Knowledge mapping of tourism demand forecasting research

Chengyuan Zhang; Shouyang Wang; Shaolong Sun; Yunjie Wei

doi:10.1016/j.tmp.2020.100715

Knowledge mapping of tourism demand forecasting research

Tour Manag Perspect. 2020 Jul:35:100715. doi: 10.1016/j.tmp.2020.100715. Epub 2020 Jul 4.

Authors

Chengyuan Zhang¹, Shouyang Wang², Shaolong Sun³, Yunjie Wei²

Affiliations

¹ School of Economics and Management, Beihang University, Beijing 100191, China.
² Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
³ School of Management, Xi'an Jiaotong University, No.28, Xianning, West Road, Beilin District, Xi'an, 710049, China.

Abstract

Utilizing a scientometric review of global trends and structure from 388 bibliographic records over two decades (1999-2018), this study seeks to advance the building of comprehensive knowledge maps that draw upon global travel demand studies. The study, using the techniques of co-citation analysis, collaboration network and emerging trends analysis, identified major disciplines that provide knowledge and theories for tourism demand forecasting, many trending research topics, the most critical countries, institutions, publications, and articles, and the most influential researchers. The increasing interest and output for big data and machine learning techniques in the field were visualized via comprehensive knowledge maps. This research provides meaningful guidance for researchers, operators and decision makers who wish to improve the accuracy of tourism demand forecasting.

Keywords: Bibliometric analysis; CiteSpace; Hospitality demand; Infographic; Knowledge map; Tourist arrival.