• The Journal of Korean Medicine
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• v.32 no.5
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• pp.66-77
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• 2011

Objectives: This study aimed to make preliminary data through the bibliometric analysis of journals registered by national research foundations of Korea for the improvement of traditional Korean medical (TKM) journals. Methods: We collected 4,396 articles from the OASIS database, which is composed of articles published by TKM societies and institutes. First, we analyzed the 'authors by year', 'average authors per article', 'articles by institute' and 'authors by institute' to get the author-related conditions. Second, we analyzed the 'reference type', 'cited times', 'IF with self citation' and 'IF without self citation' to get the citation condition. Results: First, we found that the journal order of total authors was KAOOMP (2362), KOMS (1189), and KAMS (967), and of average authors per article was KAMS (5.29), KOIMS (5.25), and KOMS (4.75). Second, we learned that the journal order of occupied article ratio by high rank institutes was SCMS (92.4%), MAS (90.03%), and KOPS (87.22%) and of occupied author ratio by high rank institutes was KOPMS (96.55%), MAS (95.19%), and SCMS (93.85%). Third, we analyzed the most highly cited reference type by journals and we found that OMCS was books, SCMS was oriental medical journals and the other 10 journals were not oriental medical journals. Finally, we observed that the journal order of self citation ratio was SCMS (16.79%), KMAS (11.77%), and OOGS (11.67%) and also that the IF order was KAMS (0.675), OOGS (0.546), and KAOH (0.430). Conclusions: Through this study we found that TKM research leans too much toward on oriental medical universities, so we insist that TKM R&D institutes must be expanded. We also found that the self citation ratio was high in TKM journals, so the ratio must be decreased to improve the quality of the TKM journals.

• The Journal of Society for e-Business Studies
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• v.22 no.4
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• pp.71-101
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• 2017

Since late 2016, protectionism has been a major trend in world trade with the Great Britain exiting the European Union and the United States electing Donald Trump as the 45th president. Consequently, there has been a huge public outcry regarding the negative prospects of heavy industry firms in Korea, which are highly dependent upon international trade with Western countries including the United States. In light of such trend and concerns, we have tried to predict business performance of heavy industry firms in Korea with data regarding trade policy of the United States. United States International Trade Commission (USITC) levies countervailing duties and anti-dumping duties to firms that violate its fair-trade regulations. In this study, we have performed data analysis with past records of countervailing duties and anti-dumping duties. With results from clustering analysis, it could be concluded that trade policy trends of the Unites States significantly affects the business performance of heavy industry firms in Korea. Furthermore, we have attempted to quantify such effects by employing long short-term memory (LSTM), a popular neural networks model that is well-suited to deal with sequential data. Our major contribution is that we have succeeded in empirically validating the intuitive argument and also predicting the future trend with rigorous data mining techniques. With some improvements, our results are expected to be highly relevant to designing regulations regarding heavy industry in Korea.