For the purpose of this study it is important to peel the layers back to reveal the core local and academic Key Performance Indicators (KPIs) for this study. Although there has been work done in relations to identifying bibilometrics and citations of singular universities and in the context of the USA; States with publications in nano; little work has been done to look at Swansea University and in particular Wales in the context of the United Kingdom over a period of time. Though Swansea University is located within Wales and that Wales is within the United Kingdom it is important to identify these relevant outputs.

It can be seen in Figure 3 that at the onset during the period of 2002 – 2004 Swansea made up 22% of Welsh and 25% of the United Kingdom publications in “nano” interestingly at the same period of time Swansea University was awarded funding for the Multidisciplinary Nanotechnology Centre. It can also be seen that over the following periods 2004-2006 and 2006-2008 that the journal article outputs in Swansea, Wales and the United Kingdom increased, identifying “nano” as a research growth area. Also within the following period of and 2008 – present there was an increase in the journal article outputs for Wales and the United Kingdom.

Figure 3: Journal Publication Search within Science Direct with key word Search “Swansea”, “Wales”, “United Kingdom” and “nano” over 2 Year Periods from 2002 to present.

In 2004 the NAno and MIcro -TEC SMEs in Integrate Projects and Networks of Excellence (NAoMITEC) was funded by the European Commission with the main objective of the promotion and participation of SMEs in the new instruments of Framework Project 6 with attention focusing on projects referring to nano and micro technologies and their application in key industrial sectors of ICT, Health Care, Aerospace, Transport and Environment.

In 2006 NAoMITEC put out two reports investigated the strengths of micro– and nanotechnology in Europe in a sector specific and country specific breakdown. Within the Country Report and Sector Report: Health it was seen that the University of Wales Swansea Multidisciplinary Nanotechnology Centre were capitalising and taking the lead in Wales.

The NAoMITEC reports highlight current availabilities and capacities within the European Union and identified that nanotechnology opens-up a multitude of new and improved applications in the biomedical field and that these applications would be for diagnostic or therapeutic use. They also highlighted that nanotechnology applications would also cover areas such as tissue engineering, biocompatible implants or bioactive materials. The report also identified low hanging fruit that could be quick wins within current trends as being; further biochip miniaturisation, advances of lab-on-chip applications and for longer periods of imaging techniques enhanced by nano-enabled contrast agents.

The reports went on to say that within previous years many of the start-up companies and SMEs set to work on developing ambitious new products. Yet many of them lack the funds that are needed for the lengthy phase of developments, demonstration and deployment. Therefore SMEs were recommended to concentrate on Research and Technical Development projects as a short-term step promising a sort of early return on investment.

Following a report by the House of Lords Science and Technology Select Committee on Nanotechnologies and Food, January 2009, on nano and food safety, the Department for Innovation, Universities and Skills were asked to respond. In March 2007 the Medical Research Council (MRC) issued a ‘highlight notice’ to encourage applications in nanotoxicology with the aim to inform policy development. The notice proved successful in stimulating a significant increase of applications and that since its launch five awards had been made at a total level of approximately £3 million. The research was focused on better understand the uptake of nanoparticles into cells and the functional consequences including oxidative stress, inflammatory response, cell death and genotoxicity, by linking this information to the physical and chemical characteristics of nanoparticles, predictive models for nanoparticle toxicity can be developed that will help risk assessment.

University of Wales Swansea was highlighted in this report as one of the five awards: “Understanding the genotoxic potential of ultra-fine superparamagnetic iron oxide nanoparticles” (University of Wales, Swansea) - £450k/3yrs, for studyingthe genotoxic properties of iron oxide nanoparticles with the aim to develop high-trough-put screening tests for genotoxic effects; Aims to understand dose-response relationships, to inform future in vivo studies and predictive approaches (DIUS 2009).

