Friend-Invitation Promotion Scheme Used in Electric Carsharing: Empirical Analysis and Policy Implications
Bibliographic record
Abstract
The combination of electric vehicle (EV) and carsharing is expected to provide social and environmental benefits, like encouraging sustainable travel behaviors (reducing car ownership and vehicle kilometres of travel) and improving the accessibility and flexibility of urban transport. Thus, electric carsharing is encouraged to be adopted for daily trips, and the operators propose the friend-invitation promotion scheme for the membership expansion. This study explores the effectiveness of this scheme and the characteristics of the scheme participants and their invited friends (e.g., age, friend-invitation pattern, and EV rental pattern). The analysis found that 28.4% of these invited friends would make at least one EV rental after registration, whereas 30.4% of the other members who registered in the same period would do so, indicating that these invited friends were less active. Therefore, suggestions are given based on the EV rental pattern of these invited friends (preferring a longer journey using a smaller but cheaper EV) to enhance the effectiveness of the friend-invitation promotion scheme.
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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 | 0.001 | 0.003 |
| 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".