• Journal of the Korean Society for information Management
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• v.29 no.1
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• pp.115-134
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• 2012

The author co-citation analysis is generally based on the frequency of the first author because most citation databases include only the first author in the bibliographic information. In this sense, the purpose of this study is to provide a better knowledge structure by utilizing the multiple authorship of author co-citation analysis. To achieve the purpose of this study, four different data sets are prepared: (1) counting the first author, (2) counting all the author without limiting the total frequency, (3) counting all the author with limiting the total frequency, and (4) counting adjusted frequencies based on the order of author subscription. The findings of this study show that there are clear differences between the knowledge structure counting all the author and the one counting only the first author. In addition, depending on the different methods, there are subtle changes of cluster members for authors.

• Journal of the Korean Society for information Management
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• v.31 no.2
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• pp.57-77
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• 2014

As co-authorship has been prevalent within science communities, counting the credit of co-authors appropriately is an important consideration, particularly in the context of identifying the knowledge structure of fields with author-based analysis. The purpose of this study is to compare the characteristics of co-author credit counting methods by utilizing correlations, multidimensional scaling, and pathfinder networks. To achieve this purpose, this study analyzed a dataset of 2,014 journal articles and 3,892 cited authors from the Journal of the Architectural Institute of Korea: Planning & Design from 2003 to 2008 in the field of Architecture in Korea. In this study, six different methods of crediting co-authors are selected for comparative analyses. These methods are first-author counting (m1), straight full counting (m2), and fractional counting (m3), proportional counting with a total score of 1 (m4), proportional counting with a total score between 1 and 2 (m5), and first-author-weighted fractional counting (m6). As shown in the data analysis, m1 and m2 are found as extreme opposites, since m1 counts only first authors and m2 assigns all co-authors equally with a credit score of 1. With correlation and multidimensional scaling analyses, among five counting methods (from m2 to m6), a group of counting methods including m3, m4, and m5 are found to be relatively similar. When the knowledge structure is visualized with pathfinder network, the knowledge structure networks from different counting methods are differently presented due to the connections of individual links. In addition, the internal validity shows that first-author-weighted fractional counting (m6) might be considered a better method to author clustering. Findings demonstrate that different co-author counting methods influence the network results of knowledge structure and a better counting method is revealed for author clustering.

• Journal of the Korean Society for information Management
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• v.33 no.4
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• pp.175-199
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• 2016

We analyzed the relationships between the co-authorship network centralities and the research performance indicators with the authors and the number of citations of the papers published for 10 years in Korean library and information science journals. In particular, the research performance indicators were calculated with normal counting and with fractional counting also. As a result of correlation analysis between the variables by setting the different ranges of the author groups to be analyzed according to the number of articles, it was possible to explain the inconsistent results of the previous studies on the correlations between the researchers' citation indicators and their co-authorship network centralities. Overall, the degree of co-authorship activities measured by collaboration coefficient showed no or negatively correlated with research performance. There were statistically significant positive correlations between the centralities and the research performance indicators, but the correlation was not significant in the analysis of the top 30 authors by number of articles.

• Journal of Information Science Theory and Practice
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• v.11 no.2
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• pp.37-66
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• 2023

Scientometrics and research evaluation describe and analyze research publications when conducting publication, citation, and topic analyses. However, what exactly is a (scientific, academic, scholarly or research) publication? This article demonstrates that there are many problems when it comes to looking in detail at quantitative publication analyses, citation analyses, altmetric analyses, and topic analyses. When is a document a publication and when is it not? We discuss authorship and contribution, formally and informally published documents, as well as documents in between (preprints, research data) and the characteristics of references, citations, and topics. What is a research publication? Is there a commonly accepted criterion for distinguishing between research and non-research? How complete and unbiased are data sources for research publications and sources for altmetrics? What is one research publication? What is the unit of a publication that causes us to count it as "1?" In this regard, we report problems related to multi-author publications and their counting, weighted document types, the unit and weighting of citations and references, the unit of topics, and counting problems-not only at the article and individual researcher level (micro-level), but also at the meso-level (e.g., institutions) and macro-level (e.g., countries). Our results suggest that scientometric counting units are not reliable and clear. Many scientometric and research evaluation studies must therefore be used with the utmost caution.