• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.95-108
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• 2017

Recently, AlphaGo which is Bakuk (Go) artificial intelligence program by Google DeepMind, had a huge victory against Lee Sedol. Many people thought that machines would not be able to win a man in Go games because the number of paths to make a one move is more than the number of atoms in the universe unlike chess, but the result was the opposite to what people predicted. After the match, artificial intelligence technology was focused as a core technology of the fourth industrial revolution and attracted attentions from various application domains. Especially, deep learning technique have been attracted as a core artificial intelligence technology used in the AlphaGo algorithm. The deep learning technique is already being applied to many problems. Especially, it shows good performance in image recognition field. In addition, it shows good performance in high dimensional data area such as voice, image and natural language, which was difficult to get good performance using existing machine learning techniques. However, in contrast, it is difficult to find deep leaning researches on traditional business data and structured data analysis. In this study, we tried to find out whether the deep learning techniques have been studied so far can be used not only for the recognition of high dimensional data but also for the binary classification problem of traditional business data analysis such as customer churn analysis, marketing response prediction, and default prediction. And we compare the performance of the deep learning techniques with that of traditional artificial neural network models. The experimental data in the paper is the telemarketing response data of a bank in Portugal. It has input variables such as age, occupation, loan status, and the number of previous telemarketing and has a binary target variable that records whether the customer intends to open an account or not. In this study, to evaluate the possibility of utilization of deep learning algorithms and techniques in binary classification problem, we compared the performance of various models using CNN, LSTM algorithm and dropout, which are widely used algorithms and techniques in deep learning, with that of MLP models which is a traditional artificial neural network model. However, since all the network design alternatives can not be tested due to the nature of the artificial neural network, the experiment was conducted based on restricted settings on the number of hidden layers, the number of neurons in the hidden layer, the number of output data (filters), and the application conditions of the dropout technique. The F1 Score was used to evaluate the performance of models to show how well the models work to classify the interesting class instead of the overall accuracy. The detail methods for applying each deep learning technique in the experiment is as follows. The CNN algorithm is a method that reads adjacent values from a specific value and recognizes the features, but it does not matter how close the distance of each business data field is because each field is usually independent. In this experiment, we set the filter size of the CNN algorithm as the number of fields to learn the whole characteristics of the data at once, and added a hidden layer to make decision based on the additional features. For the model having two LSTM layers, the input direction of the second layer is put in reversed position with first layer in order to reduce the influence from the position of each field. In the case of the dropout technique, we set the neurons to disappear with a probability of 0.5 for each hidden layer. The experimental results show that the predicted model with the highest F1 score was the CNN model using the dropout technique, and the next best model was the MLP model with two hidden layers using the dropout technique. In this study, we were able to get some findings as the experiment had proceeded. First, models using dropout techniques have a slightly more conservative prediction than those without dropout techniques, and it generally shows better performance in classification. Second, CNN models show better classification performance than MLP models. This is interesting because it has shown good performance in binary classification problems which it rarely have been applied to, as well as in the fields where it's effectiveness has been proven. Third, the LSTM algorithm seems to be unsuitable for binary classification problems because the training time is too long compared to the performance improvement. From these results, we can confirm that some of the deep learning algorithms can be applied to solve business binary classification problems.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.69-93
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• 2017

