• Journal of the Korean Home Economics Association
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• v.44 no.5 s.219
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• pp.79-87
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• 2006

The purpose of this study is to analyze whether service quality and perceived risk have an effect on purchase intention and satisfaction of the fashion merchandise in internet shopping malls. To this end, a survey was conducted from June 20 to July 30 in 2005, among married women aged in their 20s and 30s, on their purchase experience of fashion merchandise from internet shopping malls. The survey was conducted over the internet with 306 subjects. The statistical analysis methods were frequency analysis, reliability analysis, factor analysis, and multiple regression analysis. First, the service quality factors were determined to be reliability, responsibility, ease of transaction, order convenience, and site characteristics. Perceived risk factors were determined to be merchandise risk, information exposure risk, social psychological risk, and function risk. Second, service quality factors of responsibility, ease of transaction, order convenience, site characteristics and perceived risk factor of social psychological risk had an effect on internet purchase intention of the fashion merchandise. Greater site use convenience, lower price, simpler ordering, and lower social psychological risk were all positively correlated with higher internet purchase intention of fashion merchandise. Third, nice quality factors and social psychological risk had an effect on satisfaction degree in internet shopping.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2002.11a
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• pp.95-98
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• 2002

The purpose of this study was to Estimation the Risk of construction method to protect and reduce the risk of construction period. For this study we are using the pre and during construction period Inspection list. The inspection list was used by construction company for check a flaw of construction field. So we rearrange the individual item of inspection list to match the Risk factor, The results of this study were as follows: The inspection list was rearranged at Risk checklist for Risk estimation. So that we estimate the Risk of individual construction method.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.41 no.8
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• pp.619-624
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• 2013

To predict the probabilistic service life, statistical factors should be included to consider the uncertainty of parameters. Generally the probabilistic analysis is one of the common methods to account the uncertainty of parameters on the structural failure. In order to apply probabilistic analysis on the deterministic life analysis, it would be necessary to introduce Probability of Failure(PoF) and conduct risk assessment. In this work, we have studied probabilistic risk assessment of aircraft structures by using PoF approach. To achieve this goal, the Bayesian method was utilized to model PoF estimation since this method is known as the proper method to express the uncertainty of parameters. A series of proof tests were also conducted in order to verify the result of PoF estimation. The results from this efforts showed that the PoF estimation model can calculate quantitatively the value of PoF. Furthermore effectiveness of risk assessment approach for the aircraft structures was also demonstrated.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.6
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• pp.567-574
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• 2018

Risk management is a method to 1) identify risks that can adversely affect the cost, schedule, and target achievement performance of a system development project, and 2) manage the identified risks based on the severity and likelihood assigned to each risk item. Risk management is applicable to various fields, since it can manage the cost/schedule and effectively guides accomplishing the target performance by identifying and managing the risks in advance, which necessitates many concurrent studies. This paper proposes a procedure to estimate the severity value for a risk item using a Kalman filter. It is assumed that the severity can be expressed as an equation consisting of cost/schedule loss during the risk event. A linear Kalman filter is used to reduce the error between the true and estimated values, which can eventually save resources spent on the risk management procedure. A simulation test case was conducted to demonstrate the validity of the proposed method.

• Journal of Industrial Technology
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• v.22 no.A
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• pp.127-135
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• 2002

In this study, the preliminary risk assessment for the underground storage tanks(UST) in Chunchon city was implemented using the geographical information system(GIS). The estimation variables, such as the installation year, storage capacity, the distances from streams, and from groundwater pumping wells, were selected to estimate the relative risk levels. The weighting factors were given to all the estimation variables. Cumulative scores were induced by the combination of all the scores of the corresponding variables using the buffering technique and the overlay analysis in ArcView. Using the these process, the relative risk level of each UST was estimated. Some sites in this study are simplified and reduced because the number of useable data are limited or too enormous. Thus the selection of the comprehensive estimation variables and the proper weighting values are required for the future study. The methodology in this study could be served not only for the preliminary risk assessment of UST but also for the selection of the proper location of new and old UST. And, it can be used for the effective management system of UST.

• Journal of Environmental Health Sciences
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• v.38 no.1
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• pp.66-72
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• 2012

Objectives: The AHERA method by the US EPA, ASTM E2356-04, and HSG264 by the UK HSE, all of which are hazard/risk assessment methods for asbestos-containing building materials, were reviewed and compared based on 231 homogeneous areas. In addition, the current Act on Asbestos Safety Management (enforcement: April 29, 2012) was reviewed and analyzed. This trial provided fundamental data for improving the current asbestos hazard/risk assessment method. Methods: For the hazard/risk assessment of 77 asbestos-containing public buildings including schools, 231 homogeneous areas were selected, each of which was assessed using AHERA, ASTM E2356-04, and HSG264. Results: The matching rate of the hazard/risk assessment stood at 20.4 percent between AHERA and ASTM, at 71.4 percent between AHERA and HSG264 and at 17.8 percent between ASTM and HSG264. The AHERA method includes a seven-category rating scale. There were three categories, two of which have three subcategories. ASTM provides two decision-making charts consisting of ten rating scales for current condition estimation and for potential for disturbance estimation. In addition, the HSG264 method has a total of 20 scores with four items, and then provides four grades. This HSG264 method cannot clearly separate current condition and potential for disturbance. Conclusions: In the Korean Act on Asbestos Safety Management, the hazard/risk assessment method for asbestos-containing building materials should consider balance between current condition estimation and the potential for disturbance estimation.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.1017-1028
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• 2014

