A Typology of Attitudes towards the E-Bike against the Background of the Traditional Bicycle and the Car
Bibliographic record
Abstract
Although it is generally accepted that innovators and early adopters have a particular influence on the uptake of electric bikes, other groups, whose reactions to innovation have not been studied, are perhaps wrongly marginalised. Such individuals, often relying on stereotypes, also need to be included in researchers’ scope of interest. Therefore, the aim of the CAWI survey was to determine the attitudes of respondents, both users and non-users of electric bikes, and on this basis to classify by k-means analysis the attitudes towards the e-bike compared to the traditional bicycle and the car. The results obtained show a large variation between the designated classes. What most of them have in common is a strong attachment to the private car, which is typical for the cycling culture of this part of Europe, but the psycho-physical characteristics of the surveyed remain the strongest differentiating factor. Therefore, in the light of the opinions collected, it seems that the group Gadget lovers 1 and 2, whose share was about one-quarter of the total number surveyed, may be the most inclined to change their transport behaviour. This means that the e-bike, as an innovation, can overcome a critical discontinuity stage on the innovation diffusion curve in the Innovation Adaptation Lifecycle (IAL).
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".