• International Journal of CAD/CAM
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• v.4 no.1
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• pp.1-10
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• 2004

Through reviewing and comparing the current virtual shopping malls web sites integrated VR into E-commerce, this paper analyzed both the advantages and disadvantages of two kinds of methods for product presentation: 2D image based and 3D model based presentation method. Using the virtual shopping mall (EasyMall) as a showcase, we presented the architecture of the system and the development technologies, especially those in the mixed presentation method. The presentation and customization methods in the two related modules, including the PhoneShow for mobile phone and EasyShow for textile products, were discussed. It indicated that the integration of E-commerce with VR could provide consumers with virtual experience and intelligent service for business activities. Furthermore, the product presentation methods can be made available for use in different cases.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.19 no.9
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• pp.2073-2080
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• 2015

Even though users choose goods they want to buy in on-line shopping malls, real purchase is often performed in off-line shopping malls. It is called reverse showrooming. It means that users' analysis of goods based on images and description of internet shopping malls has limitation. Thus, large-scale online shopping malls provide a customized shopping information. However, in that case, the provided information is a simple list of goods users bought or retrieved. Thus, a system to analyze various needs of users and apply the result into on-line shopping mall is necessary. In this paper, an analysis system is proposed. The system contains a module to analyze user defined preference and a module to analyze users' reviews. The former designates two goods and collects preferences of individual users. the latter analyzes reviews about purchased goods based on database dictionary stored in advance for analyzing reviews. The system implemented shows that it is possible to recommend some goods that meet each users's needs

• The Journal of Industrial Distribution & Business
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• v.9 no.12
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• pp.63-72
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• 2018

Purpose - In this study, it is classified the service quality dimension of mobile shopping app using Kano model. In addition, it is evaluated quality factors suitable for strategic management from the viewpoint of service provider through mobile application through binary dimension analysis. Research design, data, and methodology - In this study, seven quality dimensions such as information quality, reliability, immediacy, convenience, design, security and customer service were derived through related studies to make binary shopping quality app quality measurement. 37 sub-variables were set by each quality dimensions. Each questionnaire was composed of positive and negative items like Kano's proposed method, and the satisfaction coefficient suggested by Timko(1993) was examined to understand the influence of each factors on customer satisfaction. Results - As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. As a result of research, shopping app users perceived unity quality factor in most items of service quality dimension such as information quality, reliability, immediacy, convenience and customer service. And, in information quality, the information overload was classified as an apathetic quality component, while the related information provision belonged to an attractive quality component. In reliability quality, customized service provision was classified as an attractive quality component. In instant connectivity, the quality of the connection during transport was classified as an attractive quality component. In convenience quality, access to product information was classified as a one-way quality component. All components of designs quality were classified as attractive quality components, and in security quality, all of their components were all classified as one quality component. Lastly, in customer service, they components were all classified as a single quality component. In addition, the satisfaction coefficient showed a good impression, quick response of the result, fast delivery, and the unsatisfactory coefficient showed more interest in personal information such as payment method safety, and transaction security. Conclusion - In the online service environment, which is difficult to differentiate in terms of upward upgrading only by technological implementation and function, the results of this study can be suggested as a differentiating factor for major channels with customers rather than improve the brand image.

• Proceedings of the KAIS Fall Conference
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• 2003.11a
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• pp.118-126
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• 2003

In data mining, having access to large amount of data sets for the purpose of predictive data does not guarantee good method, even where the size of Real data is Mobile commerce unlimited. In addition to searching expected Goods objects for Users, it becomes necessary to develop a recommendation service based on XML. In this paper, we design the optimized XML Recommender product data. Efficient XML data preprocessing is required, include of formatting, structural, and attribute representation with dependent on User Profile Information. Our goal is to find a relationship among user interested products from E-Commerce and M-Commerce to XDB. Firstly, analyzing user profiles information. In the result creating clusters with analyzed user profile such as with set of sex, age, job. Secondly, it is clustering XML data which are associative products classify from user profile in shopping mall. Thirdly, after composing categories and goods data in which associative objects exist from the first clustering, it represent categories and goods in shopping mall and optimized clustering XML data which are personalized products. The proposed personalized user profile clustering method has been designed and simulated to demonstrate it's efficient.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.11
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• pp.777-787
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• 2020

