• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.6
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• pp.25-37
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• 2022

The purpose of this study is to survey startup founders within 7 years of founding the importance and satisfaction of the components of the scale-up entrepreneurial ecosystem at the national level in Korea and analyze the direction of scale-up policy by component using IPA (importance-performance analysis). Since the perception of founders, who are the subjects of the entrepreneurial ecosystem, affects the quantity and quality of start-ups, research is needed to analyze and diagnose the perception of scale-up components. For the development of the national economy and entrepreneurial ecosystem, companies that emerge from startups to scale-up and unicorns must be produced, and for this, elements for the scale-up entrepreneurial ecosystem are needed. The results of this study are as follows. First, the importance ranking of the components of the scale-up entrepreneurial ecosystem recognized by founders was in the order of "Financial support by growth stage," "Support for customized scale-up for enterprises," "Improvement of regulations," "Funds dedicated to scale-up," "large-scale investment," and "nurturing technical talents." Second, the factors that should be intensively improved in the importance-satisfaction matrix in the future were 'Pan-Government Integration Promotion Plan', 'Scale-Up Specialized Organization Operation', 'Company Customized Scale-Up Support', 'Regulatory Improvement', and 'Building a Korean Scale-Up Model'. As a result, various and large financial capital for the scale-up entrepreneurial ecosystem, diversification of scale-up programs by business sector, linkage of start-ups and scale-up support, deregulation of new technologies and new industries, strengthening corporate-tailored scale-up growth capabilities, and providing overseas networking opportunities can be derived. In addition, it is expected to contribute to policy practice and academic work with research that derives the components of the domestic scale-up entrepreneurial ecosystem and diagnoses its perception.

• Journal of Intelligence and Information Systems
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• v.23 no.2
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• pp.123-138
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• 2017

Since the stock market is driven by the expectation of traders, studies have been conducted to predict stock price movements through analysis of various sources of text data. In order to predict stock price movements, research has been conducted not only on the relationship between text data and fluctuations in stock prices, but also on the trading stocks based on news articles and social media responses. Studies that predict the movements of stock prices have also applied classification algorithms with constructing term-document matrix in the same way as other text mining approaches. Because the document contains a lot of words, it is better to select words that contribute more for building a term-document matrix. Based on the frequency of words, words that show too little frequency or importance are removed. It also selects words according to their contribution by measuring the degree to which a word contributes to correctly classifying a document. The basic idea of constructing a term-document matrix was to collect all the documents to be analyzed and to select and use the words that have an influence on the classification. In this study, we analyze the documents for each individual item and select the words that are irrelevant for all categories as neutral words. We extract the words around the selected neutral word and use it to generate the term-document matrix. The neutral word itself starts with the idea that the stock movement is less related to the existence of the neutral words, and that the surrounding words of the neutral word are more likely to affect the stock price movements. And apply it to the algorithm that classifies the stock price fluctuations with the generated term-document matrix. In this study, we firstly removed stop words and selected neutral words for each stock. And we used a method to exclude words that are included in news articles for other stocks among the selected words. Through the online news portal, we collected four months of news articles on the top 10 market cap stocks. We split the news articles into 3 month news data as training data and apply the remaining one month news articles to the model to predict the stock price movements of the next day. We used SVM, Boosting and Random Forest for building models and predicting the movements of stock prices. The stock market opened for four months (2016/02/01 ~ 2016/05/31) for a total of 80 days, using the initial 60 days as a training set and the remaining 20 days as a test set. The proposed word - based algorithm in this study showed better classification performance than the word selection method based on sparsity. This study predicted stock price volatility by collecting and analyzing news articles of the top 10 stocks in market cap. We used the term - document matrix based classification model to estimate the stock price fluctuations and compared the performance of the existing sparse - based word extraction method and the suggested method of removing words from the term - document matrix. The suggested method differs from the word extraction method in that it uses not only the news articles for the corresponding stock but also other news items to determine the words to extract. In other words, it removed not only the words that appeared in all the increase and decrease but also the words that appeared common in the news for other stocks. When the prediction accuracy was compared, the suggested method showed higher accuracy. The limitation of this study is that the stock price prediction was set up to classify the rise and fall, and the experiment was conducted only for the top ten stocks. The 10 stocks used in the experiment do not represent the entire stock market. In addition, it is difficult to show the investment performance because stock price fluctuation and profit rate may be different. Therefore, it is necessary to study the research using more stocks and the yield prediction through trading simulation.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.1-22
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• 2017

