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Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy

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  • Zhao, Jianyu
  • Yu, Lean
  • Xi, Xi
  • Li, Shengliang

Abstract

Digital economy expands the source of knowledge for innovation and accelerates the flow and combination of knowledge to form novel knowledge combinations, thereby generating the interdisciplinary knowledge production model. In this context, complex innovation which is characterized by the knowledge production consequence based on the combinations of multiple-field knowledge has become the new way for firms to seize new development opportunities and compete in the digital economy. Given that complex innovation emerged from a gradually forming large, multilayered, combinatorial network consists of collaboration networks in various knowledge fields that are initially separated, the challenge of facillatating the emergence of complex innovation is unveiling the minimum proportion of connected paths in the combinatorial network to trigger effective transmission of multi-fields knowledge and offering applicable optimization strategies to optimize that proportion. This study incorporated Ohm's law into the percolation theoretical framework and calculate the knowledge percolation threshold of the combinatorial network and its subnetworks with patent data of Chinese strategic emerging industries. We further examined the optimization results of six strategies in terms of their optimization effects and time costs. Accordingly, we revealed the probability of knowledge percolation occurring in a combinatorial network and its subnetworks, clarified knowledge transmission characteristics according to knowledge-based cluster dynamics, and determined strategies for optimizing the knowledge percolation threshold. This study is not only highly feasible and exercisable for academics to conduct future studies, but it also has vital implications for the practitioners to utilize and control the knowledge transmission of the combinatorial network to realize the complex innovation.

Suggested Citation

  • Zhao, Jianyu & Yu, Lean & Xi, Xi & Li, Shengliang, 2023. "Knowledge percolation threshold and optimization strategies of the combinatorial network for complex innovation in the digital economy," Omega, Elsevier, vol. 120(C).
    • Handle: RePEc:eee:jomega:v:120:y:2023:i:c:s0305048323000774
    DOI: 10.1016/j.omega.2023.102913
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    as
    1. Guan, Jiancheng & Liu, Na, 2016. "Exploitative and exploratory innovations in knowledge network and collaboration network: A patent analysis in the technological field of nano-energy," Research Policy, Elsevier, vol. 45(1), pages 97-112.
    2. Jaideep Ghosh & Avinash Kshitij, 2014. "An integrated examination of collaboration coauthorship networks through structural cohesion, holes, hierarchy, and percolating clusters," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 65(8), pages 1639-1661, August.
    3. Gautam Ahuja, 2000. "The duality of collaboration: inducements and opportunities in the formation of interfirm linkages," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 317-343, March.
    4. Antonelli, Cristiano, 1999. "The Evolution of the Industrial Organisation of the Production of Knowledge," Cambridge Journal of Economics, Cambridge Political Economy Society, vol. 23(2), pages 243-260, March.
    5. Zhao, Jianyu & Wei, Jiang & Yu, Lean & Xi, Xi, 2022. "Robustness of knowledge networks under targeted attacks: Electric vehicle field of China evidence," Structural Change and Economic Dynamics, Elsevier, vol. 63(C), pages 367-382.
    6. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Andrianova, Elena, 2022. "Beyond big data – new techniques for forecasting elections using stochastic models with self-organisation and memory," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    7. Cristiano Antonelli, 1996. "Localized knowledge percolation processes and information networks," Journal of Evolutionary Economics, Springer, vol. 6(3), pages 281-295.
    8. Brennecke, Julia & Rank, Olaf, 2017. "The firm’s knowledge network and the transfer of advice among corporate inventors—A multilevel network study," Research Policy, Elsevier, vol. 46(4), pages 768-783.
    9. Stienen, V.F. & Wagenaar, J.C. & den Hertog, D. & Fleuren, H.A., 2021. "Optimal depot locations for humanitarian logistics service providers using robust optimization," Omega, Elsevier, vol. 104(C).
    10. Kadziński, Miłosz & Tervonen, Tommi & Tomczyk, Michał K. & Dekker, Rommert, 2017. "Evaluation of multi-objective optimization approaches for solving green supply chain design problems," Omega, Elsevier, vol. 68(C), pages 168-184.
    11. Azzolin, Alberto & Dueñas-Osorio, Leonardo & Cadini, Francesco & Zio, Enrico, 2018. "Electrical and topological drivers of the cascading failure dynamics in power transmission networks," Reliability Engineering and System Safety, Elsevier, vol. 175(C), pages 196-206.
    12. Hong, Wei, 2008. "Decline of the center: The decentralizing process of knowledge transfer of Chinese universities from 1985 to 2004," Research Policy, Elsevier, vol. 37(4), pages 580-595, May.
    13. Ivan Kryven, 2019. "Bond percolation in coloured and multiplex networks," Nature Communications, Nature, vol. 10(1), pages 1-16, December.
    14. Rudberg, Martin & Olhager, Jan, 2003. "Manufacturing networks and supply chains: an operations strategy perspective," Omega, Elsevier, vol. 31(1), pages 29-39, February.
    15. Jenner, RA, 1998. "Dissipative Enterprises, Chaos, and the Principles of Lean Organizations," Omega, Elsevier, vol. 26(3), pages 397-407, June.
    16. Bruce Kogut & Pietro Urso & Gordon Walker, 2007. "Emergent Properties of a New Financial Market: American Venture Capital Syndication, 1960-2005," Management Science, INFORMS, vol. 53(7), pages 1181-1198, July.
    17. Zhao, Dawei & Wang, Lianhai & Xu, Shujiang & Liu, Guangqi & Han, Xiaohui & Li, Shudong, 2017. "Vital layer nodes of multiplex networks for immunization and attack," Chaos, Solitons & Fractals, Elsevier, vol. 105(C), pages 169-175.
    18. Bakker, Hannah & Dunke, Fabian & Nickel, Stefan, 2020. "A structuring review on multi-stage optimization under uncertainty: Aligning concepts from theory and practice," Omega, Elsevier, vol. 96(C).
    19. Dibiaggio, Ludovic & Nasiriyar, Maryam & Nesta, Lionel, 2014. "Substitutability and complementarity of technological knowledge and the inventive performance of semiconductor companies," Research Policy, Elsevier, vol. 43(9), pages 1582-1593.
    20. Dmitry Zhukov & Tatiana Khvatova & Carla Millar & Anastasia Zaltcman, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Post-Print hal-03188186, HAL.
    21. Van Engeland, Jens & Beliën, Jeroen & De Boeck, Liesje & De Jaeger, Simon, 2020. "Literature review: Strategic network optimization models in waste reverse supply chains," Omega, Elsevier, vol. 91(C).
    22. Marco Iansiti, 2000. "How the Incumbent Can Win: Managing Technological Transitions in the Semiconductor Industry," Management Science, INFORMS, vol. 46(2), pages 169-185, February.
    23. repec:hal:spmain:info:hdl:2441/43aq8ffdqb82sbffkv69bt1eaa is not listed on IDEAS
    24. Lordan, Oriol & Albareda-Sambola, Maria, 2019. "Exact calculation of network robustness," Reliability Engineering and System Safety, Elsevier, vol. 183(C), pages 276-280.
    25. Zhukov, Dmitry & Khvatova, Tatiana & Millar, Carla & Zaltcman, Anastasia, 2020. "Modelling the stochastic dynamics of transitions between states in social systems incorporating self-organization and memory," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
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