Exploring the General Extended Technology Acceptance Model for e-Learning Approach on Student’s Usage Intention on e-Learning System in University of Dar es Salaam — Oak Academic Publishing
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Exploring the General Extended Technology Acceptance Model for e-Learning Approach on Student’s Usage Intention on e-Learning System in University of Dar es Salaam
College of Public Administration, Huazhong University of Science and Technology, Wuhan, China
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College of Public Administration, Huazhong University of Science and Technology, Wuhan, China
1 College of Public Administration, Huazhong University of Science and Technology, Wuhan, China
2 College of Public Administration, Huazhong University of Science and Technology, Wuhan, China
Tanzania has successfully included modern information communication technology in the education system. The main concerned is to raise the standard of education. The factors that influence university student’s usage intention of e-learning for education goals in Tanzania are importan t to be studied. The study employed the empirical techniques with the General Extended Technology Acceptance Models for E-learning for the aim of determining the factors that affect undergraduate student’s usage intention of e-learning system. Data collection lasted for one month with 172 undergraduate students and the Structural Equation Modeling (SEM) was employed for data analysis. The finding shows that subjective norm positive ly influence s perceived usefulness and perceived ease of use, experience positively influence s perceived ease of use and enjoyment positively influence s perceived ease of use. Also, computer anxiety positively influence s perceived usefulness and perceive d ease of use and perceived usefulness positively influence s intentions and perceived ease of use positively influence s perceived usefulness. This study is also one of the very few studies that use the GETAMEL model for e-learning settings in developing countries. The re sults have significant practical implications for educational institutions as well as decision makers in terms of the design of the e-learning system in the university.
Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by Analysing Commonly Used External Factors. Computers in Human Behavior, 56, 238-256. https://doi.org/10.1016/j.chb.2015.11.036
Agarwal, R., & Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology. Information Systems Research, 9, 204-215. https://doi.org/10.1287/isre.9.2.204
Agudo-Peregrina, á. F., Hernández-García, á., & Pascual-Miguel, F. J. (2014). Behavioral Intention, Use Behavior and the Acceptance of Electronic Learning Systems: Differences between Higher Education and Lifelong Learning. Computers in Human Behavior, 34, 301-314. https://doi.org/10.1016/j.chb.2013.10.035
Ajzen, I., & Fishbein, M. (1977). Attitude-Behavior Relations: A Theoretical Analysis and Review of Empirical Research. Psychological Bulletin, 84, 888. https://doi.org/10.1037/0033-2909.84.5.888
Alenezi, A. R. (2012). E-Learning Acceptance: Technological Key Factors for the Successful Students’ Engagement in e-Learning System. In The 2012 International Conference on e-Learning, e-Business, Enterprise Information Systems, and e-Government (1-6). https://pdfs.semanticscholar.org/1659/6fda1b352a7d198e189dec6702299ce358d4.pdf
Al-Gahtani, S. S. (2016). Empirical Investigation of e-Learning Acceptance and Assimilation: A Structural Equation Model. Applied Computing and Informatics, 12, 27-50. https://doi.org/10.1016/j.aci.2014.09.001
Alsabawy, A. Y., Cater-Steel, A., & Soar, J. (2016). Determinants of Perceived Usefulness of e-Learning Systems. Computers in Human Behavior, 64, 843-858. https://doi.org/10.1016/j.chb.2016.07.065
Alshehri, M., Drew, S., & AlGhamdi, R. (2013). Analysis of Citizens Acceptance for e-Government Services: Applying the UTAUT Model. arXiv Preprint arXiv:1304.3157.
Anderson, J. C., & Gerbing, D. W. (1988). Structural Equation Modeling in Practice: A Review and Recommended Two-Step Approach. Psychological Bulletin, 103, 411. https://doi.org/10.1037/0033-2909.103.3.411
Bagozzi, R. P., & Fornell, C. (1982). Theoretical Concepts, Measurements, and Meaning. A Second Generation of Multivariate Analysis, 2, 5-23.
Bandura, A. (1997). Self-Efficacy: The Exercise of Control. United States of America: W.H. Freeman and Company.
