(Un)willingness to pay to visit a national park from a sustainable entrepreneurial tourism perspective
Bibliographic record
Abstract
As the number of protected areas increases in a country, there is a need for entrepreneurial action to maximize the environmental and economic benefits of nature-based tourism and to complement government funding. Establishing an entrance fee to visit these protected areas could be a good option to ensure long-term economic sustainability. Therefore, using Herzberg's theory of motivation-hygiene and the fuzzy-set qualitative comparative analysis methodology (fsQCA) as a valid theoretical framework, this study aims to analyze the combined effects of both motivating and hygiene variables which lead to the absence of willingness to pay (WTP) an entrance fee to visit a national park. The findings indicate that motivating factors, such as prior visits to other national parks, the inclination to visit a national park, or a strong commitment to environmental issues, were more important than hygiene factors, such as value for money and overall satisfaction with the visit. Managerial implications and directions for future studies are also discussed. • New forms of financing should be explored to manage natural parks. • Motivating factors are more important than hygiene factors to affect visitors' WTP. • Visitors' habits play a key role in explaining the visitors' WTP. • Satisfaction with the visit plays a secondary role in explaining the visitors' WTP. • No differences between men and women in the intention to pay or not an entrance fee.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".