How to cite this paper
Alshurideh, M., Jdaitawi, A., Sukkari, L., Al-Gasaymeh, A., Alzoubi, H., Damra, Y., Yasin, S., Kurdi, B & Alshurideh, H. (2024). Factors affecting ChatGPT use in education employing TAM: A Jordanian universities’ perspective.International Journal of Data and Network Science, 8(3), 1599-1606.
Refrences
Agarwal, R., & Karahanna, E. (2000). Time flies when you're having fun: Cognitive absorption and beliefs about infor-mation technology usage. MIS Quarterly, 24(4), 665-694.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261-277.
Barclay, D.W., Higgins, C.A., Thompson, R., (1995). The partial least squares approach to causal modeling: personal computer adoption and use as illustration. Technology Studies 2(2), 285-309.
Bax, S., & McGill, T. (2009). From beliefs to success: Utilizing an expanded tam to predict web page development suc-cess. In Cross-Disciplinary Advances in Human Computer Interaction: User Modeling, Social Computing, and Adap-tive Interfaces (pp. 37-58). IGI Global.
Benoit, O., Marc, K., Fernand, F., Dieter, F., & Martine, H. (2009). User-centered activity management system for elderly people Empowering older people with interactive technologies to manage their activities at the retirement home. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare (pp. 1-4). IEEE.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Chen, H. L., Vicki Widarso, G., & Sutrisno, H. (2020). A chatbot for learning Chinese: Learning achievement and tech-nology acceptance. Journal of Educational Computing Research, 58(6), 1161-1189.
Ching, L. W., & Kwok, D. (2022). Factors Influencing Polytechnic Educators’ Behavioural Intentions to use Technology Enhanced Learning Tools: The Structural Equation Modelling Approach. ASCILITE Publications, (Proceedings of ASCILITE 2022 in Sydney), e22080-e22080.
Crittenden, V., & Peterson, R. A. (2019). Digital disruption: The transdisciplinary future of marketing education. Journal of Marketing Education, 41(1), 3-4.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 1(1), 1–30.
Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the academy of marketing science, 45, 616-632.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Prac-tice, 19(2), 139-152.
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.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eu-ropean business review, 31(1), 2-24.
Han, S. L., & An, M. (2019). Analysis of user telepresence and behavioral intention in virtual reality shopping environ-ment. Journal of channel and retailing, 24(1), 51-71.
Hasyim, F. (2019). Peer To Peer Lending As Alternative Online Microfinance Platform: Threat and Challenge To Islamic Microfinance. Indonesian Journal of Islamic Literature and Muslim Society, 4(2). 157-182.
Haytko, D. L., & Baker, J. (2004). It’s all at the mall: exploring adolescent girls’ experiences. Journal of retailing, 80(1), 67-83.
Hu, L., & t., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional crite-ria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and challenges of chatbots in education. Interactive Learn-ing Environments, 31(7), 4099-4112.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post
Kirana, S. A. (2017). Students’ perception of quipper as an online practice tool for the English computer-based national examination. Indonesian Journal of English Teaching, 6(2), 248-264.
Kirmani, A. R. (2022). Artificial intelligence-enabled science poetry. ACS Energy Letters, 8, 574-576.
Li, J. (2009). Training Strategy Research of MIS Commerical Application in Perspective of Career Continuing Progres-sion. In 2009 International Conference on Management and Service Science (pp. 1-4). IEEE.
Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & education, 49(4), 1066-1080.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of applied psychology, 86(1), 114-121.
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021). GPT understands, too. arXiv, 10385, 6(2).
Lohmoller, J.-B. (1989). Predictive vs. structural modeling: PLS vs. ML. In J.- .-B. Lohmoller ¨ (Ed.), Latent variable path modeling with partial least squares (pp. 199–226). Springer.
Mathieson, K. (1991) Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2, 173-191.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of per-ceived user resources. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 32(3), 86-112.
Mındajao, B. Y. (2023). Effectiveness of Chatbot as an innovative modality in grade reporting in the new normal. Europe-an Journal of Education Studies, 10(2), 244-252.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & manage-ment, 38(4), 217-230.
Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning, 15(1), 188-199.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879-903.
Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J. P., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study.
Rukhiran, M., Phaokla, N., & Netinant, P. (2022). Adoption of Environmental Information Chatbot Services Based on the Internet of Educational Things in Smart Schools: Structural Equation Modeling Approach. Sustainability, 14(23), 1-32.
