Examining Students’ Motivation to Continue Using AI-Chatbot for Academic Assignment
DOI:
https://doi.org/10.21609/jsi.v20i2.1417Keywords:
Chatbot, Expectation confirmation theory, Continuance usage intention, Technology acceptance model, perceived enjoyment, perceived ease of use, Perceived usefulnessAbstract
This study focuses on the motivations of engaged Indonesian university students who frequently utilize the AI-chatbot to assist them with their academic work. This research modifies Post-Acceptance of Information System Continue model with the ECT (Expectation Confirmation Theory) and TAM (Technology Acceptance Model) to explore constructs to determine what motivates students to continue using the emerging AI-chatbot, namely BING. The findings revealed a positive and substantial correlation between Perceived Information Quality (PEIQ), Confirmation (CON), Perceived Usefulness (PEU), Perceived Enjoyment (PEE), Satisfaction (SAT), and Continue Intention (COI). Confirmation (CON) has a significant impact on how usefulness and enjoyment BING is perceived, which influences Satisfaction and the decision to continue using BING. Confirmation (CON) and Perceived Ease of Use (PEEOU) have no clear correlation with Satisfaction (SAT). Some contributions are discussed in this study, both theoretically and practically.
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References
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Khurshed, H., & Aljader, S. (2023). Measuring the Effect of E-Learning Information Quality on Student’s Satisfaction Using the Technology Acceptance Model. International Journal on Informatics Visualization, 7(3), 878–885. https://doi.org/10.30630/joiv.7.3.1633
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Lewis, J. R., & Sauro, J. (2023). Effect of Perceived Ease of Use and Usefulness on UX and Behavioral Outcomes. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2023.2260164
Liu, C., & Huang, X. (2023). Does the selection of virtual reality video matter? A laboratory experimental study of the influences of arousal. Journal of Hospitality and Tourism Management, 54, 152–165. https://doi.org/10.1016/j.jhtm.2022.12.002
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Nie, L., Oldenburg, B., Cao, Y., & Ren, W. (2023). Continuous usage intention of mobile health services: model construction and validation. BMC Health Services Research, 23(1). https://doi.org/10.1186/s12913-023-09393-9
Nilapun, M., & Jensuttiwetchakul, T. (2023). The Effect of System Quality, Information Quality, and Service Quality on the Continued Usage of Mobile Payment Application in Thailand. ACM International Conference Proceeding Series, 101–108. https://doi.org/10.1145/3584871.3584886
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Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An Expectation Confirmation model of continue intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34–47. https://doi.org/10.1016/j.tele.2015.05.006
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Roslan, R., Ayub, A. F. M., Ghazali, N., Zulkifli, N. N., Latip, S. N. H. M., & Hanifah, S. S. A. (2023). Predictive Model for Factors Influencing Students’ Continue Usage Intention on a Gamified Formative Assessment Application. Journal of Technical Education and Training, 15(3), 12–24. https://doi.org/10.30880/jtet.2023.15.03.002
Sprenger, D. A., & Schwaninger, A. (2023). Video demonstrations can predict the intention to use digital learning technologies. British Journal of Educational Technology, 54(4), 857–877. https://doi.org/10.1111/bjet.13298
Staples, D. S., Wong, I., & Seddon, P. B. (2002). Applications of Chatbots in Education. Information & Management, 40(2), 115–131. https://doi.org/10.1016/S0378-7206(01)00138-0
Stefanovic, D., Spasojevic, I., Havzi, S., Lolic, T., & Ristic, S. (2020). Information systems success models in the e-learning context: A systematic literature review. Annals of DAAAM and Proceedings of the International DAAAM Symposium, 31(1), 555–564. https://doi.org/10.2507/31st.daaam.proceedings.077
