• Proceedings of the Korean Operations and Management Science Society Conference
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• 2001.10a
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• pp.350-353
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• 2001

As different service classifications for existing and new business data services, it is hard to gather necessary data for the service providers to set their strategies and regulations are also applied asymmetrically to each service provider. Therefore an appropriate market classification is required for the business data services. The Hendry model is selected in this paper to analyze substitution degree among services and then Hendry model is applied to competition among four business data services. As a result, it is shown that these services compete directly and future market shares of services are forecasted and positioning strategy for new services is considered.

• Journal of Labour Economics
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• v.33 no.1
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• pp.31-52
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• 2010

Internal labor market in a firm is normally designed to solve the adverse-selection problem as well as the moral hazard problem in the world of information asymmetry. The internal labor market of a Korean university as a non-profit organization should have the same function as the firm. The purpose of this paper is to evaluate the economic performances of a university's internal labor market, using confidential data. And I suggest how to improve the performances of a Korean university's interal labor market. Specifically, I suggest incentive pay for university professors. I also suggest eight job classification and job-related pay system for staffs and secretaries to enhance the efficiency of the university's internal labor market.

• Journal of the Korean Data and Information Science Society
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• v.26 no.1
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• pp.229-241
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• 2015

Nowadays, an open-market which provides sellers and consumers a cyber place for making a transaction over the Internet has emerged as a prevalent sales channel because of convenience and relatively low price it provides. However, there are few studies about CRM strategies based on VIP consumers for an open-market even though understanding VIP consumers' behaviors in open-markets is important to increase its revenue. Therefore, we propose CRM strategies targeted on VIP customers, obtained by analyzing the transaction data of VIP customers from an open-market using data mining techniques. To that end, we first defined the VIP customers in terms of recency, frequency and monetary (RFM) values. Then, we used data mining techniques to develop a model which best classifies and identifies infiluential factors customers into VIPs or non-VIPs. We also validate each of promotion types in the aspect of effectiveness and identify association rules among the types. Then, based on the findings from these experiments, we propose strategies from the perspectives of CRM dimensions for the open-market to thrive.

• Journal of the Korea Society of Computer and Information
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• v.26 no.1
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• pp.163-170
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• 2021

As a big data is being used in various industries, big data market is expanding from hardware to infrastructure software to service software. Especially it is expanding into a huge platform market that provides applications for holistic and intuitive visualizations such as big data meaning interpretation understandability, and analysis results. Demand for big data extraction and analysis using social media such as SNS is very active not only for companies but also for individuals. However despite such high demand for the collection and analysis of social media data for user trend analysis and marketing, there is a lack of research to address the difficulty of dynamic interlocking and the complexity of building and operating software platforms due to the heterogeneity of various social media service interfaces. In this paper, we propose a method for developing a framework to operate the process from collection to extraction and classification of social media data. The proposed framework solves the problem of heterogeneous social media data collection channels through adapter patterns, and improves the accuracy of social topic extraction and classification through semantic association-based extraction techniques and topic association-based classification techniques.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.26 no.12C
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• pp.280-291
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• 2001

LL (Leased Line service) is a facility-based service as a traditional business data service, but new competition services, such as FR (Frame Relay), VPN (Virtual Private Network), and ATM (Asynchronous Transfer Mode), are value-added services. Because of different service classifications, it is hard to gather necessary data for the service providers to plan their market strategies and regulations and policies are also applied asymmetrically to each service provider. Therefore an appropriate market classification is required for the business data services. After various methods of market classification are reviewed, the Hendry model is selected in this paper to analyze substitution-degree among brands or among services. Since the structure of virtual competitions is required for the Hendry model to be applied to data service market, the market is analyzed first by the well-known Porter's model. By the analysis of Porter's model, two virtual competition structures are set up - one is for the competitions among leased line service providers, and the other is for the competitions among business data services such as LL, FR, VPN and ATM. After the Hendry model is applied to each competition structure, it is confirmed that 7 LL service providers do not compete directly, but 2 sub-markets exist for the LL service provisions. However, it is shown that 4 business data services compete directly. Using the Switching Probability Matrix from Hendry model, future market shares of LL service providers and market shares of business data services are forecasted. These empirical results are helpful for service providers to set competitive strategies with the minimization of cannibalization effect and they can easily and efficiently predict their market demands.

• 제어로봇시스템학회:학술대회논문집
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• 2005.06a
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• pp.1379-1383
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• 2005

Introducing the market into the electricity industry lets the multiple participants get into new competition. These multiple participants of the market need new business strategies for providing value added services to customer. Therefore they need the accurate customer information about the electricity demand. Demand characteristic is the most important one for analyzing customer information. In this study load profile data, which can be collected through the Automatic Meter Reading System, are analyzed for getting demand patterns of customer. The load profile data include electricity demand in 15 minutes interval. An algorithm for clustering similar demand patterns is developed using the load profile data. As results of classification, customers are separated into several groups. And the representative curves for the groups are generated. The number of groups is automatically generated. And it depends on the threshold value for distance to separate groups. The demand characteristics of the groups are discussed. Also, the compositions of demand contracts and standard industrial classification in each group are presented. It is expected that the classified curves will be used for tariff design, load forecasting, load management and so on. Also it will be a good infrastructure for making a value added service related to electricity.

