• The Journal of Society for e-Business Studies
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• v.3 no.2
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• pp.57-71
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• 1998

In this paper, we describe the approaches of shopping agents and directory services for online comparison shopping on the web, and propose an information indexing and retrieval system, named InfoEye, with a new method for automatic extraction of product information. The developed method is based on the knowledge about presentation of the product information on the Web. The method from the knowledge about presentation of the product information is derived from both the point that online stores display their products to customers in easy-to-browse ways and heuristics made of analyses of product information look-and-feel of domestic online stores. In indexing process, the method is applied to product information extraction from Hypertext Markup Language (HTML) documents collected by a mirroring robot from online stores. We have made InfoEye to a readily usable stage and transferred the technology to Webnara commercial shopping engine. The proposed system is a cutting-edge solution to help customers as a shopping expert by providing information about the reasonable price of a product from dozens of online stores, saving customers shopping time, giving information about new products, and comparing quality factors of products in a same category.

• Journal of Intelligence and Information Systems
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• v.18 no.2
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• pp.143-156
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• 2012

People easily believe that news and stock index are closely related. They think that securing news before anyone else can help them forecast the stock prices and enjoy great profit, or perhaps capture the investment opportunity. However, it is no easy feat to determine to what extent the two are related, come up with the investment decision based on news, or find out such investment information is valid. If the significance of news and its impact on the stock market are analyzed, it will be possible to extract the information that can assist the investment decisions. The reality however is that the world is inundated with a massive wave of news in real time. And news is not patterned text. This study suggests the stock-index invest model based on "News Big Data" opinion mining that systematically collects, categorizes and analyzes the news and creates investment information. To verify the validity of the model, the relationship between the result of news opinion mining and stock-index was empirically analyzed by using statistics. Steps in the mining that converts news into information for investment decision making, are as follows. First, it is indexing information of news after getting a supply of news from news provider that collects news on real-time basis. Not only contents of news but also various information such as media, time, and news type and so on are collected and classified, and then are reworked as variable from which investment decision making can be inferred. Next step is to derive word that can judge polarity by separating text of news contents into morpheme, and to tag positive/negative polarity of each word by comparing this with sentimental dictionary. Third, positive/negative polarity of news is judged by using indexed classification information and scoring rule, and then final investment decision making information is derived according to daily scoring criteria. For this study, KOSPI index and its fluctuation range has been collected for 63 days that stock market was open during 3 months from July 2011 to September in Korea Exchange, and news data was collected by parsing 766 articles of economic news media M company on web page among article carried on stock information>news>main news of portal site Naver.com. In change of the price index of stocks during 3 months, it rose on 33 days and fell on 30 days, and news contents included 197 news articles before opening of stock market, 385 news articles during the session, 184 news articles after closing of market. Results of mining of collected news contents and of comparison with stock price showed that positive/negative opinion of news contents had significant relation with stock price, and change of the price index of stocks could be better explained in case of applying news opinion by deriving in positive/negative ratio instead of judging between simplified positive and negative opinion. And in order to check whether news had an effect on fluctuation of stock price, or at least went ahead of fluctuation of stock price, in the results that change of stock price was compared only with news happening before opening of stock market, it was verified to be statistically significant as well. In addition, because news contained various type and information such as social, economic, and overseas news, and corporate earnings, the present condition of type of industry, market outlook, the present condition of market and so on, it was expected that influence on stock market or significance of the relation would be different according to the type of news, and therefore each type of news was compared with fluctuation of stock price, and the results showed that market condition, outlook, and overseas news was the most useful to explain fluctuation of news. On the contrary, news about individual company was not statistically significant, but opinion mining value showed tendency opposite to stock price, and the reason can be thought to be the appearance of promotional and planned news for preventing stock price from falling. Finally, multiple regression analysis and logistic regression analysis was carried out in order to derive function of investment decision making on the basis of relation between positive/negative opinion of news and stock price, and the results showed that regression equation using variable of market conditions, outlook, and overseas news before opening of stock market was statistically significant, and classification accuracy of logistic regression accuracy results was shown to be 70.0% in rise of stock price, 78.8% in fall of stock price, and 74.6% on average. This study first analyzed relation between news and stock price through analyzing and quantifying sensitivity of atypical news contents by using opinion mining among big data analysis techniques, and furthermore, proposed and verified smart investment decision making model that could systematically carry out opinion mining and derive and support investment information. This shows that news can be used as variable to predict the price index of stocks for investment, and it is expected the model can be used as real investment support system if it is implemented as system and verified in the future.

