How to cite this paper
Alshboul, K. (2024). Artificial intelligence-based chatbots adoption among higher education institutions by integrating with UTAUT2.International Journal of Data and Network Science, 8(4), 2141-2150.
Refrences
Adam, M., Wessel, M., & Benlian, A. (2021). AI-based chatbots in customer service and their effects on user compliance. Electronic Markets, 31(2), 427-445.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of in-formation technology. Information systems research, 9(2), 204-215.
Alkawsi, G., Ali, N., & Baashar, Y. (2021). The moderating role of personal innovativeness and users experience in ac-cepting the smart meter technology. Applied Sciences, 11(8), 3297. https://doi.org/10.3390/app11083297.
Al-Okaily, M., Alqudah, H., Matar, A., Lutfi, A., & Taamneh, E. (2020). Dataset on the acceptance of e-learning system among universities students’ under the COVID-19 pandemic conditions. Data Brief, 32(5),106176. https://doi.org/10.1016/J. DIB.2020.106176.
Al-Sharafi, M. A., Arshah, R. A., Abo-Shanab, E. A., & Elayah, N. (2016). The effect of security and privacy perceptions on customers’ trust to accept internet banking services: An extension of TAM. Journal of Engineering and Applied sciences, 11(3), 545-552.
Alzoubi, A., & Azloubi, S. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students’ Perspective in Jordan. International Journal of Advanced Science and Technology, 29(4), 1361-1370.
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28-47.
Cheng, Y. (2014). Exploring the intention to use mobile learning: The moderating role of personal innovativeness. Journal of Systems and Information Technology, 16(1), 40–61. https://doi.org/10.1108/ JSIT-05-2013-0012.
Chu, T., Chao, C., Liu, H., & Chen, D. (2022). Developing an Extended Theory of UTAUT 2 Model to Explore Factors In-fluencing Taiwanese Consumer Adoption of Intelligent Elevators. SAGE Open, 12(4), 215824402211422. https://doi.org/10.1177/ 21582440221142209.
Conrad, K., Upadhyaya, S., & Joa, C. (2015). Bridging the divide: using UTAUT to predict multigenerational. Computers in Human Behaviour, 50, 186-196, doi: 10.1016/j.chb.2015.03.032.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of ac-ceptance and use of technology (UTAUT): Towards a revised theoretical model. Information systems frontiers, 21, 719-734.
Gatzioufa, P., & Saprikis, V. (2022). A literature review on users' behavioral intention toward chatbots' adoption. Applied Computing and Informatics, (ahead-of-print).
Goodboy, A., & Kline, R. (2017). Statistical and practical concerns with published communication research featuring structural equation modeling. Communication Research Reports, 34(1), 68–77. https://doi.org/10.1080/08824096.2016.1214121.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature.
Jianlin, W., & Qi, D. (2010). Moderating effect of personal innovativeness in the model for e-store loyalty. 2010 Interna-tional Conference on E-Business and E-Government, 2065–2068.
Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. (2017). Understanding behavioural intention to use information technolo-gy: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010.
Khazaei, H., & Tareq, M. (2021). Moderating effects of personal innovativeness and driving experience on factors influ-encing adoption of BEVs in Malaysia: An integrated SEM–BSEM approach. Heliyon, 7(9), e08072. https://doi.org/10.1016/j.heliyon.2021.e0.
Khoshkam, M., & Mirzaei, M. (2023). Determinants of intention to use e-Wallet: Personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 39(12), 2361–2373. https://doi.org/10.1080/10447318.2022.2076309.
Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21-38.
Laumer, S., Maier, C., & Gubler, F. (2019). Chatbot Acceptance in Healthcare: Explaining User Adoption of Conversa-tional Agents for Disease Diagnosis. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm-Uppsala, Sweden, 2 Septe.
Lee, C. T., Pan, L. Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329-1356.
Lee, S. Y., & Lee, K. (2018). Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154-163. https://doi.org/10.1016/j. techfore.2018.01.002.
Lin, C., Huang, A., & Yang, S. (2023). A Review of AIDriven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022). Sustainability, 15(5), 4012. https://doi.org/ 10.3390/su15054012.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ ucz013.
