Acceptance of Electric Car Sharing in Rural Areas
Bibliographic record
Abstract
Car sharing helps promote new drive systems to early adopters, and its market share has grown continuously worldwide. However, in rural areas, car sharing still faces challenges, such as sparse populations. In urban areas, previous research has identified underlying factors in the use of car sharing. However, these findings are yet to be transferred to rural areas. Three different methodological approaches were applied in a rural municipality in southern Germany to better understand the acceptance of electric car sharing in rural areas. Firstly, a survey was conducted with 190 participants to provide an overview of underlying factors in the acceptance of electric car sharing. Secondly, interviews were conducted with 21 participants to obtain a deeper insight into these factors. Finally, a cocreation workshop was conducted with 17 participants to identify an electric car sharing model for rural areas. The results showed that performance expectancy, hedonic motivation, and facilitating conditions were the most important factors in the use of electric car sharing in rural areas, at least when presented at a conceptual level. Furthermore, an electric car sharing service with a station-based system and a service provider to distribute vehicles to stations across the municipality’s districts was voted as the most suitable model by participants. As the car sharing system was not yet implemented at the time of the survey, future studies should examine the underlying factors in the use of electric car sharing systems in rural areas at later stages of development. Moreover, the economic and technical viability of the developed electric car sharing service should be tested.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.004 | 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".