• Management Science and Financial Engineering
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• v.19 no.2
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• pp.13-18
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• 2013

In this paper, we analyze the relationship between implementation of information technology and inventory turnover as a measure of operational efficiency. We build a regression model including a dummy variable that indicates whether a firm has been nominated for being good at implementing information technology. By using publicly available data, we have conducted an extensive empirical analysis and found that firms' using information technology is not likely to affect inventory turnover significantly. This result implies that we have to take careful consideration in adapting information technology.

• Proceedings of the KIEE Conference
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• 2003.11a
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• pp.491-493
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• 2003

Load foretasting model used generally such as times series and econometric regression model often doesn't reflect the load characteristics of small remote islands. Therefore, in this paper load demand forecast is peformed using EGARCH-M non-linear forecasting model.

• New & Renewable Energy
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• v.16 no.3
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• pp.13-26
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• 2020

This study analyzed whether the diffusion of new and renewable energy contributed to mitigating emissions of various air pollutants, including particulate matter, using panel econometric models. The theoretical foundation of such econometric models is based on the Environmental Kuznets Curve (EKC) hypothesis, which assumes an inverted U-shaped relation between national income and environmental pollution, as originally proposed by Grossman and Krueger. We examined whether there are inverted U-, U-shaped, or N-shaped relations between national income and air pollution. We demonstrate that increases in new and renewable energy significantly mitigated emissions of CO, NO_X, and PM2.5. Additionally, we included NO_X, SO_X, PM10, and VOCs as secondary emission sources of PM2.5 and found that emission of PM10 resulted in the highest PM2.5 emissions, followed by NO_X and SO_X emissions. The impact of new and renewable energy on air pollution varied across regions. Increase of new and renewable energy in the Honam region significantly mitigated CO, NO_X, and TSP emissions, while that in the Youngnam and metropolitan areas did not significantly mitigate air pollution overall. There was a U-shaped relationship between air pollution and national income for CO, NO_X, PM2.5, and SO_X, while an inverted N-shape was observed for PM10.

• Journal of Technology Innovation
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• v.19 no.3
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• pp.141-174
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• 2011

This paper presents the results of a meta-regression analysis on econometric evidence concerning the relationship between public funding of R&D and private R&D expenditure by reviewing literature and synthesizing existing results. The analysis on the effects of public financing on private investments in R&D has been the object of numerous studies, none of which having arrived at definite conclusion. A meta-analysis based upon a data-base including all relevant studies was carried out to examine whether the characteristics of the applied analysis influence the results. Three different empirical results are presented.

• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.494-506
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• 2009

This paper tests whether or not manufacturing specialization, employment growth, establishment size, employment size, industrial mix in manufacturing, regional difference between the Capital region and the others and so on are empirically related to manufacturing employment volatility levels across 203 municipalities called shi-gun-gu during the period 1990~2006. Using the spatial econometric analysis of cross sectional data, the municipalities tending to be more volatile are more specialized; they have higher-than-average employment growth rates, smaller establishment and employment sizes, regardless of any industrial mix in manufacturing; and they tend to be located in the Capital region. Unlike existing foreign literature based upon the spatial econometric analysis of cross sectional data, this paper finds that volatility of growth in a municipality is negatively rather than positively influenced by volatility of growth in its neighboring municipalities.

• Journal of the Korean Geographical Society
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• v.38 no.6
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• pp.906-921
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• 2003

This paper discusses the price effects of local growth controls on the housing markets of California jurisdictions in the late 1980s empirically. Particularly, based on spatial econometric modeling, the study focuses on the homebuilding constrained by growth controls which is one of the price effects. The modeling produces the California-wide generalizable results, differentiates among the individual effects of various growth controls on homebuilding, and covers spatial effects. Thereby, this study intends to supplement the existing work on the price effects of growth controls. The modeling results find that restrictive residential zoning had the effect of significantly restricting housing construction in the late 1980s. On the other hand, urban growth boundaries had the effect of accommodating homebuilding. Population growth or housing permit caps and adequate public facility ordinances had no significant effects on housing construction.