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel prod-ucts: application of the technology acceptance model. Fashion and Textiles, 4, 1-20.
Mohammad, N., & Muhammad, T. (2023). The effects of the internal and the external factors affecting artificial intelli-gence (AI) adoption in e-innovation technology projects in the UAE? Applying both innovation and technology ac-ceptance theories. International Journal of Data and Network Science, 7, 1321–1332.
Mohammed, A., Yueliang, D., Hind, A., & Tommy, W. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access, 11(10), 99752-99764.
Oliver, A., & Christina, A. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 12(10).
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226.
Roy, R., Babakerkhell, D., Mukherjee, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial in-telligence-based robots in the university to educate the students, IEEE Access, 10, 125666–125678, doi: 10.1109.
Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Cheikh, F. A., Hijji, M., ... & Rodrigues, J. J. (2023). A compre-hensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria En-gineering Journal, 68, 817-840.
Samsudeen, S., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educa-tional institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219–238. https://doi.org/10.1108/ITSE-11-2018-009.
Senali, M., Iranmanesh, M., & Ismail, F. (2023). Determinants of intention to use e-Wallet: Personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 39(12), 2361–2373. https://doi.org/10.1080/10447318.2022.2076309.
Sidorova, A. (2018). Understanding User Interactions with a Chatbot: A Self-determination Theory Approach. In Proceed-ings of the Twenty-Fourth Americas Conference on Information Systems, New Orleans, LA, USA, 16–18; pp. 1–5.
Sitar, D., & Mican, D. (2021). Mobile learning acceptance and use in higher education during social distancing circum-stances: An expansion and customization of UTAUT2. Online Information Review, 45(5), 1000–1019. https://doi.org/10.1108/OIR-01-202.
Tewari, A., Singh, R., Mathur, S., & Pande, S. (2023). A modified UTAUT framework to predict students’ intention to adopt online learning: Moderating role of openness to change. The International Journal of Information and Learning Technology, 40(2), 130–147.https://doi.org/10.1108/IJILT-04-2022-0093.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the uni-fied theory of acceptance and use of technology. MIS quarterly, 157-178.
Wilmer, H., Sherman, L., & Chein, J. (2017). Smartphones and cognition: A review of research exploring the links be-tween mobile technology habits and cognitive functioning. Frontiers in Psychology, 8, 605. https://doi.org/10.3389/fpsyg.2017.00605.
Yang, W., Luo, H., & Su, J. (2022). Towards inclusiveness and sustainability of robot programming in early childhood: Child engagement, learning outcomes and teacher perception. British Journal of Educational Technology, 53(6), 1486-1510.
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning Value. Education and Information Technologies, 27(9), 12065–12082. https://doi.org/10.1007/s10639-022-11.
Zhao, Y., Wang, N., Li, Y., Zhou, R., & Li, S. (2021). Do cultural differences affect users’e-learning adoption? A meta-analysis. British Journal of Educational Technology, 52(1), 20–41. https://doi.org/10.1111/bjet.13002.
Zwain, A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065.
Agarwal, R., & Prasad, J. (1998). A conceptual and operational definition of personal innovativeness in the domain of in-formation technology. Information systems research, 9(2), 204-215.
Alkawsi, G., Ali, N., & Baashar, Y. (2021). The moderating role of personal innovativeness and users experience in ac-cepting the smart meter technology. Applied Sciences, 11(8), 3297. https://doi.org/10.3390/app11083297.
Al-Okaily, M., Alqudah, H., Matar, A., Lutfi, A., & Taamneh, E. (2020). Dataset on the acceptance of e-learning system among universities students’ under the COVID-19 pandemic conditions. Data Brief, 32(5),106176. https://doi.org/10.1016/J. DIB.2020.106176.
Al-Sharafi, M. A., Arshah, R. A., Abo-Shanab, E. A., & Elayah, N. (2016). The effect of security and privacy perceptions on customers’ trust to accept internet banking services: An extension of TAM. Journal of Engineering and Applied sciences, 11(3), 545-552.
Alzoubi, A., & Azloubi, S. (2020). Determinants of E-Learning Based on Cloud Computing adoption: Evidence from a Students’ Perspective in Jordan. International Journal of Advanced Science and Technology, 29(4), 1361-1370.
Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28-47.
Cheng, Y. (2014). Exploring the intention to use mobile learning: The moderating role of personal innovativeness. Journal of Systems and Information Technology, 16(1), 40–61. https://doi.org/10.1108/ JSIT-05-2013-0012.
Chu, T., Chao, C., Liu, H., & Chen, D. (2022). Developing an Extended Theory of UTAUT 2 Model to Explore Factors In-fluencing Taiwanese Consumer Adoption of Intelligent Elevators. SAGE Open, 12(4), 215824402211422. https://doi.org/10.1177/ 21582440221142209.
Conrad, K., Upadhyaya, S., & Joa, C. (2015). Bridging the divide: using UTAUT to predict multigenerational. Computers in Human Behaviour, 50, 186-196, doi: 10.1016/j.chb.2015.03.032.
Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of ac-ceptance and use of technology (UTAUT): Towards a revised theoretical model. Information systems frontiers, 21, 719-734.
Gatzioufa, P., & Saprikis, V. (2022). A literature review on users' behavioral intention toward chatbots' adoption. Applied Computing and Informatics, (ahead-of-print).
Goodboy, A., & Kline, R. (2017). Statistical and practical concerns with published communication research featuring structural equation modeling. Communication Research Reports, 34(1), 68–77. https://doi.org/10.1080/08824096.2016.1214121.
Hair Jr, J. F., Hult, G. T. M., Ringle, C. M., Sarstedt, M., Danks, N. P., & Ray, S. (2021). Partial least squares structural equation modeling (PLS-SEM) using R: A workbook (p. 197). Springer Nature.
Jianlin, W., & Qi, D. (2010). Moderating effect of personal innovativeness in the model for e-store loyalty. 2010 Interna-tional Conference on E-Business and E-Government, 2065–2068.
Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. (2017). Understanding behavioural intention to use information technolo-gy: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010.
Khazaei, H., & Tareq, M. (2021). Moderating effects of personal innovativeness and driving experience on factors influ-encing adoption of BEVs in Malaysia: An integrated SEM–BSEM approach. Heliyon, 7(9), e08072. https://doi.org/10.1016/j.heliyon.2021.e0.
Khoshkam, M., & Mirzaei, M. (2023). Determinants of intention to use e-Wallet: Personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 39(12), 2361–2373. https://doi.org/10.1080/10447318.2022.2076309.
Lai, P. C. (2017). The literature review of technology adoption models and theories for the novelty technology. JISTEM-Journal of Information Systems and Technology Management, 14, 21-38.
Laumer, S., Maier, C., & Gubler, F. (2019). Chatbot Acceptance in Healthcare: Explaining User Adoption of Conversa-tional Agents for Disease Diagnosis. In Proceedings of the 27th European Conference on Information Systems (ECIS), Stockholm-Uppsala, Sweden, 2 Septe.
Lee, C. T., Pan, L. Y., & Hsieh, S. H. (2022). Artificial intelligent chatbots as brand promoters: a two-stage structural equation modeling-artificial neural network approach. Internet Research, 32(4), 1329-1356.
Lee, S. Y., & Lee, K. (2018). Factors that influence an individual's intention to adopt a wearable healthcare device: The case of a wearable fitness tracker. Technological Forecasting and Social Change, 129, 154-163. https://doi.org/10.1016/j. techfore.2018.01.002.
Lin, C., Huang, A., & Yang, S. (2023). A Review of AIDriven Conversational Chatbots Implementation Methodologies and Challenges (1999–2022). Sustainability, 15(5), 4012. https://doi.org/ 10.3390/su15054012.
Longoni, C., Bonezzi, A., & Morewedge, C. K. (2019). Resistance to medical artificial intelligence. Journal of Consumer Research, 46(4), 629-650. https://doi.org/10.1093/jcr/ ucz013.
Ma, Y. J., Gam, H. J., & Banning, J. (2017). Perceived ease of use and usefulness of sustainability labels on apparel prod-ucts: application of the technology acceptance model. Fashion and Textiles, 4, 1-20.
