Modeling Chatgpt Continuance Intention: The Role of Expectancy, Satisfaction, and Trust

Authors

  • Adjie Pangestu Universitas Tanjungpura
  • Harry Setiawan Universitas Tanjungpura
  • Nur Afifah Universitas Tanjungpura
  • Bintoro Bagus Purmono Universitas Tanjungpura

DOI:

https://doi.org/10.55606/jurimbik.v5i3.1164

Keywords:

Continuance Intention, Effort Expectancy, Performance Expectancy, Satisfaction, Trust

Abstract

The growth of digital technology has increased the use of online services, with AI tools like ChatGPT becoming widely used. This study examines the impact of user perceptions on their intention to continue using ChatGPT, emphasising performance expectancy, effort expectancy, satisfaction, and trust as moderating variables. Data were gathered from 200 ChatGPT users using an organised survey and analysed via Partial Least Squares, Structural Equation Modelling (PLS, SEM). Findings indicate that performance expectancy as well as effort expectancy significantly enhance satisfaction and directly affect continuance intention. Satisfaction serves as a mediating factor between the anticipation variables and the intention to continue use. Nonetheless, trust does not substantially influence the correlation between performance or effort expectancy and satisfaction. The findings indicate that users' perception of ChatGPT as beneficial and user-friendly enhances their pleasure, hence reinforcing their intention to continue utilising it. This emphasises the significance of utility and user-friendliness in fostering sustained engagement with AI services.

References

Adetha, R., & Aprilia, N. (2023). The effect of performance expectancy on behavioral intention: The mediating role of satisfaction. . Journal of Information Systems Research, .45–60, 12(2), 45–60.

Adetha, R. F., & Aprilia, R. (2023). Analisis faktor-faktor yang mempengaruhi penggunaan aplikasi GoPay di kalangan mahasiswa. Jurnal Ilmiah Administrasi Bisnis , 12(1), 45–56.

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99–110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Alharbi, S. (2017). An empirical investigation on the impact of trust mediated determinants and moderating factors on the adoption of cloud computing. International Journal of Information Technology and Computer Science, 9(11), 12–22.

Ashfaq, M., Li, X., & Raza, S. A. (2020). Factors influencing continuance intention of mobile app users: The role of satisfaction and trust. Journal of Retailing and Consumer Services.

Bagozzi, R. P. (2022). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 59(1), 1–19.

Bhattacherjee, A. (2001). Understanding Information Systems Continuance: An Expectation-Confirmation Model. MIS Quarterly, 25(3), 351. https://doi.org/10.2307/3250921

Blut, M., Chong, A. Y. L., Tsigna, Z., & Venkatesh, V. (2022). Meta-analysis of the unified theory of acceptance and use of technology (UTAUT): Challenging its validity and charting a research agenda in the red ocean. Journal of the Association for Information Systems, 23(1), 13–95.

Blut, M., Huang, S.-S., Mittal, V., Brock, C., & Hutter, K. (2022). Trust and technology acceptance: A meta-analysis. Journal of the Academy of Marketing Science. 39–58.

Chauhan, S., & Jaiswal, M. P. (2016). Factors affecting the adoption of mobile banking in India: An empirical study. International Journal of Bank Marketing, 34(7), 1025–1044. https://doi.org/10.1108/IJBM-07-2015-0101

Chen, Y., Wang, H., Yu, K., & Zhou, R. (2024). Artificial Intelligence Methods in Natural Language Processing: A Comprehensive Review. Highlights in Science, Engineering and Technology, 85, 545–550. https://doi.org/10.54097/vfwgas09

Chin, W. W. (1998). The partial least squares approach to structural equation modeling (G.A. Marcoulides, Ed.). Lawrence Erlbaum Associates.

Chiu, C. M., Fang, Y. H., & Huang, H. Y. (2020). Understanding customers’ repeat purchase intentions in B2C e‐commerce: The roles of utilitarian value, hedonic value and perceived risk. Information Systems Journal , 30(1).

Elok, N., & Hidayati, N. (2021). The influence of e-service quality on continuance intention with customer satisfaction as an intervening variable on LinkAja application users in Bandung City. International Journal of Business, Management and Economic Research, 12(2), 1090–1110.

