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The association between prior knowledge and the disruption of an article

Author

Listed:
  • Libo Sheng

    (Nanjing University)

  • Dongqing Lyu

    (Nanjing University of Finance & Economics)

  • Xuanmin Ruan

    (Nanjing University)

  • Hongquan Shen

    (Nanjing University)

  • Ying Cheng

    (Nanjing University)

Abstract

Disruptive research that reveals an important innovation in science can reshape existing pathways. This paper studies the relationship between the prior knowledge the research builds upon and disruption in science. To measure the disruption of an article and operationalize prior knowledge, we use the disruption index ( $$D\ index$$ D i n d e x ) and examine six characteristics of references of an article: amount, recency, impact, disruption, novelty and homogeneity. Ordinary least squares regression is conducted on a set of 1,310,837 articles from 2001 to 2010 from the PubMed knowledge graph (PKG) dataset. Our primary finding shows that the recency and homogeneity of prior knowledge are negatively associated with disruption, while we found positive relationships between the amount, impact, disruption and novel combinations of prior knowledge and disruption. Our robustness checks further confirm these conclusions. This study deepens our understanding of the association between prior knowledge and disruption, and has significant implications for researchers to search for and synthesize different types of prior knowledge.

Suggested Citation

  • Libo Sheng & Dongqing Lyu & Xuanmin Ruan & Hongquan Shen & Ying Cheng, 2023. "The association between prior knowledge and the disruption of an article," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(8), pages 4731-4751, August.
    • Handle: RePEc:spr:scient:v:128:y:2023:i:8:d:10.1007_s11192-023-04751-0
    DOI: 10.1007/s11192-023-04751-0
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    1. Uijun Kwon & Youngjung Geum, 2020. "Identification of promising inventions considering the quality of knowledge accumulation: a machine learning approach," Scientometrics, Springer;Akadémiai Kiadó, vol. 125(3), pages 1877-1897, December.
    2. Messeni Petruzzelli, Antonio & Ardito, Lorenzo & Savino, Tommaso, 2018. "Maturity of knowledge inputs and innovation value: The moderating effect of firm age and size," Journal of Business Research, Elsevier, vol. 86(C), pages 190-201.
    3. Kristina Dahlin & L. Weingart & P. Hinds, 2005. "Team diversity and information use," Post-Print hal-00480406, HAL.
    4. Lin, Yiling & Evans, James A. & Wu, Lingfei, 2022. "New directions in science emerge from disconnection and discord," Journal of Informetrics, Elsevier, vol. 16(1).
    5. Winnink, J.J. & Tijssen, Robert J.W. & van Raan, A.F.J., 2019. "Searching for new breakthroughs in science: How effective are computerised detection algorithms?," Technological Forecasting and Social Change, Elsevier, vol. 146(C), pages 673-686.
    6. 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.
    7. Chen, Chaomei & Chen, Yue & Horowitz, Mark & Hou, Haiyan & Liu, Zeyuan & Pellegrino, Donald, 2009. "Towards an explanatory and computational theory of scientific discovery," Journal of Informetrics, Elsevier, vol. 3(3), pages 191-209.
    8. Iman Tahamtan & Lutz Bornmann, 2019. "What do citation counts measure? An updated review of studies on citations in scientific documents published between 2006 and 2018," Scientometrics, Springer;Akadémiai Kiadó, vol. 121(3), pages 1635-1684, December.
    9. Didegah, Fereshteh & Thelwall, Mike, 2013. "Which factors help authors produce the highest impact research? Collaboration, journal and document properties," Journal of Informetrics, Elsevier, vol. 7(4), pages 861-873.
    10. Russell J. Funk & Jason Owen-Smith, 2017. "A Dynamic Network Measure of Technological Change," Management Science, INFORMS, vol. 63(3), pages 791-817, March.
    11. Stefano Mammola & Diego Fontaneto & Alejandro Martínez & Filipe Chichorro, 2021. "Impact of the reference list features on the number of citations," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(1), pages 785-799, January.
