Quantifying the consumer?s dependence on different information sources on acceptance of autonomous vehicles

Recent academic research and industrial commitments highlight the potential of connected and autonomous vehicles (CAVs) in transforming the way we travel. However, there are some anticipated barriers associated with the widespread consumer adoption of CAVs, including but not limited to low user acceptance, cybersecurity, safety, and legislation. The existing literature is non-existent in capturing the impact of information received from multiple sources (peers, car dealers, media advertisements, and personal research) on the consumers' acceptance of CAVs while considering these barriers associated with CAV. In this direction, we quantify the impact of the multiple information sources on the individuals concerned and indifferent about the anticipated barriers of CAVs to boost their acceptance while utilizing a two-step econometric framework based on an online survey of 4,448 Tennesseans. The two-step modeling framework utilizes latent class analysis followed by a multivariate ordered logit model. Results indicated that the elderly, three or more person households, and residents interested in owning a CAV are more likely to be concerned about CAV barriers. In contrast, males, physically challenged, and residents willing to pay higher for CAV are more likely to be indifferent toward CAV barriers. Among concerned individuals, the elderly, females, and residents willing to pay more than $30 k for a conventional car are more likely to rely on car dealers when purchasing a CAV. The results of this study are expected to assist automakers, technology companies, policymakers, and third-party agencies to advertise, promote and introduce CAVs effectively through appropriate information channel(s) to boost their consumer acceptance.