• Journal of the Korean Home Economics Association
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• v.36 no.4
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• pp.63-78
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• 1998

In multi-value, multi-religions situation, we need to the research on the function of religion. This study focuses on the family life & value orientation of the people with the deferent religious affiliations. The out line of the research is a as fallow: 1) The religious life of the people with the deferent religious affiliations consists of three factors: value & attitude, religious belief and family life. 2) Among these different religions, Christian has the strong intention. 3) In general, women has the strong nature of religion more than man. 4) When the husband & wife are same religions, they get more intention of religion.

• Journal of Digital Contents Society
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• v.19 no.1
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• pp.149-155
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• 2018

In this paper we propose an algorithm for generating role-performer bipartite matrix for analyzing BPM-based human resource affiliations. Firstly, the proposed algorithm conducts the extraction of role-performer affiliation relationships from ICN(Infromation Contorl Net) based business process models. Then, the role-performer bipartite matrix is constructed in the final step of the algorithm. Conclusively, the bipartite matrix generated through the proposed algorithm ought to be used as the fundamental data structure for discovering the role-performer affiliation networking knowledge, and by using a variety of social network analysis techniques it enables us to acquire valuable analysis results about BPM-based human resource affiliations.

• Journal of Physiology & Pathology in Korean Medicine
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• v.26 no.5
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• pp.615-620
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• 2012

This research aims to analyze the trend of subhealth and meebyung(未病) research as a method of network analysis from 2007 to 2011 in China. A total of 3,933 papers were involved in analysis from 5,465 searched papers, which title have '未病', '亞健康' in CNKI (China National Knowledge Infrastructure). It is carried out that counts annual paper number, authors' publicized papers, and journals paper number related to subhealth. Network analysis was performed to reveal collaboration research trend and relations between Authors, Affiliations, and Regions. As a result, Number of related studies have increased for the last 5 years. East and south regions of China, which include Beijing, Guangxi, and Zhejiang have participated most in their studies, and also as collaborated researches. As affiliations, Researches done by College of Traditional Chinese medicine and their hospital's collaborations are most counted. Because of distance limit, many colleges or institutes seem to make contacts with nearby affiliations. This study is the first attempt to perform network analysis on subhealth research trend in CNKI. This study would contribute to related studies in case of network analysis method.

• Journal of the Korean Society for information Management
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• v.26 no.1
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• pp.147-161
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• 2009

The purpose of this study is to identify interdisciplinarity among eight research areas based upon the correlations between researchers' department affiliations and research areas. More specifically, eight research areas and their sub-areas, 153 sub-areas with researchers' department affiliations were analyzed in terms of Pearson correlation analyses. The findings demonstrated that there was interdisciplinarity between Social Science and Multiple Science Areas, Social Science and Medical & Pharmaceutical Area, Natural Science and Medical & Pharmaceutical Area, and Medical & Pharmaceutical Area and Agricultural Science.

• Journal of Internet Computing and Services
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• v.14 no.2
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• pp.25-34
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• 2013

In this paper, we propose an activity-performer bipartite matrix generation algorithm for analyzing workflow-supported human-resource affiliations in a workflow model. The workflow-supported human-resource means that all performers of the organization managed by a workflow management system have to be affiliated with a certain set of activities in enacting the corresponding workflow model. We define an activity-performer affiliation network model that is a special type of social networks representing affiliation relationships between a group of performers and a group of activities in workflow models. The algorithm proposed in this paper generates a bipartite matrix from the activity-performer affiliation network model(APANM). Eventually, the generated activity-performer bipartite matrix can be used to analyze social network properties such as, centrality, density, and correlation, and to enable the organization to obtain the workflow-supported human-resource affiliations knowledge.

• The Korean Journal of Applied Statistics
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• v.24 no.6
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• pp.1259-1270
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• 2011

This study analyzed an aspect of co-author relationship in papers published in the Korean Journal of Applied Statistics by social network analysis. The data were extracted from 664 papers in the journal from 2000 to 2010. Authors at center of the network are detected by a network centrality analysis. Sub-network analysis found distinguishable research groups from the point of view of their topics or affiliations. The significance of affiliations to co-author relationship was examined by logistic regression analysis.

