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<p>This study develops a dynamic framework to examine the co-evolution between technology applicability and enterprise innovation strategies within knowledge networks. Departing from traditional static models, we conceptualize technology applicability as an endogenous construct shaped by network position, adoption patterns, and intrinsic technological characteristics. A three-equation system is formulated to capture the bidirectional relationships among innovation strategies, network centrality, and evolving technology applicability. Empirical validation is conducted using longitudinal data from the USPTO, NBER, and Compustat, incorporating patent citation networks and firm-level innovation metrics. The findings provide evidence consistent with threshold effects, feedback loops, and non-linear dynamics: firms with high absorptive capacity respond more intensively to technology applicability signals, leading to structural shifts in knowledge diffusion. Moreover, the interaction between network centrality and technology applicability appears to explain persistent heterogeneity in innovation outcomes. These results offer a framework for understanding co-evolutionary dynamics in innovation ecosystems and suggest practical implications for strategic innovation management and policy design.</p>

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Co-Evolution of Technology Applicability and Innovation Strategy in Knowledge Networks: A Dynamic Modeling and Empirical Analysis

  • 期刊论文

  • 发表日期：

    2026年5月13日

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  • Baochen Li,
  • Alfiya Abinova,
  • Shouwei Li

摘要

This study develops a dynamic framework to examine the co-evolution between technology applicability and enterprise innovation strategies within knowledge networks. Departing from traditional static models, we conceptualize technology applicability as an endogenous construct shaped by network position, adoption patterns, and intrinsic technological characteristics. A three-equation system is formulated to capture the bidirectional relationships among innovation strategies, network centrality, and evolving technology applicability. Empirical validation is conducted using longitudinal data from the USPTO, NBER, and Compustat, incorporating patent citation networks and firm-level innovation metrics. The findings provide evidence consistent with threshold effects, feedback loops, and non-linear dynamics: firms with high absorptive capacity respond more intensively to technology applicability signals, leading to structural shifts in knowledge diffusion. Moreover, the interaction between network centrality and technology applicability appears to explain persistent heterogeneity in innovation outcomes. These results offer a framework for understanding co-evolutionary dynamics in innovation ecosystems and suggest practical implications for strategic innovation management and policy design.