Tourism demand forecasting and tourists’ search behavior: evidence from segmented Baidu search volume

Abstract

Given the importance of web search volume for reflecting tourists' preferences for certain tourism services and destinations, incorporating these data into forecasting models can significantly improve forecasting performance. This study enriches the literature on tourism demand forecasting and tourists' search behavior through segmented Baidu search volume data. First, this study divides Baidu search volume data based on volume sources and periods. Then, by analyzing the most relevant keywords in tourism demand in different segments, this study captures the dynamic characteristics of tourist search behavior. Finally, this study adopts a series of econometric and machine learning models to further improve the performance of tourism demand and forecasting. The findings indicate that tourists’ search behavior has changed significantly with the prevalence and popularization of 4G technology and suggest that search volume improves forecasting performance, especially search volume on mobile terminals, from 2014M1–2019M12.

Keywords

• Baidu search volume
• Tourist search behavior
• Tourism demand forecasting
• Event study
• Selection of keywords