• 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.4
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• pp.127-146
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• 2017

Services using artificial intelligence have begun to emerge in daily life. Artificial intelligence is applied to products in consumer electronics and communications such as artificial intelligence refrigerators and speakers. In the financial sector, using Kensho's artificial intelligence technology, the process of the stock trading system in Goldman Sachs was improved. For example, two stock traders could handle the work of 600 stock traders and the analytical work for 15 people for 4weeks could be processed in 5 minutes. Especially, big data analysis through machine learning among artificial intelligence fields is actively applied throughout the financial industry. The stock market analysis and investment modeling through machine learning theory are also actively studied. The limits of linearity problem existing in financial time series studies are overcome by using machine learning theory such as artificial intelligence prediction model. The study of quantitative financial data based on the past stock market-related numerical data is widely performed using artificial intelligence to forecast future movements of stock price or indices. Various other studies have been conducted to predict the future direction of the market or the stock price of companies by learning based on a large amount of text data such as various news and comments related to the stock market. Investing on commodity asset, one of alternative assets, is usually used for enhancing the stability and safety of traditional stock and bond asset portfolio. There are relatively few researches on the investment model about commodity asset than mainstream assets like equity and bond. Recently machine learning techniques are widely applied on financial world, especially on stock and bond investment model and it makes better trading model on this field and makes the change on the whole financial area. In this study we made investment model using Support Vector Machine among the machine learning models. There are some researches on commodity asset focusing on the price prediction of the specific commodity but it is hard to find the researches about investment model of commodity as asset allocation using machine learning model. We propose a method of forecasting four major commodity indices, portfolio made of commodity futures, and individual commodity futures, using SVM model. The four major commodity indices are Goldman Sachs Commodity Index(GSCI), Dow Jones UBS Commodity Index(DJUI), Thomson Reuters/Core Commodity CRB Index(TRCI), and Rogers International Commodity Index(RI). We selected each two individual futures among three sectors as energy, agriculture, and metals that are actively traded on CME market and have enough liquidity. They are Crude Oil, Natural Gas, Corn, Wheat, Gold and Silver Futures. We made the equally weighted portfolio with six commodity futures for comparing with other commodity indices. We set the 19 macroeconomic indicators including stock market indices, exports & imports trade data, labor market data, and composite leading indicators as the input data of the model because commodity asset is very closely related with the macroeconomic activities. They are 14 US economic indicators, two Chinese economic indicators and two Korean economic indicators. Data period is from January 1990 to May 2017. We set the former 195 monthly data as training data and the latter 125 monthly data as test data. In this study, we verified that the performance of the equally weighted commodity futures portfolio rebalanced by the SVM model is better than that of other commodity indices. The prediction accuracy of the model for the commodity indices does not exceed 50% regardless of the SVM kernel function. On the other hand, the prediction accuracy of equally weighted commodity futures portfolio is 53%. The prediction accuracy of the individual commodity futures model is better than that of commodity indices model especially in agriculture and metal sectors. The individual commodity futures portfolio excluding the energy sector has outperformed the three sectors covered by individual commodity futures portfolio. In order to verify the validity of the model, it is judged that the analysis results should be similar despite variations in data period. So we also examined the odd numbered year data as training data and the even numbered year data as test data and we confirmed that the analysis results are similar. As a result, when we allocate commodity assets to traditional portfolio composed of stock, bond, and cash, we can get more effective investment performance not by investing commodity indices but by investing commodity futures. Especially we can get better performance by rebalanced commodity futures portfolio designed by SVM model.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.67-88
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• 2018

