Exploring biometric identification in FinTech applications based on the modified TAM - Jen Sheng Wang (Financial Innovation, 2021)
In recent years, biometric technologies have been widely embedded in mobile devices; these technologies were originally employed to enhance the security of mobile devices. With the rise of financial technology (FinTech), which uses mobile devices and applications as promotional platforms, biometrics has the important role of strengthening the identification of such applications for security. However, users still have privacy and trust concerns about biometrics. Previous studies have demonstrated that the technology acceptance model (TAM) can rigorously explain and predict user acceptance of new technologies. This study therefore modifies the TAM as a basic research architecture. Based on a literature review, we add two new variables, namely, "perceived privacy" and "perceived trust", to extend the traditional TAM to examine user acceptance of biometric identification in FinTech applications. First, we apply the analytic hierarchy process (AHP) to evaluate the defined objects and relevant criteria of the research framework. Second, we use the AHP results in the scenario analysis to explore biometric identification methods that correspond to objects and criteria. The results indicate that face and voice recognition are the two most preferred identification methods in FinTech applications. In addition, there are significant changes in the results of the perceived trust and perceived privacy dominant scenarios.
Keywords: 650; AHP; Biometric identification; FinTech applications; Perceived privacy; Perceived trust