• Asia pacific journal of information systems
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• v.17 no.4
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• pp.31-59
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• 2007

We frequently use search engines to find relevant information in the Web but still end up with too much information. In order to solve this problem of information overload, ranking algorithms have been applied to various domains. As more information will be available in the future, effectively and efficiently ranking search results will become more critical. In this paper, we propose a ranking algorithm for the Semantic Web resources, specifically RDF resources. Traditionally, the importance of a particular Web page is estimated based on the number of key words found in the page, which is subject to manipulation. In contrast, link analysis methods such as Google's PageRank capitalize on the information which is inherent in the link structure of the Web graph. PageRank considers a certain page highly important if it is referred to by many other pages. The degree of the importance also increases if the importance of the referring pages is high. Kleinberg's algorithm is another link-structure based ranking algorithm for Web pages. Unlike PageRank, Kleinberg's algorithm utilizes two kinds of scores: the authority score and the hub score. If a page has a high authority score, it is an authority on a given topic and many pages refer to it. A page with a high hub score links to many authoritative pages. As mentioned above, the link-structure based ranking method has been playing an essential role in World Wide Web(WWW), and nowadays, many people recognize the effectiveness and efficiency of it. On the other hand, as Resource Description Framework(RDF) data model forms the foundation of the Semantic Web, any information in the Semantic Web can be expressed with RDF graph, making the ranking algorithm for RDF knowledge bases greatly important. The RDF graph consists of nodes and directional links similar to the Web graph. As a result, the link-structure based ranking method seems to be highly applicable to ranking the Semantic Web resources. However, the information space of the Semantic Web is more complex than that of WWW. For instance, WWW can be considered as one huge class, i.e., a collection of Web pages, which has only a recursive property, i.e., a 'refers to' property corresponding to the hyperlinks. However, the Semantic Web encompasses various kinds of classes and properties, and consequently, ranking methods used in WWW should be modified to reflect the complexity of the information space in the Semantic Web. Previous research addressed the ranking problem of query results retrieved from RDF knowledge bases. Mukherjea and Bamba modified Kleinberg's algorithm in order to apply their algorithm to rank the Semantic Web resources. They defined the objectivity score and the subjectivity score of a resource, which correspond to the authority score and the hub score of Kleinberg's, respectively. They concentrated on the diversity of properties and introduced property weights to control the influence of a resource on another resource depending on the characteristic of the property linking the two resources. A node with a high objectivity score becomes the object of many RDF triples, and a node with a high subjectivity score becomes the subject of many RDF triples. They developed several kinds of Semantic Web systems in order to validate their technique and showed some experimental results verifying the applicability of their method to the Semantic Web. Despite their efforts, however, there remained some limitations which they reported in their paper. First, their algorithm is useful only when a Semantic Web system represents most of the knowledge pertaining to a certain domain. In other words, the ratio of links to nodes should be high, or overall resources should be described in detail, to a certain degree for their algorithm to properly work. Second, a Tightly-Knit Community(TKC) effect, the phenomenon that pages which are less important but yet densely connected have higher scores than the ones that are more important but sparsely connected, remains as problematic. Third, a resource may have a high score, not because it is actually important, but simply because it is very common and as a consequence it has many links pointing to it. In this paper, we examine such ranking problems from a novel perspective and propose a new algorithm which can solve the problems under the previous studies. Our proposed method is based on a class-oriented approach. In contrast to the predicate-oriented approach entertained by the previous research, a user, under our approach, determines the weights of a property by comparing its relative significance to the other properties when evaluating the importance of resources in a specific class. This approach stems from the idea that most queries are supposed to find resources belonging to the same class in the Semantic Web, which consists of many heterogeneous classes in RDF Schema. This approach closely reflects the way that people, in the real world, evaluate something, and will turn out to be superior to the predicate-oriented approach for the Semantic Web. Our proposed algorithm can resolve the TKC(Tightly Knit Community) effect, and further can shed lights on other limitations posed by the previous research. In addition, we propose two ways to incorporate data-type properties which have not been employed even in the case when they have some significance on the resource importance. We designed an experiment to show the effectiveness of our proposed algorithm and the validity of ranking results, which was not tried ever in previous research. We also conducted a comprehensive mathematical analysis, which was overlooked in previous research. The mathematical analysis enabled us to simplify the calculation procedure. Finally, we summarize our experimental results and discuss further research issues.

