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Factors affecting the citations of papers in tribology journals

Author

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  • T. Liskiewicz

    (Manchester Metropolitan University)

  • G. Liskiewicz

    (Lodz University of Technology)

  • J. Paczesny

    (Polish Academy of Sciences)

Abstract

The citations count is flawed but it still the most common way of measuring the academic impact used by scholarly journals (Impact Factor), individual researchers (h-index) and funding agencies (a proxy for quality of research). Individual papers should attract citations depending upon the importance and usefulness of the results presented. However, large enough data sets reveal that there are parameters independent of individual papers' quality that can determine an average citation rate. Here, we examine papers (4756 in total) published in six selected tribology journals in a six-year window between January 2010 and December 2015. Citations were retrieved from the Web of Science and compared with their (1) manuscript length, (2) number of authors, (3) number of affiliated institutions, (4) number of international co-authors, (5) number of cited references, (6) number of words in the title, and (7) mode of publication (open versus paid access). The results revealed that citations received by papers published in tribology journals are affected by all of these parameters. This is a significant finding for authors wishing to increase the impact of their research. This knowledge can be used effectively at the manuscript planning and writing stages to support scientific merit. We suggest that the significance of parameters not directly related to the quality of a scholarly paper will become more critical with the rise of alternative ways of measuring impact including novel generation of paper metrics (e.g., Eigenfactor, SJR), social mentions, and viral outreach.

Suggested Citation

  • T. Liskiewicz & G. Liskiewicz & J. Paczesny, 2021. "Factors affecting the citations of papers in tribology journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(4), pages 3321-3336, April.
    • Handle: RePEc:spr:scient:v:126:y:2021:i:4:d:10.1007_s11192-021-03870-w
    DOI: 10.1007/s11192-021-03870-w
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    References listed on IDEAS

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    Cited by:

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    2. Ha, Taehyun, 2022. "An explainable artificial-intelligence-based approach to investigating factors that influence the citation of papers," Technological Forecasting and Social Change, Elsevier, vol. 184(C).
    3. Yadav, Pratyush & Pervin, Nargis, 2022. "Towards efficient navigation in digital libraries: Leveraging popularity, semantics and communities to recommend scholarly articles," Journal of Informetrics, Elsevier, vol. 16(4).
    4. Giuseppe Pernagallo, 2023. "Science in the mist: A model of asymmetric information for the research market," Metroeconomica, Wiley Blackwell, vol. 74(2), pages 390-415, May.
    5. Claudia Patricia Maldonado-Erazo & María de la Cruz del Río-Rama & José Álvarez-García & Ana Carola Flores-Mancheno, 2022. "Use of Natural and Cultural Resources by Tourism as a Strategy for Regional Development: Bibliometric Analysis," Land, MDPI, vol. 11(8), pages 1-18, July.

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