Examining the company profile by sector to identify growth trends and structure this focused on employment and revenue, the results of which are as presented above in Figure 4 and Figure 5 Small companies are key indicators of growth sectors (OECD 2005, BIS 2010) interestingly all of the sectors had a higher proportion of small companies with of those firms identifying themselves as Bio-Tech having 85% of its respondents having less than 100 employees. This was followed by firms that identified themselves as Hi-Tech/Telecom with 67% of its respondents has less than 100 employees with both Manufacturing and Other having 58% of their respondents with under 100 employees. Inclusive to this was the 70% differential between the respondents of companies with less than 100 employees and those that had over 100 employees. Where as both Manufacturing and Other had about a 15% differential between companies with less than 100 employees and companies with more than 100 employees. When looking at revenue size by sector of respondents it is interesting to find that within Bio-Tech again took the lead with 98% of the respondents who identified themselves as operating within that sector had annual revenue of less than £10m. Bio-Tech was followed by Hi-Tech/Telecom with 75% of its respondents under the threshold of £10m annual revenue yet the differential between the companies with annual revenue of over £10m was only 50%. Interestingly though was that within the companies that fell outside of Bio-Tech, Manufacturing, and Hi-Tech/Telecom, noted as Other, 57% of those respondents had an annual revenue of over £10m. Looking both at company size and annual revenue it can be said that all three of the sectors identified are growth sectors, with Bio-Tech being clearly in the lead.

Figure 4: Size of companies by number of employees.

Figure 5: Size by companies by revenue.

Bio-Tech with 85% under 100 employees and 92% with annual revenues would be more in the areas of Application and Product Innovation at the stages of Early Market/Emerging Industry where the primary concern is raising funds to engage in early stage R&D, i.e., Application Innovation (creating differentiation by finding and exploiting a new application or use for an existing technology, the cornerstone of solution-oriented marketing (Moore 2005)) or Product Innovation (An innovation type in the product leadership zone that differentiates a growth market position by R&D to improve. (Moore 2005)), these companies are in the development stages and can be there for long periods, as the case is in pharmaceutical developments. These companies generally are high risk high return and often receive first stage investment from “Friends & Family” or Regional Funding Mechanisms. Being at the early stages of development also means that those companies are looking to innovate and seek assistance in several forms. Hi-Tech/Telecom with 67% under 100 employees and 75% under £10m in annual revenue would be more situated at a Process Innovation stage (a next generation of emerging offerings (Moore 2005)). Where as Manufacturing and Other had 58% less than 100 employees and Manufacturing had 68% with annual revenues under £10m, Other had 42% with annual revenue less than £10m and 58% with over £10m annual revenue.

Access to markets is an important factor to any companies no mater its size or annual revenue. Davies & Weinstein 1999 argued the evidence of the importance of increasing returns, in combination with comparative advantage resulting in the importance of market access in economic geography of course included in these comparative advantages are also regulatory factors as is the case for the US and Europe in the form of the Food and Drug Administration (FDA) and the European Medicines Association (EMA). To consider access to markets of the KTN respondent’s areas of operation by sector and size (both employment and turnover) were considered. 100% of all respondents indicated that they operate within the United Kingdom as can be seen in Figure 6 with an interesting saturation of by sectors of the US, Germany, France, Other Western Europe and China with above 50% of most sectors in operation in those regions. Within the respondents of the KTN is the Manufacturing sector that has a higher proportionality of operation within Canada, Australasia, India, Other Asia, Central/South America and Other Eastern Europe. Manufacturing traditionally is more mature in their growth phase and are more financial stability, and has slower growth, with high emphasis on spending and cost control, and less emphasis on R&D and growth strategies (Rudd 2008). These “Cost Control” may well be the reasons of choosing these locations as they historically have a low labour cost. Whereas the Bio-Tech sector, populated with more small companies (Figure 6) has a higher proportionality of areas of operation that have a larger market share and comparative advantage/influence as Davies & Weinstein 1999 and Hanson 2005 argue, and a more traditional decline in areas that are have less of a market advantage/influence.

Figure 6: Overall region(s) operational per company sector.

For the purpose of this study location of operation cannot solely be dictated by sector alone and the dichotomy of company size and annual revenue plays a key factor on a companies abilities. As could be expected and seen in Figure 7 and Figure 8, small companies find it hard to cover more space less geographical cover than large companies. Yet small companies have a higher population in the areas that could possibly give them low cost advantages.

Figure 7: Overall region(s) operational per company size.

Figure 8: Overall region(s) operational per annual revenue size.

