Statistical relationships between journal use and research output at academic institutions in South Korea

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• 作者：Youngim Jung ; Jayhoon Kim ; Minho So ; Hwanmin Kim
• 关键词：Correlation analysis ; Comparative analysis ; Diachronic analysis ; e ; Journal use ; Research output
• 刊名：Scientometrics
• 出版年：2015
• 出版时间：June 2015
• 年：2015
• 卷：103
• 期：3
• 页码：751-777
• 全文大小：1,487 KB
• 参考文献：Bollen, J., & Van de Sompel, H. (2008). Usage impact factor: the effects of sample characteristics on usage-based impact metrics. Journal of American Society for Information Science and Technology, 59(1), 136-49.View Article
  Bollen, J., Van de Sompel, H., Hagberg, A., & Chute, R. (2009). A principal component analysis of 39 scientific impact measures. PLoS One, 4(6), e6022. doi:10.-371/?journal.?pone.-006022 .View Article
  Brody, T., & Harnad, S. (2005). Earlier web usage statistics as predictors of later citation impact. http://?arxiv.?org/?abs/?cs/-503020 . Accessed October 30, 2012.
  Choi, H., Kim, B., Jung, Y., & Choi, S. (2013). Korean scholarly information analysis based on Korea Science Citation Database (KSCD). Collnet Journal of Scientometrics and Information Management, 7(1), 1-3. doi:10.-080/-9737766.-013.-02625 .View Article
  CIBER Research Ltd. (2011). The journal usage factor: Exploratory data analysis. Stage 2 Final Report. http://?www.?projectcounter.?org/?documents/?CIBER_?final_?report_?July.?pdf . Accessed May 23, 2013.
  COUNTER. (2008). Release 3 of the COUNTER code of practice for journals and databases. http://?www.?projectcounter.?org/?code_?practice.?html . Accessed November 8, 2010.
  Gorraiz, J., & Gumpenberger, G. (2010). Going beyond citation: SERUM—A new tool provided by a network of libraries. Liber Quarterly, 20(1), 80-3.
  Gumpenberger, C., Wernisch, A., & Gorraiz, J. (2012). Reality-check: Cost-related journal assessment from a practical point of view1,2. Journal of Scientometric Research, 1(1), 35-3.View Article
  Howell, D. C. (2007). The analysis of missing data. In W. Outhwaite & S. Turner (Eds.), Handbook of social science methodology. London: Sage.
  International Coalition of Library Consortia. (2006). Revised guidelines for statistical measures of usage of web-based information resources. http://?icolc.?net/?statement/?revised-guidelines-statistical-measures-usage-web-based-information-resources . Accessed November 6, 2012.
  Jung, Y., & Kim, J. (2013). Hybrid standard platform for e-journal usage statistics management. Lecture Notes in Electrical Engineering, 215, 1105-115. doi:10.-007/-78-94-007-5860-5_-32 .View Article
  Jung, Y., Kim, J., & Kim, H. (2013). STM e-journal use analysis by utilizing KESLI usage statistics consolidation platform. Collnet Journal of Scientometrics and Information Management, 7(2), 205-15. doi:10.-080/-9737766.-013.-32903 .View Article
  Larsen, P. O., & Von Ins, M. (2010). The rate of growth in scientific publication and the decline in coverage provided by Science Citation Index. Scientometrics, 84(3), 575-03. http://?www.?pubmedcentral.?nih.?gov/?articlerender.?fcgi??artid=-909426&?tool=?pmcentrez&?rendertype=?abstract . Accessed November 6, 2012.
  Matthews, T. E. (2009). Improving usage statistics processing for a library consortium: The virtual library of Virginia’s experience. Journal of Electronic Resources Librarianship, 21(1), 37-7. doi:10.-080/-941126090285857- .View Article
  Miller, R. (1992). The influence of primary task on R&D laboratory evaluation: A comparative bibliometric analysis. R&D Management, 22, 003-20. doi:10.-111/?j.-467-9310.-992.?tb00785.?x .View Article
  Moed, H. F. (2005). Statistical relationships between downloads and citations at the level of individual documents within a single journal. Journal of the American Society for Information Science and Technology, 56(10), 1088-097. doi:10.-002/?asi.-0200 .View Article
  National Science and Technology Commission. (2011). SCI Analysis Research. Daejeon: National Science and Technology Commission.
  NISO. (2007). Standardized usage statistics harvesting initiative (SUSHI): Z39.93. http://?www.?niso.?org/?apps/?group_?public/?download.?php . Accessed November 8, 2010.
  Priem, J., Piwowar, H. A., & Hemminger B. M. (2012). Altmetrics in the wild: Using social media to explore scholarly impact. http://?arxiv.?org/?html/-204.-745v1 . Accessed May, 4, 2012.
  Shepherd, P. (2011). The journal usage factor project: Results, recommendations and next steps. http://?www.?projectcounter.?org/?documents/?Journal_?Usage_?Factor_?extended_?report_?July.?pdf . Accessed December 7, 2011.
  The PLoS Medicine Editors. (2006). The impact factor game: It is time to find a better way to assess the scientific literature. PLoS Medicine, 3(6), e291. doi:10.-371/?journal.?pmed.-030291 .View Article
  Vinkler, P. (2010). Scientometric assessments: Application of scientometrics for the purposes of science policy. The evaluation of research by scientometric indicators. Oxford, UK: Chandos Publishing.View Article
  Welch, J. M. (2005). Who says we’re not busy? Library web page usage as a measure of public service activity. Reference Services Review, 33(4), 371-79. doi:10.-108/-090732051063152- .View Article
  Wood, F. (1990). Factors influencing research performance of university academic staff. Higher Educa
• 作者单位：Youngim Jung (1)
  Jayhoon Kim (1)
  Minho So (2)
  Hwanmin Kim (1)
  
