• Korea Petroleum Association Journal
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• no.12 s.94
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• pp.71-77
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• 1988

• Science & Technology Policy
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• v.10 no.3 s.123
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• pp.152-165
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• 2000

• Journal of the Korea Construction Safety Engineering Association
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• v.3 no.4 s.10
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• pp.84-107
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• 1993

• KDI Journal of Economic Policy
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• v.7 no.1
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• pp.59-73
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• 1985

• The Journal of the Korea Contents Association
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• v.12 no.6
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• pp.100-110
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• 2012

This study aims to figure out IT needs internalized in our daily lives through expert FGI, examine the general characteristics of them by verifying and classifying them through a survey, and also analyze the chief fields of industry for future society by the types of IT needs. Through this analysis, the paper suggests the directivity of future IT technology, and it can be utilized as foundational data to develop IT convergence technology. First of all, according to the result of drawing the keywords through the concrete future IT needs by the detailed activities through the FGI, this study found intellectualization, integration, virtualization, automation, customization, and safety, etc. According to the result of classifying the IT needs through the survey based on that and examining the characteristics, this study classified them into emphasis on life automation, emphasis on intellectual authenticity, emphasis on information search and convenience, and emphasis on convergence. And each type showed different general characteristics from one another. And in future society, the area of energy was valued the most, and the importance of industry showed somewhat difference by the types of IT needs.

• Management & Information Systems Review
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• v.38 no.3
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• pp.1-19
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• 2019

This paper investigates the asymmetric effects of crude oil price uncertainty on industrial stock returns under different market conditions (bearish and bullish stock markets). We consider a quantile regression method using monthly oil volatility index, KOSPI and 22 industrial stock indices from May 2007 to February 2019. Especially, we take care of the positive and negative changes of the oil volatility index to analyze asymmetric effects of the oil price uncertainty for the bearish and bullish stock market conditions. During the bearish markets, the oil volatility index has relatively strong statistically significant negative effects on the industrial stock returns. These effects gradually decrease when the market conditions became more bullish markets. In particular, positive changes in the oil volatility index yields a further significant decrease in 12 industrial stock returns during the extreme bearish markets. Moreover, during the bullish markets, negative changes in the oil volatility index have statistically significant negative effects on the 12 industrial stock returns. From the empirical results, we see that participants of the Korean stock market are sensitive to bad news in a recession.

• 사회경제평론
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• v.31 no.2
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• pp.39-69
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• 2018

We measured industrial labor income shares and investigated their determinants. Excluding industries where non-wage earners are not countable, we constructed 22 industry labor income shares from 1993 to 2015. Even though labor income shares in manufacturing industries declined more than in service industries, the economy-wide decline was not driven by structural changes but by within-industry effects. We found that rise in capital-labor ratio, R&D intensity, export dependence, and irregular job ratio contributed to the fall in labor income shares. When we examined manufacturing industries separately, overall results were about the same. But in that case we had additional findings that the rise in import dependence and outsourcing ratio lowered labor's shares, while minimum wage increases raised them.

• Environmental and Resource Economics Review
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• v.31 no.2
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• pp.233-260
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• 2022

In this paper, we analyzed how oil price fluctuations affect stock price by industry using the non-parametric quantile causality test method. We used weekly data of WTI spot price, KOSPI index, and 22 industrial stock indices from January 1998 to April 2021. The empirical results show that the effect of changes in oil prices on the KOSPI index was not significant, which can be attributed to mixed responses of diverse stock prices in several industries included in the KOSPI index. Looking at the stock price response to oil price by industry, the 9 of 18 industries, including Cloth, Paper, and Medicine show a causality with oil prices, while 9 industries, including Food, Chemical, and Non-metal do not show a causal relationship. Four industries including Medicine and Communication (0.45~0.85), Cloth (0.15~0.45), and Construction (0.5~0.6) show causality with oil prices more than three quantiles consecutively. However, the quantiles in which causality appeared were different for each industry. From the result, we find that the effects of oil price on the stock prices differ significantly by industry, and even in one industry, and the response to oil price changes is different depending on the market situation. This suggests that the government's macroeconomic policies, such as industrial and employment policies, should be performed in consideration of the differences in the effects of oil price fluctuations by industry and market conditions. It also shows that investors have to rebalance their portfolio by industry when oil prices fluctuate.

• The Journal of the Korea Contents Association
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• v.2 no.1
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• pp.52-58
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• 2002

The purpose of this study is to investigate validity of integrated information system for materials & parts industry. This purpose is carried out using cost-benefit analysis. In cost analysis, we use a present value of budget of KISTI. In Benefit analysis, we use a present value of investment on information of materials & parts industry. In the result of cost-benefit analysis, us can find that cost-benefit ratio is 4.67.

• Proceedings of the Korean Society of Computer Information Conference
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• 2020.01a
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• pp.255-256
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• 2020

생산 현장에서 발생되는 다양한 형태의 데이터는 스마트한 제조관리를 가능하게 하는 원동력으로 이를 효율적으로 저장하고 처리, 분석하는 일련의 과정이 4차 산업혁명 기반의 제조혁신에 능동적으로 대응하기 위한 핵심요소로서, 이와 관련한 다양한 연구들이 활발히 이루어지고 있다. 특히, 제조데이터 분석이라는 영역은 단순하게 기존의 데이터를 통계적인 접근 수단으로만 보는 것이 아니라 다양한 산업별 업종 도메인의 특성에 기반하여 빅데이터 분석과 기계학습 등의 인공지능 모델로 발전하고 있다. 본 논문에서는 다양한 산업별 제조현장을 이해하는 도메인 경험 및 특성을 고려하여 데이터를 효과적으로 저장, 처리, 분석할 수 있는 클라우드 형태의 빅데이터 시스템을 제안한다.