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A growing body of research acknowledges that inter-organizational networks greatly influence a firm's innovation performance. This study extends our understanding of this relationship by considering the effect of inter-organizational networks on innovation in small, knowledge-intensive companies. Based on a literature review, we formulate four propositions regarding the moderating effects of firm size and knowledge intensity on the relationship between inter-organizational networks and innovation, as well as the influence of these factors on the development of the inter-organizational networks themselves.

This study examines the benefits of network centrality (NC) in creating technological diversity. Given the role of NC in access to external technological knowledge (TK), this study uses network density and R&D resources as moderators to explore the consequences of using TK for technological diversity. The results show that NC alone does not have a positive effect on technological diversity. However, network density and R&D resources, respectively, moderate the positive relationship between NC and technological diversity. Additionally, when influenced by these two moderators simultaneously, the creation of technological diversity heavily depends on the extent to which centrally positioned firms invest in R&D resources. In this study, R&D resources play a stronger complementary role in NC than network density does.

In scientific fields such as systems biology, evaluation of the relationship between network members (vertices) is approached using a network structure. In a co-expression network, comprising genes (vertices) and gene-to-gene links (edges) representing co-expression relationships, local modular structures with tight intra-modular connections include genes that are co-expressed with each other. For detecting such modules from among the whole network, an approach to evaluate network topology between modules as well as intra-modular network topology is useful. To detect such modules, we combined a novel inter-modular index with network density, the representative intra-modular index, instead of a single use of network density. We designed an algorithm to optimize the combinatory index for a module and applied it to Arabidopsis co-expression analysis. To verify the relation between modules obtained using our algorithm and biological knowledge, we compared it to the other tools for co-expression network analyses using the KEGG pathways, indicating that our algorithm detected network modules representing better associations with the pathways. It is also applicable to a large dataset of gene expression profiles, which is difficult to calculate in a mass.