Combining survey-based and neuroscience measurements in customer acceptance of self-driving technology

Abstract

In recent years, the issue of consumer acceptance of self-driving cars has come to the forefront of interest among policymakers, researchers and automotive industry experts. Anchored in the Technology Acceptance Model (TAM) and Unified Theory of Acceptance and Use of Technology (UTAUT), these studies are typically based on survey data from respondents who have not used self-driving vehicles. The survey, being a perception-based measure has several limitations, such as social desirability bias, inaccuracy due to time pressure, just to name a few. In addition, the change in intention to use self-driving vehicles as a result of actual test use deserves more academic attention. To address this limitation, volunteers were invited to participate in a test drive as passengers in a self-driving vehicle, testing their acceptance of technology using an adapted version of UTAUT2 questionnaire before and after the ride. Neuroscience measurements were also performed: real-time electroencephalography (EEG) and eye-tracking were recorded during the ride. The explanatory power of our regression model was high (97%) using this combined research method. Our preliminary results suggest, that in a real-life test technology acceptance was related more to emotional experience during the ride and less to other elements of the UTAUT2 model – which challenges the results of previous methods based solely on surveys.