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The innovation trade-off: how following superstars shapes academic novelty

Author

Listed:
  • Sean Kelty

    (University of Rochester)

  • Raiyan Abdul Baten

    (University of South Florida)

  • Adiba Mahbub Proma

    (University of Rochester)

  • Ehsan Hoque

    (University of Rochester
    Ministry of Defense)

  • Johan Bollen

    (Luddy School of Informatics, Computing, and Engineering, Indiana University Bloomington)

  • Gourab Ghoshal

    (University of Rochester)

Abstract

Academic success is distributed unequally; a few top scientists receive the bulk of attention, citations, and resources. However, do these “superstars” foster leadership in scientific innovation? We employ a series of information-theoretic measures that quantify novelty, innovation, and impact from scholarly citation networks, and compare the academic output of scientists in the American Physical Society corpus with varying levels of connections to superstar scientists. The strength of connection is based on the frequency of citations to superstar papers, which is also related to the frequency of collaboration. We find that while strongly-connected scientists publish more, garner more citations, and produce moderately more diverse content, this comes at a cost of lower innovation, less disruption, and higher redundancy of ideas. Further, once one removes papers co-authored with superstars, the academic output of these strongly connected scientists greatly diminishes. In contrast, authors who publish at the same rate without the benefit of collaborations with scientific superstars produce papers that are more innovative, more disruptive, and have comparable citation rates, once one controls for the transferred prestige of superstars. On balance, our results indicate that academia pays a price by focusing attention and resources on superstars.

Suggested Citation

  • Sean Kelty & Raiyan Abdul Baten & Adiba Mahbub Proma & Ehsan Hoque & Johan Bollen & Gourab Ghoshal, 2025. "The innovation trade-off: how following superstars shapes academic novelty," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 12(1), pages 1-13, December.
  • Handle: RePEc:pal:palcom:v:12:y:2025:i:1:d:10.1057_s41599-025-05124-z
    DOI: 10.1057/s41599-025-05124-z
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    References listed on IDEAS

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    1. Osório, António (António Miguel) & Bornmann, Lutz, 2020. "On the disruptive power of small-teams research," Working Papers 2072/417677, Universitat Rovira i Virgili, Department of Economics.
    2. Lingfei Wu & Dashun Wang & James A. Evans, 2019. "Large teams develop and small teams disrupt science and technology," Nature, Nature, vol. 566(7744), pages 378-382, February.
    3. Michael Park & Erin Leahey & Russell J. Funk, 2023. "Papers and patents are becoming less disruptive over time," Nature, Nature, vol. 613(7942), pages 138-144, January.
    4. Trapido, Denis, 2015. "How novelty in knowledge earns recognition: The role of consistent identities," Research Policy, Elsevier, vol. 44(8), pages 1488-1500.
    5. Simon Rodan & Charles Galunic, 2004. "More than network structure: how knowledge heterogeneity influences managerial performance and innovativeness," Strategic Management Journal, Wiley Blackwell, vol. 25(6), pages 541-562, June.
    6. Pierre Azoulay & Joshua S. Graff Zivin & Jialan Wang, 2010. "Superstar Extinction," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 125(2), pages 549-589.
    7. Radicchi, Filippo & Weissman, Alexander & Bollen, Johan, 2017. "Quantifying perceived impact of scientific publications," Journal of Informetrics, Elsevier, vol. 11(3), pages 704-712.
    8. Lutz Bornmann & Hans‐Dieter Daniel, 2007. "What do we know about the h index?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1381-1385, July.
    9. Eric Abrahamson & Lori Rosenkopf, 1997. "Social Network Effects on the Extent of Innovation Diffusion: A Computer Simulation," Organization Science, INFORMS, vol. 8(3), pages 289-309, June.
    10. repec:nas:journl:v:115:y:2018:p:12603-12607 is not listed on IDEAS
    11. Weihua Li & Tomaso Aste & Fabio Caccioli & Giacomo Livan, 2019. "Early coauthorship with top scientists predicts success in academic careers," Nature Communications, Nature, vol. 10(1), pages 1-9, December.
    12. Muller, Eitan & Peres, Renana, 2019. "The effect of social networks structure on innovation performance: A review and directions for research," International Journal of Research in Marketing, Elsevier, vol. 36(1), pages 3-19.
    13. Keye Wu & Ziyue Xie & Jia Tina Du, 2024. "Does science disrupt technology? Examining science intensity, novelty, and recency through patent-paper citations in the pharmaceutical field," Scientometrics, Springer;Akadémiai Kiadó, vol. 129(9), pages 5469-5491, September.
    14. Dennis L Murray & Douglas Morris & Claude Lavoie & Peter R Leavitt & Hugh MacIsaac & Michael E J Masson & Marc-Andre Villard, 2016. "Bias in Research Grant Evaluation Has Dire Consequences for Small Universities," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-19, June.
    15. Xie, Qing & Zhang, Xinyuan & Kim, Giyeong & Song, Min, 2022. "Exploring the influence of coauthorship with top scientists on researchers’ affiliation, research topic, productivity, and impact," Journal of Informetrics, Elsevier, vol. 16(3).
    16. G. Ghoshal & M. E.J. Newman, 2007. "Growing distributed networks with arbitrary degree distributions," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 58(2), pages 175-184, July.
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