• Journal of Intelligence and Information Systems
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• v.17 no.1
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• pp.91-110
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• 2011

This paper analyzes the cases where the hotels have increased their services and enhanced their work process through IT solutions to cope with computerization globalization. Also the cases have been studies where national hotels use the CRM solution internally to respond effectively to customers requests, increase customer analysis, and build marketing strategies. In particular, this study discusses the introduction of the CRM solutions and CRM sales business and marketing services using a process for utilizing the presumed, CRM by introducing effective DEA(Data Envelopment Analysis). First, the comparison has done regarding the relative efficiency of L Company with the CCR model, then compared L Company's restaurants and facilities' effectiveness through BCC model. L Company reached a conclusion that it is important to precisely create and manage sales data which are the preliminary data for CRM, and for that reason it made it possible to save sales data generated by POS system on each sales performance database. In order to do that, it newly established Oracle POS system and LORIS POS system concerned with restaurants for food and beverage as well as rooms, and made it possible to stably generate and manage sales data and manage. Moreover, it set up a composite database to control comprehensively the results of work processes during a specific period by collecting customer registration information and made it possible to systematically control the information on sales performances. By establishing a system which unifies database and managing it comprehensively, impeccability of data has been greatly enhanced and a problem which generated asymmetric data could be thoroughly solved. Using data accumulated on the comprehensive database, sales data can be analyzed, categorized, classified through data mining engine imbedded in Polaris CRM and the results can be organized on data mart to provide them in the form of CRM application data. By transforming original sales data into forms which are easy to handle and saving them on data mart separately, it enabled acquiring well-organized data with ease when engaging in various marketing operations, holding a morning meeting and working on decision-making. By using summarized data at data mart, it was possible to process marketing operations such as telemarketing, direct mailing, internet marketing service and service product developments for perceived customers; moreover, information on customer perceptions which is one of CRM's end-products could feed back into the comprehensive database. This research was undertaken to find out how effectively CRM has been employed by comparing and analyzing the management performance of each enterprise site and store after introducing CRM to Hotel enterprises using DEA technique. According to the research results, efficiency evaluation for each site was calculated through input and output factors to find out comparative CRM system usage efficiency of L's Company four sites; moreover, with regard to stores, the sizes of workforce and budget application show a huge difference and so does the each store efficiency. Furthermore, by using the DEA technique, it could assess which sites have comparatively high efficiency and which don't by comparing and evaluating hotel enterprises IT project outcomes such as CRM introduction using the CCR model for each site of the related enterprises. By using the BCC model, it could comparatively evaluate the outcome of CRM usage at each store of A site, which is representative of L Company, and as a result, it could figure out which stores maintain high efficiency in using CRM and which don't. It analyzed the cases of CRM introduction at L Company, which is a hotel enterprise, and precisely evaluated them through DEA. L Company analyzed the customer analysis system by introducing CRM and achieved to provide customers identified through client analysis data with one to one tailored services. Moreover, it could come up with a plan to differentiate the service for customers who revisit by assessing customer discernment rate. As tasks to be solved in the future, it is required to do research on the process analysis which can lead to a specific outcome such as increased sales volumes by carrying on test marketing, target marketing using CRM. Furthermore, it is also necessary to do research on efficiency evaluation in accordance with linkages between other IT solutions such as ERP and CRM system.

• KDI Journal of Economic Policy
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• v.12 no.1
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• pp.69-91
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• 1990

A major change in tariff rates was made in January 1989 in Korea. The benchmark tariff rate, which applies to about two thirds of all commodity items, was lowered to 15 percent from 20 percent. In addition, the variation in tariff rates among different types of commodities was reduced. This paper examines the economic impact of the tariff reform using a multisectoral general equilibrium model of the Korean economy which was introduced by Lee and Chang(1988), and by Lee(1988). More specifically, this paper attempts to find the changes in imports, exports, domestic production, consumption, prices, and employment in 31 different sectors of the economy induced by the reform in tariff rates. The policy simulations are made according to three different methods. First, tariff changes in industries are calculated strictly according to the change in legal tariff rates, which tend to over-estimate the size of the tariff reduction given the tariff-drawback system and tariff exemption applied to various import items. Second, tariff changes in industries are obtained by dividing the estimated tariff revenues of each industry by the estimated imports for that industry, which are often called actual tariff rates. According to the first method, the import-weighted average tariff rate is lowered from 15.2% to 10.2%, while the second method changes the average tariff rate from 6.2% to 4.2%. In the third method, the tariff-drawback system is internalized in the model. This paper reports the results of the policy simulation according to all three methods, comparing them with one another. It is argued that the second method yields the most realistic estimate of the changes in macro-economic variables, while the third method is useful in delineating the differences in impact across industries. The findings, according to the second method, show that the tariff reform induces more imports in most sectors. Garments, leather products, and wood products are those industries in which imports increase by more than 5 percent. On the other hand, imports in agricultural, mining and service sectors are least affected. Domestic production increases in all sectors except the following: leather products, non-metalic products, chemicals, paper and paper products, and wood-product industries. The increase in production and employment is largest in export industries, followed by service industries. An impact on macroeconomic variables is also simulated. The tariff reform increases nominal GNP by 0.26 percent, lowers the consumer price index by 0.49 percent, increases employment by 0.24 percent, and worsens the trade balance by 480 million US dollars, through a rise in exports of 540 million US dollars and a rise in imports of 1.02 billion US dollars.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.1-19
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• 2018

