Leveraging scientific knowledge for technological inventions: insights from knowledge recombination theory

Introduction. Technological inventions are often built on top of previous technological developments and sometimes earlier scientific discoveries. It has been demonstrated that technologies integrating scientific knowledge exhibit superior performance in terms of their technological value compared to non-science-based inventions. However, the factors influencing the utilisation of scientific knowledge components during the technological invention process remain unclear. Method. By analysing 1,685,970 utility patents granted by the USPTO from 2001 to 2010, this study identifies five characteristics-rooted in the perspectives of knowledge recombination and inventor team-that potentially affect the adoption of scientific knowledge in technological inventions. Results. Both Logit and Tobit regression analyses reveal that recombination complexity and novelty significantly enhance the propensity and intensity of integrating scientific knowledge into technological inventions, whereas knowledge maturity exhibits an inverse relationship. The most critical determinant identified is the inventor team’s experience with science-based inventions, which significantly positively impacts the reliance on scientific knowledge, whereas a broader technical knowledge base within the team tends to reduce the use of scientific components in inventions. Conclusions. The findings of this study provide insights into the unique recombination processes underlying science-based technologies and offer guidance for optimising organisational and policy designs for innovation activities.

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Publication Details

Journal
Information Research an international electronic journal
Published
2025-03-11
DOI
https://doi.org/10.47989/ir30iconf47218
Citations
2
Primary Topic
Biomedical and Engineering Education
Type
article
Field-Weighted Citation Impact
1.35
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article

Leveraging scientific knowledge for technological inventions: insights from knowledge recombination theory

Guiyan Ou, Hongyu Chen, Kaili Wang, Jiang Wu
2 citations
Information Research an international electronic journal
Biomedical and Engineering Education
1.35
article

Leveraging scientific knowledge for technological inventions: insights from knowledge recombination theory

Guiyan Ou, Hongyu Chen, Kaili Wang, Jiang Wu
article en
2 citations

Abstract

Introduction. Technological inventions are often built on top of previous technological developments and sometimes earlier scientific discoveries. It has been demonstrated that technologies integrating scientific knowledge exhibit superior performance in terms of their technological value compared to non-science-based inventions. However, the factors influencing the utilisation of scientific knowledge components during the technological invention process remain unclear. Method. By analysing 1,685,970 utility patents granted by the USPTO from 2001 to 2010, this study identifies five characteristics-rooted in the perspectives of knowledge recombination and inventor team-that potentially affect the adoption of scientific knowledge in technological inventions. Results. Both Logit and Tobit regression analyses reveal that recombination complexity and novelty significantly enhance the propensity and intensity of integrating scientific knowledge into technological inventions, whereas knowledge maturity exhibits an inverse relationship. The most critical determinant identified is the inventor team’s experience with science-based inventions, which significantly positively impacts the reliance on scientific knowledge, whereas a broader technical knowledge base within the team tends to reduce the use of scientific components in inventions. Conclusions. The findings of this study provide insights into the unique recombination processes underlying science-based technologies and offer guidance for optimising organisational and policy designs for innovation activities.

Information Research an international electronic journalVol. 30(iConf)
Wuhan University (CN), Central China Normal University (CN)
Openalex Percentile: Top 22%
Biomedical and Engineering Education
1.35
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