Chatbot Quality and Its Impact on User Satisfaction and Continuance Usage Intention in the Indonesian Banking Industry
Keywords:
chatbot quality, user satisfaction, continuance intention, banking, perceived usefulness, Indonesia, , AI customer serviceAbstract
The research aims to investigate the role of chatbot quality in influencing user satisfaction and continuance usage intention within the Indonesian banking industry. The research is among the first to apply Expectation Confirmation Theory (ECT) to chatbot usage in the Indonesian banking industry and offers a novel integration of chatbot quality dimensions within the framework. A quantitative explanatory method is adopted, and a purposive sampling method is used to collect 347 valid responses via an online structured questionnaire. Data analysis is conducted using Partial Least Squares-Structural Equation Modeling (PLSSEM) with a focus on reflective-formative evaluation, bootstrapping for hypothesis testing, PLS-Predict for out-of-sample predictive performance, and Importance-Performance Analysis (IPMA) for managerial insights. The results show that chatbot quality significantly enhances both perceived usefulness and confirmation to subsequently reinforce user satisfaction and continuance usage intention. Satisfaction is identified as the strongest predictor of continuance usage. Meanwhile, chatbot disclosure does not have a significant impact on perceived quality, and it reflects the gap between transparency efforts and user perception. The observations underline the importance of designing chatbots that are responsive, context-aware, and linguistically adaptive specifically in the diverse communication landscape of Indonesia. The research contributes to the growing body of knowledge on AI-driven customer service technologies in emerging markets by offering practical implications for chatbot implementation in the financial sector. The identification of critical determinants of chatbot success also leads to the provision of insights for banks to enhance digital engagement, foster trust, and ensure long-term usage through optimized conversational experiences.
References
[1] V. Kaushal and R. Yadav, “Learning successful implementation of chatbots in businesses from B2B customer experience perspective,” Concurrency and Computation: Practice and Experience, vol. 35, no. 1, p. e7450, 2023.
[2] R. M. Qadiri, N. Shabir, and M. Qadri, “Conceptualizing possibilities of artificial intelligence in furtherance of the banking sector: An effective tool for improving customer relationship, customer service and public relations,” vol. 10, no. 2, pp. 44–65, 2020.
[3] D. Doherty and K. Curran, “Chatbots for online banking services,” vol. 17, no. 4, pp. 327–342, 2019.
[4] D. M. Nguyen, Y. T. H. Chiu, and H. D. Le, “Determinants of continuance intention towards banks’ chatbot services in Vietnam: A necessity for sustainable development,” Sustainability, vol. 13, no. 14, pp. 1–24, 2021.
[5] A. Kwangsawad and A. Jattamart, “Overcoming customer innovation resistance to the sustainable adoption of chatbot services: A communityenterprise perspective in Thailand,” Journal of Innovation & Knowledge, vol. 7, no. 3, pp. 1–13, 2022.
[6] J. S. Chen, T. T. Y. Le, and D. Florence, “Usability and responsiveness of artificial intelligence chatbot on online customer experience in e-retailing,” International Journal of Retail & Distribution Management, vol. 49, no. 11, pp. 1512–1531, 2021.
[7] M. Li and R. Wang, “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, vol. 71, 2023.
[8] M. Adam, M. Wessel, and A. Benlian, “AI-based chatbots in customer service and their effects on user compliance,” Electronic Markets, vol. 31, no. 2, pp. 427–445, 2021.
[9] E. M. Safitri, A. Pratama, M. A. Furqon, I. R. Mukhlis, Agussalim, and A. Faroqi, “Interaction effect of system, information and service quality on intention to use and user satisfaction,” in 2020 6th Information Technology International Seminar (ITIS). Surabaya, Indonesia: IEEE, Oct. 14–16, 2020, pp. 92–97.
[10] Y. Ruan and J. Mezei, “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, vol. 68, pp. 1–16, 2022.
[11] L. Rajaobelina, S. Prom Tep, M. Arcand, and L. Ricard, “Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot,” Psychology & Marketing, vol. 38, no. 12, pp. 2339–2356, 2021.