• Journal of Preventive Medicine and Public Health
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• v.5 no.1
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• pp.115-123
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• 1972

As a link of chain study program of school health, a survey was made up by the screening test with audiometry for hearing disturbance on 18,675 high school students who are mainly aged in 15-19 years from November 5.1969 to October 30. 1970. The results obtained were summerized as follows. According to our criteria as table 3, the rates of the profound, the severe and the moderate who required the appropriate hearing aids were 0.02%, 0.03% and 0.14% respectively:-the cumulative percentage was 0.197. When the marginal, 0.23% should be included the cumulative rate was 0.41%. But there was no-significance by sex and school classes. If we will make the special classes for them one class would be estimated out of 10,000 persons when a class is formed with about 15 persons. Otherwise when we examined that according to each ear of persons, the rates of the profound, the severe and the moderate were 0.17%, 0.22% and 0.33% respectively and their cumulative percentage wag 0.72. There was no significance also by sex and age. By the way, the rate of hearing disturbance in urban high school students tended to lower than rural. And the perceptive disturbance was higher than rural in rate. The conductive disturbance tended to oppose in comparison with the above.

• Journal of Distribution Research
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• v.17 no.4
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• pp.29-53
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• 2012

As so many marketers get to use diverse sales promotion methods, manufacturer and retailer in a channel often use them too. In this context, diverse issues on sales promotion management arise. One of them is the issue of unplanned buying. Consumers' unplanned buying is clearly better off for the retailer but not for manufacturer. This asymmetric influence of unplanned buying should be dealt with prudently because of its possibility of provocation of channel conflict. However, there have been scarce studies on the sales promotion management strategy considering the unplanned buying and its asymmetric effect on retailer and manufacturer. In this paper, we try to find a better way for a manufacturer in a channel to promote performance through the retailer's sales promotion efforts when there is potential of unplanned buying effect. We investigate via game-theoretic modeling what is the optimal cost sharing level between the manufacturer and retailer when there is unplanned buying effect. We investigated following issues about the topic as follows: (1) What structure of cost sharing mechanism should the manufacturer and retailer in a channel choose when unplanned buying effect is strong (or weak)? (2) How much payoff could the manufacturer and retailer in a channel get when unplanned buying effect is strong (or weak)? We focus on the impact of unplanned buying effect on the optimal cost sharing mechanism for sales promotions between a manufacturer and a retailer in a same channel. So we consider two players in the game, a manufacturer and a retailer who are interacting in a same distribution channel. The model is of complete information game type. In the model, the manufacturer is the Stackelberg leader and the retailer is the follower. Variables in the model are as following table. Manufacturer's objective function in the basic game is as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. And retailer's is as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+p_u(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. The model is of four stages in two periods. Stages of the game are as follows. (Stage 1) Manufacturer sets wholesale price of the first period($w_1$) and cost sharing level of channel sales promotion(${\Psi}$). (Stage 2) Retailer sets retail price of the focal brand($p_1$), the unplanned buying item($p_u$), and sales promotion level(L). (Stage 3) Manufacturer sets wholesale price of the second period($w_2$). (Stage 4) Retailer sets retail price of the second period($p_2$). Since the model is a kind of dynamic games, we try to find a subgame perfect equilibrium to derive some theoretical and managerial implications. In order to obtain the subgame perfect equilibrium, we use the backward induction method. In using backward induction approach, we solve the problems backward from stage 4 to stage 1. By completely knowing follower's optimal reaction to the leader's potential actions, we can fold the game tree backward. Equilibrium of each variable in the basic game is as following table. We conducted more analysis of additional game about diverse cost level of manufacturer. Manufacturer's objective function in the additional game is same with that of the basic game as follows: ${\Pi}={\Pi}_1+{\Pi}_2$, where, ${\Pi}_1=w_1(1+L-p_1)-{\psi}^2$, ${\Pi}_2=w_2(1-{\epsilon}L-p_2)$. But retailer's objective function is different from that of the basic game as follows: ${\pi}={\pi}_1+{\pi}_2$, where, ${\pi}_1=(p_1-w_1)(1+L-p_1)-L(L-{\psi})+(p_u-c)(b+L-p_u)$, ${\pi}_2=(p_2-w_2)(1-{\epsilon}L-p_2)$. Equilibrium of each variable in this additional game is as following table. Major findings of the current study are as follows: (1) As the unplanned buying effect gets stronger, manufacturer and retailer had better increase the cost for sales promotion. (2) As the unplanned buying effect gets stronger, manufacturer had better decrease the cost sharing portion of total cost for sales promotion. (3) Manufacturer's profit is increasing function of the unplanned buying effect. (4) All results of (1),(2),(3) are alleviated by the increase of retailer's procurement cost to acquire unplanned buying items. The authors discuss the implications of those results for the marketers in manufacturers or retailers. The current study firstly suggests some managerial implications for the manufacturer how to share the sales promotion cost with the retailer in a channel to the high or low level of the consumers' unplanned buying potential.

