Main Article Content
Innovation Diffusion, Education Business, E-learning, Attitude, Advantages-Disadvantages
The objectives of this study are two folds. Firstly is to identify the advantages and disadvantages factors of electronic learning’s adoption. Secondly is to measure the influence of innovation adoption components toward users’ attitude in using electronic learning. A mixed method of study was carried out in response to the research’s objectives. The qualitative approach was conducted by means of interviewing 25 participants of users to identify e-learning advantages and disadvantages. The quantitative approach was used to test the hypotheses. A questionnaire was distributed to 313 e-learning system users. The results show that the three advantages and disadvantages of e-learning adoption factors were formed. SEM-Smart PLS was used to test the hypothetical relationships. The results indicate that three dimensions of innovation diffusion significantly influenced the attitude toward e-learning, while two dimensions were not significant. The findings suggest that education-based business services should use the advantages factors and influential dimensions to promote their teaching-learning services delivery and eliminate weaknesses and insignificant dimensions.
Arkorful, V., & Abaidoo, N. (2015). The role of e-learning, advantages and disadvantages of its adoption in higher education. International Journal of Instructional Technology and Distance Learning, 12(1), 29-42.
Boateng, R., Mbrokoh, A. S., Boateng, L., Senyo, P. K., & Ansong, E. (2016). Determinants of e-learning adoption among students of developing countries. The International Journal of Information and Learning Technology, 33(4), 248-262. doi: 10.1108/IJILT02-2016-0008
Bouhnik, D., & Marcus, T. (2006). Interaction in distance‐learning courses. Journal of the American Society for Information Science and Technology, 57(3), 299-305. doi: 10.1002/asi.20277
Chang, V. (2016). Review and discussion: E-learning for academia and industry. International Journal of Information Management, 36(3), 476-485. doi: 10.1016/j.ijinfomgt.2015.12.007
Cheung, R., & Vogel, D. (2013). Predicting user acceptance of collaborative technologies: An extension of the technology acceptance model for e-learning. Computers & Education, 63, 160-175. doi: 10.1016/j.compedu.2012.12.003
Chu, T.-H., & Chen, Y.-Y. (2016). With good we become good: Understanding e-learning adoption by theory of planned behavior and group influences. Computers & Education, 92, 37-52. doi: 10.1016/j.compedu.2015.09.013
Daryanto, A., Ruyter, K. d., & Wetzels, M. (2010). Getting a Discount or Sharing the Cost: The Influence of Regulatory Fit on Consumer Response to Service Pricing Schemes. Journal of Service Research, 13(2), 153-167.
Duan, Y., He, Q., Feng, W., Li, D., & Fu, Z. (2010). A study on e-learning take-up intention from an innovation adoption perspective: A case in China. Computers & Education, 55(1), 237-246. doi: 10.1016/j.compedu.2010.01.009
Fernández, A., Peralta, D., Benítez, J. M., & Herrera, F. (2014). E-learning and educational data mining in cloud computing: an overview. International Journal of Learning Technology, 9(1), 25-52.
Hair, J. F., Hult, G. T. M., Ringle, C., & Sarstedt, M. (2017). A Primer on Partial Least Squares Structural Equation Modeling (PLS-SEM) (2 ed.). Thousand Oaks: Sage.
Hair, J. F., Sarstedt, M., Hopkins, L., & Kuppelwieser, V. G. (2014). Partial least squares structural equation modeling (PLS-SEM) An emerging tool in business research. European Business Review, 26(2), 106-121.
Henseler, J., Hubona, G., & Ray, P. A. (2016). Using PLS path modeling in new technology research: updated guidelines. Industrial Management & Data Systems, 116(1), 2-20. doi: 10.1108/IMDS-09-2015-0382
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.
