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A pathway study of university embeddedness in provincial innovation systems based on field theory: Empirical evidence from China

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  • Zhangyuze Wang
  • Yifei Sun
  • Lili Gu
  • Xingguang Ye

Abstract

This study employs Bourdieu’s Field Theory and fuzzy-set Qualitative Comparative Analysis (fsQCA) to investigate how university-based scientific and technological talent cultivation mechanisms influence Provincial Innovation (PI) capacity in China. Using data from 31 provincial-level regions, two high-performing innovation pathways were identified: (1) a market-driven pathway propelled by technology commercialization and international cooperation; and (2) a government-led pathway defined by robust policy support and institutional coordination. The findings highlight that high-level PI stems from the synergistic integration of HC, ERC, and international scientific exchange. Conversely, four non-high PI pathways reveal that performance is inhibited by resource scarcities, weak institutional frameworks, and ineffective collaboration mechanisms. Those findings indicate that innovation success does not depend on isolated institutional elements, but on the synergy of configurational complementarity Theoretically, this work extends Bourdieu’s Field Theory into the realms of regional innovation and higher education studies. Methodologically, it demonstrates the value of fsQCA in uncovering nonlinear, multi-factor causal mechanisms. Practically, the study offers guidance for localized talent strategies and university reforms aimed at enhancing regional innovation resilience.

Suggested Citation

  • Zhangyuze Wang & Yifei Sun & Lili Gu & Xingguang Ye, 2026. "A pathway study of university embeddedness in provincial innovation systems based on field theory: Empirical evidence from China," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-21, March.
    • Handle: RePEc:plo:pone00:0344374
    DOI: 10.1371/journal.pone.0344374
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    References listed on IDEAS

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    1. Jeffrey M Wooldridge, 2010. "Econometric Analysis of Cross Section and Panel Data," MIT Press Books, The MIT Press, edition 2, volume 1, number 0262232588, December.
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