Analysis of future business or investment opportunities, such as business feasibility analysis and company or technology valuation, necessitate objective estimation on the relevant market and expected sales. While there are various ways to classify the estimation methods of these new sales or market size, they can be broadly divided into top-down and bottom-up approaches by benchmark references. Both methods, however, require a lot of resources and time. Therefore, we propose a data-based intelligent demand forecasting system to support evaluation of new business. This study focuses on analogical forecasting, one of the traditional quantitative forecasting methods, to develop sales forecasting intelligence systems for new businesses. Instead of simply estimating sales for a few years, we hereby propose a method of estimating the sales of new businesses by using the initial sales and the sales growth rate of similar companies. To demonstrate the appropriateness of this method, it is examined whether the sales performance of recently established companies in the same industry category in Korea can be utilized as a reference variable for the analogical forecasting. In this study, we examined whether the phenomenon of "mean reversion" was observed in the sales of start-up companies in order to identify errors in estimating sales of new businesses based on industry sales growth rate and whether the differences in business environment resulting from the different timing of business launch affects growth rate. We also conducted analyses of variance (ANOVA) and latent growth model (LGM) to identify differences in sales growth rates by industry category. Based on the results, we proposed industry-specific range and linear forecasting models. This study analyzed the sales of only 150,000 start-up companies in Korea in the last 10 years, and identified that the average growth rate of start-ups in Korea is higher than the industry average in the first few years, but it shortly shows the phenomenon of mean-reversion. In addition, although the start-up founding juncture affects the sales growth rate, it is not high significantly and the sales growth rate can be different according to the industry classification. Utilizing both this phenomenon and the performance of start-up companies in relevant industries, we have proposed two models of new business sales based on the sales growth rate. The method proposed in this study makes it possible to objectively and quickly estimate the sales of new business by industry, and it is expected to provide reference information to judge whether sales estimated by other methods (top-down/bottom-up approach) pass the bounds from ordinary cases in relevant industry. In particular, the results of this study can be practically used as useful reference information for business feasibility analysis or technical valuation for entering new business. When using the existing top-down method, it can be used to set the range of market size or market share. As well, when using the bottom-up method, the estimation period may be set in accordance of the mean reverting period information for the growth rate. The two models proposed in this study will enable rapid and objective sales estimation of new businesses, and are expected to improve the efficiency of business feasibility analysis and technology valuation process by developing intelligent information system. In academic perspectives, it is a very important discovery that the phenomenon of 'mean reversion' is found among start-up companies out of general small-and-medium enterprises (SMEs) as well as stable companies such as listed companies. In particular, there exists the significance of this study in that over the large-scale data the mean reverting phenomenon of the start-up firms' sales growth rate is different from that of the listed companies, and that there is a difference in each industry. If a linear model, which is useful for estimating the sales of a specific company, is highly likely to be utilized in practical aspects, it can be explained that the range model, which can be used for the estimation method of the sales of the unspecified firms, is highly likely to be used in political aspects. It implies that when analyzing the business activities and performance of a specific industry group or enterprise group there is political usability in that the range model enables to provide references and compare them by data based start-up sales forecasting system.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.231-252
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• 2021

