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Sintesis from: A. Townsend (IFTF), A. Soojung-Kim Pang (IFTF), Rick Weddle (RTF),The Next Twenty Years of Technology-Led Economic Development, Institute for The Future Report Number SR-1236, Palo Alto (California), 2009

This paper is exploring the relationship between economical-technological-geographical trends and changes in learnig and research spaces. The issue of science and technology we are facing today is too complex to be assigned to a single place-function: i.e. university, campus, or research park.

Studies on the knowledge ecosystems are focused on how the community practices interact with the estabilished knowledge systems, and on how the tools and techniques for their updating.
The paper proposal is a link between physical and virtual spaces, macro and micro structures named regional knowledge ecosystems, considered as a new way to include ideas and to promote physical development, that will be explained due to three different scenarios:

  • “Science and Technology Parks 3.0”: A restricted number of international research platforms will have an incremental change thank to the connection with the universities and the bio & pharma firms, and thank to a reach sustainability agenda and  3.0 TLC hightway;
  • “The Rise of Research Clouds”: Digitally connected networks of small spaces will challenge existing parks by providing more collaborative, flexible and less costly spaces, the new "homes for invention";
  • "Dematerialization Innovation": We will see the decline of R&D, University as "ivory tower". The biotech future wil stagnates. This sceario will push R&D in ciberspace and reseach parch will trasform in event spaces.


  • Introduction
  • Trendds Shaping the Futureof Technology-led Development
       Economi & Society      
       Science & Tecnology
       Models & Places for Research & Development
  • Strategic Inplications

A Brief History of Technology-Led Economic Development
2009 marks an important moment in the history of technology-led development. The Research Triangle Park of North Carolina turns fifty, and over 40 parks are twenty-five years old or more.3 As we begin thinking about the next twenty years of change and innovation in this field, it makes sense to review how the movement has evolved and the source of its past successes.
The concept of the science city—a city built from the ground up to house scientific and technical research—emerged during World War II. The speed of technological development demanded by the war effort vastly exceeded any existing industrial R&D capability, and the concentration of existing research centers in cities was a security risk.
As a result, both Allied and Axis powers created massive R&D facilities, isolated far from population centers. The British concentrated cryptography researchers in Bletchley Park; German rocket developers were centered at Peenemünde; and most spectacularly, America’s Manhattan Project built remote complexes dedicated to atomic bomb research and production in Tennessee, Washington and New Mexico.
While the science city model was certainly effective at massive breakthroughs in both basic science and its technological applications, it was frighteningly expensive , and their geographic isolation meant that there were few opportunities for spin-off economic growth. The science city model was later used both successfully and unsuccessful in economies as different as Japan and the Soviet Union. Over time, the notion of science cities as a specific site gave way to “technopoles” as regional concentrations of public and private technological, financial and human capital.5
In the early 1950s, first at Stanford University and later in North Carolina, the science city model was adapted to a more manageable scale. Dubbed “industrial parks”, “research parks” and “science parks” these projects were land-driven strategies primarily aimed at attracting the regional
branch plants of large manufacturing companies. Over time, these places saw a growing share of their tenants engaging in research and development functions. In many countries such as Japan, France and the Netherlands, central governments played a major role in the creation of research parks. In the United States, research parks more often were the result of sub-national governments. Over time, parks have tended to become more specialized, targeting specific industries or sectors.
By the 1980s, the strategic focus of technology-led development shifted from the attract-and-retain model of industrial parks to a model based on business incubation. While technology transfer was an element of the business model behind industrial parks, in incubators it moved to the forefront. The thinking was two-fold: dating companies was a zero-sum game playing regions off against each other, and growing firms locally would be more “sticky” and likely to produce secondary benefits. Beginning with the first known business incubator, established in Batavia, New York in 1959, thousands of incubators opened throughout the world. Today, some 3,000 business incubators exist worldwide, along with thousands of other facilities that perform similar functions under different monikers. The incubator model also marked a shift away from lowering real estate costs as the primary strategy (though rents are still typically subsidized), to providing seed capital, management expertise and intellectual property management needed to grow small companies into big ones. Almost universally, incubators have been positioned around universities, in the hope of leveraging their research and talent.
Today, both the industrial park and business incubator model are widely used. However, in advanced industrial economies these models are less effective as the needs of startups evolve. In their 1991 study of U.S. research parks, Luger and Goldstein found that more than half of all research parks fail or shift their focus. Furthermore they found that “many research parks are unlikely to be appropriate for new start-up businesses, because minimum lots size requirements and high land prices make the cost of entry into parks high.” Yet, existing parks still create great value for tenants, surrounding properties and regions – not because of the business model – but because they have become key nodes in larger knowledge ecosystems. This accrued value is being reflected in the market. For instance, land values at the Research Triangle Park have more than tripled in the last five years.
But, as we illustrate through one of our future scenarios, the next few years may very well be a period in which no significant new research park projects are launched, and some parks fail. Any number of factors could drive this scenario to the forefront – a protracted global recession, aggressive corporate cost-cutting and dematerialization of R&D or a return to high energy costs that put “legacy” parks at a carbon disadvantage.
Signals of this future are already around us, from the endless delays of Russia’s ambitious national technopark project to bubble-era university-based efforts like the Harry Reid Technology Park at the University of Nevada Las Vegas. Even current success stories such as Singapore’s Biopolis have been called into question by the World Bank.
On the other hand, the threat to existing parks could also come not from external economic shifts but from the emergence of entirely new models for building and organizing spaces for R&D. In our second scenario, “The Rise of Research Clouds” - digitally connected networks of small spaces challenge existing parks and by providing more collaborative, more flexible and less costly homes for invention. Again, signals of this future abound if we raise our heads and look at innovative communities outside our own circle of peers.
A third possibility, a very real future for many parks, is incrementalism – evolving and upgrading infrastructure and services to the next version, “Science and Technology Parks 3.0”. Indeed, an upgrade is desperately needed. Cities and metropolitan regions are increasingly seen as the drivers of national economic growth, making it likely that we will see renewed interest in the research park model as an economic development tool. Yet, while this scenario may involve survival and a limited degree of prosperity for some, it does not realize the full potential for innovation and socioeconomic gains that future scientific breakthroughs may hold. It is a likely scenario for many parks in the absence of external threats, but not necessarily the most desirable one.

