2692-2967ISSN (Online)
2770-0216ISSN (Print)
Volume 13 , Issue 2 , PP: 01-14, 2025 | Cite this article as | XML | Html | PDF | Full Length Article
Rakhimova Gulnoza 1 * , Dilfuza Kuzikulova 2
Doi: https://doi.org/10.54216/AJBOR.130201
Digital transformation has fundamentally reshaped innovation dynamics in many parts of the global economy, and knowledge diffusion is no longer spatially bounded, as in large-scale innovation data collection, the density of collaborative ties and cross-border knowledge exchanges are increasing across institutional and technological domains. Due to structural changes in the daily organization of innovation activities, knowledge production has been reshaped by the expansion of digital infrastructures and the proliferation of networked research collaborations and innovation platforms. In this study, we aim to contribute to the understanding of global innovation systems by examining how patterns of knowledge diffusion are structured using network analysis in transnational innovation networks. This paper aims to identify structural configurations and relational mechanisms in innovation networks and how these contribute to theoretical understandings of knowledge diffusion. In this paper, we analyze the process of knowledge creation and diffusion as a networked system, using specific examples from our dataset of global innovation actors in order to examine their relational structures and positional roles of knowledge-producing entities. A sample of innovation network data from multiple sectors of global innovation systems took part in the empirical analysis, drawing from bibliometric indicators and the analysis of over large-scale relational linkages. We empirically found that we cannot assume uniformly that centrality or connectivity are either a prerequisite for innovation performance; a driver for diffusion of technological knowledge; a mechanism for individual learning; a mechanism for collective learning; and a determinant for accumulation of innovation capabilities. The findings indicate that actors adopt different strategies of using network positions in their learning: exploratory engagement or exploitative specialization. We argue for a more nuanced interpretation of innovation networks that acknowledges both its structural heterogeneity in shaping understandings of knowledge flows and providing policymakers with insights on organizations’ patterns of using digital infrastructures in other sectors and more complex configurations in the global system. The implications of this study could inform a policy framework in innovation governance on how actors can use their network resources for knowledge accumulation and coordination toward systemic innovation and that networks can function differently in alternative institutional contexts.
Global innovation systems , Knowledge diffusion , Innovation networks , Network analysis , Digital infrastructures , Learning strategies , Innovation governance
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