E learning – does individuality matter when it comes to e-learning user’s behavior in developing countries?
E-learning environments ‘reduce the cost of provision and therefore increase revenues for academic institutions’ (Saadé & Bahli, 2005). They also ‘afford students with more study flexibility and improve their learning experience and performance’ (Nora & Snyder, 2009).
However, for such results to be efficient, the tools need to be utilized correctly. Therefore, ‘the successful implementation of elearning tools depends on whether or not students are willing to adopt and accept the technology’ (Clay, Rowland, & Packard, 2009). This is crucial, especially in developing countries such as Lebanon where e-learning is still in its infancy and ‘universities and higher education institutions support traditional styles of pedagogy in education’ (Baroud & Abouchedid, 2010; Nasser, Khoury, & Abouchedid, 2008).
This research that I’m focusing on was done by: Ali Tarhini and Kate Hone – Department of Information System and Computing, Brunel University, London, United Kingdom and Xiaohui Liu – Faculty of Engineering, King Abdulaziz University, Jeddah, Saudi Arabia, which was published in Computers in Human Behavior, Volume 63, 1 October 2016.
So what kind of individuality?
User acceptance and usage behaviour towards technology can be influenced by a variety of factors such as cultural, individual differences and social influence. The method used in the research discusses a Technology Acceptance Model (TAM) to assess how much the individual matters in an e-learning process.
So, to address these limitations, this study extended TAM to include two constructs, social norms and quality of work life (Kripanont, 2007) and a set of individual differences (age, gender, educational level and experience) as moderators (Venkatesh et al., 2003) in order to enhance the understanding of the e-learning users. Specifically, the research investigated the factors that affected the acceptance and use of e-learning in the developing world, particularly in Lebanon as a cultural context.
The research found that all the individual differences (age, gender, educational level and experience) obviously impact on the e-learning user journey however the most important aspect was their own definition of ‘quality of work life’. Therefore, making sure every student recognizes how e-learning can contribute to their overall professional life should be a priority. Individuality matters less so much as making sure the importance is emphasized. So, obviously, individuality matters so I hope that future research can build on the findings of this research and offer greater insights on the social and individual factors rather than simply the technological solution.
– Saadé, R., & Bahli, B. (2005). The impact of cognitive absorption on perceived usefulness and perceived ease of use in on-line learning: An extension of the technology acceptance model. Information & Management, 42, 317–327
– Nora, A., & Snyder, B. P. (2009). Technology and higher education: The impact of elearning approaches on student academic achievement, perceptions and persistence.
– Clay, M. N., Rowland, S., & Packard, A. (2009). Improving undergraduate online retention through gated advisement and redundant communication. Journal of College Student Retention: Research, Theory and Practice, 10, 93–102.
– Baroud, F., & Abouchedid, k. (2010). E-learning in Lebanon: Patterns of E-learning development in Lebanon’s mosaic educational context. In U. Demiray (Ed.), Elearning practices: Cases on challenges facing e-learning and national development, institutional studies and practices (pp. 409–424). Eskisehir-Turkey: Anadolu University.
– Kripanont, N. (2007). Examining a technology acceptance model of internet usage by academics within Thai business schools. Australia: Victoria University Melbourne.
– Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly, 425–478.
– A. Tarhini et al (2014). The effect of individual differences on e-learning users’ behavior in developing countries. Computers in Human behavior 41, 153-163.