Hussein, Z. (2017). Leading to Intention: The Role of Attitude in Relation to Technology Acceptance Model in E-Learning. Procedia Computer Science, 105, 159-164. doi: 10.1016/j.procs.2017.01.196
Lee, Y.-H., Hsieh, Y.-C., & Hsu, C.-N. (2011). Adding innovation diffusion theory to the technology acceptance model: Supporting employees' intentions to use e-learning systems. Educational Technology & Society, 14(4), 124-137.
Liao, H.-L., & Lu, H.-P. (2008). The role of experience and innovation characteristics in the adoption and continued use of e-learning websites. Computers & Education, 51(4), 1405-1416. doi: 10.1016/j.compedu.2007.11.006
Liaw, S.-S. (2008). Investigating students’ perceived satisfaction, behavioral intention, and effectiveness of e-learning: A case study of the Blackboard system. Computers & Education, 51(2), 864-873. doi: 10.1016/j.compedu.2007.09.005
Liaw, S.-S., & Huang, H.-M. (2013). Perceived satisfaction, perceived usefulness and interactive learning environments as predictors to self-regulation in e-learning environments. Computers & Education, 60(1), 14-24. doi: 10.1016/j.compedu.2012.07.015
Makkonen, S., & Johnston, J. (2014). Innovation adoption and diffusion in business-to-business marketing. Journal of Business & Industrial Marketing, 29(4), 324-331. doi: 10.1108/JBIM-08-2013-0163
Mantle‐Bromley, C. (1995). Positive attitudes and realistic beliefs: Links to proficiency. The Modern Language Journal, 79(3), 372-386. doi: 10.1111/j.1540-4781.1995.tb01114.x
Mohammadi, H. (2015). Investigating users’ perspectives on e-learning: An integration of TAM and IS success model. Computers in human behavior, 45, 359-374. doi: 10.1016/j.chb.2014.07.044
Okazaki, S., & dos Santos, L. M. R. (2012). Understanding e-learning adoption in Brazil: Major determinants and gender effects. The International Review of Research in Open and Distributed Learning, 13(4), 91-106. doi: 10.19173/irrodl.v13i4.1266
Park, S. Y. (2009). An analysis of the technology acceptance model in understanding university students' behavioral intention to use e-learning. Educational Technology & Society, 12(3), 150-162.
Rogers, E. M. (2003). Diffusion of innovations (5th Ed ed.). New York, NY, USA: The Free Press.
Rogers, E. M. (2004). A prospective and retrospective look at the diffusion model. Journal of health communication, 9(S1), 13-19. doi: 10.1080/10810730490271449
Shee, D. Y., & Wang, Y.-S. (2008). Multi-criteria evaluation of the web-based e-learning system: A methodology based on learner satisfaction and its applications. Computers & Education, 50(3), 894-905. doi: 10.1016/j.compedu.2006.09.005
Tarhini, A., Masa’deh, R. e., Al-Busaidi, K. A., Mohammed, A. B., & Maqableh, M. (2017). Factors influencing students’ adoption of e-learning: a structural equation modeling approach. Journal of International Education in Business, 10(2), 164-182. doi: 10.1108/JIEB-09-2016-0032
Tosuntaş, Ş. B., Karadağ, E., & Orhan, S. (2015). The factors affecting acceptance and use of interactive whiteboard within the scope of FATIH project: A structural equation model based on the Unified Theory of acceptance and use of technology. Computers & Education, 81, 169-178. doi: 10.1016/j.compedu.2014.10.009
Truong, H. M. (2016). Integrating learning styles and adaptive e-learning system: Current developments, problems and opportunities. Computers in human behavior, 55, 1185-1193. doi: 10.1016/j.chb.2015.02.014
Wang, W.-T., & Wang, C.-C. (2009). An empirical study of instructor adoption of web-based learning systems. Computers & Education, 53(3), 761-774.
Zhang, L., Wen, H., Li, D., Fu, Z., & Cui, S. (2010). E-learning adoption intention and its key influence factors based on innovation adoption theory. Mathematical and Computer Modelling, 51(11), 1428-1432. doi: 10.1016/j.mcm.2009.11.013