This study utilizes social big data to investigate the factors influencing the awareness, attitude, and behavior toward vegan fashion consumption among global and Korean consumers. Social media posts containing the keyword “vegan fashion” were gathered, and meaningful discourse patterns were identified using semantic network analysis and sentiment analysis. The study revealed that diverse factors guide the purchase of vegan fashion products within global consumer groups, while among Korean consumers, the predominant discourse involved the concepts of veganism and ethics, indicating a heightened awareness of vegan fashion. The research then delved into the factors underpinning awareness (comprehension of animal exploitation, environmental concerns, and alternative materials), attitudes (both positive and negative), and behaviors (exploration, rejection, advocacy, purchase decisions, recommendations, utilization, and disposal). Global consumers placed great significance on product-related information, whereas Korean consumers prioritized ethical integrity and reasonable pricing. In addition, environmental issues stemming from synthetic fibers emerged as a significant factor influencing the awareness, attitude, and behavior regarding vegan fashion consumption. Further, this study confirmed the potential presence of cultural disparities influencing overall awareness, attitude, and behavior concerning the acceptance of vegan fashion, and offers insights into vegan fashion marketing strategies tailored to specific cultures, aiming to provide vegan fashion companies and brands with a deeper understanding of their consumer base.

The purpose of this study is to examine the relationships between citations and the research trends of The Research Journal of the Costume Culture (RJCC) using bibliometric and network analyses. The results are as follows. First, the RJCC has been cited by a greater number of journals and high-reputation journals today. The RJCC has been mentioned in global academic journals in various fields, and it has been noted the most in environmental science. Second, because of examining the articles published in the RJCC over the past three years (2019–2021), it was found that the number of topics was evenly distributed in various subfields of the clothing and textiles sector. The RJCC principally deals with traditional clothing, ethics and sustainability, and technology, which means that the RJCC reflects the past, present, and future. As a result of conducting a cluster analysis using the Wakita-Tsurumi algorithm, the subjects of ethical fashion and sustainability were derived from the subdivisions of the RJCC. This suggests that the RJCC is a journal specialized in ethical fashion and sustainability sectors such as environmental, animal, and labor ethics. This study outlined the current status and future direction of academic journals in the field of clothing through an analysis of the RJCC’s influence change and the relationship between citations. In addition, it is academically significant because it identifies research trends and knowledge-structure changes in the apparel science field by identifying changes in research keywords and significant research topics by sector.

This study analyzed the expression of hanbok in online fashion styling games, and examined the main themes of and user reactions to hanbok fashion styling games through semantic network analysis and sentiment analysis. KrKwic, Textom, and NodeXL were used for data collection and analysis. The findings of the study are as follows: First, depending on the designer’s expressive method, hanbok fashion styling games provide typical traditional hanbok, modern hanbok, and dress-like fusion hanbok, demonstrating contents with various themes for different sexes, classes, and situations. Second, as a result of analyzing the themes of hanbok-related styling game contents, it turned out that the main themes are tradition, color, historical drama, fusion hanbok, holiday, and love. Most of them produce modernized hanbok that reflect the situation and utility rather than strictly traditional ones. Third, as a result of analyzing user reviews of hanbok styling game contents, positive factors mostly turned out to be the satisfaction of the hanbok contents, with the users also showing positive intent to wear hanbok. Through this, it was found out that hanboks portrayed in game contents could potentially cause the user’s positive intent to wear hanbok. Negative factors turned out to be the discordance between the intrinsic image of hanbok and the game character’s image.

This study aimed at examining fashion consumers’ awareness during the COVID-19 pandemic. Big data analysis methods, such as text mining, social network analysis, and regression analysis, were applied to user posts about fashion on Korean portal websites and social media during COVID-19. R 3.4.4, UCINET 6, and SPSS 25.0 software were used to analyze the data. The results were as follows. In researching the popular fashion-related topics during COVID-19, the prevention of infection and prophylaxis were significant concerns in the early stage (Jan 1 to Jan 31, 2020), and changed to online channels and online fashion platforms. Then, various topics and fashion keywords appeared with COVID-19-related keywords afterwards. Fashion-related subjects concerned prophylaxis, home life, digital and beauty products, online channels, and fashion consumption. In comparing fashion consumers’ awareness during COVID-19 with SARS and MERS, “face masks” was the common keyword for all three illnesses; yet, the prevention of infection was a major consumer concern in fashion-related subjects during COVD-19 only. As COVD-19 cases increased, the search volume for face masks, shoes, and home clothes also increased. Consumer awareness about face masks shifted from blocking yellow dust and micro-dust to the sociocultural significance and short supply. Keywords related to performance turned out to be the major awareness as to shoes, and home clothes were repurposed with an expanded range of use.

This study aimed to analyze the performance of Disney-collaborated fashion lines based on online consumer reviews. To do so, the researchers employed text mining and network analysis to identify key words in the reviews of these products. Blogs, internet cafes, and web documents provided by Naver, Daum, and YoutTube were selected as subjects for the analysis. The analysis period was limited to one year after for the 2019. Data collection and analysis were conducted using Python 3.7, Textom, and NodeXL. The research terms in question were as follows: ‘Disney fashion collaboration’ and ‘Frozen fashion collaboration’. Preliminary survey results indicated that ‘Elsa’s dress’ was the most frequently mentioned term and that the domestic fashion brand Eland Retail was the most active in selling Disney branded clothing through its own brand. The writers of reviews for Disney-collaborated fashion products were primarily mothers with daughters. Their decision to purchase these products was based upon the following factors; price, size, stability of decoration, shipping, laundry, and retailer. The motives for purchasing the product were the positive response of the consumer’s child and the satisfaction of the parents due to the child’s response. The problems to be solved included insufficient quantity of supply, delay in delivery, expensive price considering the number of times children’s clothes are worn, poor glitter decoration, faded color, contamination from laundry, and undesirable smells immediately after the purchase.

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.