• Journal of the Korean Society of Clothing and Textiles
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• v.33 no.7
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• pp.1086-1097
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• 2009

Consumers have more choices for apparel products as e-shopping grows. This study examines the importance of apparel product attributes and classifies online apparel buyers into groups based on product attribute evaluation in various choice sets. For the empirical research, the online survey was conducted and Latent Gold Choice 4.0 was used for the choice-based conjoint analysis. Five consumer segments are found based on the choice selection of product attributes. The importance of product attributes (online shopping mall, brand, price, and style) and the preference of each product attribute level were different across segments. This research improves the knowledge of the purchasing behavior of online apparel buyers and provides proper attribute combinations of apparel e-shopping for each consumer segment.

• Journal of Fashion Business
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• v.19 no.6
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• pp.127-138
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• 2015

Due to the virtual nature of online businesses, online apparel stores need to enhance the consumer experience by utilizing store design attributes to provide their customers relevant and sufficient information. Since online apparel stores mainly communicate with their customers virtually and digitally, it is important to understand how consumers perceive and react to different design attributes commonly found on apparel stores' online websites. The purpose of this paper is to examine the importance of design attributes commonly found on online apparel stores' websites using three different importance measurements. The design attributes examined in this study include enlarged pictures, product detail pictures, product reviews by other buyers, coordinating items, and size measurement charts. The three different measurements used in this study include two direct measures and one indirect measure using conjoint analysis. Across the three different measures, both the men and women indicated that enlarged pictures represent the most important design attribute when they purchase clothes online followed by size measurement charts and they considered the availability of coordinating items the least important design attribute.

• Journal of the Korea Society of Computer and Information
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• v.28 no.8
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• pp.39-48
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• 2023

This study was conducted to provide a solution to the problem of sizing errors occurring in online purchases due to discrepancies and non-standardization in clothing sizes. This paper discusses an implementation approach for a machine learning-based recommender system capable of providing personalized sizes to online consumers. We trained multiple validated collaborative filtering algorithms including Non-Negative Matrix Factorization (NMF), Singular Value Decomposition (SVD), k-Nearest Neighbors (KNN), and Co-Clustering using purchasing data derived from online commerce and compared their performance. As a result of the study, we were able to confirm that the NMF algorithm showed superior performance compared to other algorithms. Despite the characteristic of purchase data that includes multiple buyers using the same account, the proposed model demonstrated sufficient accuracy. The findings of this study are expected to contribute to reducing the return rate due to sizing errors and improving the customer experience on e-commerce platforms.