Understanding mobile banking (MB) system usage is of great value and significance to both academicians and practitioners. MB use has increased to 30% of all bank users, generating over $500 m. annual revenue for bank according to WSJ. This paper provides a novel approach to study MB usage by combining data analytics technique of cluster analysis with a field survey. These users were segmented into homogeneous groups by using cluster analysis with their banking activity. In our study, we use cluster analysis to segment 4478 users based on their usage behavior from their log file activities captured with a MB system deployed by a mid-size bank in U.S. After clustering the banking transactions on a variety of MB usage attributes such as number and types of activities, we have created three homogeneous user groups who will be surveyed to determine the MB system success with IS success model. This field study will also gather their demographic background and experience with the MB system. The survey results will be analyzed while retaining the homogeneous groups allowing inter-group comparisons on the constructs from the IS success model. Theoretical contributions from this study are the innovative use of data analytics for segmenting user sample on the usage variable before the behavioral study. This reduces the risk of sample heterogeneity bias, a priori, before the field study. This approach will provide a deeper understanding of MB usage than prior studies that have used heterogeneous samples and help banks better decisions on MB system features that are more attuned to the needs of the users.
Motiwalla, Luvai F.; Albashrawi, Mousa; and Kartal, Hasan, "Understanding Mobile Banking Usage Behavior: An Analytics Approach" (2016). Proceedings of the 2016 Pre-ICIS SIGDSA/IFIP WG8.3 Symposium: Innovations in Data Analytics. 14.