Artificial intelligence (AI) is a key technology that will change the future the most. It affects the industry as a whole and daily life in various ways. As data availability increases, artificial intelligence finds an optimal solution and infers/predicts through self-learning. Research and investment related to automation that discovers and solves problems on its own are ongoing continuously. Automation of artificial intelligence has benefits such as cost reduction, minimization of human intervention and the difference of human capability. However, there are side effects, such as limiting the artificial intelligence's autonomy and erroneous results due to algorithmic bias. In the labor market, it raises the fear of job replacement. Prior studies on the utilization of artificial intelligence have shown that individuals do not necessarily use the information (or advice) it provides. Algorithm error is more sensitive than human error; so, people avoid algorithms after seeing errors, which is called "algorithm aversion." Recently, artificial intelligence has begun to be understood from the perspective of the augmentation of human intelligence. We have started to be interested in Human-AI collaboration rather than AI alone without human. A study of 1500 companies in various industries found that human-AI collaboration outperformed AI alone. In the medicine area, pathologist-deep learning collaboration dropped the pathologist cancer diagnosis error rate by 85%. Leading AI companies, such as IBM and Microsoft, are starting to adopt the direction of AI as augmented intelligence. Human-AI collaboration is emphasized in the decision-making process, because artificial intelligence is superior in analysis ability based on information. Intuition is a unique human capability so that human-AI collaboration can make optimal decisions. In an environment where change is getting faster and uncertainty increases, the need for artificial intelligence in decision-making will increase. In addition, active discussions are expected on approaches that utilize artificial intelligence for rational decision-making. This study investigates the impact of artificial intelligence on decision-making focuses on human-AI collaboration and the interaction between the decision maker personal traits and advisor type. The advisors were classified into three types: human, artificial intelligence, and human-AI collaboration. We investigated perceived usefulness of advice and the utilization of advice in decision making and whether the decision-maker's personal traits are influencing factors. Three hundred and eleven adult male and female experimenters conducted a task that predicts the age of faces in photos and the results showed that the advisor type does not directly affect the utilization of advice. The decision-maker utilizes it only when they believed advice can improve prediction performance. In the case of human-AI collaboration, decision-makers higher evaluated the perceived usefulness of advice, regardless of the decision maker's personal traits and the advice was more actively utilized. If the type of advisor was artificial intelligence alone, decision-makers who scored high in conscientiousness, high in extroversion, or low in neuroticism, high evaluated the perceived usefulness of the advice so they utilized advice actively. This study has academic significance in that it focuses on human-AI collaboration that the recent growing interest in artificial intelligence roles. It has expanded the relevant research area by considering the role of artificial intelligence as an advisor of decision-making and judgment research, and in aspects of practical significance, suggested views that companies should consider in order to enhance AI capability. To improve the effectiveness of AI-based systems, companies not only must introduce high-performance systems, but also need employees who properly understand digital information presented by AI, and can add non-digital information to make decisions. Moreover, to increase utilization in AI-based systems, task-oriented competencies, such as analytical skills and information technology capabilities, are important. in addition, it is expected that greater performance will be achieved if employee's personal traits are considered.

• 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.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.3 no.4
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• pp.1-41
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• 2008

The Korean semiconductor industry has made a great contribution to growth of Korean economy for the last decades by maintaining a top position in terms of Korean total annual export volume. However, the advanced semiconductor equipment and materials that are used for the production of semiconductor devices still depend on the suppliers from Europe, Japan, and America who have an influential position in the Korean semiconductor industry. The objective of this study is to empirically investigate the attributes determining the extent of autonomy in decision making for the Korean subsidiaries of multinational corporations in the semiconductor industry. This study found there were differences in the extent of autonomy in decision making in terms of the global strategies the multinational corporations pursue. This study surveyed employees at the Korean subsidiaries and joint venture companies of semiconductor multinational corporations and collected 726 survey questionnaires. Several statistical analyses including frequency analysis, reliability analysis, factor analysis, multiple regression analysis and ANOVA were performed using the collected sample data. Based on the analyses, this study found as follows: Firstly, from the factor analysis, this study found Korean subsidiaries faced three sources of uncertainties stemmed from political conditions, competent conditions, demand and supply conditions. The internal resources were characterized by the independencies of production capability, financial capability, marketing capability and human resource management capability. The operational performance was determined by total revenue, net profit and market share growth. Secondly, it was found the uncertainties from political condition and competent condition and the independencies of financial capability and marketing capability partially influenced the extent of autonomy in decision making. The independencies of production capability and human resource management capability significantly influenced the autonomy of decision making in the most areas. It was also found an increase of total revenue, net profit and market share growth partially affected the extent of autonomy in decision making of the Korean subsidiaries. Finally, it was found that the polycentrism of global management by multinational corporations seemed to bring a higher extent of autonomy in decision making than ethnocentrism or geocentrism of global management. Based on the results, this study provided managerial implications regarding the extent of autonomy in decision making for Korean subsidiaries of multinational corporations in order to help management to enhance their business capabilities.

