A Pre–Post COVID-19 Structural Comparison of Restaurant Franchise Success Factors Using Online Big Data: Keyword Frequency and CONCOR Analysis

This study identifies and compares restaurant franchise success factors before and after COVID-19 using big-data text mining and structural modeling. Online texts were collected from major Korean channels with the query “Franchise + Success” for 2017–2020 (Pre-COVID) and 2023–2025 (Post-COVID), excluding the transition years. After preprocessing in Textom, keyword frequency analysis extracted the top-50 terms for each period. Co-occurrence networks were modeled in UCINET/NetDraw, and CONCOR clustering derived four keyword blocks in both periods. Across periods, Franchise, Startups, and Success remained dominant, indicating a stable entry-and-success framing. However, the surrounding discourse shifted from exploratory entry preparation in the pre-COVID period (item/idea selection and opportunity-search cues) to execution, profitability, and governance in the post-COVID period, reflected in stronger emphasis on operation and the emergence of Cost, Profits, Contract, and Provision. In addition, Delivery, Experience, and Menu became structurally embedded after COVID-19, suggesting greater attention to channel adaptation and customer experience. The findings imply that franchise success narratives moved from “starting and exploring” toward “running, governing, and optimizing” franchise systems in the post-pandemic market.