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机构地区:[1]上海市城市规划设计研究院,上海200040 [2]南京大学国土资源与旅游学系,南京210093 [3]南京大学旅游研究所,南京210093
出 处:《地理科学进展》2017年第11期1391-1401,共11页Progress in Geography
基 金:联合国教科文组织合作项目(4500193250);国家旅游局重点项目(13TABK007)~~
摘 要:随着旅游业的发展,旅游情境下的主客交往方式愈发多元和复杂。现阶段旅游地主客交往的研究较多关注当地居民,而从游客视角深入研究旅游动机、主客交往意愿和旅游体验的相互影响机制较少。本文选取世界自然遗产地三清山为案例地,以实地问卷调查数据为基础,从主客交往意愿角度切入,构建了观光型旅游地游客视角的MCE(动机—交往意愿—体验质量)模型,并运用多群组的结构方程模型进行恒等性检验。结果表明:(1)三清山游客的旅游动机可以划分为体验生活、刺激/冒险、放松/逃避和社交/求知4种类型,主客交往意愿则划分为适度交往和密切交往2种维度;(2)体验生活、放松/逃避和社交/求知动机对适度交往意愿存在显著影响,刺激/冒险、放松/逃避动机和社交/求知动机对密切交往意愿存在显著影响;(3)密切交往意愿比适度交往意愿更能影响游客的旅游体验质量;(4)分别对三清山男女样本进行多群组分析,因素恒等性检验显示模型具有跨样本的稳定性。With the development of tourism, host-guest relationship at tourism destinations is increasingly diverse and complex. Existing studies on host-guest relationship have paid much attention on local residents. But few studies have explored the mutual influencing mechanism among tourist motivation, contact intention, and tourists' experience quality, especially from the perspective of tourists. This study employed an on-site survey in Mount Sanqingshan National Park to build the tourist perspective motivation-contact intention-experience quality(MCE) model. We also applied the invariance measurement with the multi-group structural equation model. The result shows that tourist motivations at the sightseeing destination can be classified into four categories: experiencing life motivation, excitement/adventure motivation, relaxation/escape motivation, and socializing/knowledge motivation. Tourists' contact intention can be divided into two dimensions: moderate contact intention and close contact intention. The modeling result indicates that tourist motivations of experiencing life, relaxation/escape and socializing/knowledge have direct effect on moderate contact intention,and tourist motivations of excitement/adventure, relaxation/escape, and socializing/knowledge have direct effect on close contact intention. Close contact intention has a greater influence on tourists' experience quality than moderate contact intention. We also divided the samples into gender groups for invariance measurement with the multi-group structural equation model. We found that the model built in this study showed stability across samples.
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