The Role, Context and Typology of Universities and Higher
Education Institutions in Innovation Systems: A UK
Perspective
Howells, J1 Ramlogan, R. and Cheng, S-L.
Manchester Institute of Innovation Research, University of Manchester, Manchester,
M13 9QH, UK
_____________________________________________________________________
This paper investigates the changing role and framework of higher education institutions
(HEIs) and assesses this within the context of their classification. The aim of this paper
is threefold. Firstly, to investigate the role of universities as increasingly significant actors
within national and regional innovation systems and to help answer why we need a better
description and articulation of what universities are and do. Secondly, to review how
universities and other higher education institutions have been described and classified
over time and how this has influenced our conceptualisation of universities as actors.
Lastly, these issues are explored in more detail by an analysis of the UK universities in
terms of their role and profile and seeks to classify them using cluster analysis. The
paper concludes by reviewing the implications of the study for future research and
innovation and higher education policy.
Keywords: University, higher education institution, systems of innovation, classification,
innovation, economic development
_____________________________________________________________________
1.
Introduction
University and higher education institutions (HEIs) have undergone profound changes
over the last thirty years. Universities as actors within the innovation system of nations
and regions have also taken on an increasing significance in terms of their role for
innovation, knowledge transfer and economic and social development (Mowery and
Sampat 2005, 212; Charles, 2003; Gunasekara, 2006). This, in turn, has been reflected in
their increased innovation policy profile (Peters and May, 2004, 268-9).
With this
increased recognition has come the knowledge that the role and function of universities,
1
Corresponding author: jeremy.howells@mbs.ac.uk Tel: (44) 1612757374 Fax: (44) 1612750923
1
such as teaching, research, ‘third mission’ activities and other economic, social and
cultural responsibilities, have become more diverse over time. However, although there
has been the growing appreciation that university and other HEIs have often very
different profiles with regard to these functions, it has only been very recently that
researchers have acknowledged that different universities or higher education colleges
will act and interact differently depending on their type. Thus, in the study of universityindustry links, very few studies have sought to recognise or categorise how different
universities may interact with firms, but instead have treated them largely as uniform,
non-variant organisations. Nevertheless a number of studies that have sought to at least
acknowledge these issues (see, for example, Wright et al. 2008, 1210-11) and clearly there
is a growing need to effectively classify universities and other HEIs in some kind of
typology or classification system for their role to be analysed more clearly.
The aim of this paper is therefore to explore these issues in more detail and to outline an
analysis that has sought to help measure and analyse the different attributes of HEIs to
produce a new typology of universities and colleges. More specifically, the paper has
three objectives. Firstly, to investigate the role of universities as increasingly significant
actors within national and regional innovation systems and to help answer why we need a
better description and articulation of what universities are and do. Secondly, to review
how universities and other higher education institutions have been described and
classified over time and how this has influenced our conceptualisation of universities as
actors within innovation systems. Lastly, these issues are explored in more detail by an
analysis of the UK universities in terms of their role and profile and seeks to classify
them using cluster analysis. The UK system has arguably been one of the most dynamic
(Halsey, 1995, 302) and open (Howells and Nedeva, 2003; Ortega et al., 2008) higher
education systems in the world, exhibiting strong dynamics in terms of institutional
diversity (Warner and Palfreyman, 2001) and, above all, one that many countries have
sought to track, if not to adapt and follow (Marginson, 1998). The paper concludes by
reviewing the implications of the study for future research and innovation and higher
education policy.
2.
Universities and Systems of Innovation
The systems of innovation (and technological systems) literature in defining a system of
innovation recognised early on universities as key catalysts of the national innovation
2
systems. Thus, within the early context of the National System of Innovation (NSI)
approach and the related technological infrastructure approach, universities represented a
key actor within the wider system (Freeman, 1988; Lundvall, 1992; Nelson, 1993). The
approach has subsequently been applied at a variety of scales and levels, many of which
have been outside the original focus of a national setting. In this context, Carlsson and
Stankiewicz (1991) developed the ‘technological systems’ approach, which indicates that
systems can be specific to particular technology fields or sectors.
Freeman (1988, 344-6) was the first to attempt to define the concept stressing the notion
of national systems of innovation as the ‘networks of institutions’ from both public and
private sectors and whose activities and interactions initiated, imported, modified and
diffused new technologies. Lundvall (1992, 13) in his distinction of narrow and broad
definitions of a system of innovation focused more in his narrow definition of a system
of innovation on organisations, including here universities. However, over time there
has been a shift back in systems of innovations analysis from the static observation of
actors towards the different types of interactions among actors within and beyond the
boundaries of a national system (Caloghirou et al., 2001, 14).
