Open and Distance Education/E-learning Readiness/Students E-readiness in Higher Education

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This chapter introduces two major models for gauging and/or determining student e-readiness to utilize ICT in Higher Education. Both models the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology are proven to be user-friendly. Each model will be described according to the developers’ perspectives and supported with empirical examples from some past studies. Students e-readiness to utilize ICT in Higher Education is significantly important as it contributes to the success of the learner and the development of e-learning at various levels of education. According to Vilkonis, Bakanoviene, and Turskiene, “the readiness of the learner is one of the core elements of the e-learning system.”[1]

Dada defines e-readiness (electronic readiness) as a measure of a nation willing to integrate and utilize information and communication technologies (ICTs) into society.[2] In another definition by the Economist Intelligence Unit (EIU) as cited in Srichanyachon, “e-learning readiness or e-readiness is a measure of the quality of a country’s ICT infrastructure and the ability of its consumers, businesses, and governments to use ICT to their benefit.”[3] The definitions imply the notion that for students, e-readiness refers also to their ability or willingness to use ICTs as a tool for enhancing their learning online.

The e-readiness of the learner, however, depends on numerous factors. These factors contribute their effectiveness in utilizing ICTs. The Technology Acceptance Model (TAM) devised by Davis (1989) highlights some of these factors. According to TAM, the perceived ease of use and perceived usefulness are the primary predictors of users’ attitude to accept or use ICT.[4] Davis initially devised TAM based on or an extension of the Theory of reasoned action.[5] Additionally, in TAM, “perceived usefulness defines as the degree to which the learner believes that using technology would enhance his/her study performance. Perceived ease of use, in contrast, refers to the degree of a person’s belief that the use of ICT would be free of effort, ease of difficulty or freedom from great effort.”[6] These factors, influence the learners’ attitude as it can escalate or de-escalate his/her motives to adapt to technology as a new learning tool.

In a number of studies conducted on the issue of students e-readiness, it verifies that perceived usefulness and perceived ease of use have significantly influenced students’ intention to use, and attitude towards using technology. In a study examining TAM and students’ acceptance of the e-learning confirmed that:

Perceived usefulness is crucially important in determining learner intention to use technology than the attitude toward using. Perceived usefulness and perceived ease of use are significantly affecting attitude towards using the technology. TAM can be employed as an explanatory model for students acceptance of e-learning technology.[7]

In contrast to the factors identified in TAM, the Unified Theory of Acceptance and Use of Technology (UTAUT) model proposes these four factors which include, performance expectancy, effort expectancy, social influence and facilitating conditions as primary determinants of online learning acceptance. According to the UTAUT model, each construct/effect represents different aspects or level of the user acceptance and usage behaviour in relation to technology. On the one hand, performance expectancy refers to the level of thrust an individual has that technology usage will improve his/her job/learning performance, and the other hand, effort expectancy refers to the degree of ease associated with the use of technology. In addition, social influence is associated to the influence of others on an individual perception that he/she should utilize the new system, while facilitation conditions refer to the degree to which an individual believes that an organizational and technical infrastructure exists to support the use of technology.[8]

Besides, UTAUT model incorporates four moderators known as user experience, voluntariness, and demographic characteristics (i.e. gender and age). Each moderator modifies the effects. In this regard, the effect of effort expectancy on intention to use ICT, for instance, is moderated by gender and age. This implies that an individual intention to use a new system or ICT is naturally dependent on effort expectancy, gender and age.

As a new model, UTAUT has been empirically tested and cross-validated for efficiency and utility. The tests result indicates that UTAUT model is an effective instrument for gauging students e-readiness to accept the use of ICT in Open and Distance Learning (ODL) at various levels of education. Venkatesh et al. pinpoint that three factors, performance expectancy, effort expectancy and social influence are direct determinants of intention to use ICT, whereas behavioural intention and facilitating conditions are direct determinants of usage behaviour.[8]

In recent years, UTAUT has been employed in several studies to investigate the learners’ e-readiness in accepting the use of any form of technology in ODL. Most of these researchers examine the students e-readiness to utilize m-learning. In one study, Wang, Wu, and Wang, verify that performance expectancy, effort expectancy, social influence, perceived playfulness and self-management of learning are determinants of behavioural intention to use m-learning.[9] Additionally, Abu-Al-Aish and Love, concur in their study that these factors (based on UTAUT) performance expectancy, effort expectancy, the influence of lecturers, with the quality of service, and personal innovative (added constructs) are significantly affecting the learners’ behavioural intention to use m-learning.[10]

Figure 3.2 The Unified Theory of Acceptance and Use of Technology (UTAUT) Model. (Venkatesh et al., 2003) URL:

Although these two models have their own distinct constructs (components/factors) for testing the e-readiness of the learner, both models are open to modification. Any future research can identify any factors relevant to the context or setting of the study area besides those previously discussed in the two models, the TAM and UTAUT model. Venkatesh et al. recommend that future studies should focus on “identifying constructs that can add to the prediction of intention and behaviour over and above what is already know and understood.”[8]

  1. Vilkonis, R., Bakanoviene, T., & Turskiene, S. (2013). Readiness of Adults to Learn Using E-Learning, M-Learning and T-Learning Technologies. Informatics in Education, 12(2), 181-190.
  2. Dada, D. (2006). E‐Readiness for Developing Countries: Moving the focus from the environment to the users. The Electronic Journal of Information Systems in Developing Countries, 27(1), 1-14.
  3. Srichanyachon, N. (2009). Key Components of E-Learning Readiness‖.
  4. Cheon, J., Lee, S., Crooks, S. M., & Song, J. (2012). An investigation of mobile learning readiness in higher education based on the theory of planned behaviour. Computers & Education, 59(3), 1054-1064. Padilla-MeléNdez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317.
  5. Padilla-MeléNdez, A., Del Aguila-Obra, A. R., & Garrido-Moreno, A. (2013). Perceived playfulness, gender differences and technology acceptance model in a blended learning scenario. Computers & Education, 63, 306-317.
  6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 319-340.
  7. Masrom, M. (2007). Technology acceptance model and e-learning. Technology, 21(24), 81.
  8. a b c 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.
  9. Wang, Y. S., Wu, M. C., & Wang, H. Y. (2009). Investigating the determinants and age and gender differences in the acceptance of mobile learning. British journal of educational technology, 40(1), 92-118.
  10. Abu-Al-Aish, A., & Love, S. (2013). Factors influencing students’ acceptance of m-learning: an investigation in higher education. The International Review of Research in Open and Distributed Learning, 14(5).