Analysis of the factors influencing customer adoption of internet banking in Nairobi

Abstract

The adoption of internet banking as a platform for offering banking services is on a steady rise globally. The purpose of this study was to examine the factors influencing customer adoption of internet banking in Kenya. The study utilized an Integrated Model Framework to investigate the factors that influence customer adoption of internet banking in Kenya. Variables were drawn from traditional models that offered separate and theoretically sound constructs, namely; Theory of Perceived Risk, Technological Acceptance Model, Theory of Planned Behavior, Theory of Reasoned Action, Diffusion of Innovation Theory and the ABC Model of Attitudes. The scope of the research was Kenyans who held an account with any of the commercial banks in Kenya between March and April 2017. Questionnaires were distributed to customers either inside banking halls or while entering or leaving the banking hall in sampled bank branches. A sample size of 384 customers was used. Data was analysed using SPSS software where various data analysis techniques including Descriptive statistics, Pearson’s Correlation Coefficients and Multiple Regression Analysis were employed. Results revealed that 47.1% of the respondents had adopted internet banking (IB) as of April 2017. Similarly, only 14.5% of the respondents had used IB frequently enough to infer full adoption. The model used in this study explained 40.9% of the variance in Adoption of Internet Banking in Nairobi. Further, Perceived Risk Facets, Diffusion of Innovation Factors, Technological Acceptance Factors, Planned Behavior Factors and Attitude were found to be predictors of Adoption of Internet Banking by customers in Nairobi. Perceived Risk Facet was found to be a negative predictor of internet banking adoption while all the other factors were found to be positive predictors of internet banking. The research may give some guidance to banks, KBA, CBK, Government of Kenya and other policy makers. For instance, policy makers may want to come up with policies and systems that mitigate risk associated with IB use thereby increasing its adoption and use. This research attempted to fill the knowledge gap existing regarding factors influencing customer adoption of internet banking in Kenya. The study suggests further research in the area to explore more factors that can explain the customer adoption of IB in Kenya as the overall research model did not explain most of the variance in the adoption of IB, suggesting that other factors exist that could account for adoption of IB in Kenya.