Mohammad, N., & Muhammad, T. (2023). The effects of the internal and the external factors affecting artificial intelli-gence (AI) adoption in e-innovation technology projects in the UAE? Applying both innovation and technology ac-ceptance theories. International Journal of Data and Network Science, 7, 1321–1332.
Mohammed, A., Yueliang, D., Hind, A., & Tommy, W. (2023). Understanding the factors influencing higher education students’ intention to adopt artificial intelligence-based robots. IEEE Access, 11(10), 99752-99764.
Oliver, A., & Christina, A. (2021). A new acceptance model for artificial intelligence with extensions to UTAUT2: An empirical study in three segments of application. Technology in Society, 12(10).
Pillai, R., & Sivathanu, B. (2020). Adoption of AI-based chatbots for hospitality and tourism. International Journal of Contemporary Hospitality Management, 32(10), 3199-3226.
Roy, R., Babakerkhell, D., Mukherjee, D., & Funilkul, S. (2022). Evaluating the intention for the adoption of artificial in-telligence-based robots in the university to educate the students, IEEE Access, 10, 125666–125678, doi: 10.1109.
Sajjad, M., Ullah, F. U. M., Ullah, M., Christodoulou, G., Cheikh, F. A., Hijji, M., ... & Rodrigues, J. J. (2023). A compre-hensive survey on deep facial expression recognition: challenges, applications, and future guidelines. Alexandria En-gineering Journal, 68, 817-840.
Samsudeen, S., & Mohamed, R. (2019). University students’ intention to use e-learning systems: A study of higher educa-tional institutions in Sri Lanka. Interactive Technology and Smart Education, 16(3), 219–238. https://doi.org/10.1108/ITSE-11-2018-009.
Senali, M., Iranmanesh, M., & Ismail, F. (2023). Determinants of intention to use e-Wallet: Personal innovativeness and propensity to trust as moderators. International Journal of Human–Computer Interaction, 39(12), 2361–2373. https://doi.org/10.1080/10447318.2022.2076309.
Sidorova, A. (2018). Understanding User Interactions with a Chatbot: A Self-determination Theory Approach. In Proceed-ings of the Twenty-Fourth Americas Conference on Information Systems, New Orleans, LA, USA, 16–18; pp. 1–5.
Sitar, D., & Mican, D. (2021). Mobile learning acceptance and use in higher education during social distancing circum-stances: An expansion and customization of UTAUT2. Online Information Review, 45(5), 1000–1019. https://doi.org/10.1108/OIR-01-202.
Tewari, A., Singh, R., Mathur, S., & Pande, S. (2023). A modified UTAUT framework to predict students’ intention to adopt online learning: Moderating role of openness to change. The International Journal of Information and Learning Technology, 40(2), 130–147.https://doi.org/10.1108/IJILT-04-2022-0093.
Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the uni-fied theory of acceptance and use of technology. MIS quarterly, 157-178.
Wilmer, H., Sherman, L., & Chein, J. (2017). Smartphones and cognition: A review of research exploring the links be-tween mobile technology habits and cognitive functioning. Frontiers in Psychology, 8, 605. https://doi.org/10.3389/fpsyg.2017.00605.
Yang, W., Luo, H., & Su, J. (2022). Towards inclusiveness and sustainability of robot programming in early childhood: Child engagement, learning outcomes and teacher perception. British Journal of Educational Technology, 53(6), 1486-1510.
Zacharis, G., & Nikolopoulou, K. (2022). Factors predicting University students’ behavioral intention to use eLearning platforms in the post-pandemic normal: An UTAUT2 approach with ‘Learning Value. Education and Information Technologies, 27(9), 12065–12082. https://doi.org/10.1007/s10639-022-11.
Zhao, Y., Wang, N., Li, Y., Zhou, R., & Li, S. (2021). Do cultural differences affect users’e-learning adoption? A meta-analysis. British Journal of Educational Technology, 52(1), 20–41. https://doi.org/10.1111/bjet.13002.
Zwain, A. (2019). Technological innovativeness and information quality as neoteric predictors of users’ acceptance of learning management system: An expansion of UTAUT2. Interactive Technology and Smart Education, 16(3), 239–254. https://doi.org/10.1108/ITSE-09-2018-0065.