Universities’ third mission profiles and

capabilities:

an exploratory analysis of the UK higher

education sector

Federica Rossi, Birkbeck College, University of London

Marti Sagarra, Universitat de Girona, Spain

Eva M. de la Torre, Universidad Autónoma de Madrid, Spain

1

Motivation

• In response to market-type incentives, HEIs tend to behave

strategically, choosing to particularly engage in activities where

they enjoy some form of advantage over their competitors

• HEIs may develop different third mission profiles in an attempt

to maximise the strategic fit between their institutional resources

(subject mix, degree levels provided, whether academics are

engaged in research) and the opportunities and needs in their

socioeconomic contexts

2

Research questions

• (R1). What are the different third mission profiles of UK HEIs and

how do they align with HEIs’ teaching and research missions?

• (R2). How do third mission profiles align with the HEIs’ third

mission capabilities?

3

Third mission (TM):

• The university’s “relationship with the non-academic outside

world: industry, public authorities and society” (Schoen et al. 2007)

which

• take the form of collaboration experiences “between institutions

of higher education and their larger communities (...) for the

mutually beneficial exchange of knowledge and resources”

(Driscoll 2008) and

• for the benefit of the economy and society (Mollas-Gallart et al.

2002).

4

Why the UK case?

• Due to the established presence of market-type incentives, the UK

is an exemplary case for countries where HEIs are increasingly

competing with each other for public funds, student enrolments

and third mission revenues

• Extensive data is available on UK HEIs’ engagement in teaching,

research and third mission activities through HESA

5

Previous research

• Sengupta and Ray (2015) clustered universities along three dimensions

(commercialization, knowledge exchange, research) and looked at the

determinants of performance in various third mission dimensions

• Hewitt-Dundas (2012) clustered universities into high and low research

intensive and looked at the performance of the two clusters in third

mission

• Rossi (2014) computed relative efficiency of universities in third mission

using a broader range of indicators (but sample sized limited by many

variables with “zero” values)

6

Our exploratory approach:

Ordinal Multidimensional Scaling (MDS)

• Allows to position the n units of observations (universities) on a multidimensional

map where the distance between observations is computed in terms of k

variables

• The distances between observations are mapped into a set of coordinates in a m-

dimensional space, where m is a subset of k

• Once we have chosen the optimal number of dimensions m, we can project the

observations into two-dimensional maps for each pair of dimensions at a time.

The dimensions are correlated to the initial variables, so each dimensions tells us

how the observations are positioned in relation to that set of variables.

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• Advantages of MDS

– Robust to outliers

– Can handle observations with zero in some variables so it

allows us to include in our sample 159 HEIs in the UK

• All except for: 4 HEIs that have recently merged with other institutions +

2 federations of independent colleges

– Non-parametric

– Allows to compute “actual” distances between observations

– Can be used to analyse trajectories in time

– Captures “trends” but there can be exceptions!

8

• MDS model specification

9

Type of variable ID Variable description

TM engagement

TM1 N. patent applications / Full Time Equivalent faculty (FTEF) TM2 Income research contracts / FTEF TM3 Income consultancy contracts / FTEF TM4 N. academic days spent on public events / FTEF TM5 Income CPD courses offered / FTEF TM6 N. current year Spin-offs with some HEI ownership / FTEF TM7 Regeneration Income/FTEF

Research engagement

ST1 Grants (research) / FTEF ST2 N. papers (Scopus) / FTEF ST3 N. papers (WoS) / FTEF ST4 N. research students / total enrolment

Teaching engagement

ST5 UG Enrolment / FTEF ST6 PG Enrolment / FTEF

Structure ST7 Total grants / total income ST8 FTEF / total faculty

Subject mix R2 Share of FTEF in medicine R3 Share of FTEF in science and engineering R4 Share of FTEF in the social sciences R5 Share of FTEF in the arts and humanities

10

• MDS: identifying TM profiles – The 6 dimensions explain the variables well (R2 higher than 0.5)

– Number of dimensions almost as high as number of initial variables: universities’ TM engagement profiles are very varied

