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The evolution of the industrial value chain in China's high-speed rail driven by innovation policies: A patent analysis

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  • Yuan, Xiaodong
  • Li, Xiaotao

Abstract

High-speed Rail (HSR) is one of the most technological breakthroughs in passenger transportation over the last decades. The rapid development of China's High-speed Rail (CRH) is astonishing. However, it seems to be a “black box” how China has achieved great success in the CRH industry for many organizations or scholars. The paper proposes a novel approach to identify the critical components of an industry value chain and then gains insight into why systemic policies can drive significant breakthroughs in the CRH industry. Our findings highlight that both large companies and universities have played a vital function in the process of technology innovation. Besides, the incentive policies induce many innovators to carry out competition and cooperation, which results in forming and perfecting the industrial value chain. Innovation policies facilitate the evolution of the industrial value chain though there is a time lag of incentive effect. In contrast, the perfect industry value chain contributes to achieving the success of technology innovation. The paper provides insight into the incentive effect of public policies on technology innovation that falls into the scope of Schumpeter Mark II, which can help policymakers perfect incentive policies and managers implement appropriate patent strategies for developing new emerging technologies.

Suggested Citation

  • Yuan, Xiaodong & Li, Xiaotao, 2021. "The evolution of the industrial value chain in China's high-speed rail driven by innovation policies: A patent analysis," Technological Forecasting and Social Change, Elsevier, vol. 172(C).
  • Handle: RePEc:eee:tefoso:v:172:y:2021:i:c:s0040162521004868
    DOI: 10.1016/j.techfore.2021.121054
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    Cited by:

    1. Li, Xiaotao & Yuan, Xiaodong, 2022. "Tracing the technology transfer of battery electric vehicles in China: A patent citation organization network analysis," Energy, Elsevier, vol. 239(PD).
    2. Jang, Hyejin & Lee, Suyeong & Yoon, Byungun, 2023. "Data-driven techno-socio co-evolution analysis based on a topic model and a hidden Markov model," Technovation, Elsevier, vol. 126(C).

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