  1. KISTI, 245 Daehangno, Yuseong-gu, Daejeon, 305-806, Republic of Korea
  2. KAIST, 291 Daehangno, Yuseong-gu, Daejeon, 305-701, Republic of Korea
  
• 刊物主题：Information Storage and Retrieval; Library Science; Interdisciplinary Studies;
• 出版者：Springer Netherlands
• ISSN：1588-2861

文摘

In this study, we analysed the statistical association between e-journal use and research output at the institution level in South Korea by performing comparative and diachronic analyses, as well as the analysis by field. The datasets were compiled from four different sources: national reports on research output indicators in science fields, two statistics databases on higher education institutions open to the public, and e-journal usage statistics generated by 47 major publishers. Due to the different data sources utilized, a considerable number of missing values appeared in our datasets and various mapping issues required corrections prior to the analysis. Two techniques for handling missing data were applied and the impact of each technique was discussed. In order to compile the institutional data by field, journals were first mapped, and then the statistics were summarized according to subject field. We observed that e-journal use exhibited stronger correlations with the number of publications and the times cited, in contrast to the number of undergraduates, graduates, faculty members and the amount of research funds, and this was the case regardless of the NA handling method or author type. The difference between the maximum correlation for the amount of external research funding with two average indicators and that of the correlation for e-journal use were not significant. Statistically, the accountability of e-journal use for the average times cited per article and the average JIF was quite similar with external research funds. It was found that the number of e-journal articles used had a strong positive correlation (Pearson’s correlation coefficients of r?>?0.9, p?<?0.05) with the number of articles published in SCI(E) journals and the times cited regardless of the author type, NA handling method or time period. We also observed that the top-five institutions in South Korea, with respect to the number of publications in SCI(E) journals, were generally across a balanced range of academic activities, while producing significant research output and using published material. Finally, we confirmed that the association of e-journal use with the two quantitative research indicators is strongly positive, even for the analyses by field, with the exception of the Arts and Humanities.