Predicting E-Learning Application in Agricultural Higher Education Using Technology Acceptance Model
E-learning is significant breakthrough in teaching and learning. Internet or web technologies are important because they facilitate and enhance communications among instructors and learners and provide tools to encourage creativity and initiative. If internet-based learning environments are to benefit students, then it is important from the student’s perspective that they are not seen as overly complex and hard to use. The introduction of e-learning may hinder the learning process if the technology is perceived as being complex and not useful to enhanced performance, and thus a distraction to learning. In line with acceptance studies, this research proposed and tested students’ acceptance behavior of agricultural higher education for application of e-learning using technology acceptance model. Results demonstrated that there was positive relationship between students’ intention to use e-learning and its perceived usefulness, internet experience, computer self-efficacy and affect. Instead computer anxiety and age had negative relationship with students’ intention to use e-learning.
___
- Agarwal, R. & Prasad, J. (1999). Are individual differences germane to the acceptance of a new information technologies? Decision Sciences, 30(2), 361-391.
- Ajzen, I., and Fishbein, M. (1980). Understanding attitudes and predicting social behavior. Englewood Cliffs, NJ: Prentice-Hall.
- Arkkelin, D. (2003). Putting prometheus‘ feet to the fire: student evaluations of Prometheus in relation to their attitudes towards and experience with computers, computer self-efficacy and preferred learning style. Syllabus 2003 procedings.
- Behrens, S.; Jamieson, K., Jones, D. and Cranston, M. (2005). predicting system success using the technology acceptance model: A case study, 16th Australasian conference on information systems, Sydney, Australia.
- Bose, K. (2003). An e-learning experience: a written analysis based on my experience with primary school teachers in an e-learning pilot project, International review of research in open and distance learning, Vol. 4, No. 2.
- Burton-Jones, A. & Hubona, G. S. (2003). The mediation of external variables in the technology acceptance model, working paper, department of computer information systems, Georgia state university.
- Churchill, G. A. (1991). Marketing Research: Methodological foundations. Forth worth, TX: Dryden Press.
- Compeau, D. R. & Higgins, C. A. (1995). Application of social cognitive theory to training for computer skills, Information systems research, 6, 118-143.
- Compeau, D. R., Higgins, C. A. & Huff, S. (1999). Social cognitive theory and individual reactions to computing technology: a longitudinal study. MIS Quarterly, 23, 145-158.
- Davis, F. D. (1985). A technology acceptance model for empirically testing new end- user systems: theory and results, unpublished doctoral dissertation, Massachusetts institute of technology.
- Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology, MIS Quarterly, 13.
- Davis, F. D., Bagozzi, R. P. and Warshaw, P. R. (1989). User acceptance of computer technology: A comparison of two theoretical models, management science, 35.
- Delacey, B., and Leonard, D. (2002). Case study on technology and distance in education at the Harvard Business School. Educational technology and society, 5(2).
- Dillon, A., and Morris, M. G. (1996). User acceptance of information technology: theories and models. Annual review of information science and technology, 31, 3-32.
- Dishaw, Mark T., Strong, Diane M. and Bandy, D. Brent. (2002). Extending the task- technology fit model with self-efficacy constructs, eighth Americas conference on information system.
- Fishbein, M., and Ajzen, I. (1975). Belief, attitude, intention and behavior: An introduction theory and research. Reading, MA: Addison-Wesley.
- Harrison, A. W. and Rainer, R. K. (1992). The influence of individual differences on skill in end-user computing. Journal of management information system, 9(1), 93-111.
- Howard, S. G. & Smith, D. R. (1986). Computer anxiety in management: Myth or reality? Communications of the ACM, 29(7), 611-615.
- Igbaria, M., Guimaraes, T., & Davis, G. B. (1995). Testing the determinants of microcomputer usage via a structural equation model, journal of management information systems, 11(4), 87-114.
- Kerka, S. (1999). Distance learning, the internet, and the World Wide Web. ERIC
- Digest. (ERIC Document Reproduction Service No. ED 395214).
- Lim, C. K. (2000). Computer self-efficacy, academic self-concept and other factors as predictors of satisfaction and future participation of adult learners in web-based distance education. Dissertation abstracts international, 61, 02A.
- Liu, Su-Houn., Liao, Hsiu-Li and Peng, Cheng-Jun. (2005). Applying the Technology Acceptance Model and Flow Theory to Online E-learning Users' Acceptance Behavior, Issues in Information Systems, Vol. VI, No. 2.
- Majchrzak, A. and Cotton, J. (1988). A longitudinal study of adjustment to technological change: from mass to computer-automated batch production. Journal of occupational psychology, 61, 43-66.
- Mathieson, K. (1991). Predicting user intentions: comparing the technology acceptance model with the theory of planned behavior. Information systems research, 2.
- Moore, G. C. and Benbasat, I. (1991). Development of an instrument to measure the perceptions of adopting an information technology adoption, Information systems research, 2.
- Morris, M. G. and Venkatesh, V. (2000). Age differences in technology adoption decisions: implications for a changing workforce. Personnel psychology, 53, 375-403.
- Nickel, G. S. and Pinto, J. N. (1986). The computer attitude scale. Computers in behavior, 2, 301-306.
- Pederson, P., & Nysveen, H. (2003). Usefulness and self-expressiveness: Extending TAM to explain the adoption of a mobile parking service. In the proceedings of the 16th Bled eCommerce Conference, Bled, Slovenia, June 9-11.
- Pituch, K. A. and Lee, Yao-Kuci. (2004). The influence of system characteristics on e- learning use, Computer and Education.
- Radcliffe, D. (2002). Technological and pedagogical convergence between work-based and campus-based learning. Educational technology and society, 5(2).
- Rogers, E. M. (1986). Communication technology: the new media in society. New York: Free Press.
- Saade, R. G. and Kira, D. (2006). The Emotional State of Technology Acceptance, Issues in informing science and information technology, Vol 3.
- Starr, R. M. (1997). Delivering instruction on the World Wide Web: overview and basic design principles. Educational technology, 37(3).
- Tan, M., & Teo, T. S. H. (2000). Factors influencing the adoption of internet banking. Journal of the association for information system, 1(5).
- Taylor, S. and Todd, P. A. (1995). Understanding information technology usage: a test of competing models, Information systems research, 6.
- Thurab-Nkhosi, D. (2003). WSI educational principles of elearning. University of Botswana, WebCT.
- Trevino, L. K., Lengel, R. H. and Daft, R. L. (1987). Media symbolism, media richness, and media choice in organizations: A symbolic interactionist perspective. Communication research, 14(5).
- Triandis, H. C. (1980). Values, attitudes and interpersonal behavior, Nebraska symposium on motivation, university of Nebraska press, Lincoln, NE, 195-259.
- Venkatesh, V. (1999). Creation of favorable user perceptions: exploring the role of intrinsic motivation, MIS Quarterly, 23, 239-260.
- Venkatesh, V. and Davis, F. D. (1996). A model of the antecedents of perceived ease of use: development of a test, decision sciences, 27.
- Venkatesh, V., Morris, M. G., Davis, G. B. and Davis, F. D. (2003). User acceptance of information technology: Toward a unified view, MIS Quarterly, 27(3).
- ISSN: 1302-6488
- Başlangıç: 2000
- Yayıncı: Anadolu Üniversitesi