北京大学学报自然科学版 ›› 2024, Vol. 60 ›› Issue (4): 691-700.DOI: 10.13209/j.0479-8023.2024.024

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基于“支付意愿法”和“优劣尺度法”的武夷山国家公园门票定价研究

王奕, 丛丽†   

  1. 北京林业大学旅游管理系, 北京 100083
  • 收稿日期:2023-08-04 修回日期:2023-12-17 出版日期:2024-07-20 发布日期:2024-07-20
  • 通讯作者: 丛丽, E-mail: congli1980(at)163.com
  • 基金资助:
    中央高校基本科研业务费专项资金人文社科振兴重点项目(2021SRZ01)资助

Research on Ticket Pricing of Mount Wuyi National Parks Based on WTP and BWS

WANG Yi, CONG Li†   

  1. Tourism Management Department, School of Landscape Architecture, Beijing Forestry University, Beijing 100083
  • Received:2023-08-04 Revised:2023-12-17 Online:2024-07-20 Published:2024-07-20
  • Contact: CONG Li, E-mail: congli1980(at)163.com

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摘要：

以武夷山国家公园为例, 使用游客支付意愿法(WTP)和优劣尺度法(BWS), 结合线性相关分析和二元logit回归分析等定量分析方法, 探究游客对武夷山公园的支付意愿以及影响因素。结果表明 , 通过BWS方法分析游客对国家公园门票的支付意愿具有可行性。游客整体支付意愿集中在61~150元, 通过Maxdiff模型得到游客支付意愿为77.04元。游客的支付意愿与游客的满意度以及在景区日平均消费之间正相关, 游客的整体满意度越高, 其支付意愿越高。日均花销、游客数量和景区基础设施等因素对支付意愿的影响较为显著, 日均消费越高的受访者的支付意愿更受游客数量和景区服务质量的影响。

关键词: 国家公园, 门票定价, 支付意愿法(WTP), 优劣尺度法(BWS), Maxdiff

Abstract:

Taking Mount Wuyi National Park as an example, this paper uses the Willingness to Pay (WTP) and Best-Worst Scaling (BWS) methods, combined with quantitative analysis methods such as linear correlation analysis and binary logit regression analysis, to explore tourists’ willingness to pay for Mount Wuyi Park and its influencing factors. Research results show that tourists’ overall willingness to pay is concentrated in the range of 61 to 150 yuan, and Maxdiff model shows that tourists’ willingness to pay is 77.04 yuan. The willingness to pay of tourists is positively correlated with their satisfaction and daily average consumption in scenic areas. The higher the overall satisfaction of tourists, the higher their willingness to pay. The impact of factors such as daily expenses, number of tourists, and infrastructure of scenic spots on willingness to pay is more significant. Respondents with higher daily expenses are more affected by the number of tourists and the quality of scenic spot services.

Key words: national parks, ticket pricing, willingness to pay (WTP), best-worst scaling (BWS), Maxdiff

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