How to cite this paper
Masadeh, R., Majali, S., Alkhaffaf, M., Thurasamy, R., Almajali, D., Altarawneh, K., Al-Sherideh, A & Altarawni, I. (2024). Antecedents of adoption and usage of ChatGPT among Jordanian university students: Empirical study.International Journal of Data and Network Science, 8(2), 1099-1110.
Refrences
Abdul-Halim, N. A., Vafaei-Zadeh, A., Hanifah, H., Teoh, A. P., & Nawaser, K. (2022). Understanding the determinants of e-wallet continuance usage intention in Malaysia. Quality & quantity, 56(5), 3413-3439.
Abbasi, G. A., Sandran, T., Ganesan, Y., & Iranmanesh, M. (2022). Go cashless! Determinants of continuance intention to use E-wallet apps: A hybrid approach using PLS-SEM and fsQCA. Technology in Society, 68, 101937.
Adu-Gyamfi, G., Song, H., Nketiah, E., Obuobi, B., Adjei, M., & Cudjoe, D. (2022). Determinants of adoption intention of battery swap technology for electric vehicles. Energy, 251, 123862.
Ajzen, I. (2014). Attitude structure and behavior. In Attitude structure and function (pp. 241-274). Psychology Press.
Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325
Al-Khateeb, M., Al-Mousa, M., Al-Sherideh, A., Almajali, D., Asassfeha, M., & Khafajeh, H. (2023). Awareness model for minimiz-ing the effects of social engineering attacks in web applications. International Journal of Data and Network Science, 7(2), 791-800.
Ali, M., Wood‐Harper, T., & Wood, B. (2023). Understanding the technical and social paradoxes of learning management systems usage in higher education: A sociotechnical perspective. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.2945
Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: Cross-market examination. Journal of business research, 63(9-10), 957-963.
Aslam, W., Ahmed Siddiqui, D., Arif, I., & Farhat, K. (2022).Chatbots in the frontline: drivers of acceptance. Kybernetes, 52(9), 3781-3810. https://doi.org/10.1108/K-11-2021-1119
Assaraira, T. Y., Alhindawi, N., Bani-Mohammad, S., Al-Anber, Z. A., & Albashaireh, Z. A. (2022). The Jordanian universities ex-perience in integrating online learning and its quality assurance. The International Arab Journal of Information Technology, 19(3A), 544-565.
Baabdullah, A.M. (2018). Consumer adoption of mobile social network games (M-SNGs) in Saudi Arabia: the role of social influ-ence, hedonic motivation and trust. Technology in Society, 53, 91-102.
Balakrishnan, J., Dwivedi, Y.K., Hughes, L., & Boy, F. (2021). Enablers and inhibitors of AI-powered voice assistants: a dual-factor approach by integrating the status quo bias and technology acceptance model. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10203-y
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards internet banking adoption in India. Jour-nal of Indian Business Research, 7(1), 67-102.
Belanche, D., Casalo, L.V., & Flavian, C. (2019). Artificial intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management and Data Systems, 119(7), 1411-1430.
Belanche, D., Casalo, L.V., Flavian, C., & Schepers, J. (2020). Service robot implementation: a theoretical framework and research agenda. The Service Industries Journal, 40(3/4), 203-225.
Bhat, M.A., & Tariq, S. (2022). Impact of Job Burnout on Performance: A Study among Hospital Employees of J&K, India. BIMTECH Business Perspectives, 1-17.
Çelik, H. (2008). What determines Turkish customers’ acceptance of internet banking?. International Journal of Bank Marketing, 26(5), 353-370.
Celik, I. (2023). Towards Intelligent-TPACK: an empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.
Chang, Y.-W., Hsu, P.-Y., Chen, J., Shiau, W.-L., & Xu, N. (2023), “Utilitarian and/or hedonic shopping – consumer motivation to purchase in smart stores. Industrial Management and Data Systems, 123(3), 821-842.
ChatGPT (2022). ChatGPT: optimizing language models for dialogue. OpenAI, available at: https:// openai.com/blog/chatgpt/ (ac-cessed 22J Jun 2023).
Chi, O.H., Jia, S., Li, Y., & Gursoy, D. (2021). Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Computers in Human Behavior, 118, 106700.