Fahira, A., & Djamaludin, M. D. (2023). The influence of brand trust and satisfaction towards consumer loyalty of a local cosmetic products brand X among Generation Z. Journal of Consumer Sciences, , 8(1), 27–44. https://www.sciencedirect.com/science/article/pii/S1029313223000246

Ferreira, A., Silva, G. M., & Dias, Á. L. (2021). Determinants of continuance intention to use mobile self-scanning applications in retail. Journal of Retailing and Consumer Services.

Gala, D., & Makaryus, A. N. (2023). The Utility of Language Models in Cardiology: A Narrative Review of the Benefits and Concerns of ChatGPT-4. International Journal of Environmental Research and Public Health, 20(15), 6438. https://doi.org/10.3390/ijerph20156438

Gefen, D., & Straub, D. (2005). A Practical Guide To Factorial Validity Using PLS-Graph: Tutorial And Annotated Example. Communications of the Association for Information Systems, 16. https://doi.org/10.17705/1CAIS.01605

Gupta, B., Dasgupta, S., & Gupta, A. (2008). Adoption of ICT in a government organization in a developing country: An empirical study. The Journal of Strategic Information Systems, 17(2), 140–154. https://doi.org/10.1016/j.jsis.2007.12.004

Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2019). Multivariate Data Analysis. (8th ed.). Cengage Learning.

Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). The SEMinR Package (pp. 49–74). https://doi.org/10.1007/978-3-030-80519-7_3

Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM). Sage Publications.

Hair, J. F., Ringle, C. M., & Sarstedt, M. (2013). Partial Least Squares Structural Equation Modeling: Rigorous Applications, Better Results and Higher Acceptance. Long Range Planning, 46(1–2), 1–12. https://doi.org/10.1016/j.lrp.2013.01.001

Handayani, P. W., Meigasari, D. A., Pinem, A. A., Hidayanto, A. N., & Ayuningtyas, D. (2024). Critical success factors for mobile health implementation in Indonesia: A study on telemedicine application Halodoc. Journal of Medical Internet Research, 26(2).

Heyns, C., & Rothmann, S. (2021). The role of trust in technology acceptance and user satisfaction. Computers in Human Behavior.

Hsu, C.-L., & Lin, J. C.-C. (2015). What drives purchase intention for paid mobile apps? – An expectation confirmation model with perceived value. Electronic Commerce Research and Applications, 14(1), 46–57. https://doi.org/10.1016/j.elerap.2014.11.003

Hsu, H.-M., Hsu, J. S.-C., Wang, S.-Y., & Chang, I.-C. (2014). Exploring the effects of unexpected outcome on satisfaction and continuance intention. Journal of Electronic Commerce Research, 15(3), 239–256.

Hsu, J. S. C., Lin, T. C., & Wang, X. (2015). The effect of unexpected features on app users’ continuance intention. Electronic Commerce Research and Applications, 14(6), 418–430.

Huang, J., Cao, X., & Liu, Y. (2023). Extending the technology acceptance model: The role of subjective norms and AI in education. Technology in Society.

Jones, D., Smith, L., & Brown, K. (2023). Trust as a mediating variable in technology acceptance: New insights. Journal of Information Technology, 38(2), 159–176.

Khurana, D., Koli, A., Khatter, K., & Singh, S. (2023). Natural language processing: state of the art, current trends and challenges. Multimedia Tools and Applications, 82(3), 3713–3744. https://doi.org/10.1007/s11042-022-13428-4

Kim, D., Ferrin, D. L., & Rao, H. R. (2009). A trust-based consumer decision-making model in electronic commerce: The role of perceived risk and uncertainty. Electronic Commerce Research and Applications, 8(2), 103–115.

Koerniawan, I., Sulartopo, S., Tobing, W.T., & Miftahurrohman, M. (2024). Cultural Dimensions and Ethical Decision-Making: A Case Study of Multinational Corporations Operating in Indonesia. Journal of Management and Informatics.

