• International conference on construction engineering and project management
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• 2017.10a
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• pp.1-7
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• 2017

Korea's domestic construction market and overseas construction order environment are experiencing a decreasing trend, and this trend is expected to continue. Therefore, domestic construction companies are seeking to enter the global construction market. This study analyzes the global construction market and the global competitiveness for global construction companies and provides the results. To this end, this study has developed a model to evaluate the global construction competitiveness level and to evaluated global construction competitiveness in 2016. The evaluation of global construction competitiveness was analyzed based on the competitiveness of construction infrastructure by country, and the evaluation results of competitiveness of construction companies. These assessments were based on 20 detailed international statistics (ENR, Global Insight, Compass, etc.). The evaluation results are as follows. First, in regard to the comprehensive global construction competitiveness by country, America ranked first among 20 countries, followed by China. European countries like Spain, Germany and the Netherlands ranked third to fifth, respectively. Korea ranked sixth, one rank higher than that of the previous year. America and European countries remain strong. Second, in regard to the comprehensive building infrastructure competitiveness by country, America ranked first followed by Germany. Korea ranked twelfth, which is the same rank as that of the previous year. When it comes to stability in the construction market, China ranked first and Korea eighth. For construction systems, Sweden ranked first and Korea thirteenth, and for infrastructure, Japan ranked first and Korea tenth. Third, according to the construction company's capability evaluation by country, America ranked first followed by China. Korea ranked fourth, two ranks higher than that of the previous year because of its building competitiveness (fifth → fourth) and design competitiveness (eleventh → eighth) which has improved. When it comes to building competitiveness, China ranked first and Korea fourth. For design competitiveness, America ranked first and Korea eighth, and for price competitiveness, India ranked first and Korea seventh. However, Korea is still in the middle of the pack rank among the 20 countries considered when it comes to design competitiveness. It is ranked eleventh for design productivity and thirteenth for foreign sales against the total sales (internationalization). Thus, Korea needs to improve technical power and tap into new markets for improved competitiveness, including increased productivity. To do so, more R&D investment is required.

• Asia Marketing Journal
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• v.15 no.1
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• pp.43-55
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• 2013

Since movies are experience goods, consumers are easily influenced by other consumers' behavior. For moviegoers, box office rank is the most credible and easily accessible information. Many studies have found that the relationship between a movie's box office rank and its revenue departs from the Pareto distribution, and this phenomenon has been named "increasing returns to information." The primary objective of the current research is to apply the empirical model proposed by De Vany and Walls (1996) to the Korean movie market in order to examine whether the same phenomenon prevails in the Korean movie market. The other purpose of the present study is to provide managers with useful implications about the release timing of a movie by finding different curvatures that depend upon seasonality. The empirical test on the Korean movie market shows similar results as prior studies conducted on the U.S., Hong Kong, and U.K. movie markets. The phenomenon of increasing returns is generated by information transmission among consumers, which makes some movies become blockbusters and others bombs. The proposed model can also be interpreted in such a way that a change in the rank has a nonlinear effect on the movie's performance. If a movie climbs up the chart, it would be rewarded more than its proportion. On the other hand, if a movie falls down in the ranks, its performance would drop rapidly. The research result also indicates that the phenomenon of increasing returns occurs differently depending on when the movies are released. Since the tendency of the increasing returns to information is stronger during the peak seasons, movie marketers should decide upon the release timing of a movie based on its competitiveness. If a movie has substantial potential to incur positive word-of-mouth, it would be more reasonable to release the movie during the peak season to enjoy increasing returns. Otherwise, a movie should be released during the low season to minimize the risk of being dropped from the chart.

• Journal of Korea Water Resources Association
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• v.46 no.2
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• pp.99-109
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• 2013

Increasing complexity of the basin environments makes it difficult for single decision maker to consider all relevant aspects of problem, and thus the uncertainty of decision making grows. This study attempts to develop an approach to quantify the spatial flood vulnerability of South Korea. Fuzzy TOPSIS is used to calculate individual preference by each group and then three GDM techniques (Borda count method, Condorcet method, and Copeland method) are used to integrate the individual preference. Finally, rankings from Fuzzy TOPSIS, TOPSIS, and GDM are compared with Spearman rank correlation, Kendall rank correlation, and Emond & Mason rank correlation. As a result, the rankings of some areas are dramatically changed by the use of GDM techniques. Because GDM technique in regional vulnerability assessment may cause a significant change in priorities, the model presented in this study should be considered for objective flood vulnerability assessment.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.317-326
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• 2018

