Service Attribute Preferences of Air–HSR Transfer Process From a Combined Perspective of Resource‐Matching, Perceived Risk, and Value Theories
Bibliographic record
Abstract
As an indispensable component of the modern high‐quality national comprehensive three‐dimensional transport network, air and high‐speed rail intermodal transportation (Air–HSR) plays a pivotal role in advancing “transportation integration.” The transfer process in Air–HSR represents a critical touchpoint of service experience, and the investigation into passengers’ preferences for transfer service attributes is instrumental in providing civil aviation enterprises with a more personalized foundation for product development. First, six resource‐matching attributes of Air–HSR are extracted by introducing the resource‐matching theory (RMT) and drawing on the consumer behavior analytical methods. Subsequently, incorporating the perceived risk theory (PRT) and perceived value theory (PVT), a quantitative framework for passengers’ preferences for service attributes of Air–HSR transfer (RPP‐F) is proposed from the dual perspectives of perceived risk (PR) and perceived value (PV). Then, an analysis model (PR–PV–SEM) is constructed with PR and PV as mediating latent variables to examine passengers’ preferences for service attributes of Air–HSR transfer, and the preferences are analyzed through the lens of latent variables. Finally, based on measurement data of passengers’ diverse preferences, passengers’ heterogeneous preferences are analyzed and interpreted through the lenses of latent variables and socioeconomic attributes via multisampling approaches, and the internal preference structures of different attribute groups are deeply explored. The model results demonstrate that the PR–PV–SEM with PR and PV as mediating latent variables exhibits excellent model fit with its explanatory power for passengers’ adoption intention reaching 73%. This finding reveals the mediating mechanism between objective services and subjective willingness via PR and PV and provides a novel perspective for subsequent analysis of travel service preferences.
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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) | 0.001 | 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".