Tourism demand forecasting based on user-generated images on OTA platforms

Shuai Ma, Hengyun Li, Mingming Hu, Haifeng Yang, Ruogu Gan

Research output: Journal article publicationJournal articleAcademic researchpeer-review

2 Citations (Scopus)

Abstract

Tourists’ images of destinations posted on online travel agency (OTA) platforms are important information sources for potential tourists to perceive and construct destination images. These perceptions can then inform travel decisions. We investigated the roles of the aesthetics of user-generated images on OTA platforms in tourism demand forecasting. Specifically, the aesthetics of images of three popular scenic spots in Hong Kong were used to predict tourism demand from the region’s two largest short-haul markets and largest long-haul market using a seasonal autoregressive moving average with exogenous factors (SARIMAX) model. Seasonal naïve, SARIMA, and SARIMAX models involving search query data were taken as benchmarks. Results showed that (1) image aesthetics could help make more accurate tourism demand forecasting; and (2) as an additional variable, image aesthetics could supplement search query-based volume variables to enhance tourism demand forecasting.

Original languageEnglish
JournalCurrent Issues in Tourism
DOIs
Publication statusPublished - Jun 2023
Externally publishedYes

Keywords

  • forecast
  • image aesthetics
  • online travel agency
  • Tourism demand
  • user-generated images

ASJC Scopus subject areas

  • Geography, Planning and Development
  • Tourism, Leisure and Hospitality Management

Fingerprint

Dive into the research topics of 'Tourism demand forecasting based on user-generated images on OTA platforms'. Together they form a unique fingerprint.

Cite this