透视图(图形)
知识管理
业务
计算机科学
产业组织
人工智能
作者
Mengyang Pan,Chen Qiong,Wenli Xiao
标识
DOI:10.1016/j.ijpe.2024.109219
摘要
In the context of radical innovation, we draw from knowledge network theory to investigate how the firm can manage its alliance portfolio to speed up radical innovation that relies predominantly on scientific knowledge. Specifically, we examine how the firm's innovation alliance network composition and its position in the network affect radical innovation speed. In analyzing empirical data on COVID-19-related radical innovation projects, we find that the presence of an industry partner reduces radical innovation speed, while the presence of a research partner increases it. The presence of government partners does not influence innovation speed unless the firm has a high level of collaboration experience with the partners. As for the firm's alliance network position, a more centrally located firm experiences faster radical innovation speed. However, we find that an industry partner's presence in the project's network attenuates the positive effect of network centrality on radical innovation speed. This study contributes to the literature by linking knowledge network theory and innovation speed to identify the individual and joint effects of the firm's innovation alliance composition and its position in the network. Implications regarding accelerating radical innovation and coordinating among firms, research labs, universities, and government partners are provided.
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