How to cite this paper
Alhumaid, K., Naqbi, S., Elsori, D & Mansoori, M. (2023). The adoption of artificial intelligence applications in education.International Journal of Data and Network Science, 7(1), 457-466.
Refrences
Al-Emran, M., & Salloum, S. A. (2017). Students’ Attitudes Towards the Use of Mobile Technologies in e-Evaluation. International Journal of Interactive Mobile Technologies (IJIM), 11(5), 195–202.
Alam, S. S., Masukujjaman, M., Susmit, S., Susmit, S., & Abd Aziz, H. (2022). Augmented reality adoption intention among travel and tour operators in Malaysia: mediation effect of value alignment. Journal of Tourism Futures.
Babatunde, S. A., Ajape, M. K., Isa, K. D., Kuye, O., Omolehinwa, E. O., & Muritala, S. A. (2021). Ease of Doing Business Index: An Analysis of Investors Practical View. Jurnal Economia, 17(1), 101–123.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463.
Chuan, C. L., & Penyelidikan, J. (2006). Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. Jurnal Penyelidikan IPBL, 7, 78–86.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Delrue, F., Setier, P.-A., Sahut, C., Cournac, L., Roubaud, A., Peltier, G., & Froment, A.-K. (2012). An economic, sustainability, and energetic model of biodiesel production from microalgae. Bioresource Technology, 111, 191–200.
Erdener, K., Perkmen, S., Shelley, M., & Ali Kandemir, M. (2022). Measuring Perceived Attributes of the Interactive Whiteboard for the Mathematics Class. Computers in the Schools, 39(1), 1–15.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have adavantages for small sample size or non-normal data? MIS Quaterly.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hooks, D., Davis, Z., Agrawal, V., & Li, Z. (2022). Exploring factors influencing technology adoption rate at the macro level: A predictive model. Technology in Society, 68, 101826.
Hsu, T., Ke, H., & Yang, W. (2006). Knowledge‐based mobile learning framework for museums. The Electronic Library.
John, C. (2016). ASSESSING FACTORS AFFECTING ADOPTION OF MOBILE MONEY PAYMENT IN TANZANIA.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124.
Lee, Y. H. (2007). Exploring key factors that affect consumers to adopt e-reading services. Unpublished Master Thesis, Huafan University.
Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 1–27.
Liu, C., Hou, J., Tu, Y.-F., Wang, Y., & Hwang, G.-J. (2021). Incorporating a reflective thinking promoting mechanism into artificial intelligence-supported English writing environments. Interactive Learning Environments, 1–19.
Liu, S.-H., Liao, H.-L., & Peng, C.-J. (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. E-Learning, 4(H6), H8.
Lou, A. T. F., & Li, E. Y. (2017). Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective.
Lubanga, J. M., Gakobo, T., Ochieng, I., & Kimando, L. N. (2017). Factors influencing adoption of e-payment system in Kenyan public transport: a case of matatu plying Nairobi-Kitengela route. International Academic Journal of Human Resource and Business Administration, 2(4), 27–48.
M Rogers, E. (1983). Diffusion of innovations. The Free Press.
Nezamdoust, S., Abdekhoda, M., & Rahmani, A. (2022). Determinant factors in adopting mobile health application in healthcare by nurses. BMC Medical Informatics and Decision Making, 22(1), 1–10.
Ntsiful, A., Kwarteng, M. A., Pilík, M., & Osakwe, C. N. (2022). Transitioning to Online Teaching During the Pandemic Period: The Role of Innovation and Psychological Characteristics. Innovative Higher Education, 1–22.
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. In McGraw-Hill, New York. https://doi.org/10.1037/018882
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510.
Peltier, M., & Mizock, L. (2012). Fox’s More to Love: Pseudo-fat acceptance in reality television. Somatechnics, 2(1), 93–106.
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.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
Sandu, N., & Gide, E. (2019). Adoption of AI-Chatbots to enhance student learning experience in higher education in India. 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), 1–5.
