Consumer attitudes towards the use of autonomous vehicles: Evidence from United Kingdom taxi services
Bibliographic record
Abstract
The primary aim of this research is to determine attitudes held towards autonomous vehicles (AVs) and understand their impact on intentions to use the service among ride-hailing users in the UK. Based on the Theory of Planned Behaviour model, an online, self-administered survey was used to collect data from 151 consumers (18-24-year-olds). The relationship between variables was measured using a Spearman’s Rank test in SPSS. The results of this study found all categories (overall attitude, perceived ease-of-use, perceived value, perceived safety, perceived risk, technology, environmentalism, subjective norms, perceived behavioural control) received a positive mean score. From these results, it can be concluded that this sample holds positive attitudes towards AVs and intend to use the service when they are made available. A positive score for perceived risk, however, indicated that this group thought there may be safety concerns when using this technology. The main contribution of this study is providing data to a new, and rapidly evolving field of research and thus the findings of the present study contribute to ongoing research related to consumers attitudes of AVs. Managerially, companies that focus on developing and implementing AV taxis need to focus more on the safety benefits of such vehicles.
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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 | 0.001 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".