• New & Renewable Energy
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• v.7 no.4
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• pp.10-17
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• 2011

Recently, electricity industry is facing high market uncertainty which has ever had and which increase risks in power market. In this study, we analyze risk factors such as discount rates, initial investment (overnight cost), plant factor, fuel cost, carbon price, etc, for the perspective of investor. For the analysis of risk factors, we used LCOE method. The results of this study show that renewable energy is more affected by plant factor and overnight cost than other risk factors. First, Renewable energy has higher proportion of overnight cost in the total investment than that of other technologies. Second, renewable energy is free of fuel cost and carbon price so plant factor is the most important factor, in other words, competitiveness of renewable energy depends on plant factor. Furthermore, we conducted economic feasibility of wind power and PV in domestic case study. The minimum requirement condition to get profitability is that plant factor 15% and overnight cost \6,000,000/kW and 26%, \2,200,000/kW for PV and Wind Power, respectively.

• Journal of the Korean Dietetic Association
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• v.11 no.1
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• pp.95-104
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• 2005

Customer satisfaction concerning foodservice quality characteristics were evaluated by using importance performance analysis(IPA) techniques in middle school foodsevice operations. One hundred students from each of 17 middle schools in Gangwon province were surveyed by self-developed questionnaire. Total of 1,675 questionnaires(female 943 and male 732 respectively) were collected and data were analyzed using SAS program. The results of this study are summarized as follows: 1.The male and female students scored the importance level as 3.99 and 4.03 out of 5 respectively, which means 'important' The performance level of female(2.71) was significantly lower than male(2.86) which means 'between bad and so-so'. it is suggested that the statistical analysis should be conducted respectively according to gender. And there is no performance level over 4(good). 2. Dissatisfied quality attributes identified were convenience of dinning room and the managerial attitude of dissatisfaction in the female and male students. 3. Satisfied quality attributes identified were hygiene of food and dinning room and spoon and cup and drinking water table and distribution process, taste of food, variety of food in the female and male students. 4. The performance scores of lower price(less than 1500 won) lunch group was higher than higher price(more than 1800 won) lunch group in the female and male students.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.2
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• pp.109-114
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• 2002

E-commerce customers can reduce purchasing cost by the help of comparison shopping agents that collect price information of products in the shopping malls. However, user expects a software agent that can recommend product information satisfying various purchase conditions besides price. In this paper, we present a MAS (multi-agent system) which retrieves and recommends book information suitable for various user needs to realize an agent-based E-Commerce. We implemented and tested our MAS to help on-line bookstore customers. From the results, we could provide E-commerce customers various book purchase conditions for several online bookstores in real-time.

• Journal of Biosystems Engineering
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• v.24 no.4
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• pp.351-358
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• 1999

In these days, we are faced to a couple of difficult problems, the one is the unstable price of the energy due to the shortage of fossil fuel resources and the other is the serious environmental pollution from the excessive consumption of fossil fuel. In order to save the thermal energy for the house heating, in this study the heat pump using the natural thermal energy resources was provided for Ondol heating and the thermal energy consumption of the heat was compared to that of oil boiler. The results could be summarized as follows: 1. In the Ondol room the temperature difference between the Ondol surface and room air was about 5∼$10^{\circ}C$ in accordance with the ambient temperature. 2. The Ondol room heating efficiency of the heat pump with compressor of 2PS was the highest at the water flow rate of 200 l/h. 3. The energy saving rate of the heat pump to the oil boiler for heating the Ondol system was 19.3%. 4. The Ondol heating cost of the heat pump was less 20.6% than that of oil boiler when oil price was 478 won/l.