The development of ICT has leaded the diversification and changes of supplies and demands in markets. It also caused the creations of a variety of values which are differentiated from those in the existing market. Therefore, a new-type market is created, which can include multi-value chains which are from ICT-based created markets as well as the existing markets. We defined the platform as the new-type market. In the platform, the multi-value chains can be coexisted with multi-values. In true market, when a new-type value chain entered into an existing market, it is general that it can be conflicted with the existing value chain in the market. The conflicted problem among multi-value chains in a market is caused by the sharing of limited market resources like suppliers, consumers, services or products among the value chains. In other words, if there are multi-value chains in the platform, then it is possible to have conflictions, overlapping, creations or losses of values among the value chains. To solve the problem, we introduce coexistence factors to reduce the conflictions to reach market equilibrium in the platform. In the other hand, it is possible to lead the creations of differentiated values from the existing market and to augment the total market values in the platform. In the early era of ICT development, ICT was introduced for improvement of efficiency and effectiveness of the value chains in the existing market. However, according to the changed role of ICT from the supporter to the promotor of the market, ICT became to lead the variations of the value chains and creations of various values in the markets. For instance, Uber Taxi created a new value chain with ICT-based new-type service or products with new resources like new suppliers and consumers. When Uber and Traditional Taxi services are playing at the same time in Taxi service platform, it is possible to create values or make conflictions among values between the new and old value chains. In this research, like Uber and traditional taxi services, if there are conflictions among the multi-value chains, then it is necessary to minimize the conflictions in the platform for the coexistence of multi-value chains which can create the value-added values in the platform. So, it is important to predict and discuss the possible conflicted problems between new and old value chains. The confliction should be solved to reach market equilibrium with multi-value chains in the platform. That is, we discuss the possibility of the coexistence of multi-value chains in the platform which are comprised of a variety of suppliers and customers. To do this, especially we are focusing on the healthcare markets. Nowadays healthcare markets are popularized in global market as well as domestic. Therefore, there are a lot of and a variety of healthcare services like Traditional-, Tele-, or Intelligent- healthcare services and so on. It shows that there are multi-suppliers, -consumers and -services as components of each different value chain in the same platform. The platform can be shared by different values that are created or overlapped by confliction and loss of values in the value chains. In this research, as was said, we focused on the healthcare services to show if a platform can be shared by different value chains like traditional-, tele-healthcare and intelligent-healthcare services and products. Additionally, we try to show if it is possible to increase the value of each value chain as well as the total value of the platform. As the result, it is possible to increase of each value of each value chain as well as the total value in the platform. Finally, we propose a coexistence model to overcome such problems and showed the possibility of coexistence between the value chains through experimentation.

• The Journal of the Petrological Society of Korea
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• v.27 no.3
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• pp.109-120
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• 2018

The distributional characteristics of fault segments in Cretaceous and Tertiary rocks from southeastern Gyeongsang Basin were derived. The 267 sets of fault segments showing linear type were extracted from the curved fault lines delineated on the regional geological map. First, the directional angle(${\theta}$)-length(L) chart for the whole fault segments was made. From the related chart, the general d istribution pattern of fault segments was derived. The distribution curve in the chart was divided into four sections according to its overall shape. NNE, NNW and WNW directions, corresponding to the peaks of the above sections, indicate those of the Yangsan, Ulsan and Gaeum fault systems. The fault segment population show near symmetrical distribution with respect to $N19^{\circ}E$ direction corresponding to the maximum peak. Second, the directional angle-frequency(N), mean length(Lm), total length(Lt) and density(${\rho}$) chart was made. From the related chart, whole domain of the above chart was divided into 19 domains in terms of the phases of the distribution curve. The directions corresponding to the peaks of the above domains suggest the directions of representative stresses acted on rock body. Third, the length-cumulative frequency graphs for the 18 sub-populations were made. From the related chart, the value of exponent(${\lambda}$) increase in the clockwise direction($N10{\sim}20^{\circ}E{\rightarrow}N50{\sim}60^{\circ}E$) and counterclockwise direction ($N10{\sim}20^{\circ}W{\rightarrow}N50{\sim}60^{\circ}W$). On the other hand, the width of distribution of lengths and mean length decrease. The chart for the above sub-populations having mutually different evolution characteristics, reveals a cross section of evolutionary process. Fourth, the general distribution chart for the 18 graphs was made. From the related chart, the above graphs were classified into five groups(A~E) according to the distribution area. The lengths of fault segments increase in order of group E ($N80{\sim}90^{\circ}E{\cdot}N70{\sim}80^{\circ}E{\cdot}N80{\sim}90^{\circ}W{\cdot}N50{\sim}60^{\circ}W{\cdot}N30{\sim}40^{\circ}W{\cdot}N40{\sim}50^{\circ}W$) < D ($N70{\sim}80^{\circ}W{\cdot}N60{\sim}70^{\circ}W{\cdot}N60{\sim}70^{\circ}E{\cdot}N50{\sim}60^{\circ}E{\cdot}N40{\sim}50^{\circ}E{\cdot}N0{\sim}10^{\circ}W$) < C ($N20{\sim}30^{\circ}W{\cdot}N10{\sim}20^{\circ}W$) < B ($N0{\sim}10^{\circ}E{\cdot}N30{\sim}40^{\circ}E$) < A ($N20{\sim}30^{\circ}E{\cdot}N10{\sim}20^{\circ}E$). Especially the forms of graph gradually transition from a uniform distribution to an exponential one. Lastly, the values of the six parameters for fault-segment length were divided into five groups. Among the six parameters, mean length and length of the longest fault segment decrease in the order of group III ($N10^{\circ}W{\sim}N20^{\circ}E$) > IV ($N20{\sim}60^{\circ}E$) > II ($N10{\sim}60^{\circ}W$) > I ($N60{\sim}90^{\circ}W$) > V ($N60{\sim}90^{\circ}E$). Frequency, longest length, total length, mean length and density of fault segments, belonging to group V, show the lowest values. The above order of arrangement among five groups suggests the interrelationship with the relative formation ages of fault segments.