    12. Michael B. Heeley & Robert Jacobson, 2008. "The recency of technological inputs and financial performance," Strategic Management Journal, Wiley Blackwell, vol. 29(7), pages 723-744, July.
    13. Subramanian, Annapoornima M. & Bo, Wang & Kah-Hin, Chai, 2018. "The role of knowledge base homogeneity in learning from strategic alliances," Research Policy, Elsevier, vol. 47(1), pages 158-168.
    14. Ismael Rafols & Martin Meyer, 2010. "Diversity and network coherence as indicators of interdisciplinarity: case studies in bionanoscience," Scientometrics, Springer;Akadémiai Kiadó, vol. 82(2), pages 263-287, February.
    15. Veugelers, Reinhilde & Wang, Jian, 2019. "Scientific novelty and technological impact," Research Policy, Elsevier, vol. 48(6), pages 1362-1372.
    16. Lutz Bornmann & Alexander Tekles, 2019. "Disruptive papers published in Scientometrics," Scientometrics, Springer;Akadémiai Kiadó, vol. 120(1), pages 331-336, July.
    17. Robert J. W. Tijssen & Martijn S. Visser & Thed N. van Leeuwen, 2002. "Benchmarking international scientific excellence: Are highly cited research papers an appropriate frame of reference?," Scientometrics, Springer;Akadémiai Kiadó, vol. 54(3), pages 381-397, July.
    18. Chen, Jiyao & Shao, Diana & Fan, Shaokun, 2021. "Destabilization and consolidation: Conceptualizing, measuring, and validating the dual characteristics of technology," Research Policy, Elsevier, vol. 50(1).
    19. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    20. Wagner, Caroline S. & Roessner, J. David & Bobb, Kamau & Klein, Julie Thompson & Boyack, Kevin W. & Keyton, Joann & Rafols, Ismael & Börner, Katy, 2011. "Approaches to understanding and measuring interdisciplinary scientific research (IDR): A review of the literature," Journal of Informetrics, Elsevier, vol. 5(1), pages 14-26.
    21. Liang, Guoqiang & Hou, Haiyan & Ding, Ying & Hu, Zhigang, 2020. "Knowledge recency to the birth of Nobel Prize-winning articles: Gender, career stage, and country," Journal of Informetrics, Elsevier, vol. 14(3).
    22. Lee Fleming, 2001. "Recombinant Uncertainty in Technological Search," Management Science, INFORMS, vol. 47(1), pages 117-132, January.
    23. Waltman, Ludo, 2016. "A review of the literature on citation impact indicators," Journal of Informetrics, Elsevier, vol. 10(2), pages 365-391.
    24. 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.
    25. John P. A. Ioannidis & Kevin W. Boyack & Henry Small & Aaron A. Sorensen & Richard Klavans, 2014. "Bibliometrics: Is your most cited work your best?," Nature, Nature, vol. 514(7524), pages 561-562, October.
    26. Rodrigo Costas & Thed N. Leeuwen & María Bordons, 2012. "Referencing patterns of individual researchers: Do top scientists rely on more extensive information sources?," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 63(12), pages 2433-2450, December.
    27. Shiyun Wang & Yaxue Ma & Jin Mao & Yun Bai & Zhentao Liang & Gang Li, 2023. "Quantifying scientific breakthroughs by a novel disruption indicator based on knowledge entities," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(2), pages 150-167, February.
    28. Henry Small, 2004. "Why authors think their papers are highly cited," Scientometrics, Springer;Akadémiai Kiadó, vol. 60(3), pages 305-316, August.
    29. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    30. Verhoeven, Dennis & Bakker, Jurriën & Veugelers, Reinhilde, 2016. "Measuring technological novelty with patent-based indicators," Research Policy, Elsevier, vol. 45(3), pages 707-723.
    31. Whalen, Ryan, 2018. "Boundary spanning innovation and the patent system: Interdisciplinary challenges for a specialized examination system," Research Policy, Elsevier, vol. 47(7), pages 1334-1343.
    32. Carole J. Lee & Cassidy R. Sugimoto & Guo Zhang & Blaise Cronin, 2013. "Bias in peer review," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(1), pages 2-17, January.
