• The Journal of Society for e-Business Studies
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• v.26 no.2
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• pp.45-59
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• 2021

As software(SW) has been considered as a key driver of the fourth industrial revolution, significant R&D investment has been made by Korean government. Despite the attention and support by the government, systematic analysis on the SW R&D efficiency has not been fully addressed. In this study, the efficiency of SW national research and development projects was analyzed using Data Envelopment Analysis(DEA) techniques. Efficiency was measured from both static and dynamic perspectives based on 1,463 projects conducted by the National IT Industry Promotion Agency(NIPA) from 2008 to 2018. The static efficiency analysis identified the causes of inefficiency as scale and technology problems. As a result of dynamic efficiency analysis, we present a sector-specific response model using an efficiency-stability matrix. This study is meaningful in that efficiency analysis was conducted on the entire SW national R&D project, and static/dynamic efficiency analysis results are expected to be used as a guideline for planning SW national R&D project.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.3
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• pp.1-15
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• 2023

Impact investment involves investing in companies that pursue both social value and financial returns. It focuses on addressing various social problems through innovative solutions while generating profits. The domestic impact investment ecosystem has experienced significant growth with the support of the government and public institutions. In 2021, it witnessed a 3.5-fold increase over three years, reaching a total of 700 billion won in operating assets. In order to foster qualitative growth alongside this quantitative expansion, it is crucial to conduct research specifically on impact investment, which sets it apart from conventional venture investment. This study aims to empirically analyze the unique factors that influence impact investment decisions. Firstly, the factors affecting investment decisions were identified through a literature analysis. Then, a consultation and Delphi survey involving 11 representatives and evaluators from impact investment companies was conducted to determine the major investment determinants. Subsequently, an AHP (Analytic Hierarchy Process) survey was carried out with 10 impact investment evaluators to ascertain the relative importance of these factors. The analysis revealed the following order of importance for the top factors: market>entrepreneur(team)>product/service>finance. Furthermore, the importance of specific factors was identified in the following order: market competition and entry barriers>new market creation>market growth and potential expansion>team expertise and capabilities. Unlike previous studies that primarily focus on general startup investment factors, this research demonstrates that impact investment places greater emphasis on market-related factors and considers the sustainability and profitability of the business model to be more important than the social impact of social ventures.

• Korean Journal of Construction Engineering and Management
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• v.14 no.4
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• pp.118-129
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• 2013

Due to the recent surge in natural disasters, with rising social concerns, investment on research & development (R&D) project in natural disaster mitigation area is continuously increasing. However, current R&D performance management system of natural disaster mitigation area does not properly reflect characteristics and social ripple effects of R&D projects. It is indicated that mainly due to the current performance index consists of easy-quantification factors and the performance evaluation system adopts simple aggregation method. Therefore, the purpose of this study is to develop new performance evaluation system considering characteristics and social ripple effects of natural disaster mitigation R&D projects. To this ends, this study derived 3-dimensional performance measurement index through literature review and natural disaster mitigation R&D projects analysis. Finally, based on the derived performance measurement index, this study suggested new and effective performance evaluation system for natural disaster mitigation R&D projects.

• Proceedings of the Korean Institute Of Construction Engineering and Management
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• 2004.11a
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• pp.414-418
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• 2004

Quality system of construction industry in Korea was a means to prevent unqualified construction work. But lately, it has became an essential requirement. So, lots of construction companies acquire ISO quality management system which provides fundamental management criteria. Now, most of the Korean construction companies have got IS0 quality management system. So, getting ISO quality management system doesn't mean advantage in public competition of construction bidding and that causes increase in the maintenance cost. That's why some construction companies abandon the 150 certificate. Therefore, the authors need to check the effect of ISO quality management system. For this, the authors provide the performance measurement index of ISO quality management system.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.4
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• pp.1-20
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• 2023

