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. / Psychology / M.A. (Psychology)
| Identifer | oai:union.ndltd.org:netd.ac.za/oai:union.ndltd.org:unisa/oai:uir.unisa.ac.za:10500/26667 |
| Date | 01 1900 |
| Creators | Bellingan, Adele |
| Contributors | Hagen, Sean Noel |
| Source Sets | South African National ETD Portal |
| Language | English |
| Detected Language | English |
| Type | Dissertation |
| Format | 1 online resource (182 leaves, illustrations), application/pdf |
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