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What Is a Complex Innovation System?

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  • J Sylvan Katz

Abstract

Innovation systems are sometimes referred to as complex systems, something that is intuitively understood but poorly defined. A complex system dynamically evolves in non-linear ways giving it unique properties that distinguish it from other systems. In particular, a common signature of complex systems is scale-invariant emergent properties. A scale-invariant property can be identified because it is solely described by a power law function, f(x) = kxα, where the exponent, α, is a measure of scale-invariance. The focus of this paper is to describe and illustrate that innovation systems have properties of a complex adaptive system. In particular scale-invariant emergent properties indicative of their complex nature that can be quantified and used to inform public policy. The global research system is an example of an innovation system. Peer-reviewed publications containing knowledge are a characteristic output. Citations or references to these articles are an indirect measure of the impact the knowledge has on the research community. Peer-reviewed papers indexed in Scopus and in the Web of Science were used as data sources to produce measures of sizes and impact. These measures are used to illustrate how scale-invariant properties can be identified and quantified. It is demonstrated that the distribution of impact has a reasonable likelihood of being scale-invariant with scaling exponents that tended toward a value of less than 3.0 with the passage of time and decreasing group sizes. Scale-invariant correlations are shown between the evolution of impact and size with time and between field impact and sizes at points in time. The recursive or self-similar nature of scale-invariance suggests that any smaller innovation system within the global research system is likely to be complex with scale-invariant properties too.

Suggested Citation

  • J Sylvan Katz, 2016. "What Is a Complex Innovation System?," PLOS ONE, Public Library of Science, vol. 11(6), pages 1-24, June.
    • Handle: RePEc:plo:pone00:0156150
    DOI: 10.1371/journal.pone.0156150
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    References listed on IDEAS

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    Cited by:

    1. Marcel Clermont & Johanna Krolak & Dirk Tunger, 2021. "Does the citation period have any effect on the informative value of selected citation indicators in research evaluations?," Scientometrics, Springer;Akadémiai Kiadó, vol. 126(2), pages 1019-1047, February.
    2. Alonso Rodríguez-Navarro & Ricardo Brito, 2019. "Probability and expected frequency of breakthroughs: basis and use of a robust method of research assessment," Scientometrics, Springer;Akadémiai Kiadó, vol. 119(1), pages 213-235, April.
    3. Ruiz-Castillo, Javier & Costas, Rodrigo, 2018. "Individual and field citation distributions in 29 broad scientific fields," Journal of Informetrics, Elsevier, vol. 12(3), pages 868-892.
    4. Mêgnigbêto, Eustache, 2018. "Modelling the Triple Helix of university-industry-government relationships with game theory: Core, Shapley value and nucleolus as indicators of synergy within an innovation system," Journal of Informetrics, Elsevier, vol. 12(4), pages 1118-1132.
    5. Jeffrey O’Neal London & Nasir Jamil Sheikh, 2020. "Innovation in African-American high-tech enterprises: a multi-agent approach," Entrepreneurship and Sustainability Issues, VsI Entrepreneurship and Sustainability Center, vol. 7(4), pages 3101-3121, June.
    6. Rodríguez-Navarro, Alonso & Brito, Ricardo, 2018. "Double rank analysis for research assessment," Journal of Informetrics, Elsevier, vol. 12(1), pages 31-41.
    7. Gruenhagen, Jan Henrik & Cox, Stephen & Parker, Rachel, 2022. "An actor-oriented perspective on innovation systems: Functional analysis of drivers and barriers to innovation and technology adoption in the mining sector," Technology in Society, Elsevier, vol. 68(C).
    8. Guillermo Armando Ronda-Pupo, 2017. "The effect of document types and sizes on the scaling relationship between citations and co-authorship patterns in management journals," Scientometrics, Springer;Akadémiai Kiadó, vol. 110(3), pages 1191-1207, March.
    9. Sueyoshi, Toshiyuki & Ryu, Youngbok, 2022. "Performance assessment on technology transition from small businesses to the U.S. Department of Defense," Socio-Economic Planning Sciences, Elsevier, vol. 80(C).
    10. 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.
    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. Hongguang Dong & Menghui Li & Ru Liu & Chensheng Wu & Jinshan Wu, 2017. "Allometric scaling in scientific fields," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(1), pages 583-594, July.
    13. Liliya Satalkina & Gerald Steiner, 2020. "Digital Entrepreneurship and its Role in Innovation Systems: A Systematic Literature Review as a Basis for Future Research Avenues for Sustainable Transitions," Sustainability, MDPI, vol. 12(7), pages 1-27, April.
    14. Guillermo Armando Ronda-Pupo, 2020. "The performance of Latin American research on economics & business," Scientometrics, Springer;Akadémiai Kiadó, vol. 122(1), pages 573-590, January.
    15. Brito, Ricardo & Rodríguez-Navarro, Alonso, 2018. "Research assessment by percentile-based double rank analysis," Journal of Informetrics, Elsevier, vol. 12(1), pages 315-329.
    16. Guillermo Armando Ronda-Pupo & J. Sylvan Katz, 2017. "The scaling relationship between degree centrality of countries and their citation-based performance on Management Information Systems," Scientometrics, Springer;Akadémiai Kiadó, vol. 112(3), pages 1285-1299, September.
    17. Jin Guo & Meng Chen & Xialing Sun & Zhanzhao Wang & Jinli Xue, 2020. "Leveraging industrial-technological innovation to achieve sustainable development: A systems thinking perspective," PLOS ONE, Public Library of Science, vol. 15(12), pages 1-23, December.
    18. 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.
    19. Petr Hajek & Roberto Henriques, 2017. "Modelling innovation performance of European regions using multi-output neural networks," PLOS ONE, Public Library of Science, vol. 12(10), pages 1-21, October.
    20. Chen, Kaihua & Zhang, Yi & Fu, Xiaolan, 2019. "International research collaboration: An emerging domain of innovation studies?," Research Policy, Elsevier, vol. 48(1), pages 149-168.
    21. Mao, Jin & Liang, Zhentao & Cao, Yujie & Li, Gang, 2020. "Quantifying cross-disciplinary knowledge flow from the perspective of content: Introducing an approach based on knowledge memes," Journal of Informetrics, Elsevier, vol. 14(4).
    22. Barbero, Javier & Zabala-Iturriagagoitia, Jon Mikel & Zofío, José L., 2021. "Is more always better? On the relevance of decreasing returns to scale on innovation," Technovation, Elsevier, vol. 107(C).

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