Interestingly though looking at areas of operation by annual revenue the companies less than £10m annual revenue are more evenly spread globally. This may be due to several factors that could leverage low cost advantage to them i.e., knowledge hubs and expertise in those locations. University expertise and knowledge, especially in the field of Nanotechnology, is important to the growth of companies and ability to innovate.

As can be seen in Figure 6 and Figure 7 that there a good proportion of both small and large companies spread through the more developed region that have a stronger ability to manage and regulate as would be expected large companies with higher annual revenue have a high percentage of global spread where those with annual revenue less than £10m have a smaller distribution globally. Yip et al. 2006 argues, “Managers of the large British companies need to be competitive internationally” (Yip et al. 2006). Yip et al. 2006 and Osegowitsch 2008 argues that revenues is a metric indicating the ability of a company to work globally, where as they are a better and more stable indicator over time because small enterprises can have very large market capitalisations (Yip et al. 2006, Osegowitsch 2008). Malbert et al. (2003) notes that companies of different sizes tended to do different things in their implementations of their resource planning and that there were differences in the outcomes and benefits attained by the enterprises. This follows suit to the regional importance to the respondents in the KTN questionnaire. In the case of China interestingly the literature argues that the Chinese Nationals educated abroad in technology fields are moving back to China as it is becoming a more Technology Economy (Bradsher 2010).

Whereas both small and large companies operate 100% in the UK it is interesting how of course due to the amount of revenue that the larger companies have they have a higher spread globally. Within the respondents of large companies, USA, France, Other Western European Countries, China and Australasia were populated over 50% by the large companies and followed by Japan, India, and Scandinavia all above 40%. Within France and Other European Countries the reason for operating in those regions may be due to the Regional Funding Assistance that is located within Europe. As for China and India, India has focused their efforts on high technology growth as a means of fuelling economic development, rather than relying only on streams of foreign aid or the more traditional approach to development in “stages of economic growth” typically advocated for developing economies (Parker 2008) as they seek to modernize through heavy investment in industrialization.

When looking at the location of where companies operate on their size Figure 7, the large companies have a higher percentage of wider global spread than the small companies. Large companies being more mature have a tendency in laying out extensive funds for infrastructure look for more cost savings in working in locations where labour cost is low, possibly the reason for the high proportion of the large companies with employees of over 100 employees 44%; and 49% of the large companies identified by having annual revenue of over £10m operating in Other Asia.

Interesting was to find that even with the size of the companies being under 100 people there was a global spread Figure 7; they appreciate the opportunities that the wider world can offer. Quinstas et al. 1997 and Davenport 2005 argues Knowledge-acquisition is one part of knowledge management which, in turn, has been defined as “the process of critically managing knowledge to meet existing needs, to identify and exploit existing and acquired knowledge assets and to develop new opportunities”. Geographic proximity to the knowledge sources with which the organization is collaborating is generally assumed to assist knowledge-acquisition. Much of the advantage of such collaboration is thought to come from efficiencies in collective learning (Belussi 1999, Davenport 2005), particularly for innovative firms. Whether understood as generating economic externalities or spillovers of R&D (Krugman, 1991; Audretsch and Feldman, 1994; Feldman, 1994) or facilitating inter-organisational transmission of tacit knowledge via social capital (Powell et al., 1996), geographic proximity is thought to be important for innovative activity. “Since knowledge is generated and transmitted more efficiently via local proximity, economic activity based on new knowledge has a high propensity to cluster within a geographic region”(Audretsch, 1998). Davenport 2005 states that, “Any exploration of geographic proximity leads directly to studies of successful knowledge sharing clusters”.

“Today’s economic map of the world is dominated by what are called clusters” Michael Porter, 1998

Localisation, regional innovation systems, industrial districts, learning regions, local production systems and agglomeration economies are other labels given to the trend of geographically co-located firms in a value chain collaborating in some way in order to gain efficiency (Rabellotti and Schmitz 1999). The observation of regional cluster economies is not new with most writers referring back to Marshall’s work Principles of Economics, originally published in1890 (Marshall 1986, Keeble and Wilkinson 1999). A rise in the number of studies of industrial districts and small-firm led economic growth in the 1980s combined with the increase in interest in ‘networks’ and social aspects of inter-organisational interaction (Granovetter 1985, Burt 1987, and Gulati, 1999) is the result of renewed activity on the part of scholars in such disciplines as economics, planning, sociology, strategic management, organisational behaviour and business history (Harrison 1991). The seeming paradox of the rise in “importance of local proximity and geographic clusters precisely when globalization seems to dominate the economic activity” has been attributed to the fact that more innovative activity is associated with high-tech SME clusters than with “footloose multinational corporations” (Audretsch 1998).

