Re|Imagine: Improving the Productivity of Federally Funded University Research

Re|Imagine
Improving the productivity of federally funded university research

© Purdue University, 2016
i
Contact Information
Ed Morrison
Purdue Agile Strategy Lab
Discovery Park
201 West State Street
West Lafayette, IN 47906
edmorrison@purdue.edu

ii
Acknowledgements
The authors would like to thank Paul Zielinski and Courtney Silverthorn of the National Institute for Standards
and Technology (NIST) for their flexibility and their willingness to experiment with a new approach. We would
also like to thank Jim Woodell of the Association of Public and Land-grant Universities (APLU) for his continuing
support of these new approaches to defining the university’s role in today’s economy. Jim took the initiative to
point Paul and Courtney in our direction. Finally, we would like to express our appreciations to the participants
for the time they invested in our workshops. The participants are listed in the Appendix.

This report presents the story of an experiment. Sometimes
we can address the challenges in front of us more effectively if
we reframe the problem. That’s the case with trying to figure
out new ways to make federal investments in research more
productive.
While it was never explicitly presented to us in this fashion,
the challenge of “removing the barriers to commercialization”
to federally funded research on university campuses implies a
sixty year old model of commercialization that limits our
thinking. Ever since Vannevar Bush set the post war direction
of federal research, federal policy has followed a logical, linear
model of commercialization. To improve the productivity of
federal investment, simply identify and remove the barriers.
Indeed, the deceptively simple term “technology transfer” im-
plies a hand-off, a linear flow.
But what if the process of innovation -- moving new ideas into
the marketplace to create and capture value -- is not linear?
What if it is more like a complex adaptive system? An ecosys-
tem? A network of networks? How do we think of the chal-
lenge in this way? What if we focus on strengthening the
start-up and innovation ecosystems surrounding universities
that receive federal research dollars? Does reframing the chal-
lenge open up the “solution space”? Does it change how the ac-
tors in these systems think and behave? Does it lead to new,
more productive collaborations? More flexible, higher lever-
age policies? Does this new perspective place us on the door-
step of what’s next?
We believe it does. This report represents a small step in that
direction.
Ed Morrison
Scott Hutcheson
Duane Dunlap
Purdue Agile Strategy Lab
Antonino Ardilio
Joachim Warschat
Fraunhofer IAO
iii
Introduction

CHAPTER 1
Setting the
Stage
Design thinking teaches important les-
sons, but none are more important than
this one: how we think about a problem
defines the boundaries we impose on the
solutions we consider.

EXPANDING OUR “SOLUTION SPACE”
1. In mathematical optimization, a solution space
refers to a set of all possible points that satisfy
a problem’s constraints.
2. The emerging discipline of design thinking has
adopted the term. Design thinking focuses on
generating solutions. The emphasis is on
designing experiments to explore a solution
space. Little time is spent on defining the
problem.
3. Most important, the solution space can be
expanded by reframing the problem.
4. “Overcoming barriers to commercialization” is
probably too narrow a definition of the
problem and limits our thinking of what is
possible.
SECTION 1
Asking the Right Question
Bernard Roth, a founding luminary of Stanford's design
school, illustrates the value of asking the right question with a
story. Some years ago, a student team went to Nepal to im-
prove the performance of incubators for premature infants. Af-
ter doing some field research, they reframed to their problem
by changing their point of view. The problem was not the incu-
bators, the problem was how to keep babies warm long
enough for them to survive. With this altered perspective, the
students developed a solution that sold for 1% of the price of a
conventional incubator. The core message is simple and pow-
erful. Reframing the problem, changing our point of view,
helps us to find more creative and productive solutions.
This lesson is crucial as we consider how to improve the pro-
ductivity – the returns to the public – from federal invest-
ments in research performed on university campuses. As we
shall see, in the 35 years or so that this issue has circulated
among policy circles in Washington, the predominant framing
of the problem is been to “eliminate the barriers" to commer-
cialization.
This report argues that more needs to be done to shift our per-
spective from “barriers" to an emerging language of "innova-
tion ecosystems”. Our framing question moves from “How do
we eliminate barriers to commercialization?” to “How to we
work together to strengthen innovation ecosystems?” By mak-
ing this shift, we open a wider range of solutions available to
us.
5

To demonstrate the value of this shift in perspective, we've
conducted three workshops in Washington focused on
strengthening the innovation ecosystems surrounding univer-
sities that conduct federally funded research. Our report is di-
vided as follows:
• The balance of this chapter provides an overview of
how academic research and policy formulation has moved
from a linear model of commercialization to an emerging
model of "innovation ecosystems”.
• Chapter 2 explores how a small team at Purdue Uni-
versity has been exploring this new perspective over the past
decade and has begun to implement new promising method-
ologies to design and guide the complex collaborations needed
to strengthen "innovation ecosystems”. We introduce our col-
laboration with Fraunhofer and their innovation and technol-
ogy management solutions that we are piloting with our part-
ners at the New Jersey Institute of Technology.
• Chapter 3 demonstrates how we designed three
workshops to apply these approaches in order to build
stronger connections among universities and federal agencies.
We demonstrate this framework with three potentially produc-
tive collaborations.
• Chapter 4 explores how this approach could be
scaled through more low-cost experiments.
6

