Encyclopedia Thursday, September 23, 2021 4115 hits

Revisit intention in terms of destination image and travel anxiety under COVID-19 in Japan among travelers who obtain travel information from social media influencer

Author: Hisashi Masuda, Kyoto University 
<insert-authors> (2021). <insert-abstract-title>. 29th Nordic Symposium on Tourism and Hospitality Research Shaping mobile futures: Challenges and possibilities in precarious times, 21-23 September 2021. Online. Retrieved: <insert-date>, from http://www.airth.global

In this study, we develop a research model that combines tourist destination anxiety against tourist destination image theory with investigating the characteristics of travelers who acquire information from social media influencers. To test the model by SEM(structured equation modeling), a survey on tourism on Kyoto under COVID-19 was conducted among metropolitan residents in Japan (n=514). The result showed that destination cognitive image influenced the intention to revisit for both the group that acquires information from social media influencers or not. But the impact of the cognitive image was significantly greater in the acquired group than the non-acquired group. In addition, in the destination cognitive image, ease of tourism, tradition and culture, nature, and local community were significantly related for the acquired group, while for the non-acquired group, the only such relationship was food/cuisine and local community. This study provides a perspective on the use of insights from social media influencer marketing research into tourism research. In addition, we show the tendency to revisit tourist destination in consideration of travel anxiety, which provides useful insights for the management of destination under the influence of infectious diseases. We have not yet been able to analyze the impact of individual components of the image of a tourist destination or a more detailed model that takes into account the diversity of tourists. In the future, we will build a model that can explain more complex structures based on this model and collect data, aiming to contribute to this research field. 


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