Definitions of clusters range from those that defer mainly to the geographic collectivity (“geographic concentrations of interconnected companies and institutions in a particular field” (Porter, 1998)) to those that emphasise the knowledge sharing aspects of such groupings: “Firms and organisations involved in clusters are able to achieve synergies and leverage economic advantage from shared access to information and knowledge networks, supplier and distribution chains, markets and marketing intelligence, competencies, and resources in a specific locality”, (Davenport 2005). Yet these respondents as previously identified as SMEs are not interested in certain aspect of access to specific equipment or sector specific material such as access to Biologic material for Biotech companies or “certified” procedures for regulatory approval. Yet they all identified a magnitude of importance of over 60% for access to testing and validation facilities for their products.

Figure 9 presents the importance of facilities and support by Sector. This addresses a range of the sub-hypotheses presented key observations of the response are as follows:

Figure 9: Magnitude of importance facilities/support per sector.

Figure 10 present the importance of facilities/support by Revenue. This presentation highlights the different needs of small and large enterprises. The following are key observations from these data:

Figure 10: Magnitude of importance facilities/support per company revenue.

Figure 11 above presents weighted proportions of “Junior” (Lectures & Researchers) and “Senior” (Readers & Professors) Academic respondents from the Control and TX/UK cohorts. These cohorts involved (24 and 21) academics respectively. It can be seen that there was just over twice as many “Senior” Academics amongst the TX/UK respondents. This point is of note for subsequent sections to give consideration regarding the nature of the respondents including their propensity to engage and effects from their age, experience, etc.

Figure 11: Academic rank.

Figure 12 presents, for the same cohorts, the academic disciplines of the respondents. It can be seen that the majority from both cohorts are based within the Schools of Engineering and Medicine. It can also be seen that the proportions representing each discipline are broadly similar for the two cohorts and wholly represent growth sectors.

Figure 12: Academic respondent discipline focus.

Figure 13 and Figure 14 highlights both the primary and secondary areas of research for both the Control and the TX/UK cohorts. The profiles of primary research areas for both cohorts are broadly similar, with slightly more variety amongst Texas/UK participants. It can be seen that both cohorts show greater diversity in Secondary areas, though the diversity, by both number and split of fields for Texas/UK participants indicates a more multidisciplinary group of academics.

Figure 13: Primary area of research.

Figure 14: Secondary area of research.

Training is an important factor when trying to enshrine the benefit of collaboration in younger researchers. The respondents within the TX/UK Collaborative recognise difficulty in conducting multi-disciplinary training; however they do recognise its importance to enhancing activities, whereas the Control cohort see multi-disciplinary training activities as diluting focus. Overall, the participants within the TX/UK Collaborative see a high level of benefit in teaching the value of multi-disciplinarily amongst earlier-stage researchers (Figure 15).

Figure 15: Training.

From Figure 16 it can be observed that the respondents involved in the TX/UK Collaborative have generally more open attitudes. For example, a significantly greater proportion of TX/UK respondents participate in groupings with the express intention of collaborating. Furthermore, although both cohorts have a propensity to collaborate, those academics from within the TX/UK Collaborative have a higher propensity to value fields outside their areas of expertise.

Figure 16: Collaborative activities.

Figure 17 presents weighted total of companies worked with by each cohort over the past four years, adjusted by size of cohort. The following key observations can be made from these data:

Figure 17: Weighted total number of companies worked with in past 4 years.

Figure 18 represents the weighted percentage of respondents who have worked with companies over the past four years. The following observations can be made:

Figure 18: Percentage of respondents who worked with companies over the past four years.

Figure 19 presents the “the repeat business” phenomenon to investigate whether collaborations lead to further work. The following key observations can be made:

Figure 19: How many of these collaborations at completion have led to follow on research collaborations.