• 한국벤처창업학회:학술대회논문집
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• 2008.11a
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• pp.135-168
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• 2008

The Korean semiconductor industry has made a great contribution to growth of Korean economy for the last decades by maintaining a top position in terms of Korean total annual export volume. However, the advanced semiconductor equipment and materials that are used for the production of semiconductor devices still depend on the suppliers from Europe, Japan, and America who have an influential position in the Korean semiconductor industry. The objective of this study is to empirically investigate the attributes determining the extent of autonomy in decision making for the Korean subsidiaries of multinational corporations in the semiconductor industry. This study found there were differences in the extent of autonomy in decision making in terms of the global strategies the multinational corporations pursue. This study surveyed employees at the Korean subsidiaries and joint venture companies of semiconductor multinational corporations and collected 726 survey questionnaires. Several statistical analyses including frequency analysis, reliability analysis, factor analysis, multiple regression analysis and ANOVA were performed using the collected sample data. Based on the analyses, this study found as follow: Firstly, from the factor analysis, this study found Korean subsidiaries faced three sources of uncertainties stemmed from political conditions, competent conditions, demand and supply conditions. The internal resources were characterized by the independencies of production capability, financial capability, marketing capability and human resource management capability. The operational performance was determined by total revenue, net profit and market share growth. Secondly, it was found the uncertainties from political condition and competent condition and the independencies of financial capability and marketing capability partially influenced the extent of autonomy in decision making. The independencies of production capability and human resource management capability significantly influenced the autonomy of decision making in the most areas. It was also found an increase of total revenue, net profit and market share growth partially affected the extent of autonomy in decision making of the Korean subsidiaries. Finally, it was found that the polycentrism of global management by multinational corporations seemed to bring a higher extent of autonomy in decision making than ethnocentrism or geocentrism of global management. Based on the results, this study provided managerial implications regarding the extent of autonomy in decision making for Korean subsidiaries of multinational corporations in order to help management to enhance their business capabilities.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.12
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• pp.803-812
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• 2014

This study assesses the strategy and performance of Eco-industrial Park (EIP) initiative implemented by Korea Industrial Complex Corporation (KICOX) with the support of Ministry of Trade, Industry and Energy (MOTIE), Korea since 2005 to 2013 and recommends future directions. After the concept of EIP based on industrial symbiosis (IS) is introduced, the background and implementation procedure of the EIP initiative are described. Then, economic and environmental achievement was assessed. During the project periods (2005-2013), 449 industrial symbiosis project were explored, among which 296 projects have been implemented. Among (Of these 296 projects,) them, 244 projects have been completed in which 118 projects have been commercialized which shows 48% commercialization rate of the completed projects. Through these commercialized projects, around 311.1 billion won/year of economic benefits and reduction of waste by-products of 828,113 tons/year, wastewater of 215,517 tons/year, reduction in energy consumption of 250,475 toe/year and GHG emission reduction of 1,107,189 $tCO_2/year$ were achieved. This results confirmed that EIP initiative based on industrial symbiosis can enhance eco-efficiency of industrial parks and harmonize economy and environment. However, there are obstacles like absence of interagency coordination and cooperation, laws and institutional barriers, increased demand for local governments, funding for project investment. Thus, to utilize EIP initiative as a strategic tool for competiveness and environmental management of industrial parks, it needs intergovernmental collaboration and interdisciplinary approach to lower barrier in implementation.