    33. Hur, Wonchang & Oh, Junbyoung, 2021. "A man is known by the company he keeps?: A structural relationship between backward citation and forward citation of patents," Research Policy, Elsevier, vol. 50(1).
    34. Schilling, Melissa A. & Green, Elad, 2011. "Recombinant search and breakthrough idea generation: An analysis of high impact papers in the social sciences," Research Policy, Elsevier, vol. 40(10), pages 1321-1331.
    35. 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.
    36. Zhentao Liang & Jin Mao & Gang Li, 2023. "Bias against scientific novelty: A prepublication perspective," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 74(1), pages 99-114, January.
    37. Fereshteh Didegah & Mike Thelwall, 2013. "Determinants of research citation impact in nanoscience and nanotechnology," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 64(5), pages 1055-1064, May.
    38. An Zeng & Ying Fan & Zengru Di & Yougui Wang & Shlomo Havlin, 2021. "Fresh teams are associated with original and multidisciplinary research," Nature Human Behaviour, Nature, vol. 5(10), pages 1314-1322, October.
    39. Dunaiski, Marcel & Visser, Willem & Geldenhuys, Jaco, 2016. "Evaluating paper and author ranking algorithms using impact and contribution awards," Journal of Informetrics, Elsevier, vol. 10(2), pages 392-407.
    40. Hohberger, Jan, 2016. "Does it pay to stand on the shoulders of giants? An analysis of the inventions of star inventors in the biotechnology sector," Research Policy, Elsevier, vol. 45(3), pages 682-698.
    41. Rodrigo Costas & Thed N. van Leeuwen & María Bordons, 2012. "Referencing patterns of individual researchers: Do top scientists rely on more extensive information sources?," Journal of the American Society for Information Science and Technology, Association for Information Science & Technology, vol. 63(12), pages 2433-2450, December.
    42. Schoenmakers, Wilfred & Duysters, Geert, 2010. "The technological origins of radical inventions," Research Policy, Elsevier, vol. 39(8), pages 1051-1059, October.
    43. Atul Nerkar, 2003. "Old Is Gold? The Value of Temporal Exploration in the Creation of New Knowledge," Management Science, INFORMS, vol. 49(2), pages 211-229, February.
    44. Haibo Liu & Jürgen Mihm & Manuel E. Sosa & Manuel E. Sosa, 2018. "Where Do Stars Come From? The Role of Star vs. Nonstar Collaborators in Creative Settings," Organization Science, INFORMS, vol. 29(6), pages 1149-1169, December.
    45. Roper, Stephen & Hewitt-Dundas, Nola, 2015. "Knowledge stocks, knowledge flows and innovation: Evidence from matched patents and innovation panel data," Research Policy, Elsevier, vol. 44(7), pages 1327-1340.
    46. Alan L. Porter & Ismael Rafols, 2009. "Is science becoming more interdisciplinary? Measuring and mapping six research fields over time," Scientometrics, Springer;Akadémiai Kiadó, vol. 81(3), pages 719-745, December.
    47. Iman Tahamtan & Askar Safipour Afshar & Khadijeh Ahamdzadeh, 2016. "Factors affecting number of citations: a comprehensive review of the literature," Scientometrics, Springer;Akadémiai Kiadó, vol. 107(3), pages 1195-1225, June.
    48. Ruan, Xuanmin & Lyu, Dongqing & Gong, Kaile & Cheng, Ying & Li, Jiang, 2021. "Rethinking the disruption index as a measure of scientific and technological advances," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
    49. Yue Wang & Ning Li & Bin Zhang & Qian Huang & Jian Wu & Yang Wang, 2023. "The effect of structural holes on producing novel and disruptive research in physics," Scientometrics, Springer;Akadémiai Kiadó, vol. 128(3), pages 1801-1823, March.
    50. Byeongwoo Kang & Kaoru Nabeshima, 2021. "National origin diversity and innovation performance: the case of Japan," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(6), pages 5333-5351, June.
    51. Xiaolin Shi & Lada A Adamic & Belle L Tseng & Gavin S Clarkson, 2009. "The Impact of Boundary Spanning Scholarly Publications and Patents," PLOS ONE, Public Library of Science, vol. 4(8), pages 1-7, August.
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