Over the course of the past four years there has been significant investment in facilities across the Swansea University campus. Figure 20 represents percentage of follow-on collaborations from each cohort based on the facilities, which they work within over the past four years. It can be seen that:

Figure 20: Collaborations due to facilities that the respondents work within.

Figure 21 represents project feed-in from past collaborative partners. This explores the phenomenon of referral between actors within a cluster (whatever is geographical boundaries may be). From the data, the following key observations can be made:

Figure 21: Number of project over past four years have come indirectly via partners with whom collaboration occurred in the past.

Focussing solely on respondents from both cohorts based at Swansea University: the questions in Figure 22 have explored whether there exists, or is perceived to exist relevant and effective support and commitment for collaborations at Swansea University.

The following can be observed:

Figure 22: Collaborative resources.

Figure 23 above presents an overview of inter-institutional activities that are occurring within the institution and whether the two cohorts were or are aware of the inter-institutional activities. It can be seen that there is:

Figure 23: Internal inter-institutional activities.

We have described how research culture is important in facilitating the identification and realisation of collaboration opportunities not only within one’s own field(s) but also the field(s) of others. This allows researchers to be able to identify synergies and enhance the collaboration with one’s own expertise and knowledge. Figure 24 demonstrates this phenomenon as follows:

Figure 24: Research ethos/culture.

Figure 25 presents an overview of the cohesiveness of the Control and the TX/UK cohorts, internally, institutionally, and regionally. This is observed within the following:

Figure 25: Cohesiveness.

As can be seen in Figure 26, both group of respondents view their own institutions favourably, yet interestingly there are a slightly higher proportion of the feelings of warmth, satisfaction and successfulness in the Control group, whereas the TX/UK Collaborative respondents found collaboration to be fuller, more exciting, stimulating, productive, facilitating, cooperative and enjoyable than the control group.

Figure 26: Overall impression about the institution.

Interestingly within the TX/UK Collaborative respondents they felt better about themselves as a part of the academic community within Swansea University than their counterparts do, and dramatically not intellectually isolated as the control does (Figure 27). This observation introduces an interesting perspective of the individuals themselves, i.e., did their optimism and integration bring them to the Collaborative, or is it an effect of the process.

Figure 27: Both cohorts feeling as a member of Swansea University the institution.

From the above (Figure 28) it can be observed that:

Figure 28: Magnitude of satisfaction of opinion of collaboration within Swansea University.

The first question asked was to identify the overall number of collaborations undertaken by the Control and “Collaborative” cohort researchers during the three years of the Collaborative. Both groups were given the range of 0 to 10 collaborations to identify, and asked to list their main and most recent collaborations over during Phase II of the Texas United Kingdom Collaboration 2007-2010. The result of this first question shows that during this period the “Collaborative” researchers were involved in 37% more collaborations than the control researchers (Figure 29). Digging further into the data shown below it was identified that one professor and one researcher involved in the “Collaborative” had undertaken 10 collaborations over the past three years.

Figure 29: Number of collaborations.

The second question explored the nature of the academics’ involvement in the identified collaborations. This tested whether they saw their involvement as being:

The results shown in Figure 30, show that while a greater proportion of Commercial Research is undertaken by the Control cohort, when combined with Applied Research the proportion is greater for the TX/UK cohort.

Figure 30: Nature of involvement of the cohort.

Nature of Interest

Both groups were asked about the nature of their interest in the collaboration (Figure 31): this being to explore the specific aspiration in terms of outcome of the collaborations. The general trend across both cohorts is a slight decrease in Commercial and Applied Research outcomes. Compared with Figure 30 it can be seen while there is increased interest in Basic Research outcomes within the TX/UK cohort, this is accompanied by a decrease in interest amongst the Control cohort for commercial outcomes.

Figure 31: Nature of interest by cohort.

What has this led to?

The outputs of academic research were also explored. The most startling of the findings was when the two groups were asked what has their collaborations led to in terms of output. Considering equal numbers of participants in both cohorts, the TX/UK cohort delivered 48% more outputs from their collaborations than the Control group (see Figure 32). This rather crude measure gives equal value to both academic and commercial outputs. However, within the overall findings this trend remains for almost every individual type of output.

