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Unraveling the impact of patent transfers on regional innovation: Empirical insights through the lens of entity relationships

Author

Listed:
  • Cai, Zhishan
  • Ma, Ding
  • Zhou, Rui
  • Zhang, Zhiwang

Abstract

Over the past two decades, the market for patent transfers (PT) has experienced substantial growth in China, driven by the expansion of main relationship types, including inter-firm, industry-university-research (IUR), and individual-to-firm PTs. This growth raises critical questions regarding the influence of these diverse PT types on regional innovation. Utilizing data mining on 1,914,750 PT events, this study constructs panel data models for China's 31 provinces from 2011 to 2020, investigating the presence, dimensions, effect decomposition, and threshold determinations of PTs on regional innovation. Findings reveal heterogeneous impacts of PT types on innovation, with individual-to-firm PT being most effective and IUR PTs the least. Among the dimensions of impact, PT strength demonstrates the most circumscribed promotional effect, predominantly within patent inflows, and particularly hinders IUR PTs due to market frictions. The importance of PT breadth mainly appears in patent outflows, suggesting geographic expansion of PT enhances innovation remuneration, critical for sustaining innovation momentum in hub provinces. PT depth uniformly affects innovation across all PT categories irrespective of patent inflows or outflows, underscoring the significance of diversified technology transfers. A universal observation across PT types is that surpassing regional thresholds of digitalization significantly amplifies the promotion of PT on regional innovation.

Suggested Citation

  • Cai, Zhishan & Ma, Ding & Zhou, Rui & Zhang, Zhiwang, 2024. "Unraveling the impact of patent transfers on regional innovation: Empirical insights through the lens of entity relationships," Technological Forecasting and Social Change, Elsevier, vol. 208(C).
    • Handle: RePEc:eee:tefoso:v:208:y:2024:i:c:s0040162524004645
    DOI: 10.1016/j.techfore.2024.123666
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