Extension of an adoption model to evaluate autonomous vehicles acceptance
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
Autonomous Vehicles (AVs) can provide safe, clean and efficient mobility by using advanced communication technologies to create an unprecedented revolution in transportation. Acceptance of AVs has a key role in their successful implementation. Most researchers have used Technology Acceptance Model (TAM), Theory of Planned Behavior (TPB) and Unified Theory of Acceptance and Use of Technology (UTAUT) to identify latent factors affecting, which focus only on individuals' internal schema of beliefs without considering the external factors of acceptance. The current study, uses Trialability (TR), Observability (OB) extracted from Diffusion of Innovations (DOI) theory, Performance Expectancy (PE), Effort Expectancy (EE), Social Influence (SI) extracted from UTAUT, as well as Perceived Risk (PR), Environmental Concerns (EC) and Consumer Innovativeness (CI)) to identify a wider set of latent factors. A stated preference survey conducted to this purpose in Tehran allowed collecting 641 responses. Considering the latent nature of research variables, Structural Equation Modeling is applied. Results show that PE, EE, PR, OB, SI, TR, CI and EC affect acceptance in decreasing order of regression weights, an explain 72.5% of the variance in the dependent variable..
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 | 0.000 |
| 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