Figure 32: What has this lead to: key performance indicators (KPIs).

It can also be seen (Figure 32) that there is a high proportion of publications that are in process by both groups yet there was 44% more amongst the TX/UK Collaborative cohort than amongst the Control cohort.

While the outputs of publications is important to the RAE standing of the university and plays an important role in the amount of research funding the university would obtain after the exercise it is the more relevant economic key performance indicators (KPI’s) of the two research groups which are of interest within this study.

Within this study consultancy was able to be captured and although both groups engage in consultancy activities the TX/UK researchers are 60% more engaged in consultancy activities than the control research group, this could infer that those researchers within research collaborations networks have a greater opportunity to gain consultancy through the network.

Both cohorts also reported a number of spinout and newly formed companies. Within the TX/UK cohort there were 2 spin-out companies formed over the past three years compared with none amongst the control group. Compared with a broader definition of newly established companies (i.e., including spin-in and other routes) the TX/UK researchers founded five newly formed companies to the Control group’s one.

One-to-One Partnerships

Figure 33 on the following page presents the geographical spread of 1-1 partnerships across the TX/UK and Control cohorts.

Figure 33: Geographical of location 1-1 partnership.

From Figure 33, the following can be observed:

• TX/UK and Control cohorts present the equal numbers of 1-1 collaborations across the UK.
• TX/UK cohort is involved in significantly more 1-1 collaborations with partners in the EU and elsewhere around the world.

Multipartite Partnerships

Figure 34 below presents the geographical spread of multipartite partnerships across the TX/UK and Control cohorts.

Figure 34: Geographical of location multi-partite partnerships.

In contrast with the 1-1 partnerships presented in Figure 33, a definite difference in number of partnerships with UK involvement can be seen between TX/UK and Control cohorts. This and other key observations can be summarized as:

• As for 1-1 partnerships, TX/UK cohort is involved in significantly more activity involving EU and Global partners.
• TX/UK cohort is involved in significantly more partnerships involving the UK.

The semi-structured interviews investigated a range of sub-hypotheses through questioning amongst the stakeholder groups. The following sections outline the key findings of these interviews, highlighting areas of alignment and non-alignment amongst stakeholders regarding these issues, together with other observations.

In addition to the specific issues explored through the core questions asked in the semi-structured interviews, a number of participants made comments and observations of interest to the study. These include the following:

Swansea University has already exploited this high-profile network, identifying collaborative research opportunities with Rice University, Baylor College of Medicine, Texas A&M University, University of Texas Health Science Center, and MD Anderson Cancer Research Center. The collaboration with Rice University and “The Richard E. Smalley Institute for Nanoscale Science and Technology” in particular, has enabled Swansea to position itself as a lead institution in Nanotechnology and Bioscience Research, generating true value both intellectually and economically.

Over the past three years Swansea University has achieved the following through its partnership in “The Collaborative”.

The vision of this formal framework collaboration is to be the conduit to preclinical animal studies from Wales, UK and possibly Europe for Texas A&M’s facilities with Swansea University providing access to Human Trials facilities and human biologic material. This will provide a throughput from preclinical to human and access to the US market for companies from UK/EU and conversely for US companies to the UK/EU (Figure 35). Texas A&M’s National Center for Therapeutics Manufacturing would conduct low level therapeutics development for usage in FDA/EMEA preclinical trials and then scale up for FDA/EMEA Human Trials.

Figure 35: Texas A&M University a Swansea University facilities collaboration for preclinical and clinical trials.

Other outcomes include:

A number of high profile visiting speakers were involved in the program:

All of the collaborative Research Bids have given an Induced Investment to Swansea University of ~£10 million.

Huggins and Johnson present the case that in more heavily relied on in economical deprived regions of the UK such as Wales, universities are more heavily relied upon for value generation, both intellectually and through translational discoveries. This means that initiatives such as the Texas/United Kingdom Collaborative which give regional researchers access not only to world-class expertise and facilities, but also to new markets and opportunities for creating the needed value generation. Their thesis therefore suggests that such activities should not only be merely encouraged, but actively supported to realize their wider regional benefit.