• Journal of the Korea Fashion and Costume Design Association
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• v.13 no.3
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• pp.31-43
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• 2011

This study investigates the differences in the purchasing behavior of infant & children's wear with the features of children and consumers. This study conducted a survey on female buyers of infant & children's wear. 558 questionnaires were analyzed by using SPSS 18.0 statistic program with factor analysis, Cronbach's ${\alpha}$, cluster analysis, ANOVA, and paired t-test. The results are as follows: 1. The buyer's value was identified with 3 factors: mental achievement value, social achievement value, and pleasure value. Three types of group by values were identified: group seeking mental social achievement, group seeking social achievement, and group seeking pleasure. 2. Fashion and conformity, pleasure pursuit, and reasonable price pursuit orientations showed significant differences among the value groups. 3. A significant difference was found in the imitation of wearing role models according to sibling relationships.

• Journal of Convergence for Information Technology
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• v.11 no.2
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• pp.31-40
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• 2021

Blockchain technology is also actively studied in the real estate transaction field, and real estate transactions have various ways. In this paper, we propose a model that simplifies the authentication procedure of auction systems using Ethereum's Ether to solve the problem of offline court auctions. The proposed model is written in Ethereum's Solidity language, the court registers the sale date and the sale date with the DApp browser, and the bidder accesses the address of the individual's wallet created through Metamask's private key. The bidder then selects the desired sale and enters the bid price amount to participate in the auction. The bidder's record of the highest bid price for the sale he wants is written on the Ethereum test network as a smart contract. and creates a block. Finally, smart contracts written on the network are distributed by the court auction manager to all nodes in the blockchain network, and each node in the blockchain network can be viewed and contract verified. As a result of analyzing the smart contracts of the proposed model and the performance of the system, there are fees incurred due to the creation and use of Ether on platforms using Ethereum, and participation. Ether's changes in value affect the price of the sale, resulting in inconsistent fees in smart contracts each time. However, in future work, we issue our own tokens to solve the market volatility problem and commission problem with the value change of Ether, and refine complex court auction systems.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.9 no.6
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• pp.153-161
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• 2014

This study is to contribute to the policy decision of cosmetic industry through analyzing marketing mix factors which affects customers' buying intention. For this study, marketing mix factors that impact on cosmetic customers purchasing intention were collected and chi-square independence analysis of SPSS and qualitative analysis technology of Kano & Timko were used. Analysis reached one conclusion, which factors were maximizing customers satisfaction, shows the price discount policy and recommendation from family and colleague. Product's effectiveness, usefulness and skin compatibility were rated as an one-dimensional quality as those factors were linearly affected when customers needs were satisfied. However product's containers, package design, colors, brand reputation, low and middle price, distribution channel and advertising medias were investigated as an indifferent quality and they were rated as low satisfaction/dissatisfaction coefficient of Timko. Therefore they do not need to invest to those factors too much budget for attaining customers' buying intention.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.9
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• pp.1802-1807
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• 2015

The cloud computing service provides physical IT resources to VM instances to users using virtual technique and the users pay cost of VM instances to service provider. The auction model based on cloud computing provides available resources of service provider to users through auction mechanism. The users bid spot instances to process their a job until its deadline time. If the bidding price of users is higher than the spot price, the user will be provided the spot instances by service provider. In this paper, we propose a new bidding strategy to minimize the total cost for job completion. Typically, the users propose bidding price as high as possible to get the spot instances and the spot price get high. we lower the spot price using proposed strategy and minimize the total cost for job completion. To evaluate the performance of our strategy, we compare the spot price and the total cost for job completion with real workload data.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2008.11a
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• pp.197-201
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• 2008

Conceptual cost estimates for domestic highway projects have generally been conducted using governmental unit-price references. Inaccuracies in governmental unit-price data has repeatedly addressed in the Korean construction industry which often lead to poor decision making and cost management practices. Thus, needs for developing a better way of conceptual cost estimating has been widely recognized. This research is considered as the first step in developing such model using real-world cost data based on actual construction activities. The data analyzed in this paper includes 41 P.S.C (Prestressed Concrete) Box bridges which broke into 4 categories based on construction methods such as I.L.M(Incremental Launching Method), M.S.S(Movable Scaffolding System), F.S.M(Full Staging Method), and F.C.M(Free Cantilever Method). Actual design documents; including actual cost estimating documents, drawings and specifications were carefully reviewed to effectively break down cost structures for PSC girder bridges. Among more than 40 cost categories for each P.S.C girder bridge type, 7 of them were identified which accounted for more than 95% of total construction cost (ILM: 99.47%, MSS: 99.22%, FSM: 98.18%, and FCM: 98.12%). In order to validate the clustering of cost categories, the variation of each cost category has been investigated which resulted in between -1.16 % and 0.59%.

• Proceedings of the Technology Innovation Conference
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• 2003.02a
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• pp.173-190
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• 2003

Demand forecasting is essential in establishing national and corporate strategy as well as the management of their resource. We forecast demand for multi-generation product using discrete choice model combining diffusion model The discrete choice model generally captures consumers'valuation of the product's qualify in the framework of a cross-sectional analysis. We incorporate diffusion effects into a discrete choice model in order to capture the dynamics of demand for multi-generation products. As an empirical application, we forecast demand for worldwide DRAM (dynamic random access memory) and each of its generations from 1999 to 2005. In so doing, we use the method of 'Technological Forecasting'for DRAM Density and Price of the generations based on the Moore's law and learning by doing, respectively. Since we perform our analysis at the market level, we adopt the inversion routine in using the discrete choice model and find that our model performs well in explaining the current market situation, and also in forecasting new product diffusion in multi-generation product markets.