Towards Regional Knowledge Ecosystems

Despite their stark differences, in all of these scenarios, we find one common element – regions will play a more important role than at any time in the last century. In fact, there will almost certainly be regions in which all three of these scenarios play out simultaneously over the next twenty years, with upgraded research parks, research clouds, and vacant tracts of research parks that never were, all existing side-by-side.
The simple fact is that the complexity of science and technology today is too big for any one campus, firm or research park to tackle in isolation.
The literature on knowledge ecosystems, developed in organizational studies over the last few years, provides robust framework upon which to develop a new understanding of how innovation happens in regions. A knowledge ecosystem refers to the events that occur as codified knowledge is transformed into tacit knowledge over time through learning and experience. Studies of knowledge ecosystems focus on how communities of practice interact with established bodies of knowledge and the tools and practices for upgrading that knowledge over time.
At least one study has explicitly applied the knowledge ecosystem framework to understand a technology region. We believe that this framework can be used by the research parks and economic development community to better understand the processes by which communities of practice, embedded in metropolitan areas, generate “sticky knowhow” that has real, unique economic value that is difficult to copy.
The regional knowledge ecosystem framework has several advantages. First, it focuses our attention not on the existing institutions of economic development  universities, research parks, large companies, venture funds, etc – but on the dynamics of how they interact with each other and new non-institutional elements
(talent, bodies of knowledge, virtual communities). While the economic development field is awash in talk of “networks”, the concept has lost all meaning. A rigorous application of knowledge ecosystem theory will allow us to begin specifying the kinds of networks and how they ought to operate. Second, it brings a holistic approach to how we think of innovation in regions – not as an isolated activity that happens within specific firms or clusters, but as a cohesive system.
Dysfunctional knowledge ecologies are costly to organizations, but in a regional context, they also impose costs on everyone else (if only opportunity costs). Finally, the knowledge ecosystems approach is particularly attuned to understanding how organizations perform in “hyper-turbulent” chaotic environments, which certainly describes the global technological and economic landscape.
Applying a knowledge ecosystem frame to regions immediately yields several insights that may dictate strategic shifts in the way we approach technology-based economic development. First, while land and leased space will continue to underpin the economics of creating research spaces of all kinds, the real added value will increasingly come not just from providing services (as many parks already do), but from actively managing activities and knowledge creation.
Second, as scientific knowledge and tools become available anywhere on-demand, focusing on global domination of any particular industry will lose effectiveness.
Growing the regional ecosystem elements that provide the capacity for repeatedly reinventing the cluster will become paramount. Third, all of these dictate a reduced emphasis on real estate development and infrastructure, and more emphasis on creating mechanisms that link local assets to global markets in ways that generate value.
Our understanding of this tool is in its earliest stages, and will require further development. However, our forecasting and scenario-building exercise points towards a crucial need in every technology region, for new governance structures that are broader than a single industry. Acting as a custodian of the regional ecosystem frame, this body could perform several functions. In the short term, new tools are needed for measuring and mapping networks and flows of knowledge, money and ideas. In the medium term, new business models for managing regional assets and creating something that is great than the sum of its parts. In the long term, the challenge will be leveraging this ecosystem and its many networks to help firms and clusters compete globally – by collectively figuring out where a region fits into global R&D “supply chain”. Their goals will be to encourage knowledge creation at the cutting edge and develop the organizational, human and social capital to compete in the global economy. It would build networks that would stretch far beyond the major regional institutions of today to include informal networks of entrepreneurs,  investors, professionals and hackers and other communities of mentoring and learning.

