Redai dili (Sep 2021)
Spatiotemporal Evolution of the Cognitive Image of a Tourism Destination: An Explorative Analysis in Chengdu Based on Online Reviews
Abstract
It has become increasingly common for tourists to share their personal travel experiences on social media. This user-generated content with spatial references provides a rich database for tracing the evolution of the cognitive image of a tourism destination over a long period of time. This is helpful for local authorities to improve destination management and marketing by prompting image adjustment and tourism product optimization to satisfy tourists' expectations. In this study, data on online travels on Ctrip.com, one of the most popular tourism websites in China, were collected and analyzed to reveal the spatiotemporal evolution of the cognitive image of Chengdu from 2000 to 2019. Based on a specific tourism-related lexicon, keywords were identified and grouped in a list, following a framework comprising six dimensions: tourism attraction, tourism leisure and entertainment, public infrastructure, tourism infrastructure, tourism environment, and local atmosphere. The six dimensions were further divided into 18 sub-dimensions. Under this framework, the general trends and spatiotemporal evolution characteristics of Chengdu's cognitive image during this period were analyzed. Specifically, changes in the shares of identified keywords related to the six dimensions during the period explained the general trend characteristics, while changes in the standard deviational ellipse (SDE) of the distribution of identified keywords with spatial reference, together with changes in the shares of identified keywords related to the 18 sub-dimensions in detail, were adopted to vividly show the spatiotemporal evolution of Chengdu's cognitive image. Our findings revealed that Chengdu's cognitive image has experienced obvious spatiotemporal evolution across different sub-dimensions over the past 20 years. Generally, the cognitive image has became substantial during this period, with a continuous increase in the total share of identified keywords related to the cognitive image of online travels. In addition, the shares of keywords related to the six dimensions and 18 sub-dimensions varied across different stages. Particularly, the spatiotemporal evolution shows that (1) there is an evident shift from sightseeing tourism to experience tourism, as SDE shifts toward the southeastern urban area and the share of identified keywords highly related to experience consumption increases; (2) the city tends to be more "leisurely and carefree" in tourism, as SDE tends to be more concentrated in the urban area with a relatively high density of leisure attractions; and (3) the cognitive image responds to natural hazards promptly, as the size and directions of SDE vary accordingly, and the share of identified keywords related to public infrastructure, tourism infrastructure, and tourism environment reached a peak after the Wenchuan earthquake. Based on our findings, the mechanism forming Chengdu's cognitive image was further discussed from the perspectives of government, residents, and commercial organizations. Methodologically, this study proposes an approach based on text mining and analysis of online travel, typical spatial big data generated by tourists, to examine the cognitive image, which could be applied to other tourism destinations. Moreover, the mechanism framework based on the Chengdu case provides recommendations on tourism destination image management and marketing to improve responses to tourists' changing expectations regarding other tourism destinations.
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