|
|
Can Combinative and Disciplinary Novelty Enhance the Technological Impact of Scientific Papers? A Dual Perspective of Direct and Indirect Technological Impact |
Yang Alex J.1, Liu Meijun2, Bu Yi3, Zhao Star X.4,5, Deng Sanhong1 |
1.School of Information Management, Nanjing University, Nanjing 210023 2.Institute for Global Public Policy, Fudan University, Shanghai 200433 3.Department of Information Management, Peking University, Beijing 100871 4.Institute of Big Data (IBD), Fudan University, Shanghai 200433 5.National Institute of Intelligent Evaluation and Governance, Fudan University, Shanghai 200433 |
|
|
Abstract Understanding whether combined novelty and interdisciplinary knowledge integration can enhance the technological impact of scientific papers is important for the development of new quality productive forces. To this end, this study employs Monte Carlo simulations to measure combinative novelty using higher-order metrics derived from real and simulated distributions of knowledge combinations. Disciplinary novelty is quantified using co-citation matrices and distributional distances across disciplines. Adopting a dual perspective of direct and indirect technological impact, this study integrates a “paper-to-patent” citation network with a deep “paper-to-paper” citation network to propose metrics such as patents directly and indirectly citing papers. These metrics capture the direct and indirect technological impact of scientific papers. Based on a fixed-effects regression analysis of 30 million scientific papers from the Microsoft Academic Database, the findings reveal that combinative novelty positively promotes both direct and indirect technological impacts of papers, with a stronger effect observed on indirect technological impact. By contrast, disciplinary novelty exclusively promotes indirect technological impact. Furthermore, the study identifies that, in STEM fields and large collaborative teams, both combinative and disciplinary novelty effectively enhance the dual technological impact of papers. Notably, the positive effect of disciplinary novelty on the indirect technological impact of papers exhibits a declining trend over time.
|
Received: 22 April 2024
|
|
|
|
1 Azoulay P, Graff-Zivin J, Uzzi B, et al. Toward a more scientific science[J]. Science, 2018, 361(6408): 1194-1197. 2 刘伟. 科学认识与切实发展新质生产力[J]. 经济研究, 2024, 59(3): 4-11. 3 Yang A J. Unveiling the impact and dual innovation of funded research[J]. Journal of Informetrics, 2024, 18(1): 101480. 4 Green J R, Scotchmer S. On the division of profit in sequential innovation[J]. The RAND Journal of Economics, 1995, 26(1): 20-33. 5 Lee F. Recombinant uncertainty in technological search[J]. Management Science, 2001, 47(1): 117-132. 6 Furman J L, Stern S. Climbing atop the shoulders of giants: the impact of institutions on cumulative research[J]. American Economic Review, 2011, 101(5): 1933-1963. 7 Fortunato S, Bergstrom C T, B?rner K, et al. Science of science[J]. Science, 2018, 359(6379): eaao0185. 8 Yin Y A, Dong Y X, Wang K S, et al. Public use and public funding of science[J]. Nature Human Behaviour, 2022, 6(10): 1344-1350. 9 Yin Y A, Gao J, Jones B F, et al. Coevolution of policy and science during the pandemic[J]. Science, 2021, 371(6525): 128-130. 10 Funk R J, Owen-Smith J. A dynamic network measure of technological change[J]. Management Science, 2017, 63(3): 791-817. 11 Wang D S, Barabási A L. The science of science[M]. Cambridge: Cambridge University Press, 2021. 12 Lane J, Bertuzzi S. Measuring the results of science investments[J]. Science, 2011, 331(6018): 678-680. 13 Baruffaldi S, Simeth M, Wehrheim D. Asymmetric information and R&D disclosure: evidence from scientific publications[J]. Management Science, 2023, 70(2): 1052-1069. 14 Ahmadpoor M, Jones B F. The dual frontier: patented inventions and prior scientific advance[J]. Science, 2017, 357(6351): 583-587. 15 Azoulay P, Graff Zivin J S, Manso G. Incentives and creativity: evidence from the academic life sciences[J]. The RAND Journal of Economics, 2011, 42(3): 527-554. 16 Schumpeter J A. Business cycles: a theoretical, historical, and statistical analysis of the capitalist process[M]. New York: McGraw-Hill, 1939. 17 Hofstra B, Kulkarni V V, Munoz-Najar Galvez S, et al. The diversity-innovation paradox in science[J]. Proceedings of the National Academy of Sciences of the United States of America, 2020, 117(17): 9284-9291. 18 Trapido D. How novelty in knowledge earns recognition: the role of consistent identities[J]. Research Policy, 2015, 44(8): 1488-1500. 19 Bush V. Science: the endless frontier[M]. Washington: United States Government Printing Office, 1945. 20 Evans J A. Electronic publication and the narrowing of science and scholarship[J]. Science, 2008, 321(5887): 395-399. 21 Uzzi B, Mukherjee S, Stringer M, et al. Atypical combinations and scientific impact[J]. Science, 2013, 342(6157): 468-472. 