The Role, Context and Typology of Universities and Higher Education Institutions in Innovation Systems: A UK Perspective Jeremy Howells, Shu-li Cheng and Ronnie Ramlogan Manchester Institute of Innovation Research, Manchester Business School, University of Manchester

AIMS to investigate how universities/HEIs are perceived within NIS/RIS approaches to review briefly how universities/HEIs have been described and classified over time and how this is related to conceptualisation of universities as NIS actors to analyse UK universities in terms of their role and profile and to classify them using methods of cluster analysis.

Universities and Systems of Innovation/1 Typically in systems of innovation; 4 (or 5) main groups (OECD, etc.) of actors: Firms Universities/other Higher Education Institutions (HEIs) Public Research Establishments (PREs) Not-for-Profit Research Organizations

Universities and Systems of Innovation/2 slightly static flavour - focus was on attributes of actors and less on their interactions (Freeman, 1998; Lundvall, 1992; Nelson, 1993) tended to be firm biased – relegate other actors to supporting status - universities/HEIs and other public knowledge actors only part of underlying infrastructure (Tassey, 1994; Stankiewicz, 1995; Justman and Teubal, 1996) universities uni-dimensional - perform a bridging role for knowledge exchange (Carlsson and Jacobsson 1997 )

Universities and Systems of Innovation/3 recent literatures consider types of interactions within and beyond the boundaries of a national system (role of intermediaries etc Howells, 2006) a wider intermediary function – contribute to forming an ‘ecology’ of influences on other agents (Van der Meulen and Rip, 1998) ‘play an enhanced role in innovation in increasingly knowledge-based societies’ (Etzkowitz and Leydesdorff, 2000)

Universities are different/1 implicitly recognition that universities are different (‘Oxbridge’, ‘redbrick’, new (1960s) universities, former polytechnics (post 1992), etc.) but systems of innovation perspective seen ‘as one of the same’ Universities: no agreed definition HEIs usually provide liberal arts and sciences education and graduate (and sometimes professional) schools legal status and have autonomy to confer degrees in various fields

Universities are different/2 Different Origins foundation v spontaneous (European Universities) Difference in respect to links with industry - two ‘souls’ (Miller, 1995) professional soul – ambivalence, disregard or hostility to industrial collaboration producing class soul – ‘the Humboldt’ influence - encourage research, technology and technology exchanges between with industry `

Universities are different/3 Changing structures internationally HEIs in many countries undergoing reform Different perspectives about whether to have broad based universities or focused vocational HEIs, or on the appropriate mix Binary systems introduced in some countries eg Finland, Austria but eliminated in others eg UK

University typologies/1 Recent UK trends Growth of league tables ranking universities on limited number of variables – HEIs are similar but some better than others Policies about diversity within and between higher education systems and these differences need to be recognised and encouraged Government see universities as one of the last remaining ‘levers’ in national and regional innovation system that can be manipulated 69% of total public expenditure on R&D executed by HEIs in UK (2003 Eurostat/OECD) and rising

University typologies/2 Classifications based on different criteria represent different perspectives on or approaches to understanding a phenomenon. No absolute standard for the “best” solution exists; rather, the value of a classification is closely linked to its intended use. Little interest in typologies in UK until 1960s (Oxbridge and ‘redbrick’ – the binary ‘two souls’ view). In USA with much more variegated system typologies & classifications of more interest: US ‘Carnegie Classification’ (1971, 1973)

University typologies/3 Tight (1988) London; Oxford/Cambridge; civic institutions; technological institutions; campus universities; unclassified universities. Scott (1995) Oxford/Cambridge; University of London; old Victorian ‘civics’; redbrick universities (late nineteenth/early twentieth centuries); new universities (1960s greenfield sites); technological universities & former colleges of advanced technology; newer universities (former polytechnics).

Clustering: Data and Method/1 Population: 174 Universities Data Sources: HE-BCIS and Resources of Higher Education data No of Variables: 13 variables; 10 static measures; 3 change items ( average growth over 3 year period ) Issues considered: Size; Research; Teaching; Third Mission (academic enterprise and technology transfer); Social inclusion and accessibility. Cluster analysis – art or science?

Clustering: Data and Method/2 Cluster analysis a multivariate statistical procedure based on measures of similarity and/or difference of specific variables. “structure-seeking” versus “structure imposing” Somewhat of an art : judgment has to be made about the number of clusters - no formal significance testing Clustering methods always place objects into groups, whether or not the groups are “real,” “natural,” and/or optimal Different clustering methods may produce radically different groups K-means method very sensitive to poor initial partitions - a problem exacerbated by selection of a random initial partition in iterative process.

Results/1 - 7 clusters – several iterations, selected on Calinski index, stability between rounds, changing variables, more even cluster groups, etc. - some anomalies but in general similar sets of universities - did not consider age but Cluster 2 contains a high proportion of post 1992 universities 1. Research peculiars (e.g. Heriot-Watt University) – low overall growth; low research income growth; above average teaching growth 2. Local access (e.g. Bournemouth University, Glamorgan), smaller in size (some exceptions e.g. MMU), high access from low participation neighbourhoods

Results/2 3. Elite Research Growth (e.g. University of Cambridge) 4. London Metropolitan Specialists (e.g. LSE) based in and around London, some similarities with Group 1 but highest income growth, 5. High Teaching Growth (e.g. University of Sussex) high student growth, below average size, above average research income, relatively low research income growth 6. Research Oriented Teaching Growth (e.g. QMC London) generally large, research intensive, enterprise focused, above average student growth 7. Open University

Conclusions/1 Move away from seeing HEIs as a single actor monotype Universities have a wide, possibly growing divergence in terms of their remits and profiles Institutional diversity essential to a healthy and dynamic system of higher education Policy in many countries responding to diversity But “one size fits all” still seem to be highlighted in government policy across developed (and developing) world

Conclusions/2 Diversity issues also related to how senior HEI managers plan the way forward for their respective universities Are they developing, adapting or imitating strategies that are ‘right’ for their universities? Are similar HEIs adopting similar strategies and should they? Are they benchmarking their institutions with the right peer groups and should they do so?

A word of warning Classifications are time-specific snapshots of institutional attributes and behavior based on (period) data …. Institutions might be classified differently using a different timeframe and indeed a different set of variables (italics added) Adapted from: 86