• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.42 no.6
• /
• pp.843-852
• /
• 2022

Despite continuous safety management, the death rate of construction workers is not decreasing every year. Accordingly, various studies are in progress to prevent construction site accidents. In this paper, we developed an AI-based priority inspection target selection model that preferentially selects sites are expected to cause construction accidents among construction sites with construction costs of less than 5 billion won (KRW). In particular, Random Forest (90.48 % of accident prediction AUC-ROC) showed the best performance among applied AI algorithms (Classification analysis). The main factors causing construction accidents were construction costs, total number of construction days and the number of construction performance evaluations. In this study an ROI (return of investment) of about 917.7 % can be predicted over 8 years as a result of better efficiency of manual inspections human resource and a preemptive response to construction accidents.

• Korea Trade Review
• /
• v.41 no.1
• /
• pp.41-66
• /
• 2016

This paper analyzes the firms value and the business performance before and after Chinese firms got listed in the U.S. First of all, it was separated into term before U.S listed and after listed, and looked whether there was any change in the Tobin'Q. After listed, as time went on Tobin'Q decreased more. In terms of net sales growth rate, it dropped significantly after U.S IPO. Operating profits and net profits rate increased more after being listed in large corporation, but in small corporation cases, it produced an opposite effect on debt ratio and net interest cost. Interest burden continued to fall after being listed in small corporation, and it couldn't find the investment profitability, nor could it utilize ROE, ROI variable. ROE, ROI continued to fall after being listed, but current ratio and quick ratio increased significantly in small corporation. From this results, we can infer that the financial liquidity showed signs of improvement after being listed.

• Transactions of the Korean Society of Pressure Vessels and Piping
• /
• v.18 no.2
• /
• pp.87-100
• /
• 2022

Unit capacity factor (utilization rate) of nuclear power plants (NPPs) is an important performance indicator. Since the first commercial operation of Kori Unit 1 began in April 1978, the utilization rate of domestic NPPs has gradually increased, reaching 90% from the end of the 1990s. However, due to various issues such as the Fukushima accident in 2011, corrosion of the CLP, the utilization rate dropped to 65~80%. In the early 1980s, the utilization rate of the U.S. NPPs was around 60%. However, since 2004, it has been consistently maintained above 90%. Therefore, in this study, we first examined the causes of declining the utilization rate in domestic NPPs. Next, the significances of the utilization rates are reviewed in five aspects: investment capability, electricity rate, safety and export, etc., with discussion on the current status of the utilization rates in the U.S. Based on this, three key factors are derived as the reasons of the increasing: equipment reliability program, on-line maintenance and the pursuit of institutional rationality. And finally, by synthesizing above results, the measures for increasing the utilization rate of domestic NPPs are proposed in terms of equipment management, institutional improvements, and personnel resources.

• The Journal of the Korea Contents Association
• /
• v.22 no.8
• /
• pp.329-338
• /
• 2022

As the non-face-to-face economic situation developed due to the COVID-19 pandemic, untact stock groups appeared in the stock market. This study proposed the Korea COVID-19 fear index following the spread of infectious diseases in the COVID-19 pandemic situation and analyzed the influence on the untact stock and contact stock returns. The results of the empirical analysis are as follows. First, as a result of the Granger causality analysis using the Korea COVID-19 fear index, significant causality was found in the return of contact stocks such as Korean Air, Hana Tour, CJ CGV, and Paradise. Second, as a result of stock price prediction based on the LSTM model, Kakao, Korean Air, and Naver's prediction performance was high. Third, the investment performances of the Alexander filter entry rule using the predicted stock price were high in Naver futures and Kakao futures. This study can find a difference from previous studies in that it analyzed the influence of the spread of the COVID-19 pandemic on untact and contact stocks in the COVID-19 situation where the non-face-to-face economy is in full swing.

• Korean Journal of Social Welfare Studies
• /
• v.42 no.1
• /
• pp.151-177
• /
• 2011

With the expansion of Voucher program, academic and practical concerns of performance evaluation of voucher service has been more emphasized. Accordingly, client satisfaction survey are expanding these days, but the study of client satisfaction as a performance indicator aimed at service client has not been yet conducted systematically. The purpose of this study is to develop Satisfaction Index, targeting at client of community-development voucher program in Community Service Investment Project conducted by the Ministry of Health and Welfare. Data were collected from 1800 client of community-development voucher program. This study summarized theoretically many kinds of satisfaction measurement and composed the Satisfaction Index reflecting characteristics of Voucher. By the item analysis and reliability analysis, the element satisfaction was identified as homogeneous items. General satisfaction were obtained using element satisfaction and overall satisfaction, it was found to be valid through the evaluation of convergent and discriminant validity as a tool for satisfaction. This study suggest using the '1step weighted approach' than '2step weighted approach' having weakness of complex overlapping manner.

