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<p>Driving open innovation (OI) in the restricted situations is a critical task. This study is based on strategic tripod theory and extracting 17 drivers from the perspectives of resources, industry, and institution. Using the typical innovation-constrained enterprises “military-civil integration enterprises” as samples, we employ six machine learning models to predict OI in enterprises and identify factors and dimensions significant for driving OI in restricted situations. The results indicate that digital transformation is a crucial factor driving OI in restricted situations; resources represent a vital dimension for driving OI in restricted situations. Moreover, within military-civil integration enterprises, risk-taking capacity emerges as the most critical factor driving OI.</p>

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Innovating Against the Odds: Driving Enterprise Open Innovation in Restricted Situations--Evidence Based on Machine Learning

  • 期刊论文

  • 发表日期：

    2025年6月23日

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  • Guimin Qu,
  • Hao Jing

摘要

Driving open innovation (OI) in the restricted situations is a critical task. This study is based on strategic tripod theory and extracting 17 drivers from the perspectives of resources, industry, and institution. Using the typical innovation-constrained enterprises “military-civil integration enterprises” as samples, we employ six machine learning models to predict OI in enterprises and identify factors and dimensions significant for driving OI in restricted situations. The results indicate that digital transformation is a crucial factor driving OI in restricted situations; resources represent a vital dimension for driving OI in restricted situations. Moreover, within military-civil integration enterprises, risk-taking capacity emerges as the most critical factor driving OI.