Until recently, as we recognize the significance of sustainable growth and competitiveness of small-and-medium sized enterprises (SMEs), governmental support for tangible resources such as R&D, manpower, funds, etc. has been mainly provided. However, it is also true that the inefficiency of support systems such as underestimated or redundant support has been raised because there exist conflicting policies in terms of appropriateness, effectiveness and efficiency of business support. From the perspective of the government or a company, we believe that due to limited resources of SMEs technology development and capacity enhancement through collaboration with external sources is the basis for creating competitive advantage for companies, and also emphasize value creation activities for it. This is why value chain network analysis is necessary in order to analyze inter-company deal relationships from a series of value chains and visualize results through establishing knowledge ecosystems at the corporate level. There exist Technology Opportunity Discovery (TOD) system that provides information on relevant products or technology status of companies with patents through retrievals over patent, product, or company name, CRETOP and KISLINE which both allow to view company (financial) information and credit information, but there exists no online system that provides a list of similar (competitive) companies based on the analysis of value chain network or information on potential clients or demanders that can have business deals in future. Therefore, we focus on the "Value Chain Network System (VCNS)", a support partner for planning the corporate business strategy developed and managed by KISTI, and investigate the types of embedded network-based analysis modules, databases (D/Bs) to support them, and how to utilize the system efficiently. Further we explore the function of network visualization in intelligent value chain analysis system which becomes the core information to understand industrial structure ystem and to develop a company's new product development. In order for a company to have the competitive superiority over other companies, it is necessary to identify who are the competitors with patents or products currently being produced, and searching for similar companies or competitors by each type of industry is the key to securing competitiveness in the commercialization of the target company. In addition, transaction information, which becomes business activity between companies, plays an important role in providing information regarding potential customers when both parties enter similar fields together. Identifying a competitor at the enterprise or industry level by using a network map based on such inter-company sales information can be implemented as a core module of value chain analysis. The Value Chain Network System (VCNS) combines the concepts of value chain and industrial structure analysis with corporate information simply collected to date, so that it can grasp not only the market competition situation of individual companies but also the value chain relationship of a specific industry. Especially, it can be useful as an information analysis tool at the corporate level such as identification of industry structure, identification of competitor trends, analysis of competitors, locating suppliers (sellers) and demanders (buyers), industry trends by item, finding promising items, finding new entrants, finding core companies and items by value chain, and recognizing the patents with corresponding companies, etc. In addition, based on the objectivity and reliability of the analysis results from transaction deals information and financial data, it is expected that value chain network system will be utilized for various purposes such as information support for business evaluation, R&D decision support and mid-term or short-term demand forecasting, in particular to more than 15,000 member companies in Korea, employees in R&D service sectors government-funded research institutes and public organizations. In order to strengthen business competitiveness of companies, technology, patent and market information have been provided so far mainly by government agencies and private research-and-development service companies. This service has been presented in frames of patent analysis (mainly for rating, quantitative analysis) or market analysis (for market prediction and demand forecasting based on market reports). However, there was a limitation to solving the lack of information, which is one of the difficulties that firms in Korea often face in the stage of commercialization. In particular, it is much more difficult to obtain information about competitors and potential candidates. In this study, the real-time value chain analysis and visualization service module based on the proposed network map and the data in hands is compared with the expected market share, estimated sales volume, contact information (which implies potential suppliers for raw material / parts, and potential demanders for complete products / modules). In future research, we intend to carry out the in-depth research for further investigating the indices of competitive factors through participation of research subjects and newly developing competitive indices for competitors or substitute items, and to additively promoting with data mining techniques and algorithms for improving the performance of VCNS.

• Journal of Intelligence and Information Systems
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• v.25 no.3
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• pp.239-251
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• 2019

Statistical methods such as moving averages, Kalman filtering, exponential smoothing, regression analysis, and ARIMA (autoregressive integrated moving average) have been used for stock market predictions. However, these statistical methods have not produced superior performances. In recent years, machine learning techniques have been widely used in stock market predictions, including artificial neural network, SVM, and genetic algorithm. In particular, a case-based reasoning method, known as k-nearest neighbor is also widely used for stock price prediction. Case based reasoning retrieves several similar cases from previous cases when a new problem occurs, and combines the class labels of similar cases to create a classification for the new problem. However, case based reasoning has some problems. First, case based reasoning has a tendency to search for a fixed number of neighbors in the observation space and always selects the same number of neighbors rather than the best similar neighbors for the target case. So, case based reasoning may have to take into account more cases even when there are fewer cases applicable depending on the subject. Second, case based reasoning may select neighbors that are far away from the target case. Thus, case based reasoning does not guarantee an optimal pseudo-neighborhood for various target cases, and the predictability can be degraded due to a deviation from the desired similar neighbor. This paper examines how the size of learning data affects stock price predictability through k-nearest neighbor and compares the predictability of k-nearest neighbor with the random walk model according to the size of the learning data and the number of neighbors. In this study, Samsung electronics stock prices were predicted by dividing the learning dataset into two types. For the prediction of next day's closing price, we used four variables: opening value, daily high, daily low, and daily close. In the first experiment, data from January 1, 2000 to December 31, 2017 were used for the learning process. In the second experiment, data from January 1, 2015 to December 31, 2017 were used for the learning process. The test data is from January 1, 2018 to August 31, 2018 for both experiments. We compared the performance of k-NN with the random walk model using the two learning dataset. The mean absolute percentage error (MAPE) was 1.3497 for the random walk model and 1.3570 for the k-NN for the first experiment when the learning data was small. However, the mean absolute percentage error (MAPE) for the random walk model was 1.3497 and the k-NN was 1.2928 for the second experiment when the learning data was large. These results show that the prediction power when more learning data are used is higher than when less learning data are used. Also, this paper shows that k-NN generally produces a better predictive power than random walk model for larger learning datasets and does not when the learning dataset is relatively small. Future studies need to consider macroeconomic variables related to stock price forecasting including opening price, low price, high price, and closing price. Also, to produce better results, it is recommended that the k-nearest neighbor needs to find nearest neighbors using the second step filtering method considering fundamental economic variables as well as a sufficient amount of learning data.

