Marketing mix of used electric vehicles in Ontario, Canada.
Bibliographic record
Abstract
Abstract \nElectric vehicles (EV) support sustainable transportation by contributing to the reduction of emissions from the light-duty (passenger) vehicles sector. Electric vehicle adoption is a topic that has been studied through a variety of disciplinary lenses, from economics to engineering; however, while many studies have looked at consumer motivations for purchasing new EVs, virtually no research has been conducted on the used (i.e., second-hand) EV market. As the EV market continues to grow, so too will the supply of used EVs. The used EV market is an interesting point of entry for those purchasing an EV for the first time or who cannot afford the cost of a new EV. Previous research has identified the point of sale of new EVs as an influential factor in the adoption of this technology, and it is through this lens that the used EV market was investigated. This study uses an exploratory approach to address the sale of used EVs in Ontario, Canada by analyzing online advertisements of used EVs by dealerships and private sellers. The aim was to determine how/if attributes that are specific to EVs (e.g., battery life and charging range) are being communicated to potential buyers. A secondary aim was to compare this information to advertisements for internal combustion vehicle (ICV) versions of the same cars. To achieve this, data from 480 advertisements for used vehicles on the autoTrader website were collected, including a sample of 408 EVs and 72 ICVs. The dataset included a mixture of quantitative (e.g., model, year, price, mileage, etc.) and qualitative information (e.g., seller’s own description of the vehicle attributes). The results from the study showed very little difference between advertisements for EVs and ICVs in terms of what information is being communicated: For all ads, the first few attributes communicated tended to be related to the condition of the vehicle and/or specific non-EV attributes such as ‘heated seats’. Findings also revealed that private sellers were more likely to talk about EV-specific features of the vehicle than were dealers. Overall, this research can conclude that the market of used EVs in Ontario lacks focus on attributes that differentiate EVs from ICVs, thus potentially making adoption by first-time potential purchasers more challenging, since the barriers often found by EV adopters are not being addressed. This presents an interesting opportunity for online platforms, such as autoTrader, to further customize advertising templates to include EV-related attributes. The results of the study also signal that further research should take place from the point of view of potential customers as well as previous purchasers in the used EV market to determine what information would be useful when shopping for vehicles online.
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 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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.016 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".