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= = =** Hierarchies and Technology Commercialization **=

By Alistair Brett

Eric D. Beinhocker in **The Origin of Wealth: Evolution, Complexity, and the Radical Remaking of Economics **, //Harvard Business School Press//, 2006, pp 154 notes that managers are urged to flatten organizations and to de-layer hierarchies for improved efficiency. “But counterintuitively hierarchy can serve to increase adaptability by reducing interdependencies and enabling an organization to reach a larger size before gridlock sets in.”

We are used to the idea of hierarchy as a successive set of subsystems. We have a boss; our boss has a boss, we are the boss of someone else, and so forth. The term hierarchical as used to describe commercialization systems does not necessarily imply that one level is more important than another as it tends to in common usage.

The recommendation to de-layer hierarchies argument has strengths and weakness. It is based on reducing the //numbers// of connections between ‘nodes’ in a network (see figure below). Not everyone needs to be copied by default on every e-mail, not everyone needs to take part in every face to face or online meeting. Amazon’s CEO Jeff Bezos was reported as telling his management team to spend less time communicating; small teams should get on with achieving things rather than constantly checking with one another. Such behavior requires a high level of trust among managers. In turn, high levels of trust have the benefit of also reducing the costs of transactions between network nodes. ==

On the left, each of n nodes is linked to every other one; optimum connectivity. On the right, connecting nodes in a hierarchy produces fewer links. For very large numbers of nodes in extensive networks the number links in a fully connected network becomes n2 and for hierarchies it remains n. = = However, it’s not quite so simple. We know from our own experience that not all connections are equally //relevant// or //efficient//. If I participate in a business meeting about developing new products but my expertise is in managing the company’s IT systems then the connection between me and the meeting group is not //relevant//. Likewise if I participate in the same meeting and I know a little about new product development (plus I probably don’t know what I don’t know) then my presence is not //efficient//. The converse obviously holds in both examples. = = The trick is to achieve the right balance between over connecting people, for example, resulting in inefficient and slow decision making or gridlock, but not allowing too much authority to reside in a hierarchical few. And, making sure connections are both relevant and efficient. = = Hierarchies need to be identified. An illustrative case comes from a small country which, as part of an economic development strategy, set about improving commercialization of research from the nation’s universities. To get started many of the activities described the above paragraph were set up as programs with a focus on training for university Technology Transfer Offices (TTOs) and staff in the basics of technology commercialization. Their focus was on setting up training (a common situation, but not necessarily effective) and mentoring (often neglected, but frequently more effective) for TTO staff. This was a necessary initiative but not a sufficient one. Missing at a higher level of hierarchy was a necessary policy and practical commitment to support TTOs. Only short term contracts were given to TTO staff, there were no incentives or reward structure in place, and TTO staff felt that the university had no confidence in their abilities or dedication. Missing at the lower level were mentors with hands on industry experience. The effort faltered. = = <span style="font-family: Arial,sans-serif; font-size: 10pt;">This story illustrates the importance of inputs from lower levels being a part of a system’s design. Tacit knowledge resided at lower levels where employees often have knowledge and information that management does not. Furthermore, when decisions are made at lower levels those making the decisions are likely to want to implement them.

= Don’t try to tame wicked problems =

<span style="font-family: Arial,sans-serif; font-size: 16px;">By Alistair Brett

//Wicked: Adjective (slang) meaning very good, excellent; “cool”; “awesome” from 13th Century Middle English wikked, wikke, an alteration of wicke, adjectival use of Old English wicca (“wizard, sorcerer”). “Going beyond reasonable or predictable limits.” Or, the Merriam-Webster dictionary’s (nicely understated) “very bad or unpleasant.”//

“A problem with many layers of nested and intractable predicaments,… complex inter-linkages between elements… small perturbations can quickly transform into catastrophic events…” This was how Nepalese citizens viewed the impact of climate change on their country in a 2009 survey of local views.

