논문 상세보기
Keyword networks in RJCC research - A co-word analysis and clustering - KCI 등재
RJCC 연구 키워드 네트워크 - 동시출현단어분석과 군집분석 -
- 언어KOR
- URLhttps://db.koreascholar.com/Article/Detail/374286
pp.193-205
A trend analysis of research articles in a field of knowledge is significant because it can help in finding out the structural characteristics of the field and the future direction of research through observing change in a time series. We identified the structural characteristics and trends in text data (keywords) gathered from research articles which in itself is an important task in various research areas. The titles and keywords were crawled from research articles published from 2016 to 2018 in the Research Journal of the Costume Culture (RJCC), one of the representative Korean journal in the field of clothing and textile. After we extracted data comprising English titles and keywords from 195 published articles, we transformed it into a 1-mode matrix. We used measures from network analysis (i.e., link, strength, and degree centrality) for evaluating meaningful patterns and trends in the research on clothing and textile. NodeXL was used for visualizing the semantic network. This study observed change in the clothing and textile research trend. In addition to covering the core areas of the field, the subjects of research have been diversifying with every passing year and have evolved onto a developmental direction. The most studied area in articles published by the RJCC was fashion retailing/consumer psychology while aesthetic/historic and fashion industry/policy studies were covered to a more limited extent. We observed that most of the studies reflecting the identity of RJCC share subject keywords to a significant extent.
I. Introduction
Ⅱ. Methods
1. Semantic network analysis
2. Data collection and analyses
Ⅲ. Results
1. The identity of RJCC based on the keywords ofthe research article
2. Research trends of RJCC (2016-2018)
3. Research trends and differentials of RJCC ineach year (2016-2018)
4. Characteristics of research area in RICC(2016-2018)
Ⅳ. Conclusion
References