• Journal of Intelligence and Information Systems
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• v.24 no.3
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• pp.1-19
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• 2018

Large amount of data is now available for research and business sectors to extract knowledge from it. This data can be in the form of unstructured data such as audio, text, and image data and can be analyzed by deep learning methodology. Deep learning is now widely used for various estimation, classification, and prediction problems. Especially, fashion business adopts deep learning techniques for apparel recognition, apparel search and retrieval engine, and automatic product recommendation. The core model of these applications is the image classification using Convolutional Neural Networks (CNN). CNN is made up of neurons which learn parameters such as weights while inputs come through and reach outputs. CNN has layer structure which is best suited for image classification as it is comprised of convolutional layer for generating feature maps, pooling layer for reducing the dimensionality of feature maps, and fully-connected layer for classifying the extracted features. However, most of the classification models have been trained using online product image, which is taken under controlled situation such as apparel image itself or professional model wearing apparel. This image may not be an effective way to train the classification model considering the situation when one might want to classify street fashion image or walking image, which is taken in uncontrolled situation and involves people's movement and unexpected pose. Therefore, we propose to train the model with runway apparel image dataset which captures mobility. This will allow the classification model to be trained with far more variable data and enhance the adaptation with diverse query image. To achieve both convergence and generalization of the model, we apply Transfer Learning on our training network. As Transfer Learning in CNN is composed of pre-training and fine-tuning stages, we divide the training step into two. First, we pre-train our architecture with large-scale dataset, ImageNet dataset, which consists of 1.2 million images with 1000 categories including animals, plants, activities, materials, instrumentations, scenes, and foods. We use GoogLeNet for our main architecture as it has achieved great accuracy with efficiency in ImageNet Large Scale Visual Recognition Challenge (ILSVRC). Second, we fine-tune the network with our own runway image dataset. For the runway image dataset, we could not find any previously and publicly made dataset, so we collect the dataset from Google Image Search attaining 2426 images of 32 major fashion brands including Anna Molinari, Balenciaga, Balmain, Brioni, Burberry, Celine, Chanel, Chloe, Christian Dior, Cividini, Dolce and Gabbana, Emilio Pucci, Ermenegildo, Fendi, Giuliana Teso, Gucci, Issey Miyake, Kenzo, Leonard, Louis Vuitton, Marc Jacobs, Marni, Max Mara, Missoni, Moschino, Ralph Lauren, Roberto Cavalli, Sonia Rykiel, Stella McCartney, Valentino, Versace, and Yve Saint Laurent. We perform 10-folded experiments to consider the random generation of training data, and our proposed model has achieved accuracy of 67.2% on final test. Our research suggests several advantages over previous related studies as to our best knowledge, there haven't been any previous studies which trained the network for apparel image classification based on runway image dataset. We suggest the idea of training model with image capturing all the possible postures, which is denoted as mobility, by using our own runway apparel image dataset. Moreover, by applying Transfer Learning and using checkpoint and parameters provided by Tensorflow Slim, we could save time spent on training the classification model as taking 6 minutes per experiment to train the classifier. This model can be used in many business applications where the query image can be runway image, product image, or street fashion image. To be specific, runway query image can be used for mobile application service during fashion week to facilitate brand search, street style query image can be classified during fashion editorial task to classify and label the brand or style, and website query image can be processed by e-commerce multi-complex service providing item information or recommending similar item.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.221-241
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• 2018

