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Abstract

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-users level data were analyzed in order to predict the adoption of automated teller machines using the 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 the adoption intention. Following from the PCA, the results show that the cumulative percentage of the predictive variables was 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 phenomena which may have lessened patronage and secondly, customers’ categorization on the basis of innovativeness which skewed in favour of early adopters rather 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 customer categorization on the basis of innovativeness, analysis of these groupings should be conducted in order to understand the characteristics of each group.

Keywords

Adoption relative advantage innovators late majority laggards

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How to Cite
Awuma, W., Danso, I., Yeboah, P., & Akentara, R. (2023). Application of the Theory of Diffusion of Innovation on the Adoption of Automated Teller Machines in the Sunyani Municipality. International Journal of Multidisciplinary Studies and Innovative Research, 11(5), 1719–1727. https://doi.org/10.53075/Ijmsirq/091232425366
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