Economy & Society

The first set of trends examines major global social and economic forces that will set the context for enterprises of every kind.
First, the current global economic crisis will echo for a decade or more, putting governments at the forefront of funding basic science and technology research and constraining big new development projects.
Second, new technologies of cooperation will elevate the economic importance of small groups in elation to corporations and individual consumers. This will transform entire industries and reshape the need for collaborative space. Third, as governments and industries work to address global warming through carbon markets and taxes, the measurement of the economic value of ecological processes will be increasingly important.

From Free Markets to Stimulus Capitalism
The economic crisis of 2009 will turn the tables on markets, putting governments at the helm of the global economy for many years to come. Public investments in basic science and research infrastructure will be used as a primary tool to stimulate both short-term and long-term growth. In the United States, this shift is well underway, and in rising science powers such as the Gulf states and China, recent large public investments in research capacity will at least be are sustained and potentially will expand.

The Group Economy
New tools for cooperation will drive down the cost of forming groups around any shared interest, identity or activity. New models for creating wealth will emerge at the intersection of the social web and grassroots movements. Existing organizations will be transformed through the adoption of these tools and processes, becoming less hierarchical, more agile and more collaborative.

Ecological Economics Comes of Age
As governments and industries work to address global warming by developing environmental trading markets, carbon taxes, and other mechanisms, the measurement of the economic value of ecological processes will be increasingly important.
The first major efforts in this area are around carbon. Today, carbon trading is based on estimation more than on the measurement of real systems. Evolving carbon markets from estimation to measurement will generate complex scientific and technical problems requiring transdisciplinary solutions.
Figuring out precisely how much carbon is sequestered in a particular preserve in Indonesia or Brazil, and how to turn that scientific knowledge into financial instruments will require basic research in botany, ecology, climate science, geology, remote sensing, and even accounting. As a scientific endeavor and information service industry, this will draw upon technological advances in sensing and measurement, simulation and supercomputing.

New structures for  economy

New kinds of organizations will seek “landing spots” for meetings of various kinds – scheduled daily, weekly or monthly meetups, and ad hoc gatherings around interests, ideas and current events. The space and infrastructure demands for these kinds of activities are dramatically different from those supplied traditional research park – less permanently occupied, private spaces and more think-tank type collaborative spaces. The need for temporary, flexible and even mobile spaces will grow dramatically.
The group economy will change the needs of existing organizations’ space as well.
Existing tenants will require more meeting and collaboration spaces, and less space for “warehoused" workers. The goal will be to put collaborative activities in spaces that can amplify group economies, and provide opportunities for discovery. As open innovation strategies spread, there will be greater need for co-locating company employees and outsiders within shared spaces.
Finally, the group economy will place new demands on the measurement techniques traditionally applied in economic development and research management.
Today, most econometric data looks at outputs and uses existing organizations as its units of analysis – the firm, the research park, the region. However, in the group economy, there is are new needs to measure both the flows between organizations – the “in-between stuff” – as well as the dynamics of small groups forming outside institutional boundaries. How do we measure the substantial impact of organizations without organizations?

Science &Technology

Evolving in parallel to trends that will transform the economy and society over the coming decades, the subjects, methods, talent and institutions of scientific research and technological innovation will shift as well.
Five trends will have the greatest impact on technology-based economic development and research parks:
Biology by design will supplant physics as the most scientifically vibrant and economically important field, letting us read and write nature’s “source code” at will.
The spread of ubiquitous computing will create massive new streams of research data, while simultaneously providing new tools for scientific collaboration in the lab.
Social networks where people and computers work together to make sense of data will enable a shift from artificial intelligence to hybrid sensemaking.
New scientists will transform the practice of science by forging transdisciplinary fields, multi-sector careers and bringing new cultural influences to bear.
Science institutions will be transformed as collaborative, open and online models for collaboration and knowledge sharing break through obsolete barriers.

Biology By Design: Nature as Source and Code
From synthetic genomics (which seeks to design micro-organisms that perform useful functions) to stem cell therapy (which seeks to harness the body’s own ability to heal itself), biology will become a central source of scientific and technological breakthroughs. Key drivers include global ecological challenges, the health needs of a richer, aging global population and advances in informatics that help decipher the code of life. Biological concepts about how to organize systems and structures will also inspire designs for everything from buildings to organizations to algorithms. Yet many experts believe the biotech industry is structurally unsound – without change it won’t be able to fully realize the commercial potential of these new technologies.