NEW MODELS OF INNOVATION
1. The notion of commercialization barriers is
inherently tied to a linear model of
commercialization that is an inadequate
description of the innovation process.
2. An alternative, emerging model sees
innovation as the product of networks and
collaboration. Multiple actors work together to
explore innovations and experiment in new
ways.
3. Creating new products and services is an
iterative process of experimenting and
prototyping in real world settings.
4. The resources required for successful
innovation are rarely housed within a single
entity. Rather, a network of actors -- embedded
in an ecosystem -- drive the innovation
process.
SECTION 2
Moving from Barriers to
Ecosystems
We begin our history with the passage of the Bayh-Dole Act in
1980. In the late 1970s, amidst growing concerns about stag-
flation and the global competitiveness of the US economy,
Congress began looking at how to accelerate innovation from
federally funded research. In 1980, Congress enacted the Pat-
ent Policy Act of 1980, also known as the Bayh-Dole Act. By
changing the patent rules, Congress hoped to increase the syn-
ergies between universities and business.
The Bayh-Dole enabled a linear model of technology transfer
that moves roughly as follows:
• A university scientist makes a discovery;
• Scientist discloses the invention to the university's technol-
ogy transfer office (TTO)
• The TTO evaluates the invention and decides whether or
not the patent
• The TTO files a patent application
• The TTO markets the technology to firms and entrepreneurs
• The TTO signs an agreement to license or acquire an equity
stake in a spinoff company
Bayh-Dole, although helpful, was not enough. By the mid-
1980’s a growing turbulence in the global economy pushed the
business community advocate for a more focused approach in
strengthening the nation's competitiveness. The rise of China,
the demise of the Soviet Union, and the emergence of India,
7

as well as lingering pressure from com-
petitors in Europe and Japan all con-
tributed to a sense of urgency. The
President’s Commission on Industrial
Competitiveness outlined the nation's
declining competitive position and ar-
gued that our weakness in commercial-
izing new technology contributed sig-
nificantly to the problem.
In an article written three years after
the commission report,
the chairman, John
Young, former CEO of
Hewlett-Packard, ex-
plained:
“Technology is one
area where the United
States has a competi-
tive advantage. In fact,
as a nation, it is proba-
bly our greatest advan-
tage… Yet, the competi-
tiveness of US industry
is seriously handi-
capped by shortcom-
ings in our ability to
commercialize tech-
nologies to develop
both the products in the manufacturing
processes that today's markets de-
mand.”
He went on to cite the loss of US posi-
tion in semiconductors, videocassette
recorders, compact disc players, and
other consumer electronics. Young be-
gan reframing the challenge as involv-
ing a wide array of actors. He cited
Stanford's Center for Integrated Sys-
tems (CIS) as an exemplar.
“CIS represents a special partnership
that joins together private industry, aca-
demia and government in an American-
ized version of the cooperative spirit
that is given Japanese industry and in-
ternational edge. This arrangement is
mutually beneficial because it pro-
motes the collaboration between aca-
demic and industrial communi-
ties and the basic research
needed to keep the United
States in the forefront of tech-
nology.”
Young's work began the proc-
ess of expanding our under-
standing of commercializa-
tion. The perspective gained
even more traction with the
publication in 1990 of Michael
Porter's Competitive Advan-
tage of Nations. As he summa-
rized his book in a companion
article in the Harvard Busi-
ness Review, Porter explained
how national competitiveness
is grounded in company com-
8

petitiveness. He proposed to that under-
standing company competitiveness re-
quired analyzing a model of five fac-
tors. It was an important effort to ex-
plain that national prosperity was
rooted in a complex system of interrela-
tionships. By managing these factors
intentionally, countries can give rise to
“clusters" of internationally competi-
tive industries.
By the mid-1990’s the landscape was
clearly shifting. In an important report,
the Office of Technology Assessment
declared that the debate on commer-
cialization “has been hampered By an
incomplete understanding of the ways
in which firms develop and market new
products, processes, and services in the
barriers that be must overcome in the
process.”
The report described the linear model
of innovation and commercialization,
but called it “an inadequate descrip-
tion of the innovation process.” The tra-
ditional model only describes one path-
way to innovation and “reinforces the
notion that government should restrict
its role to the support of basic re-
search.”
At the same time, a number of academ-
ics began contributing to our under-
standing by proposing different frame-
works that might help us understand
the complex nature of innovation
within regional economies. While Por-
ter focused on clusters, Leydesdorff
and Etzkowitz proposed the idea of a
“triple helix” of universities, govern-
ment and business. Cooke, building off
work done by Freeman in national inno-
vation systems, proposed the frame-
work of “regional innovation systems”.
Saxenian, comparing the economic per-
formance of Silicon Valley with Bos-
ton’s Route 128, saw the importance of
networks in explaining regional com-
petitiveness.
These researchers provided the founda-
tion for what has followed. In the past
fifteen years, additional research has
explored practical approaches to de-
signing and guiding the open, loosely
connected networks that characterize
commercialization and innovation. In
2001, Eisenhardt and Sull suggested
that strategy in a world of networks
was fundamentally different than tradi-
tional approaches. They suggested that
successful strategies in dynamic net-
works emerge from following simple
rules. In 2002, an important line of re-
search opened to explore the connec-
tion of social network analysis and in-
novation. In 2003, Chesbrough intro-
duced his concept of “open innova-
tion”. In 2005, Seely Brown and Hagel
suggested that “pull platforms” pro-
vided a useful metaphor for mobilizing
tangible and intangible assets needed
for innovation.
By 2010, the National Science Founda-
tion’s Directorate of Engineering sug-
gested a new policy framework focused
on strengthening “innovation ecosys-
tems”. The white paper bluntly states,
9
The linear model of commercializa-
tion is “an inadequate description of
the innovation process.”
Office of Technology Assessment

