How to cite this paper
Alshurideh, M & Kurdi, B. (2023). Factors affecting social networks acceptance: An extension to the technology acceptance model using PLS-SEM and Machine Learning Approach.International Journal of Data and Network Science, 7(1), 489-494.
Refrences
Aghazamani, A. (2010). How do university students spend their time on Facebook? An exploratory study. Journal of American Science, 6(12), 730–735.
Ajzen, I. (1991). The theory of planned behavior. Organ Behav Hum Dec 50: 179–211.
Akar, E., & Mardikyan, S. (2014). Analyzing factors affecting users’ behavior intention to use social media: Twitter case. International Journal of Business and Social Science, 5(11).
Al Kurdi, B., Alshurideh, M., Salloum, S. A., Obeidat, Z. M., & Al-dweeri, R. M. (2020). An Empirical Investigation into Examination of Factors Influencing University Students’ Behavior towards Elearning Acceptance Using SEM Approach. International Journal of Interactive Mobile Technologies (IJIM), 14(02), 19–41.
Alhashmi, S. F. S., Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2020). A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector. Joint European-US Workshop on Applications of Invariance in Computer Vision, 37–49. Springer.
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2020). Examining the Main Mobile Learning System Drivers’ Effects: A Mix Empirical Examination of Both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In Advances in Intelligent Systems and Computing (Vol. 1058). https://doi.org/10.1007/978-3-030-31129-2_37
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019). Factors affecting the Social Networks Acceptance: An Empirical Study using PLS-SEM Approach. 8th International Conference on Software and Computer Applications, 1–5. ACM.
Alshurideh, M., Salloum, S. A., Al Kurdi, B., Monem, A. A., & Shaalan, K. (2019). Understanding the quality determinants that influence the intention to use the mobile learning platforms: A practical study. International Journal of Interactive Mobile Technologies, 13(11). https://doi.org/10.3991/ijim.v13i11.10300
Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
Chang, C.-C., Hung, S.-W., Cheng, M.-J., & Wu, C.-Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting and Social Change, 95, 48–56.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, Fred D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, Fred 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.
Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14(1), 1–16.
Dutot, V. (2015). Factors influencing Near Field Communication (NFC) adoption: an extended TAM approach. The Journal of High Technology Management Research, 26(1), 45–57.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
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
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2009). Weka-a machine learning workbench for data mining. In Data mining and knowledge discovery handbook (pp. 1269–1277). Springer.
Gachago, D., & Ivala, E. (2012). Social media for enhancing student engagement: the use of Facebook and blogs at a university of technology. South African Journal of Higher Education, 26(1), 152–167.
Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., Hair, J. F. F., Hult, G. T. M., … Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Hair, J. F., Black Jr, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis”, Pearson Prentice Hall, USA.
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.
Lieberman, J. N. (2014). Playfulness: Its relationship to imagination and creativity. Academic Press.
Lin, M.-F. G., Hoffman, E. S., & Borengasser, C. (2013). Is social media too social for class? A case study of Twitter use. TechTrends, 57(2), 39–45.
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.
Mingle, J., & Adams, M. (2015). Social media network participation and academic performance in senior high schools in Ghana. Library Philosophy and Practice, 1.
Morris, M. G., & Dillion, A. (1997). How user precautions information software use, software. IEEE Software, 14(4), 58–65.
Padilla-MeléNdez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317.
Palmer, S. (2013). Characterisation of the use of Twitter by Australian Universities. Journal of Higher Education Policy and Management, 35(4), 333–344.
Prestridge, S. (2014). A focus on students’ use of Twitter–their interactions with each other, content and interface. Active Learning in Higher Education, 15(2), 101–115.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020a). Machine Learning and Deep Learning Techniques for Cybersecurity: A Review. Joint European-US Workshop on Applications of Invariance in Computer Vision, 50–57. Springer.
Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020b). Mining in Educational data: Review and Future Directions. Joint European-US Workshop on Applications of Invariance in Computer Vision, 92–102. Springer.
Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91.
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, 425–478.
Ajzen, I. (1991). The theory of planned behavior. Organ Behav Hum Dec 50: 179–211.
Akar, E., & Mardikyan, S. (2014). Analyzing factors affecting users’ behavior intention to use social media: Twitter case. International Journal of Business and Social Science, 5(11).
Al Kurdi, B., Alshurideh, M., Salloum, S. A., Obeidat, Z. M., & Al-dweeri, R. M. (2020). An Empirical Investigation into Examination of Factors Influencing University Students’ Behavior towards Elearning Acceptance Using SEM Approach. International Journal of Interactive Mobile Technologies (IJIM), 14(02), 19–41.
Alhashmi, S. F. S., Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2020). A Systematic Review of the Factors Affecting the Artificial Intelligence Implementation in the Health Care Sector. Joint European-US Workshop on Applications of Invariance in Computer Vision, 37–49. Springer.
