Personalized Route Recommendations in Public Transport Apps: The Impact on Privacy Perceptions and Usage Intention

Abstract

To encourage the modal shift from private car use to public transport, digital solutions are needed that attract new users and maintain the satisfaction of existing public transport travelers. Personalization is considered a promising way to achieve this. To support this goal, in this study, it was examined how personalized route recommendations affect usage intention and privacy perception of users of public transport mobile apps. Guided by the privacy calculus framework, a within-subject design online survey was conducted. Participants (N = 146) evaluated three scenarios that varied in personalization quality and extent of required user data. Repeated-measures ANOVAs revealed significant effects of the scenarios on the privacy calculus constructs. Risk perception increased with more extensive data use, leading to a reduced willingness to disclose in the most extensive scenario. Both perceived benefit and intention to use peaked at moderate personalization and decreased thereafter. The findings confirm the applicability of privacy calculus to mobility contexts. Moreover, they underscore the need for balancing personalization quality with privacy-sensitive data practices in the design of public transport mobile apps.