N/A

This study observes how LINE Bot quality impacts customer loyalty within the Taiwanese market, emphasizing customer satisfaction as the mediator and perceived risk as the moderator. This research employs the Information Systems (IS) Success Model to assess how system quality, information quality, and service quality of LINE Bots influence customer loyalty through LINE Bots—a simplified AI chatbot integrated into the LINE messaging platform. Unlike more complex AI systems, LINE Bots utilize a structured interaction framework that simplifies user experiences and potentially reduces miscommunication. The study was conducted using a structured questionnaire distributed to LINE users in Taiwan. After data cleaning, a total of 240 valid responses were collected. The findings show that via the mediation of customer satisfaction, both system quality and service quality significantly enhance customer loyalty. However, it was discovered that the relationship between system quality and customer loyalty is adversely moderated by perceived risk, indicating that higher perceived risks could dampen the positive effects of system quality enhancements. This study contributes to the knowledge of digital customer interactions in Taiwan and provides implications for businesses using LINE Bots to enhance customer loyalty. The study's shortcomings were also discussed, along with suggestions for additional research.

References
1. Adamopoulou, E., & Moussiades, L. (2020). An Overview of Chatbot Technology. In I. Maglogiannis, L. Iliadis, & E. Pimenidis (Eds.), Artificial Intelligence Applications and Innovations, 373–383. Springer International Publishing. https://doi.org/10.1007/978-3-030-49186-4_31
2. Anderson, E. W., Fornell, C., & Lehmann, D. R. (1994). Customer Satisfaction, Market Share, and Profitability: Findings from Sweden. Journal of Marketing, 58(3), 53–66. https://doi.org/10.1177/002224299405800304
3. Araujo, T. (2018). Living up to the chatbot hype: The influence of anthropomorphic design cues and communicative agency framing on conversational agent and company perceptions. Computers in Human Behavior, 85, 183–189. https://doi.org/10.1016/j.chb.2018.03.051
4. Statista (2021). Asia: Most popular Asia-based mobile messenger apps. https://www.statista.com/statistics/250548/most-popular-asian-mobile-messenger-apps/
5. Brown, I., & Jayakody, R. (2008). B2C e‑Commerce Success: A Test and Validation of a Revised Conceptual Model. Electronic Journal of Information Systems Evaluation, 11(3), Article 3.
6. Chen, Q., Lu, Y., Gong, Y., & Xiong, J. (2023). Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research, 33(6), 2205–2243. https://doi.org/10.1108/INTR-09-2021-0686
7. Churchill, G. A., & Surprenant, C. (1982). An Investigation into the Determinants of Customer Satisfaction. Journal of Marketing Research, 19(4), 491–504. https://doi.org/10.1177/002224378201900410
8. Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
9. Dawar, N., & Bendle, N. (2018). Marketing in the age of Alexa. Harvard Business Review, 96(3), 80–86. https://hbr.org/2018/05/marketing-in-the-age-of-alexa
10. Dehghani, M. (2018). Exploring the motivational factors on continuous usage intention of smartwatches among actual users. Behaviour & Information Technology, 37(2), 145–158. https://doi.org/10.1080/0144929X.2018.1424246
11. DeLone, W. H., & McLean, E. R. (1992). Information Systems Success: The Quest for the Dependent Variable. Information Systems Research, 3(1), 60–95. https://doi.org/10.1287/isre.3.1.60
12. Delone, W., & McLean, E. (2003). The DeLone and McLean Model of Information Systems Success: A Ten-Year Update. J. of Management Information Systems, 19, 9–30. https://doi.org/10.1080/07421222.2003.11045748
13. Dowling, G. R., & Staelin, R. (1994). A Model of Perceived Risk and Intended Risk-handling Activity. Journal of Consumer Research, 21(1), 119–134. https://doi.org/10.1086/209386
14. Gao, L., & Waechter, K. A. (2017). Examining the role of initial trust in user adoption of mobile payment services: An empirical investigation. Information Systems Frontiers, 19(3), 525–548. https://doi.org/10.1007/s10796-015-9611-0
15. Grant, R. (1989). BUILDING AND TESTING A CAUSAL MODEL OF AN INFORMATION TECHNOLOGY’S IMPACT. ICIS 1989 Proceedings. https://aisel.aisnet.org/icis1989/44
16. Heo, M., & Lee, K. J. (2018). Chatbot as a New Business Communication Tool: The Case of Naver TalkTalk. Business Communication Research and Practice, 1(1), 41–45. https://doi.org/10.22682/bcrp.2018.1.1.41
17. Ho, S. Y. (2006). The Attraction of Internet Personalization to Web Users. Electronic Markets, 16(1), 41–50. https://doi.org/10.1080/10196780500491162
18. Hoehle, H., & Venkatesh, V. (2015). Mobile Application Usability: Conceptualization and Instrument Development. MIS Quarterly, 39(2), 435–472.
