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An IP‐level analysis of usage statistics for electronic journals in chemistry: Making inferences about user behavior

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  • Philip M. Davis
  • Leah R. Solla

Abstract

This study reports an analysis of American Chemical Society electronic journal downloads at Cornell University by individual IP addresses. While the majority of users (IPs) limited themselves to a small number of both journals and article downloads, a small minority of heavy users had a large effect on total journal downloads. There was a very strong relationship between the number of article downloads and the number of users, implying that a user‐population can be estimated by just knowing the total use of a journal. Aggregate users (i.e. Library Proxy Server and public library computers) can be regarded as a sub‐sample of the entire user population. Analysis of article downloads by format (PDF versus HTML) suggests that individuals are using the system like a networked photocopier, for the purposes of creating print‐on‐demand copies of articles.

Suggested Citation

  • Philip M. Davis & Leah R. Solla, 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, Association for Information Science & Technology, vol. 54(11), pages 1062-1068, September.
    • Handle: RePEc:bla:jamist:v:54:y:2003:i:11:p:1062-1068
    DOI: 10.1002/asi.10302
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    Cited by:

    1. Guangyao Zhang & Yuqi Wang & Weixi Xie & Han Du & Chunlin Jiang & Xianwen Wang, 2021. "The open access usage advantage: a temporal and spatial analysis," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(7), pages 6187-6199, July.
    2. Bikun Chen, 2018. "Usage pattern comparison of the same scholarly articles between Web of Science (WoS) and Springer," Scientometrics, Springer;Akadémiai Kiadó, vol. 115(1), pages 519-537, April.
    3. Xianwen Wang & Chen Liu & Wenli Mao & Zhichao Fang, 2015. "The open access advantage considering citation, article usage and social media attention," Scientometrics, Springer;Akadémiai Kiadó, vol. 103(2), pages 555-564, May.
    4. Bikun Chen & Dannan Deng & Zhouyan Zhong & Chengzhi Zhang, 2020. "Exploring linguistic characteristics of highly browsed and downloaded academic articles," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(3), pages 1769-1790, March.
    5. Yufeng Duan & Zequan Xiong, 2017. "Download patterns of journal papers and their influencing factors," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1761-1775, September.
    6. Xianwen Wang & Wenli Mao & Shenmeng Xu & Chunbo Zhang, 2014. "Usage history of scientific literature: Nature metrics and metrics of Nature publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 98(3), pages 1923-1933, March.
    7. Xianwen Wang & Zhichao Fang & Xiaoling Sun, 2016. "Usage patterns of scholarly articles on Web of Science: a study on Web of Science usage count," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(2), pages 917-926, November.

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