This study attempted to identify the relationship between the investment determinants of accelerators and investment performance through empirical analysis. Through literature review, four dimensions and 12 measurement items were extracted for investment determinants, which are independent variables, and investment performance was adjusted to the cumulative amount of subsequent investment based on previous studies. Performance data from 594 companies selected by TIPS from 2017 to 2019, which are relatively reliable and easy to secure data, were collected, and the subsequent investment cumulative attraction amount, which is a dependent variable, was hypothesized through multiple regression analysis three years after the investment. As a result of the study, 'industrial experience years' in the characteristics of founders, 'market size', 'market growth', 'competitive strength', and 'number of patents' in the characteristics of products and services had a significant positive (+) effect. The impact of independent variables on dependent variables was most influenced by the competitive strength of market characteristics, followed by the number of years of industrial experience, the number of patents, the size of the market, and market growth. This was different from the results of previous studies conducted mainly on qualitative research methods, and in most previous studies, the characteristics of founders were the most important, but the empirical analysis results were market characteristics. As a sub-factor, the intensity of competition, which was the subordinate to the importance of previous studies, had the greatest influence in empirical analysis. The academic significance of this study is that it presented a specific methodology to collect and build 594 empirical samples in the absence of empirical research on accelerator investment determinants, and created an opportunity to expand the theoretical discussion of investment determinants through causal research. In practice, the information asymmetry and uncertainty of startups that accelerators have can help them make effective investment decisions by establishing a systematic model of experience-dependent investment determinants.

• Journal of Digital Convergence
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• v.19 no.10
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• pp.339-348
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• 2021

This paper aims to analyze the production efficiency in Korea's ten service sectors using DEA and its determinants utilizing a truncated-Tobit regression model and a censored-Tobit regression model in 2010-2019. This paper found: First, the Korean service sector's production efficiency in general has been significantly low and polarized. Especially, the inefficiency resulted from the scale inefficiency in the 'sewerage waste management industry.' Second, in the determinants analysis, the results show the positive effect of the investment and R&D expenses on technical efficiency, while FDI and lobbying expenses illustrate the negative impact. Moreover, it seems that the larger the industry, the higher the efficiency. Thus, the future Korean government's economic policy for the service sectors requires a mixed and integrated policy of the macroeconomic aspect such as active investment and R&D activities with microeconomic aspect including a convergence of FDI and human capital.

• Industry Promotion Research
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• v.7 no.2
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• pp.1-13
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• 2022

This study of purpose is to examine the efficiency through the current status and performance of each institution providing the Daegu community service investment project, and to find a direction for the efficient operation of each institution. In order to promote efficient and effective project promotion by checking the management status that meets the purpose of the local community service investment project, to provide systematic services necessary to consumers, improve quality, and improve the environment, the accurate situation and situation through the efficiency analysis of the service provider organization It is necessary to identify problems, develop programs suitable for regional characteristics, and actively manage services. Specifically, it seeks to provide a more stable and sustainable local community service by measuring efficiency based on information from each institution and comparing and analyzing it among local governments. For this purpose, it is meaningful in analyzing the efficiency of each institution by using the DEA (data envelope analysis) method and presenting the goals and policy implications for efficient operation by comparing them by local government.

• International Area Studies Review
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• v.22 no.4
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• pp.37-54
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• 2018

This study examines empirically on relevance between bank performance and IC(Intellectual Capital) in the field of banking sector in Korea. IC is measured by VAIC(Value Added Intellectual Capital) and VAIC consists of HCE(Human Capital Efficiency Coefficient), CEE(Capital Employed Efficiency Coefficient) and SCE(Structural Capital Efficiency Coefficient). Main results are as follows. First, the effects of IC(Intellectual capital) on banks performance show significant (+) effect on the performance of banks. Second, Human capital and bank size shows the significant effect on the banks financial performance but SCE, CEE, and other variables didn't show it. As a concluding remark, IC(Intellectual capital) is very helpful for banks to go forward financially to get information and knowledges easily. This study help stakeholders and investors assess the value creating potential of banks and policy makers to implement policies for performance establishment of a Korean banking sector.