Since stock movements forecasting is an important issue both academically and practically, studies related to stock price prediction have been actively conducted. The stock price forecasting research is classified into structured data and unstructured data, and it is divided into technical analysis, fundamental analysis and media effect analysis in detail. In the big data era, research on stock price prediction combining big data is actively underway. Based on a large number of data, stock prediction research mainly focuses on machine learning techniques. Especially, research methods that combine the effects of media are attracting attention recently, among which researches that analyze online news and utilize online news to forecast stock prices are becoming main. Previous studies predicting stock prices through online news are mostly sentiment analysis of news, making different corpus for each company, and making a dictionary that predicts stock prices by recording responses according to the past stock price. Therefore, existing studies have examined the impact of online news on individual companies. For example, stock movements of Samsung Electronics are predicted with only online news of Samsung Electronics. In addition, a method of considering influences among highly relevant companies has also been studied recently. For example, stock movements of Samsung Electronics are predicted with news of Samsung Electronics and a highly related company like LG Electronics.These previous studies examine the effects of news of industrial sector with homogeneity on the individual company. In the previous studies, homogeneous industries are classified according to the Global Industrial Classification Standard. In other words, the existing studies were analyzed under the assumption that industries divided into Global Industrial Classification Standard have homogeneity. However, existing studies have limitations in that they do not take into account influential companies with high relevance or reflect the existence of heterogeneity within the same Global Industrial Classification Standard sectors. As a result of our examining the various sectors, it can be seen that there are sectors that show the industrial sectors are not a homogeneous group. To overcome these limitations of existing studies that do not reflect heterogeneity, our study suggests a methodology that reflects the heterogeneous effects of the industrial sector that affect the stock price by applying k-means clustering. Multiple Kernel Learning is mainly used to integrate data with various characteristics. Multiple Kernel Learning has several kernels, each of which receives and predicts different data. To incorporate effects of target firm and its relevant firms simultaneously, we used Multiple Kernel Learning. Each kernel was assigned to predict stock prices with variables of financial news of the industrial group divided by the target firm, K-means cluster analysis. In order to prove that the suggested methodology is appropriate, experiments were conducted through three years of online news and stock prices. The results of this study are as follows. (1) We confirmed that the information of the industrial sectors related to target company also contains meaningful information to predict stock movements of target company and confirmed that machine learning algorithm has better predictive power when considering the news of the relevant companies and target company's news together. (2) It is important to predict stock movements with varying number of clusters according to the level of homogeneity in the industrial sector. In other words, when stock prices are homogeneous in industrial sectors, it is important to use relational effect at the level of industry group without analyzing clusters or to use it in small number of clusters. When the stock price is heterogeneous in industry group, it is important to cluster them into groups. This study has a contribution that we testified firms classified as Global Industrial Classification Standard have heterogeneity and suggested it is necessary to define the relevance through machine learning and statistical analysis methodology rather than simply defining it in the Global Industrial Classification Standard. It has also contribution that we proved the efficiency of the prediction model reflecting heterogeneity.

