Consumer Heterogeneity in the Potential Early Mainstream Market for Plug-in Electric Vehicles
Bibliographic record
Abstract
The authors characterize consumer heterogeneity in the potential early market for plug-in electric vehicles (PEVs)—including plug-in hybrids (PHEVs) and pure electric vehicles (EVs). The authors apply and compare two approaches, constructing consumer segments based on PEV preferences, and on lifestyle. Survey data were collected from 1,754 new vehicle buying households in Canada in 2013. The survey instrument collected extensive background information from each respondent, and PEV interest was elicited through a PEV stated choice experiment and a design space exercise. First, preference-based segments were constructed using latent-class analysis of the choice experiment data. The authors identified a smaller segment of “PEV-enthusiasts” respondents (8% of sample) with extremely high valuation of PHEVs and EVs, little interests in fuel cost savings, and high engagement in technology and environmental lifestyles. A broader segment of “PHEV-oriented” respondents (25%) expresses more moderate positive valuation of PHEVs, tends to engage in an environment-oriented lifestyle and also value fuel cost savings. Second, lifestyle-based segments were constructed using cluster analysis on a subset of potential early PEV buyers (33%). The six clusters varied in engagement in environment- and technology-oriented lifestyles, environmental concern and openness to change. PHEVs were most positively valued across all six segments, though apparent motivations varied substantially. Results suggest that PHEVs are the most likely PEV to have broad market appeal, but potential buyers can vary substantially in their valuations of fuel savings, environmental concern, and lifestyle.
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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 | 0.001 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".