Teo, T. (2009). Is there an attitude problem? Reconsidering the role of attitude in the TAM. British journal of educational technology, 40(6), 1139-1141.
Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tour-ism: do researchers follow practical guidelines?. International Journal of Contemporary Hospitality Management, 30(11), 3462-3512.
Vannavanit, Y. (2019, June). Educational Technology in IT and Marketing Education-The Experience of Early Thai Edu-cators. In InSITE 2019: Informing Science+ IT Education Conferences: Jerusalem (pp. 459-460).
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2012). Investigating the determinants and age and gender differences in the ac-ceptance of mobile learning. British Journal of Educational Technology, 43(4), 592-605.
Wu, J.H. and Wang, S.C. (2005) What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology Acceptance Model. Information Management, 42, 719-729.
Yamada, M., Goda, Y., Matsukawa, H., Hata, K., & Yasunami, S. (2016). A computer-supported collaborative learning de-sign for quality interaction. IEEE Annals of the History of Computing, 23(1), 48–59.
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179–211.
Ajzen, I., & Fishbein, M. (1975). A Bayesian analysis of attribution processes. Psychological bulletin, 82(2), 261-277.
Barclay, D.W., Higgins, C.A., Thompson, R., (1995). The partial least squares approach to causal modeling: personal computer adoption and use as illustration. Technology Studies 2(2), 285-309.
Bax, S., & McGill, T. (2009). From beliefs to success: Utilizing an expanded tam to predict web page development suc-cess. In Cross-Disciplinary Advances in Human Computer Interaction: User Modeling, Social Computing, and Adap-tive Interfaces (pp. 37-58). IGI Global.
Benoit, O., Marc, K., Fernand, F., Dieter, F., & Martine, H. (2009). User-centered activity management system for elderly people Empowering older people with interactive technologies to manage their activities at the retirement home. In 2009 3rd International Conference on Pervasive Computing Technologies for Healthcare (pp. 1-4). IEEE.
Bhattacherjee, A. (2001). Understanding information systems continuance: An expectation-confirmation model. MIS quarterly, 351-370.
Chen, H. L., Vicki Widarso, G., & Sutrisno, H. (2020). A chatbot for learning Chinese: Learning achievement and tech-nology acceptance. Journal of Educational Computing Research, 58(6), 1161-1189.
Ching, L. W., & Kwok, D. (2022). Factors Influencing Polytechnic Educators’ Behavioural Intentions to use Technology Enhanced Learning Tools: The Structural Equation Modelling Approach. ASCILITE Publications, (Proceedings of ASCILITE 2022 in Sydney), e22080-e22080.
Crittenden, V., & Peterson, R. A. (2019). Digital disruption: The transdisciplinary future of marketing education. Journal of Marketing Education, 41(1), 3-4.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 13(3), 319-340.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management science, 35(8), 982-1003.
Fornell, C., & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of marketing research, 18(1), 39-50.
Gefen, D., & Straub, D. W. (2000). The relative importance of perceived ease of use in IS adoption: A study of e-commerce adoption. Journal of the association for Information Systems, 1(1), 1–30.
Hair Jr, J., Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., & Sarstedt, M. (2021). A primer on partial least squares structural equation modeling (PLS-SEM). Sage publications.
Hair, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., & Thiele, K. O. (2017). Mirror, mirror on the wall: a comparative evaluation of composite-based structural equation modeling methods. Journal of the academy of marketing science, 45, 616-632.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed a silver bullet. Journal of Marketing theory and Prac-tice, 19(2), 139-152.
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.
Hair, J. F., Risher, J. J., Sarstedt, M., & Ringle, C. M. (2019). When to use and how to report the results of PLS-SEM. Eu-ropean business review, 31(1), 2-24.
Han, S. L., & An, M. (2019). Analysis of user telepresence and behavioral intention in virtual reality shopping environ-ment. Journal of channel and retailing, 24(1), 51-71.
Hasyim, F. (2019). Peer To Peer Lending As Alternative Online Microfinance Platform: Threat and Challenge To Islamic Microfinance. Indonesian Journal of Islamic Literature and Muslim Society, 4(2). 157-182.
Haytko, D. L., & Baker, J. (2004). It’s all at the mall: exploring adolescent girls’ experiences. Journal of retailing, 80(1), 67-83.
Hu, L., & t., and Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: Conventional crite-ria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1–55.
Hwang, G. J., & Chang, C. Y. (2023). A review of opportunities and challenges of chatbots in education. Interactive Learn-ing Environments, 31(7), 4099-4112.
