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Managing open innovation from a knowledge flow perspective: the roles of embeddedness and network inertia in collaboration networks

Managing open innovation from a knowledge flow perspective: the roles of embeddedness and network... Building on theories of inter-organizational knowledge flows and social network, we explored how two types of collaboration network embeddedness (NE) (i.e. structural embeddedness (SE) and relational embeddedness (RE)) drive firms' inbound and outbound open innovation (OI) practices from a knowledge flow perspective, and further examined these relationships are to what extent contingent on network inertia (NI).Design/methodology/approachIn this empirical research, the authors collected a sample of patents in the unmanned aerial vehicle (UAV) industry over the period of 2000–2018. Then the authors examined the direct roles of SE and RE in collaboration networks on firms' inbound and outbound OI practices from a knowledge flow perspective, and the moderating role of NI by using negative binomial regression.FindingsEmpirical results from our study of 96 firms showed that both bridging structural holes position in collaboration networks (i.e. SE) and having stronger tie strength (i.e. RE) would positively affects firms' inbound OI practices, whereas only having stronger tie strength in collaboration networks (i.e. RE) would facilitate outbound OI practices. In addition, NI strengthens the relationships between SE and firm OI practice, but weakens the positive roles of RE on firm OI practice.Originality/valueThis empirical research provides new insights into whether and how firms can grasp the benefits of collaboration NE to conduct OI activities by exploring NI contingencies. It further sheds lights on the scope of the NE–OI issue from a knowledge flow perspective by extending its research context to UAV industry. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png European Journal of Innovation Management Emerald Publishing

Managing open innovation from a knowledge flow perspective: the roles of embeddedness and network inertia in collaboration networks

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Publisher
Emerald Publishing
Copyright
© Emerald Publishing Limited
ISSN
1460-1060
DOI
10.1108/ejim-07-2019-0200
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Abstract

Building on theories of inter-organizational knowledge flows and social network, we explored how two types of collaboration network embeddedness (NE) (i.e. structural embeddedness (SE) and relational embeddedness (RE)) drive firms' inbound and outbound open innovation (OI) practices from a knowledge flow perspective, and further examined these relationships are to what extent contingent on network inertia (NI).Design/methodology/approachIn this empirical research, the authors collected a sample of patents in the unmanned aerial vehicle (UAV) industry over the period of 2000–2018. Then the authors examined the direct roles of SE and RE in collaboration networks on firms' inbound and outbound OI practices from a knowledge flow perspective, and the moderating role of NI by using negative binomial regression.FindingsEmpirical results from our study of 96 firms showed that both bridging structural holes position in collaboration networks (i.e. SE) and having stronger tie strength (i.e. RE) would positively affects firms' inbound OI practices, whereas only having stronger tie strength in collaboration networks (i.e. RE) would facilitate outbound OI practices. In addition, NI strengthens the relationships between SE and firm OI practice, but weakens the positive roles of RE on firm OI practice.Originality/valueThis empirical research provides new insights into whether and how firms can grasp the benefits of collaboration NE to conduct OI activities by exploring NI contingencies. It further sheds lights on the scope of the NE–OI issue from a knowledge flow perspective by extending its research context to UAV industry.

Journal

European Journal of Innovation Management – Emerald Publishing

Published: May 27, 2021

Keywords: Inbound open innovation; Outbound open innovation; Network embeddedness; Network inertia; Knowledge flow

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