Ubiquitous Computing
The spread of ubiquitous computing (ubicomp) - the diffusion of unobtrusive digital sensing, computation and communications technologies into everlarger parts of man-made and natural environments - will create vast new datasets for scientific research in fields from public health to civil engineering to marine biology. Mobilizing this computational infrastructures will require intensive collaborations between IT specialists, scientists, and engineers. But once in place, ubiquitous computing technologies will also generate very large quantities of information from everyday activities like travel, shopping, and communications. This will have substantial commercial value to companies that can manage and analyze it quickly enough. More importantly, it will enable new research in social science, public health, and field sciences that will contribute to the further quantification of these fields.

From Artificial Intelligence to Hybrid Sensemaking
For decades, computer science sought to create artificial systems capable of duplicating and even replacing human reasoning and communications. In the last few years, the excitement around collective intelligence experiments on the Web has established the value of a different approach: the creation of hybrid structures that combine social networks and more limited forms of machine intelligence, to collaboratively filter and extract meaning from data about our environments and ourselves. Such systems allow computers and humans to each do what they're good at, and mix together formal and tacit and social knowledge. More broadly, the growth of these tools reflects a more nuanced view of intelligence as an inherently social and contextual thing, not something reducible to computer cycles or logical statements.

The New Scientists

The next generation of scientists will transform scientific practice, the way scientific careers are constructed and managed, and the sources of knowledge they draw upon and develop in their work. As options outside academia grow, publishing becomes more open, collaborative and realtime, and entrepreneurship gains more legitimacy, the means by which scientists create professional reputation will be transformed. These new scientists will be both transdisciplinary and ultra-specialized, drawing on various disciplines to answer complex, focused questions. The role of amateurs will expand, as both independent researchers and partners of professional science. Scientists from emerging economies will introduce non-Western cultural, ethical and intellectual traditions into the practice of modern science. Science will also provide a path for women to achieve gender equality in nations with a high degree of gender segregation.

Science Institutions Transformed
Experiments with new organizational forms, incentive structures, and rewards will shake the foundations of centuries-old scientific institutions. Scientific publishing is already under full-scale attack, its economics and social conventions completely undermined by cheaper, faster, or more democratic online alternatives and by entirely new forms of publishing like video. Privately funded research centers like the Perimeter Institute, Kavli Institutes, and Jenalia Farms are experimenting new ways of funding and organizing research, and measuring the output of scientists. Scientific challenges like the X Prizes are coalescing into a parallel and competing incentive structure for innovation. Finally, the sheer complexity of the scientific challenges of the 21st century will require massive new global partnerships that cross political and organizational boundaries.

New Spaces for the Science: Ubiquitous Space, Mind Space, Biology Space Ubiquitous Space
Ubiquitous computing will collapse many of the distinctions we take for granted when doing everything from designing scientific research projects to designing research spaces. The distinction between online and offline environments, digital and physical worlds, even between natural and artificial, break down. This does not mean that physical places will become irrelevant: instead, the smart deployment of welldesigned digital resources, and the early adoption of new digital technologies, will set smart places ahead of the pack.
On the research front, ubiquitous computing creates opportunities and demands for new forms of cross-disciplinary research.
Because ubicomp creates the potential to blend digital resources with a wide variety of materials and environments, research parks could create value by bringing computer scientists and engineers together with sculptors, textile designers, architects, and anthropologists-- or with craftsmen and workers from established, mature local industries. It will also create a need for hyper-wired, digitally-mapped, configurable spaces that can be used as test-beds for new technologies.
Ubicomp will also create a need for "living labs" like Seoul Digital Media City or Singapore's Fusionpolis that combine vibrant real-world communities with research and prototyping. Ubicomp is as much about the use of technologies as their deployment; having spaces in which users can realistically interact with prototypes or enhanced spaces can generate valuable experiences and insights for researchers, retailers, and designers.

Mind Space
Artificial intelligence sought to make humans obsolete – as a corollary, it would have made place less relevant. But hybrid intelligence relies on a mix of unique places, strong algorithms, and vibrant human networks. Hybrid intelligences require interesting or unique working spaces, workplaces or other infrastructures that facilitate nonverbal communication.
Not only are there opportunities for research parks to provide rich physical spaces supporting hybrid intelligences. Hybrid intelligence could become a distinguishing feature of highly effective collaborative research spaces. By providing infrastructure and “reality mining” services, parks could distinguish themselves and move up the value chain.
Hybrid intelligences often mobilize around very large, uncertain bodies of information. These are too complex and specialized to be usefully analyzed using commercial-grade Internet connections and servers; the grid computing architecture developed for highenergy physics is likely to be replicated in
other fields. Research parks that can provide very fast access to grid-scale computational resources, often in support of groups of scientists or social networks, will have an advantage over less-connected competitors. The growing popularity of publications like the Journal of Visualized Experiments
(JoVE) suggest that a new generation of experimental scientists will see the value of systems that allow them to communicate tacit knowledge at a distance. By employing hybrid approaches to map what people are actually doing in research environments, labs can help codify some of the things that
were previously craft and technique.