There are four main groups of actors1 normally identified (see OECD, 2002) in relation
to innovation systems, they are: 1) Firms; 2) Universities and other Higher Education
Institutions (HEIs); 3) Public Research Establishments (PREs); and 4) Not-for-Profit
Research Organizations. These actor categories are however very broad and static
typologies of actors. They exclude a range and diversity of players that is both more
varied and complex than commonly supposed before (Howells, 2006) such as:
intermediaries (innovation intermediaries); Contract Research Organisations (CROs);
other government agencies and bodies; and Knowledge Intensive Business Service
(KIBS) firms, such as consultancy firms. These new and existing actors are important in
providing a rich, diverse and dynamic innovation system. Thus, the decision and actions
of agents within the system are enabled, as well as limited by other agents in the
innovation system (Lundvall, 1992, 10). Diversity between systems therefore occurs,
although within an innovation system, a strong selection process in terms of
technological trajectory or sectoral specialisation may ‘lock in’ a system (Wijnberg, 1994)
and through this inertia actually reduce its variety (Metcalfe, 1995, 29).
therefore, a strong process of cumulative causation at work here.
3
There is,
Facilitating the
emergence of new or different actors is, therefore, one aspect in creating diversity within
(and between) systems (Howells, 2006, 724).
Clearly, therefore, universities are seen as a central part of the actor community of an
innovation system. However, the focus of NSI studies has been preeminently on the
firm; other actors in the ‘cast’ particularly in relation to universities and higher education
institutions and other ‘public’ knowledge providers have been seen as simply supporting
infrastructure. This links in with the notion of ‘technological infrastructure’ developed
by Tassey (1991) and Justman and Teubal (1995) where a range of institutions provides
scientific, engineering and technological knowledge to private companies.
Such
infrastructures are not only characterised by physical infrastructure provision, but also
involve important knowledge elements within the national economy who are
independent and serve multiple users (Tassey, 1991, 347) and who perform a ‘bridging’
role for knowledge exchange (Carlsson and Jacobsson, 1997). More specifically, Van der
Meulen and Rip (1998, 757-8) identify universities as playing a much wider intermediary
function within national systems of innovation and in their contribution in forming an
‘ecology’ of influences on other agents within the innovation system.
Universities,
therefore, represent institutional elements within the technological infrastructure and
wider innovation system both as providers of public knowledge and as educators of
skilled professional, scientific and technical labour. There has been, as yet, though very
little analysis to examine the specific operation, management and institutional
development of these specific actors (Smith, 1997, 96 and 103) especially as the
behaviour of these actors may vary considerably but is poorly understood (Carlsson and
Jacobsson, 1997, 273).
Thus, although the NSI approach and related systems and infrastructure approaches may
provide the foundations of an important framework for examining the role and nature of
HEIs and universities, these remain largely unexplored. As such, universities remain
one-dimensional elements within the system, as supporters and ‘bridgers’ rather than of
interest in their own right. This, more recently has been challenged by, for example,
Leydesdorff and Etzkowitz (1996, 281) who see that universities and governments can
also play a lead role equal to firms. Thus, Etzkowitz and Leydesdorff (2000, 109) see the
role of universities in the system increasing so that “…. the university can play an
enhanced role in innovation in increasingly knowledge-based societies.”
4
3.
The Role and Function of Universities within Higher Education Systems
3.1
Background and Context
Although the system of innovation literature has highlighted the important role of actors
within systems, they have not developed the role or notion of actors more fully. In
particular, there has been a lack of identification of new actors and, secondly, little
discussion or analysis about the internal variety within major actor groups within the
systems of innovation approach2. In the context of the former issue, universities are part
of wider innovation ecology or system where new sets of actors, such as quasi
government agencies, public-private partnerships, consultancy companies and innovation
intermediaries may be emerging and which provide an interface between universities and
other types of actors. These new actors that have emerged around the interface of
universities and these other agents are likely to have a great impact on the development
of universities and their wider role within the wider economic, social and innovation
system (Tether and Tajar, 2008, 1092-3).
There is also a size issue here, larger
universities may internalise some of the functions that these intermediary organisations
provide; however, by contrast, smaller universities may find these intermediary
organisations providing very useful services which support a range of their research and
third mission activities.
In the latter context, there is a need to better understand the internal variety within the
main actor groups and universities are no different from other actors here. Although
there have been a few exceptions, most of the discussion and analysis of universities, in
relation their management, internal development and their wider role within an
innovation or economic system, tends to view university and higher education
institutions as largely homogenous entities; in short, a university is a university. The
focus of this paper is obviously on this latter issue, but the issue of how universities
relate to other actors remains important (Tether and Tajar, 2008).