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Annamalai, N., Rashid, R. A., Munir Hashmi, U., Mohamed, M., Harb Alqaryouti, M., & Eddin Sadeq, A. (2023). Using chatbots for English language learning in higher education. Computers and Education: Artificial Intelligence, 5. https://doi.org/10.1016/j.caeai.2023.100153
Beldad, A. D., & Hegner, S. M. (2018). Expanding the Technology Acceptance Model with the Inclusion of Trust, Social Influence, and Health Valuation to Determine the Predictors of German Users’ Willingness to Continue using a Fitness App: A Structural Equation Modeling Approach. International Journal of Human-Computer Interaction, 34(9), 882–893. https://doi.org/10.1080/10447318.2017.1403220
Bevan, N., Carter, J., Earthy, J., Geis, T., & Harker, S. (2016). New ISO standards for usability, usability reports and usability measures. Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 9731, 268–278. https://doi.org/10.1007/978-3-319-39510-4_25
Bhattacherjee, A. (2001). Understanding Information Systems Continue: An Expectation Confirmation Model 1 Motivation For The Study. MIS Quarterly, 25(3), 351–370. https://doi.org/10.2307/3250921
Campbell, S., Greenwood, M., Prior, S., Shearer, T., Walkem, K., Young, S., Bywaters, D., & Walker, K. (2020). Purposive sampling: complex or simple? Research case examples. Journal of Research in Nursing, 25(8), 652–661. https://doi.org/10.1177/1744987120927206
Carissa, N. E., Erlangga, M., Sonesha Evik, C., & Handayani, P. W. (2023). The Influence of Perceived Usefulness, Satisfaction, and Personalization on Subscription Video on Demand Continue Intentions. CommIT Journal, 17(2), 169–184. https://doi.org/10.21512/commit.v17i2.8446
Caroux, L. (2023). Presence in video games: A systematic review and meta-analysis of the effects of game design choices. In Applied Ergonomics (Vol. 107). Elsevier Ltd. https://doi.org/10.1016/j.apergo.2022.103936
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and user acceptance of information technology. MIS Quarterly: Management Information Systems, 13(3), 319–339. https://doi.org/10.2307/249008
Dwivedi, Y. K., Wade, M. R., & Schneberger, S. L. (2012). Information Systems Theory Explaining and Predicting Our Digital Society, Vol. 1 (R. Sharda & S. Voß, Eds.; Vol. 28). Springer. https://doi.org/10.1007/978-1-4419-6108-2
Gao, C. A., Howard, F. M., Markov, N. S., Dyer, E. C., Ramesh, S., Luo, Y., & Pearson, A. T. (2023). Comparing scientific abstracts generated by ChatGPT to real abstracts with detectors and blinded human reviewers. Npj Digital Medicine, 6(1), 75. https://doi.org/10.1038/s41746-023-00819-6
Ghayoomi, M. (2023). Applications of Chatbots in Education (M. A. Kuhail, B. A. Shawar, & R. Hammad, Eds.). IGI Global. https://doi.org/10.4018/978-1-6684-6234-8.ch004
Goli, M., Sahu, A. K., Bag, S., & Dhamija, P. (2023). Users’ Acceptance of Artificial Intelligence-Based Chatbots: An Empirical Study. International Journal of Technology and Human Interaction, 19(1). https://doi.org/10.4018/IJTHI.318481
Hair, J. F., Hul, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (3rd ed.). Sage Publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Evaluation of the Structural Model. In: Partial Least Squares Structural Equation Modeling (PLS-SEM) Using R. Classroom Companion: Business. Springer, Cham. https://doi.org/10.1007/978-3-030-80519-7_6
Hariguna, T., & Akmal, akmal@mpbi uad ac id. (2019). Assessing students’ continue intention in using multimedia online learning. Telkomnika (Telecommunication Computing Electronics and Control), 17(1), 187–193. https://doi.org/10.12928/TELKOMNIKA.v17i1.10328
Inan, D. I., Hidayanto, A. N., Juita, R., Lesmono, D. R., Honggo, F., Melissa, M., & Melianti, S. D. (2023). What Motivate Students to Continue Using Online Collaborative Tools: Post-Acceptance of Information System Approach. International Journal of Emerging Technologies in Learning, 18(9), 49–64. https://doi.org/10.3991/ijet.v18i09.36177
