Perceived differences in coastal tourism image under tourist experience-IPA analysis based on UGC data of 12 coastal cities

Author:

Zhang Meng,Di QianbinORCID,Liu Ying

Abstract

The destination image perceived by tourists is crucial to coastal tourism market positioning and marketing. This paper utilizes tourists’ Internet-generated content from 2017–2021, adopts the jieba text analysis method to identify the cognitive, emotional, and overall image of coastal tourism, divides the constituent elements of the destination image into four main classes and 20 subclasses through the text clustering method, and explores the tourists’ perception of the image of coastal tourism with the help of the IPA model. The study found that: 1) The commonality of the cognitive image of “ocean” in 12 coastal cities is outstanding, but the internal characteristics are obvious, tourists pay more attention to coastal tourism in Bohai Rim and southern coastal areas, and Shanghai, Ningbo and Hangzhou show strong correlation; 2) Tourists’ emotional image of coastal tourism destinations is dominated by positive attitudes, with a high overlap of adjectives representing positive emotions, but with heat differences in different cities; 3) The overall image of coastal tourism can be divided into three circles, including “traditional core-characteristic structure-peripheral perception”, and there are obvious differences in the characteristics of the social semantic network of each city; 4) Tourists are more satisfied with the components of coastal tourism image, but pay more attention to the construction of optimized coastal tourism environment. Based on this, in the process of coastal tourism development, it is necessary to focus on creating distinctive and diversified tourism values, focusing on tourists’ experience needs, improving the construction of quality tourism facilities and services, and promoting the high-quality development of coastal tourism.

Funder

Innovative Research Group Project of the National Natural Science Foundation of China

Publisher

Public Library of Science (PLoS)

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