• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.27-51
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• 2007

We analyze the effects of the sellers' strateiges on the final bid prices in internet auctions. We focus on the following three strategies of the seller adoption of the buy-price, setting the starting bid price, and adoption of 'the effective fixed price' which means that the starting bid price is set near the buy-price. In addition, the number of units sold single-unit or multi-unit, and item characteristics, such as whether the food is a search product (functional product) or an experience product (non-functional product), are also considered. We use real data on bids for 4 items from an online auction site. We find that in an auction for experience products when sold as single units, adopting the buy-price strategy raises the final bid price. We also find that in multi-unit auctions, starting the auction at 'the effective fixed price' raises the final bid price.

• Proceedings of the Korean Statistical Society Conference
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• 2005.05a
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• pp.277-283
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• 2005

In general, stock prices do not follow normal distributions and mean trend indexes, volatility indexes, and volume indicators relating to these non-normal stock price are widely used as buy-sell strategies. These general buy-sell strategies are rather intuitive than statistical reasoning. The non-normality problem can be solved by normalizing process and statistical buy-sell strategy can be obtained by using mean trend and volatility indexes together with normalized stock prices. In this paper, buy-sell strategy based on mean trend and volatility index with normalized stock prices are proposed and applied to KOSPI200 data to see the feasibility of the proposed buy-sell strategy.

• Journal of the Korea Society of Computer and Information
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• v.12 no.6
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• pp.11-20
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• 2007

Even though much research has been performed to recommend favorite items to the buyers in the internet shopping mall, to the best of our knowledge. it is hard to find previous research on the recommendation of buy points. In this paper, we propose a method which can be used to recommend buy points of an item to the buyers. To do this, a database containing normalized price patterns is constructed from the archive of past prices. Then, the future price pattern is retrieved from the database based on the similarity. Here, regression analysis is used to find and analyze the elements that affect the price. We also present performance results showing that the proposed method can be useful for shopping malls.

• Journal of the Korean Operations Research and Management Science Society
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• v.35 no.4
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• pp.17-31
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• 2010

This study investigates two-stage return policy and recycling issues in an e-marketplace supply chain consisting of consumers, a retailer and a manufacturer. The manufacturer, a focal company in the e-marketplace supply chain, considers the recycling of commercial returns so offers the retailer a buy-back contract of which transfer payment consists of a wholesale price and a buy-back price. Then, under the given contract offer, the retailer determines a selling price and a return policy to control consumers' demand and return requests. We consider the retailer's opportunistic behavior and supply chain coordination issues based on the principal-agent paradigm. We compare the first-best and second-best optima and conduct comparative static analyses to evaluate the performance results of the buy-back contract and provide important managerial implications.

• Journal of the Korean Operations Research and Management Science Society
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• v.32 no.1
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• pp.15-26
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• 2007

This study analyzes revenue-maximizing strategies of online auction sellers in terms of setting up starting bids and buy-it-now options. To this end, a series of field experiments is conducted where women's hair accessories of unique designs are listed in an established online auction site. The results of the experiments argue that high starting bids could increase sellers' revenue while buy-it-now options have no significant effects. Our findings suggest that online auction sellers listing items with uncertain demand - mainly individual sellers - should be cautious with auction tips which generally support low starting buds.

• Journal of Fashion Business
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• v.16 no.6
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• pp.109-126
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• 2012

The purpose of this study was to examine the effect of price-attitude toward apparel products on shopping values and consumption behavior. The study was carried out in Deagu and Kyungbook area. Applying the convenience sampling, total 326 questionnaire were collected from university students who were randomly selected as participants. This study used frequency, factor analysis, reliability analysis, regression analysis, and t-test for data analysis. The finding are as follows. Price-attitude toward apparel products was categorized into information leading, price dignity, price discount, list price orientation, quality value and using coupons. Shopping tendency factors were found as pursuit of pleasure, pursuit of sociality, and pursuit of economic feasibility. Consumption behavior factors were categorized into impulsive buying, ostentatious consumption, utilization of internet information, possession of material and brand trust. Price-attitude toward apparel products had a significant effect on shopping values and consumption behavior. University students seemed to consider the value of money to be very important as well as economic feasibility. They utilized information from the internet to buy products with good quality and showed high usage level of coupons. And, university students who buy at a least price tried to show dignity with expensive brand products and they consider those brands express self-confidence.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.21 no.4
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• pp.103-108
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• 2021