Alshurideh, M., Al Kurdi, B., & Salloum, S. A. (2020). Examining the Main Mobile Learning System Drivers’ Effects: A Mix Empirical Examination of Both the Expectation-Confirmation Model (ECM) and the Technology Acceptance Model (TAM). In Advances in Intelligent Systems and Computing (Vol. 1058). https://doi.org/10.1007/978-3-030-31129-2_37
Alshurideh, M., Salloum, S. A., Al Kurdi, B., & Al-Emran, M. (2019). Factors affecting the Social Networks Acceptance: An Empirical Study using PLS-SEM Approach. 8th International Conference on Software and Computer Applications, 1–5. ACM.
Alshurideh, M., Salloum, S. A., Al Kurdi, B., Monem, A. A., & Shaalan, K. (2019). Understanding the quality determinants that influence the intention to use the mobile learning platforms: A practical study. International Journal of Interactive Mobile Technologies, 13(11). https://doi.org/10.3991/ijim.v13i11.10300
Arpaci, I. (2019). A hybrid modeling approach for predicting the educational use of mobile cloud computing services in higher education. Computers in Human Behavior, 90, 181–187.
Barclay, D., Higgins, C., & Thompson, R. (1995). The Partial Least Squares (pls) Approach to Casual Modeling: Personal Computer Adoption Ans Use as an Illustration.
Chang, C.-C., Hung, S.-W., Cheng, M.-J., & Wu, C.-Y. (2015). Exploring the intention to continue using social networking sites: The case of Facebook. Technological Forecasting and Social Change, 95, 48–56.
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
Davis, Fred D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319–340.
Davis, Fred 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.
Dumpit, D. Z., & Fernandez, C. J. (2017). Analysis of the use of social media in Higher Education Institutions (HEIs) using the Technology Acceptance Model. International Journal of Educational Technology in Higher Education, 14(1), 1–16.
Dutot, V. (2015). Factors influencing Near Field Communication (NFC) adoption: an extended TAM approach. The Journal of High Technology Management Research, 26(1), 45–57.
Fishbein, M., & Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction to theory and research.
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
Frank, E., Hall, M., Holmes, G., Kirkby, R., Pfahringer, B., Witten, I. H., & Trigg, L. (2009). Weka-a machine learning workbench for data mining. In Data mining and knowledge discovery handbook (pp. 1269–1277). Springer.
Gachago, D., & Ivala, E. (2012). Social media for enhancing student engagement: the use of Facebook and blogs at a university of technology. South African Journal of Higher Education, 26(1), 152–167.
Hair, J., Hult, G. T. M., Ringle, C., Sarstedt, M., Hair, J. F. F., Hult, G. T. M., … Sarstedt, M. (2016). A primer on partial least squares structural equation modeling (PLS-SEM). Sage Publications.
Hair, J. F., Black Jr, W. C., Babin, B. J., & Anderson, R. E. (2010). Multivariate Data Analysis”, Pearson Prentice Hall, USA.
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.
Lieberman, J. N. (2014). Playfulness: Its relationship to imagination and creativity. Academic Press.
Lin, M.-F. G., Hoffman, E. S., & Borengasser, C. (2013). Is social media too social for class? A case study of Twitter use. TechTrends, 57(2), 39–45.
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.
Mingle, J., & Adams, M. (2015). Social media network participation and academic performance in senior high schools in Ghana. Library Philosophy and Practice, 1.
Morris, M. G., & Dillion, A. (1997). How user precautions information software use, software. IEEE Software, 14(4), 58–65.
Padilla-MeléNdez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306–317.
Palmer, S. (2013). Characterisation of the use of Twitter by Australian Universities. Journal of Higher Education Policy and Management, 35(4), 333–344.
Prestridge, S. (2014). A focus on students’ use of Twitter–their interactions with each other, content and interface. Active Learning in Higher Education, 15(2), 101–115.
Ringle, C. M., Wende, S., & Becker, J.-M. (2015). SmartPLS 3. Bönningstedt: SmartPLS.
Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020a). Machine Learning and Deep Learning Techniques for Cybersecurity: A Review. Joint European-US Workshop on Applications of Invariance in Computer Vision, 50–57. Springer.
Salloum, S. A., Alshurideh, M., Elnagar, A., & Shaalan, K. (2020b). Mining in Educational data: Review and Future Directions. Joint European-US Workshop on Applications of Invariance in Computer Vision, 92–102. Springer.
Tan, G. W.-H., Ooi, K.-B., Sim, J.-J., & Phusavat, K. (2012). Determinants of mobile learning adoption: An empirical analysis. Journal of Computer Information Systems, 52(3), 82–91.
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, 425–478.