• Journal of Biomedical Engineering Research
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• v.42 no.6
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• pp.277-286
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• 2021

The global market size of AI based SaMD for medical image in 2023 will be anticipated to reach around 620 billion won (518 million dollars). In order for Korean manufacturers to efficiently obtain CE marking for marketing in the EU countries, the paper is to introduce the recommendation and suggestion of how to reclassify SaMD based on classification rules of MDR because, after introducing the Regulation EU 2017/745, classification rules are quite modified and newly added compared to the Directive 93/42/EEC. In addition, the paper is to provide several rules of MDR that may be applicable to decide the classification of SaMD. Lastly, the paper is to examine and demonstrate various secondary data supported by qualitative data because the paper focuses on the suggestion and recommendation with a public trust on the basis of various secondary data conducted by the analysis of field data. In conclusion, the paper found that the previous classification of SaMD followed by the rule of MDD should be reclassified based on the Regulation EU 2017/745. Therefore, the suggestion and recommendation are useful for Korean manufacturers to comprehend the classification of SaMD for marketing in the EU countries.

• The Journal of Fisheries Business Administration
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• v.43 no.1
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• pp.35-48
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• 2012

Inverse demand models are well established as market demands in theory and practice of the existing literature. However, the derivation and its interpretation of individual demands from the market demands are not well known in the literature. This paper analyzes the fish market in Korea by the inverse demand model and shows how we deduce the consumer's responses from the market responses when the markets determine the prices by the quantities demanded. It illustrates empirically how this can be done applying to the korean fish market data. The empirical results show that all fishes are price inflexible and mackerels and hairtails are scale flexible in the market demand while mackerels, hairtails, and croakers are price elastic and mackerels and hairtails are income inelastic in the individual demand. The methodology and empirics used in the paper will make a contribution to the existing literature especially for the purpose of recovering consumer's demand from the market demand, thus implementing the policies to administer the fish markets.

• Journal of Korea Society of Digital Industry and Information Management
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• v.15 no.4
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• pp.197-212
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• 2019

The purpose of this study is to identify the maturity stages of venture firms through classification analysis, which is widely used as a big data technique. Venture companies should develop a competitive advantage in the market. And the maturity stage of a company can be classified into five stages. I will analyze a difference in the growth stage of venture firms between the survey response and the statistical classification methods. The firm growth level distinguished five stages and was divided into the period of start-up and declines. A classification method of big data uses popularly k-mean cluster analysis, hierarchical cluster analysis, artificial neural network, and decision tree analysis. I used variables that asset increase, capital increase, sales increase, operating profit increase, R&D investment increase, operation period and retirement number. The research results, each big data analysis technique showed a large difference of samples sized in the group. In particular, the decision tree and neural networks' methods were classified as three groups rather than five groups. The groups size of all classification analysis was all different by the big data analysis methods. Furthermore, according to the variables' selection and the sample size may be dissimilar results. Also, each classed group showed a number of competitive differences. The research implication is that an analysts need to interpret statistics through management theory in order to interpret classification of big data results correctly. In addition, the choice of classification analysis should be determined by considering not only management theory but also practical experience. Finally, the growth of venture firms needs to be examined by time-series analysis and closely monitored by individual firms. And, future research will need to include significant variables of the company's maturity stages.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.113-123
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• 2024

Stock market news sentiment analysis (SA) aims to identify the attitudes of the news of the stock on the official platforms toward companies' stocks. It supports making the right decision in investing or analysts' evaluation. However, the research on Arabic SA is limited compared to that on English SA due to the complexity and limited corpora of the Arabic language. This paper develops a model of sentiment classification to predict the polarity of Arabic stock news in microblogs. Also, it aims to extract the reasons which lead to polarity categorization as the main economic causes or aspects based on semantic unity. Therefore, this paper presents an Arabic SA approach based on the logistic regression model and the Bidirectional Encoder Representations from Transformers (BERT) model. The proposed model is used to classify articles as positive, negative, or neutral. It was trained on the basis of data collected from an official Saudi stock market article platform that was later preprocessed and labeled. Moreover, the economic reasons for the articles based on semantic unit, divided into seven economic aspects to highlight the polarity of the articles, were investigated. The supervised BERT model obtained 88% article classification accuracy based on SA, and the unsupervised mean Word2Vec encoder obtained 80% economic-aspect clustering accuracy. Predicting polarity classification on the Arabic stock market news and their economic reasons would provide valuable benefits to the stock SA field.