Chong, A.Y.L., Chan, F.T., Goh, M., & Tiwari, M.K. (2013). Do interorganisational relationships and knowledge-management prac-tices enhance collaborative commerce adoption?. International Journal of Production Research, 51(7), 2006-2018.
Cotton, D.R., Cotton, P.A., & Shipway, J.R. (2023). Chatting and cheating: ensuring academic integrity in the era of ChatGPT. In-novations in Education and Teaching International, 1-12.
Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, Massachusetts Institute of Technology.
Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theoretical mod-els. Management Science, 35(8), 982-1003.
De Luna, I.R., Liebana-Cabanillas, F., Sanchez-Fernandez, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: the adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
Fujs, D., Vrhovec, S., & Vavpotič, D. (2022). Towards personalized user training for secure use of information systems. The Interna-tional Arab Journal of Information Technology, 19(3), 307-313.
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
Giovanis, A.N., Binioris, S., & Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business, 7(1), 24-53.
Gong, L., & Nass, C. (2007). When a talking-face computer agent is half-human and half-humanoid: human identity and consisten-cy preference. Human Communication Research, 33(2), 163-193.
Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: a behavioral reasoning theory perspective. International Journal of Bank Marketing, 35(4), 733-747.
Haenlein, M., Kaplan, A., Tan, C.W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Man-agement Analytics, 6(4), 341-343.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed, a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hamid, A.A., Razak, F.Z.A., Bakar, A.A. and Abdullah, W.S.W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Economics and Finance, 35, 644-649.
Han, N., Chen, J., Xiao, G., Zhang, H., Zeng, Y., & Chen, H. (2021). Fine-grained cross-modal alignment network for text-video re-trieval. Proceedings of the 29th ACM International Conference on Multimedia, 3826-3834.
Hariri, W. (2023). Unlocking the potential of ChatGPT: a comprehensive exploration of its applications, advantages. Limitations, and Future Directions in Natural Language Processing, arXiv preprint arXiv:2304.02017.
Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y.K. (2022). The effects of trust on behavioral intention and use be-havior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
Howcroft, B., Hamilton, R., & Hewer, P. (2002). Consumer attitude and the usage and adoption of home-based banking in the Unit-ed Kingdom. International Journal of Bank Marketing, 20(3), 111-121.
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Jaafar, N. A., Ismail, N. A., & Yusoff, Y. A. (2021). Usability study of enhanced salat learning approach using motion recognition system. The International Arab Journal of Information Technology, 18(3A), 414-421.
Kabra, G., Ghosh, V., & Joshi, Y. (2023). Factors influencing adoption of cloud computing services in HEIs: a UTAUT approach based on students’ perception. International Journal of Business Information Systems, 42(1), 103-122.
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Keiper, M.C., Fried, G., Lupinek, J., & Nordstrom, H. (2023). Artificial intelligence in sport management education: playing the AI game with ChatGPT. Journal of Hospitality, Leisure, Sport and Tourism Education, 33, 100456.
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Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
Al-Azawei, A., & Alowayr, A. (2020). Predicting the intention to use and hedonic motivation for mobile learning: A comparative study in two Middle Eastern countries. Technology in Society, 62, 101325. https://doi.org/10.1016/j.techsoc.2020.101325
Al-Khateeb, M., Al-Mousa, M., Al-Sherideh, A., Almajali, D., Asassfeha, M., & Khafajeh, H. (2023). Awareness model for minimiz-ing the effects of social engineering attacks in web applications. International Journal of Data and Network Science, 7(2), 791-800.
Ali, M., Wood‐Harper, T., & Wood, B. (2023). Understanding the technical and social paradoxes of learning management systems usage in higher education: A sociotechnical perspective. Systems Research and Behavioral Science. https://doi.org/10.1002/sres.2945
Alsajjan, B., & Dennis, C. (2010). Internet banking acceptance model: Cross-market examination. Journal of business research, 63(9-10), 957-963.
Aslam, W., Ahmed Siddiqui, D., Arif, I., & Farhat, K. (2022).Chatbots in the frontline: drivers of acceptance. Kybernetes, 52(9), 3781-3810. https://doi.org/10.1108/K-11-2021-1119
Assaraira, T. Y., Alhindawi, N., Bani-Mohammad, S., Al-Anber, Z. A., & Albashaireh, Z. A. (2022). The Jordanian universities ex-perience in integrating online learning and its quality assurance. The International Arab Journal of Information Technology, 19(3A), 544-565.