• Journal of Korean Institute of Industrial Engineers
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• v.27 no.3
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• pp.267-273
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• 2001

While forecasting sales of a new product is very difficult, it is critical to market success. This is especially true when other products have a highly negative influence on the product because of competition effect. In this paper, we develop a choice-based competitive diffusion model and apply to the case where two digital mobile telecommunication services, that is, digital cellular and PCS services, compete. The basic premise is that demand patterns result from choice behavior, where customers choose a product to maximize their utility. In comparison with Bass-type competitive diffusion models, our model provides superior fitting and forecasting performance. The choice-based model is useful in that it enables the description of such competitive environments and provides the flexibility to include marketing mix variables such as price and advertising.

• Journal of the Korean Home Economics Association
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• v.40 no.1
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• pp.209-224
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• 2002

In this study, Rural and urban housewives were compared in terms of their clothing practical use and clothing buying behavior. For the analysis, a nation-wide stratified sampling was made across the rural and urban areas. As a result, 400 subjects of housewives were selected and were asked to answer the questionnaire. Results of the analysis were as follows: 1. Both rural and urban housewives were found to have reliable knowledge about the kinds of clothes they are possessing. However, there were significant differences in the knowledge level of clothing practice methods, in clothing patterns of one′s favor, and in clothing buying behavior by the type of clothing. 2. With regard to the clothing buying behavior by the type of clothing, several differences were found between rural and urban housewives. Purchase motivation for formal wear, was found to be "I don′t have one" in overall, yet having a significant frequency difference between rural and urban housewives. Major evaluation criteria were "color and design" and "fits to me" for formal wear and "comfort and fitness" and "price" for casual wear, also having a significant frequency difference between rural and urban housewives. 3. Rural area housewives bought their formal wear in stores offering "low price or sale" while urban housewives looked at "quality and variety". The major source of information was "display and direct observation" and "previous experience" in both cases. The rural housewives bought clothes when they had any "event" and urban housewives bought them when they had "extra money or sale".

• Fashion & Textile Research Journal
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• v.13 no.6
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• pp.909-916
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• 2011

This study explored the differences in consumer characteristics-consumer self-confidence, attitude toward, trust and purchase intention for secondhand fashion goods, self-confidence in fashion coordination, price sensitivity for fashion product, and environment conservation consciousness-according to purchasing experience of secondhand fashion goods. A set of questionnaire was administered to 400 university students in Daegu from 18 to 28 April 2011. Data were analyzed by factor analysis, Cronbach's ${\alpha}$, frequency, t-tests. Secondhand fashion goods shoppers were 135(37%) and non-shoppers were 230(63%). The difference analysis results between the two groups were as follows. First, in consumer self-confidence, shoppers showed higher than non-shoppers except persuasion knowledge, and there was no difference in marketplace interfaces between the two groups. Second, there were significant differences in attitude toward secondhand fashion goods, trust and purchase intention for secondhand fashion goods between the two groups. Third, shoppers had higher self-confidence in fashion coordination than non-shoppers, but there was no difference in environment conservation consciousness and price sensitivity for fashion product between the two groups. For future study, it is suggested to find out more general characteristics of secondhand fashion goods shoppers covering other age brackets.

• Journal of the Korean Society of Clothing and Textiles
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• v.29 no.9_10 s.146
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• pp.1285-1294
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• 2005

This study was fulfilled in the purpose of proposing construction strategies of Internet shopping-mall through the analysis of consumer moving line in Internet shopping-mall. This study was executed in two stages: theoretical study, qualitative study. In the theoretical study, hypothetical Internet shopping behavior model were constructed. Five internet shopping behavior types of apparel : purchase, searching purchase, prepurchase deliberation, information accumulation, opinion leadership and recreation were constructed. Next, consumer decision process were extracted from previous studies and a hypothetical internet shopping behavior model was constructed on the base of consumer decision process and Internet shopping behavior types. And then, through the qualitative study, Internet shopping behavior types were identified and hypothetical model was confirmed after adjustment. For qualitative study, 30 subjects were sampled by focus sampling and investigated by in-depth interview and observation. Seven internet shopping behavior types of apparel were found by the qualitative study: cautious purchase by price comparison, searching purchase, special low price purchase, impulse purchase, prepurchase deliberation, information accumulation and recreation-oriented. On the base of these behavior types, Internet shopping behavior model was adjusted and completed. Finally, according to the results of this study, Internet shopping construction methods that made customer's loyalty high and marketing strategy of Internet shopping-mall were proposed.