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
- 자료유형
- 학술저널
- 저자정보
- 발행연도
- 2025.08
- 수록면
- 101 - 117 (17page)
- DOI
- 10.7233/jksc.2025.75.4.0101
이용수
DBpia Top 10%동일한 주제분류 기준으로최근 2년간 이용수 순으로 정렬했을 때
해당 논문이 위치하는 상위 비율을 의미합니다.
초록· 키워드
This study investigates consumers’perceptions of greenwashing to better understand the limitations of sustainability marketing. Employing a text mining approach, we analyzed blog content to capture authentic consumer perspectives beyond what is typically found in official news articles. Data were, therefore, collected from major Korean blogging platforms—Naver, Tistory, and Brunch. Preprocessing included topic relevance validation and noun extraction using ChatGPT API, followed by stop word removal. A term frequency analysis and LDA topic modeling were subsequently performed. The frequency analysis revealed that keywords such as “environment,” “effort,” “responsibility,” and “ESG”appeared prominently, reflecting strong consumer expectations for genuine corporate accountability in environmental protection. Simultaneously, frequent mentions of “marketing,” “strategy,” and “regulation” underscored growing skepticism that eco-friendly messaging may be employed merely as a strategic tool, fueling concerns about greenwashing. LDA topic modeling identified three primary themes: (1) corporate responsibility for environmental protection, (2) criticism of superficial green marketing practices, and (3) the perceived necessity for regulatory measures to curb greenwashing. These findings indicate that consumers tend to not only assess eco-friendly claims but also closely scrutinize their authenticity and advocate for stronger regulatory frameworks. As such, this study underscores the importance for companies to move beyond surface-level sustainability narratives, embrace transparent practices, and proactively address consumer skepticism.
#fashion big data(패션 빅데이터)
#greenwashing(그린워싱)
#LDA topic modeling(LDA 토픽모델링)
#sustainabililtly(지속가능성)
#text-mining(텍스트마이닝)
상세정보 수정요청해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
목차
- ABSTRACT
- Ⅰ. 서론
- Ⅱ. 이론적 배경
- Ⅲ. 연구방법
- Ⅵ. 연구결과
- Ⅴ. 결론
- References