ABSTRACT 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.
Zhang et al. (2026) studied this question.