Baabdullah, A.M. (2018). Consumer adoption of mobile social network games (M-SNGs) in Saudi Arabia: the role of social influ-ence, hedonic motivation and trust. Technology in Society, 53, 91-102.
Balakrishnan, J., Dwivedi, Y.K., Hughes, L., & Boy, F. (2021). Enablers and inhibitors of AI-powered voice assistants: a dual-factor approach by integrating the status quo bias and technology acceptance model. Information Systems Frontiers. https://doi.org/10.1007/s10796-021-10203-y
Bashir, I., & Madhavaiah, C. (2015). Consumer attitude and behavioural intention towards internet banking adoption in India. Jour-nal of Indian Business Research, 7(1), 67-102.
Belanche, D., Casalo, L.V., & Flavian, C. (2019). Artificial intelligence in FinTech: understanding robo-advisors adoption among customers. Industrial Management and Data Systems, 119(7), 1411-1430.
Belanche, D., Casalo, L.V., Flavian, C., & Schepers, J. (2020). Service robot implementation: a theoretical framework and research agenda. The Service Industries Journal, 40(3/4), 203-225.
Bhat, M.A., & Tariq, S. (2022). Impact of Job Burnout on Performance: A Study among Hospital Employees of J&K, India. BIMTECH Business Perspectives, 1-17.
Çelik, H. (2008). What determines Turkish customers’ acceptance of internet banking?. International Journal of Bank Marketing, 26(5), 353-370.
Celik, I. (2023). Towards Intelligent-TPACK: an empirical study on teachers’ professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, 107468.
Chang, Y.-W., Hsu, P.-Y., Chen, J., Shiau, W.-L., & Xu, N. (2023), “Utilitarian and/or hedonic shopping – consumer motivation to purchase in smart stores. Industrial Management and Data Systems, 123(3), 821-842.
ChatGPT (2022). ChatGPT: optimizing language models for dialogue. OpenAI, available at: https:// openai.com/blog/chatgpt/ (ac-cessed 22J Jun 2023).
Chi, O.H., Jia, S., Li, Y., & Gursoy, D. (2021). Developing a formative scale to measure consumers’ trust toward interaction with artificially intelligent (AI) social robots in service delivery. Computers in Human Behavior, 118, 106700.
Chong, A.Y.L., Chan, F.T., Goh, M., & Tiwari, M.K. (2013). Do interorganisational relationships and knowledge-management prac-tices enhance collaborative commerce adoption?. International Journal of Production Research, 51(7), 2006-2018.
Cotton, D.R., Cotton, P.A., & Shipway, J.R. (2023). Chatting and cheating: ensuring academic integrity in the era of ChatGPT. In-novations in Education and Teaching International, 1-12.
Davis, F.D. (1985). A technology acceptance model for empirically testing new end-user information systems: theory and results. Doctoral dissertation, Massachusetts Institute of Technology.
Davis, F.D., Bagozzi, R.P., & Warshaw, P.R. (1989). User acceptance of computer technology: a comparison of two theoretical mod-els. Management Science, 35(8), 982-1003.
De Luna, I.R., Liebana-Cabanillas, F., Sanchez-Fernandez, J., & Muñoz-Leiva, F. (2019). Mobile payment is not all the same: the adoption of mobile payment systems depending on the technology applied. Technological Forecasting and Social Change, 146, 931-944.
Fujs, D., Vrhovec, S., & Vavpotič, D. (2022). Towards personalized user training for secure use of information systems. The Interna-tional Arab Journal of Information Technology, 19(3), 307-313.
Gefen, D. (2000). E-commerce: the role of familiarity and trust. Omega, 28(6), 725-737.
Giovanis, A.N., Binioris, S., & Polychronopoulos, G. (2012). An extension of TAM model with IDT and security/privacy risk in the adoption of internet banking services in Greece. EuroMed Journal of Business, 7(1), 24-53.
Gong, L., & Nass, C. (2007). When a talking-face computer agent is half-human and half-humanoid: human identity and consisten-cy preference. Human Communication Research, 33(2), 163-193.
