IDEAS home Printed from https://ideas.repec.org/a/eee/infome/v8y2014i2p329-339.html
   My bibliography  Save this article

Unpacking the Matthew effect in citations

Author

Listed:
  • Wang, Jian

Abstract

One problem confronting the use of citation-based metrics in science studies and research evaluations is the Matthew effect. This paper reviews the role of citations in science and decomposes the Matthew effect in citations into three components: networking, prestige, and appropriateness. The networking and prestige effects challenge the validity of citation-based metrics, but the appropriateness effect does not. Using panel data of 1279 solo-authored papers’ citation histories and fixed effects models, we test these three effects controlling for unobserved paper characteristics. We find no evidence of retroactive networking effect and only weak evidence of prestige effect (very small and not always significant), which provides some support for the use of citation-based metrics in science studies and evaluation practices. In addition, adding the appropriateness effect reduces the size of the prestige effect considerably, suggesting that previous studies controlling for paper quality but not appropriateness may have overestimated the prestige effect.

Suggested Citation

  • Wang, Jian, 2014. "Unpacking the Matthew effect in citations," Journal of Informetrics, Elsevier, vol. 8(2), pages 329-339.
  • Handle: RePEc:eee:infome:v:8:y:2014:i:2:p:329-339
    DOI: 10.1016/j.joi.2014.01.006
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S1751157714000078
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.joi.2014.01.006?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Jian Wang & Kaspars Berzins & Diana Hicks & Julia Melkers & Fang Xiao & Diogo Pinheiro, 2012. "A boosted-trees method for name disambiguation," Scientometrics, Springer;Akadémiai Kiadó, vol. 93(2), pages 391-411, November.
    2. Anthony F.J. van Raan, 2008. "Bibliometric statistical properties of the 100 largest European research universities: Prevalent scaling rules in the science system," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 59(3), pages 461-475, February.
    3. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.
    4. Vincent Larivière & Yves Gingras, 2010. "The impact factor's Matthew Effect: A natural experiment in bibliometrics," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 61(2), pages 424-427, February.
    5. Anthony F.J. van Raan, 2006. "Statistical properties of bibliometric indicators: Research group indicator distributions and correlations," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(3), pages 408-430, February.
    6. Van Looy, Bart & Ranga, Marina & Callaert, Julie & Debackere, Koenraad & Zimmermann, Edwin, 2004. "Combining entrepreneurial and scientific performance in academia: towards a compounded and reciprocal Matthew-effect?," Research Policy, Elsevier, vol. 33(3), pages 425-441, April.
    7. M. V. Simkin & V. P. Roychowdhury, 2005. "Stochastic modeling of citation slips," Scientometrics, Springer;Akadémiai Kiadó, vol. 62(3), pages 367-384, March.
    8. J Sylvan Katz, 2000. "Scale-independent indicators and research evaluation," Science and Public Policy, Oxford University Press, vol. 27(1), pages 23-36, February.
    9. Richard S.J. Tol, 2009. "The Matthew effect defined and tested for the 100 most prolific economists," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 60(2), pages 420-426, February.
    10. Joshua Aizenman & Kenneth Kletzer, 2011. "The life cycle of scholars and papers in economics - the 'citation death tax'," Applied Economics, Taylor & Francis Journals, vol. 43(27), pages 4135-4148.
    11. Jian Wang, 2013. "Citation time window choice for research impact evaluation," Scientometrics, Springer;Akadémiai Kiadó, vol. 94(3), pages 851-872, March.
    12. Katz, J. Sylvan, 1999. "The self-similar science system1," Research Policy, Elsevier, vol. 28(5), pages 501-517, June.
    13. Gonzalez-Brambila, Claudia N. & Veloso, Francisco M. & Krackhardt, David, 2013. "The impact of network embeddedness on research output," Research Policy, Elsevier, vol. 42(9), pages 1555-1567.
    14. Li, Eldon Y. & Liao, Chien Hsiang & Yen, Hsiuju Rebecca, 2013. "Co-authorship networks and research impact: A social capital perspective," Research Policy, Elsevier, vol. 42(9), pages 1515-1530.
    15. Marshall Medoff, 2006. "Evidence of a Harvard and Chicago Matthew Effect," Journal of Economic Methodology, Taylor & Francis Journals, vol. 13(4), pages 485-506.
