인문학
사회과학
자연과학
공학
의약학
농수해양학
예술체육학
복합학
개인구독
소속 기관이 없으신 경우, 개인 정기구독을 하시면 저렴하게
논문을 무제한 열람 이용할 수 있어요.
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.
학술저널
DBpia Top 5%
오류 신고하기해당 페이지 내 제목·저자·목차·페이지정보가 잘못된 경우 알려주세요!
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.
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.