Willingness to Pay for Autonomous Vehicles before and after Crashes: A Demographic Analysis for US Residents
Bibliographic record
Abstract
Background: It is anticipated that autonomous vehicles (AVs) can achieve many benefits, such as improving traffic safety and increasing mobility of people with limited modes of transportation. However, the public attitude remains the controlling factor determining the degree to which AVs can achieve these benefits. While it is expected that the public acceptance of AVs would increase over time due to the increase in the level of awareness and knowledge about this new technology, previous surveys show that people become more pessimistic towards AVs over time. While this pattern has never been investigated, some studies link this negative shift in the attitude to AVs accidents. Objective: This study focuses exclusively on understanding the impact of AV crashes on the willingness to pay extra to buy an AV for people from the US. In addition, the analysis focuses on evaluating changes in the willingness to pay for AVs before and after crashes for people with different demographics in order to understand how the different groups react to these accidents. Methods: A questionnaire survey was designed and conducted between February and September of 2022 and a total of 2,144 responses were received and analyzed to understand the impact of these crashes on respondents with different demographic properties (age, gender, household income, educational level, prior knowledge about AVs, and prior knowledge about AV crashes). In addition, hypothesis testing was utilized in order to evaluate whether the changes in the willingness to pay extra for AVs after introducing the accidents are significantly different from the willingness to pay for AVs before introducing the accidents. Results: The results show that the willingness to pay extra to buy an AV decreased by 29% after the crashes were introduced to the respondents, while the decline in the willingness to pay extras varies across the different demographic groups investigated. Conclusion: The results show the significant negative impact of AV crashes on the public attitude as the average willingness to pay extra for AVs decreased from 8,412 USD before the crashes to 6,007 USD after the crashes. In addition, the results show that the decrease in the willingness to pay for AVs is statistically significant for different demographic groups.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| 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".