However, what is particularly important here is that not all universities will be expected
to act the same; different universities will act differently. Without recognising these
variations in the types of universities we are also likely to ignore potential differences in
how they act and interact with other actors and their local environment.
This is
important not only for policy but also the (internal) strategies of the universities
themselves.
5
3.2
Universities: Definition and Evolution
The origins of universities, at least in Europe, are many and varied: some were
specifically founded through the efforts of emperors, kings and bishops (‘foundation
universities); whilst others grew up on ad hoc basis via pre-existing scholastic and cultural
nuclei (‘spontaneous universities’). However, once established, their growth depended
not just on the levies imposed on their students, but also via benefactions from wealthy
merchants and guilds (as many of these universities and colleges were centred on
religious education and training). Universities have been variously described, but they
can be broadly defined as institutions of higher education, usually provides liberal arts
and sciences education and graduate (and sometimes professional) schools that are legally
allowed and have autonomy to confer degrees in various fields. A university differs from
a higher education college in that it is usually larger, has a broader curriculum, and offers
advanced postgraduate degrees in addition to undergraduate degrees, although on the
margin these differences may not be large. This formal and rather reductionist view of
universities, however, ignores a much more fundamental, but difficult to articulate ethos
(Duderstadt, 2000, 3), that most, if not all, universities hold, namely that they represent
have a special responsibility and trust beyond the moment and this has remained a
constant part of the process of institutional self evaluation and internal debate (Rothblatt,
1997, 12-13). This is also reflected in another quality that is evident about universities,
namely they are very enduring organisations, with very few failing or dying (unless for
very specific reasons3). Their main reason for disappearing as entities is because of
mergers with other universities. The higher level notion or soul of a university described
by Rothblatt (1997) however ignores another more practical foundation and remit of
many universities established across Europe in the nineteenth century. In this respect,
Miller (1995) has described the two ‘souls’ with regard to universities, especially centred
on their links with industry. The first soul, the professional ‘soul’ is associated with
ambivalence, disregard or outright hostility towards industrial collaboration within higher
education (Lindner, 1993). However, the nineteenth century saw the establishment of
the new set of universities across Germany, and subsequently Britain and the other parts
of Europe, which were based centrally on Wilhelm von Humboldt’s influence. These
‘new’ universities sought to establish more practical and vocational teaching regimes
within the university system and where the principle of industry and academia working
together both for scientific and technical advancement in an academic sense, but where
the benefit to the local industry was also recognised (Driver, 1971). What might be
6
called this second generation of higher education institutions formed the second ‘soul’,
the producing class ‘soul’ (Miller, 1995) that sought to nurture and encourage research
and technology exchanges between industry and higher education.
These new,
vocationally oriented universities were based on different national articulations.
In
Britain, Driver (1971, 42) specifically sees the origins and core task of British, ‘second
soul’ civic universities were to provide a technically educated workforce. However, in
the United States, Geiger (1996, 190) sees such developments which were instigated by
the Morrill ‘Land Grant’ Act of 1862 as focused on yielding a much narrower agricultural
and engineering curriculum which specifically sought to teach these practical subjects
“without excluding other scientific and classical studies.” However, what the 1862 Act
did, and indeed what was occurring in the civic universities of Britain, was to help create
a combination of utilitarian education with scientific and classical learning (Jones 1988).
Lastly, there has been the recent rise of the more corporatist vision and approach in
universities. In this way, Denham (2005, 17) stresses that “universities have also become
increasingly business and customer-oriented, resulting in a transition from collegial
decision-making to a kind of corporate management.”
It is, therefore, important to recognise that the raison d’etre and hence definition of a
university has changed over time and can vary significantly between national jurisdictions
and institutional set up (see below).
Clearly at present, there is no one accepted
definition of a university, but in many ways, the fact that there in no universal definition
may be perceived as positive in the sense that institutions of advanced learning are
related to specific audiences, territorial jurisdictions, discipline-specific scholarship and
various types of research (Denham 2005, 16-17).