• Journal of Information Technology Services
• /
• v.22 no.5
• /
• pp.1-16
• /
• 2023

This study aimed to provide fundamental insights into the digital customer experience by identifying its components and analyzing their importance and satisfaction levels among youths. To achieve this objective, the components of digital customer experience were identified through a review of prior research and consultation with experts. Subsequently, a survey was conducted with 200 youths in Seoul and Gyeonggi-do. The main findings of the study are as follows: First, The components of the digital customer experience consisted of 12 items grouped into three categories. Second, an analysis of the disparity between the importance and satisfaction levels of digital customer experience revealed statistically significant differences across all items. Third, By utilizing IPA (Importance-Performance Analysis), the digital customer experience was categorized into four quadrant, each with its own characteristics and recommendations for management: The first quadrant, the "current level maintenance area," encompassed items related to "entertainment" and "recommended service." This area is currently functioning well but necessitates continuous attention and management. The second quadrant, the "area to be supported first," included items such as "personalization," "security," "inducing participation," "privacy," and "individuality expression." Intensive management and improvements are imperative in this quadrant. The third quadrant, the "long-term improvement area," consisted of items like 'consistency,' 'information quality,' and 'convenience.' These items require focus on long-term enhancement efforts. The fourth quadrant, the "areas where efforts have already been invested," encompassed items like 'accessibility' and 'deliberation.' It appears that excessive investment has been made in these areas relative to their importance, calling for selective investments while considering the specific issues associated with each factor. These research findings serve as essential data for managing the digital customer experiences of youths.

• Journal of Intelligence and Information Systems
• /
• v.25 no.2
• /
• pp.123-139
• /
• 2019

The Korean NPL market was formed by the government and foreign capital shortly after the 1997 IMF crisis. However, this market is short-lived, as the bad debt has started to increase after the global financial crisis in 2009 due to the real economic recession. NPL has become a major investment in the market in recent years when the domestic capital market's investment capital began to enter the NPL market in earnest. Although the domestic NPL market has received considerable attention due to the overheating of the NPL market in recent years, research on the NPL market has been abrupt since the history of capital market investment in the domestic NPL market is short. In addition, decision-making through more scientific and systematic analysis is required due to the decline in profitability and the price fluctuation due to the fluctuation of the real estate business. In this study, we propose a prediction model that can determine the achievement of the benchmark yield by using the NPL market related data in accordance with the market demand. In order to build the model, we used Korean NPL data from December 2013 to December 2017 for about 4 years. The total number of things data was 2291. As independent variables, only the variables related to the dependent variable were selected for the 11 variables that indicate the characteristics of the real estate. In order to select the variables, one to one t-test and logistic regression stepwise and decision tree were performed. Seven independent variables (purchase year, SPC (Special Purpose Company), municipality, appraisal value, purchase cost, OPB (Outstanding Principle Balance), HP (Holding Period)). The dependent variable is a bivariate variable that indicates whether the benchmark rate is reached. This is because the accuracy of the model predicting the binomial variables is higher than the model predicting the continuous variables, and the accuracy of these models is directly related to the effectiveness of the model. In addition, in the case of a special purpose company, whether or not to purchase the property is the main concern. Therefore, whether or not to achieve a certain level of return is enough to make a decision. For the dependent variable, we constructed and compared the predictive model by calculating the dependent variable by adjusting the numerical value to ascertain whether 12%, which is the standard rate of return used in the industry, is a meaningful reference value. As a result, it was found that the hit ratio average of the predictive model constructed using the dependent variable calculated by the 12% standard rate of return was the best at 64.60%. In order to propose an optimal prediction model based on the determined dependent variables and 7 independent variables, we construct a prediction model by applying the five methodologies of discriminant analysis, logistic regression analysis, decision tree, artificial neural network, and genetic algorithm linear model we tried to compare them. To do this, 10 sets of training data and testing data were extracted using 10 fold validation method. After building the model using this data, the hit ratio of each set was averaged and the performance was compared. As a result, the hit ratio average of prediction models constructed by using discriminant analysis, logistic regression model, decision tree, artificial neural network, and genetic algorithm linear model were 64.40%, 65.12%, 63.54%, 67.40%, and 60.51%, respectively. It was confirmed that the model using the artificial neural network is the best. Through this study, it is proved that it is effective to utilize 7 independent variables and artificial neural network prediction model in the future NPL market. The proposed model predicts that the 12% return of new things will be achieved beforehand, which will help the special purpose companies make investment decisions. Furthermore, we anticipate that the NPL market will be liquidated as the transaction proceeds at an appropriate price.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.1-21
• /
• 2021

