• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.13 no.3
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• pp.85-98
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• 2018

This study aims to investigate the effects of venture & inno-biz companies' technological innovation competences on technological performances and economic competitive advantage, using VRIO framework. The empirical data for this study were based on the technology assessment results of Korea Technology Finance Corporation (KOTEC) on 3,462 venture & inno-biz companies and the following 2 years' financial data. Sample companies with technological innovation resources, such as technology management strategy(Value), differentiation of technology(Rarity), imitation difficulty of technology(Imitability) and technical organization capability(Organization), made efforts to expand their intellectual property rights, technological development and commercialization of new products, thereby ensuring technological competitive advantage. Furthermore, technological competitive advantage were proved to improve sales growth, increase in operating revenue, and financial soundness. Compared with four VRIO factors' influence on business performance, imitation difficulty of technology(Imitability) and organization technological capability(Organization) had much more influence than any other internal resources or capabilities respectively.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.3
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• pp.98-107
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• 2007

For high-dimensional pattern recognition, such as face classification, the small number of training samples leads to the Small Sample Size problem when the number of pattern samples is smaller than the number of dimensionality. Recently, various LDA-extensions have been developed, including LDA, PCA+LDA, and Direct-LDA, to address the problem. This paper proposes a method of improving the classification efficiency by increasing the number of (sub)-classes through pre-clustering a training set prior to the execution of Direct-LDA. In LDA (or Direct-LDA), since the number of classes of the training set puts a limit to the dimensionality to be reduced, it is increased to the number of sub-classes that is obtained through clustering so that the classification performance of LDA-extensions can be improved. In other words, the eigen space of the training set consists of the range space and the null space, and the dimensionality of the range space increases as the number of classes increases. Therefore, when constructing the transformation matrix, through minimizing the null space, the loss of discriminatve information resulted from this space can be minimized. Experimental results for the artificial data of X-OR samples as well as the bench mark face databases of AT&T and Yale demonstrate that the classification efficiency of the proposed method could be improved.

• The Journal of the Petrological Society of Korea
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• v.26 no.3
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• pp.255-269
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• 2017

Five year term monitoring investigating variation of allochthonous originated gravels are has been conducted in the beach gravel deposit of the Island Dokdo natural reserve which takes purity and uniqueness with enormous attention though a number of areas. The beach gravel deposits near the dock of the Dongdo and near the accommodation facility of the Seodo comprise various types of gravels including basalt, trachyte, and tuff from the Dokdo itself, and granite, rhyolite, gneiss, quartzite, marble, and concrete from elsewhere. The types of the allochthonous gravels on the basis of the study in 2011 and in 2016 shows no difference, so is the ranking of abundance of the allochthonous gravels; granite-concrete-gneiss in turn on the Dongdo, concrete-gneiss-granite in turn on the Seodo. Nevertheless, the relative ratio of the allochthonous gravel area against the total area is decreased. While it is suspected that the disintegrated facility and the influx of material for construction are the main 2 reasons for the contamination by allochthonous gravel, diminished total contamination ratio indicate that supervising allochthonous material has been improved; at least not worse during the 5 years. On the other hand, it is inferred that gradual influence of rockfall also has been made the gravel beach changed. Therefore, consistent rockfall investigation must be inquired.

• Knowledge Management Research
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• v.24 no.1
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• pp.31-49
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• 2023

TikTok, as a leading short video platform, has attracted many users, and the resulting attention generates immense business value as a platform to diffuse knowledge. As a qualitative and explorative approach, this study reviews the knowledge payment industry and discusses the influential factors of users' payment decisions regarding knowledge products on TikTok. By conducting in-depth interviews with ten participants and observing 95 knowledge providers' videos, we find that TikTok has significant business potential in the knowledge payment industry. By using the ATLAS. ti software to code the data collected from these interviews, this study finds that demander characteristics (personal needs), product characteristics (product quality), provider characteristics (the key opinion leader effect), and platform characteristics (platform management) are the four core categories that influence users' payment decisions regarding knowledge products on TikTok. A theoretical model consisting of the ten variables of emotional needs, professional needs, quality, price, helpfulness, value, charisma, user trust, service guarantee, and scarcity is proposed based on the grounded theory. The theoretical and practical implications of the study findings are also discussed.