• Journal of the Korean Society of Food Science and Nutrition
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• v.34 no.7
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• pp.1028-1034
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• 2005

The purposes of this study were to: (a) identify students' patronage behaviors on university foodservice, (b) evaluate customers' perception and detect complaints on food service quality, and (c) recommend new design of food service operation by identifying relative importance of university foodservice attributes. The questionnaire was developed and distributed to 400 undergraduates. Statistical data analysis was completed using SPSS/Win 12.0 for descriptive analysis and conjoint analysis. The results of this study were summarized as follows: Students were patronizing university foodservice 3.4 times per week because of 'a short distance', 'a low price' and 'speedy meals' and evaluated overall satisfaction and value as low level. Customers were dissatisfied with eight among twelve food service attributes including handling customer's complain ASAP, According to conjoint analysis, price $(37\%)$ was considered as the most important factor and atmosphere $(30\%),\;food\;(22\%)$ and distance $(11\%)$ were the next factors. Profile 2, which had 4 attributes of a location within 6-10 min, comfortable circumstances, a meal served with 4 side-dish and dessert and a meal price of \2,000 firstly, were recommend as new food service operation model.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.395-398
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• 2001

Studies on quantity discounts have been in two directions since Buchanen[1] published that suppliers provide quantity discounts for price discrimination and for channel efficiency. The quantity discounts for price discrimination have been treated in economics. On the contrary, the quantity discounts for channel efficiency have been studied in production management with assumptions and focuses different from economics. In this paper, the differences in each area are analyzed and purchasing situations that supplier's motivation on quantity discounts occurs is analyzed and the purchasing situations for two-stage quantity discounts are specified. In conclusion, we suggest that suppliers can enhance channel coordination through two stage quantity discounts in these purchasing situations.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.103-128
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• 2021

Recently, investors' interest and the influence of stock-related information dissemination are being considered as significant factors that explain stock returns and volume. Besides, companies that develop, distribute, or utilize innovative new technologies such as artificial intelligence have a problem that it is difficult to accurately predict a company's future stock returns and volatility due to macro-environment and market uncertainty. Market uncertainty is recognized as an obstacle to the activation and spread of artificial intelligence technology, so research is needed to mitigate this. Hence, the purpose of this study is to propose a machine learning model that predicts the volatility of a company's stock price by using the internet search volume of artificial intelligence-related technology keywords as a measure of the interest of investors. To this end, for predicting the stock market, we using the VAR(Vector Auto Regression) and deep neural network LSTM (Long Short-Term Memory). And the stock price prediction performance using keyword search volume is compared according to the technology's social acceptance stage. In addition, we also conduct the analysis of sub-technology of artificial intelligence technology to examine the change in the search volume of detailed technology keywords according to the technology acceptance stage and the effect of interest in specific technology on the stock market forecast. To this end, in this study, the words artificial intelligence, deep learning, machine learning were selected as keywords. Next, we investigated how many keywords each week appeared in online documents for five years from January 1, 2015, to December 31, 2019. The stock price and transaction volume data of KOSDAQ listed companies were also collected and used for analysis. As a result, we found that the keyword search volume for artificial intelligence technology increased as the social acceptance of artificial intelligence technology increased. In particular, starting from AlphaGo Shock, the keyword search volume for artificial intelligence itself and detailed technologies such as machine learning and deep learning appeared to increase. Also, the keyword search volume for artificial intelligence technology increases as the social acceptance stage progresses. It showed high accuracy, and it was confirmed that the acceptance stages showing the best prediction performance were different for each keyword. As a result of stock price prediction based on keyword search volume for each social acceptance stage of artificial intelligence technologies classified in this study, the awareness stage's prediction accuracy was found to be the highest. The prediction accuracy was different according to the keywords used in the stock price prediction model for each social acceptance stage. Therefore, when constructing a stock price prediction model using technology keywords, it is necessary to consider social acceptance of the technology and sub-technology classification. The results of this study provide the following implications. First, to predict the return on investment for companies based on innovative technology, it is most important to capture the recognition stage in which public interest rapidly increases in social acceptance of the technology. Second, the change in keyword search volume and the accuracy of the prediction model varies according to the social acceptance of technology should be considered in developing a Decision Support System for investment such as the big data-based Robo-advisor recently introduced by the financial sector.

• Journal of the Society of Disaster Information
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• v.12 no.1
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• pp.10-18
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• 2016

This study presented a new NOS model through the following suggestions to apply the construction work planing and management to NOS(Network Operating System). First, This study presented CIMS(construction information classification system) reflected the characteristics of facility classification - functional component classification - functional component classification - work classification - resource classification. Based on this system. this study presented how to establish PMMB(performance measurement management baseline) with proposed master target equation which analyzed the trend of performance measurement management baseline and proposed work target equation which analyzed the execution results. Finally, this study presented NOS model that can be applied to fixed price method and cost plus fee method through the theoretical verification of executive performance analysis method.