Innovation ecosystems, as well as climate, have their share of nested and intractable predicaments where inter-linkages are hidden like the layers of an onion. New business creation is linked with leadership; leadership linked with culture; resources are linked with frameworks and policies.

In economic development, especially in developing countries, poverty is linked with education, nutrition with poverty, the economy with nutrition, and so on as described the 2014 book **Aid at the Edge of Chaos**, by Ben Ramalingam. Partly as a result of Ramalingam’s book the global aid community is starting to understand that countries and regions are complex systems, and in turn are made up of sub-complexes, rather than linear modules. In linear systems cause and effect are determinable and typically modeled using Logical Framework Analysis, or ‘logframe’ methods (ubiquitous in the global aid community). The behaviors of complex systems don’t fit into logframes which deal with inputs and outputs and the tasks which produce the latter from the former. A Balanced Scorecard strategy map outlining an organization’s plans to accomplish defined objectives is another example of heavy reliance on cause-and-effect logic as best-practice.

More on Wicked Problems and commercialization in a future blog.

**<span style="font-family: Arial,sans-serif; font-size: 12pt;">Strategies for Engineering Innovation Ecosystems in Messy Environments 2 ** <span style="font-family: Arial,sans-serif; font-size: 12pt;"> By Alistair Brett

**<span style="font-family: Arial,sans-serif; font-size: 12pt;">Is strategic planning possible ****<span style="font-family: Arial,sans-serif; font-size: 12pt;">in messy environments? ** <span style="font-family: Arial,sans-serif; font-size: 12pt;"> There are two questions to be answered: >
 * 1) <span style="font-family: Arial,sans-serif; font-size: 12pt;">How does strategic planning change when the environment is a mix of the certainty and uncertainty, of the predictable, the unpredictable, and the discontinuous, that is, when the environment is a mix of the complicated and the complex, of complex regions and attractor basins, or, in Rainforest language, a mix of farms and rainforests?
 * 1) <span style="font-family: Arial,sans-serif; font-size: 12pt;">By applying what is known about complex adaptive systems how can strategic planning and strategy development enable and sustain innovation in organizations or communities, which are a mix of Rainforests and farms and which may switch between these depending on circumstances? Organizations may have several reasons for developing strategic plans, e.g. for a merger or de-merger. We will focus on plans to improve innovation and innovative capacity.

=
<span style="font-family: Arial,sans-serif; font-size: 12pt;">A good strategic planning process yields good strategies – that is the value of the planning process whether in technology commercialization or more generally. =====

**<span style="font-family: Arial,sans-serif; font-size: 12pt;">The main conclusions which I will justify as this series of blogs continues are: **
<span style="font-family: Arial,sans-serif; font-size: 12pt;"> 1. Conventional strategic planning is not possible except in regions of linearity (‘farms’ in Rainforest language or ‘attractors’ in complex adaptive systems language).

2. Many traditional, hierarchical, sequential, strategic planning methods which were developed for //complicated// production environments, such as factory processes, cannot be applied for strategic planning and implementation in // complex // environments.

3. Applying conventional strategic planning methods may require assuming that a problem which is actually a complex one is not complex and consequently invalid strategic planning methods are used.

<span style="font-family: Arial,sans-serif; font-size: 12pt;"> 4. Planning must be incremental. Therefore strategies matter, not strategic planning which assumes a cause and effect linearity over time.

<span style="font-family: Arial,sans-serif; font-size: 12pt;">In future blogs I will give technology commercialization cases. **<span style="font-family: Arial,sans-serif; font-size: 12pt;">Not strategic planning but planning strategies ** <span style="font-family: Arial,sans-serif; font-size: 12pt;">The notion of strategic plans being useful in dynamic environments seems far-fetched. Strategies, however, when well-conceived can be highly useful. <span style="font-family: Arial,sans-serif; font-size: 12pt;">To generalize, complex or partially complex situations require complex or partially complex responses. This means letting go of the structurally familiar and accepting messiness (including uncertainty, unpredictability) which may be difficult. Messiness does not preclude analysis or as the historian Jacques Barzun put it “Wisdom lies not in choosing between [intuition and analysis] but in knowing their place and limits.”

