Innovation is described more succinctly as the ‘the transformation of knowledge into new products, processes, and services…’ (Porter and Stern 1999) and in the definition provided by the DTI in the Innovation Review as:

“…the successful exploitation of new ideas…”

Information and knowledge (though of varying value and exclusiveness) are relatively abundant. However its potential is limited by ‘the capacity to use them in meaningful ways’ (OECD 1996). The knowledge-based economy therefore applies ‘Innovation’ to turn knowledge into wealth.

Innovation is central to driving up productivity and delivering economic growth. Porter and Stern (1999), outlining how innovation not only provides a mechanism for improving productivity through efficiency, but also creates higher value goods for which businesses (subsequently amalgamated to industries and economies on a national scale) can command higher prices in comparison to the inputs required. If unskilled labour and land are cheaper in Asia and access to markets from these locations is relatively easy then it is through innovation, and the development of higher value-added goods and services that developed nations can compete (Porter 2000).

Innovation has often been approached as a linear process taking an idea through development and production to market, as in Figure 1 (OECD 1996). Each of the phases in this model itself draws upon a variety of disciplines as illustrated in the ‘Innovation Bridge’ representation of Clement (2004) (Figure 2).

Figure 1: ‘Linear’ model of innovation (OECD 1996).

Figure 2: ‘Innovation Bridge’ linear model of innovation presenting disciplines involved (Clement 2004).

Such a model implies that innovation is only ‘initiated’ by invention or discovery (OECD 1997). This sits at odds with von Hippel’s observation that the most important source of innovation is ‘end-user innovation’ (von Hippel 1988) where users’ needs rather than supply side factors drive the development and exploitation of knowledge. The ‘chain-link model’ of innovation by contrast allows for numerous stimuli and feedback to be incorporated from various stages between identifying market potential and actual sale (Figure 3).

Figure 3: ‘Chain-link’ model of innovation (OECD 1996).

Companies including leading multinationals can no longer satisfy their need for innovation internally and are therefore looking outside their own organisations for sources of innovation that will provide future growth. Traditionally, businesses used their own internal resources and capabilities to innovate, and jealously protected their results achieved in what is termed Closed Innovation. However, it has become increasingly difficult for companies to satisfy their innovation needs from internal resources. This has come about as markets become increasingly dynamic and global, disruptive technologies arrive, and opportunities require diverse multidisciplinary approaches – often involving completely new capabilities.

To address this challenge, many large firms have adopted a strategy of acquisition, buying innovative small firms to assimilate into their own product/service offerings. Meanwhile, others have looked to collaborate with partners, including academia, in order to support their innovation activity.

During recent years, collaborative approaches have received increasing interest, particularly within the paradigm of Open Innovation, which not only embraces openness in sourcing of innovations, but also in how they are developed and taken to market. As shown in Figure 4 this Open Innovation approach significantly expands innovation potential by increasing opportunity flow in terms of markets as well as ideas.

Figure 4: A representation of closed (left) and open (right) innovation paradigms (Chesbrough 2006).

Open Innovation is a concept developed by Henry Chesbrough (Chesbrough 2003, Chesbrough 2006) recognising a change in how businesses innovate. The concept is defined by Chesbrough as:

“…the use of purposive inflows and outflows of knowledge to accelerate internal innovation, and expand the markets for external use of innovation, respectively. [This paradigm] assumes that firms can and should use external ideas as well as internal ideas, and internal and external paths to market, as they look to advance their technology.”

As the definition implies, Open Innovation is not only about where companies source knowledge for their own innovations but ways in which they manage innovations that arise which may not fit with the conventional strategy. Examples of both these strands may include licensing in IP to develop, while licensing out IP, which may not fit with the core business.

Chesbrough outlines how the development of this concept is highlighted by the challenges faced by many major companies who are struggling to sustain their innovation performances. To address this they have to look beyond their (often global) internal capabilities and engage in innovation with a variety of partners. Whereas internal R&D could produce sufficient innovation he describes how this has been challenged by ‘erosion factors’ including:

Many of the concepts in Open Innovation are not new. For example, earlier models of innovation describe how ‘firms search for linkages to promote inter-firm learning and for outside partners to provide complementary assets’ (OECD 1996), which ties in with the paradigm described by Chesbrough. Furthermore, the pressure of the Knowledge Economy in challenging hierarchical structures and replacing them with flatter alternatives, often involving semi-autonomous teams is an effect that was apparent before Open Innovation (World Bank 1999).

