• 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 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 Fisheries and Marine Sciences Education
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• v.20 no.3
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• pp.442-451
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• 2008

The purpose of this study is to analyze the impacts of the rapid rise in oil prices on fisheries economy. Even though fishery oils are tax exemption items, such increase in oil prices put a great amount of pressure on Korean fishing operations. Because basically the recent oil shock is externally given, Korean fisheries themselves have little capacity to cope with the disruption of economic environments. The research results turned out that Korean fisheries are extremely vulnerable(or fragile) to external shocks. In this regard, government support issues of oil costs are in the center of debate. It is widely recognized that direct/indirect government financial supports or subsidies would result in economic inefficiency in expense of equity. However, there are second best theories which may justify government intervention into the markets. This second best theory is translated into the constitutional law that instructs the government to protect and promote the primary industries including fisheries, agriculture, and midium/small-scale enterprises. It is apparent that the constitutional law would provide the government with a variety of policy instruments such as more active buy-back programs, tax exemptions and technological development to deal with fisheries economic hardship due to the external pressure such as high oil prices and international fishery orders.

• Information Systems Review
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• v.17 no.3
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• pp.95-111
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• 2015

The recommendation system automatically provides the predicted items which are expected to be purchased by analyzing the previous customer behaviors. This recommendation system has been applied to many e-commerce businesses, and it is generating positive effects on user convenience as well as the company's revenue. However, there are several limitations of the existing recommendation systems. They do not reflect specific criteria for evaluating products or the factors that affect customer buying decisions. Thus, our research proposes a collaborative recommendation model algorithm that utilizes each customer's online product reviews. This study deploys topic modeling method for customer opinion mining. Also, it adopts a kernel-based machine learning concept by selecting kernels explaining individual similarities in accordance with customers' purchase history and online reviews. Our study further applies a multiple kernel learning algorithm to integrate the kernelsinto a combined model for predicting the product ratings, and it verifies its validity with a data set (including purchased item, product rating, and online review) of BestBuy, an online consumer electronics store. This study theoretically implicates by suggesting a new method for the online recommendation system, i.e., a collaborative recommendation method using topic modeling and kernel-based learning.

• Journal of Global Scholars of Marketing Science
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• v.18 no.1
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• pp.141-156
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• 2008

Market structure analysis is a very useful tool to analyze the competition boundary of the brand or the company. But most of the studies in market structure analysis, the concern lies in nondurable goods such as candies, soft drink and etc. because of the their availability of the data. In the field of durable goods, the limitation of the data availability and the repurchase time period constrain the study. In the analysis of the automobile market, those of views might be more persuasive. The purpose of this study is to analyze the structure of automobile market based on some idea suggested by prior studies. Usually the buyers of the automobile tend to buy upper tier when they buy in the next time. That kind of behavior make it impossible to analyze the structure of automobile market under the level of automobile model. For that reason I tried to analyze the market structure in the brand or company level. In this study, consideration data was used for market structure analysis. The reasons why we used the consideration data are summarized as following. Firstly, as the repurchase time cycle is too long, brand switching data which is used for the market analysis of nondurable good is not avaliable. Secondly, as we mentioned, the buyers of the automobile tend to buy upper tier when they buy in the next time. We used survey data collected in the U.S.A. market in the year of 2005 through questionaire. The sample size was 8,291. The number of brand analyzed in this study was 9 among 37 which was being sold in U.S.A. market. Their market share was around 50%. The brands considered were BMW, Chevrolet, Chrysler, Dodge, Ford, Honda, Mercedes, and Toyota. �� ratio was derived from frequency of the consideration set. Actually the frequency is different from the brand switch concept. In this study to compute the �� ratio, the frequency of the consideration set was used like a frequency of brand switch for convenience. The study can be divided into 2 steps. The first step is to build hypothetical market structures. The second step is to choose the best structure based on the hypothetical market structures, Usually logit analysis is used for the choice best structure. In this study we built 3 hypothetical market structure. They are type-cost, cost-type, and unstructured. We classified the automobile into 5 types, sedan, SUV(Sport Utility Vehicle), Pickup, Mini Van, and Full-size Van. As for purchasing cost, we classified it 2 groups based on the median value. The median value was $28,800. To decide best structure among them, maximum likelihood test was used. Resulting from market structure analysis, we find that the automobile market of USA is hierarchically structured in the form of 'automobile type - purchasing cost'. That is, result showed that automobile buyers considered function or usage first and purchasing cost next. This study has some limitations in the analysis level and variable selection. First, in this study only type of the automobile and purchasing cost were as attributes considered for purchase. Considering other attributes is very needful. Because of the attributes considered, only 3 hypothetical structure could be analyzed. Second, due to the data, brand level analysis was tried. But model level analysis would be better because automobile buyers consider model not brand. To conduct model level study more cases should be obtained. That is for acquiring the better practical meaning, brand level analysis should be conducted when we consider the actual competition which occurred in the real market. Third, the variable selection for building nested logit model was very limited to some avaliable data. In spite of those limitations, the importance of this study lies in the trial of market structure analysis of durable good.