Deep learning is getting attention recently. The deep learning technique which had been applied in competitions of the International Conference on Image Recognition Technology(ILSVR) and AlphaGo is Convolution Neural Network(CNN). CNN is characterized in that the input image is divided into small sections to recognize the partial features and combine them to recognize as a whole. Deep learning technologies are expected to bring a lot of changes in our lives, but until now, its applications have been limited to image recognition and natural language processing. The use of deep learning techniques for business problems is still an early research stage. If their performance is proved, they can be applied to traditional business problems such as future marketing response prediction, fraud transaction detection, bankruptcy prediction, and so on. So, it is a very meaningful experiment to diagnose the possibility of solving business problems using deep learning technologies based on the case of online shopping companies which have big data, are relatively easy to identify customer behavior and has high utilization values. Especially, in online shopping companies, the competition environment is rapidly changing and becoming more intense. Therefore, analysis of customer behavior for maximizing profit is becoming more and more important for online shopping companies. In this study, we propose 'CNN model of Heterogeneous Information Integration' using CNN as a way to improve the predictive power of customer behavior in online shopping enterprises. In order to propose a model that optimizes the performance, which is a model that learns from the convolution neural network of the multi-layer perceptron structure by combining structured and unstructured information, this model uses 'heterogeneous information integration', 'unstructured information vector conversion', 'multi-layer perceptron design', and evaluate the performance of each architecture, and confirm the proposed model based on the results. In addition, the target variables for predicting customer behavior are defined as six binary classification problems: re-purchaser, churn, frequent shopper, frequent refund shopper, high amount shopper, high discount shopper. In order to verify the usefulness of the proposed model, we conducted experiments using actual data of domestic specific online shopping company. This experiment uses actual transactions, customers, and VOC data of specific online shopping company in Korea. Data extraction criteria are defined for 47,947 customers who registered at least one VOC in January 2011 (1 month). The customer profiles of these customers, as well as a total of 19 months of trading data from September 2010 to March 2012, and VOCs posted for a month are used. The experiment of this study is divided into two stages. In the first step, we evaluate three architectures that affect the performance of the proposed model and select optimal parameters. We evaluate the performance with the proposed model. Experimental results show that the proposed model, which combines both structured and unstructured information, is superior compared to NBC(Naïve Bayes classification), SVM(Support vector machine), and ANN(Artificial neural network). Therefore, it is significant that the use of unstructured information contributes to predict customer behavior, and that CNN can be applied to solve business problems as well as image recognition and natural language processing problems. It can be confirmed through experiments that CNN is more effective in understanding and interpreting the meaning of context in text VOC data. And it is significant that the empirical research based on the actual data of the e-commerce company can extract very meaningful information from the VOC data written in the text format directly by the customer in the prediction of the customer behavior. Finally, through various experiments, it is possible to say that the proposed model provides useful information for the future research related to the parameter selection and its performance.

• Journal of the korean academy of Pediatric Dentistry
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• v.31 no.3
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• pp.516-526
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• 2004