[12] X. Cheng, Y. Bao, A. Zarifis, W. Gong, and J. Mou, “Exploring consumers’ response to textbased chatbots in e-commerce: The moderating role of task complexity and chatbot disclosure,” Internet Research, vol. 32, no. 2, pp. 496–517, 2022.
[13] N. Mozafari, W. H. Weiger, and M. Hammerschmidt, “Trust me, i’m a bot–Repercussions of chatbot disclosure in different service frontline settings,” Journal of Service Management, vol. 33, no. 2, pp. 221–245, 2022.
[14] L. Li, K. Y. Lee, E. Emokpae, and S. B. Yang, “What makes you continuously use chatbot services? Evidence from Chinese online travel agencies,” Electronic Markets, vol. 31, no. 3, pp. 575–599, 2021.
[15] K. Klein and L. F. Martinez, “The impact of anthropomorphism on customer satisfaction in chatbot commerce: An experimental study in the food sector,” Electronic Commerce Research, vol. 23, no. 4, pp. 2789–2825, 2023.
[16] W. B. Kim and H. J. Hur, “What makes people feel empathy for AI chatbots? Assessing the role of competence and warmth,” International Journal of Human–Computer Interaction, vol. 40, no. 17, pp. 4674–4687, 2024.
[17] D. El-Shihy, M. Abdelraouf, M. Hegazy, and N. Hassan, “The influence of AI chatbots in fintech services on customer loyalty within the banking industry,” Future of Business Administration, vol. 3, no. 1, pp. 16–28, 2024.
[18] F. Y. Lee and T. J. Chan, “Establishing credibility in AI chatbots: The importance of customization, communication competency and user satisfaction,” in 4th International Conference on Communication, Language, Education and Social Sciences (CLESS 2023). Melaka, Malaysia (Online): Atlantis Press, 2024, pp. 88–106.
[19] J. Rhim, M. Kwak, Y. Gong, and G. Gweon, “Application of humanization to survey chatbots: Change in chatbot perception, interaction experience, and survey data quality,” Computers in Human Behavior, vol. 126, 2022.
[20] S. C. Silva, R. De Cicco, B. Vlaˇci´c, and M. G. Elmashhara, “Using chatbots in e-retailing–How to mitigate perceived risk and enhance the flow experience,” International Journal of Retail & Distribution Management, vol. 51, no. 3, pp. 285–305, 2023.
[21] A. M. Sundjaja, P. Utomo, and F. Colline, “The determinant factors of continuance use of customer service chatbot in indonesia e-commerce: Extended expectation confirmation theory,” Journal of Science and Technology Policy Management, vol. 16, no. 1, pp. 182–203, 2025.
[22] Y. Jiang, X. Yang, and T. Zheng, “Make chatbots more adaptive: Dual pathways linking human-like cues and tailored response to trust in interactions with chatbots,” Computers in Human Behavior, vol. 138, 2023.
[23] G. Park, M. C. Yim, J. Chung, and S. Lee, “Effect of AI chatbot empathy and identity disclosureon willingness to donate: The mediation of humanness and social presence,” Behaviour & Information Technology, vol. 42, no. 12, pp. 1998–2010, 2023.
[24] M. Ashfaq, J. Yun, S. Yu, and S. M. C. Loureiro, “I, chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AIpoweredservice agents,” Telematics and Informatics, vol. 54, 2020.
[25] I. Lubbe and N. Ngoma, “Useful chatbot experience provides technological satisfaction: An emerging market perspective,” South AfricanJournal of Information Management, vol. 23,no. 1, pp. 1–8, 2021.
[26] F. A. Silva, A. S. Shojaei, and B. Barbosa, “Chatbot-based services: A study on customers’ reuse intention,” Journal of Theoretical and Applied Electronic Commerce Research, vol. 18, no. 1, pp. 457–474, 2023.
[27] Y. Zhu, J. Zhang, J. Wu, and Y. Liu, “AI is better when I’m sure: The influence of certainty of needs on consumers’ acceptance of AI chatbots,” Journal of Business Research, vol. 150, pp. 642–652, 2022.