Lee, M.-C. (2009). Factors influencing the adoption of internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3), 130–141. https://doi.org/10.1016/j.elerap.2008.11.006

Li, L. (2021). Exploring the factors influencing the continuous usage intention of AI-based voice assistants: A case of Siri. Technology in Society, 66.

Li, W. (2021). The role of trust and risk in citizens’ e-government services adoption: A perspective of the extended UTAUT model. Sustainability, , 13(14).

Li, Y., Halili, S. H., & Razak, R. A. (2023). Factors Influencing the Online Learning Success of Adults in Open and Distance Education in Southwest China. International Journal of Information and Education Technology, 13(10), 1615–1624. https://doi.org/10.18178/ijiet.2023.13.10.1670

Limayem, M., Khalifa, M., & Chin, W. W. (2004). Factors Motivating Software Piracy: A Longitudinal Study. IEEE Transactions on Engineering Management, 51(4), 414–425. https://doi.org/10.1109/TEM.2004.835087

Luo, Y. (2024). Innovative research on AI-assisted teaching models for college English listening and speaking courses. Applied and Computational Engineering, 69(1), 155–160. https://doi.org/10.54254/2755-2721/69/20241493

Ma, X., Zhang, X., Guo, X., Lai, K., & Vogel, D. (2021). Examining the role of ICT usage in loneliness perception and mental health of the elderly in China. Technology in Society, 67, 101718. https://doi.org/10.1016/j.techsoc.2021.101718

Majiid, M., Kartikasari, D., & Intyas, R. (2020). Encouraging traditional market through customer satisfaction. RSF Conference Series: Business, Management and Social Sciences, 1(1), 297–304. https://proceeding.researchsynergypress.com/index.php/rsfconferenceseries1/article/download/297/294/688

Mao, Y., Zhang, Y., Zhan, Y., & Li, Y. (2023). Investigating the determinants of IoT device continuance intentions: An extended expectation-confirmation model. . . SAGE Open, 13(3).

Mcknight, D. H., Carter, M., Thatcher, J. B., & Clay, P. F. (2011). Trust in a specific technology. ACM Transactions on Management Information Systems, 2(2), 1–25. https://doi.org/10.1145/1985347.1985353

McKnight, D. H., Choudhury, V., & Kacmar, C. (2013). The impact of initial consumer trust on intentions to transact with a web site: A trust building model. Journal of Strategic Information Systems, 19(3), 297–323.

McKnight, D. H., Liu, P., & Pentland, B. T. (2020). Trust Change in Information Technology Products. Journal of Management Information Systems, 37(4), 1015–1046. https://doi.org/10.1080/07421222.2020.1831772

Memon, M. A., T., R., Cheah, J.-H., Ting, H., Chuah, F., & Cham, T. H. (2021). PLS-SEM STATISTICAL PROGRAMS: A REVIEW. Journal of Applied Structural Equation Modeling, 5(1), i–xiv. https://doi.org/10.47263/JASEM.5(1)06

Nascimento, B., Oliveira, T., & Tam, C. (2018). Wearable technology: What explains continuance intention in smartwatches? Journal of Retailing and Consumer Services, 43, 157–169. https://doi.org/10.1016/j.jretconser.2018.03.017

Nascimento, R., Silva, T., & Santos, J. (2018). The impact of satisfaction on continuance intention in e-learning platforms. Computers & Education, 280–289.

Pack, A., & Maloney, J. (2023). Using Generative Artificial Intelligence for Language Education Research: Insights from Using OpenAI ’s ChatGPT. TESOL Quarterly, 57(4), 1571–1582. https://doi.org/10.1002/tesq.3253

Pack, A., & Maloney, J. (2024). Using Artificial Intelligence in TESOL: Some Ethical and Pedagogical Considerations. TESOL Quarterly, 58(2), 1007–1018. https://doi.org/10.1002/tesq.3320

Prasetyo, A. (2024). Understanding Information System Continuance Intention In The Indonesian Public Sector. JPEK (Jurnal Pendidikan Ekonomi Dan Kewirausahaan), 8(3). https://doi.org/10.29408/jpek.v8i3.26325

Raghulan, A., & Jayanthi, N. (2024). Revolutionizing Marketing: How Ai is Transforming Customer Engagement (pp. 478–492). https://doi.org/10.2991/978-94-6463-433-4_36

Raman, P., & Aashish, K. (2021a). Factors influencing continuance intention to use mobile payments: A developing country perspective. International Journal of Bank Marketing, 39(1), 1–25.

