• Asia pacific journal of information systems
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• v.19 no.1
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• pp.113-145
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• 2009

Recently, due to the globalization of the IT(Information Technology) market, devices and systems designed in one country are used in other countries as well. This phenomenon is becoming the key factor for increased interest on cross-cultural, or cross-national, research within the IT area. However, as the IT market is becoming bigger and more globalized, a great number of IT practitioners are having difficulty in designing and developing devices or systems which can provide optimal experience. This is because not only tangible factors such as language and a country's economic or industrial power affect the user experience of a certain device or system but also invisible and intangible factors as well. Among such invisible and intangible factors, the cultural characteristics of users from different countries may affect the user experience of certain devices or systems because cultural characteristics affect how they understand and interpret the devices or systems. In other words, when users evaluate the quality of overall user experience, the cultural characteristics of each user act as a perceptual lens that leads the user to focus on a certain elements of experience. Therefore, there is a need within the IT field to consider cultural characteristics when designing or developing certain devices or systems and plan a strategy for localization. In such an environment, existing IS studies identify the culture with the country, emphasize the importance of culture in a national level perspective, and hypothesize that users within the same country have same cultural characteristics. Under such assumptions, these studies focus on the moderating effects of cultural characteristics on a national level within a certain theoretical framework. This has already been suggested by cross-cultural studies conducted by scholars such as Hofstede(1980) in providing numerical research results and measurement items for cultural characteristics and using such results or items as they increase the efficiency of studies. However, such national level culture has its limitations in forecasting and explaining individual-level behaviors such as voluntary device or system usage. This is because individual cultural characteristics are the outcome of not only the national culture but also the culture of a race, company, local area, family, and other groups that are formulated through interaction within the group. Therefore, national or nationally dominant cultural characteristics may have its limitations in forecasting and explaining the cultural characteristics of an individual. Moreover, past studies in psychology suggest a possibility that there exist different cultural characteristics within a single individual depending on the subject being measured or its context. For example, in relation to individual vs. collective characteristics, which is one of the major cultural characteristics, an individual may show collectivistic characteristics when he or she is with family or friends but show individualistic characteristics in his or her workplace. Therefore, this study acknowledged such limitations of past studies and conducted a research within the framework of 'theoretically integrated model of user satisfaction and emotional attachment', which was developed through a former study, on how the effects of different experience elements on emotional attachment or user satisfaction are differentiated depending on the individual cultural characteristics related to a system or device usage. In order to do this, this study hypothesized the moderating effects of four cultural dimensions (uncertainty avoidance, individualism vs, collectivism, masculinity vs. femininity, and power distance) as suggested by Hofstede(1980) within the theoretically integrated model of emotional attachment and user satisfaction. Statistical tests were then implemented on these moderating effects through conducting surveys with users of four digital devices (mobile phone, MP3 player, LCD TV, and refrigerator) in three countries (US, Germany, and Russia). In order to explain and forecast the behavior of personal device or system users, individual cultural characteristics must be measured, and depending on the target device or system, measurements must be measured independently. Through this suggestion, this study hopes to provide new and useful perspectives for future IS research.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.4 no.3
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• pp.41-50
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• 2001

Recently our nature of environment has destroyed by a large scaled forest fire. In order to manage these forest fires, forecasting of it is considered as the most important thing. In this paper the database related to forest fire was first built and the efficient forest fire information management system was implemented by using GIS. The main goal of this system is that forest fire managers have GUI(graphic user interface) to analyze data of forest fire effectively and update and retrieve information in database. For the efficient GUI, this system is built in Visual Basic 6.0 and Map Object 2.0. Map Object 2.0 is combined to have various and powerful functionality of GIS analysis as component ware. The Oracle 8.0 is used as DBMS in this study to manage all the spatial and attributed information in database effectively. In the future, this system will play a critical role as making a decision supporting system for scientific forest fire protection and help real time forest fire hazard information offers service for public welfare administration business management.

• The Journal of Industrial Distribution & Business
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• v.11 no.8
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• pp.31-38
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• 2020

