionnaires were distributed in May-June 2019 with a total sample of 278 respondents. The validity was tested using factor analysis with a loading factor value of ≥0.5 (Hair et al., 2010), and the reliability was tested by a Cronbach’s Alpha value ≥0.6 (Hair et al., 2009). The hypotheses were tested by simple regression analysis (Hair, Jr, 2015). The e-service quality measurement was adopted from Lee and Lin (2005) suggesting that e-service quality is divided into five variables, including website design, reliability, and responsiveness measured by a questionnaire developed by Parasuraman et al. (1988) and Kim and Lee, (2002). Furthermore, trust and personalization variables were measured by a questionnaire developed by Djojo et al. (2015), Fang et al. (2011), Parasuraman et al. (1988) and Yang and Jun, (2002). The customer perception variables were measured with a questionnaire developed by Teoh et al. (2013) and Fang et al. (2011).

4. Results and Discussion

The questionnaires were directly distributed to respondents in North Maluku, and for those in North Sulawesi, South Sulawesi, Maluku, and Papua, they were given the questionnaire through Google Form link via WhatsApp group. Among the 350 questionnaires distributed, 298 (85%) were returned and 278 (79%) were eligible for further testing. Table 1 presents the respondent profile.

Table 1: Profile of Respondents

Table 1 shows the profile of the respondents by their gender, age, shopping frequency, occupation, purchased item price, and provinces. In general, the majority of the respondents were women, aged 15–24 years old with a shopping frequency of three times. Most of them are students purchasing products less than IDR1, 000, 000 worth. They come from five provinces, namely, South Sulawesi, North Sulawesi, Maluku, North Maluku, and Papua.

Table 1 implies that women enjoy shopping more, have more brand and price awareness, have higher self-confidence, and are hedonistic, while men prioritize functional and time-saving aspects when shopping (Seock & Bailey, 2008). By age and occupation, respondents are generally Generation Z, followed by Millennial Generation, Generation X, and the Baby Boomers Generation. This underlines that Generation Z is more proficient in using the Internet than other generations so that the average goods purchased are under IDR 1,000,000 (Ayuni, 2019; Dharmesti et al., 2019; Koksal, 2019; Ladhari et al., 2019).

Table 2 indicates the results of testing the validity and reliability among the variables studied. Website design, responsiveness, trust, personalization, and customer perception, all showed a loading factor value of ≥0.5. Only the variable reliability has to be tested for validity twice because one question item does not meet the loading factor value. Furthermore, all variables were declared valid (Hair et al., 2010). Furthermore, for the results of reliability testing for the variables studied, all of them had a Cronbach’s Alpha value ≥0.6, so that all variables (website design, reliability, responsiveness, trust, personalization, and customer perception), were declared reliable (Hair, Jr, 2015). This shows that valid questions must be reliable and the analysis can be continued to the following stages (Sekaran & Bougie, 2016).

Table 2: Validity and Reliability Testing Results

Also, the results of validity and reliability testing in Table 2 shows that among the variables studied, the website design variables, responsiveness, trust, personalization, and customer perception, all showed a loading factor value ≥0.5. Only the reliability variable, which is one question item, must be discarded because it does not meet the loading factor value. Furthermore, all variables are declared valid. Furthermore, for the results of reliability testing for the variables studied, all of them had a Cronbach’s Alpha value ≥0.6, so that all variables were declared reliable. This shows that valid questions must be reliable and can be continued at the following stages.

Table 3 indicates that the majority of respondents agreed that website design must be visually attractive with a well-organized appearance and quick and easy transaction completion. As for the responsiveness, the respondents agreed about the importance of providing fast service, customer assistance, and proper responses to customer requests. They also agreed that reliability shows a sincere desire to solve customer problems flawlessly and with adequate security.

Table 3: Respondents Perception

Furthermore, regarding the trust variable, the respondents agreed that the value of privacy protection, trustworthiness, transaction protection, low risk, proper obligation fulfillment, and e-commerce competency and good reputation. Also, as for personalization, the respondents agreed that targeted emails, product recommendations, and free personal homepages for customers will be beneficial. Table 3 also highlights that the respondents perceived online purchasing method is better and more efficient than the traditional method, and the user-friendly interfaces on e-commerce websites influence their purchasing decisions.

