, Volume 109, Issue 2, pp 917–926 | Cite as

Usage patterns of scholarly articles on Web of Science: a study on Web of Science usage count

  • Xianwen WangEmail author
  • Zhichao Fang
  • Xiaoling Sun


Usage data of scholarly articles provide a direct way to explore the usage preferences of users. Using the “Usage Count” provided by the Web of Science platform, we collect and analyze the usage data of five journals in the field of Information Science and Library Science, to investigate the usage patterns of scholarly articles on Web of Science. Our analysis finds that the distribution of usage fits a power law. And according to the time distribution of usage, researchers prefer to use more recent papers. As to those old papers, citations play an important role in determining the usage count. Highly cited old papers are more likely to be used even a long time after publication.


Article usage Usage count Altmetrics Usage metrics Web of Science 



The work was supported by the project of ‘‘National Natural Science Foundation of China’’ (61301227), the project of “Growth Plan of Distinguished Young Scholar in Liaoning Province” (WJQ2014009), the project of “the Fundamental Research Funds for the Central Universities” (DUT15YQ111) and Liaoning Province Higher Education Innovation Team Fund “Research on Responsible Innovation” (WT2015002). We appreciate the reviewers’ suggestions and comments, some of their comments are used in this paper.


  1. Amat, C. (2007). Editorial and publication delay of papers submitted to 14 selected Food Research journals. Influence of online posting. Scientometrics, 74(3), 379–389.Google Scholar
  2. Bollen, J., & Sompel, H. V. D. (2008). Usage impact factor: the effects of sample characteristics on usage-based impact metrics. Journal of the American Society for Information Science and Technology, 59(1), 136–149.CrossRefGoogle Scholar
  3. Brody, T., Harnad, S., & Carr, L. (2006). Earlier web usage statistics as predictors of later citation impact. Journal of the American Society for Information Science and Technology, 57(8), 1060–1072.CrossRefGoogle Scholar
  4. CIBER Research Limited. (2011). The journal usage factor: exploratory data analysis. Accessed April 11, 2016 from:
  5. Davis, P. M., & Price, J. S. (2006). eJournal interface can influence usage statistics: implications for libraries, publishers, and Project COUNTER. Journal of the American Society for Information Science and Technology, 57(9), 1243–1248.CrossRefGoogle Scholar
  6. Davis, P. M., & Solla, L. R. (2003). An IP-level analysis of usage statistics for electronic journals in chemistry: Making inferences about user behavior. Journal of the American Society for Information Science and Technology, 54(11), 1062–1068.CrossRefGoogle Scholar
  7. Duy, J., & Vaughan, L. (2006). Can electronic journal usage data replace citation data as a measure of journal use? An empirical examination. The Journal of Academic Librarianship, 32(5), 512–517.CrossRefGoogle Scholar
  8. Egghe, L., & Rousseau, R. (2000). Aging, obsolescence, impact, growth, and utilization: Definitions and relations. Journal of the American Society for Information Science, 51(11), 1004–1017.CrossRefGoogle Scholar
  9. Glänzel, W., & Gorraiz, J. (2015). Usage metrics versus altmetrics: Confusing terminology? Scientometrics, 3(102), 2161–2164.CrossRefGoogle Scholar
  10. Gorraiz, J., Gumpenberger, C., & Schlögl, C. (2014). Usage versus citation behaviours in four subject areas. Scientometrics, 101(2), 1077–1095.CrossRefGoogle Scholar
  11. Gosnell, C. F. (1944). Obsolescence of books in college libraries. College and Research Libraries, 5, 115–125.CrossRefGoogle Scholar
  12. Gross, P. L. K., & Gross, E. M. (1927). College libraries and chemical education. Science, 66, 385–389.CrossRefGoogle Scholar
  13. Guerrero-Bote, V. P., & Moya-Anegón, F. (2014). Relationship between downloads and citations at journal and paper levels, and the influence of language. Scientometrics, 101(2), 1043–1065.CrossRefGoogle Scholar
