A Study on the Core Attributes of Gastronomy Tourism Using Big Data: Focusing on CONCOR Analysis

This study focuses on gastronomy tourism as a practical strategy for regional revitalization in the context of the emerging crisis of local extinction. The purpose of this research is to identify the core attributes of gastronomy tourism as perceived by consumers through big-data-based text analysis and to provide strategic implications for revitalizing local economies. Text data related to “gastronomy tourism” posted between January 1, 2023 and January 1, 2025 were collected from major portal sites such as Naver, Daum, and Google. Using the big data analysis solution TEXTOM, the collected data were refined and TF-IDF, semantic network analysis and CONCOR analysis were sequentially performed. The CONCOR analysis revealed that consumer perceptions of gastronomy tourism are structured into four core attribute clusters. These clusters were labeled “Popular Destinations and General Activities”, “Premium Dining and Stay Experience”, “Authentic Local Food Exploration” and “Cultural Festivals and Regional Contents.” This study empirically demonstrates through big data that contemporary gastronomy tourism is a complex activity that combines accommodation, culture, and experiential elements rather than simple food tasting. These findings can be utilized as fundamental data for developing high value-added fusion tourism products and for establishing tailored marketing strategies aimed at regional revitalization.