• KDI Journal of Economic Policy
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• v.12 no.3
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• pp.71-94
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• 1990

This paper reviews the rationale of monetary and banking intervention by an outside authority, either the government or the central bank, and seeks to delineate clearly the optimal limits to the monetary and banking deregulation currently underway in Korea as well as on a global scale. Furthermore, this paper seeks to establish an objective and balanced view on the role of the central bank, especially in light of the current discussion on the restructuring of Korea's central bank, which has been severely contaminated by interest-group politics. The discussion begins with the recognition that the modern free banking school and the new monetary economics are becoming formidable challenges to the traditional role of the government or the central bank in the monetary and banking sector. The paper reviews six arguments that have traditionally been presented to support intervention: (1) the possibility of an over-issue of bank notes under free banking instead of central banking; (2) externalities in and the public good nature of the use of money; (3) economies of scale and natural monopoly in producing money; (4) the need for macro stabilization policy due to the instability of the real sector; (5) the external effects of bank failure due to the inherent instability of the existing banking system; and (6) protection for small banknote users and depositors. Based on an analysis of the above arguments, the paper speculates on the optimal role of the government or central bank in the monetary and banking system and the optimal degree of monetary and banking deregulation. By contrast to the arguments for free banking or laissez-faire monetary systems, which become fashionable in recent years, monopoly and intervention by the government or central bank in the outside money system can be both necessary and optimal. In this case, of course, an over-issue of fiat money may be possible due to political considerations, but this issue is beyond the scope of this paper. On the other hand, the issue of inside monies based on outside money could indeed be provided for optimally under market competition by private institutions. A competitive system in issuing inside monies would help realize, to the maxim urn extent possible, external economies generated by using a single outside money. According to this reasoning, free banking activities will prevail in the inside money system, while a government monopoly will prevail in the outside money system. This speculation, then, also implies that the monetary and banking deregulation currently underway should and most likely will be limited to the inside money system, which could be liberalized to the fullest degree. It is also implied that it will be impractical to deregulate the outside money system and to allow market competition to provide outside money, in accordance with the arguments of the free banking school and the new monetary economics. Furthermore, the role of the government or central bank in this new environment will not be significantly different from their current roles. As far as the supply of fiat money continues to be monopolized by the government, the control of the supply of base money and such related responsibilities as monetary policy (argument(4)) and the lender of the last resort (argument (5)) will naturally be assigned to the outside money supplier. However, a mechanism for controlling an over-issue of fiat money by a monopolistic supplier will definitely be called for (argument(1)). A monetary policy based on a certain policy rule could be one possibility. More importantly, the deregulation of the inside money system would further increase the systemic risk inherent in the current fractional banking system, while enhancing the efficiency of the system (argument (5)). In this context, the role of the lender of the last resort would again become an instrument of paramount importance in alleviating liquidity crises in the early stages, thereby disallowing the possibility of a widespread bank run. Similarly, prudential banking supervision would also help maintain the safety and soundness of the fully deregulated banking system. These functions would also help protect depositors from losses due to bank failures (argument (6)). Finally, these speculations suggest that government or central bank authorities have probably been too conservative on the issue of the deregulation of the financial system, beyond the caution necessary to preserve system safety. Rather, only the fullest deregulation of the inside money system seems to guarantee the maximum enjoyment of external economies in the single outside money system.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.37-51
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• 2015