22 Liu M J, Bu Y, Chen C Y, et al. Pandemics are catalysts of scientific novelty: evidence from COVID-19[J]. Journal of the Association for Information Science and Technology, 2022, 73(8): 1065-1078. 23 Liu M J, Xie Z H, Yang A J, et al. The prominent and heterogeneous gender disparities in scientific novelty: evidence from biomedical doctoral theses[J]. Information Processing & Management, 2024, 61(4): 103743. 24 Kim D, Cerigo D B, Jeong H, et al. Technological novelty profile and invention’s future impact[J]. EPJ Data Science, 2016, 5(1): Article No.8. 25 Wu L F, Wang D S, Evans J A. Large teams develop and small teams disrupt science and technology[J]. Nature, 2019, 566(7744): 378-382. 26 Park M, Leahey E, Funk R J. Papers and patents are becoming less disruptive over time[J]. Nature, 2023, 613(7942): 138-144. 27 Wang J, Veugelers R, Stephan P. Bias against novelty in science: a cautionary tale for users of bibliometric indicators[J]. Research Policy, 2017, 46(8): 1416-1436. 28 Bornmann L, Tekles A, Zhang H H, et al. Do we measure novelty when we analyze unusual combinations of cited references? A validation study of bibliometric novelty indicators based on F1000Prime data[J]. Journal of Informetrics, 2019, 13(4): 100979. 29 Stirling A. A general framework for analysing diversity in science, technology and society[J]. Journal of the Royal Society Interface, 2007, 4(15): 707-719. 30 Fontana M, Iori M, Montobbio F, et al. New and atypical combinations: an assessment of novelty and interdisciplinarity[J]. Research Policy, 2020, 49(7): 104063. 31 Foster J G, Rzhetsky A, Evans J A. Tradition and innovation in scientists’ research strategies[J]. American Sociological Review, 2015, 80(5): 875-908. 32 李茂林, 王子路, 何光辉, 等. 银行业金融科技创新、结构性普惠效应与创业活力[J]. 管理世界, 2024, 40(6): 195-224. 33 Krieger J, Li D, Papanikolaou D. Missing novelty in drug development[J]. The Review of Financial Studies, 2022, 35(2): 636-679. 34 Shi F, Evans J. Surprising combinations of research contents and contexts are related to impact and emerge with scientific outsiders from distant disciplines[J]. Nature Communications, 2023, 14(1): 1641. 35 Onodera N, Yoshikane F. Factors affecting citation rates of research articles[J]. Journal of the Association for Information Science and Technology, 2015, 66(4): 739-764. 36 Wang D S, Song C M, Barabási A L. Quantifying long-term scientific impact[J]. Science, 2013, 342(6154): 127-132. 37 Azoulay P, Li D, Graff Zivin J S, et al. Public R&D investments and private-sector patenting: evidence from NIH funding rules[J]. The Review of Economic Studies, 2019, 86(1): 117-152. 38 Waltman L. A review of the literature on citation impact indicators[J]. Journal of Informetrics, 2016, 10(2): 365-391. 39 梁镇涛, 毛进, 李纲. 融合“科学-技术”知识关联的高颠覆性专利预测方法[J]. 情报学报, 2023, 42(6): 649-662. 40 Li D, Azoulay P, Sampat B N. The applied value of public investments in biomedical research[J]. Science, 2017, 356(6333): 78-81. 41 Williams H L. Intellectual property rights and innovation: evidence from the human genome[J]. The Journal of Political Economy, 2010, 121(1): 1-27. 42 Lee Y N, Walsh J P, Wang J. Creativity in scientific teams: unpacking novelty and impact[J]. Research Policy, 2015, 44(3): 684-697. 43 Wang K S, Shen Z H, Huang C Y, et al. Microsoft Academic Graph: when experts are not enough[J]. Quantitative Science Studies, 2020, 1(1): 396-413. 44 Marx M, Fuegi A. Reliance on science by inventors: hybrid extraction of in-text patent-to-article citations[J]. Journal of Economics & Management Strategy, 2022, 31(2): 369-392. 45 Gates A J, Barabási A L. Reproducible science of science at scale: pySciSci[J]. Quantitative Science Studies, 2023, 4(3): 700-710. 46 Zeng A, Shen Z S, Zhou J L, et al. The science of science: from the perspective of complex systems[J]. Physics Reports, 2017, 714: 1-73. 47 Cimini G, Squartini T, Saracco F, et al. The statistical physics of real-world networks[J]. Nature Reviews Physics, 2019, 1: 58-71. 48 刘嘉明, 孙建军. 参考文献跨学科性与论文学术影响力的关系研究[J]. 情报学报, 2023, 42(5): 525-536. 49 Yang Y, Tian T Y, Woodruff T K, et al. Gender-diverse teams produce more novel and higher-impact scientific ideas[J]. Proceedings of the National Academy of Sciences of the United States of America, 2022, 119(36): e2200841119. 50 孔嘉, 邓三鸿, 张佳锐, 等. 技术创新中的知识融合: “科学——技术”二元知识对专利影响研究[J]. 情报学报, 2022, 41(11): 1161-1173. 51 Lin Z H, Yin Y A, Liu L, et al. SciSciNet: a large-scale open data lake for the science of science research[J]. Scientific Data, 2023, 10: Article No.315. 52 Li J C, Yin Y A, Fortunato S, et al. A dataset of publication records for Nobel laureates[J]. Scientific Data, 2019, 6(1): 33. 53 Brogaard J, Engelberg J E, Eswar S K, et al. On the causal effect of fame on citations[J]. Management Science, 2024, 70(10): 7187. 54 Lin Y L, Frey C B, Wu L F. Remote collaboration fuses fewer breakthrough ideas[J]. Nature, 2023, 623(7989): 987-991. 55 杨杰, 邓三鸿, 王昊. 科学研究的颠覆性创新测度——相对颠覆性指数[J]. 情报学报, 2023, 42(9): 1052-1064. 56 杨杰, 孔嘉, 张艺炜, 等. 融合论文颠覆性与巩固性的学者二元影响力测度[J]. 情报学报, 2023, 42(12): 1412-1423. |
|
|
|