Autonomous Vehicles Acceptance: A Perceived Risk Extension of Unified Theory of Acceptance and Use of Technology and Diffusion of Innovation, Evidence from Tehran, Iran
Bibliographic record
Abstract
This research integrates Unified Theory of Acceptance and Use of Technology (UTAUT) (Performance Expectancy [PE], Effort Expectancy [EE], and Social Influence) with Diffusion of Innovation Theory (TRialability [TR] and OBservability [OB]) as well as Perceived Risk (PR) to identify a wider set of latent factors affecting acceptance of fully automated AVs. Although research on AVs acceptance has been conducted in developed countries, it is a rather new topic in developing countries, where it has only been introduced as a promising technology to come. Structural equation modeling on stated preference surveys data of 641 Tehran residents in 2019 justifies the proposed integration of two theories and PR. Only PR shows an expected negative sign (−0.2); Among UTAUT variables, PE (0.33) and EE (0.25) were the most and least influential factors, respectively. Regression weights of DOI-related variables show that TR (0.17) and OB (0.16) have almost equal effect.
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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 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".