• The Journal of Bigdata
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• v.7 no.2
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• pp.217-224
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• 2022

Since online shopping has become common, people can easily buy fashion goods anytime, anywhere. Therefore, consumers quickly respond to various environmental variables such as weather and sales prices. Therefore, utilizing big data for efficient inventory management has become very important in the fashion industry. In this paper, the changes in sales volume of fashion goods due to changes in temperature is analyzed via the proposed big data analysis algorithm by utilizing actual big data from Korean fashion company 'A'. According to the simulation results, it was confirmed that Bidirectional-LSTM(Bi-LSTM) compared to LSTM(Long Short-Term Memory) takes more simulation time about more than 50%, but the prediction accuracy of non-periodic time series data such as clothing product sales data is the same.

• Journal of the Korean Society of Clothing and Textiles
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• v.42 no.6
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• pp.909-923
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• 2018

Online retailers' offline channel expansion is rapidly growing as an important consumer incentive strategy, despite the enormous initial costs of establishing a store. This study focuses on the offline channel operation of online shopping malls, and examines the effects and influencing factors of the channel expansion strategy from a consumer perspective. In-depth interviews were conducted with ten customers in their 20s and 30s who had visited online retailers' stores or purchased products. Major issues were extracted based on the framework of a channel effect mechanism proposed by Cao and Li (2015). Subsequently, it was found that existing online retailers could enhance brand image and perception by expanding their channels to offline stores. It was also emphasized that the consumer trust on the quality of the product in offline stores is a key variable, and it has a significant influence on consumer's continuous purchase and revisit intention. This study showed that borderless channel integration was the most important task when expanding channels of online retailers. So it will be necessary to strive for an omni-channel strategy so that channel integration can be strategically executed and consumers can interact regardless of channels.

• The Journal of Society for e-Business Studies
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• v.19 no.2
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• pp.31-51
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• 2014

This study examined the effect of product presentation on consumers' affective/cognitive states and purchase intention. The design of the study was a one factor(product presentation: garment presented flat vs. garment presented on models without faces vs. garment presented on models with faces) between-subject design with a moderator (involvement: high vs. low). A sample of 429 female college students participated in this online experiment. The results show the effectiveness of using a realistic human model on apparel websites. Also, consumers who are highly involved with clothing generally exhibit more positive responses than those who are less involved. Overall, these findings provide empirical support for the Stimulus-Organism-Response model and the Elaboration Likelihood Model, and contribute useful knowledge regarding website design for online apparel retailers.

• Journal of the Korean Society of Clothing and Textiles
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• v.31 no.12
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• pp.1721-1732
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• 2007

This study identified types of information source, and explored a path model for consumer information search by shopping attributes in the context of online decision making. Participants completed self-administered questionnaires during regularly scheduled classes. A total of 219 usable questionnaires were obtained from respondents who enroll at universities in the southwestern region of the United States. For data analysis, factor analysis and path model estimation were used. Consumer information source was classified into three types for online clothing purchases: Online source, Offline retail source, and Mass media. Consumers were more likely to rely on offline retail source for online clothing purchases, than other sources. In consumer information search by shopping attributes, online sources were more likely to be related to transaction-related attributes(e.g., incentive service), whereas offline retail source(e.g., displays in stores, manufacturer's catalogs and pamphlets) were more likely to be related to product and market related attributes(e.g., aesthetics, price) when purchasing clothing online. Also, the path model emphasizes the effect of shopping attributes on traditional retailer search behavior, leading to online purchase intention for clothing. This study supports consumer information search by attributes, and discusses a managerial implication of multi-channel retailing for apparel.

• Journal of the Korean Society of Costume
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• v.56 no.2 s.101
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• pp.83-100
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• 2006

As online fashion businesses achieve rapid growth in the last few years, they gather various kinds of customer information through customer registration procedures and expect to utilize this information for CRM programs. The purpose of this study were to understand the current practices of customer information management of online fashion companies and to discuss how to improve it for the benefit of both customers and fashion businesses. This study included three steps of empirical data collection process: First, online fashion companies were visited and content analyzed at three time periods-2002, 2003, and 2004. Second, a questionnaire was developed and surveyed with 488 customers. And third, interviews with two groups were conducted, one with customers who experienced customer registration with online fashion companies and the other with experts of web developing. Through customer registration procedures, personal and contact information such as name, citizen registration number(social security number), home address, home telephone number, and cellular phone number were most frequently required. Customers were asked to provide more specific information regarding their privacy, online behavior, and taste recently. The variety of information category in 2004 observation was larger than 2002, but the amount of required information from each company got smaller. Customers tended to provide some false infor- mation, and the most frequently cited reason for that was 'too much hassle' and 'no practical benefit from information provision'. Customers were concerned with the exposure of personal information such as citizen registration number. The ideal number of pieces of information required was identified as 3 to 5 including name, phone number, and address. The paper was concluded with the discussion of customer information management from CRM perspective, CRM program, information analysis methods, and security.