Dimension Positive correlations Negative correlations DIM 1

Research

commercialisation

- TM: Patent applications, Income research

contracts

- ST: Grants (research), publications,

research students

- R: science and engineering

- ST: undergraduate enrolment

- R: arts and humanities

DIM 2

Professional education

- TM: Income from CPD courses offered

- ST: Postgraduate enrolment

- R: social sciences

- ST: Total grants

DIM 3 - Public

engagement vs. local

development

- TM: Academic days spent on public events

- ST: Grants (research), research students

- R: arts and humanities

- TM: spin-offs, regeneration Income

- ST: undergraduate enrolment

- R: medicine

DIM 4

Research oriented

- ST: Publications (WoS), research students

- R: Medicine

- TM: Income research and

consultancy contracts,

- ST: science and engineering

DIM 5 - Local economic

development

- TM: patent applications, spin-offs,

regeneration income

- R: medicine

DIM 6 - Public and local

engagement

- TM: public events, regeneration income

- R: medicine

- ST: postgraduate enrolment, total

grants, FTEF

- R: arts and humanities

Mai

n s

trat

egi

es

Mix

ed

str

ate

gie

s

• MDS + cluster analysis: identifying TM profiles – Cluster analysis (Ward method) on the MDS coordinates

11

Clu

ster

N. TM profile Characteristics

Type of HEIs Structure Subject mix

Cl. 1

Mixed profile

research intensive

(MPRI)

54 Patent applications,

income research and

consultancy contracts,

and from CPD courses

Old universities (founded

before 1990s)

Research grants, (ISI

and Scopus)

publications, research

students

Science and

engineering

Cl. 2

Professional

teaching intensive

(PTI)

55 Income from CPD

courses

Polytechnics + some of

post-1992 modern

universities

Undergraduate

enrolment

Medicine and

social

sciences

Cl. 3

Arts and

humanities

teaching intensive

(AHTI)

40 N. academic days spent

on public events

Colleges + the rest of post-

1992 modern universities

Undergraduate and

postgraduate

enrolment

Arts and

humanities

Cl. 4

Science based

highly research

intensive

(SHRI)

8 Patent applications and

income research

contracts

Old (founded before 1990)

(Very research intensive)

Research grants, (ISI an

Scopus) publications,

research students, and

faculty

Medicine, and

science and

engineering

Cl. 5 1 Outlier lstm Outlier Outlier

Cl. 6 1 Outlier lbs Outlier Outlier

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DIM 1: Research commercialisation

DIM

2:

Pro

fess

ion

al e

du

cati

on

Positive correlations Negative correlations DIM 1 - Patent applications (TM1), Income research contracts (TM2)

- Research grants (ST1), publications (ST2, ST3), research students (ST4)

- Science and engineering FTEF (R4)

- Undergraduate enrolment (ST5)

- Arts and humanities FTEF (R5)

DIM 2 - Income from CPD courses offered (TM5)

- Postgraduate enrolment (ST6)

- Social sciences FTEF (R4)

- Total grants (ST7)

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DIM 1: Research commercialisation

DIM

3: L

oca

l eco

no

mic

dev

elo

pm

ent

vs. p

ub

lic e

nga

gem

ent

Positive correlations Negative correlations DIM 1 - Patent applications (TM1), Income research contracts (TM2)

- Research grants (ST1), publications (ST2, ST3), research students (ST4)

- Science and engineering FTEF (R4)

- Undergraduate enrolment (ST5)

- Arts and humanities FTEF (R5)

DIM 3 - Academic days spent on public events (TM4)

- Research grants (ST1), research students (ST4)

- Arts and humanities FTEF (R5)

- Spin-offs (TM6), regeneration Income (TM7)

- Undergraduate enrolment (ST5)

- Medicine FTEF (R2)

• Structured system for providing third mission advice

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• Capability to seek out licensing opportunities for IP

(patents, copyrights, designs & trademarks)

