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|Title:||Empirical examination of decision making core technology adoption theory to explain youth preferences for HIV preventive actions||Authors:||Shongwe, Njabulo Samson Melusi||Issue Date:||3-Oct-2014||Abstract:||This study reports on the application of decision making core technology adoption theory to empirically examine youth preferences for Human Immunodeficiency Virus (HIV) preventive actions. In order to contribute to the open discourse on whether technology adoption rate is higher for male or female, goal desire, goal intention, action desire and action intention elements of decision making core theory were tested. A mobile health information system was implemented as an HIV information disseminating tool and used for experimentation to determine adoption by youths. A dataset of 118 pupils from two high schools was used for pilot investigation. A dataset of 292 undergraduate youths aged 10-24 years from two universities in South Africa was generated to validate the research model. The Partial Least Square (PLS) analytic modelling technique was used to determine the predictive power of decision making core model from the input dataset. Results of experimentation show that regardless of the gender youth accepts to use mobile information system to access HIV information. The predictive power of the decision making core model was found to be independent of gender factor, which was also not found to moderate the relationship between Perceived Behavioural Control (PBC) and action intention. In addition, gender was not found to moderate the order of importance in factors that predict youth preferences for HIV preventive action. PBC, action desire and goal desire were selected as the most important predictors of HIV preventive actions. The factor of action desire was found to mediate the relationship between PBC and action intention such that the mediation effect was stronger for male youth (68%) than for female youth (19%). Finally, the decision making core model better predicted youth preferences for HIV preventive actions as compared to two models based on Theory of Reason Action (TRA) and Theory of Planned Behaviour (TPB).||Description:||Submitted in fulfillment of the requirements for the degree of Master of Technology, Information Technology, Durban University of Technology, Durban, South Africa, 2013.||URI:||http://hdl.handle.net/10321/1166|
|Appears in Collections:||Theses and dissertations (Accounting and Informatics)|
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