Using an extended technology acceptance model to investigate facial authentication

Abstract

The biometric authentication is an pioneering technology, which confirms an individual’s identity by leveraging their definite physical or behavioural traits, including facial features, retinal scans, vocal patterns, palm vein patterns. Although the potential advantages and increasing prevalence of biometric authentication in both public and private sectors are evident, its adoption by end-users has been comparatively sluggish. To investigate the driving forces behind individual acceptance of new technologies, we developed a comprehensive the Technology Acceptance Model (TAM) and examined the impact of novel constructs such as Personal Innovativeness (PI), Perceived Enjoyment (PE), Trust (T), Personal Innovativeness (PI), and Perceived Risk (PR). This study involves two phases of data collection, which involves the use of two biometric authentication devices (Palm Vein Scanner and Face Authentication device). In each phase, 100 voluntary participants interact with a biometric authentication device mounted on a self-service coffee machine. The goodness-of-fit of the collected data to the model is verified, and both the proposed model and the hypotheses are evaluated using the Structural Equation Modelling (SEM). The findings substantiate that users’ Perceived Enjoyment (PE) with facial authentication devices positively affects their Perceived Ease of Use (PEU). Perceived Usefulness (PU) was found to significantly influence the user’s Attitude Towards Usage (ATU) of the face Authentication device, while PEU and ATU were found to be in an inverse relationship. The construct T proved having a positive effect on the user’s ATU, which in turn has a significant influence on the BI of the user. These crucial factors determine the adoption of facial Authentication technology for daily usage. Moreover, this study shows that PR is the main hindrance to users accepting Facial Authentication technology. To have a more comprehensive insight into influential factors in accepting new biometric technology, we combine the collected data for both phases and analyse the feedback from participants interacting with both biometric authentication devices.