Public Perception of the Collection and Use of Connected Vehicle Data

Acharya, Sailesh; Mekker, Michelle · 2021 · ROSA P / Mountain-Plains Consortium

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Summary

This study addresses the critical gap in understanding public perception regarding the adoption of connected vehicle (CV) technology, specifically focusing on data privacy, security, and sharing intentions. While CVs offer significant benefits in safety, mobility, and environmental efficiency, their potential is contingent upon sufficient market penetration. Previous literature identified data privacy and security as potential barriers to acceptance, but lacked in-depth analysis of how these factors influence public willingness to share data and adopt the technology. The research aimed to define public data sharing intentions, ascertain the associations between data issues and CV acceptance, and develop a comprehensive model explaining the development of public attitude and behavioral intention toward CVs. To achieve these objectives, the authors conducted a questionnaire survey of 2,400 U.S. adults via an online panel between November 2020 and February 2021. The study employed exploratory and confirmatory factor analyses, as well as structural equation modeling (SEM), to analyze the data. The research examined respondents' intentions to share eight types of CV data (e.g., speed, position, braking intensity) across ten different use categories. It also investigated the relationships between perceived data privacy and security, trust, perceived usefulness, perceived ease of use, and overall CV acceptance. Additionally, the study analyzed how socio-demographic and individual characteristics, such as familiarity with connected features, influenced these attitudes. The results revealed that perceived data privacy and security significantly lower CV acceptance both directly and indirectly by reducing data sharing intention. Furthermore, concerns about privacy and security negatively impacted public trust in the technology. A key finding was that data sharing intention depended on the intended use of the data rather than the type of data collected. Respondents were highly willing to share data for purposes such as incident information but were least interested in sharing data for enforcement and fee assessment. The study also developed a novel Connected Vehicle Acceptance Model (CVAM), extending the Technology Acceptance Model, which demonstrated that CV adoption is driven by perceived trust, usefulness, and ease of use, with trust acting as a critical mediator or antecedent. Individual characteristics, such as familiarity with connected features, were positively associated with higher data sharing intentions and acceptance. The significance of this research lies in its provision of actionable insights for CV stakeholders, including developers and transportation agencies. The findings imply that to maximize CV adoption, stakeholders must actively strengthen data privacy and security systems and clearly communicate these efforts to the public. Since data sharing intention is driven by specific use cases, agencies should assure the public about the intended uses of data, avoiding applications perceived as intrusive, such as enforcement. The study also suggests that marketing strategies should focus on educating experienced drivers about connectivity advantages and providing test-drive opportunities for inexperienced drivers to increase familiarity. By addressing privacy concerns and clarifying data uses, stakeholders can mitigate barriers to acceptance and facilitate the broader deployment of connected vehicle technologies.

Key finding

Perceived data privacy and security significantly reduces connected vehicle acceptance both directly and indirectly by lowering public trust and data sharing intention.

Methodology

survey

Sample size: 2400

Provenance

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Summary generated by qwen3.6-27b-prismaquant on 2026-06-10; verification: verified.

Topics

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• acceptance adoption

Information type

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• Empirical Findings: observational prevalence, self report data
• Methodological Resource: dataset resource