An Exploratory Study in Nursing Education: Factors Influencing Nursing Students’ Acceptance of Mobile Learning
The proliferation in the development of mobile learning (m-learning) has played a vital role in the rapidly growing electronic learning market. This relatively new technology can help to encourage the development of in learning and to aid knowledge transfer a number of areas, by familiarizing students with innovative information and communications technologies (ICT). M-learning plays a substantial role in the deployment of learning methods for nursing students by using the Internet and portable devices to access learning resources ‘anytime and anywhere’. However, acceptance of m-learning by students is critical to the successful use of m-learning systems. Thus, there is a need to study the factors that influence student’s intention to use m-learning. This paper addresses this issue. It outlines the outcomes of a study that evaluates the unified theory of acceptance and use of technology (UTAUT) model as applied to the subject of user acceptance in relation to m-learning activity in nurse education. The model integrates the significant components across eight prominent user acceptance models. Therefore, a standard measure is introduced with core determinants of user behavioural intention. The research model extends the UTAUT in the context of m-learning acceptance by modifying and adding individual innovativeness (II) and quality of service (QoS) to the original structure of UTAUT. The paper goes on to add the factors of previous experience (of using mobile devices in similar applications) and the nursing students’ readiness (to use the technology) to influence their behavioural intentions to use m-learning. This study uses a technique called ‘convenience sampling’ which involves student volunteers as participants in order to collect numerical data. A quantitative method of data collection was selected and involves an online survey using a questionnaire form. This form contains 33 questions to measure the six constructs, using a 5-point Likert scale. A total of 42 respondents participated, all from the Nursing Institute at the Armed Forces Hospital in Saudi Arabia. The gathered data were then tested using a research model that employs the structural equation modelling (SEM), including confirmatory factor analysis (CFA). The results of the CFA show that the UTAUT model has the ability to predict student behavioural intention and to adapt m-learning activity to the specific learning activities. It also demonstrates satisfactory, dependable and valid scales of the model constructs. This suggests further analysis to confirm the model as a valuable instrument in order to evaluate the user acceptance of m-learning activity.
Digital Object Identifier (DOI): doi.org/10.5281/zenodo.1129766Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 669
 Venkatesh, V. et al., 2003. User acceptance of information technology: towards a unified view. MIS Quarterly, 27(3), p.425-478.
 Creswell, J.W., 2012. Qualitative inquiry and research design: Choosing among five approaches. Sage publications.
 Liu, Y., Li, H. and Carlsson, C., 2010. Factors driving the adoption of m-learning: An empirical study. Computers and Education, 55(3), p.1211-1219. Available at: http://dx.doi.org/10.1016/j.compedu.2010.05.018. (Accessed 21 April 2016).
 Yu, T.K., Lu, L.C. and Liu, T.F., 2010. Exploring factors that influence knowledge sharing behavior via weblogs. Computers in Human Behavior, 26(1), pp.32-41.
 Straub, D., Boudreau, M.C. and Gefen, D., 2004. Validation guidelines for IS positivist research. The Communications of the Association for Information Systems, 13(1), p.63.
 Cronbach, L.J. and Meehl, P.E., 1955. Construct validity in psychological tests. Psychological bulletin, 52(4), p.281.
 Zarmpou, T., Saprikis, V. and Vlachopoulou, M., 2012. Examining behavioral intention toward mobile services: An empirical investigation in Greece. Mobile opportunities and applications for E-service innovations, 37.
 Abu-Al-Aish, A., 2014. Toward mobile learning deployment in higher education. Available at: http://v-scheiner.brunel.ac.uk/handle/2438/7998. (Accessed 16 June 2016).