The results of hypothesis testing (see Table 4) show that website design has a positive effect on customer perception (β = 0.409, t = 5.121, P < 0.05), responsiveness has a positive effect on customer perception (β = 0.761, t = 9.194, P < 0.05), reliability has a positive effect on customer perception (β = 0.433, t = 6.881, P < 0.05), trust has a positive effect on customer perception (β = 0.800, t = 10, 878, P < 0.05), and personalization has a positive effect on customer perception (β = 0.228, t = 2.511, P < 0.05). This concludes that all five hypotheses are supported. The results of hypothesis testing are shown in Table 4.

Table 4: Hypotheses Testing

Note: Beta (β), t count (t), significant (Sig).

The hypothesis testing reveals that website design has a positive effect on customer perception. This implies that website design is the main medium that connects online shopping with consumers (Taherdoost, 2019). Thus, a good website must meet the elements of being visually attractive, having a well-organized appearance, and providing speed transactions (Kim & Lee, 2002; Parasuraman et al., 1988). Both Shopee and Tokopedia have fulfilled this.

The hypothesis testing also proves that responsiveness has a positive effect on customer perception. This shows that Shopee and Tokopedia have been able to handle complaints and respond quickly. E-commerce must constantly carry out its commitment to listen to the customers or provide clear information (Zemblytė, 2015), give fast service, help customers, and respond to their requests quickly (Kim & Lee, 2002; Parasuraman et al., 1988).

The hypothesis testing also suggests that reliability has a positive effect on customer perception. It is the main factor that motivates consumers to shop (Rishi, 2010). Good reliability performs the promised service accurately, reliably, and promptly (Parasuraman et al., 1988).

Also, the hypothesis testing signifies that trust has a positive effect on customer perception. Shopee and Tokopedia have been able to maintain consumer privacy and security which shape their consumer perceptions. This impacts their repurchasing decision (Wu et al., 2008; Zheng et al., 2019). This finding is in line with Teoh et al., (2013).

Subsequently, personalization has a positive effect on customer perception. This implies that positive shopping experiences in Shopee and Tokopedia form better consumer perceptions. This in turn creates a sense of joy, gratitude, and customer satisfaction (Bock et al., 2016).

5. Conclusion

This study summarizes new findings regarding the positive effect of e-service quality (website design, responsiveness, reliability, trust, personalization) on customer perception as well as providing recommendations for future research. It also provides descriptive aspects such as gender, age, and occupation. This research underlines that women dominate shopping more than men and that online shopping is dominated by Generation Z. In addition, the research provides new evidence that earnings do not determine how much online purchases consumers will make as our respondents are students who are active purchasers. The challenge for future research will be to add other variables, both antecedents (customer knowledge, experiences, and perceived usefulness) and consequences (e-consumer satisfaction and purchase intention).