The challenge for businesses to exploit external knowledge sources while ‘protecting’ their own knowledge is observed by Doring and Shnellenbach (Doring and Shnellenbach 2006) in their work examining knowledge spillovers.

The transition of multinationals to Open Innovation strategies is not only shown by high-profile endeavours such as Proctor and Gamble’s ‘Connect and Develop’ strategy (Huston and Sakkab 2006) but also through observations of phenomena such as “creation of new technological competencies through the international dispersion of corporate activities” (Cantwell and Piscitello 2005), whereby firms seek access to knowledge and opportunities in other localities.

The Procter and Gamble ‘Connect and Develop’ strategy is particularly interesting as it uses an Open Innovation system to provide “more than 35% of the company’s innovations and billions of dollars in revenue” (Huston and Sakkab 2006). Having previously focused on the internal efforts of its 8,600 scientists the company looked outside to capitalise on the 1.5 million who worked elsewhere (Chesbrough 2003).

Above we have introduced the concept of business and technology cycles as developed by Joseph Schumpeter. The concept of economic cycles including those involving technological change has also been developed by other economists such as Kondratieff, who in 1925, drew attention to the ~60 year cycles which bear his name. While his research clearly did not examine modern technologies such as ICT or Genetics, the phenomena he observed from analysis of data relating to the technology sectors of his time can still be used as a basis for contemporary analyses.

In his view, each of these long business cycles was unique, driven by entirely different clusters of industries (Figure 5). Typically, a long upswing in a cycle started when a new set of innovations came into general use - as happened with water power, textiles and iron in the late 18th century; steam, rail and steel in the mid-19th century; and electricity, chemicals and the internal-combustion engine at the turn of the 20th century. In turn, each upswing stimulated investment and an expansion of the economy. These long booms eventually petered out as the technologies matured and returns to investors declined with the dwindling number of opportunities. After a period of much slower expansion came the inevitable decline - only to be followed by a wave of fresh innovations, which destroyed the old way of doing things and created the conditions for a new upswing. The entrepreneur's role, as Schumpeter saw it, was to act as ferment in this process of creative destruction, allowing the economy to renew itself and bound onwards and upwards again (McDaniel, 2005).

Figure 5: Schumpeter’s wave (Copyright: The Economist, 1999).

By the time Schumpeter died in 1950, the third cycle of his ”successive industrial revolutions” had already run its course. The fourth, powered by oil, electronics, aviation and mass production, is now rapidly winding down, if it has not gone already. All the evidence suggests that a fifth industrial revolution - based on semiconductors, fibre optics, genetics and software - is not only well under way but even approaching maturity. This may explain why America shrugged off its lethargy in the early 1990s and started bounding ahead again, leaving behind countries too preoccupied with preserving their fourth-wave industries. If so, then Schumpeter's long economic waves are shortening, from 50-60 years to around 30-40 years.

There is good reason why they should. It was only during the third wave, in the early part of the 20th century, that governments and companies began to search for new technologies in a systematic manner. One of the oldest, Bell Laboratories at Murray Hill in New Jersey, was founded in 1925. Rather than leave the emergence of “new-wave” technologies to chance, all the major industrial countries nowadays have armies of skilled R&D workers sifting the data in pursuit of blockbuster technologies capable of carving out wholly new markets. The tools they use - computer analysers, gene sequencers, text parsers, patent searchers, citation mappers - are getting better all the time, speeding up the process. The productivity of industrial laboratories today is twice what it was a couple of decades ago (McDaniel, 2005).

