• International Commerce and Information Review
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• v.15 no.4
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• pp.481-508
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• 2013

The purpose of this study is to research the specific ways of successful localization by analyzing the success and failures case for localization through the theoretical background and the strategic models of localization. The strategic models of localization are divided by management aspects such as the localization of production and sourcing, the localization of human resources, the localization of marketing, the localization of R&D, harmonious relationship with the local community and authority transfer between headquarters and local subsidiaries. And the specific measures of the successful localization are proposed within the framework of the strategic models by comparing and analyzing the success and failures case for localization of individual companies operating in Indonesia. The results indicate that there are successful companies which develop a suitable products for the local climate and failed automobile company which is weak for assembly of complete vehicle in terms of localization of production and sourcing. In case of localization of human resources, most companies recognize the importance of this part and endeavor to secure superior human resource through a related education. It is found that most of the companies perform R & D in their native country. In part of a harmonious relationship with the local community, Korean companies should contribute to the community and be friendly with local residents and make a good image of the company focusing on the cultural environment. In aspect of authority transfer between headquarters and local subsidiaries, there is a tendency to be determined by the head office rather than the joint participation. In the future, in order for Korean enterprise to be successful one in Indonesia market, a highly interdependent and complex forms between headquarters and local subsidiaries shall be performed and an active exchange of information and the selection of best talent regardless of nationality shall be promoted.

• Management & Information Systems Review
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• v.33 no.3
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• pp.167-187
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• 2014

While Korean GAAP had detailed regulations for the measurement and disclosure of operating income in the past, K-IFRS did not provide specific rules for operating income until 2011. Some firms that adopted K-IFRS before 2011 did not disclose or calculated operating income in an inconsistent manner although operating income is usually considered as one of the core information items to assess firm valuation. Inconsistency in firms' treatment of operating income invoked much criticism from diverse users of financial statement. The Korean Accounting Institute (KAI hereafter) revised the K-IFRS rules relevant to operating income in September 2010 in response to the voices raised by the business community, whereby the operating income number is allowed to be calculated in conformity with the previous K-GAAP. This study was motivated by the revision of K-IFRS and aims to provide a clue on the validity of such policy decision. To achieve the research objective, we test the relative value relevance of the alternative operating income numbers under K-IFRS versus K-GAAP. Our main findings are as follows. The value relevance of operating income reported before K-IFRS is proved to be higher than after K-IFRS. K-IFRS operating income adjusted to the previous K-GAAP has greater explanatory power for market values relative to one calculated under the K-IFRS regime. In an additional analysis, the sample was decomposed according to whether the operating income under K-IFRS is greater than under K-GAAP. The difference in the value relevance of K-IFRS versus K-GAAP operating income is significant only in the subsample consisting of firms which reports higher operating income under K-IFRS compared to K-GAAP. Also, the firms which would have reported negative operating income on a consecutive basis are more likely to have chosen K-IFRS, resulting in higher numbers than otherwise. It is likely that firms facing the threat of delisting due to consecutive operating loss reporting are more likely to have adopted K-IFRS disclosure rules by which they could report higher operating income numbers. To sum up, these results corroborate the limitation inherent in the K-IFRS regarding operating income disclosures. This paper suggests that the recent revision of K-IFRS implemented by KAI is likely to mitigate some of afore-mentioned limitations effectively.

• Journal of Intelligence and Information Systems
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• v.16 no.4
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• pp.99-112
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• 2010