참고문헌

  1. Ayo, C. K., Oni, A. A., Adewoye, O. J., & Eweoya, I. O. (2016). E-banking users' behaviour: e-service quality, attitude, and customer satisfaction. International Journal of Bank Marketing, 34(3), 347-367. https://doi.org/10.1108/02652323199400002
  2. Ayuni, R. F. (2019). the Online Shopping Habits and E-Loyalty of Gen Z As Natives in the Digital Era. Journal of Indonesian Economy and Business, 34(2), 168. https://doi.org/10.22146/jieb.39848
  3. Bauer, H. H., Falk, T., & Hammerschmidt, M. (2006). eTransQual: A transaction process-based approach for capturing service quality in online shopping. Journal of Business Research, 59(7), 866-875. https://doi.org/10.1016/j.jbusres.2006.01.021
  4. Bilgihan, A., Kandampully, J., & Zhang, T. (Christina). (2016). Towards a unified customer experience in online shopping environments: Antecedents and outcomes. International Journal of Quality and Service Sciences, 8(1), 102-119. https://doi.org/https://doi.org/10.1108/IJQSS-07-2015-0054
  5. Bock, D. E., Mangus, S. M., & Folse, J. A. G. (2016). The road to customer loyalty paved with service customization. Journal of Business Research, 69(10), 3923-3932. https://doi.org/10.1016/j.jbusres.2016.06.002
  6. Bues, M., Steiner, M., Stafflage, M., & Krafft, M. (2017). How Mobile In-Store Advertising Influences Purchase Intention: Value Drivers and Mediating Effects from a Consumer Perspective. Psychology and Marketing, 34(2), 157-174. https://doi.org/10.1002/mar.20981
  7. Casalo, L. V., Flavian, C., & Guinaliu, M. (2010). Antecedents and consequences of consumer participation in on-line communities: The case of the travel sector. International Journal of Electronic Commerce, 15(2), 137-167. https://doi.org/10.2753/JEC1086-4415150205
  8. Chang, S. H., Chih, W. H., Liou, D. K., & Yang, Y.-T. (2016). The mediation of cognitive attitude for online shopping. Information Technology & People, 29(3), 618-646. https://doi.org/https://doi.org/10.1108/ITP-08-2014-0172
  9. Cristobal, E., Flavian, C., & Guinaliu, M. (2007). Perceived e-service quality (PeSQ): Measurement validation and effects on consumer satisfaction and web site loyalty. Managing Service Quality, 17(3), 317-340. https://doi.org/10.1108/09604520710744326
  10. Dam, S. M., & Dam, T. C. (2021). Relationships between Service Quality, Brand Image, Customer Satisfaction, and Customer Loyalty. Journal of Asian Finance, Economics and Business, 8(3), 585-593. https://doi.org/10.13106/jafeb.2021.vol8.no3.0585
  11. Dharmesti, M., Dharmesti, T. R. S., Kuhne, S., & Thaichon, P. (2019). Understanding online shopping behaviours and purchase intentions amongst millennials. Young Consumers, June. https://doi.org/10.1108/YC-12-2018-0922
  12. Djojo, B. W., Arief, M., & Heriyati, P. (2015). Exploring the relationship of distribution channel role to trust and purchase intention of microinsurance. Advanced Science Letters, 21(5), 1108-1112. https://doi.org/10.1166/asl.2015.6010
  13. Fang, Y., Chiu, C., & Wang, E. T. G. (2011). Understanding customers' satisfaction and repurchase intentions. Internet Research. https://doi.org/10.1108/10662241111158335
  14. Fang, Y. H., Chiu, C. M., & 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
  15. Ganguly, B., Dash, S. B., Cyr, D., & Head, M. (2010). The effects of website design on purchase intention in online shopping : the mediating role of trust and the moderating role of culture. Int. J. Electronic Business, 8(4-5), 302-330. https://doi.org/10.1504/IJEB.2010.035289
  16. Gao, Y. (2005). Web systems design and online consumer behavior. Idea Group Publishing Global.
  17. Gummerus, J., Liljander, V., Pura, M., & Van Riel, A. (2004). Customer loyalty to content-based Web sites: The case of an online health-care service. Journal of Services Marketing, 18(3), 175-186. https://doi.org/10.1108/08876040410536486
  18. Guo, Y., & Barnes, S. (2009). Virtual item purchase behavior in virtual worlds: An exploratory investigation. Electronic Commerce Research, 9(1-2), 77-96. https://doi.org/10.1007/s10660-009-9032-6
  19. Ha, N. T., Nguyen, T. L. H., Pham, T. Van, & Nguyen, T. H. T. (2021). Factors Influencing Online Shopping Intention: An Empirical Study in Vietnam. Journal of Asian Finance, Economics and Business, 8(3), 1257-1266. https://doi.org/10.13106/jafeb.2021.vol8.no3.1257