Polymerization shrinkage of photoinitiation type composite resin cause several clinical problems. The purpose of this study was to evaluate the shrinkage strain stress, linear polymerization shrinkage, compressive strength and microhardness of recently developed composite resins. The composite resins were divided into four groups according to the contents of matrix and filler type. Group I : $Denfil^{TM}$(Vericom, Korea) with conventional matrix, Group II : $Charmfil^{(R)}$(Dentkist, Korea) with microfiller and nanofller mixture, Group III : $Filtek^{TM}$ Z250(3M-ESPE, USA) TEGDMA replaced by UDMA and Bis-EMA(6) in the matrix, and Group IV : $Filtek^{TM}$ Supreme(3M-ESPE, USA) using pure nanofiller. Preparation of acrylic molds were followed by filling and curing with light gun. Strain gauges were attached to each sample and the leads were connected to a strainmeter. With strainmeter shrinkage strain stress and linear polymerization shrinkage was measured for 10 minutes. The data detected at 1 minute and 10 minutes were analysed statistically with ONE-way ANOVA test. To evaluate the mechanical properties of tested materials, compressive hardness test and microhardness test were also rendered. The results can be summarized as follows : 1. Filling materials in acrylic molds showed initial temporary expansion in the early phase of polymerization. This was followed by contraction with the rapid increase in strain stress during the first 1 minute and gradually decreased during post-gel shrinkage phase. After 1 minute, there's no statistical differences of strain stress between groups. The highest strain stress was found in group IV and followed by group III, I, II at 10 minutes-measurement(p>.05). In regression analysis of strain stress, group III showed minimal inclination and followed by group II, I, IV during 1 minute. 2. In linear polymerization shrinkage test, the composite resins in every group showed initial increase of shrinkage velocity during the first 1 minute, followed by gradually decrease of shrinkage velocity. After 1 minute, group IV and group III showed statistical difference(p<.05). After 10 minutes, there were statistical differences between group IV and group I, III(p<.05) and between group II and group III(p<.05). In regression analysis of linear polymerization shrinkage, group II showed minimal inclination and followed by group IV, III, I during 1 minute. 3. In compressive strength test, group III showed the highest strength and followed by group II, IV, I. There were statistical differences between group III and group IV, I(p<.05). 4. In microhardness test, upper surfaces showed higher value than lower surfaces in every group(p<.05).

• The Korean Journal of Nuclear Medicine Technology
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• v.17 no.2
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• pp.31-36
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• 2013

Purpose: Measure gastric emptying time (GET: Gastric Emptying Time) is a non-invasive and quantitative evaluation methods, mainly by endoscopic or radiological examination confirmed no mechanical obstruction in patients with symptoms of congestion is checked. Such tests are not common gastric emptying time measured esophageal cancer patients (who underwent esophagectomy) patients after surgery for gastric emptying time was measured test. And the period of time for more than one year after the gastric emptying time measurement was performed. By comparing the two kinds of tests in the chest cavity after surgery as the evaluation of gastrointestinal function tests evaluate the usefulness of GET, and will evaluate the characteristics of the image. Materials and Methods: 93 patients who underwent esophagectomy with gastric emptying time measurement of subject tests immediately after surgery and after 1 year or longer were twice. Preparation of the patient before the test is more than 12 hours of overnight fasting is important, in addition to the medicine or to stop smoking, and diabetes insulin injections should be early in the morning is ideal to test. Generally labeled with $^{99m}Tc-DTPA$ resin which is used to make steamed egg, seaweed and fermented milk with a high viscosity after eating, three hours in the standing position was measured. Evaluation of gastric emptying curves on the way intragastric radioactivity level by 50% the time (half-time [T1/2]) was calculated, based on the half-life was divided into three steps: over 180 minutes was defined as delayed gastric emptying, within 180minutes was defined as intermediate gastric emptying and when all the radioisotopes were dumped into the jejunum as soon as swallowed, was defined as rapid gastric emptying. Results: Gastric emptying time of a typical images stomach of antrum and fundus additional images appear stronger over time move on to the small intestine. but esophageal cancer who underwent esophagectomy side of the thoracic cavity showed a strong image. Immediately after surgery, the half-time (T1/2) of rapid gastric emptying appeared to 12.9%, intermediate gastric emptying appeared to 52.7%, delay gastric emptying appeared to 34.4%. After more than a year the results of the half-life after surgery, 67% of rapid gastric emptying to intermediate gastric emptying was changed, 69% of delay gastric emptying to intermediate gastric emptying changed. Intermediate gastric emptying worse in patients rapid gastric emptying and the delay gastric emptying is 24% in the case. Conclusion: Esophagectomy for esophageal cancer who underwent half-time measurement test (T1/2) rapid gastric emptying and delay gastric emptying are the result of the comparison over time, changes were observed intermediate gastric emptying. Mainly seeing of gastric emptying time measurement in the esophagus instead of thoracic cavity to check the evaluation of gastrointestinal function can be useful even means. And segmentation criteria and narrow time interval of checking if more accurate information and analysis of the clinical diagnosis and evaluation seems to be done.

