Nguyen Hoang Trieu Vy

                                                                                                                                                                                                                                                                                                      Lecturer, 

Ho Chi Minh City University of Economics and Finance

Email: vynht@uef.edu.vn

JEL Classification: M15, M31                                                                                                                                             https://doi.org/10.65176/IJLM.V2I1.01

Abstract

In the current digital era, artificial intelligence (AI) and chatbot technology play pivotal roles in enhancing customer engagement, satisfaction, and brand relationships. Based on the Stimulus-Organism-Response (S-O-R) framework, this study proposes a conceptual model that explains how chatbot service quality influences e-brand loyalty. In this model, chatbot service quality is regarded as a stimulus (S) that elicits the functional benefits perceived by users. These benefits serve as the organism (O), shaping positive experiences and satisfaction during AI interactions. Consequently, these internal evaluations lead to responses (R) manifested as e-brand loyalty. Moreover, the model highlights the mediating role of satisfaction in linking functional benefits and positive experiences to loyalty, underscoring its significance in fostering long-term customer–brand relationships. Methodologically, this study adopts a conceptual modelling approach, synthesising the literature on service quality, customer experience and digital branding to propose testable hypotheses. This study offers three major contributions to theory and practice. First, it extends service quality theory by applying and advancing the S-O-R framework in the context of AI-driven interactions, clarifying the combined role of functional benefits and experiential value in shaping digital service experiences. Second, it integrates technological, experiential, and emotional dimensions into a comprehensive framework to clarify how value co-creation occurs between chatbots and consumers. Third, it offers practical implications for managers to optimise chatbot design to enhance efficiency, enrich the user experience, and foster stronger brand attachment. By advancing both theoretical understanding and managerial practice, this study underscores the strategic role of AI chatbots in cultivating sustainable e-brand loyalty.

Keywords: AI-Chatbot, Service Quality, User Experience, Satisfaction, E-Brand Loyalty

Article received on: August 25, 2025.

Revised date: October 1, 2025.

Accepted date: October 4, 2025

Publication date: October 10, 2025

References

Ahmadi, A., & Ataei, A. (2024). Emotional attachment: A bridge between brand reputation and brand advocacy. Asia Pacific Journal of Business Administration, 16(1), 1–20.

Bagozzi, R. P. (1986). Principles of Marketing Management. Science Research Associates.

Chang, J. Y. S., Cheah, J. H., Lim, X. J., & Morrison, A. M. (2023). One pie, many recipes: The role of artificial intelligence chatbots in influencing Malaysian solo traveler purchase intentions. Tourism Management Perspectives, 49, 101191. https://doi.org/10.1016/j.tmp.2023.101191

Chaudhry, B. M., & Debi, H. R. (2024). User perceptions and experiences of an AI-driven conversational agent for mental health support. mHealth, 10, 22.

Chen, Q., Gong, Y., Lu, Y., & Tang, J. (2022). Classifying and measuring the service quality of AI chatbot in frontline service. Journal of Business Research, 145, 552–568.

Chung, M., Ko, E., Joung, H., & Kim, S. J. (2018). Chatbot e-service and customer satisfaction regarding luxury brands. Journal of Business Research, 117, 587–595. https://doi.org/10.1016/j.jbusres.2018.10.004

Davis, S. V., & Dacin, P. A. (2022). This brand is who I am... or is it? Examining changes in motivation to maintain brand attachment. Journal of Product & Brand Management, 31(7), 1125–1139.

Elkhwesky, Z., & Elkhwesky, E. F. Y. (2023). A systematic and critical review of Internet of Things in contemporary hospitality: A roadmap and avenues for future research. International Journal of Contemporary Hospitality Management, 35(2), 533–562. https://doi.org/10.1108/ijchm-01-2022-0090

Eroglu, S. A., Machleit, K. A., & Davis, L. M. (2001). Atmospheric qualities of online retailing: A conceptual model and implications. Journal of Business Research, 54(2), 177–184. https://doi.org/10.1016/S0148-2963(99)00087-9

Foroudi, P., Cuomo, M. T., & Foroudi, M. M. (2020). Continuance interaction intention in retailing: Relations between customer values, satisfaction, loyalty, and identification. Information Technology & People, 33(4), 1303–1326. https://doi.org/10.1108/itp-09-2018-0421

Godlewski, L. R., & Perse, E. M. (2010). Audience activity and reality television: Identification, online activity, and satisfaction. Communication Quarterly, 58(2), 148–169. https://doi.org/10.1080/01463371003773358

Gray, H. M., Gray, K., & Wegner, D. M. (2007). Dimensions of mind perception: Science, 315(5812), 619.

Kunal, K., Ramprakash, K. R., Arun, C. J. O. E., & Xavier, M. J. (2023). A behavioural study on the impact of artificial intelligence on customer services retention in telecom industry. Russian Law Journal, 11(5s).

Kehat, R., Hirschprung, R. S., & Alkoby, S. (2024). Enhancing user acceptance of an AI agent’s recommendation in information-sharing environments. Applied Sciences, 14(17), 7874. https://doi.org/10.3390/app14177874

Ku, E. C. S. (2020). Spreading distinctive insignia benefits from mobile service: Evidence from a mobile application of transportation. Transportation Planning and Technology, 43(7), 651–669. https://doi.org/10.1080/03081060.2020.1805542

Meria, L. (2023). Development of automatic industrial waste detection system for leather products using artificial intelligence. International Transactions on Artificial Intelligence, 1(2), 195–204.

