• The Journal of the Institute of Internet, Broadcasting and Communication
• /
• v.22 no.6
• /
• pp.185-190
• /
• 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 Information Technology Applications and Management
• /
• v.10 no.4
• /
• pp.29-50
• /
• 2003

This research provide the basic information to enhance the user-orientedness of usability design guidelines for software products and an effective empirical guidance to classify design attributes of internet shopping mall sites. The results of analysis show that design attributes can be classified into the procedural attribute group, the shopping tool attribute group, the visual attribute group, linguistic attribute group, and others. The results show that shopping tool attribute group can be divided further into the search tool attribute group and purchase tool attribute group and that the visual attribute group can be divided further into the screen condition attribute group and the character legibility attribute group. The research reveals that when designers design software interfaces and features they should take the compound effect of a group of design attributes into consideration to enhance the usability of the system.

• Fashion & Textile Research Journal
• /
• v.15 no.4
• /
• pp.533-542
• /
• 2013

Fashion products are frequently purchased on impulse and are also one of the most popular product categories sold online. Online environment attributes can facilitate flow experiences that are described as an optimal psychological state reached during an engagement in activities (e.g., games and e-shopping). This study estimated the path model to examine the causal relationships among shopping mall attributes, flow, e-impulse buying, and consumer satisfaction for fashion products. A total of 598 usable questionnaires were obtained from college students who had purchased fashion products through the Internet. Data were analyzed by exploratory factor analysis, confirmatory factor analysis, and path analysis using SPSS 18.0 and AMOS 18.0. The results showed that e-shopping mall attributes (visual attributes and product attributes) significantly influenced e-impulse buying (fashion-oriented impulse buying and promotion-oriented impulse buying) which was mediated by the consumer flow experience and then influenced by consumer satisfaction. In the path model, the flow was stimulated by shopping mall attributes, the e-impulse buying was influenced by flow, and the consumer satisfaction was influenced by e-impulse buying. Flow was the most highly related to the fashion-oriented impulse buying, and followed by the relationship of the flow and promotion-oriented impulse buying in the context of e-shopping for fashion products. A managerial implication was discussed for fashion product e-retailers to develop strategies on visual attributes and product attributes that could stimulate and increase the consumer flow to trigger impulse buying as well as consumer satisfaction.

• Journal of Global Scholars of Marketing Science
• /
• v.10
• /
• pp.165-185
• /
• 2002

The extant study on virtual store loyalty has not been comprehensive and has focused on identifying limited variables such as design, product value, contents, communication, security, that have influence on virtual store loyalty. Their limited perspectives have frequently yielded empirical studies with fragmented results and contributed to the research with low explanation. This paper thus examines the effects of virtual store image, satisfaction and trust on loyalty. The study results confirm that virtual store image have significant effects on satisfaction, trust, and loyalty. Also, satisfaction and trust directly influence vitrual store loyalty. This study contributes to the understanding of the role of virtual store providers in online shopping situation.

• Journal of Information Technology Services
• /
• v.11 no.2
• /
• pp.49-68
• /
• 2012

The purpose of this study is to analyse the country image, the pre-assessment, and the usage intention about overseas internet shopping mall service in china and how these factors are related to one another according to internet shopping mall using experience. And this study divided the country image for internet shopping mall service into two components : general country attribute and general service product attribute. In this study it is found that the country image of international internet shopping service is directly related to the pre-assessment, and the pre-assessment is related to usage intention especially in case that chinese consumer has a lot of internet shopping mall using experience. In the other case, the general country attributes affect the general service product attributes but the general service product attributes don't have any relationship with the pre-assessment. For a successful international internet shopping mall service business in China, marketer should recognize that it is important to formulate the policy extending the internet shopping mall experience as well as using the country image.

• Fashion & Textile Research Journal
• /
• v.13 no.2
• /
• pp.234-241
• /
• 2011

In this study, the effect of Internet clothing soho mall attributes on attitude toward site and revisit intention was investigated. A total of 292 female college students participated in the experiment to explore a clothing soho mall out of 20 highly-ranked soho malls for shopping and to complete a questionnaire. In exploratory factor analysis results, five factors were generated out of 18 question items indicating clothing soho mall attributes. Among those five factors, 'product assortment and presentation' was the most important factor affecting attitude toward site and revisit intention toward a clothing soho mall. In addition, 'site construct' was another factor affecting attitude toward a clothing soho mall site specifically in the group who shops clothing more often on the Internet shops than off-line shops. Based on study results, implications and insights were discussed.