Biology space
Synthetic biology may change that, and increase the demand for research space over the coming decades. So-called “white” biotechnology – industrial biotech for producing fuel and materials may just be mundane and scalable enough to produce sustainable profits, unlike earlier generations of “red” (biomedical) and “green”(agricultural) biotechnology. In the meantime, however, the sector will continue to require massive public investment in basic science to jumpstart
economic activity.
Biotech will also diverge from the IT industry in the ways and places in which it clusters. First, the translational nature of biomedicine means that researchers are often moving from lab to bedside frequently, requiring them to be located near research hospitals in large population centers, often
in the center of large cities (versus a suburban research park). Second, it often involves specimens that cannot be removed from the lab – distributed work is less important since much of the “code” is not
portable as in the software world. Third, while IT infrastructure is becoming highly distributed, many of the most advanced biomedical research tools are becoming highly centralized. For instance, the next
generation of high resolution MRIs used in brain imaging research weigh dozens of tons and take up an entire building.13 Finally, biological research will always entail a certain level of public health risk – while many factors cited above point towards centralization, the need to isolate hazardous materials may push in the other direction – some bioresearch will need to be located far from population centers.
The sheer complexity of bioscience will require radically new approaches to designing research organizations. Research at the intersection of biology and informatics, and biology and nanotechnology, for instance, requires bringing together different disciplinary skillsets in the same place or even the same person. Parks and regions that can tap multiple disciplinary centers of excellence, or partner with transdisciplinary organizations and research communities will be well-positioned for biomedical innovation.

Models & Places for R&D

The final set of trends looks at how organizational structures and business models for research and development are changing, and emerging ideas about how to configure these activities at various scales – the lab, the building, the campus, the region and the globe.
Six trends are shaping the future of R&D:

  • A new global map of science is emerging, in which smaller countries are playing an increasingly important role, challenging the Western superpowers’ centuries-long dominance;
  • New models of lightweight innovation seek to do more, faster with less, and cast a broader net for ideas;
  • Universities will continue their transformation from ivory tower to economic engine and play a greatly expanded role in economic development – in time, it could become their primary function, trumping education;
  • Economic development practice will shift from trying to copy the success of others to building sticky know-how – tacit knowledge that builds on local cultural and industrial resources, and isn’t mobile;
  • Greater attention to the social life of small research spaces will create dynamic, transdisciplinary places that bring virtual networks to ground;
  • Regional knowledge ecosystems will emerge as a new strategic frame, providing scale, efficiency and global platforms for economic development.

New Global Map of Science
If science in the 20th century was a pyramid, with the United States, the United Kingdom, Germany, Russia, and Japan at the apex, science in the 21st century will be more like a network, with multiple, linked centers of excellence. Successful countries and subnational regions will pursue strategies to blend targeted investments in basic science with local industrial or cultural resources, to create unique and hard-to-reproduce centers of excellence. These centers will be designed to capture critical niches in complex global R&D “supply chains”.
Meanwhile, the shift from brain drain to brain circulation; the rising capability of moderate Islamic states to support scientific communities; and the growth of new "South-South" collaborative networks mean that these centers of global excellence can develop in a wider range of countries than in the 20th century.

Lightweight Innovation

Over the next decade, new economic realities will increase the pressure to innovate faster and cheaper. New ideas about how to organize the innovation process, combined with dramatically cheaper tools for invention that put advanced research technology in the hands of small firms, will enable new lightweight models for commercializing knowledge.
More and more of new product and service development will happen outside of existing pipelines. Lightweight innovation will reinforce the strategic shift of innovation activities out of large firms into broadly defined “open innovation” networks.

Universities: From Ivory Tower to Economic Engine

Several interacting forces will expand the modernization of universities’ role in the economy. First, increased public investment in basic research will raise public expectations about the social and economic impacts produced by universities. Second, companies will continue to outsource research to university partners, amplifying the need for efficient technology transfer.
Third, global competition between universities will foster more entrepreneurial initiatives to secure talent and find new sources of financial support. Finally, developing countries will rely heavily upon universities to jump-start new technologybased clusters.
There is a great degree of uncertainty about this shift. There is still considerable debate about whether ”universities should Impact portal ( has cataloged over 90 economic impact studies of universities in the US and worldwide, this is largely a defensive exercise by universities seeking to avoid taxation by local authorities, not a demonstration of university vision or public policy.

