Journal of Fashion Business (패션비즈니스)

The Korean Society of Fashion Business (한국패션비즈니스학회)
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A Study of Consumer Perception on Fashion Show Using Big Data Analysis

빅데이터를 활용한 패션쇼에 대한 소비자 인식 연구

  • Kim, Da Jeong (Dept. of Clothing & Textile, Sookmyung Women's University) ;
  • Lee, Seunghee (Dept. of Clothing & Textile, Sookmyung Women's University/Research Institute of ICT Convergence, Sookmyung Women's University)
  • 김다정 (숙명여자대학교 의류학과) ;
  • 이승희 (숙명여자대학교 의류학과/숙명여자대학교 융합연구소)
  • Received : 2019.06.26
  • Accepted : 2019.07.25
  • Published : 2019.07.30
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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.

Keywords

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Figure 1. The Results of Word Clouding (2015~2016)

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Figure 2. The Results of Word Clouding (2017~2018)

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Figure 4. A Network of 40 Keyword (2017~2018)

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Figure 3. A Network of 40 Keyword (2015~2016)

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Figure 5. CONCOR Analysis Results (2015~2016)

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Figure 6. CONCOR Analysis Results (2017~2018)

Table 1. An Overview of Data Collection

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Table 2. Data Extraction Amount

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Table 3. Keyword Analysis of Fashion Show

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Table 4. A group of Text-mining Analysis on Fashion Show Keyword

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Table 5. The Analysis Centrality of Top 40 Keyword

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