PENGARUH USER EXPERIENCE, ACCURACY OF RESPONSES, PERSONALIZATION USER ENGAGEMENT DAN RETENTION CHATGPT
DOI:
https://doi.org/10.61722/jemba.v2i4.1213Keywords:
Accuracy of Responses; ChatGPT; Personalization; User Engagement; User RetentionAbstract
This study aims to analyze the influence of user experience, accuracy of responses, and personalization on user engagement and its impact on user retention in the context of ChatGPT usage. A quantitative approach was employed by distributing questionnaires to 200 active ChatGPT users and analyzing the data using path analysis with the help of SmartPLS software. The results show that all three main variables—user experience, accuracy of responses, and personalization—have a positive and significant effect on user engagement. Furthermore, user engagement significantly affects user retention. These findings highlight that a positive user experience, accurate responses, and relevant personalization features are crucial in building user engagement and loyalty toward artificial intelligence-based systems such as ChatGPT.
References
Ayers, J., Wu, Y., Lan, W., & Liu, Q. (2023). Enhancing chatbot user retention through personalized responses and adaptive interaction design. International Journal of Human-Computer Interaction, 39(4), 278–293. https://doi.org/10.1080/10447318.2023.2145678
Chandra, A., Rachmawati, E., & Kurniawan, A. (2022). The effect of response accuracy and personalization on user satisfaction and engagement: A study on AI-based platforms. Jurnal Teknologi Informasi dan Komunikasi, 21(2), 87–95.
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
Ghozali, I. (2001). Aplikasi analisis multivariate dengan program SPSS. Semarang: Badan Penerbit Universitas Diponegoro.
Ghozali, I. (2004). Model persamaan struktural: Konsep dan aplikasi dengan program AMOS Ver.5.0. Semarang: Badan Penerbit Universitas Diponegoro.
Hadian, D., & Fikriah, S. (2023). Analisis faktor-faktor yang memengaruhi penerimaan teknologi berbasis AI: Pendekatan Technology Acceptance Model. Jurnal Sistem Informasi dan Teknologi, 12(1), 15–28.
Nasrul, A., Wulandari, M., & Salim, R. (2024). Evaluating user experience of generative AI chatbot using multi-dimensional UX metrics. Journal of Digital Interaction, 3(1), 44–56.
Nielsen, J., & Norman, D. (2020). The definition of user experience (UX). Nielsen Norman Group. https://www.nngroup.com/articles/definition-user-experience
Sauer, J., Seibel, K., & Rüttinger, B. (2020). The influence of usability, workload, and performance on user acceptance of intelligent systems. Ergonomics, 63(3), 287–298. https://doi.org/10.1080/00140139.2019.1682310
Seaborn, K. (2025). Towards a framework for evaluating AI-driven conversational agents: Balancing UX and trust. AI & Society, 40(1), 112–125.
Zhai, X. (2022). Factors influencing user satisfaction and loyalty of AI-based chat platforms. Computers in Human Behavior, 130, 107163. https://doi.org/10.1016/j.chb.2021.107163
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2025 JURNAL ILMIAH EKONOMI, MANAJEMEN, BISNIS DAN AKUNTANSI
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