Gupta, A., & Arora, N. (2017). Consumer adoption of m-banking: a behavioral reasoning theory perspective. International Journal of Bank Marketing, 35(4), 733-747.
Haenlein, M., Kaplan, A., Tan, C.W., & Zhang, P. (2019). Artificial intelligence (AI) and management analytics. Journal of Man-agement Analytics, 6(4), 341-343.
Hair, J. F., Ringle, C. M., & Sarstedt, M. (2011). PLS-SEM: Indeed, a silver bullet. Journal of Marketing theory and Practice, 19(2), 139-152.
Hamid, A.A., Razak, F.Z.A., Bakar, A.A. and Abdullah, W.S.W. (2016). The effects of perceived usefulness and perceived ease of use on continuance intention to use e-government. Procedia Economics and Finance, 35, 644-649.
Han, N., Chen, J., Xiao, G., Zhang, H., Zeng, Y., & Chen, H. (2021). Fine-grained cross-modal alignment network for text-video re-trieval. Proceedings of the 29th ACM International Conference on Multimedia, 3826-3834.
Hariri, W. (2023). Unlocking the potential of ChatGPT: a comprehensive exploration of its applications, advantages. Limitations, and Future Directions in Natural Language Processing, arXiv preprint arXiv:2304.02017.
Hooda, A., Gupta, P., Jeyaraj, A., Giannakis, M., & Dwivedi, Y.K. (2022). The effects of trust on behavioral intention and use be-havior within e-government contexts. International Journal of Information Management, 67, 102553. https://doi.org/10.1016/j.ijinfomgt.2022.102553
Howcroft, B., Hamilton, R., & Hewer, P. (2002). Consumer attitude and the usage and adoption of home-based banking in the Unit-ed Kingdom. International Journal of Bank Marketing, 20(3), 111-121.
Hu, L. T., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: A Multidisciplinary Journal, 6(1), 1-55.
Jaafar, N. A., Ismail, N. A., & Yusoff, Y. A. (2021). Usability study of enhanced salat learning approach using motion recognition system. The International Arab Journal of Information Technology, 18(3A), 414-421.
Kabra, G., Ghosh, V., & Joshi, Y. (2023). Factors influencing adoption of cloud computing services in HEIs: a UTAUT approach based on students’ perception. International Journal of Business Information Systems, 42(1), 103-122.
Kasım, Ö. (2022). An efficient ensemble architecture for privacy and security of electronic medical records. The International Arab Journal of Information Technology, 19(2), 272-280.
Keiper, M.C., Fried, G., Lupinek, J., & Nordstrom, H. (2023). Artificial intelligence in sport management education: playing the AI game with ChatGPT. Journal of Hospitality, Leisure, Sport and Tourism Education, 33, 100456.
Kim, J., Merrill, K., Jr, Xu, K., & Kelly, S. (2022). Perceived credibility of an AI instructor in online education: the role of social presence and voice features. Computers in Human Behavior, 136, 107383.
Kline, T. J. (2005). Psychological testing: A practical approach to design and evaluation. Sage publications.
Kwak, Y., Seo, Y.H., & Ahn, J.W. (2022). Nursing students’ intent to use AI-based healthcare technology: path analysis using the unified theory of acceptance and use of technology. Nurse Education Today, 119, 105541.
Larue, G.S., & Watling, C.N. (2021). Acceptance of visual and audio interventions for distracted pedestrians. Transportation Re-search Part F: Traffic Psychology and Behaviour, 76, 369-383.
Lee, H. (2023). The rise of ChatGPT: exploring its potential in medical education. Anatomical Sciences Education, pp. 1-6. https://doi.org/10.1002/ase.2270
Lim, J.S., & Zhang, J. (2022). Adoption of AI-driven personalization in digital news platforms: an integrative model of technology acceptance and perceived contingency. Technology in Society, 69, 101965. https://doi.org/10.1016/j.techsoc.2022.101965
Lin, C.H., Shih, H.Y., & Sher, P.J. (2007). Integrating technology readiness into technology acceptance: the TRAM model. Psychol-ogy and Marketing, 24(7), 641-657. https://doi.org/10.1002/mar.20177
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