It is difficult to predict an increase or decrease of stock price because of uncertainty. Researches for prediction of stock price using AI technology have been done for a long time. Recently stock buy/sell recommend programs called by Robot Advisor using AI machine learning technology are used. In this paper, to develop a stock buy/sell recommend system using AI technology, an core engine of this system is designed. An analysis and design method of a stock buy/sell recommend system software using AI machine learning technology will be presented by showing user requirement analysis using object-oriented analysis method, flowchart and screen design.

• Korean Journal of Human Ecology
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• v.15 no.4
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• pp.659-667
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• 2006

The purpose of this study is to identify the combinated factors of leaf mustard kimchi which confer the highest utility on tourists, and to establish the relative factors of importance in terms of tourists' contribution to total utility to their tour purpose. Conjoint model, $X^2$ analysis, Max. Utility model, BTL model, Logit model, K-means cluster analysis, and one-way ANOVA analysis are used for this study. The findings from this study are as follows: First, the Pearson's R and Kendall's tau($\tau$) statistics shows that the model fits the data well to the tourists' visit purpose. Second, when they choose a sightseeing place, tourists' taste for food renowned in the local area is a very important factor. Third, the leaf mustard kimchi some tourists most prefer has light red color and mild taste, and they buy it in a shaped packing, at a cheap price and directly at the kimchi factory. The leaf mustard kimchi the other tourists most prefer has light red color and highly pungent taste, and they buy it in a shaped packing, at a cheap price and directly at the kimchi factory. Fourth, by the results of BTL model and Logit Model analysis, some tourists most prefer an experimental model of leaf mustard kimchi which has light red color and mild taste. They want to buy it in a shaped packing, at a cheap price and directly at the kimchi factory. The other tourists most prefer an experimental model of leaf mustard kimchi which has light red color and highly pungent taste. They want to buy it in a shaped packing, at a cheap price and directly in the kimchi factory. Finally, the writer hopes this study will provide the kimchi marketers with some insights into the types of popular leaf mustard kimchi designs that could be successfully developed.

• Journal of the Korean Society of Clothing and Textiles
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• v.21 no.2
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• pp.357-367
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• 1997

The success of apparel goods mainly depends on the purchase behavior of end use consumers. The decision making processes of apparel merchandise are very complicated according to the many information cues available to the consumers. The country-of-origin is one of the extrinsic cues to affect the consumers 'decision. To study the effect of country -of-origin, the Polo style knit shirts were chosen as stimuli to the male and the female subjects (total 527) aged from 18 to 35. The identical nine shirts (3 countries$\times$3 levels of price) were carefully manipulated for the treatments. The three countries labelled are Italy as industrialized country, China as less developed one, and Korea. In addition to the country-of -origin, the prices of the shirts were exposed to the respondents. The price levels were 14,000 won for the low, 39,000 won for the moderate, and 64,000 won for the high price level. The findings were as follows: 1) As price was increased, the perceived value and purchase intention were decresed. Price was not statistically significant to perceived quality, but it was significant to perceived value and willingness to buy. 2) The merchandise of "Made in Italy" was evaluated higher than those of "Made in Korea" and "Made in China" The country-of-origin had statistically significant influences on the perceived quality, perceived value and also willingness to buy. 3) The interaction between the two factors, country-of-origin and price, was not observed.n and price, was not observed.

• Proceedings of the CALSEC Conference
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• 2005.11a
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• pp.147-153
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• 2005

When a customer wants to buy an item at the Internet shopping mall, one of the difficulties is to decide when to buy the item because its price changes over time. If the shopping mall can be able to recommend appropriate buying points, it will be greatly helpful for the customer. Therefore, in this presentation, we propose a method to recommend buying points based on the time series analysis using a database that contains past prices data of items. The procedure to provide buying points for an item is as follows. First, we search past time series patterns from the database using normalized similarity, which are similar to the current time series pattern of the item. Second, we analyze the retrieved past patterns and predict the future price pattern of the item. Third, using the future price pattern, we recommend when to buy the item.