Predicting Students’ Intention and Actual Use of E-Learning Using the Technology Acceptance Model: A Case from Zimbabwe

Edward Elirehema Marandu, Forbes Makudza, Sothini N Ngwenya

Abstract


This study investigates the extent to which the Technology Acceptance Model (TAM) constructs predict intention to accept and use technology in learning. Data were obtained from 337 students from Bindura University of Science Education (BUSE) in Zimbabwe. The findings made three revelations. First, usefulness appears as an important driver for intention to use e-technology in education. Second, Ease-of-Use was contrary to the hypothesis, but was statistically insignificant. Third, Behavioural Intention has a positive and a strong association with Actual Use. The findings suggest several implications for theory and policy. In theoretical terms, the study provides evidence for the predominance of Usefulness over Ease-of-Use in predicting intention to adopt e-learning among students. In practical terms, the study shows that to ensure that students use particular technologies for study, a functionally useful system must be put in place. As such, technologies, which do not meet this condition, may simply be ignored.

https://doi.org/10.26803/ijlter.18.6.7


Keywords


Technology Acceptance Model, Usefulness, Ease-of-Use, Intention, E-Learning, University, Zimbabwe

Full Text:

PDF

References


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

Adams, D. A., Nelson, R. R., & Todd, P. A. (1992). Perceived usefulness, ease of use, and usage of information technology: A replication. MIS Quarterly(16), 227–247. doi:10.2307/249577

Agbatogun, A. (2013). Interactive digital technologies’ use in Southwest Nigerian Universities. Education Tech Research Dev, 333–357. https://doi.org/10.1007/s11423-012-9282-1

Ajzen, I., & Madden, T. J. (1986). Prediction of goal-directed behavior: Attitudes and perceived behavioral control. Journal of Experimental Social Psychology, 22, 453-474. https://doi.org/10.1016/0022-1031(86)90045-4

Allison, C., Bottu, S., Heckadon, M., & Leider, A. (2016). An analysis of students internet usage and its impact on the infrastructure of a school’s information technology services. Students Faculty Board Day (pp. D7.1 – D7). Pace University.

Anderson, S., & Groulx, J. (2015). Predicting elementary student teachers 'technology acceptance. The International Journal of Information and Learning Technology, 198-208. https://doi.org/10.1108/ijilt-01-2015-0003

Attuquayefio, S. N., & Addo, H. (2014). Using UTAUT model to analyse students’ ICT adoption. International Journal of Education and Development and Communication Technology (IJEDICT), 10(3), 75-86.

Barak, T., Boston, C., Shimaneni, J. S., & Dunckley, L. (2006). Users’ Satisfaction — An African Perspective. Proc. of BCS HCI, 2, 167-172.

Brown, J. D. (2002). The Cronbach alpha reliability estimate. JALT Testing & Evaluation SIG Newsletter, 6(1), 17 – 18.

Cant, T., & Bothma, C. (2010). Computer literacy and attitudes towards e-learning among first year medical students. BioMed Central . J Med Educ., 61, 410-2.

Chau, P. Y., & Hu, P. J. (2002). Examining a model of information technology acceptance by individual professionals: An exploratory study. Journal of Management Information Systems, 18(4), 191- 229. https://doi.org/10.1080/07421222.2002.11045699

Chiaburu, D. S., Oh, I. S., Berry , C. M., Li, N., & Gardner, R. G. (2011). The five-factor model of personality traits and organizational citizenship behaviors: a meta-analysis. Journal of Applied Psychology, 6, 1140-1166. doi:10.1037/a0024004

Churchill, G. A. (1979). A paradigm for developing better measures of marketing constructs. Journal of Marketing Research, 16(February), 64—73.

Cronbach, L. J. (1970). Essentials of Psychological Testing. Harper & Row.

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.

Davis, F. D. (1989). Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology. MIS Quarterly, 13(3), 319-339.

Dede, A. (2005). A Theoretical Assessment of the User-Satisfaction Construct in Information Systems Research. Management Science, 36, 76-91.

Dwivedi, Y. K., Rana, N. P., Anand, J., Clement, M., & Michael, D. W. (2017). Re-examining the Unified Theory of Acceptance and Use of Technology (UTAUT): Towards a Revised Theoretical Model. Information Systems Frontiers. https://doi.org/10.1007/s10796-017-9774-y

Fishbein, M., & Ajzen, I. (1980). Understanding attitudes and predicting social behaviour. New Jersey: Prentice-Hall.

Gujarati, D. (1978). Basic Econometrics. San Francisco: McGraw-Hill Book Company.

Hasan, H., & Ditsa, G. (1998). The Impact of Culture on the Adoption of IT: An Interpretive Study. Journal of Global Information Management (JGIM), 7(1). doi:10.4018/jgim.1999010101

Hendrickson, A. R., Massey, P. O., & Cronan, T. P. (1993). On the test-retest reliability of perceived usefulness and perceived ease of use scales. MIS Quarterly, 17(2), 227–230. doi:10.2307/249803

Holden, H., Ozok, A., & Rada, R. (2008). Technology Use and Acceptance in the Classroom:Results from an Exploratory Survey Study Among Secondary Education Teachers in the USA. Interactive Technology and Snart Education, 113-134.

