• Korean Journal of Construction Engineering and Management
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• v.19 no.3
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• pp.52-60
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• 2018

Korea's public construction projects are under construction through bidding, however, due to the nature of the bidding, collusion between participants can occur. The collusion of bids accordingly damages the client. So, it is necessary to calculate the appropriate fictitious competition price to compensate for this. In this regard, econometrics methods are generally used, but there are limitations and issues arising from the nature of construction, especially design-build bid. Therefore, this study proposes a method to estimate reasonable competitive bid-price in design-build bid. It derives the lowest bid-price from the design submitted by the proponent and estimates the competitive bid-price by examining the factors according to the penetration rate according to the technical level of the tester, the skill level of the competitor, and the type of tester. Based on the method proposed in this study, a reasonable price can be derived that reflects the characteristics of the design and construction bidding bidder selection method and also it can be used as a reference material in the actual bidding process as well as calculating the damage due to the answer.

• Journal of Korea Port Economic Association
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• v.24 no.2
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• pp.335-350
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• 2008

This paper analyzes the effects of port size on port tariff using the data for world major sixteen container ports. Some previous studies show that demand for port services have significant effects on port tariff, but we cannot find studies analyzing the correlation between the supply variables and the port tariff. In this paper, we used the five supply variables, which are the number of gantry crane, the number of berth, the quay length, the terminal area and the storage capacity for containers. The panel regression results are as follows. Port tariff generally decreases as port size increases, which shows that port tariff is explained by the economic theory. However, increase of port size, in some cases, does not reduce port tariffs, which may be due to monopolistic characteristics of port. This paper also shows that both demand and supply factors affect port tariff, but that demand factors have more consistent effect on port tariff than supply factors.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.741-743
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• 2015

오늘날 BI(Business Intelligence)시스템 다차원 데이터를 다루는 많은 방법들이 제안되어 TB 이상의 데이터를 다룰 수 있다. 하지만 IT 전문가 및 IT에 대한 투자여력이 충분하지 않은 중소 제조 기업들은 발 맞춰가기 힘들다. 또한 생산관리시스템(MES)을 미 도입한 기업이 대다수이고, 존재하는 현장데이터의 대부분도 수기데이터 또는 Excel 데이터로 보관 되어 있어, 수작업에 의한 데이터 분석과 의사결정을 수행한다. 이로 인해, 불량 요인 파악이나 이상 현상 파악이 불분명하기 때문에 데이터 분석에 어려움을 겪는다. 이에 본 연구에서는 중소제조기업의 경쟁력 강화를 위하여 제조 기업현장에서 사용되는 데이터를 자동으로 수집하여 정제 및 처리하여 저장이 가능하도록 하는 빅 데이터 분석 시스템 모델을 개발하였다. 이 분석 시스템 모델은 ERP, MIS 등에 존재하는 데이터들이 각 시스템의 DB 기능을 활용하여 데이터를 추출하고 정제하여 수집하는 ETL(Extract Transform Loading)과정을 통한다. 현장에서 비정형으로 기록되고 있는 정보들(ex. Excel)은 ODE(Office Data Excavation)모듈을 통해 문서의 패턴을 자동으로 인식하고 정형화된 정보로서 추출, 정제되어 수집된다. 저장된 데이터는 오픈소스 데이터 시각화 라이브러리인 D3.js를 이용하여 다양한 chart들을 통한 강력한 시각효과를 제공함으로써, 정보간의 연관 관계 및 다차원 분석의 기반을 마련하여 의사결정체계를 효과적으로 지원한다. 또한, 높은 가격에 형성되어 있는 빅데이터 솔루션을 대신해 오픈소스 Spago BI를 이용하여 경제적인 빅 데이터 솔루션을 제공한다. 본 연구의 기대효과로는 첫째, 현장 데이터 중심의 효과적인 의사결정 기반을 마련할 수 있다. 둘째, 통합 데이터 기반의 연관/다차원 분석으로 경영 효율성이 향상된다. 마지막으로, 중소 제조기업 환경에 적합한 분석 시스템을 구축함으로써 경쟁력과 생산력을 강화한다.

• Journal of Korean Society for Quality Management
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• v.47 no.4
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• pp.687-700
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• 2019

Purpose: The purpose of this study is to investigate the interaction effects between price determinants of artworks. We expand the methodology in art market by applying machine learning techniques to estimate the price of artworks and compare linear regression and machine learning in terms of prediction accuracy. Methods: Moderated regression analysis was performed to verify the interaction effects of artistic characteristics on price. The moderating effects were studied by confirming the significance level of the interaction terms of the derived regression equation. In order to derive price estimation model, we use multiple linear regression analysis, which is a parametric statistical technique, and k-nearest neighbor (kNN) regression, which is a nonparametric statistical technique in machine learning methods. Results: Mostly, the influences of the price determinants of art are different according to the auction types and the artist 's reputation. However, the auction type did not control the influence of the genre of the work on the price. As a result of the analysis, the kNN regression was superior to the linear regression analysis based on the prediction accuracy. Conclusion: It provides a theoretical basis for the complexity that exists between pricing determinant factors of artworks. In addition, the nonparametric models and machine learning techniques as well as existing parameter models are implemented to estimate the artworks' price.

