An exploratory analysis of internet banking adoption using decision tree induction

Although internet banking promises several benefits to bank customers, the adoption of online banking services continue to be low especially in developing countries. Prior research has examined internet banking adoption as a classification problem with a focus on adopters and non-adopters using traditional statistical approaches. In this paper we extend the problem by including partial adopters and used decision tree induction as the analytical method. Our results on data from customers in 20 countries show that household income is the most important variable in separating those who do not adopt IB or partially adopt IB from those who fully do. Education and age are the most important variables in separating partial adopters from non-adopters. While exploratory, our results provide opportunities for further theory development on internet banking adoption while practically contributing to discussions on how practitioners can develop specific strategies to targeted customers to become fully adopters of online banking services.