3.3
The Changing Structure of Higher Education Systems
Institutional status and history play a significant role in influencing the patterns of
interaction a higher education institution may have with industry, but this is being
clouded by wider changes in the nature of the system, in particular whether institutions
are becoming more homogenised or differentiated over time. Thus, the U.S. continues
to have a highly diverse system, as does Canada (Jones 1996), whilst the differentiation
appears to be increasing in Finland, but declining in Australia and the UK. This can be
seen in the differing policy approaches towards the binary system of higher education,
7
with broad-based universities on one hand and more focused vocational higher
education institutions (with varying titles to denote their different status). Thus, the UK
abolished its binary system in the early 1990s and this was followed by many other
British Commonwealth countries, but most notably Australia, who have also moved back
to unitary systems (Denham, 2005, 13) and more recently Hungary. However, at the
same time the Netherlands has sought to maintain the binary system and wants more
institutional types to emerge within that framework, whilst Finland and Austria have only
introduced the binary system over the last decade. As Meek et al. (1996, 216) have noted
such shifts appear to go, almost randomly, through cycles of peaks and troughs.
For those countries shifting back towards a more blurred unitary system, such as
Australia, the lack of differentiation between institutions, with resulting mission
convergence and institutional isomorphism, is seen as a justification for new reforms
(Scott, 2004; Moses, 2004). In the case of the UK, this blurring process is leading to a
new search for effective forms of diversity, including a renewed focus on the teaching
mission of higher education institutions, as exemplified by the Leitch Report (2006).
For universities in Europe there is another factor that is shaping national higher
education systems and that is the Bologna Accord. In short, the Bologna Accord was
signed in 1999 initially by France, Germany, Italy and the UK and committed these
countries to a reform of their higher education systems centred on a common BachelorMaster structure by 2010. The Bologna Accord is certainly encouraging convergence in
institutional forms in higher education between countries, although paradoxically this
convergence process is leading to increased diversity of higher education institution
forms within countries, such as France, Germany and the Netherlands (Witte et al.,
2008). As such, there is increasing instances of functional overlap between types of
HEIs as they seek to offer similar bachelor and master programmes. Lastly, within a
European context, the European Commission has also been advocating increased
diversity, as a condition for excellence and increased access.
Thus, the European
Commission (2005, 3-4) in reviewing the European higher education system has stressed
the need for greater differentiation between institutions to overcome bottlenecks and
lack of coverage within the system in relation to the needs of learners and the drive for
world class centres of research excellence. Insufficient diversification, the tendency of
promoting uniformity and egalitarianism, is seen by the Commission as a bottleneck for
8
including a wider range of learners and for achieving world class excellence (van der
Wende 2006, 7).
4.
A New Taxonomy of Universities: A UK Perspective
4.1
Classification of Higher Education Institutions
There have been seemingly two contrasting trends when comparing HEIs nationally and
internationally.
The first trend has been the growth in league tables, which rank
institutions on a usually limited number of variables and which imply that all higher
education organisations are basically similar (Marginson and van der Wende, 2007;
Sanoff et al., 2007), but some are ‘better’ than others. The second trend has been
associated with policy, recognising that there is diversity within and between higher
education systems and these differences need to be recognised and encouraged. League
tables and their analysis suggest that all HEIs are similar and that all HEIs can be
generally seen as mono-types (all HEIs are basically the same); on the other hand,
diversity suggests some kind of classification system is required to reflect this variation.
Classifications of universities and other forms of HEIs have been around since the mid
nineteenth century, if not earlier. In the UK the emergence in the nineteenth century of
the universities of London and Durham and the federal universities of Victoria
(Liverpool, Leeds and Manchester) and Wales to challenge Oxford and Cambridge and
the five long established Scottish universities and the need for some kind of distinction in
ethos and remit (Section 3.2). Similarly the emergence of Land Grant colleges and
subsequently extension colleges also led to diversity and the need for some kind of
categorisation (Hall-Quest, 1926).
Work on the classification of HEIs in earnest began in the early 1970s. This was
epitomised by King’s (1970) work in the UK and in the US has been the development of
the Carnegie Classification. More recent work has also emerged in such countries as
Australia (Marginson, 1998, 87-90) and in Europe more generally (van Vught et al.,
2005).
The Carnegie Classification emerged from two publications: the first ‘New
Students and New Places’ contained a simple classification framework and was followed
in 1973 by the more substantive report entitled ‘A Classification of Institutions of Higher
Education’. The aim for the Carnegie Commission was to help educational researchers
differentiate between the wide varieties of higher education institutions within the United
9
States higher education system and the classification system was centred on the basis of
the HEIs’ research and teaching objectives, the degrees offered they offered and their
size and their comprehensiveness.
It enabled researchers, and subsequently the
institutions themselves, to compare their practices and performance with their more
direct peers. The classification went through a number of iterations, and the fourth
version, the 1994 Classification, for examples, identifies the following categories: research
universities I; research universities II; doctoral universities I; doctoral universities II;
master’s (comprehensive) colleges and universities I; master’s (comprehensive) colleges
and universities II; baccalaureate (liberal arts) colleges I; baccalaureate colleges II;
associate of arts colleges; and, specialized institutions.