=
<span style="font-family: Arial,sans-serif; font-size: 12pt;">We can imagine an ideal world where everyone has all information needed to make rational decisions, where we are all perfectly rational beings thinking deductively and where causes have logical and linear connections to their effects….. or, we can try and deal with the world as it is, messy, confusing, unpredictable where we behave as real people subject to the stresses of existence, and make illogical, irrational, decisions influenced by emotions and the culture and norms in which we live our lives. The latter choice means engineering and optimizing innovation ecosystems under such real world conditions. ===== <span style="font-family: Arial,sans-serif; font-size: 12pt;">If strategic planning, like the Balanced Scorecard, is to be applied in complex systems it will be similarly necessary to (i) inspect the system’s cause-and-effect assumptions to see if they model the real world, and (ii) focus on applying the traditional strategic planning methodology within regions of quasi stability (attractors– or farms in Rainforest language) where linear cause-and-effect relationships are more likely to hold. Especially important for strategic planning is to recognize that emergence produces new causal relationships.

=
<span style="font-family: Arial,sans-serif; font-size: 12pt;">A notion of causality based on regularities can only be meaningfully defined for systems with linear interactions among their variables. This implies that we cannot understand causation in complex systems by a process of analysis of variables because complex systems have emergent properties, and a change in one variable will probably affect all the other variables. This further implies that a notion of causation can only be meaningful for regions in which the behavior of a nonlinear system is topologically equivalent to that of a linear system. However, it may be possible to track cause and effect by studying cases which take different paths to the same results. This approach puts emphasis on cases and trajectories rather than variables. Knowledge of how complex adaptive systems behave enables some leading indicators to be developed. Another way of expressing this is that because complex systems are adaptable and can self-repair it is sometimes possible to assume linearity – until the next disruption arrives. =====

** Strategies for Engineering Innovation Ecosystems in Messy Environments 1 **
By Alistair Brett ====<span style="font-family: Arial,Helvetica,sans-serif;">Innovation ecosystems are systems of people usually in organizations behaving as normal non-rational beings, making decisions, experiencing successes and failure, learning, and living. An innovation ecosystem is a complex system of connections and relationships among people and their environment needed to support innovation. We make use of a metaphor to describe these systems and call them ‘rainforests’ (after Victor W. Hwang and Greg Horowitt, The Rainforest: The Secret to Building the Next Silicon Valley, Regenwald,2012) because natural rainforest behave as complex adaptive systems. In more precise terminology these rainforests are complex adaptive systems. This recognition opens up a vast amount of existing knowledge about complexity to help us ‘engineer’ innovation ecosystems. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">It’s unfortunate that “complex” used in the sense of describing a system is the same word used colloquially, if incorrectly, by many of us to describe something which we find difficult to understand or which seems highly intricate or complicated. A critical difference is that “complicated” systems such as the passenger aircraft can be fully modeled, that is, we can deduce the behavior of the whole aircraft from its parts, whereas complex systems are inherently resistant to modeling; a study of the parts may not tell us how the whole will behave. Many of us get this distinction between complex and complicated wrong in our everyday speech. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">Understanding such complex systems helps deliver economic and social benefits: new business opportunities, new product development, workforce utilization, investment, quality of life improvement, and more in a holistic, positive manner; and, most importantly, help to create a robust innovation culture. We shall see in future blogs that the building blocks of innovation ecosystems are constructed of other building blocks, strongly or weakly connected, and these of still other building blocks. If this was not the case and the ultimate building blocks were simple linear systems then stasis would result and there could be no innovation. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">A complex adaptive system does not have a single concise definition – in future blogs I will provide more details of their behavior. Think about it this way: we can imagine an ideal world where everyone has all information needed to make rational decisions, where we are all perfectly rational beings thinking deductively and where causes have logical and linear connections to their effects….. or, we can try and deal with the world as it is, messy, confusing, unpredictable where we behave as real people subject to the stresses of existence, and make illogical, irrational, decisions influenced by emotions and the culture and norms in which we live our lives. This is the complex adaptive systems world. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">The term complex comes from the Latin complexus, which means intertwined. This suggests that a complex system has many components which are interconnected, although with different strengths and degrees of relevance, and difficult to separate. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">For now, let’s just think of such a system as having unpredictable behavior, although some parts of the system may be predictable, and one in which the whole is more than, or different from, the sum of its parts (just as our bodies are more than the sum of individual cells). ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">Our focus is on engineering innovation ecosystems to support economic and technology development and commercialization, but the methodologies and findings discussed here can be applied more generally to enhancing innovation in other settings. ====