“The linear model is an
oversimplification of the
innovation process, and
it misses many of the nu-
ances involved in the
non-linear, real life proc-
ess.”
This paper serves as a
useful bridge between
academics, focused on
fashioning theoretical
tools to guide policy, and
policy makers seeking to
improve the productivity
of federal research.
In 2013, the White House
convened the Lab to Mar-
ket Summit. The expert
panel following the sum-
mit reiterated the challenge voiced by
John Young in 1988: “[I]f the U.S. is to
remain globally competitive in the 21st
century, it must accelerate the transla-
tion of federally funded R&D into com-
mercial outcomes that create economic
and public value, thus maximizing the
return on the public dollars invested.”
The question, of course, is “How?”
There are no set answers, only experi-
ments on which to build. The federal
government has been experimenting
with different policy approaches, from
the multi-agency i6 Challenge to the
National Innovation Corps (I-Corps),
the NIH Research Evaluation and Com-
mercialization Hub (REACH) program,
the National Incuba-
tor Initiative for
Clean Energy, and the
National Network for
Manufacturing Inno-
vation (NNMI).
APLU has launched
its Innovation and
Economic Prosperity
(IEP) initiative to rec-
ognize the strategies
undertaken by univer-
sities to design and
guide the networks
needed to accelerate
innovation.
The IEP program
demonstrates that uni-
versities are experi-
menting in a wide range of activities
and collaborative investments to build
both the start up and innovation ecosys-
tems surrounding their campuses.
These experiments are teaching us how
to design the networks and platforms
leading to a more productive invest-
ment of federal research funding.
10

CHAPTER 2
Taking on the
How
We now take you inside another experi-
ment. In August 2013, Purdue and Fraun-
hofer IAO began a collaboration to ex-
plore this question: How could we com-
bine our innovation, technology manage-
ment and strategy frameworks to
strengthen the innovation ecosystems sur-
rounding U.S. universities?

WHAT IS AGILE STRATEGY?
1. Strategy enables investors to deploy scarce
resources in productive ways.
2. Designing strategy in an ecosystem cannot
follow conventional strategic planning
methods. Traditional approaches were
designed for hierarchical, command-and-
control organizations. Ecosystems, by
contrast, are open, loosely connected networks
embedded in other networks. In an ecosystem,
no one can tell anyone else what to do.
3. Strategy in open networks is inherently
complex. To design effective strategies, actors
must follow a collective discipline of simple
rules.
4. As participants follow these rules, complex
strategies emerge from continuous
experimentation and adaptation.
SECTION 1
Open Innovation and Agile
Strategy
One of the biggest challenges of moving from a linear to a
network-based model of innovation involves figuring out a
new approach to strategy. An effective strategy consists of
two components: a destination (“ Where we going?”) and a
pathway (“ How we get there?”). With conventional hierarchi-
cal organizations, the responsibility of developing a strategy
falls to top management. In an ecosystem, the design of strat-
egy is not so clear.
Meeting this challenge is crucial, if we hope to design and
guide an ecosystem intentionally. If we have no replicable,
scalable and sustainable approach to strategy, then we must
leave the formation and operation of ecosystems to serendip-
ity.
Seen from the perspective of the business firm, the challenge
of innovating within an ecosystem involves managing the proc-
ess of open innovation. The term, coined in 2003 by Henry
Chesbrough, describes how a firm identifies, links and lever-
ages resources outside the firm to accelerate the innovation.
Within large firms, managing this process is challenging, and
there is no established process for doing so.
For nearly a decade, a small group of practitioners and re-
searchers at Purdue University have been experimenting with
a new approach to strategy, an approach that is directly appli-
cable to open innovation and innovation ecosystems. De-
signed specifically for open, loosely connected networks, this
approach manages the inherent complexities of ecosystem de-
sign with a set of simple rules. These rules guide participants
12

to shared, measurable outcomes and enable them to make fre-
quent adjustments along the way, as they learn by doing.
This discipline, called Strategic Doing, focuses participants on
deep conversations framed by engaging, strategic question
about what they could do together. Participants begin to ex-
plore this question by connecting their assets and defining
new opportunities. They then go through a clear, concise proc-
ess of establishing priorities. They identify opportunities that
both have a big potential impact and are relatively easy to do.
To set these priorities they fall back on a valuable, often ne-
glected resource: their strategic intuition.
With one opportunity on which to focus they drive their con-
versation deeper to explore what a successful outcome might
look like and how they would measure their success. Once
they have an outcome, they next turn their attention to estab-
lishing a project that can move them toward that outcome.
They mark their path forward and established commitments
to move their ideas and action. Finally, they establish a time
to meet so they
can review their
progress and de-
cide on next steps.
This conversation
takes a relatively
short time to com-
plete, a matter of
hours. As partici-
pants answer
these questions
they generate all
the components
they need for a
strategy to begin implementing. By following this protocol of
collaboration, partners can quickly begin building trust by
working toward a shared outcome.
With this process in place, universities can design and guide
the development of innovating networks capable of speeding
resources to the most promising ideas. To begin developing
the ecosystems needed to speed commercialization of feder-
ally funded research, we have borrowed an important emerg-
ing idea from business strategy and product development: the
concept of platforms. Universities need to design and develop
their own platforms on which ecosystems can form.
13
“I’ve worked with large companies
trying to do open innovation, but the
Strategic Doing process is unique.
This is the most clear an concise
open innovation process I’ve seen.”
Mark Scotland
CEO, 4.0 Analytics
A company participating in an open innova-
tion network with Lockheed to develop
Condition-Based Maintenance innovations
for the Navy

