From local innovation to national policy: Cross‐boundary policy entrepreneurship in China's health regulatory reform
Alex Jingwei He et al.
Abstract
Many contemporary social problems are innately complex, increasingly requiring joint efforts across sectoral boundaries to find a resolution. Cross‐boundary policy entrepreneurship, a distinctive form of policy dynamism, represents a nascent approach to policy innovation in the face of emerging disruptive technologies. By analysing a major health regulatory reform in China that was catalysed by local innovation, this study explains how a coalition of entrepreneurial actors across the local government, the corporate sector, and public hospitals was formed, and how it secured policy adoption at both local and national levels. In‐depth interviews were extensively used in data collection, and a processual approach was adopted in qualitative analysis. This two‐episode case study characterises four types of policy entrepreneurs and explains their respective motivations and entrepreneurial strategies used in the reform. This study's findings illustrate how local innovations are scaled‐up into national policies in a multilevel governance structure through collective manoeuvres. Points for practitioners Many contemporary social problems require joint innovative efforts across sectoral boundaries. Policy entrepreneur, process broker, program champion, and technology innovator play distinctive roles in cross‐boundary policy innovations. Issue framing, strategic planning, coalition building, venue‐shopping, persuasion, and leading by example are common entrepreneurial strategies in scaling up local innovations to national policies. Caveats must be drawn from both legal and ethical grounds to scrutinise cross‐boundary policy entrepreneurship.
3 citations
Evidence weight
Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40
| F · citation impact | 0.32 × 0.4 = 0.13 |
| M · momentum | 0.57 × 0.15 = 0.09 |
| V · venue signal | 0.50 × 0.05 = 0.03 |
| R · text relevance † | 0.50 × 0.4 = 0.20 |
† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.