• The Research Journal of the Costume Culture
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• v.24 no.1
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• pp.41-53
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• 2016

The purposes of this study was to identify fashion shoppers' perceived risk and satisfaction while shopping at overseas online shopping malls based on their internet shopping values. A self-administered questionnaire was used for data collection and an internet survey was conducted from April 12~15, 2015. Most consumers purchased one or two fashion items at overseas online malls directly, motivated by low prices, and spent 200,000~400,000 won during the last one year. The factors of consumers' internet shopping values were information, hedonics, and practicality. Factors of perceived risk were delivery and refund, price and approval, and product and shopping mall. Consumers were divided into three categories: heavy pursuit shoppers, intermediate shoppers, and uninformed shoppers based on their internet shopping values. Heavy pursuit shoppers were primarily female; they spent more, felt a deeper patronage with overseas online shopping malls and their perceived risk regarding delivery and refund was higher than the other shoppers. The group of uninformed shoppers were primarily male. They spent less, had low patronage with overseas online shopping malls, and their perceived risk regarding delivery and refund was lower than other shoppers. Overall satisfaction was positively affected by information provided while shoppers were surfing the overseas online shopping malls and practicality. Satisfaction was negatively affected by perceived risk regarding price and approval and difficulty in finding specific products while shopping at overseas online shopping malls.

• The Research Journal of the Costume Culture
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• v.15 no.4
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• pp.551-568
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• 2007

The purposes of this study were to identify factors of dissatisfaction and complaining behavior in internet fashion shopping mall, and to investigate constructs of service recovery for solution to this problems. Data were obtained from 201(male: 87, female: 114) internet fashion shopping mall consumer who have experiences of dissatisfaction and complaining behavior after buying products, and were analyzed using by descriptive analysis, factor analysis, Cronbach' $\alpha$, t-test. The results were as follows. First, the most response(80.4% of reponses) was experience to dissatisfaction through internet fashion shopping mall, and the most dissatisfied items were blouse, sweater, T-shirt in upper garment. Also, in men's case, the most satisfied price zone was not exceeding $30,000{\sim}50,000\;won$ and in women's case, it was not exceeding $20,000{\sim}30,000\;won$. Second, 7 factors of dissatisfaction(called quality, payment, delivery, price, interaction, returning/changing/refunding, contract) were identified after purchasing fashion products from internet shopping mall. 3 factors of complaining behavior(called private action, legal action, remedial seeking action) were investigated. Third, constructs of perceived service recovery were extracted from literature review: perceived interaction and justice. Perceived interaction were categorized into two factors: the interaction on the part of the consumer, the interaction in the part of the shopping mall. And perceived justice were categorized into three factors: interactional justice, distributive justice, procedural justice. Usually, university students were likely to take a serious view of service recovery through interaction and justice with internet fashion shopping mall.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.6
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• pp.185-190
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• 2022

Open data has a lot of economic value. Not only Korea, but many other countries are doing their best to make various policies and efforts to expand and utilize open data. However, although Korea has a large amount of data, the data is not utilized effectively. Thus, attempts to utilize those data should be made in various industries. In particular, in the fashion industry, exchange and refund problems are the most common due to unpredictable consumers. Better feedback is necessary for service providers to solve this problem. We want to solve it by showing improved images of dissatisfactions along with user reviews including consumer needs. In this paper, user reviews are analyzed on online shopping mall websites to identify consumer needs, and product attributes are defined by utilizing the attributes of K-fashion data. The users' request is defined as a dissatisfaction attribute, and labeling data with the corresponding attribute is searched. The users' request is provided to the service provider in forms of text data or attributes, as well as an image to help improve the product.

• Journal of the Korea Society of Computer and Information
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• v.27 no.9
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• pp.59-68
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• 2022

A recommender system covers users, searches the items or services which users will like, and let users purchase them. Because recommendations from a recommender system are predictions of users' preferences for the items which they do not purchase yet, it is rarely possible to be drawn a perfect answer. An evaluation has been conducted to determine whether a prediction is right or not. However, it can be lower user's satisfaction if a recommender system focuses on only the preferences, that is caused by a 'filter bubble effect'. The filter bubble effect is an algorithmic bias that skews or limits the information an individual user sees on the recommended list. It is the reason why multiple metrics are required to evaluate recommender systems, and a diversity metrics is mainly used for it. In this paper, we compare three different methods for enhancing diversity for personalized recommendation - bin packing, weighted random choice, greedy re-ranking - with a practical e-commerce data acquired from a fashion shopping mall. Besides, we present the difference between experimental results and F1 scores.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Distribution Research
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• v.16 no.2
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• pp.1-24
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• 2011

1. Introduction: Contrast to the offline purchasing environment, online store cannot offer the sense of touch or direct visual information of its product to the consumers. So the builder of the online shopping mall should provide more concrete and detailed product information(Kim 2008), and Alba (1997) also predicted that the quality of the offered information is determined by the post-purchase consumer satisfaction. In practice, many fashion and apparel online shopping malls offer the picture information with the product on the real person model to enhance the usefulness of product information. On the other virtual product experience has been suggested to the ways of overcoming the online consumers' limited perceptual capability (Jiang & Benbasat 2005). However, the adoption and the facilitation of the virtual reality tools requires high investment and technical specialty compared to the text/picture product information offerings (Shaffer 2006). This could make the entry barrier to the online shopping to the small retailers and sometimes it could be demanding high level of consumers' perceptual efforts. So the expensive technological solution could affects negatively to the consumer decision making processes. Nevertheless, most of the previous research on the online product information provision suggests the VR be the more effective tools. 2. Research Model and Hypothesis: Presented in