• Journal of the Korean Society for information Management
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• v.36 no.1
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• pp.117-135
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• 2019

Data journals and data papers have grown and considered an important scholarly practice in the paradigm of open science in the context of data sharing and data reuse. This study investigates a total of 713 data papers published in Scientific Data in terms of author, citation, and subject areas. The findings of the study show that the subject areas of core authors are found as the areas of Biotechnology and Physics. An average number of co-authors is 12 and the patterns of co-authorship are recognized as several closed sub-networks. In terms of citation status, the subject areas of cited publications are highly similar to the areas of data paper authors. However, the citation analysis indicates that there are considerable citations on the journals specialized on methodology. The network with authors' keywords identifies more detailed areas such as marine ecology, cancer, genome, database, and temperature. This result indicates that biology oriented-subjects are primary areas in the journal although Scientific Data is categorized in multidisciplinary science in Web of Science database.

• Journal of Intelligence and Information Systems
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• v.20 no.1
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• pp.177-193
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• 2014

Over the past decade, there has been a rapid diffusion of electronic commerce and a rising number of interconnected networks, resulting in an escalation of security threats and privacy concerns. Electronic commerce has a built-in trade-off between the necessity of providing at least some personal information to consummate an online transaction, and the risk of negative consequences from providing such information. More recently, the frequent disclosure of private information has raised concerns about privacy and its impacts. This has motivated researchers in various fields to explore information privacy issues to address these concerns. Accordingly, the necessity for information privacy policies and technologies for collecting and storing data, and information privacy research in various fields such as medicine, computer science, business, and statistics has increased. The occurrence of various information security accidents have made finding experts in the information security field an important issue. Objective measures for finding such experts are required, as it is currently rather subjective. Based on social network analysis, this paper focused on a framework to evaluate the process of finding experts in the information security field. We collected data from the National Discovery for Science Leaders (NDSL) database, initially collecting about 2000 papers covering the period between 2005 and 2013. Outliers and the data of irrelevant papers were dropped, leaving 784 papers to test the suggested hypotheses. The co-authorship network data for co-author relationship, publisher, affiliation, and so on were analyzed using social network measures including centrality and structural hole. The results of our model estimation are as follows. With the exception of Hypothesis 3, which deals with the relationship between eigenvector centrality and performance, all of our hypotheses were supported. In line with our hypothesis, degree centrality (H1) was supported with its positive influence on the researchers' publishing performance (p<0.001). This finding indicates that as the degree of cooperation increased, the more the publishing performance of researchers increased. In addition, closeness centrality (H2) was also positively associated with researchers' publishing performance (p<0.001), suggesting that, as the efficiency of information acquisition increased, the more the researchers' publishing performance increased. This paper identified the difference in publishing performance among researchers. The analysis can be used to identify core experts and evaluate their performance in the information privacy research field. The co-authorship network for information privacy can aid in understanding the deep relationships among researchers. In addition, extracting characteristics of publishers and affiliations, this paper suggested an understanding of the social network measures and their potential for finding experts in the information privacy field. Social concerns about securing the objectivity of experts have increased, because experts in the information privacy field frequently participate in political consultation, and business education support and evaluation. In terms of practical implications, this research suggests an objective framework for experts in the information privacy field, and is useful for people who are in charge of managing research human resources. This study has some limitations, providing opportunities and suggestions for future research. Presenting the difference in information diffusion according to media and proximity presents difficulties for the generalization of the theory due to the small sample size. Therefore, further studies could consider an increased sample size and media diversity, the difference in information diffusion according to the media type, and information proximity could be explored in more detail. Moreover, previous network research has commonly observed a causal relationship between the independent and dependent variable (Kadushin, 2012). In this study, degree centrality as an independent variable might have causal relationship with performance as a dependent variable. However, in the case of network analysis research, network indices could be computed after the network relationship is created. An annual analysis could help mitigate this limitation.

• Journal of Contemporary Eastern Asia
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• v.13 no.1
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• pp.19-37
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• 2014

In an attempt to develop comprehensive evidence-based methods for evaluation of the R&D performance of cross-disciplinary projects, a joint bibliometric analysis of patents and publications was performed for two industry-university-government collaborative projects aimed at commercialization: Hokkaido University Research & Business Park Project (2003-2007; 63 inventors; 176 patents; 853 papers), and Matching Program for Innovations in Future Drug Discovery and Medical Care - phase I (2006-2010; 46 inventors; 235 patents; 733 papers). Besides the simple output indicators (for five years period), and citations (from the publication date to the end of 2012), science maps based on the network analysis of words and co-authorship relations were generated to identify the prominent research themes and teams. Our joint analysis of publications and patents yields objective and mutually complementing information, which provides better insights on research and commercialization performance of the large-scale projects. Hence, such analysis has potential for use in the industry-university project's performance evaluation.

