Marketing mix of used electric vehicles in Ontario, Canada.
Notice bibliographique
Résumé
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.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,016 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,003 | 0,001 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,012 | 0,003 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».