接见者模式
大数据
旅游
营销
情绪分析
分析
数据科学
知识管理
自发地理信息
体验式学习
地理
地理信息系统
款待
酒店管理学
旅游地理学
文化遗产
消费者行为
文化旅游
用户生成的内容
业务
空间分析
计算机科学
感知
遗产旅游
艺术
可持续旅游
乡村旅游
作者
Xiaobin Zhang,Yinai Zhong,Reza Asriandi Ekaputra,Hak-Seon Kim
出处
期刊:Journal of Hospitality and Tourism Technology
[Emerald Publishing Limited]
日期:2026-01-21
卷期号:: 1-24
标识
DOI:10.1108/jhtt-05-2025-0352
摘要
Purpose This study aims to examine customer satisfaction in Chinese tourism performing arts by integrating big data analytics and Geographic Information Systems (GIS), addressing the lack of data-driven consumer insights in cultural tourism research. Given the increasing significance of immersive cultural experiences in shaping visitor perceptions and destination competitiveness, this study highlights the need for advanced analytical methods to evaluate visitor experiences in spatial and emotional dimensions. Design/methodology/approach Analyzing 5,690 online reviews from three Chinese immersive performances − Encore Dunhuang, Encore Pingyao and Unique Henan − using text mining, co-occurrence network analysis, exploratory factor analysis, linear regression and spatial analysis. By combining sentiment extraction with spatial accessibility evaluation, it develops an integrated analytical framework that links experiential factors with geographic patterns of visitor satisfaction. Findings The results show that “Characteristic landscape,” “Cultural innovation” and “Performance content” enhance satisfaction, while “Value for money” and “Service quality” negatively affect it. Sentiment clusters reveal spatial disparities: Encore Pingyao benefits from urban transport, Encore Dunhuang faces accessibility challenges and Unique Henan exhibits strong regional links but weaker last-mile connectivity. These insights suggest that both experience design and spatial planning critically influence visitor engagement and perceived value. Originality/value This research contributes to tourism, hospitality and cultural management literature by integrating sentiment analysis with spatial behavior modeling through big data and GIS technologies. It offers a novel framework for evaluating customer experiences in tourism performing arts, providing actionable recommendations for smart tourism development, accessibility optimization and sustainable cultural destination management in emerging tourism economies.
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