Integrating cognitive antecedents into TAM to explain mobile banking behavioral intention: A SEM-neural network modeling

The research extends the original Technology Acceptance Model, by incorporating two cognitive antecedents, namely, autonomous motivation and controlled motivation, in addition to trust components for understanding adoption.

Abstract

Abstract Higher penetration of smartphones and 3G and 4G mobile networks have led to the higher usage of smartphones for mobile banking activities. This paper identifies key antecedents influencing the mobile banking acceptance. The research extends the original Technology Acceptance Model, by incorporating two cognitive antecedents, namely, autonomous motivation and controlled motivation, in addition to trust components for understanding adoption. Data were collected from 225 mobile banking users and analyzed using an innovative two-stage research methodology. In the first stage, structural equation modeling was employed to test the research hypotheses and identify significant antecedents influencing mobile banking acceptance. In the second stage, the significant antecedents obtained from the first stage were input to a neural network model for ranking. The results showed that trust and autonomous motivation are the two main predictors influencing mobile banking acceptance. Theoretical and practical implications of findings are discussed.