Li, M., & Wang, R. (2023). Chatbots in e-commerce: The effect of chatbot language style on customers’ continuance usage intention and attitude toward brand. Journal of Retailing and Consumer Services, 71, 103209. https://doi.org/10.1016/j.jretconser.2022.103209

Li, Y., Li, Y., Chen, Q., & Chang, Y. (2024). Humans as teammates: The signal of human–AI teaming enhances consumer acceptance of chatbots. International Journal of Information Management, 76, 102771. https://doi.org/10.1016/j.ijinfomgt.2024.102771

Loureiro, S. M. C., & Sarmento, E. M. (2018). Enhancing brand equity through emotions and experience: The banking sector. International Journal of Bank Marketing, 36(5), 868–883. https://doi.org/10.1108/IJBM-03-2017-0061

Saputra, M. C., & Andajani, E. (2024). Analysis of factors influencing intention to adopt battery electric vehicle in Indonesia. ADI Journal of Recent Innovation, 5(2), 100–109.

Maklan, S., Antonetti, P., & Whitty, S. (2017). A better way to manage customer experience: Lessons from the Royal Bank of Scotland. California Management Review, 59(2), 92–115.

Mehrabian, A., & Russell, J. A. (1974). An approach to environmental psychology. MIT Press.

Mordor Intelligence Reports. (2023). Chatbot market size & share analysis – Growth trends & forecasts (2024–2029).

Niu, B., & Mvondo, G. F. N. (2024). I am ChatGPT, the ultimate AI chatbot! Investigating the determinants of users’ loyalty and ethical usage concerns of ChatGPT. Journal of Retailing and Consumer Services, 76, 103562. https://doi.org/10.1016/j.jretconser.2023.103562

Oliver, R. L. (1997). Satisfaction: A behavioral perspective on the consumer. McGraw Hill.

Oliver, R. L. (1981). Measurement and evaluation of satisfaction processes in retail settings. Journal of Retailing, 57(3), 25–48.

Petre, M., Minocha, S., & Roberts, D. (2006). Usability beyond the website: An empirically-grounded e-commerce evaluation instrument for the total customer experience. Behaviour and Information Technology, 25(2), 189–203.

Priya, B., & Sharma, V. (2023). Exploring users’ adoption intentions of intelligent virtual assistants in financial services: An anthropomorphic perspectives and socio-psychological perspectives. Computers in Human Behavior, 148, 107912. https://doi.org/10.1016/j.chb.2023.107912

Rose, S., Clark, M., Samouel, P., & Hair, N. (2012). Online customer experience in e-retailing: An empirical model of antecedents and outcomes. Journal of Retailing, 88(2), 308–322.

Ruan, Y., & Mezei, J. (2022). When do AI chatbots lead to higher customer satisfaction than human frontline employees in online shopping assistance? Considering product attribute type. Journal of Retailing and Consumer Services, 68, 103059. https://doi.org/10.1016/j.jretconser.2022.103059

Schuhbert, A., Thees, H., & Pechlaner, H. (2023). Deep and organizational learning as innovation catalyzer in digital business ecosystems: A scenario analysis on the tourism destination Berlin. European Journal of Innovation Management. https://doi.org/10.1108/ejim-08-2022-0448

Schultz, D. E., & Peltier, J. J. (2013). Social media’s slippery slope: Challenges, opportunities and future research directions. Journal of Research in Interactive Marketing, 7(2), 86–99. https://doi.org/10.1108/JRIM-12-2012-0054

Shahzad, M. F., Xu, S., An, X., & Javed, I. (2024). Assessing the impact of AI-chatbot service quality on user e-brand loyalty through chatbot user trust, experience and electronic word of mouth. Journal of Retailing and Consumer Services, 79, 103867.

Tran, L. A. P., & Chang, T. Y. (2024). What makes customers loyal to an online booking brand? The effects of online brand experience and brand attachment. Journal of Quality Assurance in Hospitality & Tourism, 25(2), 187–214.

Yang, B., Sun, Y., & Shen, X. L. (2023). Understanding AI-based customer service resistance: A perspective of defective AI features and tri-dimensional distrusting beliefs. Information Processing & Management, 60(3), 103257. https://doi.org/10.1016/j.ipm.2022.103257

Yang, S. F., Ma, D., Shen, Z. Y., Wen, L., & Dong, L. (2023). The impact of artificial intelligence industry agglomeration on economic complexity. Economic Research-Ekonomska Istraživanja, 36(1), 1420–1448. https://doi.org/10.1080/1331677x.2022.2089194

Yang, Z., & Peterson, R. T. (2004). Customer perceived value, satisfaction, and loyalty: The role of switching costs. Psychology & Marketing, 21(10), 799–822.

Yoon, J., Kim, C., & Kang, R. (2020). Positive user experience over product usage life cycle and the influence of demographic factors. International Journal of Design, 14(2), 85–102.

Zhang, M., Hu, M., Guo, L., & Liu, W. (2017). Understanding relationships among customer experience, engagement, and word-of-mouth intention on online brand communities. Internet Research, 27(4), 839–857.

Zhang, Y., & Wang, S. (2023). The influence of anthropomorphic appearance of artificial intelligence products on consumer behavior and brand evaluation under different product types. Journal of Retailing and Consumer Services, 74, 103432.