WHY PLATFORMS?
1. Beginning in the 1990’s, as business strategy
began to shift toward networks and
ecosystems, the concept of platforms emerged.
2. A platform enables a business to “orchestrate”
collaborations and networks that create value
for markets.
3. So, for example, Apple created a platform for
music sales with iTunes, Amazon created a
platform for books and publishing, and Intel
created a platform to accelerate the
development of the personal computer.
4. Following a similar approach, universities and
national labs can design platforms that will
speed the commercial development of federally
funded research.
SECTION 2
Platforms and Ecosystems
In the business world, the idea of platforms first emerged in
product development in the 1980’s. Companies assembled
foundation of components around which they developed multi-
ple products. So, for example, auto companies like Toyota
would develop a series of models on the same platform of
shared components. Similarly, Microsoft developed its Office
suite using a shared set of components for file sharing, text
processing, and graphics.
As the concept of business ecosystems developed in the
1990’s, however, the term platform took on a different mean-
ing. Companies began designing platforms in order to engage
partners and customers in new networks. In other words,
their business strategy focused on designing and guiding their
own business ecosystems using a platform business model. Ap-
ple disrupted the music industry by creating a new platform,
iTunes, which enabled consumers to download single music
tracks and develop their own playlists. By designing the plat-
form and setting the rules of operation, a company can orches-
trate the networks that form on the platform. In this way, a
business ecosystem emerges that provides a competitive ad-
vantage to the platform owner.
Universities can use the same approach to design and guide
two overlapping ecosystems to accelerate the commercializa-
tion of federally funded research. The first ecosystem focuses
on startup companies. Here, faculty and students leverage uni-
versity intellectual property from federally funded research to
create spinoff companies. The second ecosystem involves ac-
celerating innovation among existing companies.
14

In the United States, a great deal of attention focuses on accel-
erating the rate of university startups and spinoff companies.
In Germany, by contrast, much more attention is paid to accel-
erating innovation among existing companies, small and
large.
Universities pursuing federally funded research can do both,
but each requires a different strategy and a different set of net-
works. Equally important, the assets around which networks
form vary from university to university. Each set of university
ecosystems will be different.
Meeting the challenge of designing and guiding these ecosys-
tems -- with universities as a orchestrator or “platform owner”
-- is not easy. Universities are organized around academic de-
partments that often block cross disciplinary collaborations.
Faculty are not easily rewarded for devoting time and re-
sources to innovation. The lack of faculty incentives to inno-
vate represents a common “obstacle” often cited but difficult
to address.
The Purdue Agile Strategy Lab is addressing this challenge by
developing “platforms” on which faculty are attracted to col-
laborate. The platforms are “open” to participation by faculty,
students and outside organizations, including companies.
These platforms “slide underneath” existing organizational
structures and do not disrupt existing power relationships. As
a result, they are unlikely to provoke an “immune response”
from power players within the existing organizations. Each
platform provides a safe place where “networks of the willing”
can form to explore innovation opportunities. By becoming a
15

platform designer,
universities can ac-
celerate the develop-
ment of both start
up and innovation
ecosystems.
The Purdue team
has been working on
this approach with
Fraunhofer IAO over
the past three years.
In the past year, the
team has collabo-
rated with the New
Jersey Innovation Institute to design innovation platforms to
address particular opportunities, including developing an in-
novation network of smaller companies working with Lock-
heed Martin to address the challenge of Condition Based Main-
tenance (CBM) with the Navy. (CBM represents a strategy in
which maintenance is performed on heavy equipment when
certain indicators show that performance is degrading or a fail-
ure is imminent.)
The university uses its convening power and the discipline of
agile strategy to form networks quickly, generate hypotheses
about how value could be created collaboratively, and design
experiments to test these hypotheses. The formation of these
networks takes time, but an agile strategy discipline speeds
the process. Participants learn that by linking and leveraging
their assets, they can gener-
ate new opportunities. In
the case of CBM, the
Purdue-NJII team used
this process uncover a
promising set of compa-
nies in sensors, data ana-
lytics, machine learning,
predictive analytics, and
augmented reality.
Through a series of agile
strategy workshops, a new
innovation network
formed on the platform
within six months.
16

CHAPTER 3
A Lab to
Market
Experiment
What would it look like if we introduce
these concepts of platforms, innovation
networks and ecosystems to the ongoing
Lab to Market initiative in Washington?
We launched an experiment to find out.