With the development of the Internet and the spread of mobile devices, the online market is growing rapidly. As the number of customers using online shopping malls explodes, research is being conducted on the analysis of usage behavior from customer data, personalized product recommendations, and service development. Thus, this paper seeks to analyze the overall process of online shopping malls through process mining, and to identify the factors that influence users' purchases. The data used are from a large online shopping mall, and R was the analysis tool. The results show that customer activity was most prominent in categories with event elements, such as unconventional discounts and monthly giveaway events. On the other hand, searches, logins, and campaign activity were found to be less relevant than their importance. Those are very important, because they can provide clues to a customer's information and needs. Therefore, it is necessary to refine the recommendations from related search words, and to manage activity, such as coupons provided when customers log in. In addition to the previous discussion, this paper proposes various business strategies to enhance the competitiveness of online shopping malls and to increase profits.

• Spatial Information Research
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• v.18 no.2
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• pp.67-75
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• 2010

When people are shopping at a large shopping mall, they usually become to go around many stores for looking for better products and comparing them. In this paper, we design and implement a purchasing activity support system based on an Ambient Service Model that provides relevant stores information on map interface hierarchically through user contexts based search, to support such user's purchasing activities with the help of relevant information. In this system, users can search for relevant stores information through the system by Ambient Query which is created by their location and stores information with a mobile device. Then, users obtain relevant stores information provided in the form of hierarchy of keywords as a highly condensed summary and easily figure out the locations of the stores on a map interface. Moreover, users search additional other kinds of relevant stores information over the hierarchy of keywords. Eventually, users can obtain relevant stores information intuitively and conveniently without complex search processes. We implemented this system by integrating the subordinate technologies such as RFID, map-based, location-based and ontology technology. We also performed experiments on a well-known shopping region (Ilsan Lapesta shopping mall, Goyang-city Gyeonggi-do, Korea). Finally, we also confirmed that users' shopping activities were significantly improved by utility the present system.

• Proceedings of the Korea Society for Industrial Systems Conference
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• 2000.05a
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• pp.33-41
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• 2000

최근 정보기술, 정보통신기술등의 발달로 기존의 상거래 방식과는 많이 다른 방식의 거래가 이루어지고 있다. Electronic Commerce의 활성화는 가상상점(Electronic Shopping Mall), 전자지불체계 (Electronic Payment System), Web EDI 등 인터넷을 기반으로 한 전자적 경제활동을 다양하게 하였다. 이러한 EC의 한 패러다임으로 이동 상거래 방식이 등장하게 되었다. 이것이 개방 네트워크와 공중망을 통하여 이용자의 위치, 시간과 관계없이 텍스트, 음성, 화상의 디지털 데이터를 이용하여 기업 또는 개인의 상거래 활동을 정의하는 전자상거래(Electronic Commerce) 방식 중의 하나인 모빌커머스(Mobile Commerce)이다. 본 연구에서는 이러한 모빌커머스가 기존의 인터넷 비즈니스의 한 방향으로서 유, 무선 통신망을 이용한 자유스런 정보의 전달을 통한해 어떤 상거래를 구축하는 것이 성공할 수 있는 것인가 이해하고자 전자상거래에서의 모빌커머스 역할에 대해 연구하고자 한다.

• Journal of Information Technology Services
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• v.8 no.4
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• pp.151-163
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• 2009

In this paper, we design and develop a personalized advertisement and information services system based on mobile RFID using RFID virtual tag. It is a system to perform the use of the contents information or the commercial transaction on the contents products through internet server connection by using RFID tag information with a mobile according as transmitting immediately to a cell phone after a tag generator derived RFID tag information to be substituted for the contents information being output at present, after being transmitted from a cell phone the information request signal on the contents information being output from video media. This service system provides a mechanism for gathering advertisement information left behind by many clients visiting advertisement sites for analysis of customers property, service model for selecting personalized services, and mechanism for providing them to customers who visited in a advertisement mall joined to the shopping site.

