The Role of Information Technology in motivating students to accept e-learning adoption in universities: A case study in Jordanian universities
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https://doi.org/10.25255/jbm.2016.4.1.36.46关键词:
theoretical study, technology acceptance model, E-learning摘要
The present study aimed to develop a theoretical framework toward e-learning adoption in universities through conducting a comprehensive review of literature, and empirical studies. Based on the technology acceptance model, the researchers developed a model designed to measure the impact of ease of use, perceived usefulness, and training on e-learning usage on the actual use of e-learning systems via the initial trend towards the use of e-learning systems and the intention towards the use of e-learning systems as mediating factors. The researchers also developed a survey questionnaire that will be distributed to a selected sample study on the students at the Jordanian public and private universities in order to answer the research questions and test its hypotheses by applying structural equation modeling.
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参考
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Allen, I., and Seaman, J. (2004). Entering the mainstream: The quality and extent of online education in the United States, 2003 and 2004. Retrieved April 15, 2005 from: http://sloan-c.org/resources/entering_mainstream.pdf.
Clark, Jr. T.D., Jones, M.C., and Zmud, R.W. (2006). Post adoptive ERP system analysis: A system dynamic modeling approach, Working Paper,
http://www.bus.lsu.edu./centers/decid/WorkingPaper.asp.
Compeau, D.R., Higgins, C.A., and Huff, S.L. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F., Bagozzi, R., and Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Diana, O. (1992). Teaching and learning with computers. (URL: http://orders.edrs.com).
Doll, W.J., Deng, X., and Scazzero, J.A. (2003). A process for post-implementation IT benchmarking, Information & Management, 41, 199-212.
George, P. (2000). Breaking ranks. Principal Leadership, 1(4), 56-61.
Imel, S. (1998). Myths and realities. (Report No. BBB16032). Columbus, OH. ERICClearinghouse on Adult, Career, and Vocational Education.
Kulik, J. A., and Kulik, C. L. (1991). Effectiveness of computer-based instruction. Computers in Human Behavior, 7(1), 75-94.
Masa’deh, R., Shannak, R., and Maqableh, M. (2013). A structural equation modeling approach for determining antecedents and outcomes of students’ attitude toward mobile commerce adoption. Life Science Journal, 10(4), 2321-2333.
Porter,C and Donthu,N. (2006) . Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59, 999–1007.
Russell, A. I. (1995). Stages in learning new technology: Naïve adult e-mail users. Computers in Education, 25(4), 173-178.
Stoehl, L., and Lee, K. (2003). Modeling the effect of experience on student acceptance of Web-based courseware. Internet Research: Electronic Networking Applications and Policy, 13(5), 364-374.
Tarhini, A., Hone, K., and Liu, X. (2013). Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modelling approach. International Journal of Information and Education Technology, 3(1), 54-59.
Tarhini, A., Hone, K., and Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184.
Theriot, P. (2004). Student values and ethics in an e-learning environment. In D. Christopher & S. Jaderstrom (Eds.), NBEA 2004 Yearbook, 42, 13-25.
Wiersma, W. (2000). Research methods in education: An introduction (7nd ed.). Boston:Allyn and Bacon.
Willis, T (2008). An evaluation of the Technology Acceptance Model as a means of understanding online social networking behaviour, Master thesis, University of South Florida.
Zamfiroiu, A., and Sbora, C. (2014). Statistical analysis of the behavior for mobile E-learning. Procedia Economics and Finance, 10, 237-243.
Allehaibi, M. (2001). Faculty adoption of Internet technology in Saudi Arabian universities. PhD, Florida State University. Available: http://www.sssgrp.com/Menu/Di ssAbstracts /InnovationDiffusion/Allehaibi.htm. Retrieved on 12/4/2015.
Allen, I., and Seaman, J. (2004). Entering the mainstream: The quality and extent of online education in the United States, 2003 and 2004. Retrieved April 15, 2005 from: http://sloan-c.org/resources/entering_mainstream.pdf.
Clark, Jr. T.D., Jones, M.C., and Zmud, R.W. (2006). Post adoptive ERP system analysis: A system dynamic modeling approach, Working Paper,
http://www.bus.lsu.edu./centers/decid/WorkingPaper.asp.
Compeau, D.R., Higgins, C.A., and Huff, S.L. (1999). Social cognitive theory and individual reactions to computing technology: A longitudinal study. MIS Quarterly, 23(2), 145-158.
Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319-340.
Davis, F., Bagozzi, R., and Warshaw, P. (1989). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
Diana, O. (1992). Teaching and learning with computers. (URL: http://orders.edrs.com).
Doll, W.J., Deng, X., and Scazzero, J.A. (2003). A process for post-implementation IT benchmarking, Information & Management, 41, 199-212.
George, P. (2000). Breaking ranks. Principal Leadership, 1(4), 56-61.
Imel, S. (1998). Myths and realities. (Report No. BBB16032). Columbus, OH. ERICClearinghouse on Adult, Career, and Vocational Education.
Kulik, J. A., and Kulik, C. L. (1991). Effectiveness of computer-based instruction. Computers in Human Behavior, 7(1), 75-94.
Masa’deh, R., Shannak, R., and Maqableh, M. (2013). A structural equation modeling approach for determining antecedents and outcomes of students’ attitude toward mobile commerce adoption. Life Science Journal, 10(4), 2321-2333.
Porter,C and Donthu,N. (2006) . Using the technology acceptance model to explain how attitudes determine Internet usage: The role of perceived access barriers and demographics. Journal of Business Research, 59, 999–1007.
Russell, A. I. (1995). Stages in learning new technology: Naïve adult e-mail users. Computers in Education, 25(4), 173-178.
Stoehl, L., and Lee, K. (2003). Modeling the effect of experience on student acceptance of Web-based courseware. Internet Research: Electronic Networking Applications and Policy, 13(5), 364-374.
Tarhini, A., Hone, K., and Liu, X. (2013). Factors affecting students’ acceptance of e-learning environments in developing countries: A structural equation modelling approach. International Journal of Information and Education Technology, 3(1), 54-59.
Tarhini, A., Hone, K., and Liu, X. (2014). Measuring the moderating effect of gender and age on e-learning acceptance in England: A structural equation modeling approach for an extended technology acceptance model. Journal of Educational Computing Research, 51(2), 163-184.
Theriot, P. (2004). Student values and ethics in an e-learning environment. In D. Christopher & S. Jaderstrom (Eds.), NBEA 2004 Yearbook, 42, 13-25.
Wiersma, W. (2000). Research methods in education: An introduction (7nd ed.). Boston:Allyn and Bacon.
Willis, T (2008). An evaluation of the Technology Acceptance Model as a means of understanding online social networking behaviour, Master thesis, University of South Florida.
Zamfiroiu, A., and Sbora, C. (2014). Statistical analysis of the behavior for mobile E-learning. Procedia Economics and Finance, 10, 237-243.
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