In 2015, representatives from NIST approached Purdue about
conducting a workshop on the barriers to commercialization
of federally funded research by universities. The Purdue team
instead proposed an alternative of three workshops designed
as an experiment. The purpose is to explore how we could de-
velop practical initiatives to strengthen the innovation ecosys-
tem surrounding universities conducting federal research. So
the question guiding the design of these workshops was as fol-
lows:
Could we design a series of workshops that generated practi-
cal initiatives to strengthen university start-up and innovation
ecosystems?
To test this idea, we conducted a series of three workshops in
August 2015, October 2015, and February 2016. We began
with the premise that there are three types of solutions that
could strengthen these innovation ecosystems:
• Collaborative solutions —These consist of three types of col-
laboration: collaborations within universities, such as multi-
disciplinary research teams tackling grand challenges; col-
laborations between university and industry; and collabora-
tions among federal agencies.
• Administrative solutions — These are actions by federal
agencies that can speed collaboration by increasing the flexi-
bility and flow of information and funding within innovation
ecosystems;
• Legislative solutions — These are Congressional authoriza-
tions of appropriations specifically designed to support col-
laborations within innovation ecosystems.
In the August workshop, 20 participants convened to explore
solutions that they could generate. We arbitrarily divided into
three teams. Using the Strategic Doing process, each team
quickly generated an inventory of potential solutions that
could be launched by the participants from the assets within
their networks. In the October and February workshops, the
teams continued to refine their work.
The three initiatives they developed include:
1. Connect network of IEP universities with the Federal Lab
Consortium to form opportunities for new collaborations
to form.
2. Pilot an SBIR Phase 0 initiative to bridge the gap between
federal research funding and Phase 1 SBIRs.
3. Promote funding alignment with NIST’s Washington-
based innovation fellows initiative to serve as “innovation
guides”. They would develop tools and methods to navi-
gate among different federal programs to support
technology-based start-ups and early stage high growth
firms. They would also advise federal program managers
on how to make their initiatives more open, flexible, trans-
parent and collaborative.
18

The process demonstrated that with short bursts of time,
teams of professionals from universities, university associa-
tions, and government agencies could do complex thinking in
a new way.
In the course of three four hour workshops, the teams devel-
oped creative, sophisticated ideas for productive collabora-
tions, set priorities among these opportunities, and began im-
plementing an action plan to develop their top priority.
A deeper dive into the process
Let’s take a deeper look into the opportunities that emerged
from the first workshop.
On August 31, 2015 a diverse group of twenty participants
gathered at the office of the Association of Public and Land
Grant Universities (APLU) in Washington D.C. The partici-
pants completed an agile strategy workshop, guided by a team
from Purdue University. The first step in this process involves
identifying opportunities for linking and leveraging assets
among the participants. Opportunities emerge when partici-
pants link these assets across their networks.
Working on three teams, the participants identified 29 poten-
tial solutions including 13 that could likely be accomplished
through administrative action [A], 12 that could be imple-
mented through focused collaboration [C], and four that
would require legislative action [L]. Each of the three teams
selected one of their solutions to begin developing. They de-
signed a Strategic Action Plan to begin moving forward. Each
team ranked their opportunities along two dimensions: im-
pact and ease of implementation. On a five point scale, they
ranked the potential impact of each opportunity (1=low;
5=high). Next, they ranked the ease of implementation for
each opportunity (1=low; 5=high). In this way, each team de-
cided on their “Big Easy”, the opportunity that had a relatively
high impact, but that was also relatively easy to do. In subse-
quent workshops, the teams continued to develop their Big
Easy.
A more detailed assessment of each team’s activity follows.
For each team, their opportunities are outlined, along with
their scoring in terms of impact and ease of implementation.
19

Team One: Opportunities Identiﬁed
Business Intelligence Web Platform
Create a business intelligence web platform that can assist in
finding technologies and markets, doing market analysis, and
regional economic development analytics. [A] Impact: 5,
Ease: 3
Industry-Inspired I/UCRC
Assure that Industry/University Cooperative Research Cen-
ters are truly industry inspired so that fundamental research
is motivated by industry-relevant problems. [A] Impact: 4,
Ease: 3
SBIR Phase Zero Program*
Design a “Phase Zero” (may not actually be called that) pro-
gram as a first step in the SBIR/STTR program. The program
could fund teams to go through an I-Corps-like experience as
a first step in the SBIR/STTR process. This could result in bet-
ter Phase 1 proposals. [A] Impact: 4, Ease: 4
Venture Capital Community Integration
Form a consortium of public and private funding entities that
includes, not just inter-agency grants, but also inter-
governmental and private sector funding that includes the ven-
ture capital community. This would be a hybrid of federal
grant and sponsored research. [C] Impact: 4, Ease: 4
Cluster-Based Collaborative Network
Design a cluster-based collaborative network that goes beyond
a firm-by-firm approach. [C] Impact: 4, Ease: 4
Innovation Academy
Develop an Innovation Academy program that designs and of-
fers short courses, workshops, etc. on innovation and enter-
prise development. [A] Impact: 3, Ease: 5
Clear & Transparent University Policies on Tech Transfer
Goals & Objectives
Establish and clearly state what the goals and policies are for
the university as it relates to tech transfer/communications
(i.e., have a clear tech transfer policy). Often times the objec-
tives of the university, as they relate to tech transfer, are un-
clear and difficult, depending who you talk to. [A] Impact: 5,
Ease: 4
Systematic Collaboration Among Federal Relations Officers,
University Innovators, and Higher Education Associations
Foster greater collaboration among federal relations officers,
university innovators, and higher education associations. Fed-
eral relations officers can help educate innovators about regu-
latory and legislative trends – and more importantly, the inno-
vators can convey obstacles to Washington, DC for advocacy
and action. [C] Impact: 3, Ease: 4
20