Due to the development of internet technology and the rapid increase of internet data, various studies are actively conducted on how to use and analyze internet data for various purposes. In particular, in recent years, a number of studies have been performed on the applications of text mining techniques in order to overcome the limitations of the current application of structured data. Especially, there are various studies on sentimental analysis to score opinions based on the distribution of polarity such as positivity or negativity of vocabularies or sentences of the texts in documents. As a part of such studies, this study tries to predict ups and downs of stock prices of companies by performing sentimental analysis on news contexts of the particular companies in the Internet. A variety of news on companies is produced online by different economic agents, and it is diffused quickly and accessed easily in the Internet. So, based on inefficient market hypothesis, we can expect that news information of an individual company can be used to predict the fluctuations of stock prices of the company if we apply proper data analysis techniques. However, as the areas of corporate management activity are different, an analysis considering characteristics of each company is required in the analysis of text data based on machine-learning. In addition, since the news including positive or negative information on certain companies have various impacts on other companies or industry fields, an analysis for the prediction of the stock price of each company is necessary. Therefore, this study attempted to predict changes in the stock prices of the individual companies that applied a sentimental analysis of the online news data. Accordingly, this study chose top company in KOSPI 200 as the subjects of the analysis, and collected and analyzed online news data by each company produced for two years on a representative domestic search portal service, Naver. In addition, considering the differences in the meanings of vocabularies for each of the certain economic subjects, it aims to improve performance by building up a lexicon for each individual company and applying that to an analysis. As a result of the analysis, the accuracy of the prediction by each company are different, and the prediction accurate rate turned out to be 56% on average. Comparing the accuracy of the prediction of stock prices on industry sectors, 'energy/chemical', 'consumer goods for living' and 'consumer discretionary' showed a relatively higher accuracy of the prediction of stock prices than other industries, while it was found that the sectors such as 'information technology' and 'shipbuilding/transportation' industry had lower accuracy of prediction. The number of the representative companies in each industry collected was five each, so it is somewhat difficult to generalize, but it could be confirmed that there was a difference in the accuracy of the prediction of stock prices depending on industry sectors. In addition, at the individual company level, the companies such as 'Kangwon Land', 'KT & G' and 'SK Innovation' showed a relatively higher prediction accuracy as compared to other companies, while it showed that the companies such as 'Young Poong', 'LG', 'Samsung Life Insurance', and 'Doosan' had a low prediction accuracy of less than 50%. In this paper, we performed an analysis of the share price performance relative to the prediction of individual companies through the vocabulary of pre-built company to take advantage of the online news information. In this paper, we aim to improve performance of the stock prices prediction, applying online news information, through the stock price prediction of individual companies. Based on this, in the future, it will be possible to find ways to increase the stock price prediction accuracy by complementing the problem of unnecessary words that are added to the sentiment dictionary.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.79-92
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• 2015

Knowledge map is widely used to represent knowledge in many domains. This paper presents a method of integrating the national R&D data and assists of users to navigate the integrated data via using a knowledge map service. The knowledge map service is built by using a lightweight ontology and a topic modeling method. The national R&D data is integrated with the research project as its center, i.e., the other R&D data such as research papers, patents, and reports are connected with the research project as its outputs. The lightweight ontology is used to represent the simple relationships between the integrated data such as project-outputs relationships, document-author relationships, and document-topic relationships. Knowledge map enables us to infer further relationships such as co-author and co-topic relationships. To extract the relationships between the integrated data, a Relational Data-to-Triples transformer is implemented. Also, a topic modeling approach is introduced to extract the document-topic relationships. A triple store is used to manage and process the ontology data while preserving the network characteristics of knowledge map service. Knowledge map can be divided into two types: one is a knowledge map used in the area of knowledge management to store, manage and process the organizations' data as knowledge, the other is a knowledge map for analyzing and representing knowledge extracted from the science & technology documents. This research focuses on the latter one. In this research, a knowledge map service is introduced for integrating the national R&D data obtained from National Digital Science Library (NDSL) and National Science & Technology Information Service (NTIS), which are two major repository and service of national R&D data servicing in Korea. A lightweight ontology is used to design and build a knowledge map. Using the lightweight ontology enables us to represent and process knowledge as a simple network and it fits in with the knowledge navigation and visualization characteristics of the knowledge map. The lightweight ontology is used to represent the entities and their relationships in the knowledge maps, and an ontology repository is created to store and process the ontology. In the ontologies, researchers are implicitly connected by the national R&D data as the author relationships and the performer relationships. A knowledge map for displaying researchers' network is created, and the researchers' network is created by the co-authoring relationships of the national R&D documents and the co-participation relationships of the national R&D projects. To sum up, a knowledge map-service system based on topic modeling and ontology is introduced for processing knowledge about the national R&D data such as research projects, papers, patent, project reports, and Global Trends Briefing (GTB) data. The system has goals 1) to integrate the national R&D data obtained from NDSL and NTIS, 2) to provide a semantic & topic based information search on the integrated data, and 3) to provide a knowledge map services based on the semantic analysis and knowledge processing. The S&T information such as research papers, research reports, patents and GTB are daily updated from NDSL, and the R&D projects information including their participants and output information are updated from the NTIS. The S&T information and the national R&D information are obtained and integrated to the integrated database. Knowledge base is constructed by transforming the relational data into triples referencing R&D ontology. In addition, a topic modeling method is employed to extract the relationships between the S&T documents and topic keyword/s representing the documents. The topic modeling approach enables us to extract the relationships and topic keyword/s based on the semantics, not based on the simple keyword/s. Lastly, we show an experiment on the construction of the integrated knowledge base using the lightweight ontology and topic modeling, and the knowledge map services created based on the knowledge base are also introduced.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.53-77
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• 2015

