Data Issues and the Road Ahead: Multivariate Modeling of Public Interest in Connected Vehicle Adoption

Abstract

Connected vehicles (CVs) present a wide range of potential benefits, including the distribution of reliable and critical information to motorists and providing valuable big data to transportation professionals. With all the prospective benefits, the challenge is to bring CVs to the real world via widespread adoption. Regardless of the timeline of deployment, prior understanding of the possible barriers to the adoption and usage of CVs will help stakeholders improve public attitude and intention towards adoption. This study splits CV adoption into three distinct but related forms: intentions to ride, own, and recommend CVs, and uses a multivariate ordered probit model to assess the impact of individual characteristics and latent variables—perceived data privacy, perceived data security, and importance of reputation of data manager—on these three forms of CV adoption. While all three latent variables have a positive impact on all forms of adoption, they have the greatest impact on intention to ride compared to intentions to own and recommend. Based on the findings, this study recommends stakeholders to increase transparency and strength of data privacy and security practices as well as to focus educating and marketing on certain population segments to increase CV adoption.