The Car-Purchasing Intention of the Youth in the Context of Online Car-Hailing: The Extended Theory of Planned Behavior
Bibliographic record
Abstract
Online car-hailing services have become an integral part of people’s daily travel in China. Considering that young people are the major consumer group in car purchases, it is worth investigating how the experience of online car-hailing services affects their intention to purchase a car. Based on the extended theory of Planned Behavior, this study found that the factors that negatively impact the car purchase intention of the youth are firstly the public transportation service quality, followed by the risks of private cars, and finally the online car-hailing services quality. Elevating the convenience and comfort of public transportation is conducive to reducing car purchasing intention. The indirect effect of online car-hailing services on car purchase intention is greater than its direct effect, and the most important factor is attitude. The car purchase intention is significantly heterogeneous across age and annual household income groups. Improving the convenience of public transportation will reduce the car purchase intention of people in the early youth. For middle and later youth, providing demand-responsive transit for important individuals to meet their diverse needs can reduce car purchase intention. As for online car-hailing services, youth care most about their convenience and comfort and worry most about their safety. Providing better online car-hailing services can reduce the car-purchasing intention of youth.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".