ScholarWorks@숙명여자대학교

초록

This study aims to provide insights into franchise café entry restrictions and positioning strategies for non-franchise cafés through two complementary studies. Study 1 examines location-related factors influencing café revenue, while Study 2 analyzes consumer reviews of top-grossing cafés. The analysis focuses on two types of commercial districts: an office district and a high-traffic leisure district (‘hot place’). Estimated sales data were obtained from the big data commercial district analysis platform Openup, and review data were collected from NAVER Map. In Study 1, multiple regression analysis was used to examine the impact of surrounding franchise cafés on estimated sales. The results indicate that in both districts, a higher number of high-end franchise cafés within 300 meters was associated with increased average estimated sales per square meter. However, in the ‘hot place’ district, a greater number of mid-range franchise cafés within the same radius was associated with lower average estimated sales per square meter. Study 2 applied LDA topic modeling to reviews of the top 20 cafés based on average estimated sales per square meter. The findings show that non-franchise cafés in both districts can be classified into three clusters reflecting different consumption purposes: coffee, dessert purchase, and café exploration.

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