Social acceptance of autonomous vehicles. A cross-country model validation
Bibliographic record
Abstract
• A modified version of UTAUT2 theory is validated on acceptance of Autonomous Vehicles. • A structural equation model approach is used with survey data of six different countries. • Performance expectancy, trust and social influence are the strongest predictors. • The weakest predictors of behavioral intentions vary by country. • Findings offer guidance of targeted strategies for policymakers. Autonomous vehicles (AVs) are expected to offer significant benefits, including improved mobility, reduced energy consumption and emissions, shorter travel times or enhanced road safety. While field tests are being conducted in controlled environments worldwide, AVs still face major challenges related to technical aspects, regulations and public acceptance. This study examines public acceptance of AVs by proposing and validating a modified version of the extended Unified Theory of Acceptance and Use of Technology (UTAUT2). The proposed model, incorporates additional constructs (trust, perceived risk, environmental awareness, green perceived usefulness and user innovativeness) while excludes others (facilitating conditions, price value and habit). The model was tested using a structural equation model approach to survey data from 2,221 participants across 6 different countries—Spain, Mexico, the United Kingdom, Canada, the United States and Australia—. The results confirm the cross-country validity of the model, and reveals that performance expectancy, trust and social influence are the strongest predictors of behavioral intentions in both the pooled and country-specific samples. However, the weakest predictors vary by country: environmental awareness showed the lowest impact on behavioral intentions in Spain, the United Kingdom, Canada and the United States, while in Mexico and Australia, the weakest predictors were perceived risk and effort expectancy, respectively. These findings offer valuable guidance for policymakers and industry stakeholders, emphasizing the need for targeted strategies to foster social acceptance of AVs more effectively.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".