Browsing PhD e-theses by Subjects
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Enhanced technology acceptance model to explain and predict learners' behavioural intentions in learning management systemsE-learning has become the new paradigm for modern teaching moreover, the technology allows to break the resurrection of time and place by enabling people to learn whenever and wherever they want. In information system research, learners' acceptance of e-learning can be predicted and explained using technology acceptance models. This research developed enhanced technology acceptance model to explain students' acceptance of learning management systems (LMSs) in Saudi Arabia. The research model aims to investigate the viability of TAM constructs in a nonwestern country. Moreover, due to the cultural impact of the Saudi Arabian culture towards genders, the research addresses the moderating effect of gender towards LMSs acceptance. The developed model variables identification focuses on two motivation aspects, extrinsic and intrinsic. The developed model consisted of ten variables in total, which can be categorised into three groups. First, the extrinsic variables consisting of information quality, functionality, accessibility, and user interface design. Second, the intrinsic variables are consisting of computer playfulness, enjoyment, and learning goal orientation. Third, the TAM variables consisting of perceived usefulness, perceived ease of use and behavioural intention. Moreover, to validate and examine the developed model, a questionnaire tool was developed for data collection. Furthermore, the data was collected from electronically from three universities over six weeks. The research findings supported the developed model. Additionally, the identified variables were good critical in predicting and explaining students' acceptance of LMSs. The research applied structural equation modelling for statistical analysis using IBM AMOS. The research results confirmed the applicability of the developed model to explain the Saudi students' acceptance of LMSs. The developed model explained high variance among the dependent variables outperforming the excising models. The research improved the explanatory power of the TAM model through the identified variables. Furthermore, the research results showed that the extrinsic variables were stronger predictors of students' perceived usefulness, perceived ease of use and behavioural intention. In addition, the results showed that males and females perception towards the LMS was significantly different. The male students' acceptance towards LMSs was higher than females. Moreover, enjoyment was the stronger determinant of females' behavioural intention.