• Journal of the Society of Disaster Information
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• v.16 no.1
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• pp.163-177
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• 2020

Purpose: While it is very important to maintain facilities recently, the introduction and its application of high technology in the facility maintenance industry has increased. It is necessary for high technology to secure reliability through the verification and certification system of diagnostic equipment to have an effective impact in the field, but there is difference between the industry's perspective and realistic level of technology apart from social demand for the system of the system. This paper dealt with the introduction of a verification and certification system for rational facility diagnostic equipment with the opinion survey on managers about the current situation. Method: Survey is carried out targeting managers in the maintenance and construction regarding the necessity and urgency of introducing a verification and certification system to promote the introduction and its application of high technology of diagnostic equipment and facility inspection. Also, the introduction to a verification and certification system was reviewed for advanced facility diagnostic equipment through foreign research about similar systems and comparative analysis of similar systems related to the certification of 21 domestic equipment. Result: It showed that, regarding the application of high technology, it is necessary for most managers to introduce high technology such as drones, robots, etc., in the maintenance industry, and when high technology is introduced, it will have a considerable effect in the field. On the other hand, the current technology level in Korea is relatively low, so it turned out to take a certain amount of time for the application of technology. Also, it was found that the management of reliable facility diagnostic equipment will be possible through the introduction of the verification and certification system for facility diagnosis equipment. Meanwhile, the survey is conducted on similar systems about foreign and domestic diagnosis and measuring equipment, etc., but there is no system to verify and certify equipment applied with high technology directly to facility diagnosis maintenance. However, because Japan has a system of verifying the performance of diagnostic equipment and South Korea has 21 similar inspection and diagnostic equipment certification systems among 186 certification systems, it is considered to be possible to design systems which utilize them. Conclusion: According to the managers' opinion, it seems that the introduction of the system supporting the application of 4th industrial technology for the equipment and the use of the equipment with high reliability has sufficient validity. However, because our high technology level is undervalued compared to the urgency, the system for checking high technology facilities and certifying diagnostic equipment should be to be implemented in form of escalation considering technical use and verification level. Apart from the introduction of the verification and certification system, it is necessary for special investment, support and efforts to promote advanced facility diagnostic equipment.

• Archives of design research
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• v.19 no.4 s.66
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• pp.163-174
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• 2006

The purpose this study is to discuss effects of product design innovations on small and medium enterprises' business accomplishments and further on raising those enterprises' international competitiveness through reviewing previous studies that quantitatively analyzed economic and technological performance and ripple effects of products developed through design innovations. To determine how much design innovations are influential and contributing to small and medium enterprises' international competitiveness, then, the researcher took most advantage of statistical data from quantitative analyses of business accomplishments brought by design innovation development and investment, or economic effects like sales and exports increase. Results of the study can be summarized as follows. First, product design innovations by small and medium enterprises directly contribute to creating plenty of technological and economical achievements, for example, improved product quality, increased product profitability, the effect of product differentiation, improved price competitiveness and increased business sales and exports. Second, technological and economic achievements brought by product design innovations can directly lead to ripple effects like accumulating related knowledge and know-hows, strengthening the competitiveness of products, improving corporate image, increasing business sales and net profit, and meeting many different consumer requirements. Third, technological and economic achievements and ripple effects brought by product design innovations all become very important factors and sources on which small and medium enterprises strengthen their international competitiveness in world markets and maintain their sustainable competitive advantage. Fourth, business accomplishments or economic effects brought by design innovations can be quantitatively measured and analyzed with statistical data. Additional data from the moves can help understand and express the very value or nature of design in a quantitative way. This study is significant in that its results was made based on statistical data from empirical, objective measurements and quantification. The researcher hopes that the study contributes to promoting design innovations by small and medium enterprises and helps CEOs of those businesses better understand the very value and nature of design.