The Influence of Heritage Tourism Destination Reputation on Tourist Consumption Behavior: A Case Study of World Cultural Heritage Shaolin Temple
Bibliographic record
Abstract
The increasing drastic competition between tourism destinations decides on only the sites with good reputation can attract more and more tourists. The tourism destination reputation will affect tourists’ choices before traveling and consumption behavior during traveling. In order to analyze tourist consumption behavior during traveling, this article initially builds a theoretical model of tourism destination reputation, tourist enjoyment, tourist memorability, and tourist consumption behavior. Then, 490 valid questionnaires are collected based on a field survey. Meanwhile, a basic sample information collection, reliability, and validity testing (confirmatory factor analysis) and testing analysis based on structural equation model are conducted on the collected data. The result of the confirmatory factor analysis shows that the tourism destination reputation is measured by five factors: catering, accommodation, landscape, culture, and recreation and entertainment; tourist enjoyment is measured by the feeling of joy and relaxation; tourist memorability is measured by emotional memory, expectation memory, benefit memory, and interest memory; tourist consumption behavior is represented by five indexes: willing consumption, urgent consumption, guided consumption, repeated consumption, and recommended consumption. The hypothesis testing shows that the heritage tourism destination reputation not only directly and positively influences tourist consumption behavior but also indirectly affects tourist consumption behavior through tourist memorability and the chain relationship between tourist enjoyment and memorability. Finally, some suggestions are put forward to improve the tourism destination reputation and tourist enjoyment and memorability.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".