Karahanna, E., Straub, D. W., & Chervany, N. L. (1999). Information technology adoption across time: A cross-sectional comparison of pre-adoption and post
Kirana, S. A. (2017). Students’ perception of quipper as an online practice tool for the English computer-based national examination. Indonesian Journal of English Teaching, 6(2), 248-264.
Kirmani, A. R. (2022). Artificial intelligence-enabled science poetry. ACS Energy Letters, 8, 574-576.
Li, J. (2009). Training Strategy Research of MIS Commerical Application in Perspective of Career Continuing Progres-sion. In 2009 International Conference on Management and Service Science (pp. 1-4). IEEE.
Liaw, S. S., Huang, H. M., & Chen, G. D. (2007). Surveying instructor and learner attitudes toward e-learning. Computers & education, 49(4), 1066-1080.
Lindell, M. K., & Whitney, D. J. (2001). Accounting for common method variance in cross-sectional research designs. Journal of applied psychology, 86(1), 114-121.
Liu, X., Zheng, Y., Du, Z., Ding, M., Qian, Y., Yang, Z., & Tang, J. (2021). GPT understands, too. arXiv, 10385, 6(2).
Lohmoller, J.-B. (1989). Predictive vs. structural modeling: PLS vs. ML. In J.- .-B. Lohmoller ¨ (Ed.), Latent variable path modeling with partial least squares (pp. 199–226). Springer.
Mathieson, K. (1991) Predicting User Intentions: Comparing the Technology Acceptance Model with the Theory of Planned Behavior. Information Systems Research, 2, 173-191.
Mathieson, K., Peacock, E., & Chin, W. W. (2001). Extending the technology acceptance model: the influence of per-ceived user resources. ACM SIGMIS Database: the DATABASE for Advances in Information Systems, 32(3), 86-112.
Mındajao, B. Y. (2023). Effectiveness of Chatbot as an innovative modality in grade reporting in the new normal. Europe-an Journal of Education Studies, 10(2), 244-252.
Moon, J. W., & Kim, Y. G. (2001). Extending the TAM for a World-Wide-Web context. Information & manage-ment, 38(4), 217-230.
Muniasamy, A., & Alasiry, A. (2020). Deep learning: The impact on future eLearning. International Journal of Emerging Technologies in Learning, 15(1), 188-199.
Podsakoff, P. M., MacKenzie, S. B., Lee, J. Y., & Podsakoff, N. P. (2003). Common method biases in behavioral research: a critical review of the literature and recommended remedies. Journal of applied psychology, 88(5), 879-903.
Raman, R., Mandal, S., Das, P., Kaur, T., Sanjanasri, J. P., & Nedungadi, P. (2023). University students as early adopters of ChatGPT: Innovation Diffusion Study.
Rukhiran, M., Phaokla, N., & Netinant, P. (2022). Adoption of Environmental Information Chatbot Services Based on the Internet of Educational Things in Smart Schools: Structural Equation Modeling Approach. Sustainability, 14(23), 1-32.
Teo, T. (2009). Is there an attitude problem? Reconsidering the role of attitude in the TAM. British journal of educational technology, 40(6), 1139-1141.
Usakli, A., & Kucukergin, K. G. (2018). Using partial least squares structural equation modeling in hospitality and tour-ism: do researchers follow practical guidelines?. International Journal of Contemporary Hospitality Management, 30(11), 3462-3512.
Vannavanit, Y. (2019, June). Educational Technology in IT and Marketing Education-The Experience of Early Thai Edu-cators. In InSITE 2019: Informing Science+ IT Education Conferences: Jerusalem (pp. 459-460).
Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sci-ences, 39(2), 273-315.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., & Davis, F. D. (2000). A theoretical extension of the technology acceptance model: Four longitudinal field studies. Management science, 46(2), 186-204.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 27(3), 425-478.
Wang, Y.-S., Wu, M.-C., & Wang, H.-Y. (2012). Investigating the determinants and age and gender differences in the ac-ceptance of mobile learning. British Journal of Educational Technology, 43(4), 592-605.
Wu, J.H. and Wang, S.C. (2005) What Drives Mobile Commerce? An Empirical Evaluation of the Revised Technology Acceptance Model. Information Management, 42, 719-729.
Yamada, M., Goda, Y., Matsukawa, H., Hata, K., & Yasunami, S. (2016). A computer-supported collaborative learning de-sign for quality interaction. IEEE Annals of the History of Computing, 23(1), 48–59.