Tourism Recovery Nowcasting Based on Combined Survey-Based and Search Engine Data: A Forward-Looking Micro–Macro Approach

The uncertainty of tourism demand in the aftermath of a crisis makes high-frequency nowcasts valuable for fast decision-making in destination management. Through the lens of modern macroeconomic theories, this study proposes an innovative macro–micro tourism demand nowcasting approach using forward-looking survey-based and search engine data to capture the post-COVID-19 recovery trajectory. The empirical results show that combining survey-based confidence data and search engine data significantly improves high-frequency nowcasting performance during the tourism recovery stage. Macro-level survey-based data and micro-level search engine data capture different information on tourism demand and supplement each other. Incorporating survey-based data, including indexes from businesses (BCI) or consumers (CCI), into nowcasting models alongside search volumes enhances nowcasting performance. Specifically, BCI may have higher short-term predictive power than CCI. Industry managers and policy makers are advised to pay more attention to survey-based data released by governments and search engine or other internet big data when predicting futures.