Predicting mobile banking adoption in Bangladesh: a neural network approach

Abstract

Rapid development in technology amplifies the need of examining technology adoption. Financial sectors, nowadays, are providing a long list of both financial and nonfinancial services to attract their potential customers using mobile banking (m-banking). Thus, m-banking is becoming a part of modern life style. This paper critically predicts the key factors of m-banking adoption in Bangladesh from the user perspective. A three layer neural network is used with 10-fold cross validation as a prediction model. For robustness, factor analysis is run using principal component analysis and verimax rotation technique. After pilot study, a structured questionnaire was used and a total of 314 respondents successfully returned their filled survey questionnaire. The results revealed that Perceived Ease of Use is the most influencing factor. One significant finding is that gender has no significance on m-banking adoption in Bangladesh explaining both men and women are flexible in technology adoption. Policy makers can find significant results in this paper for implementing future service design. Limitations and future research scope are also discussed.

Keywords:

• E-TAM
• factor analysis
• m-banking
• ANN
• Bangladesh

Disclosure statement

The author reports no conflicts of interest. The author alone is responsible for the content and writing of this article.

Notes on contributor

Md. Abul Kalam Azad received his MSc from University of Bedfordshire, UK and his MBA & BBA from International Islamic University Chittagong (IIUC), Bangladesh. His research fields are finance, corporate governance, and business. He is an Associate professor of Banking at Department of Economics & Banking at IIUC. Currently, he is pursuing PhD at University of Malaya, Malaysia.