• Journal of the Korean association of regional geographers
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• v.2 no.2
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• pp.75-92
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• 1996

Geography education is one of the best ways to improve the understanding of other countries. By analyzing Korean and Japanese textbooks on world geography, I tried to find out how well they explain the other country and to set forth guiding principles for geography education. To achieve these aims, weight analysis are used. The major findings in this study can be summarised as follow. The contents of Korean and Japanese geography textbooks were analyzed deviding into 2 major topics, 6 minor topics, and 20 key concepts. (1) By analyzing Korean geography textbook of the 5th curriculum the weight percentages which had been given to each minor topics were found. They are as follow: resource problem(57.7%), human right problem(21.4%), population problem (9.0%), mutual dependence(6.0%), environmental problem(3.3%), international competition(2.6%). (2) By analyzing Korean geography text-book of the 6th curriculum the weight percentages which had been give to each minor topics were found. They are as follow: resource problem(42.7%), human right problem(21.7%), mutual dependence (20.9%), environmental problem(7.7%), population problem(4.6%), international competition(2.4%) (3) By analyzing Japanise geography text-book of 5th curriculum ammendment the weight percentages which had been give to each minor topics were found. They are as follows: resource problem(49.9%) human right problem(21.7%), mutual dependence(15.5%), population problem (7.1%), international competition(6.2%), environmental problem(3.8%) (4) By analyzing Japanise geography textbook of 6th curriculum ammendment the weight percentages which had been give to each minor topics were found. They are as follows human right problem (31.6%), mutual dependence(22.8%), resource problem(20.7%), population problem(12.7%), environmental problem(8.6%), international competition(3.6%). We can see that in the field of dependence Korea and Japan put the similar weight but in the field of common problem they put the fairly different weight. It can be viewed as the difference of curriculum. That is to say Korea used both the systematic method on the basis of unit but Japan used only topical method on the basis of unit. Therefore Korean geography textbook introduce agriculture, forestry, fishery, mining industry and manufacturing industry. Japanese textbook, however gives a detailed account about residents' lives in specific area. For that reason in Korean textbook, resource was stressed, while in Japanese textbook, culture was stressed.

• Economic and Environmental Geology
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• v.18 no.1
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• pp.65-92
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• 1985

A total of 12 mineral commodities significant in domestic output, economy and/or strategy of the Republic of Korea are chosen to examine the structural changes in production and demand-supply of these minerals during the last two decades of her industrialization. These include iron and manganese ores as the raw materials for iron and steel making, copper, zinc and tungsten ores among other non-ferrous metallic minerals, limestone (cement), kaolin, talc, pyrophyllite and graphite among other non-metallic minerals, and anthracite coal as the only domestic source of fossil energy. These are reviewed historically in time-series based on the statistical data which are tabulated and graphed in terms of domestic output, export, import, apparent demand-supply, its increasing rate, and self-sufficiency rate of each commodity. The increasing rates of demand-supply (IRDS) of some more important commodities are compared with those of Gross Domestic Production (GDP) and Economic Growth Rate (EGR) to evaluate how the IRDS contributed to the GDP and EGR. The major results revealed are as follows: Among the 12 commodities, the domestic output of 8 commodities appeared to have grown with steady upward trends: they are ores of lead, zinc and tungsten, limestone (cement), kaolin, talc, pyrophyllite and anthracite coal. Two commodities, ores of iron and copper, continued with unchanging or slightly declining trends and varied fluctuations, in spite of their cardinal importance to the heavy industry and strategy of Korea. The remaining two, graphite and manganese ore, have gradualy declined in domestic output in which the former has still enough resource potential but the latter has not and virtually ceased its domestic output. Trade patterns for mineral commodities in the Republic of Korea during the last two decades have changed greatly, being marked by a shift from mineral-exporting to mineral importing, mainly because of increasing consumption of mineral raw materials for industrialization rather than beceuse of decreasing output of domestic mineral commodities in quantity. In terms of trade patterns, the 12 commodities concerned in this study can be classified into the following four groups. The 1st group - ores of lead and tungsten have only been exported without imports. The 2nd group - amorphous graphite, and pyrophyllite have mainly been exported but partly been imported. The 3rd group - kaolin, talc and crystalline graphite have equally been exported and imported, but quantity of imports have rapidly been increased with time. The 4th group - ores of iron, manganese and zinc have shifted from exports to imports during the industrialization, particularly owing to the initiation of iron and steel making by the Pohang Iron and Steel Company in the middle 1970' s and the new establishment of the Onsan Zinc Refinery in the late 1970' s. All of the 12 commodities under considerations were far above 100% in self-sufficiency rate before or in the early 1960' s. Recently, however, most of them have been declined to below 100% except for those of limestone (cement) and pyrophyllite. It is particularly serious to identify that the self-sufficiency rates of the three important metallic minerals, iron, copper and manganese ores in 1982 appeared to be 5.1%, 0.5%, and 0.01%, respectively. The average self-sufficiency rate of the total domestic minerals produced in 1982 was 14.4% (in value) for that year. Mining industry appeared to be extremely high in its intermediate demand rate whereas its intermediate input rate to be quite low indicating that mineral raw materials have been exerted strong forward linkage effects upon the other industries rather than backward linkage effects. In comparing the curves of increasing rates of demand-supply of several major minerals - iron ore, manganese ore, copper ore, limestone (cement), kaolin, and anthracite coal - with those of Gross Domestic Production and Economic Growth Rate drawn on every graph, it is clearly shown that the curves of increasing rates of demand-supply comprise around 6 to 7 periods of cycles which roughly harmonious with those of the curves of GDP and EGR, except for the curve of anthracite coal of which the configuration seems to have resulted from the (artificial) government's mineral policy rather than from economic free market mechanism. The harmonic feature of these curves well suggests that the increasing rates of demand-supply of major minerals have been significantly contributed to the GDP and EGR. In addition, the wider amplitudes of the iron, manganese and copper curves than those of the limestone (cement) and kaolin curves indicate that the contribution of the former, metallic commodities, has been greater than that of the latter, non-metallic commodities.