Jia, X., Pang, Y., Huang, B., & Hou, F. (2023). Understanding consumers’ continue intention to watch streams: A value-based continue intention model. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1073301
Jo, H., & Baek, E. M. (2023). Customization, loneliness, and optimism: drivers of intelligent personal assistant continue intention during COVID-19. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-02021-1
Jo, H., & Bang, Y. (2023). Understanding continue intention of enterprise resource planning (ERP): TOE, TAM, and IS success model. Heliyon, 9(10). https://doi.org/10.1016/j.heliyon.2023.e21019
Khan, M. I., & Saleh, M. A. (2023). Facebook Users’ Satisfaction and Intention to Continue Using It: Applying the Expectation Confirmation Model. Social Science Computer Review, 41(3), 983–1000. https://doi.org/10.1177/08944393211066584
Khurshed, H., & Aljader, S. (2023). Measuring the Effect of E-Learning Information Quality on Student’s Satisfaction Using the Technology Acceptance Model. International Journal on Informatics Visualization, 7(3), 878–885. https://doi.org/10.30630/joiv.7.3.1633
Labadze, L., Grigolia, M., & Machaidze, L. (2023). Role of AI chatbots in education: systematic literature review. In International Journal of Educational Technology in Higher Education (Vol. 20, Issue 1). Springer Science and Business Media Deutschland GmbH. https://doi.org/10.1186/s41239-023-00426-1
Lee, J. C., Tang, Y., & Jiang, S. Q. (2023). Understanding continue intention of artificial intelligence (AI)-enabled mobile banking applications: an extension of AI characteristics to an expectation Confirmation model. Humanities and Social Sciences Communications, 10(1). https://doi.org/10.1057/s41599-023-01845-1
Lewis, J. R., & Sauro, J. (2023). Effect of Perceived Ease of Use and Usefulness on UX and Behavioral Outcomes. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2023.2260164
Liu, C., & Huang, X. (2023). Does the selection of virtual reality video matter? A laboratory experimental study of the influences of arousal. Journal of Hospitality and Tourism Management, 54, 152–165. https://doi.org/10.1016/j.jhtm.2022.12.002
Liu, Y., Li, Q., Edu, T., & Negricea, I. C. (2023). Exploring the Continue Usage Intention of Travel Applications in the Case of Chinese Tourists. Journal of Hospitality and Tourism Research, 47(1), 6–32. https://doi.org/10.1177/1096348020962553
Lu, J., Liu, C., & Wei, J. (2017). How important are enjoyment and mobility for mobile applications? Journal of Computer Information Systems, 57(1), 1–12. https://doi.org/10.1080/08874417.2016.1181463
Maeda-Minami, A., Yoshino, T., Yumoto, T., Sato, K., Sagara, A., Inaba, K., Kominato, H., Kimura, T., Takishita, T., Watanabe, G., Nakamura, T., Mano, Y., Horiba, Y., Watanabe, K., & Kamei, J. (2023). Development of a novel drug information provision system for Kampo medicine using natural language processing technology. BMC Medical Informatics and Decision Making, 23(1). https://doi.org/10.1186/s12911-023-02230-3
Mao, Z., Zou, Q., Bu, T., Dong, Y., & Yan, R. (2023). Understanding the Role of Service Quality of Government APPs in Continue Intention: An Expectation–Confirmation Perspective. SAGE Open, 13(4). https://doi.org/10.1177/21582440231201218
Meena, R., & Sarabhai, S. (2023). Extrinsic and intrinsic motivators for usage continue of hedonic mobile apps. Journal of Retailing and Consumer Services, 71. https://doi.org/10.1016/j.jretconser.2022.103228
Mehdi, Y. (2023, March 8). The New Bing and Edge – Progress from Our First Month. Blogs.Bing.Com. https://blogs.bing.com/search/march_2023/The-New-Bing-and-Edge-%E2%80%93-Momentum-from-Our-First-Month/
Meng, Z., & Li, R. (2023). Understanding Chinese teachers’ informal online learning continue in a mobile learning community: an intrinsic–extrinsic motivation perspective. Journal of Computing in Higher Education. https://doi.org/10.1007/s12528-023-09352-7
