How Much Polish Consumers Know about Alternative Fuel Vehicles? Impact of Knowledge on the Willingness to Buy
Bibliographic record
Abstract
Limited consumer knowledge reduces the chances of the spread of alternative fuel vehicles (AFVs), and hence slows down AFV market spread. In our empirical survey conducted in the first quarter of 2020 among 1002 Poles planning to buy a car in the next 12 months or who have just bought one, we examine what socio-economic and attitudinal factors influence their willingness to buy an AFV. In particular, we are interested in exploring how AFV knowledge related to understanding of the differences between hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs) and battery electric vehicles (BEVs), as well as brand recognition associate with the willingness to buy. To the best our knowledge, this is a unique study among consumers in Central and Eastern Europe, characterized by lower exposure to AFVs and lower purchasing power. Our results indicate that males with pro-environmental beliefs and behaviors who are interested in modern automotive technologies and have good AFV brand recognition are predominated to be willing to buy an AFV in the near future.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| 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".