Visitor satisfaction and behavioral intentions in nature-based tourism during the COVID-19 pandemic: A case study from Zhangjiajie National Forest Park, China
Bibliographic record
Abstract
Nature-based tourism (NBT) has become a popular tool for developing countries to achieve economic growth by the non-destructive use of their natural resources. COVID-19 has caused severe financial impacts on tourism-dependent areas. Revitalizing NBT is needed for economic recovery in those regions and can also help deal with mental health issues worldwide. Zhangjiajie National Forest Park (ZNFP), the first national park created in China, was selected to examine the important factors that influence visitor satisfaction during the COVID-19 pandemic and the relationship between satisfaction and visitors' environmentally responsible behavior (ERB) intention. The authors collected 788 onsite and online questionnaires from visitors to ZNFP during June–September 2020. This paper reveals previously underestimated factors and offers practical applications for park development at ZNFP and other NBT destinations. Visitors had a high level of satisfaction with the natural scenery of the park but were relatively dissatisfied with price reasonableness, park services, activities and events, and artificial attractions. Younger visitors, especially students, and well-educated visitors looking for environmental education opportunities tended to have lower satisfaction rates. Visitor satisfaction may have a positive but limited influence on promoting visitors' ERB intentions. We propose group-specific strategies for national park managers to attract more visitors and increase their length of stay.
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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) | 0.001 | 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".