Who will adopt private automated vehicles and automated shuttle buses? Testing the roles of past experience and performance expectancy
Bibliographic record
Abstract
To better plan for potential impacts of automated vehicles (AV), this study investigates the effects of performance expectancy and experience, based on the Unified Theory of the Acceptance and Use of Technology (UTAUT), on willingness to pay for private automated vehicles (PAVs) and intention to use an automated shuttle bus. Using survey data (N = 2658) from Southern Ontario, Canada, experience is separated into two constructs: experience with partially automated vehicles and experience with public transit. Results indicate that the impacts of performance expectancy are strongest, that AV experience and transit experience impact PAV adoption, but that transit experience is only linked with intention to use an automated shuttle bus. Findings paint a complex picture of the application of common technology adoption models to transportation planning, as the notion of ‘experience’ is multi-dimensional and suggests complex pathways towards shifting from existing mobility options towards new alternatives, such as PAVs and shuttle buses.
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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 | 0.004 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".