• International Area Studies Review
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• v.21 no.2
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• pp.219-245
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• 2017

This study is intended to empirically investigate how Korean consumers evaluate foreign CSRs conducted by Korean firms. Specifically, the study investigates the effects of domestic vs. foreign CSR targets & domestic vs. global NGO affiliations by employing a two-way 2-by-2 factorial design. Field experiments were carried out with adult consumers in Seoul aged over 25. Our findings, first, suggest that Korean consumers perceive domestic CSRs more positively than foreign CSRs. But Korean consumers were found to respond more positively to foreign CSRs when the focal firm was perceived as a global firm. As regards to NGO affiliations, the study found that consumers were partially favorable towards domestic NGOs. However, interestingly, foreign CSRs with domestic NGOs were perceived more favorably than foreign CSRs with foreign NGOs.

• Asian Journal for Public Opinion Research
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• v.11 no.4
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• pp.304-326
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• 2023

News shows often deliver crosscutting information to their audiences by inviting commentators from rival political parties. If these news shows foster the formation of informed and balanced views of the audience, mass media could provide countermeasures against political polarization. To test the effect of such news shows, this study conducted an experiment with two variants of a simulated radio talk show. In the partisan scenario, the two guest commentators' affiliations suggested their ideological orientation. In the non-partisan scenario, the commentators had neutral affiliations. We divided participants into two ideology groups, liberals and conservative, and compared each group's evaluation of the commentators in the two scenarios. Two multivariate analysis of variance (MANOVA) tests were conducted to analyze the effect of the perceived ideology of the commentators on respondents' attitudes toward the commentators' arguments depending on their own ideological inclinations. The analyses results did not support the hypothesis that anticipated partisan attitudes towards the commentators' arguments. It was only the liberal respondents who showed statistically significant different attitudes toward commentators' arguments in each of the two scenarios. The findings suggest that such broadcast shows do not automatically trigger partisan message processing and may help the audience to develop informed and balanced opinions. While the current study failed to find conclusive evidence to support the hypotheses, it also found that the perceived ideology of the information source may trigger partisan attitudes for certain types of issues. Future studies with different experiment designs are needed to investigate the issue further.

• International Journal of Computer Science & Network Security
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• v.24 no.5
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• pp.217-223
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• 2024

The current study aimed to evaluate the effectiveness of using Support Vector Machine (SVM) for political affiliation classification. The system was designed to analyze the political tweets collected from Twitter and classify them as positive, negative, and neutral. The performance analysis of the SVM classifier was based on the calculation of metrics such as accuracy, precision, recall, and f1-score. The results showed that the classifier had high accuracy and f1-score, indicating its effectiveness in classifying the political tweets. The implementation of SVM in this study is based on the principle of Structural Risk Minimization (SRM), which endeavors to identify the maximum margin hyperplane between two classes of data. The results indicate that SVM can be a reliable classification approach for the analysis of political affiliations, possessing the capability to accurately categorize both linear and non-linear information using linear, polynomial or radial basis kernels. This paper provides a comprehensive overview of using SVM for political affiliation analysis and highlights the importance of using accurate classification methods in the field of political analysis.

• International Commerce and Information Review
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• v.1 no.1
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• pp.3-24
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• 2008

This paper examines the corporate parent effect on the diversification strategies of triad and Asian subsidiaries in the ASEAN region. I find that triad subsidiaries tend to participate in high technology intensive industries, and are more likely to diversify. In addition, I also find that the subsidiary diversification strategy is significantly affected by the number of affiliations under the same corporate parent that perform the same or different activities in the region. These results shed light on the influence of corporate parent on subsidiary-level strategy and the role of the subsidiary within an economically integrated region. Various implications are discussed.