So the fifth industrial revolution that started in America in the late 1980s may last no more than 25-30 years. If, as seems likely, we are already a decade into this new industrial cycle, it may now be almost too late for the dilatory to catch up. The rapid-upswing part of the cycle - in which successful participants enjoy fat margins, set standards, kill off weaker rivals and establish themselves as main players - looks as though it has already run two-thirds of its course, with only another five or six years left to go. Catching the wave at this late stage will depend on governments' willingness to free up their technical and financial resources, invest in the infrastructure required and let their fourth-wave relics go (The Economist, 1999). Failing that, latecomers can expect only crumbs from the table before the party comes to an end - and a new wave of technologies begins, once again, to wash everything aside (Table 1 and Figure 6).

Figure 6: The Economic wave theories.

The knowledge that is created in the market allows for the cycles to shorten, this is not done in isolation there are other factors such as government and policy that aid or impede the shortening of these cycles.

In the Juglar cycle, that is often called the business cycle, recovery and prosperity are associated with increases in productivity, consumer confidence, aggregate demand and price. In the cycles before World War II or that of the late 1990’s in the United States, the growth periods usually ended with the failure speculative investments built on a bubble of confidence that bursts or deflates. In these cycles, the periods of contraction and stagnation reflect a purging of unsuccessful enterprises as resources are transferred by market forces from less productive uses to more productive uses. Cycles between 1945 and 1990’s in the United States were generally more restrained and followed political factors, such as fiscal policy, and monetary policy.

If one lays the idea of knowledge over this cycle; as the growth period ended and the failures occurred the knowledge of those people involved in the cycle and enterprises utilise knowledge of lessons learned before into the new emerging cycle to create value and opportunities in new cross over emerging technologies and enterprises (Figure 7).

Figure 7: Economic wave theories and innovation knowledge.

The linkages between academia and industry have received much interest over recent years by governments (WAG 2004, Lambert 2003), academics (Nelson 1986, Varga 2000) and other organisations including the private sector, though many commentators observe that it is the private sector that will deliver the fruits of innovation in the knowledge economy (Porter and Stern 1999).

The above studies recognise the importance of universities and academic knowledge in driving innovation and the knowledge economy. Nelson (1986) was one of the earliest to clearly demonstrate the positive effect of university on industry and technological advance, based on research undertaken in the US. This came at a time when American academia was undergoing the start of a seismic shift in technology transfer following the Bayh-Dole Act. This important pieced of legislation is regarded as a paradigm shift in US academia-industry relations for it clarified ownership of IP developed during research, and incentivised and charged universities to exploit its value.

Higher education institutions (HEIs) and public research facilities play a variety of roles in supporting the Knowledge-Based economy including ‘knowledge production’ developing new knowledge, ‘knowledge transmission’ – in developing human capital, and ‘knowledge transfer’ – by disseminating knowledge and supporting industry (OECD 1996, WAG 2004). HEIs are also recognised as important knowledge businesses that are often ‘anchor tenants’ in regional knowledge economies (WAG 2004). The importance of HEIs in supporting knowledge-based industrial clusters in their regions is acknowledged by the UK and Welsh Governments (DTI 2001 and WAG 2003b).

The opportunities and challenges for each region and individual collaboration are unique to its ambition, environment and the efforts invested. This context includes for example: the existing vibrancy of knowledge-based enterprise within the region; the presence of research activity allied to growth sectors; and the mobilisation of collective efforts within the region to develop the initiative.

It is possible to identify key factors that can affect the likelihood and potential extent of success for collaboration. These include individual and organisational factors as well as broader issues such as funding availability. For example, a first weakness lies in the way that the United Kingdoms Research Assessment Exercise (RAE) operates. The Research Assessment Exercise (RAE) is intended to recognise world-class research undertaken with business partners, as well as other forms of academic excellence. In practice, however, the assessment panels tend to concentrate on purely academic benchmarks, such as output in important journals. This may be partly because this kind of output is what most interests the people who sit on the peer review panels. It is also because such work is easier to measure than business collaboration. An article in an academic journal has by definition been through a rigorous process of assessment even before it appears, and can be judged against similar work from other sources. It is much harder to define what constitutes world-class research undertaken with business partners (Lambert 2003).

This bias has an impact on the way that research departments operate. Given the choice between producing an academic paper and working with industry, an ambitious academic is more likely to take the former option: that way lies extra funding for the department, and an increased chance of promotion. The Review came across a number of cases where departments had deliberately decided not to work with business in order to concentrate all their efforts on raising their RAE rankings.

