Willingness to Pay for Conditional Automated Driving among Segments of Potential Buyers in Europe
Bibliographic record
Abstract
This study aims to investigate the willingness to pay for conditionally automated cars (CACs) among 8,084 respondents in seven European countries by segmenting potential buyers of CACs. Future deployment of CACs depends on a sufficient willingness to pay among a sufficient large part of the population. Latent profile analysis was employed to identify the variables with the highest loadings on the latent factor “willingness to pay,” based on latent constructs from the Unified Theory of Acceptance and Use of Technology (UTAUT2) model. In addition, we analyzed which factors were associated with willingness to pay for different automated systems in CACs, i.e., for driving on urban roads, motorways, congested motorways, and parking areas. We find that a large share of respondents indicates a generally high willingness to pay for CACs, but classes with a high share of conservatives and young respondents have the lowest willingness to pay.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".