• Journal of Chest Surgery
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• v.33 no.7
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• pp.531-540
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• 2000

Baclgrpimd; Recent studies have suggested that the cardioprotective effect of ischemic preconditioning(IP) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G) is closely related to glycogen depletion and attenuation of intracellular acidosis. In the present study, the authors tested this hypothesis by perfusion isolated rabbit hearts with glucose(G)-free perfusate. Material and Method; Hearts isolated from New Zealand white rabbits(1.5~2.0 kg body weight) were perfused with Tyrode solution by Langendorff technique. After stabilization of baseline hemodynamics, the hearts were subjected to 45 min global ischemia followed by 120 min reperfusion with IP(IP group, n=13) or without IP(ischemic control group, n=10). IP was induced by single episode of 5 min global ischemia and 10 min reperfusion. In the G-free preconditioned group(n=12), G depletion was induced by perfusionwith G-free Tyrode solution for 5 min and then perfused with G-containing Tyrode solution for 10 min; and 45 min ischemia and 120 min reperfusion. Left ventricular functionincluding developed pressure(LVDP), dP/dt, heart rate, left ventricular end-distolic pressure(LVEDP) and coronary flow (CF) were measured. Myocardial cytosolic and membrane PKC activities were measured by 32P-${\gamma}$-ATP incorporation into PKC-specific peptide and PKC isozymes were analyzed by Western blot with monoclonal antibodies. Infarct size was determined by staining with TTC(tetrazolium salt) and planimetry. Data were analyzed by one-way analysis of variance (ANOVA) and Turkey's post-hoc test. Result ; In comparison with the ischemic control group, IP significantly enhanced functional recovery of the left ventricle; in contrast, functional significantly enhanced functional recovery of the left ventricle; in contrast, functional recovery were not significantly different between the G-free preconditioned and the ischemic control groups. However, the infarct size was significantly reduced by IP or G-free preconditioning(39$\pm$2.7% in the ischemic control, 19$\pm$1.2% in the IP, and 15$\pm$3.9% in the G-free preconditioned, p<0.05). Membrane PKC activities were increased significantly after IP (119%), IP and 45 min ischemia(145%), G-free [recpmdotopmomg (150%), and G-free preconditioning and 45 min ischemia(127%); expression of membrane PKC isozymes, $\alpha$ and $\varepsilon$, tended to be increased after IP or G-free preconditioning. Conclusion; These results suggest that in isolated Langendorff-perfused rabbit heart model, G-free preconditioning (induced by single episode of 5 min G depletion and 10 min repletion) colud not improve post-ischemic contractile dysfunction(after 45-minute global ischemia); however, it has an infarct size-limiting effect.

• The Journal of Small Business Innovation
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• v.19 no.4
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• pp.19-39
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• 2016