From Knowledge Diffusion to Sticky Know-How

Advocates of innovation economies often see knowledge as both infinitely mobile and disconnected from its origins. Knowledge can be produced anywhere, this thinking goes, and high value-added, knowledgeintensive activities can be decoupled entirely from manufacturing. Both are wrong.
Many bench scientists can't take their work home, and some can't work outside one-ofa- kind facilities. Innovation often has a geographical or social "stickiness" to it because it can draw on combinations of scientific knowledge, technical skill and tacit knowledge that are place-specific. Nor is innovation so easily distinguished from manufacturing: many high-tech innovations have emerged while solving manufacturing problems, and contrary to popular perception, making things—especially innovative new products-- is a highly complex, creative activity. Indeed, future industries, like the translational research paradigm emerging in the biomedical world, are likely to place a higher value on the tacit knowledge required to move new scientific discoveries from the laboratory to store shelves, doctors' offices, and living rooms.

The Social Life of Small Research Spaces

Traditional business incubators will fade away, replaced by new kinds of spaces for entrepreneurship and collaborative research.
Pop-up labs, co-working hubs, mobile incubators and disposable research parks will provide flexible physical spaces for R&D. Rather than warehousing workers, they will meet a need for communal collaborative meeting space in a world of increased mobility within and between workplaces. They will be neutral places where networks of investors, entrepreneurs, hackers and customers converge for collaborative knowledge creation and trustbuilding, cementing relationships initiated and cultivated online. Overlaid grids of social software will enhance serendipitous discovery inside these spaces and knit them together in local, regional and global networks of collaboration.

From Research Parks to Regional Knowledge Ecosystems
Translational research – science that transcends basic and applied research – and successful commercialization of the resulting technology, will grow increasingly complex. To succeed, these efforts will require coordinated investments at the regional level, because no single organization will have the capacity to perform all of many steps between lab and market. Because of this, we will see an expansion of new institutions and governance structures operating at the regional level whose goal will be to encourage knowledge creation at the cutting edge and develop the organizational, human and social capital at the scale needed to compete globally. These institutions will stretch far beyond the regional networks of today to include not just university and corporate leaders but also entrepreneurs, investors, professionals and amateurs. By their very nature, regional knowledge ecosystems will transcend traditional industry boundaries, seeking to create capacity to quickly re-invest resources and re-invent industries in response to global shifts.

R&D Spaces Science Globalization
Globally networked science will necessitate a shift from zero-sum competition and efforts to replicate Silicon Valley’s broad knowledge ecosystem, in favor of highly
focused efforts to develop niches in global technology supply chains. This strategic shift will be pioneered by new clusters in emerging economies, seeking to be globally competitive at the cutting edge in narrow areas of opportunity.
Global science also means greater mobility of talent. As wage differentials shrink, returning home will be more attractive to foreign students – developed countries will need to offer additional value, such as a better business environment or easier access to startup funding. U.S. universities are responding by exporting their “brands” to developed and less-developed countries. We will also see scientists with global mobility that is more complex than simply moving between two countries – they may migrate
multiple times to emerging centers of excellence.
Finally, global science will create more demand for “soft landing” zones for foreign companies expanding into new markets and joint ventures, which could provide an additional source of science park tenants, as “soft landing” companies outgrow their incubator space. Innovative regions will
need to provide a broad variety of these spaces and market them through existing business networks.

University Spaces
The most aggressive universities will completely transform their promotion
systems, deeply integrating incentives for entrepreneurship. Some universities (such as the University of Iowa and Texas A&M) now identify patents, patent applications and involvement in tech transfer as evidence in tenure review. Some universities are even willing to reward faculty who have proven
their effectiveness in economic development as highly as academic stars. As the share and volume of basic research done at universities rises, technology transfer will either exceed or fail to meet public and corporate expectations. Flaws in prevailing models for managing technology transfer will become more apparent, such as the preference for patents that produce shortterm profits over more challenging longterm commercialization projects. The backlog of research generated by stimulus funding may skew incentives even further in the wrong direction, and leave many promising technologies languishing in the lab.
On the other hand, greater competition between universities will encourage more experimentation by universities in technology transfer and IP management. More universities will develop strategies and resources for supporting other means of promoting commercialization and entrepreneurship than only patent licensing. Others will create internal competition - putting outside agents on equal footing to compete with their own technology licensing office. Still others will partner to create multi-university offices that can achieve a more efficient and effective scale.
The role of research parks, incubators and other facilities for technology transfer will change rapidly. As expectations for technology transfer grow, universities will diversify their strategies for spin-off spaces. This will mean shifting from a single research park model to investing in entire “innovation zones”. In this model, rather than merely developing an urban research campus, universities act as long-term participants in the ongoing revitalization of urban neighborhoods or districts. These districts are mixed-use, combining both academic and commercial research activities with residential, office, retail, and cultural uses. The goal is to create an environment that helps attract, nurture and retain talent, and to encourage innovation across a wide range of other enterprises as well. Extending this strategy, more incubation spaces may be inserted directly into campuses and university buildings.
Entrepreneurial universities are not without their critics. Gary Pisano argues that aggressive commercialization of university bioscience research is actually limiting the industry’s development by reducing the pool of shared scientific knowledge. His solution: “[t]hey should focus primarily on maximizing their contributions to the scientific community, not maximizing their licensing revenues and equity returns."
And there is a clear impact on the academic environment in entrepreneurial universities - when research parks are close by, the curriculum tends to shift from basic to applied research.
Some universities will be unwilling or unable adopt a new model, and will produce limited economic benefits. We are also likely to see the emergence of new universities where economic development, not education, is the primary mission. Most will fall in the middle. As one study summarized the future: “In our view, universities… increasingly have no choice whether to be entrepreneurial, although like for-profit firms, they do have a choice about how they go about doing so.”

