PurposeThis study aims at developing a better understanding of the relationship between network embeddedness and incremental innovation capability and further examines the moderating effect of open innovation.Design/methodology/approachThis paper adopts hierarchical regressions to validate the theoretical model and collect the patent data of the top 54 firm patentees in the smartphone industry as empirical sample. Using patent citation network data, this paper estimates the relationship between open innovation, network embeddedness and incremental innovation capability.FindingsThis paper empirically shows that structural embeddedness exerts a negative effect on incremental innovation capability, while relational embeddedness is positively related to incremental innovation capability. And open innovation strengthens the relationship between network embeddedness and incremental innovation capability.Originality/valueThis paper shifts the focus of the determinants of incremental innovation capability from internal factors to the external network features by exploring the linkage between network embeddedness and incremental innovation capability. A counterintuitive conclusion is that structural embeddedness shows a negative effect on firm's incremental innovation capability. Furthermore, in contrast to most previous studies, which only focus on the direct effect of open innovation on the firm's incremental innovation capability, our study examines the moderating effect of open innovation on the relationships between network embeddedness and incremental innovation capability. At last, the results provide practical guidance for firms to occupy the beneficial network positions and adopt appropriate open innovation strategies to improve their incremental innovation capability.
A firm's superior innovation performance is embodied not only by its average innovation performance but also by the likelihood of extremely high innovative outcomes. The former benchmark is associated with the mean of the performance distribution, while the latter is associated with the variance, both of which play an important role in instructing the achievement of superior innovation performance. In this paper, we explored how inbound open innovation impacts superior innovation performance by considering both the average and variance effects of inbound openness. We conducted agent-based modeling and simulation research to untangle the relationship between inbound openness and superior innovation performance and how the relationship is moderated by the disruptiveness of industrial innovation. We found that inbound openness significantly influences both the benchmarks. Specifically, search breadth positively influences the likelihood of extremely high innovative outcomes in general, whereas search depth positively influences average innovation performance; and the strength of these effects varies under different disruptiveness.
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