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

Abstract

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.

Unravelling the entrepreneurial ecosystem conditions spurring the global value chains: a configurational approach

“…Furthermore, results in Tables 3–5 confirm that the fsQCA models are adequate, informative, and valid as under all dimensions of GVC, the overall configurational (solution) consistencies are above 0.80 (Wang et al ., 2022), except for input-output structure. Thus, all identified EE conditions’ configurations are sufficient for supporting the GVC activities except for the input-output structure.…”

“…To assess the robustness of fsQCA, the authors reperformed the analyses by setting new mid- (cross-over) calibration membership breakpoints at the 45th (−0.1) and the 55th (+0.1) percentile, following Wang et al . (2022).…”

“…The authors then performed the sufficiency analysis of the conditions. In calculating the sufficiency conditions, the fsQCA analysis consists of two stages (Wang et al ., 2022). First, a truth table algorithm transforms the scores in a fuzzy data set into a truth table that lists all logically possible combinations of causal conditions and the empirical result of each configuration.…”

“…The conditions eliminated in the parsimonious solution, and appearing only in the intermediate solution are referred to as “peripheral conditions”. Therefore, merging the parsimonious and intermediate solutions offers a more detailed and aggregated view of the findings (Wang et al ., 2022). Thus, the authors highlighted the intermediate solution by identifying the “core” conditions (those appearing in both parsimonious and intermediate solutions) and “peripheral” conditions (those that appear only in the intermediate solutions).…”

“…To assess the robustness of fsQCA, the authors reperformed the analyses by setting new mid-(cross-over) calibration membership breakpoints at the 45th (À0.1) and the 55th (þ0.1) percentile, following Wang et al (2022). The authors also edited the truth tables by raising the consistency threshold from 0.80 to 0.90 (Pappas and Woodside, 2021).…”