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- W3036206579 abstract "The realization of exploratory innovation is a complex and nonlinear evolutionary problem. Existing works point out that it is closely related with knowledge governance and boundary-spanning search. However, the intricate relationship among them still lacks exact quantitative explanations. Motivated by this, using four machine learning methods, namely, linear regression (LR), neural network (NN), support vector machine (SVM) and k-nearest neighbors (KNN), we explore how boundary-spanning search combined with knowledge governance influences innovation. Results show that SVM has the highest values of both stability and goodness of fitting. The SVM results show that the combination of low knowledge governance and high boundary-spanning search boosts innovation most efficiently, while high knowledge governance combined with low boundary-spanning search caused the most detrimental effect on innovation. Our results reveal enhancing boundary-spanning search is essential and beneficial to innovation." @default.
- W3036206579 created "2020-06-25" @default.
- W3036206579 creator A5057973707 @default.
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- W3036206579 date "2020-06-15" @default.
- W3036206579 modified "2023-09-23" @default.
- W3036206579 title "The influence of knowledge governance and boundary-spanning search on innovation performance" @default.
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- W3036206579 doi "https://doi.org/10.1142/s0217984920503261" @default.
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