• Fashion & Textile Research Journal
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• v.20 no.5
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• pp.556-568
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• 2018

In this study, we surveyed the current status and sizing system of the custom dress shirts sold through online shopping, compared with the sizing system of the ready-made dress shirts. We tried to collect the information needed to make the well fitted dress shirts for middle-aged men from this study. The 17 online custom dress shirt brands were selected and the sales type, sales price, design options and size options of each brand were analyzed. The sizing systems of online custom dress shirt brands were compared with the sizing system of the 10 ready made dress shirt brands. The result showed that online custom dress shirts brands offered a variety of design options and size options to meet the consumers' individuality, taste and demand for good fit. In the ready-made brands, all 10 brands were using the same size notation system. In the same size designation, the difference in product size among the ready-made brands showed a tendency to be smaller than the online custom brands. The online custom brands had the different size notation system among brands. The size notation, the number of size designation and the size interval were different for each brand. Also, in the online custom brands, the product size among brands differed from each other in the same size designation. Therefore, the standardized size information and sizing system for middle-aged men that could be used as criteria when making the product size and pattern design in online custom brands were needed.

• Journal of the Korea Fashion and Costume Design Association
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• v.24 no.3
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• pp.145-158
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• 2022

To grasp the size system of children's clothing online shopping malls, basic size charts for clothing products from 14 shopping malls were collected, compared, and analyzed. Looking at the age range of the basic size chart for each company, the minimum age is 1 year old, the maximum age is 14-15 years old, and all companies included ages up to 7 years. In addition, as for the number of size designations, children's clothing companies used seven to eight designations, and there were six types of size designation methods. Next, looking at the height range for each size, even with the same size designation method, the height range differed depending on the company. Also, the KS size standard does not consider body weight, but many companies use weight as a reference body size. Compared with the child body size data of the 6th Korean Body Size Data, the height and weight range for each size provided by the company showed differences between companies. The results of this study can be used in the future as basic data when revising and supplementing the clothing sizes in children's clothing companies.

• Journal of Intelligence and Information Systems
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• v.23 no.4
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• pp.77-110
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• 2017

Recently, the diversification and individualization of consumption patterns through the web and mobile devices based on the Internet have been rapid. As this happens, the efficient operation of the offline store, which is a traditional distribution channel, has become more important. In order to raise both the sales and profits of stores, stores need to supply and sell the most attractive products to consumers in a timely manner. However, there is a lack of research on which SKUs, out of many products, can increase sales probability and reduce inventory costs. In particular, if a company sells products through multiple in-store stores across multiple locations, it would be helpful to increase sales and profitability of stores if SKUs appealing to customers are recommended. In this study, the recommender system (recommender system such as collaborative filtering and hybrid filtering), which has been used for personalization recommendation, is suggested by SKU recommendation method of a store unit of a distribution company that handles a homogeneous brand through a plurality of sales stores by country and region. We calculated the similarity of each store by using the purchase data of each store's handling items, filtering the collaboration according to the sales history of each store by each SKU, and finally recommending the individual SKU to the store. In addition, the store is classified into four clusters through PCA (Principal Component Analysis) and cluster analysis (Clustering) using the store profile data. The recommendation system is implemented by the hybrid filtering method that applies the collaborative filtering in each cluster and measured the performance of both methods based on actual sales data. Most of the existing recommendation systems have been studied by recommending items such as movies and music to the users. In practice, industrial applications have also become popular. In the meantime, there has been little research on recommending SKUs for each store by applying these recommendation systems, which have been mainly dealt with in the field of personalization services, to the store units of distributors handling similar brands. If the recommendation method of the existing recommendation methodology was 'the individual field', this study expanded the scope of the store beyond the individual domain through a plurality of sales stores by country and region and dealt with the store unit of the distribution company handling the same brand SKU while suggesting a recommendation method. In addition, if the existing recommendation system is limited to online, it is recommended to apply the data mining technique to develop an algorithm suitable for expanding to the store area rather than expanding the utilization range offline and analyzing based on the existing individual. The significance of the results of this study is that the personalization recommendation algorithm is applied to a plurality of sales outlets handling the same brand. A meaningful result is derived and a concrete methodology that can be constructed and used as a system for actual companies is proposed. It is also meaningful that this is the first attempt to expand the research area of the academic field related to the existing recommendation system, which was focused on the personalization domain, to a sales store of a company handling the same brand. From 05 to 03 in 2014, the number of stores' sales volume of the top 100 SKUs are limited to 52 SKUs by collaborative filtering and the hybrid filtering method SKU recommended. We compared the performance of the two recommendation methods by totaling the sales results. The reason for comparing the two recommendation methods is that the recommendation method of this study is defined as the reference model in which offline collaborative filtering is applied to demonstrate higher performance than the existing recommendation method. The results of this model are compared with the Hybrid filtering method, which is a model that reflects the characteristics of the offline store view. The proposed method showed a higher performance than the existing recommendation method. The proposed method was proved by using actual sales data of large Korean apparel companies. In this study, we propose a method to extend the recommendation system of the individual level to the group level and to efficiently approach it. In addition to the theoretical framework, which is of great value.