基于混合Logit模型的仙女山国家森林公园游客行为的影响因素研究
Research on the influencing factors of tourist behavior in Xiannvshan Mountain National Forest Park based on mixed logit models
张罗雪 1王昕 1孙天怡 1龚凤1
作者信息
- 1. 重庆师范大学地理与旅游学院,重庆 401331
- 折叠
摘要
研究森林公园的游客行为特征及影响因素,有助于促进当地的旅游发展,推动经济可持续增长,为森林公园的高质量发展提供研究基础.以仙女山国家森林公园为例,通过实地调查获取游客的社会经济特征、态度量表及行为特征,运用S-O-R框架构建森林公园游客的行为特征分析框架,利用MIMIC模型拟合行为特征分析框架,引入混合Logit模型探索社会经济特征和态度变量对游客行为的影响.结果表明:①仙女山国家森林公园以市内游客为主,多以陪伴家人、休闲度假为主要动机.②研究证实了构建的行为分析框架对森林公园游客的行为具有解释效力.③各变量对旅游行为的影响依次为主体认知(-0.278)＞主体选择(-0.160)＞旅游地条件(0.204)＞主体感受(0.373).
Abstract
Studying the behavioral characteristics and influencing factors of tourists in forest parks can help promote local tourism development,promote sustainable economic growth,and provide a research basis for the high-quality development of forest parks.Taking Xiannvshan Mountain National Forest Park as a case site,the socio-economic characteristics,attitude scales and behavioral characteristics of tourists are obtained through field surveys,constructed a behavioral characterization framework for forest park tourists using the S-O-R framework,fitted the behavioral characteristics analysis framework using the MIMIC model,and introduced a mixed Logit model to explore the influence of socioeconomic characteristics and attitude variables on tourists'behavior.The results show that ① Xiannvshan Mountain National Forest Park is dominated by tourists from the city,and most of them are mainly motivated by accompanying their families and taking leisure vacations.(2)The study confirms the explanatory validity of the constructed behavioral analysis framework on the behavior of forest park tourists.(3)The influence of each variable on tourism behavior is in the order of subject cognition(—0.278)＞subject choice(—0.160)＞tourist place conditions(0.204)＞subject feelings(0.373).
关键词
混合Logit模型/S-O-R理论/森林公园/行为特征Key words
Hybrid Logit models/S-O-R theory/Forest parks/Behavioral characteristics引用本文复制引用
基金项目
国家社会科学基金(18XSH013)
重庆市社会科学基金资助项目(21SKGH408)
重庆市教育科学规划课题(K22YC205040)
出版年
2024
NETL
NSTL
维普
万方数据