Purpose: This study uses 'Autoregressive Integrated Moving Average Model' to predict the impact of a sharp drop in the base rate due to COVID-19 at the present time when government policies for stabilizing house prices are in progress. The purpose of this study is to predict implications for the direction of the government's house policy by predicting changes in house transaction prices and house rental prices after a sharp cut in the base rate. Research design, data, and methodology: The ARIMA intervention model can build a model without additional information with just one time series. Therefore, it is a time-series analysis method frequently used for short-term prediction. After the subprime mortgage, which had shocked since the global financial crisis in April 2007, the bank's interest rate in 2020 is set at a time point close to zero at 0.75%. After that, the model was estimated using the interest rate fluctuations for the Bank of Korea base interest rate, the house transaction price index, and the house rental price index as event variables. Results: In predicting the change in house transaction price due to interest rate intervention, the house transaction price index due to the fall in interest rates was predicted to change after 3 months. As a result, it was 102.47 in April 2020, 102.87 in May 2020, and 103.21 in June 2020. It was expected to rise in the short term. In forecasting the change in house rental price due to interest rate intervention, the house rental price index due to the drop in interest rate was predicted to change after 3 months. As a result, it was 97.76 in April 2020, 97.85 in May 2020, and 97.97 in June 2020. It was expected to rise in the short term. Conclusions: If low interest rates continue to stimulate the contracted economy caused by COVID-19, it seems that there is ample room for house transaction and rental prices to rise amid low growth. Therefore, In order to stabilize the house price due to the low interest rate situation, it is considered that additional measures are needed to suppress speculative demand.

• Proceedings of the Korea Society for Simulation Conference
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• 2001.10a
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• pp.170-202
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• 2001

Simulation is used to reduce a risk involved in the new project and decision-making in an organization and to save cost and time by forecasting different situations. The objectives of this research are to acknowledge the need of simulation through the real life sample and to encourage the use of the simulation method in the future consulting project by continuously making the necessary improvements. This research analyzed the effectiveness of the simulation based on the sample use of simulation method in 1994 and 1997 for the BPR project of certification issuance process at the Supreme Court. In order to evaluate the value of the proposed simulation model, we examined the gap, which existed between the simulation result and the operational data collected by visiting the actual sites where AROS (Automated Registry Office System: automation system developed by LG-EDS Systems) is being utilized. We also identified the causes for the existing gap. According to the analysis result, (1) the gap came from the status change of thinking that the concentration of certification issuance request has eased after the computerization, (2) the gap existed in the operational process because they failed to consider the situational factors of each registry office in the simulation model, and (3) lastly the gap came from the difficulty of formulating the mathematical model for predicting the complex and diverse behavior pattern of individuals requesting the certification issuance. In order to narrow the existing gaps, we made a proposal to improve the certification issuance process where software of certification issuance vending machine was upgraded in order to help the people to use the service conveniently, more part time workers were hared when there was a overload of certification issuance request, and the quality of the certification Issuance vending machine is improved, In this research, we examined an efficient way of resource allocation based on the simulation conducted in 1994 and 1997. By reflecting changes since the simulation of 1994 and allocating the clerk and machine based on the predicted results of the simulation, we maximized the efficiency of the certification issuance process. In conclusion, this research examined the future usability of simulation method based on the analysis result and identified the key issues to consider when using the simulation method in the future consulting project.

• The Journal of Sustainable Design and Educational Environment Research
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• v.15 no.2
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• pp.13-22
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• 2016

The purpose of this study is to provide a basis for determining when a school established long term school plans accepted by analyzing the trend of increase or decrease elementary school students 30 years to target of Daejeon Metropolitan City. Most were analyzed for land development area of Yuseong-Gu, and most of the old downtown area of Dong-Gu for this purpose. Results of this study are as follows: First, the results of this research to analyze the number of students increase or decrease trend forecasting model specific case in residential development projects in the district are Model8 4 schools (11.8%) in Yuseong-Gu were compared higher than one schools (4.3%) in the Dong-Gu. This suggests the need for a cautious approach in the planning of schools accommodated in a large housing development district. Second, large-scale land development trend analysis results Students sensitized Yuseong-Gu business district is followed by a new school in 11 schools (29.7%), Old town in Dong-Gu, which is contrary 13 schools (56.5%) were in a downturn.

• Journal of Service Research and Studies
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• v.7 no.3
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• pp.57-70
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• 2017

It is anticipated that the employment structure of the whole industry will change drastically as the Fourth Industrial Revolution era arrives. Particularly, there are numerous reseraches that the development of artifical intelligence will promote automation causing jobs in manufacturing industry to decrease; thus, the economy will be reorganized with service-centered jobs, which heavily depend on human ability. This study was conducted to verify the trend-forecasting model based on the theoretical analysis. We analyzed the change in employment structure over the past decades in each country and period to gain insights from the changes in the employment structure caused by the Fourth Industrial Revoltion. The results of this study are as follows: First, we investigaed whether the current economy is moving along the U-shaped model suggested by an existing researcher. As a result of the analysis, the data substantiated that the change of the employment structure is moving along the U-shaped model. It is also suggested that this U-shaped trend is expected to accelerate in the era of the Fourth Industrial Revolution. In the future, more accurate data analyses are needed to verify the model, and additional researches on the change in the employment structed is also needed.

