TY - GEN
T1 - How Knowledge Recombination Fuels Technological Innovation? Insights from IPC Co-occurrence Networks
AU - Xie, Ziyue
AU - Wu, Keye
AU - Du, Jia Tina
AU - Xie, Yunhao
AU - Chen, Ya
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
PY - 2025
Y1 - 2025
N2 - As knowledge recombination increasingly shapes technological advancement, scholarly efforts have focused on how integrating knowledge elements can enhance technological innovation performance. Our study contributes to this literature by adopting a network perspective and assessing the predictive value of various knowledge linkage features. Using granted patents in the pharmaceutical field, we constructed the annual International Patent Classification (IPC) co-occurrence network and extracted the real-time linkage features of pairwise co-occurring IPCs, including tie strength, knowledge distance, node assortativity, and betweenness centrality. Based on explainable machine learning, we found that XGBoost outperformed in predicting both patent impact and patent disruptiveness. More importantly, feature interpretation based on the Shapley value illustrates that patent impact and patent disruptiveness have different determinants, implicating different knowledge combinative strategies. Specifically, we found that betweenness centrality and node assortativity contribute significantly in predicting patent impact, suggesting that localised search combining hotspot and marginal knowledge components are more likely to produce impactful inventions. Conversely, tie strength between combined knowledge components is the most important predictor for patent disruptiveness, indicating that deeper exploitation along existing technology portfolios can effectively enhance patent disruptiveness. These results provide new insights into how knowledge recombination fuels technological innovation and offer practitioners valuable strategies for developing target technologies.
AB - As knowledge recombination increasingly shapes technological advancement, scholarly efforts have focused on how integrating knowledge elements can enhance technological innovation performance. Our study contributes to this literature by adopting a network perspective and assessing the predictive value of various knowledge linkage features. Using granted patents in the pharmaceutical field, we constructed the annual International Patent Classification (IPC) co-occurrence network and extracted the real-time linkage features of pairwise co-occurring IPCs, including tie strength, knowledge distance, node assortativity, and betweenness centrality. Based on explainable machine learning, we found that XGBoost outperformed in predicting both patent impact and patent disruptiveness. More importantly, feature interpretation based on the Shapley value illustrates that patent impact and patent disruptiveness have different determinants, implicating different knowledge combinative strategies. Specifically, we found that betweenness centrality and node assortativity contribute significantly in predicting patent impact, suggesting that localised search combining hotspot and marginal knowledge components are more likely to produce impactful inventions. Conversely, tie strength between combined knowledge components is the most important predictor for patent disruptiveness, indicating that deeper exploitation along existing technology portfolios can effectively enhance patent disruptiveness. These results provide new insights into how knowledge recombination fuels technological innovation and offer practitioners valuable strategies for developing target technologies.
KW - IPC co-occurrence network
KW - Knowledge recombination
KW - patent disruptiveness
KW - patent impact
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U2 - 10.1007/978-981-96-0868-3_2
DO - 10.1007/978-981-96-0868-3_2
M3 - Conference paper
AN - SCOPUS:85213024947
SN - 9789819608676
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 19
EP - 38
BT - Sustainability and Empowerment in the Context of Digital Libraries - 26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024, Proceedings
A2 - Oliver, Gillian
A2 - Frings-Hessami, Viviane
A2 - Du, Jia Tina
A2 - Tezuka, Taro
PB - Springer Science and Business Media Deutschland GmbH
T2 - 26th International Conference on Asia-Pacific Digital Libraries, ICADL 2024
Y2 - 4 December 2024 through 6 December 2024
ER -