Collaborative Spaces

The collaborative magic of small research places depends heavily upon the ability of managers to “produce” and “direct” the space like a “show” on a daily basis. This involves coordinating events, both formal and informal, ensuring a steady flow of new people and ideas through the space, and making connections between participants.
This is a very different set of skills than the typical research park manager or economic developer today. The shift from managing land use and real estate to managing activity (or both) will require a fundamental shift in perspective.
Small research spaces are the physical side of lightweight innovation, allowing big companies and their smaller partners to come into direct contact. As architect Frank Duffy writes, "Conventional office
developments exclude or marginalize workspace at lower rental levels and thus diminish the possibility of mutually beneficial interactions between large, mature businesses and smaller, growing enterprises." Simply moving small bits of the company out of the main campus (like Corning, Yahoo! and Intel have done in recent years) will not be enough. Corporations and startups will need to colocate within the same buildings, forming “coalitions of interest”. Small research spaces, because they lack the
scale of research parks, are heavily dependent upon social networks to extend their reach and connect to external resources. Social networks are the demand generators for these spaces, as online communities develop needs for ad hoc, temporary or on-demand meetings, these spaces will need to develop business models to meet those needs.
The new leasing arrangements of small research spaces – monthly, weekly, daily, and even hourly rate structures – will overturn the supply chain for commercial real estate, which evolved around long-term leases of 10 years or more. As Duffy points out, conventional leases block feedback from users in the design and construction business. By providing direct daily feedback to property managers, research “hotels” might introduce end-user innovation to architecture for the first time in a century.
Many of these small spaces are driven by more than just business objectives. A growing number seek to further social goals by incubating social ventures (Front Seat Software in Seattle, The Hub in London) or by gathering disparate firms and communities in just-emerging sectors like sustainable design (Treehouse Brooklyn).
Finally, small research spaces present an opportunity to make R&D more transparent – engaging not only partners, customers and suppliers, but also a broader public as well. Already, we see many firms engaging lead users through beta tests and iterative design processes – it is only a matter of time before the physical organization of research adapts to support these activities.

Strategic Implications

Here we highlight some broader strategic takeaways that arise from these trends.

Building biomedical places: From Silicon Valley to Biopolis

Too many assumptions about how technology-led development works are based on lessons learned from the Silicon Valley experience. However, these successes have not only proven incredibly difficult to duplicate but are unlikely to be a good model for successfully growing biomedical and biotechnology industries.
More and more we are beginning to understand the fundamentally different nature of biomedical R&D, the current and optimal industry structure, and the needs of growing firms. While a place like Biopolis in Singapore has literally reframed our thinking about how to build a “city of biology”, it has by no means perfected the model. Bio-industrial regions will cluster along very different rules than IT hardware and software did. We have identified several driving forces in this study, but more focused research is needed to understand how location decisions happen in these future growth sectors.

Building responsive universities
As universities become bigger players in R&D and economic development, their relationship with research parks and regions needs to be carefully rethought. On some level, the very notion of a university as solely a center of research and teaching needs to be re-examined.
In our scenarios, universities are among the least adaptive institutions. While universities do routinely respond to market and economic shifts, they do so over very long periods of time. Today, economic development often responds to the needs of universities. For regional knowledge ecosystems to become more resilient, they will need to encourage universities that are responsive to well-articulated regional needs. Structuring these engagements around mechanisms that produce tangible benefits for the universities will be crucial.

Future business models: from products to services

Each of our scenarios point towards a need to develop new business models for technology-led economic development efforts. The first-generation and secondgeneration models in use today are mainly driven by revenue from real estate development, sales and leasing and government subsidy. Potential new models are more likely to be built on venture investments, knowledge brokering and event management. The overall shift will continue to evolve rapidly from products (buildings, sites, infrastructure) to services (research “hotels”, incubation, technology transfer, knowledge commons).

