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빅데이터를 활용한 패션쇼에 대한 소비자 인식 연구A Study of Consumer Perception on Fashion Show Using Big Data Analysis

Other Titles
A Study of Consumer Perception on Fashion Show Using Big Data Analysis
Authors
김다정이승희
Issue Date
Jul-2019
Publisher
한국패션비즈니스학회
Keywords
fashion show; big data; text mining; semantic network analysis; consumer perception; 패션쇼; 빅데이터; 텍스트마이닝; 의미연결망 분석; 소비자 인식
Citation
패션 비즈니스, v.23, no.3, pp 85 - 100
Pages
16
Journal Title
패션 비즈니스
Volume
23
Number
3
Start Page
85
End Page
100
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/1803
DOI
10.12940/jfb.2019.23.3.85
ISSN
1229-3350
2288-1867
Abstract
This study examines changes in consumer perceptions of fashion shows, which are critical elements in the apparel industry and a means to represent a brand’s image and originality. For this purpose, big data in clothing marketing, text mining, semantic network analysis techniques were applied. This study aims to verify the effectiveness and significance of fashion shows in an effort to give directions for their future utilization. The study was conducted in two major stages. First, data collection with the key word, “fashion shows,” was conducted across websites, including Naver and Daum between 2015 and 2018. The data collection period was divided into the first- and second-half periods. Next, Textom 3.0 was utilized for data refinement, text mining, and word clouding. The Ucinet 6.0 and NetDraw, were used for semantic network analysis, degree centrality, CONCOR analysis and also visualization. The level of interest in “models” was found to be the highest among the perception factors related to fashion shows in both periods. In the first-half period, the consumer interests focused on detailed visual stimulants such as model and clothing while in the second-half period, perceptions changed as the value of designers and brands were increasingly recognized over time. The findings of this study can be utilized as a tool to evaluate fashion shows, the apparel industry sectors, and the marketing methods. Additionally, it can also be used as a theoretical framework for big data analysis and as a basis of strategies and research in industrial developments.
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