• Journal of the Korea Safety Management & Science
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• v.17 no.1
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• pp.203-213
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• 2015

Recently, online grocery shopping has been increasing with the development of internet, mobile, and IT technology due to the proportion of consumers changes like increasing single households and double-income couples. Therefore, online sales from distributors with offline stores have also increased, and the offline retailers are facing their limits in dealing with store-based online channel they have carried out. Domestic offline retailers benchmarked overseas advanced retailers to solve this problem by reviewing about developing the online-only distribution center. However, much investment is needed in order to operate the distribution center with the new concept from abroad. In this study, we have reviewed the current online grocery market trend and the theory related to developing distribution system of the online mall. For offline retailers, we have reviewed the case which developed the distribution center applied to the nation's first online-only distribution center. The purpose of this study is reducing trial and error for local retailers in developing online-only distribution centers and suggesting ways to improve investment effect.

• Journal of Intelligence and Information Systems
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• v.20 no.2
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• pp.123-136
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• 2014

Recently, online shopping has further developed as the use of the Internet and a variety of smart mobile devices becomes more prevalent. The increase in the scale of such shopping has led to the creation of many Internet shopping malls. Consequently, there is a tendency for increasingly fierce competition among online retailers, and as a result, many Internet shopping malls are making significant attempts to attract online users to their sites. One such attempt is keyword marketing, whereby a retail site pays a fee to expose its link to potential customers when they insert a specific keyword on an Internet portal site. The price related to each keyword is generally estimated by the keyword's frequency of appearance. However, it is widely accepted that the price of keywords cannot be based solely on their frequency because many keywords may appear frequently but have little relationship to shopping. This implies that it is unreasonable for an online shopping mall to spend a great deal on some keywords simply because people frequently use them. Therefore, from the perspective of shopping malls, a specialized process is required to extract meaningful keywords. Further, the demand for automating this extraction process is increasing because of the drive to improve online sales performance. In this study, we propose a methodology that can automatically extract only shopping-related keywords from the entire set of search keywords used on portal sites. We define a shopping-related keyword as a keyword that is used directly before shopping behaviors. In other words, only search keywords that direct the search results page to shopping-related pages are extracted from among the entire set of search keywords. A comparison is then made between the extracted keywords' rankings and the rankings of the entire set of search keywords. Two types of data are used in our study's experiment: web browsing history from July 1, 2012 to June 30, 2013, and site information. The experimental dataset was from a web site ranking site, and the biggest portal site in Korea. The original sample dataset contains 150 million transaction logs. First, portal sites are selected, and search keywords in those sites are extracted. Search keywords can be easily extracted by simple parsing. The extracted keywords are ranked according to their frequency. The experiment uses approximately 3.9 million search results from Korea's largest search portal site. As a result, a total of 344,822 search keywords were extracted. Next, by using web browsing history and site information, the shopping-related keywords were taken from the entire set of search keywords. As a result, we obtained 4,709 shopping-related keywords. For performance evaluation, we compared the hit ratios of all the search keywords with the shopping-related keywords. To achieve this, we extracted 80,298 search keywords from several Internet shopping malls and then chose the top 1,000 keywords as a set of true shopping keywords. We measured precision, recall, and F-scores of the entire amount of keywords and the shopping-related keywords. The F-Score was formulated by calculating the harmonic mean of precision and recall. The precision, recall, and F-score of shopping-related keywords derived by the proposed methodology were revealed to be higher than those of the entire number of keywords. This study proposes a scheme that is able to obtain shopping-related keywords in a relatively simple manner. We could easily extract shopping-related keywords simply by examining transactions whose next visit is a shopping mall. The resultant shopping-related keyword set is expected to be a useful asset for many shopping malls that participate in keyword marketing. Moreover, the proposed methodology can be easily applied to the construction of special area-related keywords as well as shopping-related ones.