• Journal of the Korea Academia-Industrial cooperation Society
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• v.11 no.1
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• pp.272-277
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• 2010

In respect complication of group, rate of arrearage in domestic bank is composed of various factors. This paper studies focus on estimating the determinants of the rate of arrearage in domestic bank using panel data model. The volume of analysis consist of 3 groups(loaned patterns of enterprise, housekeeping, credit card). Analyzing period be formed over a 54 point(2005. 1~ 2009. 06). In this paper dependent variable setting up rate of arrearage in domestic bank, explanatory(independent) variables composed of the consumer price index, composite stock price index, rate of exchange, the coincident composite index, national housing bonds and employment rate. The result of estimating the rate of arrearage in domestic bank provides empirical evidences of significance positive relationships between the consumer price index However this study provides empirical evidences of significance negative relationships between the coincident composite index and the composite stock price index. The explanatory variables, that is, rate of exchange, national housing bonds and the employment rate are non-significance variables of negative factor. Implication of these findings are discussed for content research and practices.

• Journal of Internet Computing and Services
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• v.16 no.1
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• pp.91-100
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• 2015

Recently many companies began to feel the pressures of cost savings due to the global recession, so they have been interested in the Cloud Computing. Cloud Computing is one of using method of IT resources through the network. Users can borrow softwares or hardwares instead of buying them. Many people expect remarkable growth in Cloud Computing industry because of it's effectiveness. But Cloud Computing industry is still at an early stage. Especially, people who in the public sector hesitate to adopt Cloud Computing Services due to security issues and their conservative views. Also, they just have limited understanding, so we need to investigate what they really know and understand. Researches about the Cloud Computing generally focus on technical issues, so we can hardly find researches reference for decision making in considering the services. The study aims to investigate diverse factors for agencies' adoption decisions, such as benefits, costs, and risk in developing the most ideal type of cloud computing service for them, and performs priority analyses by applying ANP (Analytic Network Process). The results identify that features pertaining to the risk properties were considered the most significant factors. According to this research, the usage of private cloud computing services may prove to be appropriate for public environment in Korea. The study will hopefully provide the guideline to many governmental agencies and service providers, and assist the related authorities with cloud computing policy in coming up with the relevant regulations.

• Journal of the Korean Data and Information Science Society
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• v.28 no.4
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• pp.853-862
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• 2017

In this study, we analyse the determinants of the youth employment rate of public agencies and local public enterprises. On the other hand the youth employment rate contains information of the youth employment rate and the size of the youth employment. We use pooled tobit model and panel tobit model since dependent variable is a censored form observed only in a certain area. The results of the analysis are summarized as follows. First, the panel tobit model is more statistically significant as compared to the combined tobit model. Second, the youth employment rate is more statistically significantly higher in 2014 and 2015 than in 2011. Third, the youth employment rate in public enterprises is more statistically significantly higher than that in local public agencies. Finally, the higher the average wage is, the lower the youth employment ratio is.

• Journal of Intelligence and Information Systems
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• v.26 no.4
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• pp.149-171
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• 2020

The period at university is to make decision about getting an actual job. As our society develops rapidly and highly, jobs are diversified, subdivided, and specialized, and students' job preparation period is also getting longer and longer. This study analyzed the log data of college students to see how the various activities that college students experience inside and outside of school might have influences on employment. For this experiment, students' various activities were systematically classified, recorded as an activity data and were divided into six core competencies (Job reinforcement competency, Leadership & teamwork competency, Globalization competency, Organizational commitment competency, Job exploration competency, and Autonomous implementation competency). The effect of the six competency levels on the employment status (employed group, unemployed group) was analyzed. As a result of the analysis, it was confirmed that the difference in level between the employed group and the unemployed group was significant for all of the six competencies, so it was possible to infer that the activities at the school are significant for employment. Next, in order to analyze the impact of the six competencies on the qualitative performance of employment, we had ANOVA analysis after dividing the each competency level into 2 groups (low and high group), and creating 6 groups by the range of first annual salary. Students with high levels of globalization capability, job search capability, and autonomous implementation capability were also found to belong to a higher annual salary group. The theoretical contributions of this study are as follows. First, it connects the competencies that can be extracted from the school experience with the competencies in the Human Resource Management field and adds job search competencies and autonomous implementation competencies which are required for university students to have their own successful career & life. Second, we have conducted this analysis with the competency data measured form actual activity and result data collected from the interview and research. Third, it analyzed not only quantitative performance (employment rate) but also qualitative performance (annual salary level). The practical use of this study is as follows. First, it can be a guide when establishing career development plans for college students. It is necessary to prepare for a job that can express one's strengths based on an analysis of the world of work and job, rather than having a no-strategy, unbalanced, or accumulating excessive specifications competition. Second, the person in charge of experience design for college students, at an organizations such as schools, businesses, local governments, and governments, can refer to the six competencies suggested in this study to for the user-useful experiences design that may motivate more participation. By doing so, one event may bring mutual benefits for both event designers and students. Third, in the era of digital transformation, the government's policy manager who envisions the balanced development of the country can make a policy in the direction of achieving the curiosity and energy of college students together with the balanced development of the country. A lot of manpower is required to start up novel platform services that have not existed before or to digitize existing analog products, services and corporate culture. The activities of current digital-generation-college-students are not only catalysts in all industries, but also for very benefit and necessary for college students by themselves for their own successful career development.