• Journal of Intelligence and Information Systems
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• v.25 no.1
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• pp.63-83
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• 2019

Investors prefer to look for trading points based on the graph shown in the chart rather than complex analysis, such as corporate intrinsic value analysis and technical auxiliary index analysis. However, the pattern analysis technique is difficult and computerized less than the needs of users. In recent years, there have been many cases of studying stock price patterns using various machine learning techniques including neural networks in the field of artificial intelligence(AI). In particular, the development of IT technology has made it easier to analyze a huge number of chart data to find patterns that can predict stock prices. Although short-term forecasting power of prices has increased in terms of performance so far, long-term forecasting power is limited and is used in short-term trading rather than long-term investment. Other studies have focused on mechanically and accurately identifying patterns that were not recognized by past technology, but it can be vulnerable in practical areas because it is a separate matter whether the patterns found are suitable for trading. When they find a meaningful pattern, they find a point that matches the pattern. They then measure their performance after n days, assuming that they have bought at that point in time. Since this approach is to calculate virtual revenues, there can be many disparities with reality. The existing research method tries to find a pattern with stock price prediction power, but this study proposes to define the patterns first and to trade when the pattern with high success probability appears. The M & W wave pattern published by Merrill(1980) is simple because we can distinguish it by five turning points. Despite the report that some patterns have price predictability, there were no performance reports used in the actual market. The simplicity of a pattern consisting of five turning points has the advantage of reducing the cost of increasing pattern recognition accuracy. In this study, 16 patterns of up conversion and 16 patterns of down conversion are reclassified into ten groups so that they can be easily implemented by the system. Only one pattern with high success rate per group is selected for trading. Patterns that had a high probability of success in the past are likely to succeed in the future. So we trade when such a pattern occurs. It is a real situation because it is measured assuming that both the buy and sell have been executed. We tested three ways to calculate the turning point. The first method, the minimum change rate zig-zag method, removes price movements below a certain percentage and calculates the vertex. In the second method, high-low line zig-zag, the high price that meets the n-day high price line is calculated at the peak price, and the low price that meets the n-day low price line is calculated at the valley price. In the third method, the swing wave method, the high price in the center higher than n high prices on the left and right is calculated as the peak price. If the central low price is lower than the n low price on the left and right, it is calculated as valley price. The swing wave method was superior to the other methods in the test results. It is interpreted that the transaction after checking the completion of the pattern is more effective than the transaction in the unfinished state of the pattern. Genetic algorithms(GA) were the most suitable solution, although it was virtually impossible to find patterns with high success rates because the number of cases was too large in this simulation. We also performed the simulation using the Walk-forward Analysis(WFA) method, which tests the test section and the application section separately. So we were able to respond appropriately to market changes. In this study, we optimize the stock portfolio because there is a risk of over-optimized if we implement the variable optimality for each individual stock. Therefore, we selected the number of constituent stocks as 20 to increase the effect of diversified investment while avoiding optimization. We tested the KOSPI market by dividing it into six categories. In the results, the portfolio of small cap stock was the most successful and the high vol stock portfolio was the second best. This shows that patterns need to have some price volatility in order for patterns to be shaped, but volatility is not the best.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.39-55
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• 2019