<span style="font-family: Arial,Helvetica,sans-serif;">Typical examples of ecosystems designed to support innovation are:
====<span style="font-family: Arial,Helvetica,sans-serif;">Consider the common case of an innovation ecosystem within a community, region, or country which has been engineered to support new business creation, entrepreneurship, and commercializing research. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">Such an innovation ecosystem may have a wide variety of building blocks; its hardware and software. Hardware building blocks probably include companies, universities and colleges, a new business incubator, an accelerator, an early stage investment fund, grants to support proof of concept and prototype development, economic development organizations, a contract research organization, and perhaps a science and technology park together with miscellaneous organizations which were formed for different times but are still functioning. Software building blocks may include a culture of innovation, mentors to coach new businesses, leaders and role models, respect for intellectual property, human resources, education and training programs, and trusted cooperation among all building blocks and the ecosystem’s external environment. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">We shall see as we proceed that a collection of building blocks such as these do not in themselves constitute an innovation ecosystem. A community or organization which has companies, universities, leadership, a culture of innovation, and so forth may believe they have an innovation ecosystem. Maybe yes; maybe no. An innovation ecosystem is created by how the building blocks are connected and interact among themselves and with their environment. Better fit among building blocks correlates with better ecosystem performance. It should also be remembered that building blocks are not static but will change over time. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">Therefore, an innovation ecosystem is not a fixed scaffolding to support innovative activities. It must be adaptable; changing as the needs of what it supports change. It must be sustainable, scalable, and resilient to internal and external shocks. ==== ====<span style="font-family: Arial,Helvetica,sans-serif;">Innovation ecosystems do not have to be fully engineered to be complete. It is better to build a ‘minimum viable product’ rather than to over-engineer and end up with unneeded building blocks. ==== More in my next post **<span style="font-family: Arial,Helvetica,sans-serif; font-size: 10pt;">Strategies for Engineering Innovation Ecosystems in Messy Environments 2 **
 * ====<span style="font-family: Arial,Helvetica,sans-serif;">An innovation ecosystem within a community, region, or country ====
 * ====<span style="font-family: Arial,Helvetica,sans-serif;">An innovation ecosystem within a company or other organization ====
 * ====<span style="font-family: Arial,Helvetica,sans-serif;">An innovation ecosystem within a cluster of organizations ====
 * ====<span style="font-family: Arial,Helvetica,sans-serif;">A cross border or distributed innovation ecosystem ====

**The Tech Entrepreneurship Blog** []

The following was originally published March 27, 2011 at http://www.business-strategy-innovation.com/wordpress/2011/03/accumulating-patents-at-universities/  **by Melba Kurman** Most US universities maintain three core businesses that earn most of their revenue: Since the 1980s, universities have ventured into a new line of business: patenting inventions from university research labs and brokering these patents to businesses and start-ups. Thirty years later, university patent holdings have swelled into the tens of thousands and larger research universities spend millions of dollars each year on filing for new patents. Yet, on average, over three-quarters of university patents are never licensed to companies for commercial use. Since US universities own 5% of our nation’s patents, and a growing number of patents in cutting-edge fields such as nanotechnology and biotech, even on human genes, people get worried that needlessly “locking up” basic university research will stifle innovation and create a patent anti-commons.
 * 1) Selling diplomas
 * 2) Competing for federal and industry research sponsorships
 * 3) Trying to crack open the checkbooks of wealthy alumni