• Journal of Korean Society of Transportation
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• v.30 no.2
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• pp.7-20
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• 2012

The objective of this paper is to construct pseudo-panel data set and estimate price and income elasticities of car travel demand, using 1995-2007 household income and expenditure survey data, in order to provide quantitative information for analyzing related policy effects in the transport sector. We categorized household survey data into 14 cohorts based on the birth year of the household head. As the result, a total of 133 pseudo-panel data sets was created for estimating price and income elasticities of car travel demand. Especially, price and income elasticities of car travel demand were separately estimated both short-term and long-term. We analyzed the panel model considering fixed effect within cohorts, using explanatory variables such as previous year's fuel consumption, real household income after tax, education level of the household head, the number of children under five, and the share of household type averaged by cohorts. As results, the short-term and long-term price elasticities of car travel demand were calculated as 0.2974-0.4280 and 0.4087-0.6275, respectively. Similarly, the short-term and long-term income elasticities were calculated as 0.3364-0.6281 and 0.7098, respectively.

• Culinary science and hospitality research
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• v.15 no.1
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• pp.79-93
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• 2009

This study was conducted to suggest a way to increase profitability by figuring out the factors that affect the satisfaction and revisit intention of customers who visit a theme park, studying the property and correlation of service quality. The purpose of this study was to figure out the relation between the service quality of foods and beverages and its influence on the satisfaction of the customers who visited the theme park. As service quality and price of foods and beverages which occupy 20% of the whole sales of the theme park are related with each other, they positively affect the satisfaction of visitors of the theme park and their revisit intention. It means it is necessary to improve the service quality of foods and beverages and to set the reasonable price based on quality in order to raise the rate of the customers' revisit through increase in profits and customer satisfaction. One of the most important quality factors to improve service quality is service attitude of employees who help customers. It requires to take action to improve employers' morals and service mind via education and training. Thus, it is possible to raise customer satisfaction and revisit intention by improving service quality.

• Culinary science and hospitality research
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• v.9 no.4
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• pp.163-178
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• 2003

The purpose of this study is to find the effect of restaurant brand image upon service quality, price acceptability, and revisit intention, and to propose the importance of brand image to operators and managers who manage restaurants. To accomplish the purpose of this study, sampling was taken among customers who visit six deluxe hotels and six family restaurants in Seoul. Six hundreds questionnaires were distributed to each hotel and restaurant and 487 valid samples were selected for statistical analysis. The questionnaire consists of 77 items about demographical characteristics, brand image, service quality, revisit intention, price acceptability, and spending patterns. SPSS WIN 10.0 was used for statistical analysis. A research model was built up and three null hypotheses were established. Based on theses research model and three null hypotheses, the test was conducted, and the results are as follows. Brand image has an effect upon service quality, and furthermore this can be preceding variable of service quality. Also Service quality has an effect upon price acceptability and revisit intention.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.10a
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• pp.136-138
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• 2015

Recently, with the rapid expansion of the mobile phone, such as the high price of the smartphone is being constantly handset subsidies at issue in the social harm caused by excessive competition in the mobile communications market. In this research, we aim to analyze factors influencing of the policy to subsidy regulatory on decision to continue purchasing intention of consumers. Predictor factors were selected the perceived usefulness, the perceived ease of use and the policy to subsidy regulatory on the previous study. Participants of this study be going to 200 mobile users in Busan Gyeongnam and Jeonbuk province in accordance with convenience sampling. IBM SPSS Statistics 19 were employed for descriptive statistics, Smart PLS(partial least squares) was employed for confirmatory factor analysis and path analysis of casual relationship among variables and effect. This study suggests practical and theoretical implications based on the results.

• Journal of the Korea Convergence Society
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• v.6 no.6
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• pp.221-229
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• 2015

This study aims to identify and analyze the main factors that determine the properties and buying behavior in the premium bottled select and analyze the degree of impact on the relevant variables are premium water demand. When applied to the truncated negative binomial model to derive the study results: The results of estimating the variables that affect the demand for premium mineral water are as follows. Premium bottled water demand of this group my purchases with a choice between buying behavior variables are significantly higher than the relative population. To also do a good ingredient water, it appeared to be on a statistically significant positive effect on the demand for the more groups you purchase a premium bottled water for the purpose of receiving special feeling, just buy purpose is called to drinking water does not significantly affect to be analyzed. Among demographic characteristics it showed that demand for premium bottled water purchases are significantly higher in women than in men, professional / clerical job, such as the military, college graduates were more consumer research as significant in comparison to the relative population. Taste and package design factors of premium bottled mineral water among the select attribute factors are having a significant positive impact on the purchasing demand, local conditions and cost factors have been estimated to be insignificant.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.181-200
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• 2020

This study used the OLS model to estimate the determinants affecting the tenure of a home and then compared the predictive power of each model with SVM, Decision Tree, Random Forest, Gradient Boosting, XGBooest and LightGBM. There is a difference from the preceding study in that the Stacking model, one of the ensemble models, can be used as a base model to establish a more predictable model to identify the volume of housing transactions in the housing market. OLS analysis showed that sales profits, housing prices, the number of household members, and the type of residential housing (detached housing, apartments) affected the period of housing ownership, and compared the predictability of the machine learning model with RMSE, the results showed that the machine learning model had higher predictability. Afterwards, the predictive power was compared by applying each machine learning after rebuilding the data with the influencing variables, and the analysis showed the best predictive power of Random Forest. In addition, the most predictable Random Forest, Decision Tree, Gradient Boosting, and XGBooost models were applied as individual models, and the Stacking model was constructed using Linear, Ridge, and Lasso models as meta models. As a result of the analysis, the RMSE value in the Ridge model was the lowest at 0.5181, thus building the highest predictive model.