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Research brief · Sustainability & Future Competitiveness

Bringing the knowledge spillover theory of entrepreneurship to circular economies: Knowledge and values in entrepreneurial ecosystems

International Small Business Journal · 2024 DOI: 10.1177/02662426231218357 ↗

The idea in brief

Circular-economy transitions require more than technological knowledge. Entrepreneurs can introduce and validate new knowledge and values about reuse, repair and resource loops, which then flow back to established firms. The article extends knowledge spillover theory by showing this reverse direction: entrepreneurs are not only recipients of incumbent knowledge; their experiments can change what incumbents recognise as valuable and redirect established R&D.

What the article shows

  • Reverse knowledge spillovers from entrepreneurs can complement technological spillovers originating in incumbent firms.
  • Entrepreneurial experimentation helps advocate circular-economy values and validate circular knowledge in the market.
  • Once validated, that knowledge can alter incumbents’ knowledge filters, R&D priorities and willingness to embrace new approaches.

Implications for entrepreneurs

  • Treat pilots and early market experiments as evidence that can influence suppliers, incumbents and investors—not only as tests of the venture itself.
  • Communicate both technical performance and the values underpinning circular solutions so that new knowledge becomes legitimate and transferable.
  • Build partnerships that return learning to established firms and create cross-organisational loops for materials, information and innovation.

Implications for policymakers

  • Support two-way knowledge transfer in which startups are recognised as producers of valuable knowledge, not merely recipients of university or corporate expertise.
  • Use procurement, demonstration projects, standards and shared data to validate circular solutions and lower adoption uncertainty.
  • Fund intermediaries that connect entrepreneurs with incumbents and help translate experimental knowledge into changes in established R&D and operations.

This plain-language summary was prepared with AI assistance from the published abstract and available article information. The published article is the authoritative source; practical and policy implications are interpretive.