Szalavetz, A. (2019). Industry 4.0 and capability development in manufacturing subsidiaries. Technological Forecasting and Social Change, 145, 384–395.
Teo, T., & Tan, L. (2012). The theory of planned behavior (TPB) and pre-service teachers’ technology acceptance: A validation study using structural equation modeling. Journal of Technology and Teacher Education, 20(1), 89–104.
Tyson, M. M., & Sauers, N. J. (2021). School leaders’ adoption and implementation of artificial intelligence. Journal of Educational Administration.
Ukobitz, D. V., & Faullant, R. (2022). The relative impact of isomorphic pressures on the adoption of radical technology: Evidence from 3D printing. Technovation, 113, 102418.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710
Varghese, J. (2020). Artificial intelligence in medicine: chances and challenges for wide clinical adoption. Visceral Medicine, 36(6), 443–449.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education. Educational Technology & Society, 24(3), 116–129.
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2021). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 1–15.
Alam, S. S., Masukujjaman, M., Susmit, S., Susmit, S., & Abd Aziz, H. (2022). Augmented reality adoption intention among travel and tour operators in Malaysia: mediation effect of value alignment. Journal of Tourism Futures.
Babatunde, S. A., Ajape, M. K., Isa, K. D., Kuye, O., Omolehinwa, E. O., & Muritala, S. A. (2021). Ease of Doing Business Index: An Analysis of Investors Practical View. Jurnal Economia, 17(1), 101–123.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25(5), 3443–3463.
Chuan, C. L., & Penyelidikan, J. (2006). Sample size estimation using Krejcie and Morgan and Cohen statistical power analysis: A comparison. Jurnal Penyelidikan IPBL, 7, 78–86.
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1992). Extrinsic and intrinsic motivation to use computers in the workplace. Journal of Applied Social Psychology, 22(14), 1111–1132.
Delrue, F., Setier, P.-A., Sahut, C., Cournac, L., Roubaud, A., Peltier, G., & Froment, A.-K. (2012). An economic, sustainability, and energetic model of biodiesel production from microalgae. Bioresource Technology, 111, 191–200.
Erdener, K., Perkmen, S., Shelley, M., & Ali Kandemir, M. (2022). Measuring Perceived Attributes of the Interactive Whiteboard for the Mathematics Class. Computers in the Schools, 39(1), 1–15.
Fornell, C., & Larcker, D. F. (1981). Evaluating Structural Equation Models With Unobservable Variables and Measurement Error. Journal of Marketing Research, 18(1), 39–50. https://doi.org/10.2307/3151312
Goodhue, D. L., Lewis, W., & Thompson, R. (2012). Does PLS have adavantages for small sample size or non-normal data? MIS Quaterly.
Hair, J., Hollingsworth, C. L., Randolph, A. B., & Chong, A. Y. L. (2017). An updated and expanded assessment of PLS-SEM in information systems research. Industrial Management & Data Systems, 117(3), 442–458. https://doi.org/10.1108/IMDS-04-2016-0130
Hair Jr, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115–135.
Hooks, D., Davis, Z., Agrawal, V., & Li, Z. (2022). Exploring factors influencing technology adoption rate at the macro level: A predictive model. Technology in Society, 68, 101826.
Hsu, T., Ke, H., & Yang, W. (2006). Knowledge‐based mobile learning framework for museums. The Electronic Library.
John, C. (2016). ASSESSING FACTORS AFFECTING ADOPTION OF MOBILE MONEY PAYMENT IN TANZANIA.
Kline, R. B. (2015). Principles and practice of structural equation modeling. Guilford publications.
Krejcie, R. V, & Morgan, D. W. (1970). Determining sample size for research activities. Educational and Psychological Measurement, 30(3), 607–610.
Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees’ intentions to use e-learning systems. Journal of Educational Technology & Society, 14(4), 124.
Lee, Y. H. (2007). Exploring key factors that affect consumers to adopt e-reading services. Unpublished Master Thesis, Huafan University.