Recently banks and large financial institutions have introduced lots of Robo-Advisor products. Robo-Advisor is a Robot to produce the optimal asset allocation portfolio for investors by using the financial engineering algorithms without any human intervention. Since the first introduction in Wall Street in 2008, the market size has grown to 60 billion dollars and is expected to expand to 2,000 billion dollars by 2020. Since Robo-Advisor algorithms suggest asset allocation output to investors, mathematical or statistical asset allocation strategies are applied. Mean variance optimization model developed by Markowitz is the typical asset allocation model. The model is a simple but quite intuitive portfolio strategy. For example, assets are allocated in order to minimize the risk on the portfolio while maximizing the expected return on the portfolio using optimization techniques. Despite its theoretical background, both academics and practitioners find that the standard mean variance optimization portfolio is very sensitive to the expected returns calculated by past price data. Corner solutions are often found to be allocated only to a few assets. The Black-Litterman Optimization model overcomes these problems by choosing a neutral Capital Asset Pricing Model equilibrium point. Implied equilibrium returns of each asset are derived from equilibrium market portfolio through reverse optimization. The Black-Litterman model uses a Bayesian approach to combine the subjective views on the price forecast of one or more assets with implied equilibrium returns, resulting a new estimates of risk and expected returns. These new estimates can produce optimal portfolio by the well-known Markowitz mean-variance optimization algorithm. If the investor does not have any views on his asset classes, the Black-Litterman optimization model produce the same portfolio as the market portfolio. What if the subjective views are incorrect? A survey on reports of stocks performance recommended by securities analysts show very poor results. Therefore the incorrect views combined with implied equilibrium returns may produce very poor portfolio output to the Black-Litterman model users. This paper suggests an objective investor views model based on Support Vector Machines(SVM), which have showed good performance results in stock price forecasting. SVM is a discriminative classifier defined by a separating hyper plane. The linear, radial basis and polynomial kernel functions are used to learn the hyper planes. Input variables for the SVM are returns, standard deviations, Stochastics %K and price parity degree for each asset class. SVM output returns expected stock price movements and their probabilities, which are used as input variables in the intelligent views model. The stock price movements are categorized by three phases; down, neutral and up. The expected stock returns make P matrix and their probability results are used in Q matrix. Implied equilibrium returns vector is combined with the intelligent views matrix, resulting the Black-Litterman optimal portfolio. For comparisons, Markowitz mean-variance optimization model and risk parity model are used. The value weighted market portfolio and equal weighted market portfolio are used as benchmark indexes. We collect the 8 KOSPI 200 sector indexes from January 2008 to December 2018 including 132 monthly index values. Training period is from 2008 to 2015 and testing period is from 2016 to 2018. Our suggested intelligent view model combined with implied equilibrium returns produced the optimal Black-Litterman portfolio. The out of sample period portfolio showed better performance compared with the well-known Markowitz mean-variance optimization portfolio, risk parity portfolio and market portfolio. The total return from 3 year-period Black-Litterman portfolio records 6.4%, which is the highest value. The maximum draw down is -20.8%, which is also the lowest value. Sharpe Ratio shows the highest value, 0.17. It measures the return to risk ratio. Overall, our suggested view model shows the possibility of replacing subjective analysts's views with objective view model for practitioners to apply the Robo-Advisor asset allocation algorithms in the real trading fields.

• Journal of Intelligence and Information Systems
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• v.20 no.4
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• pp.89-105
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• 2014