Mobile commerce provides a convenient shopping experience in which users can buy products without the constraints of time and space. Mobile commerce has already set off a mega trend in Korea. The market size is estimated at approximately 15 trillion won (KRW) for 2015, thus far. In the Korean market, social commerce and open market are key components. Social commerce has an overwhelming open market in terms of the number of users in the Korean mobile commerce market. From the point of view of the industry, quick market entry, and content curation are considered to be the major success factors, reflecting the rapid growth of social commerce in the market. However, academics' empirical research and analysis to prove the success rate of social commerce is still insufficient. Henceforward, it is to be expected that social commerce and the open market in the Korean mobile commerce will compete intensively. So it is important to conduct an empirical analysis to prove the differences in user experience between social commerce and open market. This paper is an exploratory study that shows a comparative analysis of social commerce and the open market regarding user experience, which is based on the mobile users' reviews. Firstly, this study includes a collection of approximately 10,000 user reviews of social commerce and open market listed Google play. A collection of mobile user reviews were classified into topics, such as perceived usefulness and perceived ease of use through LDA topic modeling. Then, a sentimental analysis and co-occurrence analysis on the topics of perceived usefulness and perceived ease of use was conducted. The study's results demonstrated that social commerce users have a more positive experience in terms of service usefulness and convenience versus open market in the mobile commerce market. Social commerce has provided positive user experiences to mobile users in terms of service areas, like 'delivery,' 'coupon,' and 'discount,' while open market has been faced with user complaints in terms of technical problems and inconveniences like 'login error,' 'view details,' and 'stoppage.' This result has shown that social commerce has a good performance in terms of user service experience, since the aggressive marketing campaign conducted and there have been investments in building logistics infrastructure. However, the open market still has mobile optimization problems, since the open market in mobile commerce still has not resolved user complaints and inconveniences from technical problems. This study presents an exploratory research method used to analyze user experience by utilizing an empirical approach to user reviews. In contrast to previous studies, which conducted surveys to analyze user experience, this study was conducted by using empirical analysis that incorporates user reviews for reflecting users' vivid and actual experiences. Specifically, by using an LDA topic model and TAM this study presents its methodology, which shows an analysis of user reviews that are effective due to the method of dividing user reviews into service areas and technical areas from a new perspective. The methodology of this study has not only proven the differences in user experience between social commerce and open market, but also has provided a deep understanding of user experience in Korean mobile commerce. In addition, the results of this study have important implications on social commerce and open market by proving that user insights can be utilized in establishing competitive and groundbreaking strategies in the market. The limitations and research direction for follow-up studies are as follows. In a follow-up study, it will be required to design a more elaborate technique of the text analysis. This study could not clearly refine the user reviews, even though the ones online have inherent typos and mistakes. This study has proven that the user reviews are an invaluable source to analyze user experience. The methodology of this study can be expected to further expand comparative research of services using user reviews. Even at this moment, users around the world are posting their reviews about service experiences after using the mobile game, commerce, and messenger applications.