The purpose of this study is to develop an AHP model to evaluate the relative importance and priorities of multi-cultural policies under bounded Rationality. The results of the study are as follows. First, in the evaluation elements for each measurement area, the following are the stable social settlement support policy (1rank), social capability development policy of multi-cultural family second generation (2rank), socio-economic activity policy (3rank), collaborative governance policy enforcement(4rank). Second, the priority of the measurement element is as follows. social settlement service target expansion policy was proved to be the top priority project stable social settlement support policy aspect and social capacity development policies of the second generation of multi-cultural families, social support policy was most important evaluated. Active economic activity support policy was as the top priority project socio-economic activity policy, and construct cooperation system of policy practice main agents was proved to be the top priority collaborative governance policy enforcement. These results will contribute to explain the reality of multi-cultural policy.

• Magazine of the Korea Concrete Institute
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• v.10 no.6
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• pp.149-159
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• 1998

The reasonable prediction of time-dependent deformation of prestressed concrete(PSC) box girder bridges is very important for accurate construction as well as good serviceability. The long-term behavior is mostly influenced by the probabilistic characteristic of creep and shrinkage. This paper presents a method of statistical analysis and sensitivity analysis of creep and shrinkage effects in PSC box been taken into account - model uncertainty, parameter variation and environmental condition. The statistical and sensitivity analyses are performed by using the numerical simulation of Latin Hypercube sampling. For each sample, the time-dependent structural analysis is performed to produce response data, which are then statistically analyzed. The probabilistic prediction of the confidence limits on long-term effects of creep and shrinkage is then expressed. Three measure are examined to quantify the sensitivity of the outputs of each of the input variables. These are rank correlation coefficient(RCC), partical rank correlation coefficient(PRCC) and standardiozed rank regression coefficient(SRRC) computed on the ranks of the observations. Three creep and shrinkage models - i. e., ACI model. CEB-FIP model and the model in Korea Highway Bridge Specification - are studied. The creep model uncertainy factor and the relative humidity appear to be the most dominant factors with regard to the model output uncertainty.

• Communications for Statistical Applications and Methods
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• v.19 no.3
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• pp.367-379
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• 2012

In baseball rankings, prediction has been a subject of interest for baseball fans. To predict these rankings, (based on 2011 data from Korea Professional Baseball records) the arithmetic mean method, the weighted average method, principal component analysis, and principal component regression analysis is presented. By standardizing the arithmetic average, the correlation coefficient using the weighted average method, using principal components analysis to predict rankings, the final model was selected as a principal component regression model. By practicing regression analysis with a reduced variable by principal component analysis, we propose a rank predictability model of a pitcher part, a batter part and a pitcher batter part. We can estimate a 2011 rank of pro-baseball by a predicted regression model. By principal component regression analysis, the pitcher part, the other part, the pitcher and the batter part of the ranking prediction model is proposed. The regression model predicts the rankings for 2012.

• Journal of Pharmaceutical Investigation
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• v.38 no.3
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• pp.183-189
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• 2008

The present study compared the feasibility of Caco-2 and MDCK cells as an efficient in-vitro model for the drug classification based on Biopharmaceutics Classification System (BCS) as well as an in-vitro model for drug interactions mediated by P-gp inhibition or P-gp induction. Thirteen model drugs were selected to cover BCS Class I{\sim}IV$ and their membrane permeability values were evaluated in both Caco-2 and MDCK cells. P-gp inhibition studies were conducted by using vinblastine and verapamil in MDCK cells. P-gp induction studies were also performed in MDCK cells using rifampin and the P-gp expression level was determined by western blot analysis. Compared to Caco-2 cells, MDCK cells required shorter period of time to culture cells before running the transport study. Both Caco-2 and MDCK cells exhibited the same rank order relationship between in-vitro permeability values and human permeability values of all tested model compounds, implying that those in-vitro models may be useful in the prediction of human permeability (rank order) of new chemical entities at the early drug discovery stage. However, in the case of BCS drug classification, Caco-2 cells appeared to be more suitable than MDCK cells. P-gp induction by rifampin was negligible in MDCK-cells while MDCK cells appeared to be feasible for P-gp inhibition studies. Taken all together, the present study suggests that Caco-2 cells might be more applicable to the BCS drug classification than MDCK-cells, although MDCK cells may provide some advantage in terms of capacity and speed in early ADME screening process.

