AIMS to investigate how universities/HEIs are perceived within NIS/RIS approaches - PowerPoint PPT Presentation

the role context and typology of universities n.
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AIMS to investigate how universities/HEIs are perceived within NIS/RIS approaches

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AIMS to investigate how universities/HEIs are perceived within NIS/RIS approaches
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AIMS to investigate how universities/HEIs are perceived within NIS/RIS approaches

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  1. 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

  2. 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.

  3. 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

  4. 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)

  5. 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)

  6. 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

  7. 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`

  8. 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

  9. 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

  10. 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)

  11. 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).

  12. 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?

  13. 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.

  14. 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

  15. 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, • High Teaching Growth (e.g. University of Sussex) high student growth, below average size, above average research income, relatively low research income growth • Research Oriented Teaching Growth (e.g. QMC London) generally large, research intensive, enterprise focused, above average student growth • Open University

  16. 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

  17. 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?

  18. 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: