Tourist Behavioral Intentions in a UNESCO World Cultural Heritage Site in China: A Multi-Method Analysis Integrating PLS-SEM and fsQCA of Motivational and Perceptual Factors

旅游 遗产旅游 感知 接见者模式 背景(考古学) 心理学 文化遗产 认知 定性比较分析 社会心理学 预测能力 社会学 认知心理学 价值(数学) 结构方程建模 样品(材料) 比较案例 认知地图 概念框架 风险感知 霍夫斯泰德的文化维度理论 功率(物理) 概念模型 鉴定(生物学) 代理(哲学)
作者
Xin Su,SURAIYATI BINTI RAHMAN
出处
期刊:SAGE Open [SAGE Publishing]
卷期号:16 (1) 被引量:1
标识
DOI:10.1177/21582440251410718
摘要

This study integrates PLS-SEM and fsQCA to explore how push and pull motivations, perceived authenticity (object-based, constructive, and existential), satisfaction, and prior knowledge shape two key post-visit behaviors: revisit intention and word-of-mouth intention, in the context of cultural heritage tourism. The PLS-SEM results indicate that motivational factors and authenticity perceptions significantly influence revisit intention but show limited effects on word-of-mouth intention. The fsQCA further reveals multiple distinct configurational pathways leading to both revisit and absence of WOM intentions, highlighting the asymmetric and non-compensatory nature of causal conditions. Notably, some non-WOM configurations feature high levels of cognitive engagement (constructive authenticity and prior knowledge) but lack affective engagement (existential authenticity and satisfaction), suggesting that cognitive understanding alone does not guarantee advocacy behaviors. Moreover, by incorporating prior knowledge as a moderator, the study introduces the concept of the expertise reversal effect into cultural heritage tourism, demonstrating how tourist expertise reshapes motivational influence and co-creation dynamics. These findings extend the theoretical boundary of value co-creation in symbolically rich environments and provide actionable segmentation strategies for heritage site managers based on fsQCA-derived visitor profiles. Additionally, the predictive validity analysis conducted through fsQCA confirms that the identified causal configurations exhibit good predictive power across different sample subsets.
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