Microsoft. (2023, February). The new Bing: Our approach to Responsible AI. Www.Microsoft.Com. https://support.microsoft.com/en-us/topic/the-new-bing-our-approach-to-responsible-ai-45b5eae8-7466-43e1-ae98-b48f8ff8fd44
Mishra, A., Shukla, A., Rana, N. P., Currie, W. L., & Dwivedi, Y. K. (2023). Re-examining post-acceptance model of information systems continue: A revised theoretical model using MASEM approach. International Journal of Information Management, 68. https://doi.org/10.1016/j.ijinfomgt.2022.102571
Neo, M., Lee, C. P., Tan, H. Y. J., Neo, T. K., Tan, Y. X., Mahendru, N., & Ismat, Z. (2022). Enhancing Students’ Online Learning Experiences with Artificial Intelligence (AI): The MERLIN Project. International Journal of Technology, 13(5), 1023–1034. https://doi.org/10.14716/ijtech.v13i5.5843
Nie, L., Oldenburg, B., Cao, Y., & Ren, W. (2023). Continuous usage intention of mobile health services: model construction and validation. BMC Health Services Research, 23(1). https://doi.org/10.1186/s12913-023-09393-9
Nilapun, M., & Jensuttiwetchakul, T. (2023). The Effect of System Quality, Information Quality, and Service Quality on the Continued Usage of Mobile Payment Application in Thailand. ACM International Conference Proceeding Series, 101–108. https://doi.org/10.1145/3584871.3584886
Nurdin, N., Pettalongi, S. S., Ahsan, M. N., & Febrianti, V. (2023). Factors affecting students’ continue intention to use teaching performance assessment application from technology continue theory. International Journal of Electrical and Computer Engineering, 13(5), 5342–5353. https://doi.org/10.11591/ijece.v13i5.pp5342-5353
Oghuma, A. P., Libaque-Saenz, C. F., Wong, S. F., & Chang, Y. (2016). An Expectation Confirmation model of continue intention to use mobile instant messaging. Telematics and Informatics, 33(1), 34–47. https://doi.org/10.1016/j.tele.2015.05.006
Oliver, R. L. (1980). A Cognitive Model of the Antecedents and Consequences of Satisfaction Decisions. Journal of Marketing Research, 17(4), 460–469. https://doi.org/10.2307/3150499
Ozili, P. K. (2023). The Acceptable R-square in Empirical Modelling for Social Science Research. https://ssrn.com/abstract=4128165
Rese, A., & Tränkner, P. (2023). Perceived conversational ability of task-based chatbots – Which conversational elements influence the success of text-based dialogues? International Journal of Information Management, 74. https://doi.org/10.1016/j.ijinfomgt.2023.102699
Roslan, R., Ayub, A. F. M., Ghazali, N., Zulkifli, N. N., Latip, S. N. H. M., & Hanifah, S. S. A. (2023). Predictive Model for Factors Influencing Students’ Continue Usage Intention on a Gamified Formative Assessment Application. Journal of Technical Education and Training, 15(3), 12–24. https://doi.org/10.30880/jtet.2023.15.03.002
Sprenger, D. A., & Schwaninger, A. (2023). Video demonstrations can predict the intention to use digital learning technologies. British Journal of Educational Technology, 54(4), 857–877. https://doi.org/10.1111/bjet.13298
Staples, D. S., Wong, I., & Seddon, P. B. (2002). Applications of Chatbots in Education. Information & Management, 40(2), 115–131. https://doi.org/10.1016/S0378-7206(01)00138-0
Stefanovic, D., Spasojevic, I., Havzi, S., Lolic, T., & Ristic, S. (2020). Information systems success models in the e-learning context: A systematic literature review. Annals of DAAAM and Proceedings of the International DAAAM Symposium, 31(1), 555–564. https://doi.org/10.2507/31st.daaam.proceedings.077
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2024-10-11
How to Cite
Ramadan, F., Sari, P. K., & Murti, Y. R. (2024). Examining Students’ Motivation to Continue Using AI-Chatbot for Academic Assignment. Jurnal Sistem Informasi, 20(2), 18–31. https://doi.org/10.21609/jsi.v20i2.1417
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