• Information Systems Review
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• v.19 no.2
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• pp.1-19
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• 2017

Original scripts of e-learning lectures for the CEOs of corporation S were analyzed using topic analysis, which is a text mining method. Twenty-two topics were extracted based on the keywords chosen from five-year records that ranged from 2011 to 2015. Research analysis was then conducted on various issues. Promising topics were selected through evaluation and element analysis of the members of each topic. In management and economics, members demonstrated high satisfaction and interest toward topics in marketing strategy, human resource management, and communication. Philosophy, history of war, and history demonstrated high interest and satisfaction in the field of humanities, whereas mind health showed high interest and satisfaction in the field of in lifestyle. Studies were also conducted to identify topics on the proportion of content, but these studies failed to increase member satisfaction. In the field of IT, educational content responds sensitively to change of the times, but it may not increase the interest and satisfaction of members. The present study found that content production for CEOs should draw out deep implications for value innovation through technology application instead of simply ending the technical aspect of information delivery. Previous studies classified contents superficially based on the name of content program when analyzing the status of content operation. However, text mining can derive deep content and subject classification based on the contents of unstructured data script. This approach can examine current shortages and necessary fields if the service contents of the themes are displayed by year. This study was based on data obtained from influential e-learning companies in Korea. Obtaining practical results was difficult because data were not acquired from portal sites or social networking service. The content of e-learning trends of CEOs were analyzed. Data analysis was also conducted on the intellectual interests of CEOs in each field.

• Journal of Intelligence and Information Systems
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• v.26 no.1
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• pp.23-45
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• 2020

Big data is creating in a wide variety of fields such as medical care, manufacturing, logistics, sales site, SNS, and the dataset characteristics are also diverse. In order to secure the competitiveness of companies, it is necessary to improve decision-making capacity using a classification algorithm. However, most of them do not have sufficient knowledge on what kind of classification algorithm is appropriate for a specific problem area. In other words, determining which classification algorithm is appropriate depending on the characteristics of the dataset was has been a task that required expertise and effort. This is because the relationship between the characteristics of datasets (called meta-features) and the performance of classification algorithms has not been fully understood. Moreover, there has been little research on meta-features reflecting the characteristics of multi-class. Therefore, the purpose of this study is to empirically analyze whether meta-features of multi-class datasets have a significant effect on the performance of classification algorithms. In this study, meta-features of multi-class datasets were identified into two factors, (the data structure and the data complexity,) and seven representative meta-features were selected. Among those, we included the Herfindahl-Hirschman Index (HHI), originally a market concentration measurement index, in the meta-features to replace IR(Imbalanced Ratio). Also, we developed a new index called Reverse ReLU Silhouette Score into the meta-feature set. Among the UCI Machine Learning Repository data, six representative datasets (Balance Scale, PageBlocks, Car Evaluation, User Knowledge-Modeling, Wine Quality(red), Contraceptive Method Choice) were selected. The class of each dataset was classified by using the classification algorithms (KNN, Logistic Regression, Nave Bayes, Random Forest, and SVM) selected in the study. For each dataset, we applied 10-fold cross validation method. 10% to 100% oversampling method is applied for each fold and meta-features of the dataset is measured. The meta-features selected are HHI, Number of Classes, Number of Features, Entropy, Reverse ReLU Silhouette Score, Nonlinearity of Linear Classifier, Hub Score. F1-score was selected as the dependent variable. As a result, the results of this study showed that the six meta-features including Reverse ReLU Silhouette Score and HHI proposed in this study have a significant effect on the classification performance. (1) The meta-features HHI proposed in this study was significant in the classification performance. (2) The number of variables has a significant effect on the classification performance, unlike the number of classes, but it has a positive effect. (3) The number of classes has a negative effect on the performance of classification. (4) Entropy has a significant effect on the performance of classification. (5) The Reverse ReLU Silhouette Score also significantly affects the classification performance at a significant level of 0.01. (6) The nonlinearity of linear classifiers has a significant negative effect on classification performance. In addition, the results of the analysis by the classification algorithms were also consistent. In the regression analysis by classification algorithm, Naïve Bayes algorithm does not have a significant effect on the number of variables unlike other classification algorithms. This study has two theoretical contributions: (1) two new meta-features (HHI, Reverse ReLU Silhouette score) was proved to be significant. (2) The effects of data characteristics on the performance of classification were investigated using meta-features. The practical contribution points (1) can be utilized in the development of classification algorithm recommendation system according to the characteristics of datasets. (2) Many data scientists are often testing by adjusting the parameters of the algorithm to find the optimal algorithm for the situation because the characteristics of the data are different. In this process, excessive waste of resources occurs due to hardware, cost, time, and manpower. This study is expected to be useful for machine learning, data mining researchers, practitioners, and machine learning-based system developers. The composition of this study consists of introduction, related research, research model, experiment, conclusion and discussion.