  14. Jung, Y., Kim, J., & Kim, H. (2013). Stm e-journal use analysis by utilizing kesli usage statistics consolidation platform. Collnet Journal of Scientometrics & Information Management, 7(2), 205–215.CrossRefGoogle Scholar
  15. Jung, Y., Kim, J., So, M., & Kim, H. (2015). Statistical relationships between journal use and research output at academic institutions in South Korea. Scientometrics, 103(3), 751–777.CrossRefGoogle Scholar
  16. Kurtz, M. J., & Bollen, J. (2010). Usage bibliometrics. Annual Review of Information Science and Technology, 44, 3–64.CrossRefGoogle Scholar
  17. Ladwig, J. P., & Sommese, A. J. (2005). Using cited half-life to adjust download statistics. College and Research Libraries, 66(6), 527–542.CrossRefGoogle Scholar
  18. Line, M. B., & Sandison, A. (1975). Practical interpretation of citation and library use studies. College an Research Libraries, 36(5), 393–396.CrossRefGoogle Scholar
  19. 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), 1088–1097.CrossRefGoogle Scholar
  20. O’ Leary, D. E. (2008). The relationship between citations and number of downloads in Decision Support Systems. Decision Support Systems, 45(4), 972–980.MathSciNetCrossRefGoogle Scholar
  21. Peng, T. Q., & Zhu, J. J. (2012). Where you publish matters most: A multilevel analysis of factors affecting citations of internet studies. Journal of the American Society for Information Science and Technology, 63(9), 1789–1803.CrossRefGoogle Scholar
  22. Schloegl, C., & Gorraiz, J. (2010). Comparison of citation and usage indicators: The case of oncology journals. Scientometrics, 82(3), 567–580.CrossRefGoogle Scholar
  23. Schloegl, C., & Gorraiz, J. (2011). Global usage versus global citation metrics: The case of pharmacology journals. Journal of the American Society for Information Science and Technology, 62(1), 161–170.CrossRefGoogle Scholar
  24. Schloegl, C., Gorraiz, J., Gumpenberger, C., Jack, K., & Kraker, P. (2014). Comparison of downloads, citations and readership data for two information systems journals. Scientometrics, 101(2), 1113–1128.CrossRefGoogle Scholar
  25. Subotic, S., & Mukherjee, B. (2014). Short and amusing: The relationship between title characteristics, downloads, and citations in psychology articles. Journal of Information Science, 40(1), 115–124.CrossRefGoogle Scholar
  26. Wan, J. K., Hua, P. H., Rousseau, R., & Sun, X. K. (2010). The journal download immediacy index (DII): experiences using a Chinese full-text database. Scientometrics, 82(3), 555–566.CrossRefGoogle Scholar
  27. Wang, X., Mao, W., Xu, S., & Zhang, C. (2014a). Usage history of scientific literature: Nature metrics and metrics of Nature publications. Scientometrics, 98(3), 1923–1933.CrossRefGoogle Scholar
  28. Wang, X., Peng, L., Zhang, C., Xu, S., Wang, Z., Wang, C., et al. (2013a). Exploring scientists’ working timetable: A global survey. Journal of Informetrics, 7(3), 665–675.CrossRefGoogle Scholar
  29. Wang, X., Wang, Z., Mao, W., & Liu, C. (2014b). How far does scientific community look back? Journal of Informetrics, 8(3), 562–568.CrossRefGoogle Scholar
  30. Wang, X., Wang, Z., & Xu, S. (2013b). Tracing scientist’s research trends realtimely. Scientometrics, 95(2), 717–729.CrossRefGoogle Scholar
  31. Wang, X., Xu, S., & Fang, Z. (2016). Tracing digital footprints to academic articles: An investigation of PeerJ publication referral data. arXiv preprint arXiv:1601.05271.
  32. Wang, X., Xu, S., Peng, L., Wang, Z., Wang, C., Zhang, C., et al. (2012). Exploring scientists’ working timetable: Do scientists often work overtime? Journal of Informetrics, 6(4), 655–660.CrossRefGoogle Scholar

Copyright information

© Akadémiai Kiadó, Budapest, Hungary 2016

Authors and Affiliations

  1. 1.WISE Lab, Faculty of Humanities and Social SciencesDalian University of TechnologyDalianChina

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