2692-2967ISSN (Online)
2770-0216ISSN (Print)
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.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130201
Vol. 13 Issue. 2 PP. 01-14, (2025)
In this study, we look at globalization processes over a longitudinal time horizon in the global system to reduce the fragmentation of analytical perspectives while integrating structural and relational dimensions. The analysis examines the dynamics of a complex network in global contexts, including economic, technological, institutional, and informational linkages, to identify systemic patterns that have implications for governance in the area of global integration. Based on a theoretical framework, we position this research to improve the understanding of globalization dynamics into empirically observable structures for the scholarly community. In this paper, we provide empirical insights into the structure of global networks by showing how connectivity and centrality have jointly shaped interaction patterns and asymmetries in the globalization process, affecting the stability of the system. Within each of these dimensions, we integrated observations into a multi-level repeated-measures analysis of network indicators (nodes × ties). Differences were assessed by use of a combination of correlation techniques and regression models, and network metrics within the global system that are relevant to these dynamics. Gephi-based visualization resulted in the exclusion of isolated components not being used for explanatory modeling and statistical testing. A significant main effect was found for network type and it influenced only the strength of associations and structural dependencies. The interaction of global actors of different system positions with other forms of global connectivity through network structures suggests that actors who are new to operating in a highly connected system may be at an increased risk of marginalization. Because increases in these structural imbalances have been associated with an increased likelihood of system-level instability, network-oriented analysis is an effective and integrative approach with potential to improve analytical rigor, policy relevance, and to inform globalization-related decision-making.
Read MoreDoi: https://doi.org/10.54216/AJBOR.130202
Vol. 13 Issue. 2 PP. 15-25, (2025)