Network embeddedness indicates the innovation potential of firms

The R&D network of 14,000 firms over 25 years was reconstructed to understand how network embeddedness affects innovation potential. This study found that firms with higher weighted k-core centrality have a higher innovation output. This means that being well-connected in the network can lead to more patents.

Firms with a higher weighted k-core centrality in a given year also have a higher innovation output in the following year.
Why This Matters for Scientists

As a scientist, you may want to consider how embeddedness can impact your research. This study found a correlation between a firm's embeddedness and its innovation output. You may want to explore how this concept can be applied to your own research.

Quick Technical Overview

The researchers used data on R&D alliances and patenting activity to study the evolution of the alliance network and the effect of embeddedness on patenting activity. They analyzed a measure, coreness, to quantify the position of firms in the R&D network.

The weighted k-core centrality has the advantage to control for repeated interactions, while the unweighted centrality treats repeated interactions as a single interaction.
Summary for Policy Makers

Policy decisions can be influenced by understanding the impact of network embeddedness on innovation potential. This study suggests that firms with higher embeddedness are more likely to innovate. This has implications for how we design and implement policies to support innovation.

Firms with a higher weighted k-core centrality in a given year also have a higher innovation output in the following year.
Disclaimer

The above summaries were generated with the assistance of an AI system.

Abstract

Firms' innovation potential depends on their position in the R&D network. But details on this relation remain unclear because measures to quantify network embeddedness have been controversially discussed. We propose and validate a new measure, coreness, obtained from the weighted k-core decomposition of the R&D network. Using data on R&D alliances, we analyse the change of coreness for 14,000 firms over 25 years and patenting activity. A regression analysis demonstrates that coreness explains firms' R&D output by predicting future patenting.

Tags

Related Posts

Success in Science - Special Issue

Success in Science - Special Issue

Scientific Networks and Success Every researcher is affected by how scientific performance is measured. How should it be measured? Do we have the right data to do it? How can we make it fair and unbiased?

Read More
Reproducing scientists' mobility: a data-driven model

Reproducing scientists' mobility: a data-driven model

Scientists often move around the world to share ideas and work together, but how do these moves actually happen? This study looked at millions of career paths to map out how researchers travel between cities, countries and institutions. It found that most scientists prefer to move shorter distances, usually less than 1000 kilometers, and tend to choose places that are both close and well-regarded. The research also showed that the way we visualize these moves changes depending on the scale. At the city level, scientists move more freely, while at the country or institution level, clear pathways called “knowledge corridors” emerge. This helps us understand how knowledge spreads and how scientific careers develop over time, with important implications for both scientists and policymakers.

Read More
Foreword to the special issue on success in science

Foreword to the special issue on success in science

Science is a complex social endeavour that relies on collaboration networks to advance knowledge. This meta-science, or the 'science of science', helps stakeholders understand, shape and guide the development of this system. A key observation is that the number of references and co-authors per paper has increased, illustrating the importance of collaboration in synthesizing research findings. Understanding these networks and their evolution is crucial for identifying knowledge-generating processes in science and allocating resources efficiently.

Read More