    16. Anthony F. J. van Raan, 2006. "Performance‐related differences of bibliometric statistical properties of research groups: Cumulative advantages and hierarchically layered networks," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 57(14), pages 1919-1935, December.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tol, Richard S.J., 2013. "The Matthew effect for cohorts of economists," Journal of Informetrics, Elsevier, vol. 7(2), pages 522-527.
    2. Wang, Jian, 2016. "Knowledge creation in collaboration networks: Effects of tie configuration," Research Policy, Elsevier, vol. 45(1), pages 68-80.
    3. Anthony F J van Raan, 2013. "Universities Scale Like Cities," PLOS ONE, Public Library of Science, vol. 8(3), pages 1-14, March.
    4. Vieira, Elizabeth S. & Lepori, Benedetto, 2016. "The growth process of higher education institutions and public policies," Journal of Informetrics, Elsevier, vol. 10(1), pages 286-298.
    5. Patrick Röhm, 2018. "Exploring the landscape of corporate venture capital: a systematic review of the entrepreneurial and finance literature," Management Review Quarterly, Springer, vol. 68(3), pages 279-319, August.
    6. Manuel Acosta & Daniel Coronado & Esther Ferrándiz & M. Dolores León & Pedro J. Moreno, 2017. "The geography of university scientific production in Europe: an exploration in the field of Food Science and Technology," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 215-240, July.
    7. Leila Tahmooresnejad & Catherine Beaudry & Andrea Schiffauerova, 2015. "The role of public funding in nanotechnology scientific production: Where Canada stands in comparison to the United States," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(1), pages 753-787, January.
    8. Lee, You-Na & Walsh, John P. & Wang, Jian, 2015. "Creativity in scientific teams: Unpacking novelty and impact," Research Policy, Elsevier, vol. 44(3), pages 684-697.
    9. Sylvan Katz, 2012. "Science Policy, Complex Innovation Systems and Performance Measures," SPRU Working Paper Series 198, SPRU - Science Policy Research Unit, University of Sussex Business School.
    10. Tie, Ying & Wang, Zheng, 2022. "Publish or perish? A tale of academic publications in Chinese universities," China Economic Review, Elsevier, vol. 73(C).
    11. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2018. "The power law relationship between citation impact and multi-authorship patterns in articles in Information Science & Library Science journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 114(3), pages 919-932, March.
    12. Calabrese, Armando & Capece, Guendalina & Costa, Roberta & Di Pillo, Francesca & Giuffrida, Stefania, 2018. "A ‘power law’ based method to reduce size-related bias in indicators of knowledge performance: An application to university research assessment," Journal of Informetrics, Elsevier, vol. 12(4), pages 1263-1281.
    13. Önder Nomaler & Koen Frenken & Gaston Heimeriks, 2014. "On Scaling of Scientific Knowledge Production in U.S. Metropolitan Areas," PLOS ONE, Public Library of Science, vol. 9(10), pages 1-6, October.
    14. Saeed Roshani & Mohammad-Reza Bagherylooieh & Melika Mosleh & Mario Coccia, 2021. "What is the relationship between research funding and citation-based performance? A comparative analysis between critical disciplines," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(9), pages 7859-7874, September.
    15. Gao, Xia & Guan, Jiancheng, 2009. "A scale-independent analysis of the performance of the Chinese innovation system," Journal of Informetrics, Elsevier, vol. 3(4), pages 321-331.
    16. Mario Fernandes & Andreas Walter, 2023. "The times they are a-changin’: profiling newly tenured business economics professors in Germany over the past thirty years," Journal of Business Economics, Springer, vol. 93(5), pages 929-971, July.
    17. Birkmaier, Daniel & Wohlrabe, Klaus, 2014. "The Matthew effect in economics reconsidered," Journal of Informetrics, Elsevier, vol. 8(4), pages 880-889.
    18. Ricardo S. Santos & Jose Soares & Pedro Carmona Marques & Helena V. G. Navas & José Moleiro Martins, 2021. "Integrating Business, Social, and Environmental Goals in Open Innovation through Partner Selection," Sustainability, MDPI, vol. 13(22), pages 1-25, November.
    19. Giancarlo Ruocco & Cinzia Daraio, 2013. "An empirical approach to compare the performance of heterogeneous academic fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 97(3), pages 601-625, December.
    20. Na Liu & Jiancheng Guan, 2015. "Dynamic evolution of collaborative networks: evidence from nano-energy research in China," Scientometrics, Springer;Akadémiai Kiadó, vol. 102(3), pages 1895-1919, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:infome:v:8:y:2014:i:2:p:329-339. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/joi .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.