[28] M. Chung, E. Ko, H. Joung, and S. J. Kim, “Chatbot e-service and customer satisfaction regarding luxury brands,” Journal of Business Research, vol. 117, pp. 587–595, 2020.
[29] C. L. Hsu and J. C. C. Lin, “Understanding the user satisfaction and loyalty of customer service chatbots,” Journal of Retailing and Consumer Services, vol. 71, 2023.
[30] R. Pillai, Y. Ghanghorkar, B. Sivathanu, R. Algharabat, and N. P. Rana, “Adoption of Artificial Intelligence (AI) based Employee Experience (EEX) chatbots,” Information Technology & People,vol. 37, no. 1, pp. 449–478, 2024.
[31] P. K. Tan and C. M. Lim, “Factors that affect user satisfaction of using e-commerce chatbot: A study on Generation Z,” International Journal of Business and Technology Management, vol. 5, no. 1, pp. 292–303, 2023.
[32] P. Singh and V. Singh, “The power of AI: Enhancing customer loyalty through satisfaction and efficiency,” Cogent Business & Management, vol. 11, no. 1, pp. 1–14, 2024.
[33] H. Oppewal, “Causal research,” in Wiley International Encyclopedia of Marketing. Wiley, 2010.
[34] N. Rai and B. Thapa, A study on purposive sampling method in research. Kathmandu School of Law, 2015.
[35] U. Sekaran and R. Bougie, Research methods forbusiness: A skill building approach. John Wiley & Sons, 2016.
[36] M. Sarstedt, J. F. Hair Jr, J. H. Cheah, J. M. Becker, and C. M. Ringle, “How to specify, estimate, and validate higher-order constructs in PLS-SEM,” Australasian Marketing Journal, vol. 27, no. 3, pp. 197–211, 2019.
[37] G. Shmueli, M. Sarstedt, J. F. Hair, J. H. Cheah, H. Ting, S. Vaithilingam, and C. M. Ringle, “Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict,” European Journal of Marketing, vol. 53, no. 11, pp. 2322–2347, 2019.
[38] A. F. Hariansyah, S. Audre, J. Owen, and A. M. Sundjaja, “Unveiling the determinants of marketplace customer service chatbot continuous intention to use in Indonesia: A descriptive analysis,” in 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS). Jakarta Selatan, Indonesia: IEEE, Nov. 7–8, 2023, pp. 452–457.
[39] J. F. Hair, G. T. M. Hult, C. Ringle, and M. Sarstedt, A primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). SAGE Publications, 2016.
[40] J. F. Hair, J. J. Risher, M. Sarstedt, and C. M. Ringle, “When to use and how to report the results of PLS-SEM,” European Business Review, vol. 31, no. 1, pp. 2–24, 2019.
[41] G. Shmueli, S. Ray, J. M. V. Estrada, and S. B. Chatla, “The elephant in the room: Predictive performance of PLS models,” Journal of business Research, vol. 69, no. 10, pp. 4552–4564, 2016.
[42] F. N. Shabrina, A. Anggraeni, A. S. Ramadhan, and S. Putra, “Negative emotions on a digital bank brand: How do scandals impact brand love?” in 2023 International Conference on Informatics, Multimedia, Cyber and Informations System (ICIMCIS). Jakarta Selatan, Indonesia: IEEE, Nov. 7–8, 2023, pp. 467–472.
[43] D. Darmayanti and H. Cahyono, “The influence of perceived service quality, attitudinal loyalty and corporate social responsibility on repeat patronage intention in retail banking in indonesia,” Journal of Business and Retail Management Research, vol. 8, no. 2, pp. 16–23, 2014.
[44] A. F. Utami, I. A. Ekaputra, and A. Japutra, “Adoption of FinTech products: A systematic literature review,” Journal of Creative Communications, vol. 16, no. 3, pp. 233–248, 2021.
[45] V. Tohang, E. Lo, and A. Anggraeni, “Financial Technology 3.0 adoption in financial and nonfinancial institutions from modified UTAUT perspective,” in Conference on International Issues in Business and Economics Research (CIIBER 2019). Malang, Indonesia: Atlantis Press, 2021, pp. 1–6.
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