Well-known Bayes and empirical Bayes estimators have a disadvantage in respecting to overshink the parameter estimator error; therefore, a constrained Bayes estimator is suggested by matching the first two moments. Also traditional loss function such as mean square error loss function only considers the precision of estimation and to consider both precision and goodness of fit, balanced loss function is suggested. With these reasons, constrained Bayes estimators under balanced loss function is recommended for non-life insurance pricing.; however, most studies focus on the performance of estimation since Bayes risk of newly suggested estimators such as constrained Bayes and constrained empirical Bayes estimators under specific loss function is difficult to derive. This study compares the Bayes risk of several Bayes estimators under two different loss functions for estimating the risk in the auto insurance business and indicates the effectiveness of the newly suggested Bayes estimators with regards to Bayes risk perspective through auto insurance real data analysis.

• Restorative Dentistry and Endodontics
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• v.49 no.1
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• pp.4.1-4.9
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• 2024

Objectives: This study aims to correlate caries-causing microorganism load, lactic acid estimation, and blood groups to high caries risk in diabetic and non-diabetic individuals and low caries risk in healthy individuals. Materials and Methods: This study includes 30 participants divided into 3 groups: Group A, High-risk caries diabetic individuals; Group B, High-risk caries non-diabetic individuals; and Group C, Low-risk caries individuals. The medical condition, oral hygiene, and caries risk assessment (American Dental Association classification and International Caries Detection and Assessment System scoring) were documented. Each individual's 3 mL of saliva was analyzed for microbial load and lactic acid as follows: Part I: 2 mL for microbial quantity estimation using nutrient agar and blood agar medium, biochemical investigation, and carbohydrate fermentation tests; Part II: 0.5 mL for lactic acid estimation using spectrophotometric analysis. Among the selected individuals, blood group correlation was assessed. The χ² test, Kruskal-Wallis test, and post hoc analysis were done using Dunn's test (p < 0.05). Results: Group A had the highest microbial load and lactic acid concentration, followed by Groups B and C. The predominant bacteria were Lactobacilli (63.00 ± 15.49) and Streptococcus mutans (76.00 ± 13.90) in saliva. Blood Group B is prevalent in diabetic and non-diabetic high-risk caries patients but statistically insignificant. Conclusions: Diabetic individuals are more susceptible to dental caries due to high microbial loads and increased lactic acid production. These factors also lower the executing tendency of neutrophils, which accelerates microbial accumulation and increases the risk of caries in diabetic individuals.

• Journal of Korea Technology Innovation Society
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• v.11 no.2
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• pp.241-263
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• 2008

Within any income approach, a discount rate is used to convert some projected free cash flow to its presented value. In case of valuing companies, the most frequently used discount rate is the weighted average cost of capital(WACC) at the aggregate level. But technology valuation is different to discounting aggregate corporate cash flow since it is concerned about individual Intellectual property. Therefore, blindly applying standard discount rate such as WACC in technology valuation is unlikely to lead to the right result. The primary focus of this paper is to establish the structure of discount rate for technology valuation and to suggest the method of estimation. To determine an appropriate discount rate for technology valuation, the level of technology risk, market risk and competitive risk should be included in the structure of discount rate. This paper suggests the build-up model which consists of three components as a expansion of the CAPM. It includes (1) a risk-free rate of return, (2) general market risk premium and beta and (3) intellectual property risk premium related to technology risk and specific target market risk. However, there is no specific check list for examining the intellectual property risk until now and no specific method for quantifying its risk into risk premium. This paper developed the 10 element to determine the level of the intellectual property risk and applied estimation function such as linear function, natural log function and exponential function to transform the level of risk into risk premium. The limitation of this paper is that the range of intellectual property risk premium is inferred based on the information of foreign and domestic valuation agency. Finally, this paper explored the development of an intellectual property discount rate for technology valuation and presented the method in order to quantify the intellectual property risk premium.

• Journal of Korea Water Resources Association
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• v.52 no.12
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• pp.963-973
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• 2019

This study reviews a urban flooding risk criteria estimation model to predict risk criteria in areas where flood risk criteria are not precalculated by using watershed characteristic data and limit rainfall based on damage history. The risk criteria estimation model was designed using Support Vector Machine, one of the machine learning algorithms. The learning data consisted of regional limit rainfall and watershed characteristic. The learning data were applied to the SVM algorithm after normalization. We calculated the mean absolute error and standard deviation using Leave-One-Out and K-fold cross-validation algorithms and evaluated the performance of the model. In Leave-One-Out, models with small standard deviation were selected as the optimal model, and models with less folds were selected in the K-fold. The average accuracy of the selected models by rainfall duration is over 80%, suggesting that SVM can be used to estimate flooding risk criteria.