How to cite this paper
Al-Ali, R., Shishakly, R., Almaiah, M & Shehab, R. (2025). Factors influencing students' attitude toward to use mobile learning applications using SEM-ANN hybrid approach.International Journal of Data and Network Science, 9(1), 115-124.
Refrences
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Al-Emran, M., Abbasi, G. A., & Mezhuyev, V. (2021). Evaluating the impact of knowledge management factors on M-learning adoption: A deep learning-based hybrid SEM-ANN approach. In Recent advances in technology acceptance mod-els and theories (pp. 159-172). Cham: Springer International Publishing.
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1), 100143.
Alhumaid, K., Habes, M., & Salloum, S. A. (2021). Examining the factors influencing the mobile learning usage during COVID-19 Pandemic: An Integrated SEM-ANN Method. IEEE Access, 9, 102567-102578.
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. M. (2019). Analysis the effect of different factors on the development of Mobile learning applications at different stages of usage. IEEE Access, 8, 16139-16154.
Almaiah, M. A., Ayouni, S., Hajjej, F., Lutfi, A., Almomani, O., & Awad, A. B. (2022). Smart mobile learning success model for higher educational institutions in the context of the COVID-19 pandemic. Electronics, 11(8), 1278.
Alowayr, A. (2022). Determinants of mobile learning adoption: extending the unified theory of acceptance and use of technol-ogy (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12.
Althunibat, A., Altarawneh, F., Dawood, R., & Almaiah, M. A. (2022). Propose a New Quality Model for M‐Learning Applica-tion in Light of COVID‐19. Mobile Information Systems, 2022(1), 3174692.
Dutta, S., & Shivani, S. (2020). Modified utaut2 to determine intention and use of e-commerce technology among micro & small women entrepreneurs in jharkhand, india. In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18–19, 2020, Proceedings, Part II (pp. 688-701). Springer International Publishing.
Hameed, F., Qayyum, A., & Khan, F. A. (2024). A new trend of learning and teaching: Behavioral intention towards mobile learning. Journal of Computers in Education, 11(1), 149-180.
Hoang-Tung, N., Kojima, A., & Kubota, H. (2017). Transformation from intentions to habits in travel behavior: An awareness of a mediated form of intention. Transportation Research Part F: Traffic Psychology and Behaviour, 49, 226-235.
Jiang, T., Luo, G., Wang, Z., & Yu, W. (2024). Research into influencing factors in user experiences of university mobile li-braries based on mobile learning mode. Library Hi Tech, 42(2), 564-579.
Juera, L. C. (2024). Digitalizing skills development using simulation-based mobile (SiM) learning application. Journal of Computers in Education, 11(1), 29-50.
Katayeva, M. (2023). Analysis And Recommendations On Mobile Learning In The Educational Process. Farg'ona davlat uni-versiteti, 3, 40-40.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Li, M., & Liu, L. (2023). Students' perceptions of augmented reality integrated into a mobile learning environment. Library Hi Tech, 41(5), 1498-1523.
Meng, Z., & Li, R. (2024). Understanding Chinese teachers’ informal online learning continuance in a mobile learning com-munity: an intrinsic–extrinsic motivation perspective. Journal of Computing in Higher Education, 36(2), 275-297.
Ogata, H., Majumdar, R., Flanagan, B., & Kuromiya, H. (2024). Learning analytics and evidence-based K12 education in Ja-pan: usage of data-driven services for mobile learning across two years. International Journal of Mobile Learning and Or-ganisation, 18(1), 15-48.
Raman, A., & Thannimalai, R. (2021). Factors Impacting the Behavioural Intention to Use E-learning at Higher Education amid the Covid-19 Pandemic: UTAUT2 Model. Psychological Science & Education, 26(3).
Sarrab, M., Elbasir, M., & Alnaeli, S. (2016). Towards a quality model of technical aspects for mobile learning services: An empirical investigation. Computers in Human Behavior, 55, 100-112.
Sitar‐Tăut, D. A. (2021). Mobile learning acceptance in social distancing during the COVID‐19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366-378.
Sternad Zabukovšek, S., Bobek, S., Zabukovšek, U., Kalinić, Z., & Tominc, P. (2022). Enhancing PLS-SEM-enabled research with ANN and IPMA: Research study of enterprise resource planning (ERP) systems’ acceptance based on the technology acceptance model (TAM). Mathematics, 10(9), 1379.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296.
Thabet, Z., Albashtawi, S., Ansari, H., Al-Emran, M., Al-Sharafi, M. A., & AlQudah, A. A. (2023). Exploring the factors af-fecting telemedicine adoption by integrating UTAUT2 and IS success model: a hybrid SEM–ANN approach. IEEE Trans-actions on Engineering Management.
Al-Emran, M., Abbasi, G. A., & Mezhuyev, V. (2021). Evaluating the impact of knowledge management factors on M-learning adoption: A deep learning-based hybrid SEM-ANN approach. In Recent advances in technology acceptance mod-els and theories (pp. 159-172). Cham: Springer International Publishing.
