• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.2
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• pp.59-72
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• 2022

The online fashion market in the 21st century has shown rapid growth. Against this backdrop, using consumer activity data to provide customized customer services has emerged as a viable business model that draws attention. Algorithm-based personalized recommendation services are a good example. But their application in fashion products has clear limitations. It is not easy to identify consumers' perceptions of the attributes of fashion, which are various, hard to define, and very sensitive to trends. So there is a need to compile data on consumers' underlying awareness and to carry out defined research to increase the utilization of such services in the fashion industry and further engage consumers. This research aims to classify the attributes and types of fashion products and to identify consumers' perceptions of a given situation where a recommendation service is offered. To find out consumers' perceptions of and satisfaction with recommendation services, an online and mobile survey was conducted on women in their 20s and 30s, a group that uses recommendation services frequently. A total of 455 responses were used for analysis. SPSS 28.0 was used, combined with Conjoint Analysis and multiple regression, to analyze data. The study results could provide insights into a better understanding of recommendation services and be used as basic data for companies to identify consumers' preferences and draw up a detailed strategy for market segmentation.

• Fashion & Textile Research Journal
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• v.21 no.2
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• pp.141-150
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• 2019

Online recommendation services help people search for an appropriate product among a huge assortment in stores that also minimize consumers' choice overload. People with a need for uniqueness are likely to prefer this online recommendation service based on individual needs and tastes. This study verifies the effect of consumers' choice overload and similarity avoidance in consumers' evaluation towards an online recommendation service with a focus on innovativeness and use comparability. Two-hundred consumers participated in this study and data were collected through an online survey firm. A mock retailer's webpage was created and showed six types of sneakers, which was presented as a result of product recommendation based on consumers' personal information. Data was analyzed using confirmatory factor analysis (CFA), analysis of variance (ANOVA), and regression analysis. The results show that people with a high similarity avoidance perceive an online recommendation service as an innovative and compatible service. They also perceive a high level of use compatibility for an online recommendation service, especially when it is difficult to choose a product under choice overload. Innovativeness and use compatibility of an online recommendation service increase behavioral intention. The results of this study can contribute to strategies to start online recommendation services from online retailers' websites that identify circumstances in which consumers can adopt innovative services in a positive manner.

• Journal of the Korean Society of Clothing and Textiles
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• v.35 no.8
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• pp.890-905
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• 2011

This study investigated service quality perception, purchase satisfaction, recommendation intention, and switching intention of fashion consumers according to the types of internet shopping malls. The survey was conducted from February 7 to 21 in 2011, and 294 responses were used in the data analysis. The statistical analysis methods were frequency analysis, factor analysis, reliability analysis, ANOVA, and regression analysis. The result of the service quality perception of internet fashion consumers was classified by site characteristics, reliability, enjoyment, product diversity, responsibility, security, and order convenience. There were significant differences in the site characteristics, reliability, enjoyment, responsibility, and security of service quality perception by the types of internet shopping malls. In addition, the factors of service quality perception that could affect the purchase satisfaction of fashion consumers showed differently according to the types of internet shopping malls. The purchase satisfaction of fashion consumers influenced the recommendation intention in all types of internet shopping malls and the purchase satisfaction influenced the switching intention in fashion specialized internet shopping malls.

• Journal of Fashion Business
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• v.27 no.3
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• pp.50-62
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• 2023

The e-commerce fashion market has experienced a remarkable growth, leading to an overwhelming availability of shared information and numerous choices for users. In light of this, chatbots have emerged as a promising technological solution to enhance personalized services in this context. This study aimed to develop user-product attributes for a chatbot-based personalized fashion recommendation service using big data text mining techniques. To accomplish this, over one million consumer reviews from Coupang, an e-commerce platform, were collected and analyzed using frequency analyses to identify the upper-level attributes of users and products. Attribute terms were then assigned to each user-product attribute, including user body shape (body proportion, BMI), user needs (functional, expressive, aesthetic), user TPO (time, place, occasion), product design elements (fit, color, material, detail), product size (label, measurement), and product care (laundry, maintenance). The classification of user-product attributes was found to be applicable to the knowledge graph of the Conversational Path Reasoning model. A testing environment was established to evaluate the usefulness of attributes based on real e-commerce users and purchased product information. This study is significant in proposing a new research methodology in the field of Fashion Informatics for constructing the knowledge base of a chatbot based on text mining analysis. The proposed research methodology is expected to enhance fashion technology and improve personalized fashion recommendation service and user experience with a chatbot in the e-commerce market.

