Application of the Theory of Diffusion of Innovation on the Adoption of Automated Teller Machines in the Sunyani Municipality

The ongoing debate concerning economic losses associated with human tellers and the negative impact of queuing in the banking halls have led to new technologies and innovation diffusion. This paper applied a regression model in which end-user’s level data were analyzed in order to predict adoption of automated teller machines using theory of diffusion of innovation (e.g., relative advantage, complexity, observability, trialability and compatibility) empirically. Applying the principal component analysis and regression as analytical techniques, the results were compatible with adoption intention. Following from the PCA, the results show that the cumulative percentage of the predictive variables were above the 50% threshold with KMO measure and Cronbach Alphas recording scores above 70%, suggesting the appropriateness of PCA in data reduction. The predictive variables have strong predictability and were significant. Abstracting from the results there may be two reasons relating to the low adoption decisions. The first reason may be due to some inherent inefficiencies or unwarranted phenomenon which may have lessen patronage and secondly, customers’ categorization on the basis of innovativeness which skewed in favour of early adopters than late adopters. The banks should take steps to update the existing technologies relating to automated teller machine operations in particular in order to address the challenges before enforcing any future deployment to meet end-users’ expectations. Because adoption can be influenced by customers categorization on the basis of innovativeness, analysis of these groupings should be conducted in order to understand the characteristics of each group.

The ongoing debate concerning economic losses associated with human tellers and the negative impact of queuing in the banking halls have led to new technologies and innovation diffusion. This paper applied a regression model in which end-user’s level data were analyzed in order to predict adoption of automated teller machines using theory of diffusion of innovation (e.g., relative advantage, complexity, observability, trialability and compatibility) empirically. Applying the principal component analysis and regression as analytical techniques, the results were compatible with adoption intention. Following from the PCA, the results show that the cumulative percentage of the predictive variables were above the 50% threshold with KMO measure and Cronbach Alphas recording scores above 70%, suggesting the appropriateness of PCA in data reduction. The predictive variables have strong predictability and were significant. Abstracting from the results there may be two reasons relating to the low adoption decisions. The first reason may be due to some inherent inefficiencies or unwarranted phenomenon which may have lessen patronage and secondly, customers’ categorization on the basis of innovativeness which skewed in favour of early adopters than late adopters. The banks should take steps to update the existing technologies relating to automated teller machine operations in particular in order to address the challenges before enforcing any future deployment to meet end-users’ expectations. Because adoption can be influenced by customers categorization on the basis of innovativeness, analysis of these groupings should be conducted in order to understand the characteristics of each group.