Exploring tourists' preferences and willingness to pay for national park recreation improvements based on regret and utility comparison.
Discrete choice experiments
National park recreation improvement
Preferences heterogeneity
Regret minimization
Utility maximization
Willingness to pay
Journal
Scientific reports
ISSN: 2045-2322
Titre abrégé: Sci Rep
Pays: England
ID NLM: 101563288
Informations de publication
Date de publication:
14 Sep 2024
14 Sep 2024
Historique:
received:
21
03
2024
accepted:
09
09
2024
medline:
15
9
2024
pubmed:
15
9
2024
entrez:
14
9
2024
Statut:
epublish
Résumé
Research on the improvement of national park recreation policies has attracted much attention to discrete choice experiments to obtain tourists' preferences and willingness to pay. However, individual choice behavior is extremely complex, and the single Random Utility Maximization (RUM) model ignores anticipated regret and is insufficient to explain individuals' actual choice behavior. To investigate whether regret influences tourists' choices regarding the improvement of national park recreation attributes, this study introduces the Random Regret Minimization (RRM) model and explores the performance of polynomial logit models and hybrid latent class models in analyzing discrete choice models based on utility and regret. By constructing a hybrid utility-regret model, we examine how tourists trade off between attributes such as vegetation coverage, water clarity, amount of litter, and level of crowding in national park recreation. Results indicate that the RRM model has better goodness-of-fit and predictive ability than the RUM model, indicating that regret is a significant choice paradigm, and the hybrid model better explains respondents' choices. Specifically, 62.5% of tourists' choices are driven by regret, and regret-driven respondents are more inclined to increase vegetation coverage and improve water clarity, while utility-driven respondents are more inclined to reduce litter and crowding. This study not only provides a reference for managers to develop more optimal recreation improvement strategies but also offers theoretical insights for national park recreation improvement policies.
Identifiants
pubmed: 39277648
doi: 10.1038/s41598-024-72494-w
pii: 10.1038/s41598-024-72494-w
doi:
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
21524Subventions
Organisme : Design and Application Research of China's Marine Economy Big Data Monitoring System
ID : number 21ATJ006
Organisme : Shandong Business College Wealth Management Characteristic Research Project, China under Grant
ID : number 2022YB11
Organisme : Research on the Path of Improving the Quality and Efficiency of Scenic Village Tourism in Shandong Province under the Background of Rural Revitalization
ID : number 22CJJJ26
Informations de copyright
© 2024. The Author(s).
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