With increasing adoption of smart products and complexity, companies have shifted their strategies from stand alone and competitive strategies to business ecosystem oriented and cooperative strategies. The win-win growth of business refers to corporate efforts undertaken by companies to pursue the healthiness of business between conglomerates and partnering companies such as suppliers for mutual prosperity and a long-term corporate soundness based on their business ecosystem and cooperative strategies. This study is designed to validate a theoretical proposition that the win-win growth strategy of Samsung Electronics and cooperative efforts among companies can create a healthy business ecosystem, based on results of case studies and surveys. In this study, a level of global market access of small and mid-sized companies is adopted as the key achievement index. The foreign market entry is considered as one of vulnerabilities in the ecosystem of small and mid-sized enterprises (SMEs). For SMEs, the global market access based on the research and development (R&D) has become the critical component in the process of transforming them into global small giants. The results of case studies and surveys are analyzed mainly based on a model of a virtuous cycle of Creativity, Opportunity, Productivity, and Proactivity (the COPP model) that features the characteristics of the healthiness of a business ecosystem. In the COPP model, a virtuous circle of profits made by the first three factors and Proactivity, which is the manifestation of entrepreneurship that proactively invests and reacts to the changing business environment of the future, enhances the healthiness of a given business ecosystem. With the application of the COPP model, this study finds major achievements of the win-win growth of Samsung Electronics as follows. First, Opportunity plays a role as a parameter in the relations of Creativity, Productivity, and creating profits. Namely, as companies export more (with more Opportunity), they are more likely to link their R&D efforts to Productivity and profitability. However, companies that do not export tend to fail to link their R&D investment to profitability. Second, this study finds that companies with huge investment on R&D for the future, which is the result of Proactivity, tend to hold a large number of patents (Creativity). And companies with significant numbers of patents tend to be large exporters as well (Opportunity), and companies with a large amount of exports tend to record high profitability (Productivity and profitability), and thus forms the virtuous cycle of the COPP model. In addition, to access global markets for sustainable growth, SMEs need to build and strengthen their competitiveness. This study concludes that companies with a high level of proactivity to invest for the future can create a virtuous circle of Creativity, Opportunity, Productivity, and Proactivity, thereby providing a strategic implication that SMEs should invest time and resources in forming such a virtuous cycle which is a sure way for the SMEs to grow into global small giants.

• The Korean Journal of Air & Space Law and Policy
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• v.29 no.2
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• pp.3-54
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• 2014

The Republic of Korea enacted the Air Transport Act in Commercial Law which was entered into force in November, 2011. The Air Transport Act in Korean Commercial Law was established to regulate domestic carriage by air and damages to the third party which occur within the territorial area caused by aircraft operations. There are some problems to be reformed in the Provisions of Korean Commercial Law for the aircraft operator's liability of compensation for damages to the third party caused by aircraft operation as follows. First, the aircraft operator's liability of compensation for damages needs to be improved because it is too low to compensate adequately to the third party damaged owing to the aircraft operation. Therefore, the standard of classifying per aircraft weight is required to be detailed from the current 4-tier into 10-tier and the total limited amount of liability is also in need of being increased to the maximum 7-hundred-million SDR. In addition, the limited amount of liability to the personal damage is necessary to be risen from the present 125,000 SDR to 625,000 SDR according to the recent rate of prices increase. This is the most desirable way to improve the current provisions given the ordinary insurance coverage per one aircraft accident and various specifications of recent aircraft in order to compensate the damaged appropriately. Second, the aircraft operator shall be liable without fault to damages caused by terrorism such as hijacking, attacking an aircraft and utilizing it as means of attack like the 9 11 disaster according to the present Air Transport Act in Korean Commercial Law. Some argue that it is too harsh to aircraft operators and irrational, but given they have also some legal duties of preventing terrorism and in respect of helping the third party damaged, it does not look too harsh or irrational. However, it should be amended into exempting aircraft operator's liability when the terrorism using of an aircraft by well-organized terrorists group happens like 9 11 disaster in view of balancing the interest between the aircraft operator and the third party damaged. Third, considering the large scale of the damage caused by the aircraft operation usually aircraft accident, it is likely that many people damaged can be faced with a financial crisis, and the provision of advance payment for air carrier's liability of compensation also needs to be applied to the case of aircraft operator's liability. Fourth, the aircraft operator now shall be liable to the damages which occur in land or water except air according to the current Air Transport Act of Korean Commercial Law. However, because the damages related to the aircraft operation in air caused by another aircraft operation are not different from those in land or water. Therefore, the term of 'on the surface' should be eliminated in the term of 'third parties on the surface' in order to make the damages by the aircraft operation in air caused by another aircraft operation compensable by Air Transport Act of Korean Commercial Law. It is desired that the Air Transport Act in Commercial Law including the clauses related to the aircraft operator's liability of compensation for damages to the third party be developed continually through the resolutions about its problems mentioned above for compensating the third party damaged appropriately and balancing the interest between the damaged and the aircraft operator.