• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.211-216
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• 2006

Within the frame of the MERCOSUR (South Common Market), one of the most important goals to achieve by its member states is a better cost effective international cargo transportation system. For this purpose the project of developing a commercial waterway linking the east region of Uruguay with the south of Brazil has been under study for a number of years now. Because of the high costs involved on the development of such waterway, the project has been indefinitely delayed. It is our intention to show an alternative way to reduce the present obstacles by using a budget oriented approach in order to determine the vessel best suited to use on the proposed waterway. So far, every study related to the project has been focused on the amount of work needed to modify the environment in order to accommodate the hardware already available in the region. The conclusions show that the cost of opening and maintaining the required navigation channel is high enough to discourage investment; the added responsibility to take care of any environmental damage incurred during the building and/or operation of the waterway makes searching for a less costly and hazardous option an interesting challenge. The proposed terminal on the Cebollat? River would be located at the heart of the Uruguayan rice growing region. Uruguay exports 90% of its rice production, being Brazil its biggest buyer. Wood chips and clinker are the other types of cargo considered to use the proposed waterway in route to either Brazil or to overseas destinations through the deep water port of Rio Grande. Through the analysis of local data by a Geographical Information System, international regulations regarding inland waterways and shallow draught vessel characteristics, we seek to propose a cost efficient alternative to apply on this particular scenario.

• The Journal of Society for e-Business Studies
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• v.18 no.2
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• pp.279-299
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• 2013

The problem of choosing the right product that will best fit a consumer's taste and preferences extends to the field of electronic commerce. However, e-commerce has been able to create a technological proxy for the social filtering process, known as online recommender systems (RSs). RSs aid users in filtering products and decisions on matters relating to personal taste. RSs have the potential to support and improve the quality of the decisions consumers make when searching for and selecting products and services online. However, most previous research on RSs has focused on the accuracy of the algorithms, with little emphasis on user interface and perspectives. This study identified transparency and feedback as possible ways to effectively evaluate RSs from the user's perspective. Thus, this research focused on examining and identifying the roles of transparency and feedback in recommender systems and how they affect users' attitudes toward the system. Results of the study showed that both transparency and feedback positively and significantly affected perceived trust, perceived value of the process, and perceived enjoyment. Furthermore, we found that perceived trust, perceived value of the process, and perceived enjoyment positively and directly affected users' intentions to use/reuse a recommender system.

• Journal of KIISE:Computing Practices and Letters
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• v.8 no.5
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• pp.540-550
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• 2002

Electronic Book(eBook) is a publication that stored and processed the contents of a book using digital mechanisms and has advantages such as easiness in saving and searching and the possibility of carrying. To activate Electronic Book which has the advantages mentioned above, studies on related techniques are required and a development of an editor exclusive for eBooks which is appropriate for eBook structure is still not adequate. In this paper, we design and implement Electronic Book editor providing form-based interface for eBook genre-based structure so that it would be easier for users to write. Especially because Electronic Book has genre-based structure due to the characteristic of literature, it is necessary to provide forms for each different genres. Therefore, compared to the problem of having to study XML grammar when writing Electronic Book using the existing XML editor, the proposed system can solve this problem by providing form-based interface. Additionally, with regard to the characteristic of eBook which have structures according to the intention of users, we provided the flexibility of adding dynamic forms to the form provided in default so that it will be more effective in writing Electronic Books. Therefore by providing form-based interface according to the genre and dynamic structure according to the intention of users, Electronic Book can be wrote more easily.

• Korean Journal of Culture and Social Issue
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• v.19 no.2
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• pp.235-257
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• 2013

The purpose of this study is to develop the Leisure Obsession Scale and examine the validity of the scale. The Leisure Obsession Scale was developed and identified its validity by exploratory factor analysis, confirmatory factor analysis, and correlation analysis. The Leisure Obsession Scale consists of two factors, which are 'Leisure Preoccupation' and 'Leisure Stereotype'. Those two factors indicated the reasonable fit index by confirmatory factor analysis. In addition, this scale displayed discriminant validity via measurement of obsession, workaholism, leisure anxiety, and leisure constraint. Also, the results of criterion validation analysis shows that the Leisure Obsession Scale and its subscale are correlated with measure of age, leisure information searching, intention of participation to new leisure activities, and intention of increase in leisure time. Conceptualizing leisure obsession and exploring components of leisure obsession would be valuable for understanding the nature of leisure obsession and its effects on leisure satisfaction, and suggesting more effective psychological intervention in a diverse population.