After emergence of Internet, social media with highly interactive Web 2.0 applications has provided very user friendly means for consumers and companies to communicate with each other. Users have routinely published contents involving their opinions and interests in social media such as blogs, forums, chatting rooms, and discussion boards, and the contents are released real-time in the Internet. For that reason, many researchers and marketers regard social media contents as the source of information for business analytics to develop business insights, and many studies have reported results on mining business intelligence from Social media content. In particular, opinion mining and sentiment analysis, as a technique to extract, classify, understand, and assess the opinions implicit in text contents, are frequently applied into social media content analysis because it emphasizes determining sentiment polarity and extracting authors' opinions. A number of frameworks, methods, techniques and tools have been presented by these researchers. However, we have found some weaknesses from their methods which are often technically complicated and are not sufficiently user-friendly for helping business decisions and planning. In this study, we attempted to formulate a more comprehensive and practical approach to conduct opinion mining with visual deliverables. First, we described the entire cycle of practical opinion mining using Social media content from the initial data gathering stage to the final presentation session. Our proposed approach to opinion mining consists of four phases: collecting, qualifying, analyzing, and visualizing. In the first phase, analysts have to choose target social media. Each target media requires different ways for analysts to gain access. There are open-API, searching tools, DB2DB interface, purchasing contents, and so son. Second phase is pre-processing to generate useful materials for meaningful analysis. If we do not remove garbage data, results of social media analysis will not provide meaningful and useful business insights. To clean social media data, natural language processing techniques should be applied. The next step is the opinion mining phase where the cleansed social media content set is to be analyzed. The qualified data set includes not only user-generated contents but also content identification information such as creation date, author name, user id, content id, hit counts, review or reply, favorite, etc. Depending on the purpose of the analysis, researchers or data analysts can select a suitable mining tool. Topic extraction and buzz analysis are usually related to market trends analysis, while sentiment analysis is utilized to conduct reputation analysis. There are also various applications, such as stock prediction, product recommendation, sales forecasting, and so on. The last phase is visualization and presentation of analysis results. The major focus and purpose of this phase are to explain results of analysis and help users to comprehend its meaning. Therefore, to the extent possible, deliverables from this phase should be made simple, clear and easy to understand, rather than complex and flashy. To illustrate our approach, we conducted a case study on a leading Korean instant noodle company. We targeted the leading company, NS Food, with 66.5% of market share; the firm has kept No. 1 position in the Korean "Ramen" business for several decades. We collected a total of 11,869 pieces of contents including blogs, forum contents and news articles. After collecting social media content data, we generated instant noodle business specific language resources for data manipulation and analysis using natural language processing. In addition, we tried to classify contents in more detail categories such as marketing features, environment, reputation, etc. In those phase, we used free ware software programs such as TM, KoNLP, ggplot2 and plyr packages in R project. As the result, we presented several useful visualization outputs like domain specific lexicons, volume and sentiment graphs, topic word cloud, heat maps, valence tree map, and other visualized images to provide vivid, full-colored examples using open library software packages of the R project. Business actors can quickly detect areas by a swift glance that are weak, strong, positive, negative, quiet or loud. Heat map is able to explain movement of sentiment or volume in categories and time matrix which shows density of color on time periods. Valence tree map, one of the most comprehensive and holistic visualization models, should be very helpful for analysts and decision makers to quickly understand the "big picture" business situation with a hierarchical structure since tree-map can present buzz volume and sentiment with a visualized result in a certain period. This case study offers real-world business insights from market sensing which would demonstrate to practical-minded business users how they can use these types of results for timely decision making in response to on-going changes in the market. We believe our approach can provide practical and reliable guide to opinion mining with visualized results that are immediately useful, not just in food industry but in other industries as well.

• Proceedings of the Korean System Dynamics Society
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• 2004.08a
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• pp.87-107
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• 2004