Ensemble learning is a method for improving the performance of classification and prediction algorithms. It is a method for finding a highly accurateclassifier on the training set by constructing and combining an ensemble of weak classifiers, each of which needs only to be moderately accurate on the training set. Ensemble learning has received considerable attention from machine learning and artificial intelligence fields because of its remarkable performance improvement and flexible integration with the traditional learning algorithms such as decision tree (DT), neural networks (NN), and SVM, etc. In those researches, all of DT ensemble studies have demonstrated impressive improvements in the generalization behavior of DT, while NN and SVM ensemble studies have not shown remarkable performance as shown in DT ensembles. Recently, several works have reported that the performance of ensemble can be degraded where multiple classifiers of an ensemble are highly correlated with, and thereby result in multicollinearity problem, which leads to performance degradation of the ensemble. They have also proposed the differentiated learning strategies to cope with performance degradation problem. Hansen and Salamon (1990) insisted that it is necessary and sufficient for the performance enhancement of an ensemble that the ensemble should contain diverse classifiers. Breiman (1996) explored that ensemble learning can increase the performance of unstable learning algorithms, but does not show remarkable performance improvement on stable learning algorithms. Unstable learning algorithms such as decision tree learners are sensitive to the change of the training data, and thus small changes in the training data can yield large changes in the generated classifiers. Therefore, ensemble with unstable learning algorithms can guarantee some diversity among the classifiers. To the contrary, stable learning algorithms such as NN and SVM generate similar classifiers in spite of small changes of the training data, and thus the correlation among the resulting classifiers is very high. This high correlation results in multicollinearity problem, which leads to performance degradation of the ensemble. Kim,s work (2009) showedthe performance comparison in bankruptcy prediction on Korea firms using tradition prediction algorithms such as NN, DT, and SVM. It reports that stable learning algorithms such as NN and SVM have higher predictability than the unstable DT. Meanwhile, with respect to their ensemble learning, DT ensemble shows the more improved performance than NN and SVM ensemble. Further analysis with variance inflation factor (VIF) analysis empirically proves that performance degradation of ensemble is due to multicollinearity problem. It also proposes that optimization of ensemble is needed to cope with such a problem. This paper proposes a hybrid system for coverage optimization of NN ensemble (CO-NN) in order to improve the performance of NN ensemble. Coverage optimization is a technique of choosing a sub-ensemble from an original ensemble to guarantee the diversity of classifiers in coverage optimization process. CO-NN uses GA which has been widely used for various optimization problems to deal with the coverage optimization problem. The GA chromosomes for the coverage optimization are encoded into binary strings, each bit of which indicates individual classifier. The fitness function is defined as maximization of error reduction and a constraint of variance inflation factor (VIF), which is one of the generally used methods to measure multicollinearity, is added to insure the diversity of classifiers by removing high correlation among the classifiers. We use Microsoft Excel and the GAs software package called Evolver. Experiments on company failure prediction have shown that CO-NN is effectively applied in the stable performance enhancement of NNensembles through the choice of classifiers by considering the correlations of the ensemble. The classifiers which have the potential multicollinearity problem are removed by the coverage optimization process of CO-NN and thereby CO-NN has shown higher performance than a single NN classifier and NN ensemble at 1% significance level, and DT ensemble at 5% significance level. However, there remain further research issues. First, decision optimization process to find optimal combination function should be considered in further research. Secondly, various learning strategies to deal with data noise should be introduced in more advanced further researches in the future.

• Journal of the Society of Disaster Information
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• v.17 no.3
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• pp.487-499
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• 2021

Purpose:The purpose of the paper is to review the problems of performance enhancement of safety certification standards and to suggest directions for improvement in order to rationalize safety certification standards for future industrial development and environmental changes. Method: The problems and limitations of the safety certification system are summarized through literature review and interview with manager, and the status of safety certification standards is classified into design standards, performance standards, and detailed standards, and the status analysis is performed. In addition, by synthesizing the results of the investigation and analysis, improvements are suggested to improve the performance of the safety certification standards. Result: Through the survey, the problems and limitations of safety certification could be grouped into six categories: government-led certification system operation, standardized certification standards, long time required to improve certification, poor certification standards preparation system, and lack of reflection of industry opinions. And, as a result of analyzing the certification standards by dividing them into performance and design standards, in the case of machinery, equipment, and protection devices, the design standards were high at 69.7% and 64.9%, whereas in the case of protective equipment, the performance standards were high at 61.1%. In order to improve the performance of safety certification standards centered on design standards, it is necessary to determine the possibility of performance enhancement of the certification standards and determine the feasibility of the inspection test method. In order to improve performance, it was reviewed that it was necessary to establish a systemic foundation and infrastructure, such as strengthening the Product Liability Act, systematizing market monitoring, etc., distributing certification test tasks, and participating in the preparation of certification standards by the private sector. Conclusion: Through this study, the problems and limitations of Korea's safety certification system were summarized and the necessity for performance improvement was reviewed. Performance improvement of safety certification standards is a matter that requires preparatory work, such as legislative revision and infrastructure construction, and requires mid-to-long-term promotion. In addition, rather than improving the overall safety certification standards, the performance requirements for each item subject to certification should be reviewed and promoted, and details should be specified through additional research.

• Journal of Korean Home Economics Education Association
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• v.26 no.4
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• pp.21-34
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• 2014

This study examined the levels of recognition, knowledge, and intake behavior of foods containing caffeine and analyzed the factors affecting amount of caffeine intake to get basic materials for development of educational program for proper intake behavior of foods containing caffeine of high school students in Gyeongbuk region. Of 300 questionnaires that were distributed on December 27, 2013 to February 14, 2014, 255 responses were used for analysis. Frequency distributions, Cronbach's ${\alpha}$, t tests, one-way ANOVA, Pearson's correlation analysis, and regression analysis were conducted by SPSS Windows V.21.0. The mean of the level of concern about foods containing caffeine was 2.76/5.00 points; male and second and third grade students showed lower concern level than female and first grade students. The respondents answered that intake of foods containing caffeine had somewhat harmful effect on health, but it did not much help to refresh and increase concentration. The mean for the percentage of correct answers about knowledge of caffeine was 54.3. Small number of the respondents were advised to reduce intake of caffeine from school teachers or parents. The average of caffeine intake amount was 71.67mg/day; the average for male was 78.98mg/day, whereas for female 61.23mg/day. Almost thirteen percentage of the respondents took caffeine over RDA(Recommended Daily Allowance). According to the result of regression analysis for amount of caffeine intake, grade, BMI, concern about health, perception of whether foods containing caffeine help with refreshment and increase of concentration or not, and experiences of having advice from school teachers to decrease caffeine intake were significant. These results imply that it is necessary for high school students to educate how to take caffeine properly.