Comparative analysis of mobile and online banking use in South Africa
Online and mobile banking are both channels with potential to make banking accessible to the majority of people, and in particular in South Africa where there is a high uptake of mobile communication usage. Yet the adoption of both these channels has been very low, especially when one compares the rate at which mobile communication devices continue to grow. The purpose of this study was to identify and compare the factors that influence the adoption of mobile and online banking in South Africa using the Theory of Acceptance and Use of Technology (UTAUT) and to develop a deeper understanding of the antecedent factors that influence the adoption of one service over the other in South Africa. The aim was to understand these factors and test and verify these factors in the South African context. The research data was collected by using self-administered questionnaires that were distributed to mobile and online banking customers and prospective customers, and in addition, interviews were conducted with both digital banking experts and thought leaders. A total of 215 responses was collected from the survey questionnaire administered with 177 usable responses. A further 6 interviews were conducted with digital banking experts and thought leaders in order to cross-examine the quantitative results from the surveyed population. The demographic information as well as technology usage is useful to understand the type of customer profile that the banks have to focus their marketing efforts on in order to improve on the adoption of both online and mobile banking services. Principal component analysis was applied on the ii surveyed data to test whether the proposed factors are applicable in the South African context. The factors that were identified to influence the adoption of mobile and online banking are performance expectancy, effort expectancy, social influence and facilitating conditions as per the unified theory of acceptance and use of technology (UTAUT) model. Effort expectancy was not an influential factor in online banking contrary to the UTAUT model. Furthermore, the study tested how factors like age and gender moderate some of these factors in order to further understand the adoption influence across some groupings. In comparison, the identified factors were more influential on online banking than mobile banking, highlighting the relationship that might exist between factors and the product lifecycle. The study further showed that there are multiple factors that influence the adoption of technologies. Some of the factors are more influential than others under certain circumstances; and some of the factors could even be more influential to certain technologies than others. It is therefore important for organisations to continuously study these factors throughout the technology lifecycle as this will enhance decision-making and increase successful technology implementation.
Mobile banking , Online banking , Banks and banking