• Knowledge Management Research
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• v.25 no.2
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• pp.193-218
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• 2024

The current study investigated the factors influencing the buyer's repurchase intention for second-hand products. This study first identified perceived risk and purchase value as the two primary influencing variables. Additionally, some exogenous variables influencing these two variables were examined. Statistical analysis using Partial Least Squares (PLS) revealed that product uncertainty, seller uncertainty, and site trust had statistically significant relationships with perceived transaction risk. However, while economic benefit showed a significant impact on purchase value, product scarcity and resale value did not exhibit a significant relationship with purchase value. Perceived transaction risk was found to have an insignificant relationship with repurchase intention, but indirectly influenced repurchase intention through purchase value. Purchase value was identified as having a significant influence on repurchase intention. Therefore, it was concluded that purchase value is the most important factor influencing repurchase intention in the purchase of second-hand products, while transaction risk indirectly influences repurchase intention through purchase value. The study indicates that product uncertainty and economic benefit are the most significant exogenous factors influencing transaction risk and purchase value, respectively.

• Journal of Intelligence and Information Systems
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• v.26 no.2
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• pp.105-129
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• 2020

This study uses corporate data from 2012 to 2018 when K-IFRS was applied in earnest to predict default risks. The data used in the analysis totaled 10,545 rows, consisting of 160 columns including 38 in the statement of financial position, 26 in the statement of comprehensive income, 11 in the statement of cash flows, and 76 in the index of financial ratios. Unlike most previous prior studies used the default event as the basis for learning about default risk, this study calculated default risk using the market capitalization and stock price volatility of each company based on the Merton model. Through this, it was able to solve the problem of data imbalance due to the scarcity of default events, which had been pointed out as the limitation of the existing methodology, and the problem of reflecting the difference in default risk that exists within ordinary companies. Because learning was conducted only by using corporate information available to unlisted companies, default risks of unlisted companies without stock price information can be appropriately derived. Through this, it can provide stable default risk assessment services to unlisted companies that are difficult to determine proper default risk with traditional credit rating models such as small and medium-sized companies and startups. Although there has been an active study of predicting corporate default risks using machine learning recently, model bias issues exist because most studies are making predictions based on a single model. Stable and reliable valuation methodology is required for the calculation of default risk, given that the entity's default risk information is very widely utilized in the market and the sensitivity to the difference in default risk is high. Also, Strict standards are also required for methods of calculation. The credit rating method stipulated by the Financial Services Commission in the Financial Investment Regulations calls for the preparation of evaluation methods, including verification of the adequacy of evaluation methods, in consideration of past statistical data and experiences on credit ratings and changes in future market conditions. This study allowed the reduction of individual models' bias by utilizing stacking ensemble techniques that synthesize various machine learning models. This allows us to capture complex nonlinear relationships between default risk and various corporate information and maximize the advantages of machine learning-based default risk prediction models that take less time to calculate. To calculate forecasts by sub model to be used as input data for the Stacking Ensemble model, training data were divided into seven pieces, and sub-models were trained in a divided set to produce forecasts. To compare the predictive power of the Stacking Ensemble model, Random Forest, MLP, and CNN models were trained with full training data, then the predictive power of each model was verified on the test set. The analysis showed that the Stacking Ensemble model exceeded the predictive power of the Random Forest model, which had the best performance on a single model. Next, to check for statistically significant differences between the Stacking Ensemble model and the forecasts for each individual model, the Pair between the Stacking Ensemble model and each individual model was constructed. Because the results of the Shapiro-wilk normality test also showed that all Pair did not follow normality, Using the nonparametric method wilcoxon rank sum test, we checked whether the two model forecasts that make up the Pair showed statistically significant differences. The analysis showed that the forecasts of the Staging Ensemble model showed statistically significant differences from those of the MLP model and CNN model. In addition, this study can provide a methodology that allows existing credit rating agencies to apply machine learning-based bankruptcy risk prediction methodologies, given that traditional credit rating models can also be reflected as sub-models to calculate the final default probability. Also, the Stacking Ensemble techniques proposed in this study can help design to meet the requirements of the Financial Investment Business Regulations through the combination of various sub-models. We hope that this research will be used as a resource to increase practical use by overcoming and improving the limitations of existing machine learning-based models.

• Journal of Intelligence and Information Systems
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• v.22 no.1
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• pp.119-138
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• 2016