• Fashion & Textile Research Journal
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• v.23 no.5
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• pp.586-597
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• 2021

Personalized product recommendation service is receiving attention as a new marketing strategy while supporting consumer information search and purchasing decisions. This study attempted to verify the effect of self-reference on service use behavior through the dual path of cognitive attitude and emotional attachment. Using convenience sampling, an online survey was conducted with 324 women who were in their 20s and 30s. After collecting and compiling the survey data, the reliability and validity of variables constituting the conceptual research model were verified through confirmatory factor analysis using AMOS 22.0. Next, the significance of sequentially mediated pathways was verified using Process 3.5 Model 80. The results showed that self-referencing not only significantly affects service use intention by simply mediating cognitive attitudes but also sequentially mediates cognitive attitudes and additional information search. Furthermore, self-referencing was significant as an indirect path to service use intention by mediating additional information search. However, in the path mediated by emotional attachment, self-referencing was considered as a simple mediated path leading to service usage intention. These results indicate a dual path in the psychological mechanism, through cognitive and emotional evaluation, that prompts consumer behavioral responses to the personalized product information provided in the shopping process.

• Journal of the Korea Fashion and Costume Design Association
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• v.14 no.4
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• pp.43-61
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• 2012

This research aims to examine the impact of store personality and service quality on the customers' intention of revisiting the department store and their intention of recommendation to others. The participants were women in their 20s to 50s with experiences of purchasing apparel from major department stores. A total of 324 survey responses were used for the final analysis. The data were analyzed using factors analysis, reliability analysis, and multiple regression analysis with PASW 18.0. The results were as follows. First, the department store personality was composed of 3 factors; prestige, passion, sincerity. Service quality factors were defined as tangibility, responsiveness, and empathy. Second, the three dimensions of brand personality-prestige, passion and sincerity turned out to be influential factors affecting the customers' revisiting intention and recommendation intention. Also, tangibility and responsiveness of service quality factors had a significant influence on their revisiting intention, whereas tangibility, responsiveness and empathy factors had a significant influence on their recommendation intention. Third, the sub-dimensions of store personality and service quality had a different influence on the customers' revisiting intention and recommendation intention according to the department store brand.

• Journal of the Korean Home Economics Association
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• v.47 no.7
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• pp.83-95
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• 2009

This study investigated the differences in the effects of the perception of the internet fashion shopping mall characteristics on satisfaction and loyalty of internet fashion shopping mall between Korean and American college students. Questionnaires were administered to 251 Korean and 221 American college students. The results were as follows. First, service and product diversity had significant effects on satisfaction of internet fashion shopping mall in both group, while convenience had a significant impact on satisfaction of internet fashion shopping mall in American group. Second, service, product diversity, and promotion had significant influences on repurchase intention of internet fashion shopping mall in Korean consumer group, while service and reliability, product diversity, and convenience had significant effects on repurchase intention of internet fashion shopping mall in American consumer group. Third, service had a significant effect on recommendation intention in Korean group, while service and reliability and product diversity had significant influences on recommendation intention in American group.

• Journal of the Korean Society of Clothing and Textiles
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• v.43 no.3
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• pp.349-360
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• 2019

This study analyzes the trends of recommendation services for customized fashion styles in relation to artificial intelligence. To achieve this goal, the study examined filtering technologies of collaborative, content based, and deep-learning as well as analyzed the characteristics of recommendation services in the users' purchasing process. The results of this study showed that the most universal recommendation technology is collaborative filtering. Collaborative filtering was shown to allow intuitive searching of similar fashion styles in the cognition of need stage, and appeared to be useful in comparing prices but not suitable for innovative customers who pursue early trends. Second, content based filtering was shown to utilize body shape as a key personal profile item in order to reduce the possibility of failure when selecting sizes online, which has limits to being able to wear the product beforehand. Third, fashion style recommendations applied with deep-learning intervene with all user processes of buying products online that was also confirmed to penetrate into the creative area of image tag services, virtual reality services, clothes wearing fit evaluation services, and individually customized design services.

• Journal of Korea Artificial Intelligence Association
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• v.1 no.1
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• pp.7-10
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• 2023

This study proposes an AI-based shoe recommendation model based on user clothing image data to solve the problem of the global fashion industry, which is worsening due to factors such as the economic downturn. Shoes are an important part of modern fashion, and this research aims to improve user satisfaction and contribute to economic growth through a generative AI-based shoe recommendation service. By utilizing generative AI in the personalized consumer market, we show the feasibility, efficiency, and improvements through an accessible web-based implementation. In conclusion, this study provides insights to help fulfill consumer needs in the ever-changing fashion market by implementing a generative AI-based shoe recommendation model.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.5
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• pp.871-883
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• 2018

This study tests consumer responses to online product recommendation service offered by a website. A product recommendation service refers to a filtering system that predicts and shows items that consumers would like to purchase based on their searches or pre-purchase information. The survey is conducted on 300 people in an age group between 20 and 40 years in a panel of an online survey firm. Data are analyzed using confirmatory factor analysis and structural equation modeling by AMOS 20.0. The results show that personalization quality does not have a significant effect on trust, but relationship quality and technology quality have a positive effect on trust. Three types of quality of recommendation service also have a positive effect on commitment. Trust and commitment are factors that increase service usage intentions. In addition, this study reveals the moderating effect of light users vs heavy users based on online shopping time. Light users show a negative effect of personalization quality on trust, indicating that they are likely to be uncomfortable to the service using personal information, compared to heavy users. This study also finds that trust vs commitment is an important factor increasing service usage intentions for heavy users vs light users.