  20. Hair, Jr, J. F. (2015). Essentials of Business Research Methods. In Essentials of Business Research Methods. Routledge. https://doi.org/10.4324/9781315704562
  21. Hair, J., Black, W., Babin, B., & Anderson, R. (2010). Multivariate Data Analysis: A Global Perspective. In: Hall, P. P. (Ed.), Multivariate Data Analysis: A Global Perspective (7th Ed., Vol. 7). Pearson.
  22. Hair, J. F., Black, W., Babin, B., & Anderson, R. E. (2009). Multivariate Data Analysis. Upper Saddle River, NJ: Prentice Hall.
  23. Hapsawati, T. (2019). Service Quality and Consumer's Trust Using PT. JNE Gorontalo Branch. International Journal of Applied Business & International Management, 4(1), 103-111. https://doi.org/https://doi.org/10.32535/ijabim.v4i1.387
  24. Harcar, T., & Yucelt, U. (2012). American Consumer's Attitudes towards Different Airline Companies Channels: A Comparison of transaction Methods. PASOS. Revista de Turismo y Patrimonio Cultural, 10(2), 59-68. https://doi.org/10.25145/j.pasos.2012.10.027
  25. Hong, I. B., & Cho, H. (2011). The impact of consumer trust on attitudinal loyalty and purchase intentions in B2C e-marketplaces: Intermediary trust vs. seller trust. International Journal of Information Management, 31(5), 469-479. https://doi.org/10.1016/j.ijinfomgt.2011.02.001
  26. Hu, Y. C., Wang, J. H., & Hung, L. P. (2012). Evaluating Microblogging e-Service Quality using ETAL. Journal of Multi-Criteria Decision Analysis, 19(1-2), 89-111. https://doi.org/10.1002/mcda.491
  27. Jun, M., Yang, Z., & Kim, D. S. (2004). Customers' perceptions of online retailing service quality and their satisfaction. International Journal of Quality and Reliability Management, 21(8), 817-840. https://doi.org/10.1108/02656710410551728
  28. Kalia, P. (2016). Tsunamic E-Commerce in India: The Third Wave. The Global Analyst, 5(7), 47-49.
  29. Kalia, P., Arora, R., & Kumalo, S. (2016). E-service quality, consumer satisfaction and future purchase intentions in e-retail. E-Service Journal, 10(1), 24. https://doi.org/10.2979/eservicej.10.1.02
  30. Kalia, P., Kaur, N., & Singh, T. (2017). E-commerce in India: Evolution and revolution of online retail. Mobile Commerce: Concepts, Methodologies, Tools, and Applications, 2, 736-758. https://doi.org/10.4018/978-1-5225-2599-8.ch036
  31. Katz, K. L., Larson, B. M., & Larson, R. C. (1991). Prescription for waiting-in line blues: Entertain, enlighten and engage. (winter), 32(2). Sloan Management Review (Winter), 32(2), 44-55.
  32. Kim, B. C., & Park, Y. W. (2012). Security versus convenience? An experimental study of user misperceptions of wireless internet service quality. Decision Support Systems, 53(1), 1-11. https://doi.org/10.1016/j.dss.2011.08.006
  33. Kim, J. H., & Kim, M. (2020). Conceptualization and assessment of E-service quality for luxury brands. Service Industries Journal, 40(5), 436-470. https://doi.org/10.1080/02642069.2018.1517755
  34. Kim, J., & Lee, J. (2002). Critical design factors for successful e-commerce systems. Behaviour and Information Technology, 21(3), 185-199. https://doi.org/10.1080/0144929021000009054
  35. Koksal, M. H. (2019). Differences among baby boomers, Generation X, millennials, and Generation Z wine consumers in Lebanon: Some perspectives. International Journal of Wine Business Research, 31(3), 456-472. https://doi.org/10.1108/IJWBR-09-2018-0047
  36. Ladhari, R., Gonthier, J., & Lajante, M. (2019). Generation Y and online fashion shopping: Orientations and profiles. Journal of Retailing and Consumer Services, 48(February), 113-121. https://doi.org/10.1016/j.jretconser.2019.02.003
  37. Law, R., & Leung, R. (2000). A study of airlines' online reservation services on the internet. Journal of Travel Research, 39(2), 202-211. https://doi.org/10.1177/004728750003900210
  38. Lee, G. G., & Lin, H. F. (2005). Customer perceptions of e-service quality in online shopping. International Journal of Retail and Distribution Management, 33(2), 161-176. https://doi.org/10.1108/09590550510581485
  39. Liao, Z., & Cheung, M. T. (2002). Internet-based e-banking and consumer attitudes: an emprirical study. Information and Management Journal, 39(4), 283-295. https://doi.org/10.1016/S0378-7206(01)00097-0
