EAGER: PBI: Paving the Path to Market: The Role of Place-Based Innovation in R&D Product Availability

The proposed research project will enhance understanding of factors affecting how innovative research and development effectively translate into commercial products by focusing on place-based innovation (PBI) ecosystems. PBIs are regions where a variety of stakeholders collaborate in close geographic proximity (for example, Silicon Valley is a well-known example of a PBI ecosystem).

The National Science Foundation's Regional Innovation Engines (NSF Engines) program has recently started funding PBI ecosystems to bridge the regional technological divide in the United States. By uncovering critical factors that facilitate or hinder successful R&D commercialization, this project will provide valuable insights to support the continuous improvement of NSF Engines and PBI ecosystems more broadly.

The significance of this project therefore lies in its potential to promote inclusive economic growth and resilience throughout the United States.

Technically, the study will involve linking vast, regionalized data sources that cover the innovation pipeline, from academic publications and patents to market-ready products. The study aims to characterize the rate at which R&D outputs are translated into new products and the time it takes for this translation to occur through advanced statistical modeling.

By testing a broad range of factors that can be optimized through PBI interventions, the research will address critical questions about these factors’ influence on R&D translation and commercialization timelines. The project will leverage transdisciplinary perspectives and novel data on patent-protected products, advancing existing methods of measuring regional innovative activities.

The project will also innovate on various methods to enhance understanding of regional innovation dynamics. As an example, the measurement of a region’s related capabilities in a specific area will be improved by relying on co-citation as opposed to specialization patterns. The study will notably inform NSF’s monitoring, evaluation, and learning approaches for the NSF Engines.

It will also support the monitoring of regional R&D initiatives more broadly, as well as provide methods to obtain relevant local baselines for assessing other regional innovation ecosystems.

This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.