In addition, the importance attached to Quality-related Research (QR) funding has tended to homogenise the research efforts of the university system. Less research-intensive universities invest large amounts of time and money in preparing for the RAE even though they may have very little hope of gaining significant extra funding as a result. Instead of concentrating on their own areas of comparative advantage – which may be of real value to their local and regional economy – they strive to be measured against a world-class benchmark.

Another criticism by business of the RAE is that it fails to give sufficient weight to multidisciplinary research. Because the assessment is undertaken by a large number of panels divided up on the basis of subject areas or units of assessment, it can be difficult to reward work that cuts across different disciplines – precisely the kind of research that is of increasing importance to business.

There are broadly similar concerns about the ways in which the Research Councils operate. One of them, the Engineering and Physical Sciences Research Council (EPSRC), has made a particular effort to develop collaborative projects with business. It says that such work represents around 40 per cent of its current research programmes, up from just 13% a decade ago. Other Research Councils have much less exposure to the business sector, with relatively few active business people on their boards.

There is no doubt it is easier for the EPSRC, which covers the engineering sectors, to develop collaborative links than it is for, say, the Particle Physics and Astronomy Research Council. All the Councils have mechanisms for funding research in collaboration with industry. These include set piece schemes which are often funded jointly with the DTI, such as LINK and Knowledge Transfer Partnerships; network-type projects such as the Faraday Partnerships; funding for joint business university projects; and the financing of PhD students in the workplace (Lambert 2003).

Over the past decade growth in Research Council funding has significantly outstripped the growth in QR funding. The increasing imbalance between the two funding streams has led some observers to question the present dual support system. Business has a real interest in the sustainability of strong university departments, and in public funding which supports creative and innovative research (Lambert 2003).

Universities are being increasingly recognised as a source of ideas for new commercial products and services (Siegel et al. 2003). University research produces new knowledge and builds upon existing knowledge. This makes it valuable for fuelling innovation, through both incremental improvements to existing technology and by major fundamental breakthroughs.

Forms of technology and knowledge transfer that are simple to measure and compare include: contract research; new company spinout; (Di Gregorio and Shane 2003); patenting and licensing activity. Each of these activities is easily numerated, be it by research income, number of new companies founded, patents filed or licenses executed. Studies in many countries, including extensive national surveys, have quantified and analysed these outputs of technology transfer (AUTM 1995, 2005, HEFCE 2003).

Consultancy, Contract Research and Licensing

As described above there exists a host of mechanisms for universities to transfer knowledge to the industrial community. Consultancy can provide businesses with the opportunity to appraise what a university could offer before embarking upon larger research contracts, leading to a different type of interaction, plus it can provide SMEs with university expertise for relatively low fees. Other fields of technology transfer could also benefit such as licensing, where more than 50% of licenses go to companies already known by the academic concerned (Lambert 2003).

The manner in which universities manage their IPR portfolios and anticipate revenues is an important issue. Using a portfolio of patents (patent pooling can be within and between institutions) (Parish and Jargosch 2003) in a targeted manner rather than relying on individual patents is a strategy advocated and applied by the Association of University Technology Managers (AUTM) in the United States. This strategy helps facilitate successful licensing and commercialisation. This strategy also helps balance revenues, as revenues from all patents are not equal. During 2002 only 0.6% of licenses negotiated by U.S. universities (N.B. licences not patents) provided revenues of over $1million (Pressman 2002). When considering the possible revenues it must be born in mind that on average it takes six years to commercialise university research, thereby putting much of the onus of risk and investment onto the shoulders of the licensee.

Management of IP raises many issues before embarking upon the patent application process and searching for potential licensees. The appropriateness of patent protection and to what extent is important considerations along with ensuring freedom to operate. 70% of R&D in the U.S. infringes IPR of another party (WAG 2004), which can place substantial obstacles in the path of continued the development, let alone eventual commercialisation. The importance of the right of freedom to operate in the university case has been highlighted by high-profile cases such as Madey versus Duke University (Guttag 2003) in the U.S. and has led to much discussion about the legal position of educational institutions.

