Examining the human factors that influence the adoption of self-service business intelligence within the banking sector of South Africa - De Waal, Sean (Department of Information Systems, 2026)
By making use of Business Intelligence (BI), the business is able to extract relevant information that can be used within their decision-making processes. However, due to the challenges faced within the traditional form of BI, such as limited human capital, complex processes and tools, the ability to provide effective and efficient information to the business is hindered. According to Considine & Cormican, (2016), simply adopting self-service technologies into the current business processes, allows the business to observe benefits such as cost savings and an increase in customer satisfaction. Self- Service Business Intelligence (SSBI) is one of these solutions, that has been shown to provide business users with the opportunity to become self-reliant in making business-critical decisions within the need of a BI specialist. The aim of this research has been to examine the human factors which play a role in the adoption SSBI within the banking sector of South Africa. During the literature review, it was deemed that the Model of PC Utilisation (MPCU) framework was the best fit to examine the human factors that influence the adoption of SSBI and testable hypotheses were formulated based on its constructs. The research employed a quantitative approach via the use of an online survey through Google Forms to collect responses from a specific group of employees within the “Big Four” banks of South Africa. A total of 257 usable responses were received and using IBM SPSS Statistics v28, statistical analyses were conducted in order to determine the significance of the hypotheses. The regression analysis revealed that the MPCU framework could explain 69.2% of the phenomena of SSBI adoption, indicating that the human aspect plays a significant role in the adoption of SSBI within the banking sector of South Africa. Two of the model constructs namely, Job Fit and Complexity were found not to be significant predictors of the adoption of SSBI while Affect Towards Use, Long-Term Consequences, Social Factors and Facilitating Conditions all predicted the adoption of SSBI. The results indicated that the participants were more concerned with the availability of the SSBI models and data while being heavily influenced by management or social factors to adopt and integrate into the SSBI methodology. The study contributes to academia by providing evidence that human factors are significant contributors to the adoption of SSBI while also affirming that the users highly consider the formatting of the reporting dashboards during their adoption. It is believed that these findings should be considered by the industry while developing and implementing SSBI solutions within their businesses. Keywords: Business Intelligence, Self-Service Business Intelligence, Banking, Adoption, Model of PC Utilisation (MPCU).