In the terminology of Henry Chesbrough, in the open innovation ecosystem, universities have branched beyond their traditional role of innovation explorers (they generate knowledge) to become innovation merchants (they license their knowledge to other organizations). While not everyone agrees it’s good for society for university’s to own and broker patents, from the perspective of business strategy it makes sense if a university files for a patent at the request of company that plans to license the invention. What’s is harder to understand, however, is what motivates universities to continue to file for patents when there’s no licensee in sight. My question is are not intended to point the finger at the capabilities of university tech transfer staff who adeptly juggle large volumes of very complicated patent paperwork. Nor do I mean to question the effectiveness of any given university’s technology transfer strategy. I am genuinely curious.

Why university accumulate patents is a controversial, complicated and poorly understood topic. The debate around the topic brings to mind the seven blind men and the elephant. If you ask an optimist or a politician why universities continue to invest in unlicensed patents, they’ll tell you that it’s to incent companies to invest in developing a product, to motivate the faculty, and to make sure the university invention gets a fair shot at finding a home in the commercial marketplace. If you ask a pessimistic or someone who’s had a bad experience, they’ll tell you that universities file patents willy-nilly based on the political pull of the inventing faculty member. Or that staff don’t know what they’re doing. Or worse, that universities have become genteel patent trolls, guarding piles of un-used patents and suing companies and researchers that made money on an unlicensed university invention.

Some patent accumulation, perhaps, could be explained by the motivations above. However, the real reason universities end up with largely unlicensed patent portfolios is this: university patent accumulation is an unintended consequence of the inefficiencies inherent in our current university technology transfer model. The current tech transfer model creates a situation in which universities accumulate patents because they patent more inventions than they can license. Then, after a patent is issued, if there’s still no licensee in sight, a university’s technology transfer office is reluctant to let outside parties try their hand at finding a licensee in exchange for commission.

Two indications point to the fact that university patent accumulation reflects underlying inefficiencies in our current tech transfer model: one, data indicates that each year, university file for utility patents independent of that year’s licensing activity. Two, when comparing patent and licensing activity across universities of similar size and resources, the numbers are all over the map. In theory, if a university only filed a utility patent application at the request of a company wishing to license the invention, there would be no patent accumulation. Although some universities report that this indeed, is their patent strategy, in fact, AUTM data from the top 50 US research university says otherwise. Most research universities file new utility patent applications without the presence of a signed license. If it getting patents was indeed a license-driven process, the chart below that depicts the number of utility patent applications vs. new licenses executive should look more like a line. (Other factors may be in play here: part of this seeming random filing could be that a single license covers several patents. Also, since a typical license takes 6-8 months to negotiate, some license completion may be spilling into over into the next year). The only consistently demonstrated patent strategy across US universities was filing a provisional “placeholder” patent on new inventions to buy time to find a licensee and to figure out the potential commercial value and use of the invention. Across the board, most universities file provisional patents on roughly three-quarters of their new invention disclosures. Perhaps since most utility patent applications are not motivated by company request, nor directly related to a university’s three core businesses (teaching, research and alumni donations) AUTM data indicates that patenting activity between university varies wildly, even between universities of similar size, funding and technology transfer activity. (For a clearer picture, I pulled out the California and Texas systems since their research funding and number of new disclosures is significantly higher.) University patent-related activity is all over the map. For example, at leading US research universities in 2009 The fact that universities file for patents without a clear commercial license in sight is not necessarily a bad thing, despite legitimate concerns about the impact of patent accumulation on our innovation ecosystem. Nor is it necessarily a bad sign that across universities file for patents in such widely varying numbers and with no apparent underlying strategy. As patent brokers, universities have the right to take an individualistic approach that reflects their own unique internal logic and is driven by local need and local mission. Extreme local variance could indicate an underlying strategy unique to that university’s areas of research expertise. If patent filings reflect the number of high-potential new inventions that came in that year and are based on sound market research and an intelligent case-by-case basis, then cross-university variance may simply indicate diligence and adherence to a locally-defined patent strategy. At the end of the day, a university’s relationship with its patent portfolio is a complicated one. In comparison, a company’s patent strategy reflects and is a tool to support its larger business strategy (see the fantastic Kauffman-funded report by Ted Sichelman and Stuart Graham). It’s not so straightforward with university patent strategy. The current technology transfer method rests on a model in which universities attempt to be innovation merchants, despite the fact their core business is not brokering IP and the patents they broker have no relation to their core revenue streams. In other words, successfully licensing university inventions is an incredibly difficult task! Patent accumulation is not the fault of under-performing tech transfer offices, nor the malevolent master plan of greedy university administrators or faculty. Instead, it’s much more subtle and complicated than that. Universities simply don’t have the same incentives for filing and hanging onto a patent that companies and startups do. Nor do they have the same resources and business models that permit IP brokering companies to succeed (although even successful IP brokers accumulate patents and regularly go out of business).
 * A few universities filed zero utility patent applications; one filed more than 200
 * Some universities filed 50 to 60 new provisional patent applications, while another university with similar resources filed more than 300 new provisional patent applications
 * The percentage of provisional patents filed per utility patents filed was completely inconsistent across universities. For example, one university filed 145 provisional patents and not a single utility patent application. The same year, another filed 221 utility patent applications and only 134 provisional patent applications.
 * Spending on patent-related expenses ranged from less than a million dollars at one university, to over $16 million at another
 * A hefty patent portfolio does not attract tuition-paying students and does not play a part in faculty advancement.
 * Federal funding isn’t allocated according to universities that own a particular type or certain number of patent.
 * Companies bearing research sponsorships are typically drawn by faculty expertise and university research capabilities.
 * Alumni donate money because they get their name on a bench.