• Journal of vocational education research
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• v.36 no.1
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• pp.47-75
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• 2017

The purpose of this study was to provide basic data for policy making for secondary vocational education in each region and transformation in vocational high schools. To achieve this, the regional strategic industries in Daejeon and Jeonnam were selected, new demand for vocational high school graduates was forecasted in each industry and occupation. The results of the study are as follows. First, locational quotient analysis and regional shift-share analysis revealed that Daejon and Jeonnam have different strategic industries. Daejon, unlike Jeonnam strategically develops 'manufacturing food, beverage and tobacco', 'manufacturing timber and paper, printing and copying', 'public service and administration of national defense and social security' and 'manufacturing electrical devices, electronics and precision devices'. Jeonnam has specialized industries distinguished from Daejon's, which are 'manufacturing of machinery transportation equipments and etc', 'manufacturing of non-metallic minerals and metal products', 'electric, gas, steam and water supply systems/industries', 'manufacturing coal and chemical products, refining petroleum', 'mining' and 'agriculture, forestry and fishery'. Second, new demand for vocational high school graduates by occupations and industries showed regional differences(in Daejon and Jeonnam). According the forecast, Daejon will have many workforce demands based on manufacturing industries, on the other hand Jeonnam's focused on service industries. Analysis by occupations was also different, Daejon showed high demands on professional and related workers, while Jeonnam requested many new office and service workers. Third, new workforce demand by occupations in regional strategic industries is big part of overall new workforce demand both in Daejon and Jeonnam. Forth, according to the results of analyzing the new demand for vocational high school graduates in Daejeon and Jeonnam in terms of industry location quotient and change effect, there was high demand in industries with positive total change effects. In terms of location quotient, Daejeon and Jeonnam showed different results.

• Journal of Technology Innovation
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• v.26 no.4
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• pp.199-232
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• 2018

There are increasing needs for understanding and fathoming of business management environment through big data analysis at industrial and corporative level. The research using the company disclosure information, which is comprehensively covering the business performance and the future plan of the company, is getting attention. However, there is limited research on developing applicable analytical models leveraging such corporate disclosure data due to its unstructured nature. This study proposes a text-mining-based analytical model for industrial and firm level analyses using publicly available company disclousre data. Specifically, we apply LDA topic model and word2vec word embedding model on the U.S. SEC data from the publicly listed firms and analyze the trends of business topics at the industrial and corporate levels. Using LDA topic modeling based on SEC EDGAR 10-K document, whole industrial management topics are figured out. For comparison of different pattern of industries' topic trend, software and hardware industries are compared in recent 20 years. Also, the changes of management subject at firm level are observed with comparison of two companies in software industry. The changes of topic trends provides lens for identifying decreasing and growing management subjects at industrial and firm level. Mapping companies and products(or services) based on dimension reduction after using word2vec word embedding model and principal component analysis of 10-K document at firm level in software industry, companies and products(services) that have similar management subjects are identified and also their changes in decades. For suggesting methodology to develop analysis model based on public management data at industrial and corporate level, there may be contributions in terms of making ground of practical methodology to identifying changes of managements subjects. However, there are required further researches to provide microscopic analytical model with regard to relation of technology management strategy between management performance in case of related to various pattern of management topics as of frequent changes of management subject or their momentum. Also more studies are needed for developing competitive context analysis model with product(service)-portfolios between firms.

