• Knowledge Management Research
• /
• v.10 no.3
• /
• pp.17-33
• /
• 2009

The emergence of the Internet shopping agent enabled consumers to enjoy Internet shopping more easily and quickly. Especially, the role of Internet shopping agent is becoming more important following the information overload trend on the Internet in that the consumers can promptly obtain information about a certain product and its price among countless items on the Internet. As a result, consumers can now enjoy shopping more easily, compared to the offline shopping which requires a lot of efforts in comparing the products and purchasing them. Since the Internet shopping agents collect extensive information about the products' price, delivery period, detailed characteristics, etc., and present a comparison table containing the information to the consumers, the consumers can shop more quickly at lower price using such shopping agents. However, it has not been sufficiently studied about how the various functions of shopping agents actually support consumers' purchase decision making procedure in everyday life, and if they do, in which stages they play a supporting role in consumers' purchase decision making system. Therefore, this study conducts an empirical analysis on the role of the Internet shopping agents in the purchase decision making process of the consumers, considering the Internet shopping agent as a decision making supporting system. Moreover, it analyzes how the effects of the Internet shopping agent differs according to the consumers' knowledge level about the products.

• Information Systems Review
• /
• v.6 no.1
• /
• pp.53-66
• /
• 2004

In Korea, The Consumer Protection Law in Electronic Commerce, which came into effect in July 2002, forced internet shopping malls to provide a minimum of 7 forms of business information, including the name of the business, the name of the representative, geographical address, telephone number, fax number, e-mail address, and business license number, so that consumers could easily identify them. To investigate the shopping malls which do not provide their business information, can lead to confidence in the electronic commerce environment. To investigate the completeness of the business information with the internet shopping malls, this paper proposes the methods of gathering URLs of internet shopping malls, of extracting business information attributes, and an architecture of the agent system. Information extraction in our research is based on synonyms and indicator words of the attributes. With the experiment we showed that the accuracy of our agent system to find out the right business information is 89.3%.

• Journal of Internet Computing and Services
• /
• v.2 no.1
• /
• pp.63-75
• /
• 2001

With the popularization of internet, its application fields are spreading out every industry parts, especially there are great change in marketing areas by the internet. One of them is shopping mall. Assembly PC of old method being sale shopping mall is not consider in compatibility of each devices, because of using the assembling method of it. In this paper, in order to their has not specific knowledge on computer and consider of a compatibility to each devices, our implemented shopping mall that on the provided convenience by using the on-line customer, M implemented intelligent agent can advise compatibility of each devices using Fuzzy value.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.33 no.3
• /
• pp.29-42
• /
• 2008

Semantic Web society initially focused only on data but has gradually moved toward knowledge. Recently rule beyond ontology has emerged as a key element of the Semantic Web. All of these activities are obviously aiming at making data and knowledge on the Web sharable and reusable between various entities around the world. If one of ultimate visions of the Semantic Web is to increase human's decision making quality assisted by machines, there is a missing but important part to be shared and reused. It is knowledge about constraints on data and concepts represented by ontology which should be emphasized more. In this paper, we propose Semantic Web Constraint Language (SWCL) based on OWL and show how effective SWCL can be in representing and solving an internet shopper's decision making problem by an implementation of a shopping agent in the Semantic Web environment.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.27 no.1
• /
• pp.38-46
• /
• 2004

To be successful in increasingly competitive Internet marketplace, it is essential to capture customer loyalty. This paper deals with an intelligent agent approach to incorporate customer's sensibility into an one-to-one recommendation service in on-line shopping mall. In this paper the focus of interest is on-line recommendation service algorithm for development of Human Sensibility based web agent system. The recommendation agent system composed of seven services including specialized algorithm. The on-line recommendation service algorithm use human sensibility ergonomics and on-line preference matching technologies to tailor to the customer the suggestion of goods and the description of store catalog. Customizing the system's behavior requires the parallel execution of several tasks during the interaction (e.g., identifying the customer's emotional preference and dynamically generating the pages of the store catalog). Most of the present shopping malls go through the catalog of goods, but the future shopping malls will have the form of intelligent shopping malls by applying the on-line recommendation service algorithm.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.35 no.1
• /
• pp.1-9
• /
• 2012

It is well known that internet shoppers are very sensitive to sale prices. They visit the various shopping malls and collect the product information including purchase conditions for goods purchase decision-making. Recently the necessity of information support is increasing because of increase of information amount which is necessary and complexity of goods purchase decision-making process. The comparison shopping agent systems have provided price comparison information which is collected from various shopping malls to satisfy internet shoppers information craving. But the frequent price change caused by keen price competition is becoming the primary reason of information quality decline among price comparison sites. RSS which is a family of web feed formats used to publish frequently updated is applied even in on-line shopping malls. This paper develops a RSS product information collection system to get real-time product information. The proposed product information system consists of (1) web crawler module for searching RSS feed shopping malls automatically, (2) RSS reader module for parsing product information from RSS feed file, (3) product DB and (4) product searching module. Performance of the proposed system is higher than the comparison shopping agent systems when it is defined with the volume of collecting product information per unit time.

• Journal of the Korea Computer Industry Society
• /
• v.4 no.12
• /
• pp.919-936
• /
• 2003

Nowadays Electronic Commerce shopping mall is welcomed more and more on the Internet. It is expected that Shopping mall systems come to be various and adaptable to complex requirements according to customers who have these various needs, but just show products list, instead. This thesis suggests various structures of shopping malls showing interface agent model using Case-Based Reasoning one of reasoning method of Artificial Intelligence instead of the method of prior EC shopping mall. 1 constructed case base by making index with shopping mall members and customers' private informations, and pursued difference from prior EC shopping malls by proposing to customers cases of other users' selection of products who have similar private informations with them.

• Fashion & Textile Research Journal
• /
• v.12 no.4
• /
• pp.450-458
• /
• 2010

The purpose of this study was to investigate the effect of shopping orientation on price perception and the effect of shopping orientation and price perception on purchase satisfaction. This study administered a questionnaire survey to adults in their 20s and 30s who had once purchased fashion products in surrogate internet shopping malls. Among 327 questionnaires, 263 were collected through convenience sampling and 94 were collected from six internet communities. Data were analyzed by factor analyses and regression analyses. The results of this study are as follows. First, factor analysis of price perception revealed that two factors such as price reasonability and price reduction were extracted and the mean of price reduction was higher than one of price reasonability. Second, factor analysis of purchase satisfaction extracted four factors such as service quality, shopping convenience, product scarcity and product variety/price satisfaction. Mean comparisons showed that the mean of product scarcity was the highest among four factors. Third, regression analyses that recreational, economic, and convenience shopping orientation affected price reduction, a factor of price perception. Fourth, regression analyses showed that shopping orientations and the price perception had significant effects on the purchase satisfaction.

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 1999.10a
• /
• pp.33-51
• /
• 1999

• Proceedings of the Korea Inteligent Information System Society Conference
• /
• 2002.05a
• /
• pp.342-351
• /
• 2002

A personalized product recommendation is an enabling mechanism to overcome information overload occurred when shopping in an Internet marketplace. Collaborative filtering has been known to be one of the most successful recommendation methods, but its application to e-commerce has exposed well-known limitations such as sparsity and scalability, which would lead to poor recommendations. This paper suggests a personalized recommendation methodology by which we are able to get further effectiveness and quality of recommendations when applied to an Internet shopping mall. The suggested methodology is based on a variety of data mining techniques such as web usage mining, decision tree induction, association rule mining and the product taxonomy. For the evaluation of the methodology, we implement a recommender system using intelligent agent and data warehousing technologies.