TITLE:
The Influencing Factors and Mechanism of Tourists’ Revisit Intention in Chinese Tourism Characteristic Towns—Take Gankeng Hakka Town in Shenzhen as an Example
AUTHORS:
Mu Zhang, Qing Chen, Wenyu Li
KEYWORDS:
Chinese Tourism Characteristic Towns, Tourists’ Revisit Intention, Structural Equation Model
JOURNAL NAME:
Journal of Service Science and Management,
Vol.12 No.3,
April
11,
2019
ABSTRACT: Tourists’ revisit intention is of great significance to the sustainable development of Chinese tourism characteristic towns. This study took Gankeng Hakka Town which is a typical Chinese tourism characteristic town as an example, using confirmatory factor analysis (CFA) method of Structural Equation Modeling (SEM) to build a structural equation model regarding tourists’ revisit intention. This model included ten paths and four variables which were tourist satisfaction, tourist motivation, tourists’ perceived value and tourists’ revisit intention. Through CFA, this paper found out the dimensions of the three variables (tourist satisfaction, tourist motivation, tourists’ perceived value), also tested the reliability and validity of the measurement items. Through SEM, CMIN/DF, RMR, RMSEA, GFI, AGFI, NFI, RFI, IFI, TLI, CFI, PGFI, PCFI and PNFI were selected to evaluate the degree of finesse between the model and the data. The results indicate that the tourist satisfaction has a direct and significant effect on the tourists’ revisit intention; tourist motivation and tourists’ perceived value have no direct influence on tourists’ revisiting intention, but they can influence the revisiting intention through the influence of tourist satisfaction. Therefore, tourist satisfaction has a mediating effect in the mechanism of the entire model. Among them, the factors of “Increase Knowledge”, “Rich Atmosphere” and “Satisfactory Landscape” have great influence on tourists’ revisit intention. It is suggested that the construction of Chinese tourism characteristic towns should focus on the development of scenic cultural atmosphere and park landscape in the future.