Effective policy mixes in entrepreneurial ecosystems: A configurational analysis in China

Hongqi Wang, Tianyi Zhao, Sarah Cooper, Shanshan Wang, Richard Harrison, Zhongji Yang

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Policy-makers are seeking to build vibrant entrepreneurial ecosystems (EEs) to promote innovative entrepreneurship. However, the role of government policy in EEs, especially in the emerging economy context, is poorly understood, lacking a systematic concept of policy and theoretical framework, clear policy classification, and empirical research considering distinctive characteristics of policy mixes in EEs. This paper introduces the concept of EE policy mixes and classifies them into seven policy instruments. Using data from 21 national high-tech zones (NHZs) in China, fuzzy-set qualitative comparative analysis (fsQCA) is applied to identify effective configurations of EE policy mixes that support early-stage and late-stage technology start-ups to produce high entrepreneurial performance. The configurational analysis shows that EE policy mixes present causal complexity and stage differences. Based on interpreting multiple policy interactions, we reveal the differentiated support mechanisms of EE policy mixes in NHZs and draw out their implications for research, policy, and practice. This study contributes to the EE literature by adopting a policy-mix perspective to examine the role of policy, exploring the specific emerging economy context of China, suggesting further extensions of the research to other contexts, and applying a methodology that reveals causal mechanisms. It also supports better-informed EE policy and governance.
Original languageEnglish
JournalSmall Business Economics
Early online date15 Aug 2022
Publication statusE-pub ahead of print - 15 Aug 2022

Keywords / Materials (for Non-textual outputs)

  • Entrepreneurial ecosystem
  • Government policy
  • Policy mixes
  • Emerging economies
  • China
  • Fuzzy-set qualitative comparative analysis (fsQCA)


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