• Economic and Environmental Geology
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• v.56 no.4
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• pp.457-473
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• 2023

Asbestos has been produced, imported and used in various industries in Korea over the past decades. Since asbestos causes fatal diseases such as malignant mesothelioma and lung cancer, the use of asbestos has been generally banned in Korea since 2009. However, there are still many asbestos-containing materials around us, and safe management is urgently needed. This study aims to examine asbestos-related trend changes using major asbestos-related keywords based on the asbestos trend analysis using big data for the past 32 years (1991 to 2022) in Korea. In addition, we reviewed both domestic trends related to the production, import, and use of asbestos before 1990 and asbestos-related policies from 2023 to 2027. From 1991 to 2000, main keywords related to asbestos were research, workers, carcinogens, and the environment because the carcinogenicity of asbestos was highlighted due to domestic production, import, and use of asbestos. From 2001 to 2010, the main keywords related to asbestos were lung cancer, litigation, carcinogens, exposure, and companies because lawsuits were initiated in the US and Japan in relation to carcinogenicity due to asbestos. From 2011 to 2020, the high ranking keywords related to asbestos were carcinogen, baseball field, school, slate, building, and abandoned asbestos mine due to the seriousness of the asbestos problem in Korea. From 2021 to present (2023), the main search keywords related to asbestos such as school, slate (asbestos cement), buildings, landscape stone, environmental impact assessment, apartment, and cement appeared.

• Knowledge Management Research
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• v.23 no.4
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• pp.315-338
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• 2022

In 1986, Korea established legal systems to support small and medium-sized start-ups, which becomes the main pillars of national development. The legal systems have stimulated start-up ecosystems to have more than 1 million new start-up companies founded every year during the past 30 years. To analyze the trend of Korea's start-up ecosystem, in this study, we collected 1.18 million news articles from 1991 to 2020. Then, we extracted news articles that have the keywords "start-up", "venture", and "start-up". We employed network analysis and topic modeling to analyze collected news articles. Our analysis can contribute to analyzing the government policy direction shown in the history of start-up support policy. Specifically, our analysis identifies the dynamic characteristics of government influenced by external environmental factors (e.g., society, economy, and culture). The results of our analysis suggest that the start-up ecosystems in Korea have changed and developed mainly by the government policies for corporation governance, industrial development planning, deregulation, and economic prosperity plan. Our frequency keyword analysis contributes to understanding entrepreneurial productivity attributed to activities among the networked components in industrial ecosystems. Our analyses and results provide practitioners and researchers with practical and academic implications that can help to establish dedicated support policies through forecast tasks of the economic environment surrounding the start-ups. Korean entrepreneurial productivity has been empowered by growing numbers of large companies in the mobile phone industry. The spectrum of large companies incorporates content startups, platform providers, online shopping malls, and youth-oriented start-ups. In addition, economic situational factors contribute to the growth of Korean entrepreneurial productivity the economic, which are related to the global expansions of the mobile industry, and government efforts to foster start-ups. Our research is methodologically implicative. We employ natural language processes for 30 years of media articles, which enables more rigorous analysis compared to the existing studies which only observe changes in government and policy based on a qualitative manner.

• Information Systems Review
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• v.20 no.2
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• pp.39-62
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• 2018

Thanks to the growth of computing power and the recent development of data analytics, researchers have started to work on the data produced by users through the Internet or social media. This study is in line with these recent research trends and attempts to adopt data analytical techniques. We focus on the impact of "internal marketing" factors on firm performance, which is typically studied through survey methodologies. We looked into the job review platform Jobplanet (www.jobplanet.co.kr), which is a website where employees and former employees anonymously review companies and their management. With web crawling processes, we collected over 40K data points and performed morphological analysis to classify employees' reviews for internal marketing data. We then implemented econometric analysis to see the relationship between internal marketing and market capitalization. Contrary to the findings of extant survey studies, internal marketing is positively related to a firm's market capitalization only within a limited area. In most of the areas, the relationships are negative. Particularly, female-friendly environment and human resource development (HRD) are the areas exhibiting positive relations with market capitalization in the manufacturing industry. In the service industry, most of the areas, such as employ welfare and work-life balance, are negatively related with market capitalization. When firm size is small (or the history is short), female-friendly environment positively affect firm performance. On the contrary, when firm size is big (or the history is long), most of the internal marketing factors are either negative or insignificant. We explain the theoretical contributions and managerial implications with these results.