Alfalah, A. A. (2023). Factors influencing students’ adoption and use of mobile learning management systems (m-LMSs): A quantitative study of Saudi Arabia. International Journal of Information Management Data Insights, 3(1), 100143.
Alhumaid, K., Habes, M., & Salloum, S. A. (2021). Examining the factors influencing the mobile learning usage during COVID-19 Pandemic: An Integrated SEM-ANN Method. IEEE Access, 9, 102567-102578.
Almaiah, M. A., Alamri, M. M., & Al-Rahmi, W. M. (2019). Analysis the effect of different factors on the development of Mobile learning applications at different stages of usage. IEEE Access, 8, 16139-16154.
Almaiah, M. A., Ayouni, S., Hajjej, F., Lutfi, A., Almomani, O., & Awad, A. B. (2022). Smart mobile learning success model for higher educational institutions in the context of the COVID-19 pandemic. Electronics, 11(8), 1278.
Alowayr, A. (2022). Determinants of mobile learning adoption: extending the unified theory of acceptance and use of technol-ogy (UTAUT). The International Journal of Information and Learning Technology, 39(1), 1-12.
Althunibat, A., Altarawneh, F., Dawood, R., & Almaiah, M. A. (2022). Propose a New Quality Model for M‐Learning Applica-tion in Light of COVID‐19. Mobile Information Systems, 2022(1), 3174692.
Dutta, S., & Shivani, S. (2020). Modified utaut2 to determine intention and use of e-commerce technology among micro & small women entrepreneurs in jharkhand, india. In Re-imagining Diffusion and Adoption of Information Technology and Systems: A Continuing Conversation: IFIP WG 8.6 International Conference on Transfer and Diffusion of IT, TDIT 2020, Tiruchirappalli, India, December 18–19, 2020, Proceedings, Part II (pp. 688-701). Springer International Publishing.
Hameed, F., Qayyum, A., & Khan, F. A. (2024). A new trend of learning and teaching: Behavioral intention towards mobile learning. Journal of Computers in Education, 11(1), 149-180.
Hoang-Tung, N., Kojima, A., & Kubota, H. (2017). Transformation from intentions to habits in travel behavior: An awareness of a mediated form of intention. Transportation Research Part F: Traffic Psychology and Behaviour, 49, 226-235.
Jiang, T., Luo, G., Wang, Z., & Yu, W. (2024). Research into influencing factors in user experiences of university mobile li-braries based on mobile learning mode. Library Hi Tech, 42(2), 564-579.
Juera, L. C. (2024). Digitalizing skills development using simulation-based mobile (SiM) learning application. Journal of Computers in Education, 11(1), 29-50.
Katayeva, M. (2023). Analysis And Recommendations On Mobile Learning In The Educational Process. Farg'ona davlat uni-versiteti, 3, 40-40.
Kline, R. B. (2023). Principles and practice of structural equation modeling. Guilford publications.
Li, M., & Liu, L. (2023). Students' perceptions of augmented reality integrated into a mobile learning environment. Library Hi Tech, 41(5), 1498-1523.
Meng, Z., & Li, R. (2024). Understanding Chinese teachers’ informal online learning continuance in a mobile learning com-munity: an intrinsic–extrinsic motivation perspective. Journal of Computing in Higher Education, 36(2), 275-297.
Ogata, H., Majumdar, R., Flanagan, B., & Kuromiya, H. (2024). Learning analytics and evidence-based K12 education in Ja-pan: usage of data-driven services for mobile learning across two years. International Journal of Mobile Learning and Or-ganisation, 18(1), 15-48.
Raman, A., & Thannimalai, R. (2021). Factors Impacting the Behavioural Intention to Use E-learning at Higher Education amid the Covid-19 Pandemic: UTAUT2 Model. Psychological Science & Education, 26(3).
Sarrab, M., Elbasir, M., & Alnaeli, S. (2016). Towards a quality model of technical aspects for mobile learning services: An empirical investigation. Computers in Human Behavior, 55, 100-112.
Sitar‐Tăut, D. A. (2021). Mobile learning acceptance in social distancing during the COVID‐19 outbreak: The mediation effect of hedonic motivation. Human Behavior and Emerging Technologies, 3(3), 366-378.
Sternad Zabukovšek, S., Bobek, S., Zabukovšek, U., Kalinić, Z., & Tominc, P. (2022). Enhancing PLS-SEM-enabled research with ANN and IPMA: Research study of enterprise resource planning (ERP) systems’ acceptance based on the technology acceptance model (TAM). Mathematics, 10(9), 1379.
Taber, K. S. (2018). The use of Cronbach’s alpha when developing and reporting research instruments in science education. Research in science education, 48, 1273-1296.
Thabet, Z., Albashtawi, S., Ansari, H., Al-Emran, M., Al-Sharafi, M. A., & AlQudah, A. A. (2023). Exploring the factors af-fecting telemedicine adoption by integrating UTAUT2 and IS success model: a hybrid SEM–ANN approach. IEEE Trans-actions on Engineering Management.