  40. Muafi, M., Syafri, W., Prabowo, H., & Nur, S. A. (2021). Digital Entrepreneurship in Indonesia: A Human Capital Perspective. Journal of Asian Finance, Economics and Business, 8(3), 351-359. https://doi.org/10.13106/jafeb.2021.vol8.no3.0351
  41. Parasuraman, A., Zeithaml, V. A., & Berry, L. L. (1988). SERVQUAL: A multiple-item scale for measuring consumer perceptions of service quality. Journal of Retailing, 64(1), 12-40.
  42. Pavlou, P. A., & Fygenson, M. (2006). Understanding and Predicting Electronic Commerce Adoption: An Extension of the Theory of Planned Behavior. MIS Quarterly, 30(1), 115-143. https://doi.org/10.1109/GLOCOMW.2012.6477580
  43. Pudaruth, S., & Nursing, R. K. (2017). Exploring the Determining Factors Influencing Online Purchase Behaviour among Consumers in Emerging Economies: A Case of Mauritius. International Journal of Arts & Sciences, 10(1), 1-22.
  44. Putra, P., Jayadi, R., & Steven, I. (2021). The Impact of Quality and Price on the Loyalty of Electronic Money Users: Empirical Evidence from Indonesia. Journal of Asian Finance, Economics and Business, 8(3), 1349-1359. https://doi.org/10.13106/jafeb.2021.vol8.no3.1349
  45. Rakhmawati, T., Sumaedi, S., Bakti, I. G. M. Y., Astrini, N. J., Yarmen, M., Widianti, T., Sekar, D. C., & Vebriyanti, D. I. (2013). Developing a Service Quality Measurement Model of Public Health Center in Indonesia. Management Science and Engineering, 7(2), 1-15. https://doi.org/10.3968/j.mse.1913035X20130702.1718
  46. Riegger, A. S., Klein, J. F., Merfeld, K., & Henkel, S. (2021). Technology-enabled personalization in retail stores: Understanding drivers and barriers. Journal of Business Research, 123, 140-155. https://doi.org/10.1016/j.jbusres.2020.09.039
  47. Rishi, B. (2010). Motivators and decisional influencers of online shopping. International Journal of Business Innovation and Research, 4(3), 195-209. https://doi.org/10.1504/IJBIR.2010.032383
  48. Sekaran, U., & Bougie, R. (2016). Research Methods For Business: A Skill Building Approach (7th ed.). Hoboken, NJ: Wiley-Blackwell.
  49. Seock, Y. K., & Bailey, L. R. (2008). The influence of college students' shopping orientations and gender differences on online information searches and purchase behaviours. International Journal of Consumer Studies, 32(2), 113-121. https://doi.org/10.1111/j.1470-6431.2007.00647.x
  50. Setiawan, E. B., Wati, S., Wardana, A., & Ikhsan, R. B. (2020). Building trust through customer satisfaction in the airline industry in Indonesia: Service quality and price fairness contribution. Management Science Letters, 10(5), 1095-1102. https://doi.org/10.5267/j.msl.2019.10.033
  51. Statista. (2018a). Number of Digital Buyers in indonesia from 2016-2022 (In Millions). 2018.
  52. Statista. (2018b). Number of Internet Users in indonesia from 2015 to 2022. Statista.
  53. Statista. (2018c). The statistics portal. Statista. https://www.statista.com/sta-tistics/272314/advertising-spending-in-the-us
  54. Suhartanto, D., Helmi Ali, M., Tan, K. H., Sjahroeddin, F., & Kusdibyo, L. (2019). Loyalty toward online food delivery service: the role of e-service quality and food quality. Journal of Foodservice Business Research, 22(1), 81-97. https://doi.org/10.1080/15378020.2018.1546076
  55. Taherdoost, H. (2019). Electronic service quality measurement: development of a survey instrument to measure the quality of e-service. International Journal of Intelligent Engineering Informatics, 7(6), 491-526. https://doi.org/10.1504/ijiei.2019.104559
  56. Tam, K. Y., & Ho, S. Y. (2006). Understanding the Impact of Web Personalization on User Information Processing and Decision Outcomes. MIS Quarterly, 30(4), 865-890. https://doi.org/https://doi.org/10.2307/25148757
  57. Tan, C. M., & Goh, T. N. (2017). Theory and practice of quality and reliability engineering in Asia industry. Theory and Practice of Quality and Reliability Engineering in Asia Industry, 1-300. https://doi.org/10.1007/978-981-10-3290-5
  58. Tandiono, J., Djojo, B. W., Candra, S., & Heriyati, P. (2020). Finding Customer Perception of Peer-to-Peer ( P2P ) Lending Financial Technology in Pohon Dana, 11(March), 51-58. https://doi.org/10.21512/bbr.v11i1.6014