• Journal of Intelligence and Information Systems
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• v.21 no.4
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• pp.111-131
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• 2015

There are only a handful number of research conducted on pattern analysis of corporate distress as compared with research for bankruptcy prediction. The few that exists mainly focus on audited firms because financial data collection is easier for these firms. But in reality, corporate financial distress is a far more common and critical phenomenon for non-audited firms which are mainly comprised of small and medium sized firms. The purpose of this paper is to classify non-audited firms under distress according to their financial ratio using data mining; Self-Organizing Map (SOM). SOM is a type of artificial neural network that is trained using unsupervised learning to produce a lower dimensional discretized representation of the input space of the training samples, called a map. SOM is different from other artificial neural networks as it applies competitive learning as opposed to error-correction learning such as backpropagation with gradient descent, and in the sense that it uses a neighborhood function to preserve the topological properties of the input space. It is one of the popular and successful clustering algorithm. In this study, we classify types of financial distress firms, specially, non-audited firms. In the empirical test, we collect 10 financial ratios of 100 non-audited firms under distress in 2004 for the previous two years (2002 and 2003). Using these financial ratios and the SOM algorithm, five distinct patterns were distinguished. In pattern 1, financial distress was very serious in almost all financial ratios. 12% of the firms are included in these patterns. In pattern 2, financial distress was weak in almost financial ratios. 14% of the firms are included in pattern 2. In pattern 3, growth ratio was the worst among all patterns. It is speculated that the firms of this pattern may be under distress due to severe competition in their industries. Approximately 30% of the firms fell into this group. In pattern 4, the growth ratio was higher than any other pattern but the cash ratio and profitability ratio were not at the level of the growth ratio. It is concluded that the firms of this pattern were under distress in pursuit of expanding their business. About 25% of the firms were in this pattern. Last, pattern 5 encompassed very solvent firms. Perhaps firms of this pattern were distressed due to a bad short-term strategic decision or due to problems with the enterpriser of the firms. Approximately 18% of the firms were under this pattern. This study has the academic and empirical contribution. In the perspectives of the academic contribution, non-audited companies that tend to be easily bankrupt and have the unstructured or easily manipulated financial data are classified by the data mining technology (Self-Organizing Map) rather than big sized audited firms that have the well prepared and reliable financial data. In the perspectives of the empirical one, even though the financial data of the non-audited firms are conducted to analyze, it is useful for find out the first order symptom of financial distress, which makes us to forecast the prediction of bankruptcy of the firms and to manage the early warning and alert signal. These are the academic and empirical contribution of this study. The limitation of this research is to analyze only 100 corporates due to the difficulty of collecting the financial data of the non-audited firms, which make us to be hard to proceed to the analysis by the category or size difference. Also, non-financial qualitative data is crucial for the analysis of bankruptcy. Thus, the non-financial qualitative factor is taken into account for the next study. This study sheds some light on the non-audited small and medium sized firms' distress prediction in the future.

• Journal of the Korean Geographical Society
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• v.30 no.3
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• pp.269-295
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• 1995