Why do universities own significantly more patents than they can license? Well, for several reasons: We should turn the debate away from the supposed motives (both for good, and for evil) underlying university patent accumulation. That debate barks up the wrong tree and doesn’t provide a solid foundation for stakeholders to rationally think about what to do next. Here’s what comes to my mind: Don’t miss an article (2,400+) – Subscribe to our RSS feed and join our Innovation Excellence group!
 * Being a patent merchant is darn hard work and even the best IP brokers end up with more inventory than they can sell
 * Patents that staff can’t find licensees for are set aside and no longer marketed. Most universities are reluctant to permit third party agents to take over their commercialization process
 * Picking out patents that may have future commercial appeal is nearly impossible given the fact that by design, most university research is early stage and covers a huge range of territory
 * Releasing patents into the public domain or regional IP pools is scary and involves its own legal and political complications;given the fact that patents can costs tens of thousands of dollars, letting patents loose is difficult to fiscally justify
 * Licensing unlicensed patents older than 2-3 years with non-exclusive, royalty-free, no cost, no terms, “go in peace” license may not please some companies, but it may lure others out of the woodwork; again, hard to justify, may invoke resistance from inventors; politically risky
 * Staff are given too many patents to handle; finding the right companies to invest in one’s raw IP is time-consuming and sophisticated work
 * Universities don’t get to choose their patents or develop their portfolio in a particular direction, say becoming a “nanotech specialist; patents get dropped off at the door
 * Universities don’t need their patents to build up their core business; university technology transfer is a sideline at most universities, not a core function
 * It’s not unusual for patents to be pursued at the request of a faculty member, even though there’s no commercial interest in the technology
 * The number of patents issued remains a core “performance” metric at many university technology transfer office
 * 1) Does patent accumulation harm anyone? If so, whom and how?
 * 2) Is getting and hanging onto unlicensed patents worth the university’s time and money?
 * 3) After 30 years of the current model, are universities effective innovation merchants?
 * 4) If you think change is needed, what, specifically would help?
 * 5) Would the introduction of commercial free agents into the university tech transfer process help break up the back log of unused patents?

