The Role of Perceived Quality on High-Speed Railway Tourists’ Behavioral Intention: An Application of the Extended Theory of Planned Behavior
Bibliographic record
Abstract
Since the COVID-19 pandemic, airlines worldwide have enforced strict travel restrictions, driving passengers to seek alternative transportations such as High-Speed Railway (HSR). Nevertheless, the current understanding of HSR travelers’ behavior is scarce. Moreover, despite the extensive application of the Theory of Planned Behavior (TPB) to explain and predict patrons and tourists’ behavior in the tourism and hospitality context, little research has employed this theory to examine HSR travelers’ processes in making travelling decisions. This study developed a research model by integrating perceived quality into the TPB and examined the relation between HSR passengers’ service quality and intention to travel in the future and the mediating effect of TPB attributes. It is found that perceived train service quality and perceived travel quality have a significant influence on the TPB attributes, which further influence HSR travelers’ travel intention. This study contributes practical implications to destinations and the travel industry that they might attract visitors by marketing the corresponding HSR train service quality and travel experience quality. This also provides recovery guidelines for the transportation and travel industry after the COVID-19 pandemic.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".