• Journal of Distribution Science
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• v.11 no.10
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• pp.63-72
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• 2013

Purpose - This study analyzes the relationship and dynamic interactions between stock price index, interest rate, price index, and housing price indices using Korean monthly data from 2000 to 2013, based on a VAR model. This study also examines Granger causal relationships among these variables in order to determine whether the time series of one is useful in forecasting another, or to infer certain types of causal dependency between stochastic variables. Research design, data, and methodology - We used Korean monthly data for all variables from 2000: M1 to 2013: M3. First, we checked the correlations among different variables. Second, we conducted the Augmented Dickey-Fuller (ADF) test and the co-integration test using the VAR model. Third, we employed Granger Causality tests to quantify the causal effect from time series observations. Fourth, we used the impulse response function and variance decomposition based on the VAR model to examine the dynamic relationships among the variables. Results - First, stock price Granger affects interest rate and all housing price indices. Price index Granger, in turn, affects the stock price and six metropolitan housing price indices. However, none of the Granger variables affect the price index. Therefore, it is the stock markets (and not the housing market) that affects the housing prices. Second, the impulse response tests show that maximum influence on stock price is its own, and though it is influenced a little by interest rate, price index affects it negatively. One standard deviation (S.D.) shock to stock price increases the housing price by 0.08 units after two months, whereas an impulse shock to the interest rate negatively impacts the housing price. Third, the variance decomposition results report that the shock to the stock price accounts for 96% of the variation in the stock price, and the shock to the price index accounts for 2.8% after two periods. In contrast, the shock to the interest rate accounts for 80% of the variation in the interest rate after ten periods; the shock to the stock price accounts for 19% of the variation; however, shock to the price index does not affect the interest rate. The housing price index in 10 periods is explained up to 96.7% by itself, 2.62% by stock price, 0.68% by price index, and 0.04% by interest rate. Therefore, the housing market is explained most by its own variation, whereas the interest rate has little impact on housing price. Conclusions - The results of the study elucidate the relationship and dynamic interactions among stock price index, interest rate, price index, and housing price indices using VAR model. This study could help form the basis for more appropriate economic policies in the future. As the housing market is very important in Korean economy, any changes in house price affect the other markets, thereby resulting in a shock to the entire economy. Therefore, the analysis on the dynamic relationships between the housing market and economic variables will help with the decision making regarding the housing market policy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.13 no.5
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• pp.2045-2053
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• 2012

The primary purpose of this paper is to identify the cognition of the convergence human resource for firms. For this purpose, Data have been collected from 110 firms in Daejeon city and studied the convergence human resource which recognized by firms on the company competitive power level. The results of this empirical studies are summarized as fellows. We classified in company competitive power as leading chaser, innovators and late chaser. In the talent, leading chasers demanded the total designer, problem solver and insighter, innovators demanded the total designer, problem solver and specialist, and late chasers demanded the traditional humanity. In technology management, all group think important fields. In the demanding forecasting for the market and technologies, leading chasers and innovators demanded the CEO, R&D researcher and a middle manager as the important position. For this education, they demanded the convergence ability of other technologies, the up-to-date abilities of product and technology and the understandings of market needs. In the convergence talent or leader, leader chaser and innovator demanded the specialist who attempted to combine others industries, who know well in technology and management, and the specialist who attempted to combine others technologies. But, late chaser demanded the specialist who attempted to combine others industries.

• Journal of the Korea Society of Computer and Information
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• v.25 no.5
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• pp.47-56
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• 2020

The stock index is used not only as an economic indicator for a country, but also as an indicator for investment judgment, which is why research into predicting the stock index is ongoing. The task of predicting the stock price index involves technical, basic, and psychological factors, and it is also necessary to consider complex factors for prediction accuracy. Therefore, it is necessary to study the model for predicting the stock price index by selecting and reflecting technical and auxiliary factors that affect the fluctuation of the stock price according to the stock price. Most of the existing studies related to this are forecasting studies that use news information or macroeconomic indicators that create market fluctuations, or reflect only a few combinations of indicators. In this paper, this we propose to present an effective combination of the news information sentiment analysis and various macroeconomic indicators in order to predict the US Dow Jones Index. After Crawling more than 93,000 business news from the New York Times for two years, the sentiment results analyzed using the latest natural language processing techniques BERT and NLTK, along with five macroeconomic indicators, gold prices, oil prices, and five foreign exchange rates affecting the US economy Combination was applied to the prediction algorithm LSTM, which is known to be the most suitable for combining numeric and text information. As a result of experimenting with various combinations, the combination of DJI, NLTK, BERT, OIL, GOLD, and EURUSD in the DJI index prediction yielded the smallest MSE value.