Who will adopt private automated vehicles and automated shuttle buses? Testing the roles of past experience and performance expectancy
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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 it