패션 테크 기업의 자기·외부 설명 비교: CES 2025 수상 기업을 중심으로

Abstract

This study explores the linguistic differences and alignment between self-descriptions and external descriptions of fashion tech companies, focusing on seven award-winning firms from CES 2025. It aims to understand how these companies articulate their brand identity through carefully crafted self-descriptions, and how external stakeholders interpret and reframe these messages. Using Orange3-based text mining techniques, the analysis examined two types of English-language texts for each company: self-descriptions sourced from official websites and product pages, and external descriptions drawn from online articles, blogs, and industry media. Quantitative methods, including term frequency (TF), term frequency-inverse document frequency (TF-IDF), 2-gram analysis, topic modeling, and cosine similarity, were employed to evaluate term usage, thematic structures, and the degree of semantic alignment. The findings reveal that self-descriptions predominantly emphasize technological expertise, innovation, and a forward-looking vision, while external descriptions focus on practical functionality, visual attributes, and user experience. While some companies exhibited a strong alignment between self- and external descriptions, others displayed significant discrepancies. These results highlight the need for fashion tech startups, especially those in their early stages, to align internal brand communication with external perceptions. The study contributes to branding research in emerging industries by providing practical insights for developing communication strategies that minimize mismatches between corporate identity and audience reception.