In the U.S. the Carnegie Classification has remained a key taxonomy for categorising
institutions operating in the US higher education and has undergone further revisions,
the latest being in 2005 (McComick and Zhao, 2005, 56). However, elsewhere there has
often been less consensus. Thus, on the basis of his analysis, Tight (1988) identified six
categories of universities within the UK, namely:
1)
London;
2)
Oxford and Cambridge;
3)
civic institutions;
4)
technological institutions;
5)
campus universities; and,
6)
unclassified universities.
Some years later Scott (2001) used a similar categorisation of HEIs in England, but this
time in five main types:
1)
Oxford and Cambridge;
2)
the University of London;
3)
the old Victorian ‘civics’;
4)
the redbrick universities founded in the late nineteenth and early twentieth
centuries;
5)
the new universities built on greenfield sites during the 1960s;
6)
technological universities and former colleges of advanced technology; and,
10
7)
the newer universities (former polytechnics).
As Scott (2001) admitted, his typology was heuristic and based on history. Others see a
clear distinction between two basic types of classification; namely, a top-down approach
and an institutional driven process. Firstly, those that take a ‘top down’ systems view are
often driven by government policy and distinctions often made within a legal framework
or perspective; most strikingly seen in the binary distinction seen in many European
higher education systems. The second type is based on the institution’s behaviour and
categorises the institutions on similarities and differences, related to an institution’s
attributes and how institutions identify themselves (Duderstadt, 2000). Being able to
classify HEIs on this latter basis depends on the availability of data relating to an
institutions’ profile and some kind of analytical framework, which only a few studies
have sought to undertake (King, 1970; Dolton and Makepeace, 1982; Tight, 1988;
Bonaccorsi et al. 2007; Bonaccorsi and Daraio, 2008). In practice, making a distinction
between these two basic types is difficult as there is often a blurring of approaches.
Many of the key issues and problems associated with developing a robust classification
system for HEIs are explored below in our analysis of the UK higher education system
using cluster analysis. On a more fundamental level, associated with organisational
research in other areas (see, for example, Carper and Snizek, 19780; Rich, 1992; Doty and
Glick, 1994), it can help define or redefine what universities are and how they are shaped
and also influence their external task environment.
4.2
Data and Cluster Methodology
The data used in the analysis were drawn from the latest higher education-business and
community interaction (HE-BCI) surveys and the Resources of Higher Education
Institutions. The former is an annual survey starting in 2001 and is managed by the
Higher Education Funding Council for England (HEFCE). The objective of the survey
is to collect data on a wide range of third stream activities, which capture the
contribution of HEIs to the economy and society. The latter is managed by the Higher
Education Statistics Agency (HESA) and provides data relating to academic staff and the
finances of UK universities and Higher Education colleges.
A series of variables were chosen to help identify and distinguish the 174 universities
used in the survey. After a series of iterations thirteen variables were finally selected to
11
gauge a variety of organisational dimensions that were seen as being important in
defining the nature and role of universities (Table 1) and also revealing the
multidimensional nature of (Rich 1992, 760) and complex multiple characteristics of such
organisations (Doty and Glick, 1994, 244). These variables can be grouped under five
main areas relating to aspects associated with, trying to reveal the comprehensive nature
of higher education institutions (Rich, 1992, 764):
1.
size;
2.
research;
3.
teaching;
4.
third mission (academic enterprise and technology transfer); and,
5.
social inclusion and accessibility.
---- Table 1 about here ----
To provide some measure of change within the cluster profiles three out of the thirteen
indicators measured change over a three year period, 2003-04 to 2005-06. All the
variables were standardised to have zero mean and unit variance to ensure they have
equal weight in the calculation of distance in the algorithm applied in cluster analysis.
For the explanatory purposes, scatter plots for each pair of variables were used to
examine whether there were clusters among the universities. The graphs showed that
Open University as a cluster itself. Moreover, despite the fact that there was a bulk of
universities had close proximity, there were a few discernible groups.
Cluster analysis is a useful exploratory tool for finding groups among observations and it
is relatively easy to apply in data analysis. However, different clustering algorithms have
their advantages and disadvantages. For example, the k-means algorithm, which was
used in this analysis, has the advantage that it always provides results for each run of
analysis.
However, there are few disadvantages of the k-means analysis, which need highlighting.
Firstly, it does not necessarily reach the optimal grouping. To reduce this risk the
analysis was run many times. Secondly, k-means algorithm randomly chooses the cluster
centres and as a consequence the resulting grouping sometimes varies. Thirdly, the
results are probably influenced by the metric of the variables. To reduce the influence of
12
different measures, the variables were standardised to ensure each of them had equal
contribution.
