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Mobile banking (MB) has emerged as a strategic differentiator for financial institutions. This study explores the limitations associated with using subjective measures in MB studies that solely rely on survey-based approaches and traditional structural analysis models. We incorporate an objective data analytic approach into measuring usage experiences in MB to overcome potential limitations and to provide further insight for practitioners. We first utilize a multi-phase path analytical approach to validate the UTAUT model in order to reveal critical factors determining the success of MB use and disclose any nonlinearities within those factors. Proposed data analytics approach also identifies non-hypothesized paths and interaction effects. Our sample is collected from computer-recorded log data and self-reported data of 472 bank customers in the northeastern region of USA. We have analyzed the data using the conventional structural equation modeling (SEM) and the Bayesian neural networks-based universal structural modeling (USM). This holistic approach reveals non-trivial, implicit, previously unknown, and potentially useful results. To exemplify, effort expectancy is found to relate positively (but nonlinearly) with behavioral intention and is also ranked as the most important driving factor in UTAUT affecting the MB system usage. Theoretical and practical implications are discussed and presented in terms of both academic and industry-based perspectives.
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- Self-Reported and Computer-Recorded Experience in Mobile Banking: a Multi-Phase Path Analytic Approach
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