Since the Industrial Revolution, which made the mass production and mass distribution of standardized goods possible, machine-made (manufactured) products have accounted for the majority of the market. However, in recent years, the phenomenon of purchasing even more expensive handmade products has become a noticeable trend as consumers have started to acknowledge the value of handmade products, such as the craftsman's commitment, belief in their quality and scarcity, and the sense of self-esteem from having them,. Consumer interest in these handmade products has shown explosive growth and has been coupled with the recent development of three-dimensional (3D) printing technologies. Etsy.com is the world's largest online handmade platform. It is no different from any other online platform; it provides an online market where buyers and sellers virtually meet to share information and transact business. However, Etsy.com is different in that shops within this platform only deal with handmade products in a variety of categories, ranging from jewelry to toys. Since its establishment in 2005, despite being limited to handmade products, Etsy.com has enjoyed rapid growth in membership, transaction volume, and revenue. Most recently in April 2015, it raised funds through an initial public offering (IPO) of more than 1.8 billion USD, which demonstrates the huge potential of online handmade platforms. After the success of Etsy.com, various types of online handmade platforms such as Handmade at Amazon, ArtFire, DaWanda, and Craft is ART have emerged and are now competing with each other, at the same time, which has increased the size of the market. According to Deloitte's 2015 holiday survey on which types of gifts the respondents plan to buy during the holiday season, about 16% of U.S. consumers chose "homemade or craft items (e.g., Etsy purchase)," which was the same rate as those for the computer game and shoes categories. This indicates that consumer interests in online handmade platforms will continue to rise in the future. However, this high interest in the market for handmade products and their platforms has not yet led to academic research. Most extant studies have only focused on machine-made products and intelligent services for them. This indicates a lack of studies on handmade products and their intelligent services on virtual platforms. Therefore, this study used signaling theory and prior research on the effects of sellers' characteristics on their performance (e.g., total sales and price premiums) in the buyer-seller relationship to identify the key influencing e-Image factors (e.g., reputation, size, information sharing, and length of relationship). Then, their impacts on the performance of shops within the online handmade platform were empirically examined; the dataset was collected from Etsy.com through the application of web harvesting technology. The results from the structural equation modeling revealed that the reputation, size, and information sharing have significant effects on the total sales, while the reputation and length of relationship influence price premiums. This study extended the online platform research into online handmade platform research by identifying key influencing e-Image factors on within-platform shop's total sales and price premiums based on signaling theory and then performed a statistical investigation. These findings are expected to be a stepping stone for future studies on intelligent online handmade services as well as handmade products themselves. Furthermore, the findings of the study provide online handmade platform operators with practical guidelines on how to implement intelligent online handmade services. They should also help shop managers build their marketing strategies in a more specific and effective manner by suggesting key influencing e-Image factors. The results of this study should contribute to the vitalization of intelligent online handmade services by providing clues on how to maximize within-platform shops' total sales and price premiums.

• 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 Sasang Constitutional Medicine
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• v.11 no.2
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• pp.119-150
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• 1999

In spite of recent remarkable recent development in both western and oriental medical sciences, there is still only a shallow understanding of individual differences for various prognoses of incurable diseases and immunopathy diseases. Nevertheless, the care, cure and prevention methods of Sasang Constitutional Medicine are broadly used as an effective treatment of incurable diseases like immunopathy diseases and stress-related diseases and diseases due to aging. In this sense, the establishment of classification norms is urgent and essential for the worldwide application of Sasang Constitutional Medicine(SCM). This study began with the confirmation process of whether Sasang Constitutional types exist in Americans. To accomodate for cultural differences, the distinguishing tool was readjusted so that Sasang Constitutional Types in Americans could be determined. Hence, the selected tool is the new QSCCII+, which is a newly revised English version of the QSCCII. QSCCII was made and standardized by Dept. of SCM in Kyung Hee Medical Center and Dr. Kim7). The evaluation methods of the old version were improved in the new QSCCII+ through necessary statistical manipulation. The original QSCCII was officially authorized by the Korean Society of Sasang Constitutional Medicine as the only computerized version of Sasang diagnostics. This study is the first attempt to design a new diagnostic tool for the classification of Sasang Constitutional types in North Americans with the revision of QSCCII. The subjects of this study were selected from the cooperative people among the students and staffs of the University of Bridgeport and the patients who visited the Clinic in the Health Science Center. This study takes for about 1 year from 1998. 8 to 1999. 8 The conclusions of the study can be summarized as follows: 1. Sasang constitutional types also exist in Americans. It can also naturally be inferred that Sasang Constitutional types exist in all human beings, for there are many different human races in America. 2. There are more So-Yang In's than any other types in American white people. This result confirms the hypothesis that there also exist Sasang Constitutional types in westerners. 3. The result of repetitive tests suggests that the new QSCCII+ is an effective diagnostic tool for westerners when we consider the constant diagnostic results of the QSCCII+. 4. Sasang Constitutional types exit in the sample group regardless of racial difference. 5. The question items that were not often checked by Americans need to be modified into more understandable expressions. 6. The standardization of diagnosis for Americans should be established by use of the QSCCII+ 7. It can be guessed that there are many Tae-yang In's among the 71 persons who could not be clearly classified by the QSCCII+. Due to the scarcity of Tae-yang-In in general, it is important to improve upon the discernability of the QSCC II+. 8. The results of the Sasang Constitutional distribution in North Americans are as follows: The percentage of So-yang In distribution in the sample group is 36.25%(87persons), that of Tae-eum In is 13.75%(33persons), and that of So-eum In is 20.41%(49persons).