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Analyzing readers behavior in downloading articles from IEEE digital library: a study of two selected journals in the field of education

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  • Muhammad Salman Khan

    (COMSATS Institute of Information Technology)

  • Muhammad Younas

    (Punjab University College of Information Technology)

Abstract

In this study, we investigate the downloads behavior of readers for two well-known IEEE journals in the field of education, i.e., IEEE Transactions on Learning Technologies (TLT) and IEEE Transactions on Education (ToE). In our analysis, we found that articles in both journals are not downloaded rapidly in earlier months. The majority of articles reach to 50 and 80% of their first 12 months downloads’ total in later months. Using linear regression analysis, we discovered that the cumulative first 12 months downloads of articles cannot be predicted by earlier months downloads. However, it can be predicted more accurately by using cumulative downloads count of later months. Moreover, we found that average downloads of articles in both journals increases rapidly as soon as they are assigned to an issue. In case of TLT which follows a delayed open access policy, we observed that average downloads after open access increases marginally for 2 months, and then it declines and continues to progress more or less in a consistent manner for 2 years. While in ToE which does not follow such policy, the average downloads decreases persistently.

Suggested Citation

  • Muhammad Salman Khan & Muhammad Younas, 2017. "Analyzing readers behavior in downloading articles from IEEE digital library: a study of two selected journals in the field of education," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1523-1537, March.
    • Handle: RePEc:spr:scient:v:110:y:2017:i:3:d:10.1007_s11192-016-2232-7
    DOI: 10.1007/s11192-016-2232-7
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    Cited by:

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    5. Wencan Tian & Yongzhen Wang & Zhigang Hu & Ruonan Cai & Guangyao Zhang & Xianwen Wang, 2024. "Does Granger causality exist between article usage and publication counts? A topic-level time-series evidence from IEEE Xplore," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(6), pages 3285-3302, June.

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