Factors Affecting User Satisfaction and Loyalty of the MITA Chatbot Bank Mandiri

Riswan Septriayadi Sianturi; M. Alfian Noor; Issa Arwani; Deden WItarsyah

doi:10.61973/apjisdt.v10124.2

Authors

Riswan Septriayadi Sianturi Universitas Brawijaya https://orcid.org/0000-0002-7853-741X
M. Alfian Noor Universitas Brawijaya
Issa Arwani Department Information System, University of Brawijaya, Malang, Indonesia
Deden WItarsyah Universitas Brawijaya, Universitas Tun Hussein Onn Malaysia https://orcid.org/0000-0001-8102-0326

DOI:

https://doi.org/10.61973/apjisdt.v10124.2

Keywords:

Chatbot, Artificial Intelligence Features, Service Quality, Satisfaction, Loyalty

Abstract

The adoption of artificial intelligence (AI)-based technologies in customer service has increased substantially in recent years, particularly through the deployment of chatbots capable of simulating human conversation to support and guide users. Within the banking sector, chatbots have become essential tools for improving service accessibility, operational efficiency, and user engagement. Bank Mandiri introduced MITA (Mandiri Intelligent Assistant) as a non-transactional AI-based chatbot designed to provide customers with product and service information quickly and conveniently. However, a decline in MITA’s performance ranking, coupled with a rise in user complaints, suggests the presence of service quality issues that may affect both user satisfaction and loyalty. To address these concerns, this study investigates the determinants of user satisfaction and loyalty toward MITA by employing the AI Bot Service Quality model, which includes Core AI Bot Service Quality, AI Bot Service Recovery Quality, and AI Bot Conversational Quality. The model is further extended to incorporate key Artificial Intelligence Features, including Trendiness, Visual Attractiveness, Problem-Solving Ability, and Communication Quality, to provide a more comprehensive understanding of the factors influencing user perceptions of chatbot-based banking services. A quantitative approach was used, with data collected through online questionnaires distributed to 126 active MITA users located on Java Island. Structural Equation Modeling using Partial Least Squares (SEM-PLS) was applied to test the proposed hypotheses. The results reveal that Core AI Bot Service Quality, Problem-Solving Ability, and Communication Quality significantly enhance user satisfaction. Moreover, user satisfaction emerges as a strong predictor of user loyalty, while Core AI Bot Service Quality indirectly influences loyalty through satisfaction. Overall, the findings highlight the importance of enhancing the fundamental capabilities of AI chatbots, specifically their accuracy in problem resolution and the clarity of their communication, to enhance user experience and foster long-term loyalty toward AI-enabled digital banking services.

References

[1] Agnihotri A, Bhattacharya S. Chatbots’ effectiveness in service recovery. Int J Inf Manage 2024;76. https://doi.org/10.1016/j.ijinfomgt.2023.102679.

[2] Aliyu Dantsoho M, Jinjiri Ringim K, Maitama Kura K. The Relationship between Artificial Intelligence (AI) Quality, Customer Preference, Satisfaction and Continuous Usage Intention of e-Banking Services. Indonesian Business Review, 4 (1), 24–43 2021.

[3] Bhandari U, Chang K, Neben T. Understanding the impact of perceived visual aesthetics on user evaluations: An emotional perspective. Information & Management 2019;56:85–93. https://doi.org/10.1016/j.im.2018.07.003.

[4] BPPT. National Strategy for Artificial Intelligence 2020-2045. 2020.

[5] Candiwan C, Annikmah RR. Exploring the Impact of Artificial Intelligence on User Satisfaction and Acceptance in Digital Banking Services in Indonesia. 2024 IEEE 30th International Conference on Telecommunications, ICT 2024, Institute of Electrical and Electronics Engineers Inc.; 2024. https://doi.org/10.1109/ICT62760.2024.10606022.

[6] Chen Q, Lu Y, Gong Y, Xiong J. Can AI chatbots help retain customers? Impact of AI service quality on customer loyalty. Internet Research 2023;33:2205–43. https://doi.org/10.1108/INTR-09-2021-0686.

[7] Chung M, Ko E, Joung H, Kim SJ. Chatbot e-service and customer satisfaction regarding luxury brands. J Bus Res 2020;117:587–95. https://doi.org/10.1016/j.jbusres.2018.10.004.

[8] Ghozali Imam. Aplikasi analisis multivariate dengan program SPSS. Badan Penerbit Universitas Diponegoro; 2006.

[9] Hair JF, Hult GTM, Ringle CM, Sarstedt M, Thiele KO. Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. J Acad Mark Sci 2017;45:616–32. https://doi.org/10.1007/s11747-017-0517-x.

[10] Hair JF, Risher JJ, Sarstedt M, Ringle CM. When to use and how to report the results of PLS-SEM. European Business Review 2019;31:2–24. https://doi.org/10.1108/EBR-11-2018-0203.

[11] Henseler J, Ringle CM, Sarstedt M. A new criterion for assessing discriminant validity in variance-based structural equation modeling. J Acad Mark Sci 2015;43:115–35. https://doi.org/10.1007/s11747-014-0403-8.