Melba Kurman writes and speaks about innovative tech transfer from university research labs to the commercial marketplace. Melba is the president of Triple Helix Innovation, a consulting firm dedicated to improving innovation partnerships between companies and universities.

<span style="font-family: Arial,Helvetica,sans-serif; font-size: 120%;">**Blog Posts**

**To add relevant posts from your favorite blogs: In the left side Navigation panel click on "Manage Wiki" then click on "Tools" then "Import Blog Posts"**

The following was originally published June 19, 2007 at http://ifcblog.ifc.org/emergingmarketsifc/2007/06/creating_the_co.html

The financing of small and medium enterprises (SMEs) and equity markets can play a critical role in fostering economic productivity by financing innovation. That's the central premise of a new [|World Bank Group] paper, which discusses the components of an SME-friendly market architecture and links to policies that foster a new class of investable equities.

More than 24 countries in emerging markets operate separate boards and exchanges aimed at SMEs, the paper says. But only a few SME exchanges function properly, providing coveted fresh capital and liquidity. The key steps to building an efficient exchange to provide risk capital for SMEs are addressed in detail.

The following was originally published April 1, 2009 at http://universitydiary.wordpress.com/2009/04/01/commercialising-university-research/

Today I attended a meeting at which I was closely questioned by a businessman along the following lines. What, he wanted to know, had been the benefit to the country of the hundreds of millions invested in university research programmes? How many jobs had been created? How much taxation had been secured for the state? In fact, how much value (if any) was all this research generating? Not particularly waiting for my answers (which I think he felt could only be ‘nothing’ and ‘none’), he said the time had come for the government to stop funding this unaffordable luxury and give the money instead to the IDA (the foreign direct investment agency of the state) so that ‘real jobs’ could be created.

I suspect that this is a beguiling argument for some, but it needs to be stopped dead in its tracks. The problem is, many academic leaders will answer such questions by saying that investment in research is an investment fore the long term, that it cannot yield results quickly, that it creates the right mood for international investors, that product development arising from research in life sciences takes a generation, and so forth. All reasonable points, but they are argument losers in this setting.

Still, we need to make it clear in public debate that the old model of taxpayer investment in economic development is dead: building advance factories, filling them with relatively low level manufacturing, or more recently call centres, paying international companies grants and subsidies to make their investments here worthwhile - all these are yesterday’s game and won’t come back. It’s not that we won’t be able to create jobs any more, but rather that the methods have changed. In the past you could almost calculate that just so many Pounds (or Euros) would create an industrial job. Today we have no idea what the cost is, because it is not a direct process. Most of the high value jobs that we must create in the future will have been made a realistic prospect not by direct grants and subsidies (now more difficult anyway under EU laws and other regulations) but by the steady creation of a knowledge infrastructure that will allow the companies’ own investment to be a productive one.

I still believe that the announcement by Trinity College and UCD recently that their Innovation Alliance would create 30,000 jobs was a bad misjudgement, because it won’t. Even if wholly successful, their Alliance may not create 3,00o (or even 300) jobs. But this alliance, and other initiatives already long under way by other institutions or yet others still to come, may create conditions in which major investments by others will be attractive and will create significant employment. The key issue in this is how we develop, protect and commercialise intellectual property arising out of research. Some research will not in any immediate sense produce IP that can or should be commercialised. But some will, and what this requires is an infrastructure of expertise and assistance that identifies the potential value of research early and puts in place the supports that will move it towards commercialisation, either through an appropriate spin-out corporate structure or the licensing of the IP. All universities and most other colleges now have incubator facilities that have this capacity, and these various facilities now require appropriate inter-institutional coordination (preferably initiated by the institutions themselves) to ensure that they create and maintain value and pool resources, particularly expertise.

We cannot go back to being the Ireland of 1995. That’s gone. We need to ensure that the Ireland of 2015 built on the successful commercialisation of knowledge and research. Without that it will not have recovered its prosperity. And unless businesspeople and politicians of all shades absolutely understand that and work with it we are doomed to long term decline.