Mobile learning readiness : psychological factors influencing student's behavioural intention to adopt mobile learning in South Africa

Abstract

With recent advances in technology, distance education has seen a move towards online and e-learning programmes and courses. However, many students in South Africa have limited access to computer technology and/or the Internet resources necessary for online learning. Worldwide trends have recently seen a growing emphasis on the use of mobile technology for learning purposes. High mobile penetration rates in South Africa means that mobile learning can potentially overcome many of the challenges associated with distanceand online learning. This research therefore aimed to explore adult distance education students’ mobile learning readiness in the South African context. Specifically, this study examined the influence of mobile learning self-efficacy, locus of control, subjective norm, perceived usefulness, perceived ease of use, perceived behavioural control and attitude towards mobile learning on students’ behavioural intention to adopt mobile learning. In order to test a model predicting students’ behavioural intention, the conceptual framework guiding the investigation combined the Technology Acceptance Model (TAM) and the Theory of the investigation combined the Technology Acceptance Model (TAM) and the Theory of Planned Behaviour (TPB) and extended the model to include locus of control and mobile learning self-efficacy. A sample of 1070 students from a private higher education institution in South Africa participated in this study. Data were collected using an online survey questionnaire. Multiple regression analysis indicated that perceived ease of use contributed most significantly to behavioural intention to adopt mobile learning, followed by attitude towards mobile learning, subjective norm, perceived usefulness, perceived behavioural control and locus of control. Mobile learning self-efficacy did not significantly influence behavioural intention to adopt mobile learning. Overall, the model accounted for 44.8% of the variance in behavioural intention to adopt mobile learning. Significant differences in age, gender, race and household income existed with regard to several of the psychological constructs hypothesised to influence behavioural intention to adopt mobile learning. 4 Structural equation modelling was used to examine the fit between the data and the proposed model. The chi square goodness for fit test and the RMSEA indicated poor fit between data and model. Considering the sensitivity of the chi square statistic for sample size and the negative influence of too many variables and relationships on the RMSEA, a variety of alternative fit indices that are less dependent on the sample size and distribution were used to examine model fit. The GFI, AGFI, NFI and CFI all exceeded their respective acceptable levels, indicating a good fit with the data.