Industry-Funded Basic Research
Develop university/industry partnerships to support basic re-
search that is of value to industry. [C] Impact: 5, Ease: 2
Statewide/Regional Buffer Organizations
Create statewide or regional
buffer organizations with mis-
sions focused exclusively on holis-
tic economic development and
technology transfer and with a
board made up of government,
industry, and research institu-
tions. [L] Impact 4, Ease 4
Multi-Agency Proof of Concept
Research Funding Program
Establish a multi-agency ap-
proach to supporting early proof
of concept research at universi-
ties to help support the innova-
tion gap that often exists, prevent-
ing ideas from being successful
and advancing in the marketplace. [A] Impact: 4, Ease: 2
I-Corps at State Level
Develop a state-level version of I-Corps to engage beyond
NSF-funded PIs. Would likely require both state and federal
legislative action. [L] Impact: 3, Ease: 2
Federal Agency Navigation Tool
Develop a tool that serves as a concierge or guidance
counselor-like service to help navigate the funding, programs,
tools, and personnel from the various federal agencies. This
could result in more unification among funding agencies and
the ability to refer between agencies. [A]
Impact: 3, Ease: 3
Industrial IGERT Training Program
for Grad Students
Develop an NSF/NIH training/
internship program for grad students
that provides them with multidisciplin-
ary research experiences working with
industry problems. This could result in
broader grad training and culture
change. [A] Impact: 3, Ease:3
Catalogue of University Programs
Develop a sharable catalogue of univer-
sity tools of bite-size program and tools
they have implemented about which
they would be willing to take questions from other university
colleagues. This would be one-page data sheets rather than
large slide decks and longer documents. [C] Impact: 2, Ease 4
21

Team Two: Opportunities Identiﬁed
Innovation Campaign
Launch an innovation campaign to create better understand-
ing of innovation and the role universities play. Looking for-
ward, connect innovation to the Grand Challenges that we
face in food, energy, water, climate change, and so on. Look-
ing retrospectively, tell the backstory of important innova-
tions that we use today. [C] Impact: 4, Ease: 3
More Flexible DOE Funding
Provide more federal funding flexibility for DOE. In the 2011/
2012 appropriations cycle, Congress placed new restrictions
on how funds could be appropriated. These restrictions re-
duce the ability of DOE to experiment and adapt to new inno-
vation opportunities as they occur. They impose significant
lag times in budgeting. DOE is not working in real time. [L]
Impact: 5, Ease: 2
Network of Networks
Convene a team to design a “network of networks” of organiza-
tions involved in university-based innovation and economic
growth. They would include university associations (APLU,
ASCU), professional associations (AUTM, UEDA, ASEE) and
others. This network of network will probably need a full time
staff, and it could provide a more coherent way to align promo-
tion and advocacy. (Example: A green network of organiza-
tions involved in sustainability; this idea contributed by
Jetta). [C] Impact: 4, Ease: 3
Innovation and Economic Prosperity (IEP) Network with
Federal Partners*
APLU has organized network of leading edge universities in-
volved in economic growth (IEP universities). The network, in
its third year, includes 48 universities. The mutual advantage
of connecting IEP with federal agencies: 1) IEP universities
have a broader network of federal professionals with whom to
interact; and 2) federal professionals have a network that can
use when they are seeking policy guidance. [C] Impact: 3,
Ease: 5
Model IP Policies
Develop model IP policies, using an emerging compilation of
innovative IP policies that APLU is compiling. Connect this
work with AUTM. [C] Impact: 2.5, Ease: 5
Team Three: Opportunities Identiﬁed
UIDP Participation*
Increase the participation of government agencies in the Uni-
versity Industry Demonstration Partnership. [C] Impact: 5,
Ease 5.
22

Funding Alignment
Reorganization of funding from agencies so there is a contin-
uum of mechanisms to provide support all the way to full com-
mercialization. [A] Impact: 5, Ease: 3.
Dedicated Funding
Create new funding
mechanisms for dedi-
cated entities to con-
nect research to inno-
vation. Could be inter-
agency funding. [L]
Impact: 5, Ease: 2
Consistent External
Messaging
Create consistent mes-
saging to the public
about the value con-
necting better re-
search and industry to
better respond to pub-
lic skepticism. [A] Im-
pact: 3, Ease: 3.
Internal Messaging
Create internal mes-
saging about the value
to of the connections between better research and industry to
respond to skepticism. [A] Impact: 4, Ease: 3
Conflict of Interest Model
Create a model for conflict of interest between industry and
university. COGR, APLU, and AAU to could take the lead. [C]
Impact: 3, Ease 1.
Inter-Agency SBIR
Find a way to test an inter-
agency SBIR program. [C]
Impact: 2.5, Ease 4.
Expansion of Entrepreneur
in Residence
Expand the role of the Entre-
preneurship in Residence
program to take advantage
of cross-agency possibilities.
[A] Impact: 2, Ease: 5.
Peer-Review of Commer-
cialization
Build into the peer-review
process the consideration of
commercialization factors
(not just SBIR). [A] Impact:
2, Ease: 3.
23

CHAPTER 4
Insights and
Next Steps
We started with a simple idea. Rather
than convene another workshop on “bar-
riers to commercialization” why not re-
frame the challenge and introduce some
new approaches to designing and guiding
platforms, innovation networks, and uni-
versity ecosystems? This approach gener-
ated some useful insights and practical
initiatives. The next step involves follow-
ing up on this work and designing a more
ambitious set of experiments.

