Tourist arrival forecasting using feed search information

Kaijian He, Qian Yang, Don Wu and Yingchao Zou

Current Issues in Tourism, 2024, vol. 27, issue 19, 3199-3230

Abstract: The feed index is a weighted sum of the number of reactions (i.e. reading, comments, retweets, likes and dislikes, and so on.) that the content engine actively recommends and distributes to the users. It provides valuable information from big data on the Internet and high marketing value to Destination Marketing Organization as the content can be customized. A large-scale empirical study on the impact of the feed index on tourist arrival forecasting accuracy has been conducted, with a new approach proposed to incorporate the feed index into the tourist arrival forecasting model with higher forecasting accuracy. Firstly, the empirical results suggest that the feed index for different keywords reflects varying tourist preferences and has different impacts on tourist arrival movements, with variant lead-lag relationships. Secondly, the study shows that keywords need to be carefully selected based on theoretical analysis plus new methods such as entropy analysis. Therefore, it is proposed that entropy is employed to select the keywords and time lags, thus helping improve forecasting accuracy.

Date: 2024
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/13683500.2023.2259573 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:rcitxx:v:27:y:2024:i:19:p:3199-3230

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/rcit20

DOI: 10.1080/13683500.2023.2259573

Access Statistics for this article

Current Issues in Tourism is currently edited by Jennifer Tunstall

More articles in Current Issues in Tourism from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().