• Information Systems Review
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• v.10 no.1
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• pp.135-164
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• 2008

This research is to offer a structured yet practical ex-ante evaluation methodology for IT investment. Benchmarking the best practices of four Korean organizations, we try to integrate core processes, relevant measures, and evaluation dimensions into a consistent and wholesome body of evaluating methodology. The best practices we considered encompass a wide range of business enterprises, including for-profit, non-profit, service-oriented, and manufacturing entities. The proposed methodology consists of three stages; the first stage checks the validity of investments by looking into comprehensiveness of planning, willingness to accomplish, justifiable grounds for the investments, overlapping investments, and obstructing risks; the second do so by putting an IT investment into economic, strategic, and technological perspectives; and the last third would produce a unified quantity that summarizes outcome of the previous stages. Incorporating the proven knowledge, guidelines, and quantifying tools, the methodology could make a valuable reference model for IT evaluation practitioners who have been bedeviled by having to going through such ex-ante evaluations.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.107-122
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• 2017

Volatility in the stock market returns is a measure of investment risk. It plays a central role in portfolio optimization, asset pricing and risk management as well as most theoretical financial models. Engle(1982) presented a pioneering paper on the stock market volatility that explains the time-variant characteristics embedded in the stock market return volatility. His model, Autoregressive Conditional Heteroscedasticity (ARCH), was generalized by Bollerslev(1986) as GARCH models. Empirical studies have shown that GARCH models describes well the fat-tailed return distributions and volatility clustering phenomenon appearing in stock prices. The parameters of the GARCH models are generally estimated by the maximum likelihood estimation (MLE) based on the standard normal density. But, since 1987 Black Monday, the stock market prices have become very complex and shown a lot of noisy terms. Recent studies start to apply artificial intelligent approach in estimating the GARCH parameters as a substitute for the MLE. The paper presents SVR-based GARCH process and compares with MLE-based GARCH process to estimate the parameters of GARCH models which are known to well forecast stock market volatility. Kernel functions used in SVR estimation process are linear, polynomial and radial. We analyzed the suggested models with KOSPI 200 Index. This index is constituted by 200 blue chip stocks listed in the Korea Exchange. We sampled KOSPI 200 daily closing values from 2010 to 2015. Sample observations are 1487 days. We used 1187 days to train the suggested GARCH models and the remaining 300 days were used as testing data. First, symmetric and asymmetric GARCH models are estimated by MLE. We forecasted KOSPI 200 Index return volatility and the statistical metric MSE shows better results for the asymmetric GARCH models such as E-GARCH or GJR-GARCH. This is consistent with the documented non-normal return distribution characteristics with fat-tail and leptokurtosis. Compared with MLE estimation process, SVR-based GARCH models outperform the MLE methodology in KOSPI 200 Index return volatility forecasting. Polynomial kernel function shows exceptionally lower forecasting accuracy. We suggested Intelligent Volatility Trading System (IVTS) that utilizes the forecasted volatility results. IVTS entry rules are as follows. If forecasted tomorrow volatility will increase then buy volatility today. If forecasted tomorrow volatility will decrease then sell volatility today. If forecasted volatility direction does not change we hold the existing buy or sell positions. IVTS is assumed to buy and sell historical volatility values. This is somewhat unreal because we cannot trade historical volatility values themselves. But our simulation results are meaningful since the Korea Exchange introduced volatility futures contract that traders can trade since November 2014. The trading systems with SVR-based GARCH models show higher returns than MLE-based GARCH in the testing period. And trading profitable percentages of MLE-based GARCH IVTS models range from 47.5% to 50.0%, trading profitable percentages of SVR-based GARCH IVTS models range from 51.8% to 59.7%. MLE-based symmetric S-GARCH shows +150.2% return and SVR-based symmetric S-GARCH shows +526.4% return. MLE-based asymmetric E-GARCH shows -72% return and SVR-based asymmetric E-GARCH shows +245.6% return. MLE-based asymmetric GJR-GARCH shows -98.7% return and SVR-based asymmetric GJR-GARCH shows +126.3% return. Linear kernel function shows higher trading returns than radial kernel function. Best performance of SVR-based IVTS is +526.4% and that of MLE-based IVTS is +150.2%. SVR-based GARCH IVTS shows higher trading frequency. This study has some limitations. Our models are solely based on SVR. Other artificial intelligence models are needed to search for better performance. We do not consider costs incurred in the trading process including brokerage commissions and slippage costs. IVTS trading performance is unreal since we use historical volatility values as trading objects. The exact forecasting of stock market volatility is essential in the real trading as well as asset pricing models. Further studies on other machine learning-based GARCH models can give better information for the stock market investors.