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The link between dissertation metadata completeness and user engagement in an institutional repository

Author

Listed:
  • Behrooz Rasuli

    (Iranian Research Institute for Information Science and Technology (IranDoc))

  • Michael Boock

    (Oregon State University)

  • Joachim Schöpfel

    (University of Lille)

  • Brenda Wyk

    (The University of Pretoria)

Abstract

This study investigates the role of metadata quality in Electronic Theses and Dissertations (ETDs), focusing on its completeness and its impact on discoverability and user engagement within institutional repositories (IRs). Using DSpace@MIT as a case study, the current research analyzed 22,276 doctoral dissertations to assess metadata completeness and its correlation with the number of views and downloads. Various metadata fields and usage statistics were extracted for detailed analysis. The study identified a moderate positive correlation between the numbers of unique metadata fields and both the Department Views Ratio (DVR) and Department Download Ratio (DDR), suggesting that enriched metadata can improve the visibility and accessibility of dissertations. Additionally, the length of abstracts is positively correlated with engagement metrics (significance level for all reported results

Suggested Citation

  • Behrooz Rasuli & Michael Boock & Joachim Schöpfel & Brenda Wyk, 2025. "The link between dissertation metadata completeness and user engagement in an institutional repository," Scientometrics, Springer;Akadémiai Kiadó, vol. 130(5), pages 2875-2899, May.
    • Handle: RePEc:spr:scient:v:130:y:2025:i:5:d:10.1007_s11192-025-05331-0
    DOI: 10.1007/s11192-025-05331-0
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    1. Besiki Stvilia & Les Gasser & Michael B. Twidale & Linda C. Smith, 2007. "A framework for information quality assessment," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(12), pages 1720-1733, October.
    2. Babak Sohrabi & Hamideh Iraj, 2017. "The effect of keyword repetition in abstract and keyword frequency per journal in predicting citation counts," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(1), pages 243-251, January.
    3. Hamid R. Jamali & Mahsa Nikzad, 2011. "Article title type and its relation with the number of downloads and citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 88(2), pages 653-661, August.
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