[12] Henseler J, Ringle CM, Sinkovics RR. The use of partial least squares path modeling in international marketing, 2009, p. 277–319. https://doi.org/10.1108/S1474-7979(2009)0000020014.

[13] Hsiao K-L, Chen C-C. What drives continuance intention to use a food-ordering chatbot? An examination of trust and satisfaction. Library Hi Tech 2022;40:929–46. https://doi.org/10.1108/LHT-08-2021-0274.

[14] Hsu CL, Lin JCC. Understanding the user satisfaction and loyalty of customer service chatbots. Journal of Retailing and Consumer Services 2023;71. https://doi.org/10.1016/j.jretconser.2022.103211.

[15] Jenneboer L, Herrando C, Constantinides E. The Impact of Chatbots on Customer Loyalty: A Systematic Literature Review. Journal of Theoretical and Applied Electronic Commerce Research 2022;17:212–29. https://doi.org/10.3390/jtaer17010011.

[16] Kotler P, Armstrong G. Principles of Marketing. 17th Edition. Pearson Education; 2018.

[17] Kuo R-Z. Why do people switch mobile payment service platforms? An empirical study in Taiwan. Technol Soc 2020;62:101312. https://doi.org/10.1016/j.techsoc.2020.101312.

[18] Kwangsawad A, Jattamart A. Overcoming customer innovation resistance to the sustainable adoption of chatbot services: A community-enterprise perspective in Thailand. Journal of Innovation and Knowledge 2022;7. https://doi.org/10.1016/j.jik.2022.100211.

[19] Kwong K, Wong K. Partial least squares structural equation modeling (PLS-SEM) techniques using SmartPLS. 2015.

[20] Malabadi RB, Kolkar KP, Chalannavar RK, Mudigoudra BS, Abdi G, Baijnath H. International Journal Of Research And Innovation In Applied Science (IJRIAS) Applications of Artificial Intelligence (AI) in Cannabis Industries: In Vitro Plant Tissue Culture 2023. https://doi.org/10.51584/IJRIAS.

[21] Maroufkhani P, Asadi S, Ghobakhloo M, Jannesari MT, Ismail WKW. How do interactive voice assistants build brands’ loyalty? Technol Forecast Soc Change 2022;183:121870. https://doi.org/10.1016/j.techfore.2022.121870.

[22] Mi Alnaser F, Rahi S, Alghizzawi M, Ngah AH. Does artificial intelligence (AI) boost digital banking user satisfaction? Integration of expectation confirmation model and antecedents of artificial intelligence enabled digital banking. Heliyon 2023;9. https://doi.org/10.1016/j.heliyon.2023.e18930.

[23] Monecke A, Leisch F. semPLS : Structural Equation Modeling Using Partial Least Squares. J Stat Softw 2012;48. https://doi.org/10.18637/jss.v048.i03.

[24] Omoge AP, Gala P, Horky A. Disruptive technology and AI in the banking industry of an emerging market. International Journal of Bank Marketing 2022;40:1217–47. https://doi.org/10.1108/IJBM-09-2021-0403.

[25] Parasuraman A, Zeithaml VA, Malhotra A. E-S-QUAL a multiple-item scale for assessing electronic service quality. J Serv Res 2005;7:213–33. https://doi.org/10.1177/1094670504271156.

[26] Russo D, Stol K-J. PLS-SEM for Software Engineering Research. ACM Comput Surv 2022;54:1–38. https://doi.org/10.1145/3447580.

[27] Sarstedt M, Hair JF, Cheah J-H, Becker J-M, Ringle CM. How to Specify, Estimate, and Validate Higher-Order Constructs in PLS-SEM. Australasian Marketing Journal 2019;27:197–211. https://doi.org/10.1016/j.ausmj.2019.05.003.

[28] So KKF, Kim H, Oh H. What Makes Airbnb Experiences Enjoyable? The Effects of Environmental Stimuli on Perceived Enjoyment and Repurchase Intention. J Travel Res 2021;60:1018–38. https://doi.org/10.1177/0047287520921241.

[29] Wang W, Li H. Factors influencing mobile services adoption: a brand‐equity perspective. Internet Research 2012;22:142–79. https://doi.org/10.1108/10662241211214548.

[30] Zehir C, Narcıkara E. E-Service Quality and E-Recovery Service Quality: Effects on Value Perceptions and Loyalty Intentions. Procedia Soc Behav Sci 2016;229:427–43. https://doi.org/10.1016/j.sbspro.2016.07.153.

[31] Zhou Q, Lim FJ, Yu H, Xu G, Ren X, Liu D, et al. A study on factors affecting service quality and loyalty intention in mobile banking. Journal of Retailing and Consumer Services 2021;60. https://doi.org/10.1016/j.jretconser.2020.102424.

Factors Affecting User Satisfaction and Loyalty of the MITA Chatbot Bank Mandiri

Authors

DOI:

Keywords:

Abstract

References

Downloads

Published

Issue

Section

License

Make a Submission

About Journal

APJISDT Template

E-ISSN

Tools Recommended

Indexed By

Visitors

Information