This research is concerned with the social division of labor within the traditional industry district: Damyang bamboo ware industry district and Yeoju pottery industry district in South Korea, Damyang bamboo ware and Yeoju pottery are well known of the Korean traditional industry. The social division of labor in an industry district is considered as an important factor. The social division of labor helps the traditional industry to survive today. This summary shows five significant points from the major findings. First, Damyang bamoo ware industry and Yoeju pottery industry have experienced the growth stages until 1945, the stagnation in the 1960s, and the business recovery in the 1980s. Most Korean traditional industries had been radically declined under the Japanese colonization; while, Damyang bamboo ware industry and Yeoju pottery industry district have been developed during above all stages. The extended market to Japan helped the local government to establish a training center, and to provide financial aids and technical aids to crafts men. During the 1960s and 1970s, mass production of substitute goods on factory system resulted in the decrease of demand of bamboo ware and pettery. During the 1980s, these industries have slowly recovered as a result of the increased income per capita. The high rate of economic growth in the 1960s and 1970s was playing an important role in the emerging the incleased demand of the bamboo ware and pottery. Second the production-and-marketing system in a traditional industry district became diversified to adjust the demand of products. In Damyang bamboo ware industry district, the level of social division of labor was low until the high economic development period. Bamboo ware were made by a farmer in a small domestic system, The bamboo goods were mainly sold in the periodic market of bamboo ware in Damyang. In the recession period in the 1960s and 1970s, the production-and-marketing system were diversified; a manufacturing-wholesale type business and small-factory type business became established; and the wholesale business and the export traders in the district appeared. In the recovery period in the 1980s, the production-and-marketing systems were more diversified; a small-factory type business started to depend On subcontractors for a part of process of production; and a wholesale business in the district engaged in production of bamboo ware. In Yeoju pottery industry district, the social division of labor was limited until the early 1970s. A pottery was made by a crafts man in a small-business of domestic system and sold by a middle man out of Yeoju. Since the late 1970s, production-and-marketing system become being diversified as a result of the increased demand in Japan and South Korea. In the 1970s, Korean traditional craft pottery was highiy demanded in Japan. The demand encouraged people in Yoeju to become craftsmen and/or to work in the pottery related occupation. In South Korea, the rapid economic growth resulted in incline to pottery due to the development of stainless and plastic bowls and dishes. The production facilities were modernized to provide pottery at the reasonable price. A small-busineas of domestic system was transformed into a small-factory type business. The social division of labor was intensified in the pottery production-and-maketing system. The manufacturing kaoline began to be seperated from the production process of pottery. Within the district, a pottery wholesale business and a retail business started to be established in the 1980s. Third the traditional industry district was divided into "completed one" and "not-completed one" according to whether or not the district firms led the function of the social division of labor. The Damyang bamboo ware industry district is "completed one": the firm within the district is in charge of the supply of raw material, the production and the marketing. In the Damyang bamboo ware district, the social division of labor w and reorganized labor system to improve the external economics effect through intensifying the social division of labor. Lastly, the social division of labor was playing an important role in the development of traditional industry districts. The subdivision of production process and the diversification of business reduced the production cost and overcame the labor shortage through hiring low-waged workers such as family members, the old people and housewives. An enterpriser with small amount of capital easily joined into the business. The risk from business recession were dispersed. The accumulated know-how in the production and maketing provided flexiblility to produce various goods and to extend the life-cycly of a product.d the life-cycly of a product.

• The Korean Journal of Air & Space Law and Policy
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• v.32 no.1
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• pp.327-353
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• 2017

The issue submitted to the Court of Justice on the merits of case C---573/13 originated from a claim brought in the context of a dispute between Air Berlin and the German Federal Union of Consumer Organisations and Associations. The challenge concerned the way in which air fares were displayed in Air Berlin's computerised booking system. The system was organised in such a way that, after selecting a date and a departure airport, one would find all possible flight connections in a summary table. However, the final price of the ticket was displayed only for the clicked connection, and not for all connections, thus preventing customers from being able to compare such price with the prices of other connections. The German Federal Union took the view that this practice did not meet the requirements laid down by Article 23 of Regulation (EC) No. 1008/2008, which requires transparency in the prices set for air services. This led the German State to bring an injunctive action to cause Air Berlin to discontinue said practice. The claim was upheld at both the application and appeal stage of the relevant proceedings. Subsequently, Air Berlin submitted the matter to the German Federal High Court, which decided to stay the proceedings and ask for a preliminary ruling from the Court of Justice as to 1. whether Article 23 of Regulation (EC) No. 1008/2008 must be interpreted as meaning that, during the computerised booking process, the final price to be paid must be indicated at all times when prices of air services are shown, including when they are shown for the first time; and 2. whether, during the computerised booking process, the final price must be indicated only for the air service specifically selected by the customer or for each air service shown. In a nutshell, the Court, by the here---discussed judgment determined that Article 23 of Regulation (EC) No. 1008/2008 must be interpreted as meaning that, in the context of a computerised air ticket booking system, the final price to be paid must be indicated not only for the air service specifically selected by the customer, but also for each air service in respect of which the fare is shown. Clearly the above judgment will place air companies under an obligation to update and adjust (when needed) their computerised ticket booking and payment systems, in consideration of the primary need for consumers to be aware at all times of the actual price payable for a ticket and be able to compare the price of the service selected with the prices for other air services in respect of which the fare is shown.