Review of tourism forecasting research with internet data

Xin Li, Rob Law, Gang Xie, Shouyang Wang

Research output: Journal article publicationReview articleAcademic researchpeer-review

37 Citations (Scopus)

Abstract

Internet techniques significantly influence the tourism industry and Internet data have been used widely used in tourism and hospitality research. However, reviews on the recent development of Internet data in tourism forecasting remain limited. This work reviews articles on tourism forecasting research with Internet data published in academic journals from 2012 to 2019. Then, the findings ae synthesized based on the following Internet data classifications: search engine, web traffic, social media, and multiple sources. Results show that among such classifications, search engine data are most widely incorporated into tourism forecasting. Time series and econometric forecasting models remain dominant, whereas artificial intelligence methods are still developing. For unstructured social media and multi-source data, methodological advancements in text mining, sentiment analysis, and social network analysis are required to transform data into time series for forecasting. Combined Internet data and forecasting models will help in improving forecasting accuracy further in future research.

Original languageEnglish
Article number104245
JournalTourism Management
Volume83
DOIs
Publication statusPublished - Apr 2021

Keywords

  • Internet data
  • Search engine
  • Social media
  • Systematic review
  • Tourism forecasting

ASJC Scopus subject areas

  • Development
  • Transportation
  • Tourism, Leisure and Hospitality Management
  • Strategy and Management

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