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The Innovation Arena: A Method for Comparing Innovative Problem-Solving Across Groups
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Predicting success in the worldwide start-up network.

Moreno Bonaventura1,2, Valerio Ciotti3,4, Pietro Panzarasa5

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Analyzing startup networks reveals employee flow predicts long-term success. This network analysis offers valuable recommendations, improving venture capital performance and innovation ecosystem assessments.

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Area of Science:

  • Network science
  • Entrepreneurship studies
  • Economic sociology

Background:

  • Startups are crucial for innovation but face high failure rates.
  • Assessing startup potential is challenging and resource-intensive for investors.
  • Understanding inter-company relationships is key to startup success.

Purpose of the Study:

  • To construct and analyze the global network of professional relationships among startups.
  • To assess the predictive power of network centrality measures for startup economic performance.
  • To offer data-driven recommendations for venture capital and policy-makers.

Main Methods:

  • Utilized large-scale online data to build a time-varying worldwide network of startups.
  • Represented companies as nodes and employee flow/know-how transfer as links.
  • Applied network centrality measures to evaluate early-stage startup potential.

Main Results:

  • The startup network exhibits significant predictive power for long-term economic performance.
  • Network centrality measures can enhance venture capital fund performance, potentially doubling current success rates.
  • Startup position within its ecosystem is a critical factor for future success.

Conclusions:

  • Network analysis provides a valuable, objective tool for assessing startup potential and innovation ecosystems.
  • This approach complements traditional, labor-intensive screening methods used by venture capital firms.
  • Findings support policymakers and entrepreneurs in identifying and nurturing high-potential innovation hubs.