인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
지원사업
학술연구/단체지원/교육 등 연구자 활동을 지속하도록 DBpia가 지원하고 있어요.
커뮤니티
연구자들이 자신의 연구와 전문성을 널리 알리고, 새로운 협력의 기회를 만들 수 있는 네트워킹 공간이에요.
논문 기본 정보
- 저자정보
초록·키워드
The global spread of K-Food and Korean cultural content has expanded interest in Korean sauces as convenient products that convey traditional flavors and support diverse culinary applications. Nevertheless, limited research has examined how K-Sauce is represented across English-language online environments beyond individual fermented sauces or menu items. This study therefore investigated the semantic structure of K-Sauce in global online discourse and derived implications for product development and overseas market expansion. A total of 9,926 English-language texts containing the search term “Korean sauce” were collected from Google web pages, Google News, and Facebook between July 1, 2024, and June 30, 2026. Following data cleaning and morphological analysis, term frequency analysis, TF-IDF, semantic network, centrality, and CONCOR analyses were conducted. The CONCOR analysis identified four semantic clusters. “Product Attributes & Choice Factors” encompassed sensory qualities, health, vegan and reduced-content attributes, convenience, price, brand, and review information. “Ingredients & Culinary Characteristics” comprised ingredients, seasonings, and cooking methods. “Consumption Experience & Globalization” reflected dining and street-food experiences, purchasing and recommendation information, and interactions with global and fusion food cultures. “Traditional Fermented Sauces & K-Food” connected gochujang, ganjang, doenjang, and ssamjang with representative Korean dishes, including bulgogi, bibimbap, tteokbokki, and chicken. These findings indicate that global online discourse frames K-Sauce as a multidimensional product category combining product attributes, Korean ingredients, culinary applications, consumption experiences, and Korean food culture. Academically, this study extends the conceptual scope of K-Sauce and empirically demonstrates its semantic structure using multi-channel, English-language unstructured data. Practically, the results suggest developing reduced-sugar, reduced-sodium, vegan, dipping, marinade, and multipurpose products; adapting flavor intensity and usage information to local food cultures; and linking traditional sauces with familiar dishes through localized recipes, brand storytelling, and experiential marketing.
본문·목차
인공지능 문자 인식 모델을 통해 추출된 텍스트로, 일부 오타나 오류가 포함될 수 있으나 지속적으로 개선 중입니다.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.
오류를 발견하셨다면 해당 부분을 드래그한 후 ' 를 통해 신고해주세요.