Determinants behind the acceptance of autonomous vehicles in mandatory and optional trips
Bibliographic record
Abstract
Due to the potential of automated vehicles (AVs) to change the transportation system radically, it is essential to investigate the factors affecting users’ intention to use them. Although previous studies have mostly focused on the latent variables of the internal schema of beliefs, this paper aims to examine the impact of internal and external factors by integrating the variables of the unified theory of acceptance and use of technology along with environmental concerns and perceived risk. Moreover, the effects of latent variables are also compared for different trip purposes (mandatory and optional), which have received less attention in previous studies. Using a stated-preference survey, 641 valid responses from citizens of Tehran, Iran have been collected. The estimation results of structural equation modelling show a significant difference between the determinants of AV acceptance across mandatory and optional trips. The estimated coefficients indicate that social influence and performance expectancy are the strongest explanatory factors in the intention to use AVs in optional and mandatory trips, respectively. However, no significant difference is observed for the impact of environmental concerns on the intention to use AVs across both trip types.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".