  59. Teoh, W. M.-Y., Chong, S. C., Lin, B., & Chua, J. W. (2013). Factors affecting consumers' perception of electronic payment: an empirical analysis. Internet Research, 23(4), 465-485. https://doi.org/https://doi.org/10.1108/IntR-09-2012-0199
  60. Teoh, W. M. Y., Chong, S. C., Lin, B., & Chua, J. W. (2013). Factors affecting consumers' perception of electronic payment: An empirical analysis. Internet Research, 23(4), 465-485. https://doi.org/10.1108/IntR-09-2012-0199
  61. Trivedi, V., & Trivedi, A. (2018). Interpretive structural modelling of website quality factors for repurchase intention in online context. International Journal of Electronic Business, 14(4), 309-325. https://doi.org/10.1504/ijeb.2018.098127
  62. Tshin, E. Y. H., Tanakinjal, G. H., & Stephen Jr, L. S. (2014). The key dimensions of online service quality: a study of consumer perceptions. The IUP Journal Of Marketing Management, 13(2), 7-18.
  63. Udo, G. J., Bagchi, K. K., & Kirs, P. J. (2010). An assessment of customers' e-service quality perception, satisfaction and intention. International Journal of Information Management, 30(6), 481-492. https://doi.org/10.1016/j.ijinfomgt.2010.03.005
  64. Vesanen, J. (2007). What is personalization? A conceptual framework. European Journal of Marketing, 41(5-6), 409-418. https://doi.org/10.1108/03090560710737534
  65. Warrier, U., Singh, P., Jien, C. W., Mui, D., Kee, H., Zi, G., Liang, T. Y., SB, G., Nair, S., Nair, R. K., Lokhande, S. D., & Ganatra, V. (2021). Factors that Lead Amazon . com to A Successful Online Shopping Platform. International Journal of Tourism and Hospitality in Asia Pacific (IJTHAP), 4(1), 7-17. https://doi.org/https://doi.org/10.32535/ijthap.v2i2.523
  66. Wattimena, R. E., & Sin, L. G. (2020). Building Trust and Quality of Customer Service Through Customer Satisfaction (Study of Gojek's Customers in Malang City). Journal of The Community Development in Asia, 3(3), 79-87. https://doi.org/10.32535/jcda.v3i3.892
  67. Weinberg, B. D. (2000). Don't keep your internet customers waiting too long at the (virtual) front door. Journal of Interactive Marketing, 14(1), 30-39. https://doi.org/10.1002/(SICI)1520-6653(200024)14:1<30::AID-DIR3>3.0.CO;2-M
  68. Wolfinbarger, M., & Gilly, M. C. (2003). eTailQ: Dimensionalizing, measuring and predicting etail quality. Journal of Retailing, 79(3), 183-198. https://doi.org/10.1016/S0022-4359(03)00034-4
  69. Wu, Y. H., Chu, S. Y., & Fang, W. C. (2008). An empirical study of trust and TAM: An example of online shopping. Journal of Information Management, 15(1), 123-152.
  70. Yang, Z., & Fang, X. (2004). Online service quality dimensions and their relationships with satisfaction: A content analysis of customer reviews of securities brokerage services. International Journal of Service Industry Management, 15(3), 302-326. https://doi.org/10.1108/09564230410540953
  71. Yang, Z., & Jun, M. (2002). Consumer perception of e-service quality: From Internet purchaser and non-purchaser perspectives. Journal of Business Strategies, 19(1), 19-41. https://doi.org/10.54155/jbs.19.1.19-42
  72. Zeithaml, V. A., Parasuraman, A., & Malhotra, A. (2002). Service quality delivery through web sites: A critical review of extant knowledge. Journal of the Academy of Marketing Science, 30(4), 362-375. https://doi.org/10.1177/009207002236911
  73. Zemblyte, J. (2015). The Instrument for Evaluating E-Service Quality. Procedia - Social and Behavioral Sciences, 213, 801-806. https://doi.org/10.1016/j.sbspro.2015.11.478
  74. Zheng, X., Men, J., Yang, F., & Gong, X. (2019). Understanding impulse buying in mobile commerce: An investigation into hedonic and utilitarian browsing. International Journal of Information Management, 48(February), 151-160. https://doi.org/10.1016/j.ijinfomgt.2019.02.010

피인용 문헌

  1. The Effects of Online Social Influencers on Purchasing Behavior of Generation Z: An Empirical Study in Vietnam vol.8, pp.11, 2021, https://doi.org/10.13106/jafeb.2021.vol8.no11.0179
  2. The Impact of Language on Customer Intentions to Use Localized E-Commerce Websites in Arabic Countries: The Mediating Role of Perceived Risk and Trust vol.9, pp.1, 2021, https://doi.org/10.13106/jafeb.2022.vol9.no1.0273