Chu, H.-C., Hwang, G.-J., Huang, S.-X., & Wu, T.-T. (2008). A Knowledge Engineering Approach to Developing e-Libraries for Mobile Learning. The Electronic Library, 26, 303-317. https://doi.org/10.1108/02640470810879464
Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13, 319-340. https://doi.org/10.2307/249008
Davis, F. D., Bagozzi, R. P., & Warshaw, P. R. (1989). User Acceptance of computer Technology: A Comparison of Two Theoretical Models. Management Science, 35, 982-1003. https://doi.org/10.1287/mnsc.35.8.982
De Smet, C., Bourgonjon, J., De Wever, B., Schellens, T., & Valcke, M. (2012). Researching Instructional Use and the Technology Acceptation of Learning Management Systems by Secondary School Teachers. Computers & Education, 58, 688-696. https://doi.org/10.1016/j.compedu.2011.09.013
Divaris, K., Barlow, P. J., Chendea, S. A., Cheong, W. S., Dounis, A., Dragan, I. F., Mitrirattanakul, S. et al. (2008). The Academic Environment: The Students’ Perspective. European Journal of Dental Education, 12, 120-130. https://doi.org/10.1111/j.1600-0579.2007.00494.x
Hair, J. F., Anderson, R. E., Babin, B. J., & Black, W. C. (2010). Multivariate Data Analysis: A Global Perspective (Vol. 7). Upper Saddle River, NJ: Pearson.
Hair, J. F., Black, W. C., Babin, B. J., Anderson, R. E., & Tatham, R. L. (2006). Multivariate Data Analysis (Vol. 6). Upper Saddle River, NJ: Pearson Prentice Hall.
Hayduk, L. A. (1987). Structural Equation Modeling with LISREL: Essentials and Advances. Baltimore, MD: Johns Hopkins University Press.
Hsia, J.-W., Chang, C.-C., & Tseng, A.-H. (2012). Effects of Individuals’ Locus of Control and Computer Self-Efficacy on Their e-Learning Acceptance in High-Tech Companies. Behaviour & Information Technology, 33, 51-64. https://doi.org/10.1080/0144929X.2012.702284
Igbaria, M., & Parasuraman, S. (2016). A Path Analytic Study of Individual Characteristics, Computer Anxiety and Attitudes toward Microcomputers. Journal of Management, 15, 373-388. https://doi.org/10.1177/014920638901500302
Kurfali, M., Arifoglu, A., Tokdemir, G., & Pacin, Y. (2017). Adoption of e-Government Services in Turkey. Computers in Human Behavior, 66, 168-178. https://doi.org/10.1016/j.chb.2016.09.041
Lee, Y.-C. (2006). An Empirical Investigation into Factors Influencing the Adoption of an e-Learning System. Online Information Review, 30, 517-541. https://doi.org/10.1108/14684520610706406
Lee, Y.-H., Hsieh, Y.-C., & Chen, Y.-H. (2013). An Investigation of Employees’ Use of e-Learning Systems: Applying the Technology Acceptance Model. Behaviour & Information Technology, 32, 173-189. https://doi.org/10.1080/0144929X.2011.577190
Lin, Y.-C., Chen, Y.-C., & Yeh, R. C. (2010). Understanding College Students’ Continuing Intentions to Use Multimedia e-Learning Systems. World Transactions on Engineering and Technology Education, 8, 488-493.
Ngai, E. W. T., Poon, J. K. L., & Chan, Y. H. C. (2007). Empirical Examination of the Adoption of Web CT Using TAM. Computers & Education, 48, 250-267.
Park, S. Y., Nam, M.-W., & Cha, S.-B. (2012). University Students’ Behavioral Intention to Use Mobile Learning: Evaluating the Technology Acceptance Model. British Journal of Educational Technology, 43, 592-605. https://doi.org/10.1111/j.1467-8535.2011.01229.x
Tarhini, A., Hone, K., & Liu, X. (2013). User Acceptance towards Web-Based Learning Systems: Investigating the Role of Social, Organizational and Individual Factors in European Higher Education. Procedia Computer Science, 17, 189-197. https://doi.org/10.1016/j.procs.2013.05.026
Tedre, M., Ngumbuke, F. D., Bangu, N., & Sutinen, E. (2008). Implementing a Contextualized IT Curriculum: Ambitions and Ambiguities. In Proceedings of the 8th International Conference on Computing Education Research (51-61). New York: ACM. https://doi.org/10.1145/1595356.1595365
Teo, T., & Zhou, M. (2014). Explaining the Intention to Use Technology among University Students: A Structural Equation Modeling Approach. Journal of Computing in Higher Education, 26, 124-142. https://doi.org/10.1007/s12528-014-9080-3
Venkatesh, V., & Davis, F. D. (2000). A Theoretical Extension of the Technology Acceptance Model: Four Longitudinal Field Studies. Management Science, 46, 186-204. https://doi.org/10.1287/mnsc.46.2.186.11926
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User Acceptance of Information Technology: Toward a Unified View. MIS Quarterly, 27, 425-478. https://doi.org/10.2307/30036540
Williams, M., & Williams, J. (2010). Evaluating a Model of Business School Students’ Acceptance of Web-Based Course Management Systems. International Journal of Management Education, 8, 59-70. https://doi.org/10.3794/ijme.83.264
Zare, H., & Yazdanparast, S. (2013). The Causal Model of Effective Factors on Intention to Use of Information Technology among Payamnoor and Traditional Universities Students. Life Science Journal, 10, 46-50.