• The International Journal of Costume Culture
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• 제8권2호
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• pp.111-123
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• 2005

In the fashion retail research, the role of fashion salesperson and their job satisfaction have been getting attentions. The purpose of this study was to investigate the elements affecting the job satisfaction of fashion salesperson. A hypothetical path model of job satisfaction of fashion salesperson was developed and tested. Empirical data were collected with a written survey instrument. Data were collected from 150 fashion salespersons during 2001 fall, and finally 101 responses were analyzed. As results of series of regression analysis, final job satisfaction model was identified. Job satisfaction of fashion salesperson was affected by subjective job aptitudity, salary, fashion product knowledge. fashion involvement, and work experience. Managerial implication and research limitation were discussed.

• 패션비즈니스
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• 제10권2호
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• pp.60-82
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• 2006

The purpose of this study was to confirm and practical use the street fashion design analysis system(Web-SFAS) which was designed in preceding research. Web-SFAS was developed to analyze data fast, accurately, conveniently, and to provide them to related fields by using Information Technology (IT) in fashion design industry. By inputting, sorting and analyzing actual image data into this system, it purposes to check if it needs to be corrected and to verify its operation and application. For this study, 191 street fashion image and paper questionaries were collected on Oct. 16th from 4pm to 7pm in Gyeong-Nam area(4 markets), 2004. This study was processed basically cross research(real time research). The collected data and paper questionaries were analysed by 4 experts who had over Master Degrees, and the results were input to the Web-SFAS system. This system analyzed the results as follow ; First, Top is usually wear T-shirts, cardigan item of soft material, Bottom is usually wear Skirt, jean item of hard material. Second, As for shoes, pumps were the most popular, and as for accessories, diverse items such as shoulder bag, jewelry, and totebag were preferred. Third, fashion image, most people wear a sportive casual style with semi-casual in a close second. Therefore, We also expect that this data can be used a prediction for the next seasons design trends and needs, especially if we make an online database through this development system, then it will be easier to access faster and more accurate fashion information.

• 패션비즈니스
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• 제23권5호
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• pp.81-95
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• 2019

The purpose of this study is to research consumers' perception of the HiSeoul fashion show, which is being used by new designers as a means of promotion, and to propose a strategy for revitalizing new designer brands. This was done in order to secure basic data from fashion consumers, to help guide marketing strategies and promote rising designers. In this research, the consumers' perception of HiSeoul fashion show was verified using text-mining, data refinement and word clouding that was undertaken by TEXTOM3.0. Also, semantic network analysis, CONCOR analysis and visualization of the analysis results were performed using Ucinet 6.0 and NetDraw. "HiSeoul fashion show" was used as the keyword for text-mining and data was collected from March 1, 2018 to April 30, 2019. Using frequency analysis, TF-IDF, and N-gram, it was also shown that consumers are aware of places where shows are held, such as DDP and Igansumun. It was also revealed that consumers recognize rising designer brands, designer's names, the names of guests attending the show and the photo times. This study is meaningful in that it not only confirmed consumers' interest in new designer brands participating in the HiSeoul Fashion Show through big data but also confirmed that it is available as a marketing strategy to boost brand sales. This study suggests using HiSeoul show room to induce consumer sales, or inviting guests that match the brand image to promote them on SNS on the day the show is held for a marketing strategy.

• 복식문화연구
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• 제1권2호
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• pp.113-130
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• 1993

The purpose of this study was to investigate the role of perceived attributes of innovation in predicting the fashion innovativeness of female college students and to compare results with the predictive efficacy of selected psychographic variables. The data were analyzed by factor analysis and stepwise multiple regression. Frequency, percentage and man values were used to evaluate the descriptive data. The major findings derived from analysis are as follows: 1. Of the psychographic variables used to predict fashion innovativeness fashion interest was the most predictive of fashion innovativeness followed by venturesomeness. 2. So only perceived attributes variables found to be predictive of fashion innovativeness was perceived risk.

• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 추계학술대회
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• pp.35-45
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• 2005

Data fusion is method to combination data. The purpose of this study is to design and implementation for street fashion information analysis system using data fusion. It can offer variety and actually information because it can fuse image data and survey data for street fashion. Data fusion method exists exact matching method, judgemental matching method, probability matching method, statistical matching method, data linking method, etc. In this study, we use exact matching method. Our system can be visual information analysis of customer's viewpoint because it can analyze both each data and fused data for image data and survey data.

