认知-情感视角下遗产旅游难忘体验的形成与演变研究——基于平遥古城游客追踪数据的多层次分析
Formation and evolution of memorable experiences in heritage tourism from perspective of cognitive-emotion:A case of Pingyao Ancient City
李曼 1李燕燕 2厉建梅 1乐凌云1
作者信息
- 1. 郑州大学管理学院,郑州 450001
- 2. 山西大学历史文化学院,太原 030006
- 折叠
摘要
为游客塑造难忘的旅游体验是文化遗产类旅游目的地实现可持续发展的中心任务.现有旅游体验相关研究呈现由静态结构性探讨向动态过程性分析的转变.由此,文中基于认知-情感-行为理论构建研究框架,借助结构方程模型和多层线性模型对调查数据进行分析,综合探究认知、情感及时间对旅游难忘体验的多层次影响.研究发现:1)同一时间阶段,游客对遗产地功能属性的认知与地方依附对旅游难忘体验的影响作用更为显著.2)随时间的流逝,旅游难忘体验存在波动,呈现短时间内减弱,某一时间被激活,又再次减弱的复杂变化.3)不同时间阶段,遗产地属性在旅游难忘体验的动态变化中存在调节作用.探索和把握游后体验形成路径和遗忘规律,将有助于强化和唤醒游客体验记忆,从而提升目的地重游率,实现可持续发展.
Abstract
Helping tourists to create memorable tourism experiences is a central task in achieving sustainable development for cultural heritage tourism destinations.The existed researches on tourism experience show a trend of shifting from static structural exploration to dynamic process analysis.Thus,in this paper,we construct a research framework based on cognitive-emotional-behavioral theory,and analyze survey data with the help of structural equation modeling and hierarchical linear modeling,to investigate the multi-level influence of cognition,emotion and time on the memorable tourism experiences comprehensively.It is found that:1)At the same time stage,tourists'cognition to the functional attributes of heritage sites and place attachment more significantly affect the memorable tourism experiences.2)With the pass of time,the memorable tourism experiences fluctuate,showing a complex change of being weakened for a short period of time,activated at a certain time,and weakened again.3)At different time stages,the attributes of heritage sites play a regulating role in the dynamic changes of memorable tourism experiences.Exploring and grasping the path of post-tour experience formation and forgetting law will help strengthen and awaken tourists'experience memory,thus enhancing the revisit rate of the destination and achieving sustainable development.
关键词
认知-情感/旅游难忘体验/追踪数据/多层线性模型Key words
cognition-emotion/memorable tourism experiences/tracking data/Hierarchical Liner Modeling引用本文复制引用
出版年
2024
国家科技期刊平台
NSTL
万方数据