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Collaborating with top scientists may not improve paper novelty: A causal analysis based on the propensity score matching method

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  • Ren, Linlin
  • Guo, Lei
  • Yu, Hui
  • Guo, Feng
  • Wang, Xinhua
  • Han, Xiaohui

Abstract

In previous collaboration studies, a majority of them concentrate on examining cooperation models, often overlooking the pivotal role played by a Top Scientist (TS) in scientific advancements. As far as my knowledge extends, only one relevant work delves into the correlation between innovation and collaboration with TSs, and no research has explored this relationship from a causal perspective. More precisely, previous studies suffer from several limitations in their examination of this topic: 1) Existing studies on Papers' Novelty (PN) primarily focus on calculating methods, with limited exploration of its relationship with scientific cooperation. 2) Research that has explored the link between collaboration with TSs and output innovation often adopts a correlational perspective, lacking a causal analysis that could correct for potential confounding factors. 3) Previous methodologies overlook the attributes of citation networks as potential confounding factors, a crucial consideration in identifying identical papers in causal analyses. 4) The impact of disciplinary diversity of papers on the innovation output when collaborating with TSs is often overlooked in prior research. To address these limitations, we conduct a causal analysis of publications in three subfields of computer science from the Web of Science (WoS) database to demonstrate the impact of collaborating with TSs on PN. Specifically, to tackle Limitations 1) and 2), we employ PN as a metric to assess the quality of academic output and explore its causal relationship with collaborating with TSs using the Propensity Score Matching (PSM) method. To address Limitation 3), we comprehensively consider potential confounding factors influencing PSM matching by further incorporating the attributes of citation networks, thereby minimizing selection bias. To deal with Limitation 4), we not only focus on the overall treatment effect but also delve into the treatment effect of intra-disciplinary and interdisciplinary collaboration modes. The research findings indicate that the papers collaborating with TSs exhibit lower PN compared to those without the participation of TSs. This suggests that collaboration with TSs may come at the cost of reduced novelty. This discovery prompts profound reflections on scientific collaboration, emphasizing the challenges and trade-offs that may exist in collaboration.