As the global business environment changes, uncertainties in technology development and market needs increase, and competition among companies intensifies, interests and demands for R&D activities of individual companies are increasing. In order to cope with these environmental changes, R&D companies are strengthening R&D investment as one of the means to enhance the qualitative competitiveness of R&D while paying more attention to facility investment. As a result, facilities or R&D investment elements are inevitably a burden for R&D companies to bear future uncertainties. It is true that the management strategy of increasing investment in R&D as a means of enhancing R&D capability is highly uncertain in terms of corporate performance. In this study, the structural factors that influence the R&D capabilities of companies are explored in terms of technology management capabilities, R&D capabilities, and corporate classification attributes by utilizing data mining techniques, and the characteristics these individual factors present according to the level of R&D capabilities are analyzed. This study also showed cluster analysis and experimental results based on evidence data for all domestic R&D companies, and is expected to provide important implications for corporate management strategies to enhance R&D capabilities of individual companies. For each of the three viewpoints, detailed evaluation indexes were composed of 7, 2, and 4, respectively, to quantitatively measure individual levels in the corresponding area. In the case of technology management capability and R&D capability, the sub-item evaluation indexes that are being used by current domestic technology evaluation agencies were referenced, and the final detailed evaluation index was newly constructed in consideration of whether data could be obtained quantitatively. In the case of corporate classification attributes, the most basic corporate classification profile information is considered. In particular, in order to grasp the homogeneity of the R&D competency level, a comprehensive score for each company was given using detailed evaluation indicators of technology management capability and R&D capability, and the competency level was classified into five grades and compared with the cluster analysis results. In order to give the meaning according to the comparative evaluation between the analyzed cluster and the competency level grade, the clusters with high and low trends in R&D competency level were searched for each cluster. Afterwards, characteristics according to detailed evaluation indicators were analyzed in the cluster. Through this method of conducting research, two groups with high R&D competency and one with low level of R&D competency were analyzed, and the remaining two clusters were similar with almost high incidence. As a result, in this study, individual characteristics according to detailed evaluation indexes were analyzed for two clusters with high competency level and one cluster with low competency level. The implications of the results of this study are that the faster the replacement cycle of professional managers who can effectively respond to changes in technology and market demand, the more likely they will contribute to enhancing R&D capabilities. In the case of a private company, it is necessary to increase the intensity of input of R&D capabilities by enhancing the sense of belonging of R&D personnel to the company through conversion to a corporate company, and to provide the accuracy of responsibility and authority through the organization of the team unit. Since the number of technical commercialization achievements and technology certifications are occurring both in the case of contributing to capacity improvement and in case of not, it was confirmed that there is a limit in reviewing it as an important factor for enhancing R&D capacity from the perspective of management. Lastly, the experience of utility model filing was identified as a factor that has an important influence on R&D capability, and it was confirmed the need to provide motivation to encourage utility model filings in order to enhance R&D capability. As such, the results of this study are expected to provide important implications for corporate management strategies to enhance individual companies' R&D capabilities.

• Journal of the Society of Disaster Information
• /
• v.16 no.2
• /
• pp.276-290
• /
• 2020

Purpose: The construction history of domestic port facilities has been more than 100 years, and until recently, modern facilities have been continuously built and expanded. However, it is not easy to keep the required performance conditions at the time of initial construction due to changes in the marine environment and increase in volume. In particular, in the case of harbor structures that have a long service life, safety performance and function management are becoming very important due to the increase in the size of ships, the increasing frequency of use, and the increase in the scale of natural disasters. Method: Therefore, this study investigates the state change by structural type of port facilities and analyzes the rehabilitation activities and the history that contribute to the performance improvement and life extension activities. Result: Through this, we distinguished between performance improvement cost (CAPEX) and repair maintenance activity (OPEX) that can be used to establish port facility maintenance strategy, and suggested cost model that can be used to establish maintenance strategy. Conclusion: These studies are expected to contribute greatly to mid- to long-term investment decisions.

• International Commerce and Information Review
• /
• v.19 no.2
• /
• pp.213-236
• /
• 2017

World trade has entered a stagnant state, protection trade measures are spread due to delayed economic recovery in developed countries, sluggish investment in emerging economies such as China, economic recession in resource exporting countries, and geopolitical and political uncertainties along with the election period in the US and other major industrialized countries. Thus, in the economic structure of our country with a focus on export, for small and medium enterprises to grow, efforts for having various markets are necessary. The importance of the trade insurance system, which can support the risk management of enterprises, is emphasized by the fact that the majority of SME exporters have a risk management level and a lack of corporate capacity to enter the global market. This study was surveyed with 87 small and medium export companies in South Korea. The purpose of this study is to verify the effect relationship how service quality of trade insurance and utilization of trade insurance impact on the risk management of trade payment and export performance. The research hypothesis and model was derived from the basis of existing theory and empirical research, and obtained the following results. Firstly, Service Quality of Trade Insurance showed positive (+) effect on Export Performance. Secondly, Utilization of Trade Insurance showed positive (+) effect on Risk Management of Trade Payment. Thirdly, Risk Management of Trade Payment showed positive (+) effect on Export Performance. This study is differentiated from previous research information by empirically evaluating the relationship between the risk management of trade payment and export performance through utilization of trade insurance. This study contributed to academic by examining the research on the risk management of trade insurance and also practically suggested the direction how small and medium export company is to take the advantage of the trade insurance.