Understanding the complexity of regional innovation capacity dynamics in china: From the perspective of hidden markov model

Abstract

This study aimed to explore the state transition of regional innovation capacity (RIC) and analyze the heterogeneous effects of determinants in an innovative subject and environment of RIC state transition based on the data collected from 30 provinces in China during 2000–2017. By applying a hidden Markov model (HMM), this study identified three RIC states: low, medium, and high. The results suggested that (1) the overall state of RIC rapidly improved but with a significant disparity across regions in China; (2) the lock-in effect of RIC is most significant in regions with a medium state, while the enterprise-dominated mode of regional innovation helps RIC transition from a medium state to a high state or to remain in a high state; and (3) the interaction and collaboration between universities and enterprises in a region can stimulate RIC to higher states for all regions. Intellectual property administrative protection exerts positive impacts on RIC transitions to higher states. Intellectual property judicial protection only exerts positive impacts on an RIC’s transition from a medium state to a high state or remaining in a high state, while these positive impacts are not significant when RIC is in a low state. Highlighting the dynamic nature of RIC evolution and the heterogeneity of determinants affecting RIC state transition, the findings provide policymakers a roadmap to identify RIC states and make precise policies based on the current RIC state.