Here are some insights that emerge from this process.
• University-based ecosystems will be improved through mul-
tiple, practical collaborations. A relatively small group of
participants generated a large number of practical initiatives
that could strengthen the collaborations and speed the flow
of resources into innovation. Many of these ideas can be im-
plemented now, in the short term.
• Despite a challenging political and budget environment,
there are important, practical steps that can be taken to im-
prove the productivity of federal investment in research
and development. Federal agencies and their university part-
ners can design and implement these collaborations them-
selves, since they draw on assets within their own networks.
• In order to move in this direction, the participants need a
common strategic framework, a simple set of rules to de-
sign and guide their collaborations. The workshops demon-
strated that participants who had never worked together be-
fore could learn the simple rules needed to design and guide
complex strategies in an open, loosely connected network.
• We take on complex challenges by relentless focusing on
“doing the doable”. As we focus on some relatively big ideas
that are relatively easy to do, important “network effects” im-
prove our productivity. First, our networks grow. As they
do, we gain access to more resources. We meet new people,
and we uncover new assets. Second, focusing on "doing the
doable” accelerates our learning. We figure out what works
to improve the performance of complex systems. Finally, as
we collectively work together, trust builds. Stronger bonds of
trust lead to more effective networks. Our collective skills
improve. We can do more complex work together faster.
• Designing and guiding new collaborations to strengthen
university innovation ecosystems is likely to be a high lever-
age investment for the federal government. Convening agile
strategy workshops is not expensive. It requires a new ap-
proach in our thinking, a new set of collective skills that we
can improve through practice, and a disciplined commit-
ment of short bursts of time.
A team from Purdue and Fraunhofer IAO have been building
this “innovation ecosystem” approach for the past three years.
The team is now collaborating with New Jersey Institute of
Technology to pilot this approach in its NJMarketShift initia-
tive, funded by the Department of Defense.
Here are some practical next steps to consider.
1. Design a Lab to Market process that builds on the ideas
generated from this three workshop pilot. NIST and its
university partners could build on the work that has been
started. This process would lead to regular workshops in
Washington at a time when university representatives are
in the city to attend other events.
2. Design a series of agile strategy workshops in one Fed-
eral Lab Consortium region over the next six months. If
results are promising, the process can be replicated and
scaled to other regions. Federal research labs and re-
28

search universities, even those within the same U.S. re-
gion, function largely as independent actors rather than
as interconnected components within a regional innova-
tion ecosystem. Significant inventories of new technolo-
gies exist within the walls of each of these institutions;
but currently there is no methodology for regional innova-
tion. There is no systematic, replicable approach to ex-
plore how these individual technologies might be linked
and leveraged to focus on specific market opportunities
and grand challenges.
3. Learn more about the Purdue-Fraunhofer approach to
designing and guiding innovation ecosystems in New Jer-
sey. The Purdue Agile Strategy Lab and Fraunhofer IAO
have been building this “innovation ecosystem” approach
for the past three years. The team is now collaborating
with New Jersey Institute of Technology the pilot this ap-
proach in its NJMarketShift initia-
tive, funded by the Department of
Defense.
4. Conduct training in agile strategy
disciplines for professionals in fed-
eral agencies. Moving toward the
perspective of innovation ecosys-
tems calls on professionals in fed-
eral agencies to develop a deeper
set of skills to collaborate. These
skills can be taught, and the proc-
ess of teaching is scalable across
an agency. (In a different context, a small team from the
DC Department of Employment Services is transforming
that agency by teaching Strategic Doing. In months, they
have trained 700 employees and launched collaborative
initiatives to improve performance in five focus areas.)
Innovation is a discipline, and networks can speed the flow of
resources to start-up and innovating companies. These net-
works can be intentionally designed and guided. But before
this approach can be replicated on a broader scale, federal
agencies, universities and federal labs will need to substitute
old thinking about “barriers” and begin learning how to link
and leverage their assets to build new networks. They will
need to learn and practice the deeper, collective skills of col-
laboration.
The good news is that moving down this path is relatively easy
and low cost. Importantly, because we are dealing with com-
plex systems, we can
identify leverage
points through continu-
ous experimentation.
We can find places
where 20% of our ef-
fort can yield 80% of
our results. Small
steps can lead to big
payoffs.
29

APPENDIX
Selected References
The literature on innovation networks, open innovation, inno-
vation ecosystems and platforms is varied and growing. Here
is a selection of references.
Adner, R. & Kapoor, R., 2010. Value creation in innovation
ecosystems: How the structure of technological interdepend-
ence affects firm performance in new technology generations.
Strategic Management Journal, 31(3), pp.306–333.
Autio, E. et al., 2014. Entrepreneurial innovation: The impor-
tance of context. Research Policy, 43(7), pp.1097–1108.
Bercovitz, J. & Feldmann, M., 2006. Entrepreneurial Universi-
ties and Technology Transfer: A Conceptual Framework for
Understanding Knowledge-Based Economic Development.
Journal of Technology Transfer, 31, pp.175–188.
Bercovitz, J. et al., 2015. Entrepreneurial orientation, technol-
ogy transfer and spinoff performance of U.S. universities.
Journal of Technology Transfer, 37(1), pp.994–1009.
Cantner, U., Conti, E. & Meder, A., 2010. Networks and inno-
vation: the role of social assets in explaining firms’ innovative
capacity. European Planning Studies, 18(12), pp.1937–1956.
Chandler, V. et al., 2013. Thriving in Open Innovation Ecosys-
tems: Toward a Collaborative Market Orientation, Working
Paper, Cambridge Service Alliance, Cambridge University.
Choudary, S., Van Alstyne, M., & Parker, G., 2016. Platform
Revolution: How Networked Markets Are Transforming the
Economy and How to Make Them Work for You. Norton.
Cooke, P. & Uranga, M.G., 1997. Regional innovation systems:
Institutional and organisational dimensions. Research Policy,
26, pp.475–491.
Freeman, C., Onofrei, M. et al., 1995. The “National System of
Innovation” in historical perspective. The Cambridge Journal
of Economics, 19(March 1993), pp.5–24.
Gastaldi, Luca; Appio, Francesco Paolo; Martini, Antonella;
Corso, M., 2015. Academics As Orchestrators of Continuous
Innovation Ecosystems: Towards a Fourth Generation of Ci
Initiatives. International Journal of Technology Management,
68(1/2), pp.1–20.
Gawer, A. & Cusumano, M.A., 2008. How Companies Become
Platform Leaders. MIT Sloan Management Review, 49(2),
pp.28–35.
30

