Abstract

Tourism research increasingly uses search query data to forecast demand, but the literature rarely explores the mechanisms of the factors influencing demand. A time-varying parameter factor vector auto-regression model is constructed based on Baidu Index on six aspects (dining, shopping, transportation, tours, attractions, and lodging) of tourism demand from January 2011 to March 2019. The model can quantitatively and comprehensively analyze the mechanisms of tourism demand and its six important influencing factors, and can provide suggestions for subsequent planning, construction, and services in the tourism industry. The empirical results show that the relationship between the six factors and domestic tourism demand is time-varying. Dining, attractions, and shopping have a driving effect on tourism demand, and are thus stimulative factors; transportation, tours, and lodging hinder the growth of tourism demand, and are thus baffle factors.

Keywords

influencing factor; tourism demand; TVP-Fa-VAR; generalized dynamic factor model; search query data

DOI: https://doi.org/10.1177/1096348020944435