• Journal of Global Scholars of Marketing Science
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• v.11
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• pp.147-163
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• 2003

Recently it is necessary that Hotel business introduce Information Technology to enhance competitive advantage and cope with changeable business promptly in management. Thus in an effort of using Information Technology strategically, Many Hotel business tries to install CRM system (Customer Relationship Management). This study tries to analyze the effects on customer performance by installing CRM system if it is in charge of major strategic system, it can get successful customer performance. I hypothesize to resolve the problem, and search preceding study results concerning the elements of CRM Installment and Customer performance. The survey was taken to emplyees in the field of CRM installment in Luxury hotel to test the hypothesis. To summarize the results, first, CRM intallment affects CRM customer performance. in short, systematic feature, management environment, information intention, and technologic element affect it. Through this study, facing the limitation and future study are below. first, additional parameter should be considered though I reviewed the elements affecting CRM customer performance by searching and abstracting preceding studies. Second, There are lack of preceding studies because it has passed only a couple of years since Korean businesses deal with CRM system and there is the limitation to compare this result with others due to few empirical analysisses. Especilly, I can hardly find the preceding study concerning hotel industry but tries to search preceding parameter as to the customer performance of CRM system. Until now, It is needed to continual study its measurement later. I believe that more specific study and precise theoretical test be performed and they deal with current CRM system installment and facing problems in all of the korean hotels.

• Journal of Intelligence and Information Systems
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• v.25 no.2
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• pp.25-38
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• 2019

Selecting high-quality information that meets the interests and needs of users among the overflowing contents is becoming more important as the generation continues. In the flood of information, efforts to reflect the intention of the user in the search result better are being tried, rather than recognizing the information request as a simple string. Also, large IT companies such as Google and Microsoft focus on developing knowledge-based technologies including search engines which provide users with satisfaction and convenience. Especially, the finance is one of the fields expected to have the usefulness and potential of text data analysis because it's constantly generating new information, and the earlier the information is, the more valuable it is. Automatic knowledge extraction can be effective in areas where information flow is vast, such as financial sector, and new information continues to emerge. However, there are several practical difficulties faced by automatic knowledge extraction. First, there are difficulties in making corpus from different fields with same algorithm, and it is difficult to extract good quality triple. Second, it becomes more difficult to produce labeled text data by people if the extent and scope of knowledge increases and patterns are constantly updated. Third, performance evaluation is difficult due to the characteristics of unsupervised learning. Finally, problem definition for automatic knowledge extraction is not easy because of ambiguous conceptual characteristics of knowledge. So, in order to overcome limits described above and improve the semantic performance of stock-related information searching, this study attempts to extract the knowledge entity by using neural tensor network and evaluate the performance of them. Different from other references, the purpose of this study is to extract knowledge entity which is related to individual stock items. Various but relatively simple data processing methods are applied in the presented model to solve the problems of previous researches and to enhance the effectiveness of the model. From these processes, this study has the following three significances. First, A practical and simple automatic knowledge extraction method that can be applied. Second, the possibility of performance evaluation is presented through simple problem definition. Finally, the expressiveness of the knowledge increased by generating input data on a sentence basis without complex morphological analysis. The results of the empirical analysis and objective performance evaluation method are also presented. The empirical study to confirm the usefulness of the presented model, experts' reports about individual 30 stocks which are top 30 items based on frequency of publication from May 30, 2017 to May 21, 2018 are used. the total number of reports are 5,600, and 3,074 reports, which accounts about 55% of the total, is designated as a training set, and other 45% of reports are designated as a testing set. Before constructing the model, all reports of a training set are classified by stocks, and their entities are extracted using named entity recognition tool which is the KKMA. for each stocks, top 100 entities based on appearance frequency are selected, and become vectorized using one-hot encoding. After that, by using neural tensor network, the same number of score functions as stocks are trained. Thus, if a new entity from a testing set appears, we can try to calculate the score by putting it into every single score function, and the stock of the function with the highest score is predicted as the related item with the entity. To evaluate presented models, we confirm prediction power and determining whether the score functions are well constructed by calculating hit ratio for all reports of testing set. As a result of the empirical study, the presented model shows 69.3% hit accuracy for testing set which consists of 2,526 reports. this hit ratio is meaningfully high despite of some constraints for conducting research. Looking at the prediction performance of the model for each stocks, only 3 stocks, which are LG ELECTRONICS, KiaMtr, and Mando, show extremely low performance than average. this result maybe due to the interference effect with other similar items and generation of new knowledge. In this paper, we propose a methodology to find out key entities or their combinations which are necessary to search related information in accordance with the user's investment intention. Graph data is generated by using only the named entity recognition tool and applied to the neural tensor network without learning corpus or word vectors for the field. From the empirical test, we confirm the effectiveness of the presented model as described above. However, there also exist some limits and things to complement. Representatively, the phenomenon that the model performance is especially bad for only some stocks shows the need for further researches. Finally, through the empirical study, we confirmed that the learning method presented in this study can be used for the purpose of matching the new text information semantically with the related stocks.