Gawer, A. & Cusumano, M.A., 2014. Industry platforms and
ecosystem innovation. Journal of Product Innovation Manage-
ment, 31(3), pp.417–433.
Gobble, M.M., 2014. Charting the innovation ecosystem. Re-
search Technology Management, 57(4), pp.55–57.
Jucevičius, G. & Grumadaitė, K., 2014. Smart Development of
Innovation Ecosystem. Procedia - Social and Behavioral Sci-
ences, 156(April), pp.125–129.
Leydesdorff, L. & Etzkowitz, H., 1996. Emergence of a triple
helix of university-industry-government relations. Science
and Public Policy, 23(5), pp.279–286.
Klein, E. & Woodell, J., 2015. Higher Education Engagement
in Economic Development : Foundations for Strategy and
Practice. Association of Public in Land-grant Universities and
University Economic Development Association. pp.1–28.
Klein, R., de Haan, U. & Goldberg, A.I., 2010. Overcoming ob-
stacles encountered on the way to commercialize university
IP. Journal of Technology Transfer, 35(6), pp.671–679.
Moore, J.F., 1993. Predators and Prey: A New Ecology of Com-
petition. Harvard Business Review, 71(3), pp.75–86.
O’Shea, R.P. et al., 2005. Entrepreneurial orientation, technol-
ogy transfer and spinoff performance of U.S. universities. Re-
search Policy, 34(7), pp.994–1009.
Porter, M.E., 1990. The Competitive Advantage of Nations.
Harvard Business Review, 68, pp.73–93.
Porter, M.E., 1998. Clusters and the new economics of com-
petitiveness. Harvard Business Review, 76, pp.77 – 90.
Prince, K., Barrett, M. & Oborn, E., 2014. Dialogical strategies
for orchestrating strategic innovation networks: The case of
the Internet of Things. Information and Organization, 24(2),
pp.106–127.
Roth, B., 2015. The Achievement Habit (HarperBusiness).
Saxenian, A. 1996. Regional Advantage (Harvard University
Press).
Schneiderman, B. 2016. The New ABC’s of Research: Achiev-
ing Breakthrough Collaborations. Oxford University Press.
Smith, T. et al., 2014. Joint Response to OSTP RFI for Strat-
egy for American Innovation, Memorandum to the Office of
Science and Technolgy Policy, Association of American Univer-
sities, Association of Public and Land-grant Universities,
American Council on Education, Association of University
Technolgy Managers, Association of American Medical Col-
leges, Council on Government Relations.
US Congress, Office of Technology Assessment, 1995. Innova-
tion and Commercialization of Emerging Technologies.
Young, J., 1988. Technology and competitiveness: a key to the
economic future of the United States. Science (New York,
31

N.Y.), 241(4863), pp.313–6. Available at:
http://www.ncbi.nlm.nih.gov/pubmed/17734861
32

APPENDIX
Participants
The participants in the workshops included:
• Joseph Allen, Allen Associates
• Amanda Arnold, Arizona State University
• Dorn Carranza, VentureWell
• Duane Dunlap, Purdue University
• Tim Franklin, New Jersey Innovation Institute
• Ann Hammersla, NIH
• Bob Hardy, Council on Government Relations
• Janet Holston, Arizona State University
• Rick Huebsch, University of Minnesota
• Scott Hutcheson, Purdue University
• Barry Johnson, National Science Foundation
• Nicole Kuehl, NIST
• Wiley Larsen, Arizona State University
• Ed Morrison, Purdue University
• Liz Nilsen, Purdue University
• Kate O’Mara, NIST
• Diane Palmintera, Innovation Associates
• Angela Phillips Diaz, University of California, San Diego
• Doug Rand, OSTP
• Rebecca Ranich, Quester Corporation
• Fred Rheinhart, AUTM & University of Massachusetts-
Amherst
• Carmel Ruffolo, Marquette University
• Don Sebastian, New Jersey Innovation Institute
• Jessica Sebeok, Association of American Universities
• Courtney Silverthorn, NIST
• Toby Smith, Association of American Universities
• Tony Stanco, National Council of Entrepreneurial Tech
Transfer
33

• Ryan Umstattd, Advanced Research Projects Agency-Energy
• Phil Wellerstein, VentureWell
• Dave Winwood, Pennington Biomedical Research Center
• Jetta Wong, Department of Energy
• Jim Woodell, APLU
• Marc Wynne, OSTP
• Paul Zielinski, NIST
34

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