Analysis of Destination Images in the Emerging Ski Market: The Case Study in the Host City of the 2022 Beijing Winter Olympic Games

Ping Yin,Yuanxiang Peng,Kurt Matzler

doi:10.3390/su14010555

Ping Yin, Yuanxiang Peng + Show 1 more

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https://doi.org/10.3390/su14010555

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Abstract

This study aims to propose a text mining framework suitable for destination image (DI) research based on UGC (User Generated Content), which combines the LDA (Latent Dirichlet Allocation) model and sentiment analysis method based on custom rules and lexicon to identify and analyze the DI in the emerging ski market. The ski resorts in the host city of the 2022 Winter Olympic Games are selected as a case study. The findings reveal that (1) 9 image attributes, out of which two image attributes have not been identified before in winter destination studies, namely beginner suitability and ticketing service. (2) In the past seven snow seasons, the negative sentiment of tourists has shown a continuous downward trend. The positive sentiment has exhibited a slow upward trend. (3) For tourists from destination countries affected by the Winter Olympic Games, the destination image will be improved when the destination meets their expectations. When the destination cannot meet their expectations, the tourists still believe that the holding of the Winter Olympic will enhance the destination’s situation. The theoretical and managerial implications of these findings are discussed.

Highlights

Destination image (DI) is generally regarded as a critical aspect of destination development as it affects both the supply and demand sides of the markets [1]
The LDA model can classify topics based on the relevance of the feature words in the text, there is no standard or unified method to concisely express the topic of each classification result
This study has attempted to propose a text mining framework suitable for DI research based on the UGC, the ski resorts in the host city of the 2022 Winter Olympic Games was selected as a case study

Summary

Introduction

Destination image (DI) is generally regarded as a critical aspect of destination development as it affects both the supply and demand sides of the markets [1]. With the pervasiveness of online social media and we-media, a large volume of data called UGC has become an essential material for researchers to analyze and predict the tourism market [7,8]. How to develop a comprehensive and effective approach to using massive UGC such as tourist reviews is an urgent issue in tourism marketing and management research [9]. The present DI research is mainly restricted to questionnaires and scale measurements [10,11]. It has not yet developed a competent approach to take full advantage of UGC. Topic modeling technique named LDA model in machine learning can classify irregular UGC data according to specific topics

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