• 한국의류산업학회지
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• 제3권4호
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• pp.313-322
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• 2001

This study was designed to investigate actual conditions(the types and the personal physical job related factors) of fashion model in Korea. Survey was done through questionnaire data, 194 fashion models were involved survey. The data were analyzed by using frequency, cluster analysis. The results of this study were as follows : 1) The largest sample were as follows (about personal physical related factors) : unmarried, college graduate and undergraduate, resident in the Seoul, 2-24 yrs female with 175-177 cm, 52-54 kg, B-W-H (33-24-35 inch). 2) The largest sample (about job related factors) were as follows: getting private educational institution, 1-2 yrs job experience, B grade, less than 600,000 won for salaries, 100,000-190,000 won at a stage, 5-6 yrs duration of model work, inexperience in the international fashion stage, take up a foreign language, fashion magazine for fashion source, image and look of individuality for a necessary condition. The motive for job was the concern in the job. Problems with a guarantee were pointed out for the betterment working conditions. 3) The types of fashion model were classified into 4 types : the type of lack of professionalism, the type of show off one's talents, the type of dissatisfaction with working condition, the type of maturity of professionalism.

• 패션비즈니스
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• 제23권3호
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• pp.85-100
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• 2019

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.

• 한국의상디자인학회지
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• 제22권3호
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• pp.49-61
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• 2020

This study analyzed the social media accounts and performed a Big Data analysis of Z-generation fashion using Textom Text Mining Techniques program and Ucinet Big Data analysis program. The research results are as follows: First, as a result of keyword analysis on 67.646 Z-generation fashion social media posts over the last 5 years, 220,211 keywords were extracted. Among them, 67 major keywords were selected based on the frequency of co-occurrence being greater than more than 250 times. As the top keywords appearing over 1000 times, were the most influential as the number of nodes connected to 'Z generation' (29595 times) are overwhelmingly, and was followed by 'millennials'(18536 times), 'fashion'(17836 times), and 'generation'(13055 times), 'brand'(8325 times) and 'trend'(7310 times) Second, as a result of the analysis of Network Degree Centrality between the key keywords for the Z-generation, the number of nodes connected to the "Z-generation" (29595 times) is overwhelmingly large. Next, many 'millennial'(18536 times), 'fashion'(17836 times), 'generation'(13055 times), 'brand'(8325 times), 'trend'(7310 times), etc. appear. These texts are considered to be important factors in exploring the reaction of social media to the Z-generation. Third, through the analysis of CONCOR, text with the structural equivalence between major keywords for Gen Z fashion was rearranged and clustered. In addition, four clusters were derived by grouping through network semantic network visualization. Group 1 is 54 texts, 'Diverse Characteristics of Z-Generation Fashion Consumers', Group 2 is 7 Texts, 'Z-Generation's teenagers Fashion Powers', Group 3 is 8 Texts, 'Z-Generation's Celebrity Fashions' Interest and Fashion', Group 4 named 'Gucci', the most popular luxury fashion of the Z-generation as one text.

• 패션비즈니스
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• 제26권1호
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• pp.1-15
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• 2022

Digital transformation has induced changes in human life patterns; consumption patterns are also changing to digitalization. Entering the era of industry 4.0 with the 4th industrial revolution, it is important to pay attention to a new paradigm in the fashion industry, the shift from developer-centered to user-centered in the era of the 3rd industrial revolution. The meaning of storing users' changing life and consumption patterns and analyzing stored big data are linked to consumer sentiment. It is more valuable to read emotions, then develop and distribute products based on them, rather than developer-centered processes that previously started in the fashion market. An AI(Artificial Intelligence) deep learning algorithm that analyzes user emotion big data from user experience(UX) to emotion and uses the analyzed data as a source has become possible. By combining AI technology, the fashion industry can develop various new products and technologies that meet the functional and emotional aspects required by consumers and expect a sustainable user experience structure. This study analyzes clear and useful user experience in the fashion industry to derive the characteristics of AI algorithms that combine emotions and technologies reflecting users' needs and proposes methods that can be used in the fashion industry. The purpose of the study is to utilize information analysis using big data and AI algorithms so that structures that can interact with users and developers can lead to a sustainable ecosystem. Ultimately, it is meaningful to identify the direction of the optimized fashion industry through user experienced emotional fashion technology algorithms.

• 한국의류학회지
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• 제46권1호
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• pp.165-181
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• 2022

The fashion industry is creating innovative business models using artificial intelligence. To efficiently utilize artificial intelligence (AI), fashion data must be classified. Until now, such data have been classified focusing only on the objective properties of fashion products. Their subjective attributes, such as fashion brand sensibilities, are holistic and heuristic intuitions created by a combination of design elements. This study aims to improve the performance of collaborative filtering in the fashion industry by extracting fashion brand sensibility using computer vision technology. The image data set of fashion brand sensibility consists of high-end fashion brand photos that share sensibilities and communicate well in fashion. About 26,000 fashion photos of 11 high-end fashion brand sensibility labels have been collected from the 16FW to 21SS runway and 50 years of US Vogue magazines beginning from 1971. We use EfficientNet-B1 to establish the main architecture and fine-tune the network with ImageNet-ILSVRC. After training fashion brand sensibilities through deep learning, the proposed model achieved an F-1 score of 74% on accuracy tests. Furthermore, as a result of comparing AI machine and human experts, the proposed model is expected to be expanded to mass fashion brands.