Liang, J.-C., Hwang, G.-J., Chen, M.-R. A., & Darmawansah, D. (2021). Roles and research foci of artificial intelligence in language education: an integrated bibliographic analysis and systematic review approach. Interactive Learning Environments, 1–27.
Liu, C., Hou, J., Tu, Y.-F., Wang, Y., & Hwang, G.-J. (2021). Incorporating a reflective thinking promoting mechanism into artificial intelligence-supported English writing environments. Interactive Learning Environments, 1–19.
Liu, S.-H., Liao, H.-L., & Peng, C.-J. (2005). Applying the technology acceptance model and flow theory to online e-learning users’ acceptance behavior. E-Learning, 4(H6), H8.
Lou, A. T. F., & Li, E. Y. (2017). Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective.
Lubanga, J. M., Gakobo, T., Ochieng, I., & Kimando, L. N. (2017). Factors influencing adoption of e-payment system in Kenyan public transport: a case of matatu plying Nairobi-Kitengela route. International Academic Journal of Human Resource and Business Administration, 2(4), 27–48.
M Rogers, E. (1983). Diffusion of innovations. The Free Press.
Nezamdoust, S., Abdekhoda, M., & Rahmani, A. (2022). Determinant factors in adopting mobile health application in healthcare by nurses. BMC Medical Informatics and Decision Making, 22(1), 1–10.
Ntsiful, A., Kwarteng, M. A., Pilík, M., & Osakwe, C. N. (2022). Transitioning to Online Teaching During the Pandemic Period: The Role of Innovation and Psychological Characteristics. Innovative Higher Education, 1–22.
Nunnally, J. C., & Bernstein, I. H. (1978). Psychometric theory.
Nunnally, J. C., & Bernstein, I. H. (1994). Psychometric theory. In McGraw-Hill, New York. https://doi.org/10.1037/018882
Oliveira, T., Thomas, M., & Espadanal, M. (2014). Assessing the determinants of cloud computing adoption: An analysis of the manufacturing and services sectors. Information & Management, 51(5), 497–510.
Peltier, M., & Mizock, L. (2012). Fox’s More to Love: Pseudo-fat acceptance in reality television. Somatechnics, 2(1), 93–106.
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.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
Sandu, N., & Gide, E. (2019). Adoption of AI-Chatbots to enhance student learning experience in higher education in India. 2019 18th International Conference on Information Technology Based Higher Education and Training (ITHET), 1–5.
Szalavetz, A. (2019). Industry 4.0 and capability development in manufacturing subsidiaries. Technological Forecasting and Social Change, 145, 384–395.
Teo, T., & Tan, L. (2012). The theory of planned behavior (TPB) and pre-service teachers’ technology acceptance: A validation study using structural equation modeling. Journal of Technology and Teacher Education, 20(1), 89–104.
Tyson, M. M., & Sauers, N. J. (2021). School leaders’ adoption and implementation of artificial intelligence. Journal of Educational Administration.
Ukobitz, D. V., & Faullant, R. (2022). The relative impact of isomorphic pressures on the adoption of radical technology: Evidence from 3D printing. Technovation, 113, 102418.
Urbach, N., & Ahlemann, F. (2010). Structural equation modeling in information systems research using partial least squares. Journal of Information Technology Theory and Application, 11(2), 5–40. https://doi.org/10.1037/0021-9010.90.4.710
Varghese, J. (2020). Artificial intelligence in medicine: chances and challenges for wide clinical adoption. Visceral Medicine, 36(6), 443–449.
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
Wang, Y., Liu, C., & Tu, Y.-F. (2021). Factors Affecting the Adoption of AI-Based Applications in Higher Education. Educational Technology & Society, 24(3), 116–129.
Zheng, L., Niu, J., Zhong, L., & Gyasi, J. F. (2021). The effectiveness of artificial intelligence on learning achievement and learning perception: A meta-analysis. Interactive Learning Environments, 1–15.