However, despite these limitations and in order to take account of all the available
characteristics, k-means cluster analysis was selected to carry out for further examination
of the dataset. Although the number of clusters can be pre-determined in the k-means
algorithm, a wide range of possible number of clusters was run (2–20) to help us to
choose the optimal result. This was done by employing the Caliński stopping rule (see
Caliński and Harabasz, 1974) in each run of the analysis. The Caliński and Harabasz
index calculates the ratio of total variation between clusters versus total variation within
cluster. In other words, the higher the value, the more distinct the clustering will be as
the differences among clusters are larger than the differences within clusters (and by
contrast smaller values indicate less clearly defined structures).
However, the
configuration with the highest Caliński score (Caliński Harabasz pseudo-F score 32.67)
gave only three clusters and was not considered to be sufficiently disaggregated enough
for analysis. Instead the next highest score of 25.36 was selected and this provided seven
clusters.
4.3
Cluster Results and Analysis
On this selection basis, the seven cluster groups have the following characteristics:
Cluster 1 - Research Peculiars: These HEIs are large (with the exception of the Institute of
Cancer), international, and highly research-intensive with high knowledge exploitation
and enterprise orientation in terms of intellectual property (IP) and consultancy income
generation. However, these institutions exhibit low overall growth and very low research
income growth, although above average teaching growth.
Cluster 2 - Local Access: These HEIs exhibit high levels of access in terms of ratio of
students from low participation neighbourhoods and high state school participation, low
overall growth in terms of university and college funds, but high research growth,
although this was on average from a very low base. They are also generally smaller in
size, although there are a number of exceptions to this, most notably Manchester
Metropolitan University.
13
Cluster 3 - Elite Research: These are universities, which are large, internationally oriented (at
least in terms of the ratio of foreign students), research intensive universities. Although
this group of universities exhibited the fastest income growth rate of the seven clusters,
their research growth is below average.
Cluster 4 - London Metropolitan Specialists: This group of institutions based in and around
London (Cranfield can be considered within the London city region in terms of broad
Travel To Work Area (TTWA)) has some similarities with Group 1 in terms of their
overall profile, however whereas Group 1 HEIs recorded the lowest growth in terms of
research income of all the seven groups identified, this group recorded the highest
(although they are generally less oriented towards knowledge exploitation and enterprise
orientation).
Cluster 5 - High Teaching Growth: These institutions exhibit the highest rate of student
growth rate between 2002/3-2005/6, average overall growth and below average size,
slightly above average research income, but low research growth.
Cluster 6 - Research Oriented, Teaching Growth: These are generally large (with the exception
of the Institute of Education), research intensive institutions, enterprise focused, with
above average high student growth, but below average research growth.
Cluster 7 - Open: This cluster is represented by one single institution, the Open University,
a very large, high access, domestic focused university. This remains a unique university
within the UK higher education system and for this reason has no close associate in
terms of institutional profile.
---- Table 2 about here ----
Table 2 lists those HEIs allocated to each of the seven groups. There may be one or two
anomalies in cluster membership, but intuitively the groups overall seem to allocate
similar sets of universities and colleges together and, although age of HEI (for various
technical reasons) was not used, Cluster 2, for example, contains a high proportion of
post 1992 universities. The Open University has its own ‘cluster’ group (if you can call a
single institution a cluster), but again given its exceptional profile and remit the authors
14
believe this to be a sensible outcome. Clusters 1, 4 and 6 represent smaller ‘outlier’
cluster groups but remain distinct at least in a number of key dimensions associated with
the five main profile groups and also in terms of their growth profiles. HEIs of similar
size also tend to be grouped together, but in a number of instances, most notably the
Institute of Cancer in Cluster 1 and the Institute of Education in Cluster 6 this is not
always the case.
The clustering process is useful here in also not setting out to rank higher education
universities and colleges but to indicate that universities and colleges have profiles similar
to other institutions in their groupings. As such, being in one group rather than in
another should not be seen as a failure (or indeed an injustice perpetrated by the authors)
but rather that their institution at this present time ‘fits’ that profile and indeed plays an
important role in meeting the many and increasingly varied role, such as social inclusion,
that universities and colleges are expected to play in the UK’s and other advanced
economies’ higher education system. Variation of remit and profile in the ecology of the
higher education institutions is important if the higher education system overall is to
meet its objectives and play a wider role in the innovation system of a nation. A diverse
set of institutions is essential if a healthy and dynamic system of higher education is to be
fostered and the all the objectives of higher education are to be met. In this respect it
will also be interesting to see if there are changes in the dynamics of these groups over
time, as senior management of HEIs consciously, through coordinated strategic change,
or indeed unconsciously through more ‘natural’ sets of uncoordinated or unplanned
change shift the profile of their university or college.
