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Innovation

Modeling Innovation EcosystemsU.S. Economic Development AdministrationApril 7, 2016

C. Scott Dempwolf, PhDAssistant Research Professor& DirectorUMD Morgan State JointCenter for Economic Development

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University of Maryland & Morgan State Universitys University CenterMorgan State Universityleads Baltimore-focused effortsMorgan Mile development district

University of Maryland College Park leads Innovation-Led Economic Development (ILED) efforts:New innovation metricsNew ILED toolsNew innovation / ILED understanding

Our University Center has pursued two tracks. One focused on technical assistance and place-based economic development wit a particular emphasis on Baltimore led by Morgan State. The other focused on innovation-led economic development. In this second focus we seek to leverage the unique capabilities of a tier 1 research university to do three things: 1) develop new metrics for innovation and its impacts; 2) develop new tools for economic development practitioners & policymakers to manage ILED more effectively; and 3) develop new knowledge and understanding of the process of innovation, its impacts, and the effective use of policy to accelerate innovation-led economic growth. This presentation is in some respects a prelude to the session this afternoon with Tom Guevara and Victor Hwang.

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Innovation & Innovation-Led Economic Development

InnovationA process of transforming knowledge and scientific research into a new product in the marketplace.Think of that process as a sequence of related activities ResearchInventionProofCommercializationProductWith this intended outcome

New MetricsNew ToolsNew Understanding

We are now in an innovation-driven economy. Understanding that economy and responding to it with innovation-led economic development policies and practices must start with a clear understanding of what is involved in that process. We can think of innovation as a sequence of connected but often independent activities that ultimately result in a new product in the marketplace. While shown here as a simple linear sequence we know that in reality it is more complex and iterative.3

Innovation

Each activity involves people and organizations producing intermediate outcomesContributing to this intended innovation outcome

Each activity has inputs, outputs, & associated documentsNew MetricsNew ToolsNew Understanding

Adding some detail to our simple model we see that each innovation activity whether it is a research project or an invention or prototype has inputs, outputs and documents associated with it. They also have people and organization involved in supporting and carrying out the activity. We can extract information about all of these factors from data sources like grant and patent databases among many other sources. In some cases, particularly with research, the people and organizations are not working towards a specific innovation outcome. Rather, they publish their research and train students. Later, this knowledge and talent gets used and embedded in inventions, prototypes or commercialization processes that contribute to an intended innovation outcome.4

Innovation MetricsNew MetricsNew ToolsNew Understanding

Some are based on organizations & resourcesNone are based on intended outcome

Some arebased on inputsSome arebased on outputsSome are based on talentSome are comparative indexes

There are many types of innovation metrics, but importantly, none of them are based on products which are the intended outcome of innovation. This is one problem we are addressing. Partnering with faculty and students in computer science who have developed new software for analyzing event sequences, we are modeling innovation sequences for drugs and medical devices where final FDA approvals serve as good proxies for product launch dates.5

How long does innovation take? Drug development from first patent to final FDA approval

Modeled with EventFlow; Funded byNSF award #1551041New MetricsNew ToolsNew Understanding

This is a screenshot from EventFlow, the new software that we are using. Here we are modeling a subset of drugs and showing how long it takes from the date of the first patent application to the date of final FDA approval. We see that the average time is 10 years 11 months, but that the standard deviation is still pretty large at 5 years 4 months. Tightening up the confidence interval is one of our objectives. We intend to use these new metrics later to help understand why some regions are more innovative than others; to identify what specific things regions can do to improve their innovation outcomes; and to build predictive models of innovation based on machine learning and big data.6

Innovation

The people and organizations from each activity create an activity networkNew MetricsNew ToolsNew Understanding

Returning to our model we note that the people and organizations involved in each activity are connected to each other through that activity, forming a simple little activity network. Here I show the activity network for the NSF grant supporting the EventFlow research. Individually these activity networks are not very interesting or useful. But when we combine them what we end up modeling are innovation ecosystems.7

Stem cell products groupCommercialization supportAccelerationAttract complementary firmsDelivery devices groups,ECM groupFacilitate collaborationNiche market developmentAttract complementary firmsRegenerative Medicine & Nutraceuticals groupsDevelop KeystonesPromote local sourcing Industry partnershipsFDI / Business expansionAttraction - supply chain University partnershipsUniversity groups (JHU, UMCP, UMB)Leads for licensing (green ties)Key labs (dense subgroups)Opportunities for faculty spin outsAccelerate student startupsCorporate PartnershipsTargeted Economic Development StrategiesAt the Cluster Level

Regenerative Medicine Cluster Howard County, MDInnovation-Led Economic Development

Drill-down to Company ProfilesClick to follow link

Nascent / emerging Growth stageInfrastructure for maturing clusterNew MetricsNew ToolsNew Understanding

As a practical matter innovation ecosystems and regional innovation clusters are the same thing. Here we show one ecosystem / cluster for regenerative medicine in Howard County Maryland, comprised of those aggregated activity networks. This small, emerging cluster does not show up in traditional cluster analysis because 1) the activity is too recent and 2) the activity is not organized according to existing NAICS codes. Thus this analysis was valuable to Howard County Economic Development. Each group includes people and organizations that are connected based on what they are working on together. The graph is organized with the largest, most connected group in the upper left and the smallest, least connected group in the lower right. It turns out that this layout is useful in helping to organize and target different types of economic development strategies to specific companies and groups so that the overall cluster strategy appropriately targets limited resources for effective economic development. The interactive network tool allows economic developers to zoom in and explore different parts of the cluster in detail. Users can also click on certain nodes to get more detailed information. This interactive network model was build using NodeXL developed in part by the same computer scientists who created EventFlow.8

Stem cell products groupCommercialization supportAccelerationAttract complementary firmsDelivery devices groups,ECM groupFacilitate collaborationNiche market developmentAttract complementary firmsRegenerative Medicine & Nutraceuticals groupsDevelop KeystonesPromote local sourcing Industry partnershipsFDI / Business expansionAttraction - supply chain University partnershipsUniversity groups (JHU, UMCP, UMB)Leads for licensing (green ties)Key labs (dense subgroups)Opportunities for faculty spin outsAccelerate student startupsCorporate PartnershipsTargeted Economic Development StrategiesAt the Cluster Level

Regenerative Medicine Cluster Howard County, MDInnovation-Led Economic Development

Drill-down to Company ProfilesClick to follow link

Nascent / emerging Growth stageInfrastructure for maturing cluster

New MetricsNew ToolsNew Understanding

For example, clicking on the Osiris node brings up a brief profile of the company.9

Illinois Battery Cluster 2010 2014Modeled with NodeXL

Bridge

Emerging Theory and Research: the Illinois Battery ClusterInnovation Ecosystemsresearch componentIndustry componentBridging component New MetricsNew ToolsNew Understanding

We prepared network models fir the Illinois Science & Technology Roadmap. One of those was for the Battery Cluster. From an academic perspective, one of the things we noticed was that the activities tended to be organized into two main components research and industry with a small group of activities that seemed to span these two components. Well call this third component the bridge show here in collapsed form as simply a gray band.

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Bridge

The Innovation Ecosystem and the Valley of DeathA network representationof the valley of deathNew MetricsNew ToolsNew Understanding

Then we recognized similarities between the network graph and our graphic of the so-called valley of death. We realized that we might actually be looking at a network representation of that valley of death. If that was the case, what would we expect to find in the bridge? 1) corporate sponsored research; 2) SBIR / STTRs; 3) intermediaries like accelerators and incubators; and 4) public-priva