• Journal of Intelligence and Information Systems
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• v.18 no.3
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• pp.97-117
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• 2012

Nowadays, Internet users tend to do a variety of actions at the same time such as web browsing, social networking and multimedia consumption. While watching a video, once a user is interested in any product, the user has to do information searches to get to know more about the product. With a conventional approach, user has to search it separately with search engines like Bing or Google, which might be inconvenient and time-consuming. For this reason, a video annotation platform has been developed in order to provide users more convenient and more interactive ways with video content. In the future of smart TV environment, users can follow annotated information, for example, a link to a vendor to buy the product of interest. It is even better to enable users to search for information by directly discussing with friends. Users can effectively get useful and relevant information about the product from friends who share common interests or might have experienced it before, which is more reliable than the results from search engines. Social networking services provide an appropriate environment for people to share products so that they can show new things to their friends and to share their personal experiences on any specific product. Meanwhile, they can also absorb the most relevant information about the product that they are interested in by either comments or discussion amongst friends. However, within a very huge graph of friends, determining the most appropriate persons to ask for information about a specific product has still a limitation within the existing conventional approach. Once users want to share or discuss a product, they simply share it to all friends as new feeds. This means a newly posted article is blindly spread to all friends without considering their background interests or knowledge. In this way, the number of responses back will be huge. Users cannot easily absorb the relevant and useful responses from friends, since they are from various fields of interest and knowledge. In order to overcome this limitation, we propose a method to filter a user's friends for information search, which leverages semantic video annotation and social networking services. Our method filters and brings out who can give user useful information about a specific product. By examining the existing Facebook information regarding users and their social graph, we construct a user profile of product interest. With user's permission and authentication, user's particular activities are enriched with the domain-specific ontology such as GoodRelations and BestBuy Data sources. Besides, we assume that the object in the video is already annotated using Linked Data. Thus, the detail information of the product that user would like to ask for more information is retrieved via product URI. Our system calculates the similarities among them in order to identify the most suitable friends for seeking information about the mentioned product. The system filters a user's friends according to their score which tells the order of whom can highly likely give the user useful information about a specific product of interest. We have conducted an experiment with a group of respondents in order to verify and evaluate our system. First, the user profile accuracy evaluation is conducted to demonstrate how much our system constructed user profile of product interest represents user's interest correctly. Then, the evaluation on filtering method is made by inspecting the ranked results with human judgment. The results show that our method works effectively and efficiently in filtering. Our system fulfills user needs by supporting user to select appropriate friends for seeking useful information about a specific product that user is curious about. As a result, it helps to influence and convince user in purchase decisions.

• Journal of Digital Convergence
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• v.12 no.3
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• pp.69-77
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• 2014

Because smart televisions (TVs) have various and innovative functions that are different from traditional TVs, consumers are front with a complex decision-making problem when they want to buy a best TV among alternatives made by many TV makers. TV manufactures have developed and announced different types of smart TV models and they need a comparative study for evaluating their characteristics. In this paper, we suggest the Analytic Hierarchy Process(AHP) method for deciding the best smart TVs regarding many selection criteria. The method provides a decision support for consumers who like to purchase a smart TV. We describe criteria affecting the smart TV selection through a literature review and suggest a user testing method in order to derive accurate judgments from consumers. Using the Expert Choice software package, we show a an numerical example how the priority of smart TVs are computed.

• Journal of the military operations research society of Korea
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• v.33 no.1
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• pp.1-18
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• 2007

In the early stage of weapon system acquisition process, acquisition policy is necessarily established to acquire weapon system in a faster, better, cheaper way. Several alternatives, such as "buy", "domestic research and development", and "technical corporative production", can be considered for the best acquisition policy making. However, the comparison factors for those alternatives have different metrics and values. Therefore, the aim of this paper is to suggest KAAM(Knowledge-based Alternatives Analysis Model) as a scientific method to compare the alternatives having such different metrics and values and giving a weighted and normalized single measurement for the easy comparison. KAAM is a hybrid model incorporating Satty technique, Delphi/Shang method, Consensus method, and SAW method. KAAM is implemented on Microsoft Excel environment and provided tabular form user interface. Finally, an illustrative example is shown using KAAM.

• Journal of the Korean Operations Research and Management Science Society
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• v.17 no.2
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• pp.145-162
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• 1992

This paper proposes two design principles for expert systems to solve a stock market timing (SMART) problems : machine knowledge and fuzzy post-adjustment, Machine knowledge is derived from past SMART instances by using an inductive learning algorithm. A knowledge-based solution, which can be regarded as a prior SMART strategy, is then obtained on the basis of the machine knowledge. Fuzzy post-adjustment (FPA) refers to a Bayesian-like reasoning, allowing the prior SMART strategy to be revised by the fuzzy evaluation of environmental factors that might effect the SMART strategy. A prototype system, named K-SISS2 (Knowledge-based Stock Investment Support System 2), was implemented using the two design principles and tested for solving the SMART problem that is aimed at choosing the best time to buy or sell stocks. The prototype system worked very well in an actual stock investment situation, illustrating basic ideas and techniques underlying the suggested design principles.

• Korean Journal of Organic Agriculture
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• v.9 no.2
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• pp.1-23
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• 2001

It was supposed that CSA could be one of the best way to promote the movement of organic agriculture since there is little reliability on the organically grown food by consumer. In the replies submitted to a questionnaire to farmer and consumer, both group responded that the need of development of cultivation technique for organic farming and the permitted substances for organic farming and production. Both of them also replied that it is necessary to establish the lectures or division/department for organic agriculture in the agricultural education program of University. Their response to CSA was so much positive that they are willing to participate the CSA farm as a active CSA farmers or consumers. Based on the evaluation of questionnaire survey, it was suggested to do the utmost efforts that farmer preferentially practice an organic farming , md consumer consciously buy an organic food to protect an ecosystem and environment pollution.