• The Journal of the Korea Contents Association
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• v.18 no.11
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• pp.430-446
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• 2018

In this paper, we conducted a comparative study on the methodology for impact analysis as the economic grounds for formulating policy and investment plan concerned with utilizing spectrum resource. In order to provide numerical results for objective comparison and selection among policy and investment planning, methods to be analyzed are focused on quantitative methodology based on mathematical models, consequently the utility and limits of econometric model, input-output analysis, computable general equilibrium and system dynamics are compared from various viewpoints including analysis cost. Besides, we compared the methodologies in the standpoint of utilizing spectrum and discussed the recent findings of mixed models combining multiple methodologies to exploit the advantages of each methodology and to offset the limit. Results of the research can be used as reference indicators to select the method that conforms to the purpose and priority of analysis verifying the efficiency of execution of policies and investment plans.

• Journal of Climate Change Research
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• v.9 no.3
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• pp.223-234
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• 2018

Estimation of future land cover based on climate change scenarios is an important factor in climate change impact assessment and adaptation policy. This study estimated future land cover considering Shared Socioeconomic Pathways (SSP) using Scenario Generators. Based on the storylines of SSP1-3, future population and estimated urban area were adopted for the transition matrix, which contains land cover change trends of each land cover class. In addition, limits of land cover change and proximity were applied as spatial data. According to the estimated land cover maps from SSP1-3 in 2030, 2050, and 2100, respectively, urban areas near a road were expanded, but agricultural areas and forests were gradually decreased. More drastic urban expansion was seen in SSP3 compared to SSP1 and SSP2. These trends are similar with previous research with regard to storyline, but the spatial results were different. Future land cover can be easily adjusted based on this approach, if econometric forecasts for each land cover class added. However, this requires determination of econometric forecasts for each land cover class.

• Journal of Digital Convergence
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• v.17 no.8
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• pp.141-148
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• 2019

Based on the theory of information ecology, this paper conducts a quantitative analysis on the research literature of e-commerce ecosystem in China, and clarifies the research hotspots and development trends, so as to provide reference for subsequent studies. With the key words related literatures collected by CNKI as the data source, and the software CiteSpaceIV is used to draw a visual knowledge map, the distribution of e-commerce ecosystem research papers in China is statistically analyzed by econometric analysis method, and literature research is summarized and evaluated. The research results show that previous studies mainly focus on the concept of e-commerce ecosystem, and there are relatively few research results on the construction and evolution mechanism of e-commerce ecosystem, so the depth and breadth of research should be expanded. Applying the concept of e-commerce ecosystem to solve the problems in cross-border e-commerce will be the future research trend.

• The Journal of Information Systems
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• v.30 no.2
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• pp.29-56
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• 2021

Purpose Cyber deviance of adolescents has become a serious social problem. With a widespread use of smartphones, incidents of cyber deviance have increased in Korea and both quantitative and qualitative damages such as suicide and depression are increasing. Research has been conducted to understand diverse factors that explain adolescents' delinquency in cyber space. However, most previous studies have focused on a single theory or perspective. Therefore, this study aims to comprehensively analyze motivations of juvenile cyber deviance and to develop a predictive model for delinquent adolescents by integrating four different theories on cyber deviance. Design/methodology/approach By using data from Korean Children & Youth Panel Survey 2010, this study extracts 27 potential factors for cyber deivance based on four background theories including general strain, social learning, social bonding, and routine activity theories. Then this study employs econometric analysis to empirically assess the impact of potential factors and utilizes a machine learning approach to predict the likelihood of cyber deviance by adolescents. Findings This study found that general strain factors as well as social learning factors have positive effects on cyber deviance. Routine activity-related factors such as real-life delinquent behaviors and online activities also positively influence the likelihood of cyber diviance. On the other hand, social bonding factors such as community commitment and attachment to community lessen the likelihood of cyber deviance while social factors related to school activities are found to have positive impacts on cyber deviance. This study also found a predictive model using a deep learning algorithm indicates the highest prediction performance. This study contributes to the prevention of cyber deviance of teenagers in practice by understanding motivations for adolescents' delinquency and predicting potential cyber deviants.