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<p>Science and technology (S&amp;T) constitute significant forces in shaping innovation, yet empirical investigations of their interaction and convergence fostering technological advancement remain limited. This study aims to delineate the multidimensional dynamics of S&amp;T interactions guided by synergetics principles, including strength, time-lag, depth, and synchronization, and explore their influence on technological innovation. It also examines the moderating impact of technological topic divergence. Utilizing a knowledge network representation framework, we conducted an examination of papers and patents within the artificial intelligence domain, spanning the period from 2000 to 2022. Our findings indicate that the strength, time-lag, and synchronization of S&amp;T interactions positively correlate with technological innovation. Strong interaction between technology with deep-level scientific knowledge facilitates technological innovation and vice versa inhibits it. Moreover, the impact of S&amp;T interactions on innovation tends to be greater in domains with higher technological topic popularity and centrality. By elucidating these associations, this study contributes to the methodological understanding of S&amp;T intrinsic interactions and furnishes valuable insights for R&amp;D organizations in formulating strategic S&amp;T decisions.</p>

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How science-technology interactions affect technological innovation: the moderating role of topic divergence

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月27日

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  • Zhichao Ba,
  • Kai Meng,
  • Leilei Liu,
  • Yujie Zhang

摘要

Science and technology (S&T) constitute significant forces in shaping innovation, yet empirical investigations of their interaction and convergence fostering technological advancement remain limited. This study aims to delineate the multidimensional dynamics of S&T interactions guided by synergetics principles, including strength, time-lag, depth, and synchronization, and explore their influence on technological innovation. It also examines the moderating impact of technological topic divergence. Utilizing a knowledge network representation framework, we conducted an examination of papers and patents within the artificial intelligence domain, spanning the period from 2000 to 2022. Our findings indicate that the strength, time-lag, and synchronization of S&T interactions positively correlate with technological innovation. Strong interaction between technology with deep-level scientific knowledge facilitates technological innovation and vice versa inhibits it. Moreover, the impact of S&T interactions on innovation tends to be greater in domains with higher technological topic popularity and centrality. By elucidating these associations, this study contributes to the methodological understanding of S&T intrinsic interactions and furnishes valuable insights for R&D organizations in formulating strategic S&T decisions.