5.
Conclusions
This study has sought to reflect on the widening role and expectations of HEIs as key
emerging actors within national and regional innovation systems. It has however also
sought to stress that in analysing HEIs, often perceived as a distinct actor or institutional
group (as categorised by for example by the OECD), we should try and move away from
seeing them as being essentially a single actor monotype. Universities and colleges have a
wide and arguably growing divergence in terms of their remits and profiles. The study
therefore went on to explore in more detail the various aspects of higher education
activities and how these various dimensions could then be used to classify universities
15
and other HEIs in a typology or classification system, using the UK higher education
system as an example. This, in turn, led the authors to use cluster analysis to frame a set
of similar HEIs into a group of seven clusters.
It was emphasised towards the end of the analysis that a diverse set of institutions is
essential if a healthy and dynamic system of higher education is to be fostered and all the
objectives of higher education are to be met. Indeed many national governments seem
to be fostering this diversity in terms of how they are seeking to change their respective
higher education systems to meet and ever widening and deepening of their roles.
However, a ‘one size fits all’ policy strategy with regard to universities and colleges still
seems to be highlighted in government policy statements across the developed (and
indeed developing) world, which certainly does not readily align itself to this overall
recognition of, and need for, diversity in higher education institutions and their
responses. Thus, in the UK, in relation to fostering industry-academic links, there
appears to be little or no recognition of differences in university profile in how they may
seek to further links with industry.
These implications for policy associated with diversity can also be paralleled in how the
senior management teams of universities, colleges and institutes plan and manage their
own institutions and the series of questions that are associated with this issue. Are they
actually developing and, perhaps more particularly, copying and adapting strategies,
which are actually ‘right’ for their institution?
Are similar HEIs adopting similar
strategies and indeed should they? Are they benchmarking their own institution with the
right peer group or indeed should they do this at all if this in a sense de facto subjugates
their institution to always remaining within a particular group?
Thus some newer
universities, perhaps most notably the University of Warwick, do seem to have
undertaken significant change and repositioning in recent years which is reflected in the
cluster analysis undertaken as part of this study. These remain fundamental questions for
further reflection.
Acknowledgements
This paper arises out of research funded by the UK Economic and Social Research
Council (Grant Number ESRC RES 171 25). The views expressed are the authors’
alone.
16
Notes
1
Elsewhere the OECD (1999), has identified a slightly different set of five main
categories of NIS institutions:
1. Governments (local, regional, national and international, with different weights by
country) that play the key role in setting broad policy directions;
2. Bridging institutions, such as research councils and research associations, which act
as intermediaries between governments and the performers of research;
3. Private enterprises and the research institutes they finance;
4. Universities and related institutions that provide key knowledge and skills;
5. Other public and private organizations that play a role in the national innovation
system (public laboratories, technology transfer organizations, joint research
institutes, patent offices, training organizations and so on).
2
Thus, in an otherwise excellent analysis of innovation systems through an evolutionary
perspective, McKelvey (1997, 212-3) just makes the distinction between ‘firms’ and ‘nonfirms’ in a system of innovation.
3
One example is Stamford in Lincolnshire, which briefly became a university town in
1334 (see Curtis and Boutlwood 1960, 366).
17
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22
Table 1
List of variables used in cluster analysis
Variable type
Variable description
Category
Time Period
1
Growth
Size
2
Static
3
Growth
4
Static
2003/42005/6
2003/42005/6
2003/42005/6
2005/6
5
Static
6
Static
7
Static
8
Static
9
Static
10
Static
11
Static
12
Static
13
Growth
Average change of total
university income
Mean of total university
income
Average change of research
income
Ratio of research income v.
total income
Ratio of average IP income2 v.
average total university income
Ratio of average consultancy
income3 v. average total
university income
Ratio of young full-time first
degree entrants from low
participation neighbourhoods
v. total young entrants
Ratio of young full-time first
degree entrants from state
schools or colleges v. total
young entrants
Ratio of part-time
postgraduates to full-time
postgraduates
Ratio of postgraduates to all
students
PhD awarded per academic
staff costs
Ratio of foreign (EU and nonEU) students v. UK students
Average change of total FTE
students
Size
Research
Research
Third mission
Third mission
2003/42005/6
2003/42005/6
Social inclusion and
accessibility
2005/6
Social inclusion and
accessibility
2005/6
Teaching/social
inclusion and
accessibility
Teaching/research
Modified4
2005/6
Teaching/research
2005/6
Teaching
2005/06
Teaching
2003/42005/6
2005/6
Average IP income was calculated from HE-BCI surveys (2002/3 -2005/6).
