To Share or to Own? Understanding the Willingness to Adopt Shared and Owned Electric Automated Vehicles on Three Continents
Bibliographic record
Abstract
Electric automated vehicles (AVs) are expected to become part of the transportation system within the coming years. The implications of their implementation are still uncertain. What is known is that human behaviour will be central to determining AV adoption. This research aims to gain insight into how potential users of privately owned (PAVs) and shared (SAV) electric automated vehicles are characterised across three different continents assessing the influence of cultural and geographic features, personal attitudes and characteristics and the perceived advantages and disadvantages of AVs. Using survey data collected among residents (N = 1440) in Greater Sydney, Australia; Greater Montréal, Canada; and the Randstad, the Netherlands, this paper explores individuals’ willingness to adopt PAVs and SAVs using statistical descriptive analysis and logistic regression models. The study supports the impact of personal characteristics (e.g., age and travel characteristics) and attitudes towards personal and societal gains on the willingness to adopt AVs. Furthermore, this paper provides cross-continental evidence for the regional socio-urban context, affecting the desire to adopt AVs in different forms. Policy-makers should consider these factors and tailor different strategies according to cultural norms in order to motivate a coherent and sustainable implementation of AVs into existing and future mobility landscapes.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 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".