Network analysis of big data research in tourism

Xin Li, Rob Law

Research output: Journal article publicationJournal articleAcademic researchpeer-review

10 Citations (Scopus)

Abstract

This study aims to provide a comprehensive network analysis to understand the current state of big data research in tourism by investigating multi-disciplinary contributions relevant to big data. A comprehensive network analytical method, which includes co-citation, clustering and trend analysis, is applied to systematically analyse publications from 2008 to 2017. Two unique data sets from Web of Science are collected. The first data set focuses on big data research in tourism and hospitality. The second data set involves other disciplines, such as computer science, for a comparison with tourism. Results suggest that applications of social media and user-generated content are gaining momentum, whereas theory-based studies on big data in tourism remain limited. Tourism and other relevant domains have similar concerns with the challenges involved in big data, such as privacy, data quality and appropriate data use. This comparative network analysis has implications for future big data research in tourism.

Original languageEnglish
Article number100608
JournalTourism Management Perspectives
Volume33
DOIs
Publication statusPublished - Jan 2020

Keywords

  • Big data
  • Co-citation analysis
  • Network analysis
  • Research trends
  • Tourism studies

ASJC Scopus subject areas

  • Tourism, Leisure and Hospitality Management

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