우리나라에 이동통신이 처음 소개된 이래로 눈부신 발전을 거듭하여 왔으며, 급기야. 무선통신 서비스를 중심으로 새로운 성장력과 패러다임 전환의 가능성에 대한 전망을 논할 수 있는 수준이 되었다. 이러한 추세에 맞추어 휴대인터넷 시장에 대한 연구가 활발히 진행되고 있으며 국민경제적 효과변화나 시장 경쟁환경의 변화에 가장 영향을 미칠 수 있는 요인들 중, 사업자 수를 어떻게 선정할 것인가에 대해 다양한 접근이 시도되고 있다. 기존의 연구들은 휴대인터넷 시장을 분석하는데 있어 시장규모가 일방향으로 사업자 수에 영향을 미친다는 측면에서 이루어지고 있으며, 대부분 휴대인터넷 시장을 단일시장으로 범위를 한정하고 성장중인 시장을 정적으로 가정하여 시장성장 추이 분석 등에 주안점을 두는 단편적 연구가 수행되어져 왔다. 휴대인터넷 시장의 단편적 분석이 아닌 '모바일인터넷' , '초고속유선인터넷', '무선인터넷', '휴대인터넷' 등 네 가지 영역을 동시에 고려함으로써 영역간 복잡성과 동적인 관계 속에서 시장이 성장해 나아간다는 가정을 바탕으로, 시장에 내재되어 있는 관련요소간 상호영향과 신규정책 및 제도적 변화 수용에 있어 발생하는 시간적 공간적 지연 등을 고려한 동태적 분석을 수행하였다. 연구를 수행하기 위해 다양한 변수간의 인과관계, 피드백 구조와 시간흐름에 따른 시스템의 변화를 파악하는데 매우 유용한 도구인 시스템다이내믹스 기법을 활용하여 휴대 인터넷 시장의 동적인 구조를 알아보고 사업자 선정정책의 시행을 앞두고 있는 현재시점에서 의미있는 시사점을 제공하였다.시하고자 한다.채취하여 임신진단키트(제네디아프로테 트, 녹십자)를 이용하여 임신여부를 1차적으로 확인하였다. 과배란을 유기한 13두의 공란우중 9두(69.2%)가 과배란 반응을 나타내었으며, 회수된 수정란 51개중 이식가능수정란은 38개(74.5%) 였다. 발정동기화를 유도한 수란우 40두중에서 35두(87.5%)가 발정이 동기화되었으며, 그 중 황체검사를 통하여 30두의 수란우에 수정란을 이식하였다. 수정란이식후 13일(발정주기 21일)에 혈액을 이용한 임신진단에서 농가별 수태율은 각각 37.5%, 70.0%, 60.0% 및 71.4% 로서 평균 60.0%를 나타내었다.서 39$^{\circ}C$, 5% $CO_2$ 배양기에 48시간 배양하면서 생존여부를 판단하였다. 실험 2에서 확장배반포배 수정란이 25.3%의 생존율을 나타내었으며, 실험 1과 실험 3에서는 수정란의 형태와 관계없이 생존성을 확인할 수 없었다. 이상의 결과로 보아 glycerol 완만동결에서는 확장배반포기 수정란 이상이 보존가능한 것으로 추정되나 더 추가적인 연구가 요구된다.c kinase 활성의 변동은 정소 내 간충조직, 세정관 상피의 증식 및 기능적 분화 과정을 매개하는 생리적 활성분자 수용체 하위의 신호전달 과정에 Src-Csk loop에 의한 조절가능성을 확인할 수 있었다.rugrene의 향기성분이 주요 성분군으로 확인되었다. 2. 생강나무에서 생강의 향기를 발산하는 성분으로는 $\beta$-myrcene, o-terpinolene, phellandrone, ι-limonene, $\b

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.6 no.4
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• pp.111-135
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• 2011

While previous studies and publications all assert a strong correlation between company's business plan and performance, very few have actually conducted practical analyses to support that. This study takes a practical approach in its analysis of Korean small and mid-sized enterprises(SME) with the view to finding an answer to the question. In addition, with the considerations of entrepreneur type and company's development stage, I analyzed the differences of business plan components' effects on performances. I selected business plan's components, which have been suggested only in theory and in concept, through the literature review and preliminary examination. Corporate performances were the recent improvements of ROS, ROA, market share and the number of employees to measure how greatly each is impacted by the components of a business plan. Results show that business plan components have influenced upon the number of employees. The business plan components discriminated superior company group and inferior company group properly. Especially, finance & related system and advertising & distribution factors showed statistically significant classification forecasting power. Technical/Craftsman evaluated the effects of producing & sales and profit & quality factors high and General/Opportunistic evaluated the effects of finance & related system, advertising & distribution, corporate mission factors high. The effect of corporate mission was highest among company development stages. Finance & related system and advertising & distribution factors showed the statistically significant difference in entrepreneur type and company development stages.

• Korean Journal of Construction Engineering and Management
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• v.7 no.1 s.29
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• pp.138-147
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• 2006

Construction companies have been interested in the construction duration which importantly affects the performance and the success of the construction projects in accordance with the systemic changes such as five days per week system, introduction of construction duration reduction bidding system and post sale system nowadays. It is also very important to estimate and forecast properly the construction duration as the construction companies compete for the projects in the situation of construction market reduction and the lowest bidding system. Recognizing the importance about the construction duration, the researches about comparing and analyzing or estiamting the construction duration have been performed. However, comparing studies about the construction duraion have been limited to the apartment and office building in domestic area. Many studies about forecasting construction duration have been performed through stochastic analysis and simulations. Little research has been addressed the comparison analysis of the real construction duration about the mix-use building and officetel building which occured according to the changes of the building requirements. Therefore, the objective of this study is to compare and analyze the real construction duration and the hypothetical construction duration about the mix-use building and officetel building of the domestic companies. Moreover, we select the most competitve construction company to get the strengths and analyze the competitive advatages of the construction companies about construction duration.