Average consultancy income was calculated from HE-BCI surveys (2002/3-2005/6).
4 RCN Institute (H-0006), Cumbria Institute of the Arts (H-0192) and Arts Institute at Bournemouth (H0197) have been dropped from the analysis since there are only part-time postgraduates in these institutes.
2
3
23
Table 2 Cluster classifications of UK higher education institutions
Cluster
name
1 ResearchLed, Third
Mission
HEIs
University of Birmingham
Keel University
University of Strathclyde
Herriot-Watt University
University of Dundee
Institute of Cancer
2 Local Access Bishop Grosseteste College Lincoln
University of Buckinghamshire
University of Chester
Canterbury Christ Church University
York St John University College
Dartington College of Arts
Edge Hill University
University of Winchester
Liverpool Hope University
University of the Arts London
University of Luton
University of Northampton
Newman College of Higher Education
Roehampton University Southampton Solent
University St Martin's College
St Mary's College
Trinity & All Saints
University of Worcester
Anglia Ruskin University
Bath Spa University
University of Bolton
Bournemouth University
University of Central England
University of Central Lancashire
University of Gloucestershire
Coventry University
University of Derby
University of East London
University of Greenwich
University of Hertfordshire
University of Huddersfield
University of Lincoln
Kingston University
Leeds Metropolitan University
Liverpool John Moores University
Manchester Metropolitan University
De Montfort University
Northumbria University
24
Cluster type &
description
Large, international,
highly researchintensive, high
knowledge
exploitation and
enterprise oriented,
but low overall growth
and low research
income growth
High access, low
overall growth, high
research growth (but
from small base)
3 Elite
Research
Nottingham Trent University
University of Portsmouth
Sheffield Hallam University
London South Bank University
Staffordshire University
University of Sunderland
University of Teesside
Thames Valley University
University of Chichester
University of Westminster
Wimbledon School of Art
University of Wolverhampton
University of Wales, Newport
North East Wales Institute
University of Glamorgan
Swansea Institute of Higher Education
University of Abertay Dundee
Royal Scottish Academy of Music & Drama
Robert Gordon University
University of Paisley
Glasgow Caledonian University
Napier University
Birkbeck College
University of Salford
University of Stirling
University of Wales, Lampeter
University of Wales, Bangor
Norwich School of Art & Design
UHI Millennium Institute
Bell College
London Metropolitan University
University of Bristol
University of Cambridge
University of Durham
University of Leeds
University of Liverpool
Imperial College London
King’s College London
Queen Mary College London
University College London
University of Newcastle
University of Nottingham
University of Oxford
University of Sheffield
University of Southampton
University of Warwick
University of Edinburgh
University of Glasgow
Cardiff University
University of Manchester
25
Large, international,
research intensive
universities, low
research growth, high
overall growth
4 London
Cranfield University
Metropolitan London Business School
Specialists
London School of Economics & Political
Science
London School of Hygiene & Tropical
Medicine
School of Oriental and African Studies
University of London
Courtauld Institute of Art
5 High
University College Falmouth
Teaching
Harper Adams University College
Growth
Trinity Laban
University of Brighton
Middlesex University
Oxford Brookes University
University of Plymouth
University of West of England, Bristol
Edinburgh College of Art
Glasgow School of Art
Queen Margaret University College Edinburgh
Aston University
University of Bath
University of Bradford
Brunel University
City University
University of East Anglia
University of Essex
University of Exeter
University of Hull
Keele University
University of Kent
Lancaster University
University of Leicester
University of Liverpool
Goldsmiths College
Royal Holloway
Royal Veterinary College
St George's Hospital Medical School
School of Pharmacy
Loughborough University
University of Reading
University of Sussex
University of York
University of Strathclyde
University of Aberdeen
University of St Andrews
University of Wales, Aberystwyth
University of Wales Swansea Queen's
University Belfast
University of Ulster
Royal Agricultural College
26
Research intensive,
high research growth,
but low overall growth
High student growth
rate, average overall
growth and below
average size, slightly
above average
research income, but
low research growth
6 Research
Oriented,
Teaching
Growth
University of Wales Institute, Cardiff
University of Bristol
University of Durham
Institute of Education
Queen Mary, University of London
University of Surrey
7 Open
Open University
27
Generally large,
research intensive
institutions, enterprise
focused, with high
student growth, but
below average
research growth
Large, high access,
domestic focused
university