Evolution Of Innovation Networks Research Articles

Economic Crises and the Evolution of Innovation Networks: A Contextual Approach

Economic Crises and the Evolution of Innovation Networks: A Contextual Approach

Evolution of innovation networks in industrial clusters and multidimensional proximity: A case of Chinese cultural clusters

The innovation and optimization of cultural industry clusters is important to the promotion of economic structural transformation and high-quality development. Taking 31 provinces (municipalities directly under the Central Government and autonomous regions in China) as the research object, the cross-sectional data included the number of joint patent applications for culture and related industries among provinces (regions) in 2014, 2016, 2018 and 2020. Then, the cluster innovation network was constructed using social network analysis (SNA) based on scientific identification of cultural industry clusters. This new cluster innovation network structure and its spatial evolution characteristics are accessible with the help of UCINet and ArcGIS tools. Finally, the multiple regression-quadratic assignment procedure (MR-QAP) method is used to determine the influence of geographical proximity, technological proximity, and social proximity on the evolution pattern of the cultural industry cluster innovation network. The results of the study are important for improving the efficiency of national cultural industry innovation and promoting regional synergistic development.

Evolution and structure of technological systems - An innovation output network

The mechanisms responsible for the structure and evolution of technological systems are not fully understood. This study examines the network of supply and use of significant innovations across industries in Sweden, 1970-2013. The results suggest that at least 30% of innovation patterns can be statistically predicted by network stimulus from both backward and forward linkages in the Swedish economy. The network is hierarchical, characterized by hubs that connect diverse industries in closely knitted communities. To account for the structure and evolution of innovation networks, the study proposes a preferential weight assignment process, allowing historical technological linkages and proximities to influence future outcomes. The empirical results show that the properties of the Swedish innovation network are strongly predicted by this process, while skill, knowledge and economic interdependencies have significant but more complex effects on link formation. The results are consistent with the idea that innovations emerge in synergistic communities, but suggest that the transformation of technological systems is shaped by technological requirements, imbalances, and opportunities that are not straightforwardly related to other economic or knowledge proximities.

The Evolution of Innovation Networks and Spin-off Entrepreneurship: The Case of RAD

We conducted an in-depth analysis of an Israeli startup, RAD Bynet, founded in 1981, that has intentionally, through the vision of its founder, given rise to 129 other startups employing some 15,000 workers, and created a unique “cloud”. Through a survey of the existing firms, we sought to explore the nature of this ecosystem and to quantify the relationships that exist between the mother company and the enterprises that emerge from it. Our main findings were: (a) social and technological proximity encourages the tendency of the companies to maintain business relationships that probably contribute to knowledge exchange, while technological diversity drives innovation and startup formation; and (b) firms will choose to cooperate on the basis of a shared past and personal proximity relations, as well as technological proximity at a certain level; “viral clouds” of startups like the one we studied can thus intentionally be designed and developed.

The evolution of innovation networks: the case of a publicly funded German automotive network

In this paper we analyze the evolution of a publicly funded interfirm R&D network in the German automotive industry between 1998 and 2007. A stochastic actor-based model allows us to estimate the role of various drivers of the evolutionary change process. We test hypotheses derived in an innovation and evolutionary economics framework and show that structural positions of firms as well as actor covariates and dyadic covariates are influential determinants of network evolution. Our results indicate that knowledge-related effects such as absorptive capacity, technological distance and knowledge-base modularity are significant determinants of network evolution. Moreover, transitivity is found to be a relevant endogenous structural effect. Also, firms with an experience in cooperation tend to be engaged more intensive in cooperation both as initiators and as targets. The same holds for geographically neighboring firms.

Evolution of Innovation Networks across Geographical and Organizational Boundaries: A Study of R&D Subsidiaries in the Bangalore IT Cluster

In this paper, we investigate the evolution of multinational corporation (MNC) research and development (R&D) subsidiaries through evolution of innovation networks. Their evolution within and outside MNC R&D subsidiaries has not been investigated in the context of MNCs that source innovation from emerging economies. We do so, using two dimensions: geographical and organizational boundaries. In order to identify a pattern, we chose the information technology (IT) cluster in Bangalore, India, as the context for a qualitative study; for there are MNC subsidiaries that operate and innovate within and outside organizational boundaries, and have strong links with firms within and outside of Bangalore cluster. The globalized nature of the cluster helps us infer the evolution of innovation networks by taking a knowledge flow perspective. We identify four distinct phases based on where and how knowledge flows. We find that the innovation networks of these MNC subsidiaries in emerging economies first develop as hierarchical networks and then extend to the local markets. Within the first part, the networks start with a non‐local nature (phase A) and get embedded into local networks (phase B and phase C), finally developing into non‐local (phase D) market ties that enable MNC headquarters to source innovation from the host country. In an emerging economy context, clusters can serve as a springboard by providing a local environment that can help overcome institutional voids.

Recombinant knowledge and the evolution of innovation networks

We introduce a new model for the evolution of networks of firms exchanging knowledge in R&D partnerships. Innovation is assumed to result from the recombination of knowledge among firms in an R&D intensive industry. The decision of two firms to establish a new partnerships or to terminate an existing one, is based on their marginal revenues and costs, which in turn depend on the position they occupy in the network. Moreover, the formation of a collaboration has significant external effects on the other firms in the same connected component of the network. We show that this decentralized partner selection process leads to the existence of multiple equilibrium structures. Finally, by means of computer simulations, we study the properties of the emerging equilibrium networks and we show that they reproduce the stylized facts of R&D networks.

Network-Independent Partner Selection and the Evolution of Innovation Networks

Empirical research on strategic alliances has focused on the idea that partners are selected on the basis of social capital considerations. In this paper we emphasize instead the role of complementary knowledge stocks and knowledge dynamics, which have received surprisingly limited attention relative to social capital as forces behind the formation and dynamics of innovation networks. To marshal evidence in this regard, we design a simple model of partner selection in which firms ally for the purpose of learning and innovating, and in doing so create an industry network. We abstract completely from network-based structural and strategic motives for partner selection and focus instead on the idea that firms' knowledge bases must “fit” for joint learning and innovation to be possible, and thus for an alliance to be feasible. The striking result is that, despite containing no social capital considerations, this simple model replicates the firm conduct, network structure, and contingent effects of network position on performance observed and discussed in the empirical literature.

NETWORK-INDEPENDENT PARTNER SELECTION AND THE EVOLUTION OF INNOVATION NETWORKS.

The article focuses on the evolution of innovation networks and strategic alliances. It states that organizations create partnerships to access competence or technological innovations they do not p...

Research and technology policies in innovation systems: zero tillage in Brazil

The increasing complexity of technology development and adoption is rapidly changing the effectiveness of scientific and technological policies. Complex technologies are developed and disseminated by networks of agents. The impact of these networks depends on the assets they command, their learning routines, the socio-economic environment in which they operate and their history. In this new environment, scientific and technology policies should: (1) foster interactions among agents (whether public or private), (2) increase the effectiveness of public research, extension and funding institutions, (3) give sufficient freedom to researchers to set their research programs, and (4) monitor the quality of research (rather of research outputs). The evolution of innovation networks is analyzed looking at the development and diffusion of zero tillage (ZT) in Brazil.