• Journal of Technology Innovation
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• v.24 no.4
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• pp.1-26
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• 2016

The period after the PhD has a huge impact on the careers of researcher from a researcher lifecycle perspective. This is a turning point which student receives guidance from professor and become an independent researcher. Furthermore, they learn to develop ideas for independent research, apply for grants and manage a project; they also form expert networks in related filed and publish papers to share their findings. This study focuses on the period between earning doctoral degree and being employed as a stable position in university. This study starts from a research questions that asks which factors of postdoctoral experience affect research output. In this study, the paper performance, especially co-authorship of paper, of postdoctoral researchers was investigated. The cumulative advantage theory and Matthew effect were employed to shed a light on this research question. The empirical work is based on the Survey & Analysis of National R&D program in Korea conducted by Korea Institute of S&T Evaluation and Planning (KISTEP). The correlations between the research output and characteristics of postdoctoral experience were verified. These results are expected to contribute as new empirical evidences on investigating knowledge transfer activities of new PhDs.

• Journal of the Korean Society for information Management
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• v.31 no.3
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• pp.153-179
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• 2014

This study explores the characteristics of centrality measures for analyzing researchers' impact and structural positions in research collaboration networks. We investigate four binary network centrality measures (degree centrality, closeness centrality, betweenness centrality, and PageRank), and seven existing weighted network centrality measures (triangle betweenness centrality, mean association, weighted PageRank, collaboration h-index, collaboration hs-index, complex degree centrality, and c-index) for research collaboration networks. And we propose SSR, which is a new weighted centrality measure for collaboration networks. Using research collaboration data from three different research domains including architecture, library and information science, and marketing, the above twelve centrality measures are calculated and compared each other. Results indicate that the weighted network centrality measures are needed to consider collaboration strength as well as collaboration range in research collaboration networks. We also recommend that when considering both collaboration strength and range, it is appropriate to apply triangle betweenness centrality and SSR to investigate global centrality and local centrality in collaboration networks.

• Journal of the Korean Society for information Management
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• v.34 no.4
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• pp.7-32
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• 2017

Recently, deep learning has been rapidly spreading as an innovative machine learning technique in various domains. This study explored the research trends of deep learning via modified ego centered topic citation analysis. To do that, a few seed documents were selected from among the retrieved documents with the keyword 'deep learning' from Web of Science, and the related documents were obtained through citation relations. Those papers citing seed documents were set as ego documents reflecting current research in the field of deep learning. Preliminary studies cited frequently in the ego documents were set as the citation identity documents that represents the specific themes in the field of deep learning. For ego documents which are the result of current research activities, some quantitative analysis methods including co-authorship network analysis were performed to identify major countries and research institutes. For the citation identity documents, co-citation analysis was conducted, and key literatures and key research themes were identified by investigating the citation image keywords, which are major keywords those citing the citation identity document clusters. Finally, we proposed and measured the citation growth index which reflects the growth trend of the citation influence on a specific topic, and showed the changes in the leading research themes in the field of deep learning.

• Land and Housing Review
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• v.12 no.2
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• pp.61-78
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• 2021

This study aims to understand the thematic trends globally developed in the 'Green Urbanism' related research. Research methodology is based on systemic review of international literature published for the past 20 years period between 2000 and 2020. The specific methods applied include not only literature search by citation, co-authorship, and co-occurrence but social network analysis in order to find correlations among the publication. The correlations are visualized and analysed using VOSviewer and Ucinet software. The analysis indicates that total of 51 studies were carried out by 89 authors from 54 institutions across 21 countries during the period. The majority of the research was done by a country-specific study and only a few research were collaborative studies with other countries. The most common theme that occurred in the early years was 'sustainability and the theme evolved toward specific ones such as 'built environment', 'infrastructure', and 'health'. Having considered that climate change has become a global challenge, green urbanism is expected to be a future direction to pursue environmentally sustainable urban spaces. This study also implies that governance, policy support, and intervention are crucial factors in developing sustainable urban spaces.