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Data-driven journal meta-ranking in business and management

Author

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  • Gianfranco Ennas

    (University of Cagliari)

  • Battista Biggio

    (University of Cagliari)

  • Maria Chiara Di Guardo

    (University of Cagliari)

Abstract

Ranking journals is a longstanding problem and can be addressed quantitatively, qualitatively or using a combination of both approaches. In the last decades, the Impact Factor (i.e., the most known quantitative approach) has been widely questioned, and other indices have thus been developed and become popular. Previous studies have reported strengths and weaknesses of each index, and devised meta-indices to rank journals in a certain field of study. However, the proposed meta-indices exhibit some intrinsic limitations: (1) the indices to be combined are not always chosen according to well-grounded principles; (2) combination methods are usually unweighted; and (3) some of the proposed meta-indices are parametric, which requires assuming a specific underlying data distribution. We propose a data-driven methodology that linearly combines an arbitrary number of indices to produce an aggregated ranking, using different techniques from statistics and machine learning to estimate the combining weights. We additionally consider correlations between indices and meta-indices, to quantitatively evaluate their differences. Finally, we empirically show that the considered meta-indices are also robust to significant perturbations of the values of the combined indices.

Suggested Citation

  • Gianfranco Ennas & Battista Biggio & Maria Chiara Di Guardo, 2015. "Data-driven journal meta-ranking in business and management," Scientometrics, Springer;Akadémiai Kiadó, vol. 105(3), pages 1911-1929, December.
    • Handle: RePEc:spr:scient:v:105:y:2015:i:3:d:10.1007_s11192-015-1751-y
    DOI: 10.1007/s11192-015-1751-y
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    References listed on IDEAS

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    Cited by:

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    2. Dejian Yu & Wanru Wang & Shuai Zhang & Wenyu Zhang & Rongyu Liu, 2017. "A multiple-link, mutually reinforced journal-ranking model to measure the prestige of journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(1), pages 521-542, April.
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    4. Yu Zhang & Min Wang & Morteza Saberi & Elizabeth Chang, 2020. "Knowledge fusion through academic articles: a survey of definitions, techniques, applications and challenges," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 2637-2666, December.
    5. Mingers, John & Yang, Liying, 2017. "Evaluating journal quality: A review of journal citation indicators and ranking in business and management," European Journal of Operational Research, Elsevier, vol. 257(1), pages 323-337.
    6. Chen, Kun & Ren, Xian-tong & Yang, Guo-liang, 2021. "A novel approach for assessing academic journals: Application of integer DEA model for management science and operations research field," Journal of Informetrics, Elsevier, vol. 15(3).
    7. Thomas C. Erren & J. Valérie Groß, 2016. "Research metrics: What about weighted citations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 315-316, April.
    8. Rosenthal, Edward C. & Weiss, Howard J., 2017. "A data envelopment analysis approach for ranking journals," Omega, Elsevier, vol. 70(C), pages 135-147.
    9. Antonio García-Romero & Daniel Santín & Gabriela Sicilia, 2016. "Another brick in the wall: a new ranking of academic journals in Economics using FDH," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(1), pages 91-101, April.

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