Raman, P., & Aashish, K. (2021b). Factors influencing continuance intention to use mobile payments: A developing country perspective. International Journal of Bank Marketing, 39(1), 1–25.

Riyanto, S., & Hatmawan, A. A. (2020). Metode Riset Penelitian Kuantitatif: Penelitian di Bidang Manajemen, Teknik, Pendidikan dan Eksperimen. Deepublish.

Rughoobur-Seetah, S., Chittoo, H. B., & Moheeputh, R. (2021). Determinants of continuance intention in e-learning: A structural equation modelling approach. Education and Information Technologies, 26(6), 6825–6849.

San Martín, H., & Herrero, Á. (2012). Influence of the user’s psychological factors on the online purchase intention in rural tourism: Integrating innovativeness to the UTAUT framework. Tourism Management, 33(2), 341–350. https://doi.org/10.1016/j.tourman.2011.04.003

Sarstedt, M., Ringle, C. M., & Hair, J. F. (2020). Treating unobserved heterogeneity in PLS-SEM: A multi-method approach (J. Henseler, Ed.). Springer.

Shah, T. R., Kautish, P., & Mehmood, K. (2023). Influence of robots service quality on customers’ acceptance in restaurants. Asia Pacific Journal of Marketing and Logistics, 35(12), 3117–3137. https://doi.org/10.1108/APJML-09-2022-0780

Slamet, S., & Aglis, A. (2020). Metode Penelitian Kuantitatif: Teori dan Aplikasi. Deepublish.

Son, S. M., Lee, H. S., & Kim, Y. J. (2022). Winning customer satisfaction toward omnichannel logistics service providers: The role of service quality . Journal of Retailing and Consumer Services. https://www.sciencedirect.com/science/article/pii/S1029313223000246

Stanford & Berkeley. (2023). Performance decline in ChatGPT models: How is ChatGPT’s behavior changing over time? arXiv preprint. Https://Arxiv.Org/Abs/2307.09009.

Tenakwah, E. S., Boadu, G., Tenakwah, E. J., Parzakonis, M., Brady, M., Kansiime, P., Said, S., Ayilu, R., Radavoi, C., & Berman, A. (2023). Generative AI and Higher Education Assessments: A Competency-Based Analysis. https://doi.org/10.21203/rs.3.rs-2968456/v2

Tudoran, A. A., Olsen, S. O., & Dopico, D. C. (2012). Satisfaction strength and intention to purchase a new product. Journal of Consumer Behaviour, 11(5), 391–405. https://onlinelibrary.wiley.com/doi/abs/10.1002/cb.1384

Venkatesh, Morris, Davis, & Davis. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27(3), 425. https://doi.org/10.2307/30036540

Venkatesh, Thong, & Xu. (2012). Consumer Acceptance and Use of Information Technology: Extending the Unified Theory of Acceptance and Use of Technology. MIS Quarterly, 36(1), 157. https://doi.org/10.2307/41410412

Wang, M. X., Kim, K. S., & Kim, J. K. (2023). Investigating the Determinants of IoT Device Continuance Intentions: An Empirical Study of Smart Speakers Through the Lens of Expectation-Confirmation Theory. Sage Open, 13(3). https://doi.org/10.1177/21582440231197067

Wang, Y. S., & Yang, Y. F. (2005). The moderating effect of personality traits on the relationship between perceived usefulness and continuance intention. Journal of Computer Information Systems, 45(1), 1–10.

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Published

2025-10-04

How to Cite

Pangestu, A., Setiawan, H., Afifah, N., & Purmono, B. B. (2025). Modeling Chatgpt Continuance Intention: The Role of Expectancy, Satisfaction, and Trust. Jurnal Ilmiah Manajemen, Bisnis Dan Kewirausahaan, 5(3), 55–74. https://doi.org/10.55606/jurimbik.v5i3.1164

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