15

0.00%

10.00%

20.00%

30.00%

40.00%

50.00%

60.00%

70.00%

80.00%

90.00%

100.00%

SHRI MPRI PTI AHTI

Both

In-house

Externalagency

• Structures responsible for Business & Community

interactions

16

• Institutional policies to support academic staff’s third

mission engagement

17

0.00%20.00%40.00%60.00%80.00%100.00%

SHRI

MPRI

PTI

AHTI

Indemnityinsuranceforstaff

StaffasindividualsarerewardedfortheIPthattheygenerate

• Spin-off development capabilities

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19

Main positive vectors Main negative vectors DIM 1 - Total B-C representation on

governing body (11a) - Support for spin-offs (16b, 16c) and

start-ups (17c, 17f)

- Institutional support for ac. staff TM engagement (9d, v14) - Lack of support for SMEs (9a, 9b) - B-C representation on governing body from comercial business (11b) - Lack of structures for seek licensing ops (8) and for B-CI (10d) - Lack of support for spin-offs (16d, 16e, 16g) and start-ups (17a, 17d, 17g)

DIM 2 - B-C representation on governing body (11a, 11c)

- Lack of B-C representation on governing body (total – 11a) - Lack of support to SMEs (9a, 9d) - Lack of structures for seek licensing ops (8)

DIM 1: general TM infrastructure development

DIM

2:

Spin

-off

s an

d s

tart

-up

s vs

. in

tern

al T

M s

tru

ctu

res

DIM

3:

B&

C r

ep

rese

nta

tio

n o

n g

ove

rnin

g b

od

ies

vs. B

&C

co

mp

anie

s

20

Main positive vectors Main negative vectors DIM 1 - Total B-C representation on governing body (11a)

- Support for spin-offs (16b, 16c) and start-ups (17c, 17f)

- Institutional support for ac. staff TM engagement (9d, v14) - Lack of support for SMEs (9a, 9b) - B-C representation on governing body (comercial business - 11b) - Lack of structures for seek licensing ops (8) and for B-CI (10d) - Lack of support for spin-offs (16d, 16e, 16g) and start-ups (17a,

17d, 17g)

DIM 3 - B-CI exploitation company owned (10b, 10c) - Lack B-C representation on governing body (11a)

- B-C representation on governing body (11a, 11c, 11d)

DIM 1: general TM infrastructure development

Conclusions • Third mission profiles are closely linked to universities’ research or

teaching orientation, to their subject mix and type of institutions

• Very varied TM profiles, but some main ones

1. Science-based highly research intensive universities (SHRI), with high share of staff in medicine and in science and engineering: • Commercialization of scientific research through patents and research

contracts

2. Mixed profile research intensive universities (MPRI) with high share of staff in science and engineering: • Research commercialization through patents and research contracts, as

well as consultancies and PCD courses

SHRI and MPRI tend to have a lot more internal capabilities in third mission, particularly for the commercialization of IP and for the set up of spinoffs through incubators

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Conclusions

3. Professional teaching intensive universities (PTI) with high share of staff

in social science:

• Provision of professional education, with high income from CPDs and

numerous postgraduate students

• Offer support to SMEs and entrepreneurship training, as well as business

advice

4. Arts and humanities-based teaching intensive universities (AHTI) with

high share of staff in the arts and humanities:

• Public events

• Score low on all variables measuring capabilities dedicated to third mission

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What next?

• Only 8 universities (SHRI) show a strong focus on patents and research contracts - need to understand the other profiles:

– Which are the stakeholders of each TM profile? Which type of industry do they target?

– Relationship between TM different profiles and efficiency in TM?

– How do universities with different profiles fare in terms of HEIF funding: could some universities do better if the incentive system was different? What means to be successful?

– Longitudinal analysis exploring how universities’ profiles evolve over time? Does the evolution overtime responds to the policy incentives to differentiation and concentration?

– Relationship between TM different profiles and (national and global) ranking positions?

– Which are the factors that determine the profile of the institution? Causal analysis

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Thank you!

Federica Rossi, f.rossi@bbk.ac.uk

Marti Sagarra, marti.sagarra@udg.edu

Eva M. de la Torre, eva.torre@uam.es

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