• Journal of Information Technology Services
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• v.17 no.2
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• pp.143-163
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• 2018

The business models has a great impact on the successful management of enterprises. Business environment has been shifting from industrial economy to knowledge-based economy. Enterprises go through numerous trials for successful management in the changing environment. Along with trial tests, research areas have been growing simultaneously. Although many researches have been conducted with regard to business models, it is very insufficient to systematically analyze the knowledge flow of research. Accordingly, successive researchers who want to study the business model may find it difficult to establish the orientation of future application research based on understanding the process of changing the knowledge structure that have accumulated so far. This study is intended to determine the current state of the business model research and to understand the process of knowledge structure changes in keywords that appear in 2,667 business model articles in the SCOPUS database. Identifying the knowledge structure has been completed through social network analysis, a methodology based on the 'relationship', and the changes in the knowledge structure were identified by classifying them into four different periods. The analysis showed that, first, the number of business model co-author increases over time with the need for academic diversity. Second, the 'innovation' keyword has the biggest center in the network, and over time, the lower-rank keyword which was in the former period has emerged as the top-rank keyword. Third, the cohesiveness group decreased from 12 before 2000 to 5 in 2015 and also the modularity decreased as well. Finally, examining characteristics of study area through a cognitive map showed that the relationships between domains increased gradually over time. The study has provided a systematic basis for understanding the current state of the business model research and the process of changing knowledge structure. In addition, considering that no research has ever systematically analyzed the knowledge structure accumulated by individual researches, it is considered as a significant study.

• Asian Pacific Journal of Cancer Prevention
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• v.16 no.18
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• pp.8567-8571
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• 2016

Background: The Cox model is known as one of the most frequently-used methods for analyzing survival data. However, in some situations parametric methods may provide better estimates. In this study, a Weibull parametric model was employed to assess possible prognostic factors that may affect the survival of patients with breast cancer. Materials and Methods: We studied 438 patients with breast cancer who visited and were treated at the Cancer Research Center in Shahid Beheshti University of Medical Sciences during 1992 to 2012; the patients were followed up until October 2014. Patients or family members were contacted via telephone calls to confirm whether they were still alive. Clinical, pathological, and biological variables as potential prognostic factors were entered in univariate and multivariate analyses. The log-rank test and the Weibull parametric model with a forward approach, respectively, were used for univariate and multivariate analyses. All analyses were performed using STATA version 11. A P-value lower than 0.05 was defined as significant. Results: On univariate analysis, age at diagnosis, level of education, type of surgery, lymph node status, tumor size, stage, histologic grade, estrogen receptor, progesterone receptor, and lymphovascular invasion had a statistically significant effect on survival time. On multivariate analysis, lymph node status, stage, histologic grade, and lymphovascular invasion were statistically significant. The one-year overall survival rate was 98%. Conclusions: Based on these data and using Weibull parametric model with a forward approach, we found out that patients with lymphovascular invasion were at 2.13 times greater risk of death due to breast cancer.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.2
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• pp.176-182
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• 2023

With about 80% of the global economy expected to shift to the global market by 2030, exports of reverse direct purchase products, in which foreign consumers purchase products from online shopping malls in Korea, are growing 55% annually. As of 2021, sales of reverse direct purchases in South Korea increased 50.6% from the previous year, surpassing 40 million. In order for domestic SMEs(Small and medium sized enterprises) to enter overseas markets, it is important to come up with export strategies based on various market analysis information, but for domestic small and medium-sized sellers, entry barriers are high, such as lack of information on overseas markets and difficulty in selecting local preferred products and determining competitive sales prices. This study develops an AI-based product recommendation and sales price estimation model to collect and analyze global shopping malls and product trends to provide marketing information that presents promising and appropriate product sales prices to small and medium-sized sellers who have difficulty collecting global market information. The product recommendation model is based on the LTR (Learning To Rank) methodology. As a result of comparing performance with nDCG, the Pair-wise-based XGBoost-LambdaMART Model was measured to be excellent. The sales price estimation model uses a regression algorithm. According to the R-Squared value, the Light Gradient Boosting Machine performs best in this model.