Historically Welsh Higher Education Institutions (HEIs) have engaged in a limited amount of licensing activity with more focus given to development of spin-out companies. However, there have been instances where inventions have been licensed for significant sums. The most notable example concerns a life science technology relating to fluorescence technology used in genetic research, which was licensed by the University of Wales, College of Medicine for £710,000 (WAG 2004).

While licensing activity has been modest other mechanisms such as consultancy have been growing consistently since the mid 1990’s as shown in Figure 10.

Figure 10: Consultancy income of Welsh HEIs 1995-2002 (WAG 2004).

Providing the talent to generate harness and exploit new knowledge and opportunities is critical for the success of a region. The correlation between regional economic performance and the quality of human capital has been clearly demonstrated in numerous studies. (ONS 2004, Work Foundation 2006) The mobility of talent between regions is a key feature in the European Union’s Knowledge Economy Strategy and is underpinned by actions ranging from ERASMUS through to FP7: People. In addition it must be stressed that human capital perspective is equally important with regard to the commercial and entrepreneurial perspectives, as it is to the scientific. It could be argued strongly that this is where the Universities have a major role to play. Do regional education programmes deliver the training that the knowledge economy needs? Is there sufficient business skills development in all undergraduate programmes? Are there policy instruments in place, particularly in the HE system to encourage entrepreneurial activity? Many observers fear that the answer to these questions is a very firm NO but that subject is another matter for discussion. A major US think tank Faster Cures in a recent publication entitled ‘The Critical Need for Innovative Approaches to Disease Research’ (April 2010) observed that a critical issue affecting progress in the traditional academic research system was:

Whatever the situation, for reasons already argued is critical. Other regions have identified the nanotechnology field as the ’next big thing’

The transition from closed to open innovation paradigms is a prime example of the need for cultural change within organisations and amongst individuals in order to harness the opportunities of collaborative, open and multidisciplinary working. Activities such as KTN and KTP aim to support development of such a culture within and between academic and industrial sectors. Building upon the people component as described above the culture of successful regional clusters supports “serial entrepreneurs and innovators”, retaining their talents and supporting the transfer of their skills to and development in to in others.

In essence, for a region and cluster to prosper and maximise impacts of its resources it cannot swim against the tide and must harness opportunities to reinvent enterprises operating in declining sectors and support new firms in emerging sectors. A region must have and intelligent, patient and informed funding infrastructure. So often early stage funding is either absent or very difficult to access. The concept of building value in ideas or enterprises is little understood and hardly ever taught to Science, Technology, Engineering and Maths (STEM) students (Abbey and Lane 2005). This establishes a chasm between the entrepreneur and the source of finance one failing to understand why the financier cannot see how brilliant the idea is and other failing to explain to the inventor why a return on investment has to be calculated and convincingly articulated. The key performance indicators, kpi’s used must be meaningful, so often for example patents filed are used as a metric when all practitioners know that the majority of patents lead to no value and represent a major financial burden. The interplay between economic benefit and the technical knowledge generators is key and must interface using the language of value generated.

Above we described the models of planned versus spontaneous clusters and how a common factor in successful regions is the good governance that facilitates rather than micromanages development. This chimes with supporting the ethos of collaborative, open global and multidisciplinary working, embedding a culture described above. Governance is key to agenda. It is the governance process that sets, protects, sustains and refreshes the vision, mission and core values. It the governance that defines the kpi’s and monitors progress against plan holding the executive to account. More importantly still it is the governance that allows the executive to deliver, protecting it form the ‘short-termism’ an influence so often embedded in the political system due to the priorities imposed by the need to be re-elected. Good governance facilitates partnership and collaboration and mitigates against vision drift.

The generation and development of new knowledge and opportunities is vital to spawn new enterprise through commercialization of academic output, attraction of inward investment and retention of talent. As will be discussed in the next chapter the increasing complexity of science and innovation requires greater multidisciplinary working often requiring collaboration on a global scale. Drilling down into any opportunity in emerging technologies demonstrates a convergence of scientific and technological disciplines, and commercial acumen. There is a need for pockets of ‘world class’ science within the cluster, but also a need to recognise limitations, identify weaknesses and to be prepared to work with others in open, collaborative, multidisciplinary and global partnership.