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  • Ren, Linlin & Guo, Lei & Yu, Hui & Guo, Feng & Wang, Xinhua & Han, Xiaohui, 2025. "Collaborating with top scientists may not improve paper novelty: A causal analysis based on the propensity score matching method," Journal of Informetrics, Elsevier, vol. 19(1).
  • Handle: RePEc:eee:infome:v:19:y:2025:i:1:s1751157724001214
    DOI: 10.1016/j.joi.2024.101609
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    as
    1. Adams, James D. & Black, Grant C. & Clemmons, J. Roger & Stephan, Paula E., 2005. "Scientific teams and institutional collaborations: Evidence from U.S. universities, 1981-1999," Research Policy, Elsevier, vol. 34(3), pages 259-285, April.
    2. Péter Érdi & Kinga Makovi & Zoltán Somogyvári & Katherine Strandburg & Jan Tobochnik & Péter Volf & László Zalányi, 2013. "Prediction of emerging technologies based on analysis of the US patent citation network," Scientometrics, Springer;Akadémiai Kiadó, vol. 95(1), pages 225-242, April.
    3. Bornmann, Lutz & Haunschild, Robin & Mutz, Rüdiger, 2020. "Should citations be field-normalized in evaluative bibliometrics? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 14(4).
    4. Rishika Rishika & Ashish Kumar & Ramkumar Janakiraman & Ram Bezawada, 2013. "The Effect of Customers' Social Media Participation on Customer Visit Frequency and Profitability: An Empirical Investigation," Information Systems Research, INFORMS, vol. 24(1), pages 108-127, March.
    5. An Zeng & Ying Fan & Zengru Di & Yougui Wang & Shlomo Havlin, 2022. "Impactful scientists have higher tendency to involve collaborators in new topics," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 119(33), pages 2207436119-, August.
    6. Bedoor K. AlShebli & Talal Rahwan & Wei Lee Woon, 2018. "The preeminence of ethnic diversity in scientific collaboration," Nature Communications, Nature, vol. 9(1), pages 1-10, December.
    7. Ludo Waltman & Nees Jan van Eck, 2012. "The inconsistency of the h-index," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(2), pages 406-415, February.
    8. Dahlin, Kristina B. & Behrens, Dean M., 2005. "When is an invention really radical?: Defining and measuring technological radicalness," Research Policy, Elsevier, vol. 34(5), pages 717-737, June.
    9. Rüdiger Mutz & Tobias Wolbring & Hans-Dieter Daniel, 2017. "The effect of the “very important paper” (VIP) designation in Angewandte Chemie International Edition on citation impact: A propensity score matching analysis," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 68(9), pages 2139-2153, September.
    10. repec:nas:journl:v:115:y:2018:p:12603-12607 is not listed on IDEAS
    11. Wang, Jian & Veugelers, Reinhilde & Stephan, Paula, 2017. "Bias against novelty in science: A cautionary tale for users of bibliometric indicators," Research Policy, Elsevier, vol. 46(8), pages 1416-1436.
    12. Ludo Waltman & Nees Jan van Eck, 2012. "The inconsistency of the h‐index," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(2), pages 406-415, February.
    13. 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.
    14. 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.
    15. Seyed Reza Mirnezami & Catherine Beaudry & Leila Tahmooresnejad, 2020. "The effect of collaboration with top-funded scholars on scientific production," Science and Public Policy, Oxford University Press, vol. 47(2), pages 219-234.
    16. Samuel F. Way & Allison C. Morgan & Daniel B. Larremore & Aaron Clauset, 2019. "Productivity, prominence, and the effects of academic environment," Proceedings of the National Academy of Sciences, Proceedings of the National Academy of Sciences, vol. 116(22), pages 10729-10733, May.
    17. Arts, Sam & Hou, Jianan & Gomez, Juan Carlos, 2021. "Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures," Research Policy, Elsevier, vol. 50(2).
    18. Trapido, Denis, 2015. "How novelty in knowledge earns recognition: The role of consistent identities," Research Policy, Elsevier, vol. 44(8), pages 1488-1500.
    19. Tahamtan, Iman & Bornmann, Lutz, 2018. "Creativity in science and the link to cited references: Is the creative potential of papers reflected in their cited references?," Journal of Informetrics, Elsevier, vol. 12(3), pages 906-930.
    20. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2022. "Remote Collaboration Fuses Fewer Breakthrough Ideas," Papers 2206.01878, arXiv.org, revised Oct 2023.
    21. Uddin, Shahadat & Khan, Arif, 2016. "The impact of author-selected keywords on citation counts," Journal of Informetrics, Elsevier, vol. 10(4), pages 1166-1177.
    22. Leydesdorff, Loet & Rafols, Ismael, 2011. "Indicators of the interdisciplinarity of journals: Diversity, centrality, and citations," Journal of Informetrics, Elsevier, vol. 5(1), pages 87-100.
    23. Henk F. Moed, 2002. "The impact-factors debate: the ISI's uses and limits," Nature, Nature, vol. 415(6873), pages 731-732, February.
    24. Kristina Dahlin & Deans M. Behrens, 2005. "When is an invention really radical? Defining and measuring technological radicalness," Post-Print hal-00480416, HAL.
    25. 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).
    26. D’Este, Pablo & Llopis, Oscar & Rentocchini, Francesco & Yegros, Alfredo, 2019. "The relationship between interdisciplinarity and distinct modes of university-industry interaction," Research Policy, Elsevier, vol. 48(9), pages 1-1.
    27. Yiling Lin & Carl Benedikt Frey & Lingfei Wu, 2023. "Remote collaboration fuses fewer breakthrough ideas," Nature, Nature, vol. 623(7989), pages 987-991, November.
    28. Liu, Qiuling & Guo, Lei & Sun, Yiping & Ren, Linlin & Wang, Xinhua & Han, Xiaohui, 2024. "Do scholars' collaborative tendencies impact the quality of their publications? A generalized propensity score matching analysis," Journal of Informetrics, Elsevier, vol. 18(1).
    29. 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.
    30. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Di Costa, 2019. "The collaboration behavior of top scientists," Scientometrics, Springer;Akadémiai Kiadó, vol. 118(1), pages 215-232, January.
    31. An Zeng & Ying Fan & Zengru Di & Yougui Wang & Shlomo Havlin, 2022. "Impactful scientists have higher tendency to involve collaborators in new topics," Decision Analysis, INFORMS, vol. 119(33), pages 2207436119-, August.
    32. Mengjiao Qi & An Zeng & Menghui Li & Ying Fan & Zengru Di, 2017. "Standing on the shoulders of giants: the effect of outstanding scientists on young collaborators’ careers," Scientometrics, Springer;Akadémiai Kiadó, vol. 111(3), pages 1839-1850, June.
    33. Qianjin Zong & Yafen Xie & Jiechun Liang, 2020. "Does open peer review improve citation count? Evidence from a propensity score matching analysis of PeerJ," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(1), pages 607-623, October.
    34. Kim, Sangil & Park, Keon Chul, 2021. "Government funded R&D collaboration and it's impact on SME's business performance," Journal of Informetrics, Elsevier, vol. 15(3).
    35. Loet Leydesdorff, 2007. "Betweenness centrality as an indicator of the interdisciplinarity of scientific journals," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 58(9), pages 1303-1319, July.
    36. Giovanni Abramo & Ciriaco Andrea D’Angelo & Anastasiia Soldatenkova, 2016. "The dispersion of the citation distribution of top scientists’ publications," Scientometrics, Springer;Akadémiai Kiadó, vol. 109(3), pages 1711-1724, December.
    37. Giovanni Abramo & Ciriaco Andrea D’Angelo & Flavia Costa, 2019. "A gender analysis of top scientists’ collaboration behavior: evidence from Italy," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(2), pages 405-418, August.
    38. ., 2017. "Standing on the shoulders of giants," Chapters, in: Endogenous Innovation, chapter 1, pages 3-24, Edward Elgar Publishing.
    39. Alberto Abadie & Guido W. Imbens, 2002. "Simple and Bias-Corrected Matching Estimators for Average Treatment Effects," NBER Technical Working Papers 0283, National Bureau of Economic Research, Inc.
    40. Amjad, Tehmina & Ding, Ying & Xu, Jian & Zhang, Chenwei & Daud, Ali & Tang, Jie & Song, Min, 2017. "Standing on the shoulders of giants," Journal of Informetrics, Elsevier, vol. 11(1), pages 307-323.
    41. Fan, Lingxu & Guo, Lei & Wang, Xinhua & Xu, Liancheng & Liu, Fangai, 2022. "Does the author’s collaboration mode lead to papers’ different citation impacts? An empirical analysis based on propensity score matching," Journal of Informetrics, Elsevier, vol. 16(4).
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