Rewards for grand visions
While the Great Recession may mean the end of big real estate projects, it does not mean the end of grand visions. In fact, it is during the downtime of a recession that the window for long-term strategic planning opens most widely.
Conflicts in large-scale efforts almost always arise from a failure to reach consensus or develop a shared vision early on. So, as a point of beginning, regions need to frame and embed a grand strategy in their thinking. For example, Research Triangle Park served as a primary mechanism for sustaining a much broader grand vision of re-inventing North Carolina’s economy to stem the “brain drain” of young talent leaving for other parts of the country. The park’s business model, and the grand strategy of developing the Triangle region worked together over a period of several decades.

Making know-how sticky

That original grand strategy for the Research
Triangle sought to address that generation’s challenge of a mobile workforce – the “brain drain” migration of educated workers out of the South. But regions and places today face a different kind of mobility – of talent, but also of knowledge.
Figuring out how to create and maintain “sticky know-how” as an immobile asset will be a central challenge for technology regions and research parks.
The first step is simply to assess what your “know how” assets are?
What tacit knowledge is locked up in local manufacturing firms?
How can strategic discussions be focused around core competence that can be upgraded and transformed rather than replaced?

Working at the very large and very small scale simultaneously

As they develop grand visions, and align interests behind them, successful regions are going to need to work simultaneously at the very small scale – unlocking the secrets of small research spaces and finding new ways to scale them quickly and coherently.
Understanding the research cloud requires understanding its overall mass and shape, but also the diversity of its many finegrained parts.
The first step in mapping this cloud will be engaging it. Identifying various elements and players in the cloud will be challenging, but we have identified many new players, groups and elements here – science bloggers, coworking spaces, angel investor networks. These can be the foundation upon which to begin discovery of the truly offthe- radar assets. The challenge will be creating venues and opportunities to bring the cloud out into the open so you can engage them.

Cultivating a regional knowledge ecosystem

Beyond visioning, there are also several possible drivers of new institutions that take on the role of knowledge ecosystem managers at a regional level. As we discussed earlier, in highly successful regions, this role is played by venture capitalists – the ultimate brokers of tacit knowledge in technology-based economies.
In aspiring regions, future ecosystem managers might:

  • Support and coordinate research across a network of “boutique” research facilities;
  • Coordinate research among universities across a region, acting as a broker for national research funding streams;
  • Funding and making available major technology commercialization infrastructure (e.g. wind tunnels, supercomputing centers, etc);
  • Rather than operate venture funds, invest in capacity for entrepreneurship broadly to develop the talent and high-quality startups that will attract private capital as a natural development.

Leadership for the “Long Now”

Regions need a leadership structure that can prepare for the “long now” – an extended view of how today’s actions connect to future outcomes. Just like the massive science projects it will support, building and supporting regional knowledge ecosystems will require sustained, coordinated effort over many years. This is not something that will be accomplished overnight or under the influence or control of any one leadership group. This structure will need to bring about trans-generational hand off of stewardship over the grand vision, to avoid the zigs and zags that kill most plans. It won’t happen accidentally, so it needs to be “designed in” from the beginning.

From managing dirt to managing activity

As research spaces become more collaborative, and the boundaries between firms, between institutions and between individuals will need to be re-designed. Places like the Network Oasis in Joensuu, Finland, are beginning to develop the tools and skills for “serendipity management”.
The notion of planning for chance encounters is counter intuitive, but that is exactly why it is important and why it works. Creating spaces where firms, individuals and small groups can develop new trusted relationships will be an enormous source of value creation.

Re-assessing assessment tools
There is a pressing need across all aspects of the economic development profession to develop better ways of measuring assets and outcomes, and re-thinking just what it is that needs to be measured. As we shift towards more open innovation networks and regional knowledge ecosystems, the most important things to understand will be what happens between institutions. But most assessment tools measure what happens inside institutions. In addition to understanding the scope of institutional activity, we need to map the pipelines of people, ideas and money moving through regions. The goal is to develop a vocabulary for talking about networks in detailed and specific ways, rather than the vague ways we do today.

Developing brands

Because regional knowledge ecosystems will grow increasingly complex and multiinstitutional, brands will become more important, not only in marketing to outsiders but in describing just what people and organizations are doing and inspiring them to new achievements.
Today, not many regions do a good job at brand management. In the future, building a brand as an identity that can describe and communicate the unique value of a knowledge ecosystem will require active cultivation on an ongoing basis. The “grand strategy” discussed earlier can be a powerful tool in testing and maintaining consistent and effective brands.
Brands will be crucially important in attracting globally mobile talent and earning reputation in new group economies.