19. Huang, M.-H., & Rust, R. T. (2018). Artificial Intelligence in Service. Journal of Service Research, 21(2), 155–172. https://doi.org/10.1177/1094670517752459
20. Jenneboer, L., Herrando, C., & Constantinides, E. (2022). The Impact of Chatbots on Customer Loyalty: A Systematic Literature Review. Journal of Theoretical and Applied Electronic Commerce Research, 17(1), Article 1. https://doi.org/10.3390/jtaer17010011
21. Kandampully, J., Zhang, T. (Christina), & Bilgihan, A. (2015). Customer loyalty: A review and future directions with a special focus on the hospitality industry. International Journal of Contemporary Hospitality Management, 27(3), 379–414. https://doi.org/10.1108/IJCHM-03-2014-0151
22. Lai‐Ming Tam, J. (2012). The moderating role of perceived risk in loyalty intentions: An investigation in a service context. Marketing Intelligence & Planning, 30(1), 33–52. https://doi.org/10.1108/02634501211193903
23. Li, D., Shenghui, A., & Kai, Y. (2008). Exploring Chinese consumer repurchasing intention for services: An empirical investigation. Journal of Consumer Behaviour, 7(6), 448–460. https://doi.org/10.1002/cb.263
24. LINE Corporation. (n.d.). LINE Announces Plans for Expanded Development and Proliferation of Chatbots. Retrieved May 31, 2024. https://LINEcorp.com/en/pr/news/en/2016/1517
25. Liu, X., & Wang, Q. (2005). Study on application of a quantitative evaluation approach for software architecture adaptability. Fifth International Conference on Quality Software (QSIC’05), 265–272. https://doi.org/10.1109/QSIC.2005.59
26. Mitchell, V. (1998). A role for consumer risk perceptions in grocery retailing. British Food Journal, 100(4), 171–183. https://doi.org/10.1108/00070709810207856
27. Ndubisi, N. O., Malhotra, N. K., & Wah, C. K. (2008). Relationship Marketing, Customer Satisfaction and Loyalty: A Theoretical and Empirical Analysis From an Asian Perspective. Journal of International Consumer Marketing. https://www.tandfonLINE.com/doi/abs/10.1080/08961530802125134
28. Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499
29. Oliver, R. L. (1997). Satisfaction: A Behavioral Perspective on the Consumer. McGraw Hill.
30. Oliver, R. L. (1999). Whence Consumer Loyalty? Journal of Marketing, 63(4_suppl1), 33–44. https://doi.org/10.1177/00222429990634s105
31. Prentice, C., Lopes, S. D., & Wang, X. (2020). The impact of artificial intelligence and employee service quality on customer satisfaction and loyalty. Journal of Hospitality Marketing & Management. https://doi.org/10.1080/19368623.2020.1722304.
32. Sundar, S. S., & Kim, J. (2019). Machine Heuristic: When We Trust Computers More than Humans with Our Personal Information. Proceedings of the 2019 CHI Conference on Human Factors in Computing Systems, 1–9. https://doi.org/10.1145/3290605.3300768
33. Sundar, S. S., & Marathe, S. S. (2010). Personalization versus Customization: The Importance of Agency, Privacy, and Power Usage. Human Communication Research, 36(3), 298–322. https://doi.org/10.1111/j.1468-2958.2010.01377.x
34. Swanson, E. B. (1997). Maintaining IS quality. Information and Software Technology, 39(12), 845–850. https://doi.org/10.1016/S0950-5849(97)00043-8
35. Trivedi, J. P., & Trivedi, H. (2018). Investigating the Factors That Make a Fashion App Successful: The Moderating Role of Personalization. Journal of Internet Commerce, 17(2), 170–187. https://doi.org/10.1080/15332861.2018.1433908
36. Wang, E. S.-T., & Lin, R.-L. (2017). Perceived quality factors of location-based apps on trust, perceived privacy risk, and continuous usage intention. Behaviour & Information Technology, 36(1), 2–10. https://doi.org/10.1080/0144929X.2016.1143033
37. Yi, Y. (1989). A Critical review of consumer satisfaction http://deepblue.lib.umich.edu/handle/2027.42/36290
38. Yi, Y., & Gong, T. (2009). An integrated model of customer social exchange relationship: The moderating role of customer experience. The Service Industries Journal, 29(11), 1627–1641. https://doi.org/10.1080/02642060902793474
39. Yu, J. (2021). The moderating role of perceived risk between AI chatbots, purchase intentions and customer loyalty in customer service (Master's dissertation). Auckland University of Technology.
40. Zhao, X., Lynch Jr., J. G., & Chen, Q. (2010). Reconsidering Baron and Kenny: Myths and truths about mediation analysis. Journal of Consumer Research, 37(2), 197–206. https://doi.org/10.1086/651257
41. Zhou, D., Nakatani, K., & Chuang, T.-T. (2011). Data quality in collaborative commerce. International Journal of Information Quality, 2(3), 264–278. https://doi.org/10.1504/IJIQ.2011.040672