İbili, E. (2019). Applying the technology acceptance model to understand maths teachers’ perceptions towards an augmented reality tutoring system. Education and Information Technologies, June, 1–23. doi:10.1007/s10639-019-09925-z

Im, I., Seongtae, H., & Myung, S. K. (2011). An International Comparison of Technology Adoption. Information & Management, 48, 1–8. https://doi.org/10.1016/j.im.2010.09.001

Kar, D., Birbal, S., & Mondal, B. C. (2014). Attitude of university students towards e-learning in west Bengal. American Journal of Educational Research, 2(8), 669-673. https://doi.org/10.12691/education-2-8-16

Keil, M., Beranek, P. M., & Konsynski, B. R. (1995). Usefulness and ease of use: field study evidence regarding task considerations. Decision Support Systems, 13(1), 75–91. doi:10.1016/0167-9236(94)e0032-m

Keller, C., & Cernerud, L. (2002). Students' perception of e-learning in university education. Journal of Educational Media, 27(1-2), 55-65. https://doi.org/10.1080/0305498032000045458

Khumalo, P. (2014). Students’ use of digital libraries in higher learning institutions. A dissertation submitted in partial fulfilment of the requirements for the degree MTech Business Information Systems of the Department of Informatics (Unpublished).

Kimathi, F. A., & Zhang, Y. (2019). 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. Creative Education, 10, 208-223. https://doi.org/10.4236/ce.2019.101017

Kotler, P., & Armstromg, G. (2009). Principles of Marketing (9 ed.). London: Pearson Education.

LaRose, R., Gregg, J., & Eastin, M. (2006). Audio graphic tele-courses for the Web: An experiment. Journal of Computer Mediated Communications. 4(2). Retrieved from https://doi.org/10.1111/j.1083-6101.1998.tb00093.x

Mahmood, M. A., Hall, L., & Swanberg, D. L. (2001). Factors affecting information technology usage: A meta-analysis of the empirical literature. Journal of Organizational Computing and Electronic Commerce, 11(2), 107-130.

Malhotra, N. K. (1996). Marketing Research: An Applied Analysis (2 ed.). London: Prentice.

McEachan, R. R., Conner, M., Taylor, N. J., & Lawton, R. J. (2011). Prospective prediction of health-related behaviours with the Theory of Planned Behaviour: A meta-analysis. Health Psychology Review, 5(2), 97-144. doi:10.1080/08870446.2011.613995

Nunnaly, J. (1967). Psychometric theory. New York: McGraw-Hill.

Pavel, R.-M., & Rajagopal. (2015). Driving Consumers Toward Online Retailing Technology: Analyzing Myths and Realities. Journal of Transnational Management, 20(3), 155-171. doi:10.1080/15475778.2015.1058688

Poropat, A. E. (2009). A meta-analysis of the five-factor model of personality and academic performance. Psychological Bulletin, 135, 322-338. doi:10.1037/a0014996

Rahimi, B., Nadri, H., Hadi, L. n., & Timpka, T. (2018). A Systematic Review of the Technology Acceptance Model in Health Informatics. Applied Clinical Informatics, 09(3), 604–634. doi:10.1055/s-0038-1668091

Rahman, M. M., Deb, S., Carruth, D., & Strawder, L. (2019). Using Technology Acceptance Model to Explain Driver Acceptance of Advanced Driver Assistance Systems. Advances in Human Factors of Transportation(June), 44-56. doi:10.1007/978-3-030-20503-4_5

Reliability Analysis. (2007). Reliability Analysis. Retrieved November 8, 2019, from http://www2.chass.ncsu.edu/garson/pa765/reliab.htm

Rhodes, R. E., & Smith, N. E. (2006). Personality correlates of physical activity: A review and meta-analysis. British Journal of Sports Medicine, 40(12), 958-965. doi:10.1136/bjsm.2006.028860

Rob, E., Thorpe, M., & Grainne, C. (2012). Student attitudes towards and use of ICT in course study, work and social activity: a technology acceptance model approach. British Journal of Educational Technology, 43(1), 71–84. https://doi.org/10.1111/j.1467-8535.2010.01142.x

Rogers, E. M. (1995). Diffusion of Innovations (4 ed.). New York: Free Press.

Segars, A. H., & Grover, V. (1993). Re-examining perceived ease of use and usefulness: A confirmatory factor analysis. MIS Quarterly, 17(4), 517–525. doi:10.2307/249590

Sheeran, P. (2002). Intention-behaviour relations: A conceptual and empirical review. European Review of Social Psychology, 12, 1-36. https://doi.org/10.1080/14792772143000003

Sheeran, P; Webb, T L. (2016). The Intention–Behavior Gap. Social and Personality Psychology Compass, 10(9), 503-518.

Sheppard, B. H., Hartwick, I., & Warshaw, P. R. (1988). The theory of reasoned action: A meta-analysis of past research with recommendations for modifications and future research. Journal of Consumer Research, 15(3), 325–343. https://doi.org/10.1086/209170

Szajna, B. (1994). Software evaluation and choice: predictive evaluation of the Technology Acceptance Instrument. MIS Quarterly, 18(3), 319–324. doi:10.2307/249621

Tsekea, S. (2016). The use of digitised information resources by faculty of commerce undergraduate students at Bindura University. MSC dissertation (Unpublished).

Venkatesh, V. M., Davis, M. G., & Davis, F. D. (2003). User acceptance of information technology: toward a unified view. MIS Quarterly, 27(3), 425 – 478. https://doi.org/10.2307/30036540


Refbacks

  • There are currently no refbacks.


e-ISSN: 1694-2116

p-ISSN: 1694-2493