• Journal of Korea Technology Innovation Society
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• v.21 no.1
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• pp.454-490
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• 2018

This paper examined the nonlinear relationships between external knowledge sources and the innovation performance of SMEs (small and medium-sized enterprises). Using 3,218 firm-year panel data in South Korea, this study found that increasing the number of external knowledge channels out of the value chain increases radical innovation. Meanwhile, increasing the number of external knowledge channels within the value chain increases the incremental innovation. Further, the external sources of knowledge both out of and in the value chain had inverted U-shaped relationships on radical and incremental innovation respectively. This finding implies that a mechanism of diminishing returns works in the relationship between the external sources of knowledge and innovation. The study also identified the synergistic effects between the external sources of knowledge out of the value chain and within the value chain, and confirmed that the synergistic effects strengthen the linear mechanism between the external sources of knowledge and innovation. In addition, this study found that the sources of knowledge both out of and within the value chain positively moderate the relationships between R&D investment and radical innovation of SMEs.

• Korean Journal of Construction Engineering and Management
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• v.11 no.3
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• pp.134-144
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• 2010

In China, the enhancement of water services has become the most crucial issue confronted with the rapid urbanization and industrialization process. A huge financial gap to meet the demand for water infrastructure and need for adopting advanced operation technology precipitated the rapid growth of PPP over the last 10 years. Diverse schemes of PPP such as TOT, Divestiture, and Management Contract and Lease have been practiced. Local governments and private investors/operator have adjusted their objectives and strategies to avoid potential pitfalls behind BOT projects in China. However, current academic research outcomes do not properly reflect important issues of BOT projects or related case studies in China. This limitation has brought in the lack of assessment of important risks and success factors required for the improvement of the body of risk management. In this regard, this study uses the market analysis method to identify major schemes of PPP water projects and conducts case studies on five PPP projects to identify key risk and success factors in association with each different scheme. It is expected that the risk and success factors identified from the cases will be used as reference to Korean companies which plan to enter the Chinese water market.

• Journal of Technology Innovation
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• v.27 no.3
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• pp.1-26
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• 2019

The Korean economy has undergone growth based on manufacturing end products. The Korean government aims to advance industry by strengthening the materials and components industry, which is attracting more attention in terms of maintaining the competitiveness of existing key industries, and fostering new industries required in the era of the Fourth Industrial Revolution. Despite this importance, there is insufficient related research on the efficient R&D of the materials and components industry. This study analyzed the R&D efficiency. In addition, exploratory research was conducted on the impact of corporate size and type of cooperation on R&D efficiency. Output variables were set to reflect economic performance and the empirical analysis revealed that overall R&D efficiency is low. Small firms were found to perform better than large firms in terms of firm-size, and the efficiency of business-university-research cooperation is worse than other types of cooperation. This study is exploratory research considering the materials and components industry, and the results provide implications for research institutions and regarding types of cooperation. This is expected to help develop polices for qualitative growth and R&D strategies for investment and allocation.

• Journal of Technology Innovation
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• v.21 no.2
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• pp.169-197
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• 2013

Despite many theoretical and empirical studies, general causality between IPRs system, firm technological innovation and financial performance is not clear. This study notices that the core factor to create financial performance is different by each industry. The study analyzed the effect of IPRs system on innovation and economic growth targeting 3 industries; pharmaceutical industry to which the basic track of creating performance is applied (strengthening IPRs${\rightarrow}$increasing R&D input/output${\rightarrow}$increasing sales); semiconductor industry where the relationship between stronger IPRs and R&D input/output is weak; and shipbuilding industry which has weak correlation between R&D and sales. It used panel data for 15 years since TRIPs when the patent institution in Korea reached up to the level of advanced countries, and applied the dynamic regression model which estimates the fixed effect model with difference-GMM. As a result, stronger IPRs increased R&D input/output, and financial performance in pharmaceutical industry, but has no influence on semiconductor and shipbuilding industries. That is, it is necessary to customize the construction of system and policy for strengthening IPRs by each industry, and unitary strengthening or weakening may have no significant impact on financial performance improvement in specific sectors.