• Journal of the Korea Society of Computer and Information
• /
• v.19 no.2
• /
• pp.193-200
• /
• 2014

Due to the advent of ubiquitous computing environment, it is becoming a part of our common life style. And tremendous information is cumulated rapidly. In these trends, it is becoming a very important technology to find out exact information in a large data to present users. Collaborative filtering is the method based on other users' preferences, can not only reflect exact attributes of user but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. In this paper, we propose clustering method by user's features based on SOM for predicting purchase pattern in u-Commerce. it is necessary for us to make the cluster with similarity by user's features to be able to reflect attributes of the customer information in order to find the items with same propensity in the cluster rapidly. The proposed makes the task of clustering to apply the variable of featured vector for the user's information and RFM factors based on purchase history data. To verify improved performance of proposing system, we make experiments with dataset collected in a cosmetic internet shopping mall.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.2
• /
• pp.197-206
• /
• 2012

A exisiting recommedation system using association rules has the problem, such as delay of processing speed from a cause of frequent scanning a large data, scalability and accuracy as well. In this paper, using a Implicit method which is not used user's profile for rating, we propose the personalized recommendation system which is a new method using the FP-tree mining based on RFM. It is necessary for us to keep the analysis of RFM method and FP-tree mining to be able to reflect attributes of customers and items based on the whole customers' data and purchased data in order to find the items with high purchasability. The proposed makes frequent items and creates association rule by using the FP-tree mining based on RFM without occurrence of candidate set. We can recommend the items with efficiency, are used to generate the recommendable item according to the basic threshold for association rules with support, confidence and lift. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Journal of the Korea Society of Computer and Information
• /
• v.17 no.6
• /
• pp.163-172
• /
• 2012

Collaborative filtering which is used explicit method in a existing recommedation system, can not only reflect exact attributes of item but also still has the problem of sparsity and scalability, though it has been practically used to improve these defects. This paper proposes the personalized recommendation system using RFM method and k-means clustering in u-commerce which is required by real time accessablity and agility. In this paper, using a implicit method which is is not used complicated query processing of the request and the response for rating, it is necessary for us to keep the analysis of RFM method and k-means clustering to be able to reflect attributes of the item in order to find the items with high purchasablity. The proposed makes the task of clustering to apply the variable of featured vector for the customer's information and calculating of the preference by each item category based on purchase history data, is able to recommend the items with efficiency. To estimate the performance, the proposed system is compared with existing system. As a result, it can be improved and evaluated according to the criteria of logicality through the experiment with dataset, collected in a cosmetic internet shopping mall.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.5
• /
• pp.29-39
• /
• 2018

Recently, In traditional markets, visitors are declining. The reason is the growth of large stores and Internet shopping malls. The government continues to support and policy to revitalize traditional markets. Government support has been focused on the selective attributes of traditional markets. However, the purchase intention of users in traditional markets is lowered. The reason is that it is in the hygiene of the traditional market. This study analyzed whether the optional attributes of traditional markets and service quality increase the intention of visit, In addition, the users of the traditional market analyzed the hygiene factor as an important factor in the intention of the visit. The results of the analysis is First, convenience, accessibility, transparency, attractiveness, and economic feasibility of selective attributes of traditional markets were analyzed to affect the intention to visit. Second, the merchant efficiency, the display efficiency, the product efficiency, and the transaction efficiency of the service quality of the traditional market influence on the visit intention. However, facility efficiency was not found to have any effect. Third, merchant hygiene factors, facility hygiene factors, and commodity hygiene factors were found to affect the intention to visit. These traditional market hygiene factors were analyzed to control the intention to visit. Therefore, it can be said that the hygiene factor of the traditional market plays a role in raising the intention of visiting the traditional market in activating the traditional market. The conclusion is that merchants and support groups should be prioritized in order to revitalize traditional markets. The importance of environmental hygiene is introduced and implications for research results are suggested.