What are university-industry research links about?

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What are university-industry research links
about?
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Structure of the Lecture

• The university-industry complex A yet poorly
  understood system.
• University-industry relationships The importance
  of searching, screening and signalling
• The Governance of University-Industry Knowledge
  Transfer Are Different Models Coexisting?

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The university-industry complex
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What do we know?

• 30 years after the start of the
  institutionalisation (with policy support) of
  uni-ind relationships we know something but not
  yet enough to have a consolidated understanding
  (conflicting results)
• Field/sector effect
• Researcher characteristics
• University characteristics
• Firm characteristics

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Field/sector

• Most of the evidence is based on hightech
  industries and especially biomedical in most
  recent years also other fields (engineering) have
  been increasingly studied Fields with most
  intense collaborations.
• We still fail to recognize the importance of non
  hightech fields see for example Food
• We know very little of the interactions in
  services (important in the UK case)

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Field/sector

• Across fields/sectors there are extremely
  important differences in
• type of knowledge,
• the codification of knowledge,
• incentives and reward system,
• supply or demand led, etc

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Researcher Characteristics

• Recent wave of studies at the individual level
• Previous experience
• Entrepreneurial capacity in raising funding
  (public and private)
• Seniority and tenure
• Male
• Teaching ?

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University characteristics

• More likely to occur in some universities than in
  others due to differences in
• Type (disciplinary orientation, local development
  focus) of the UNI
• Environment of the UNI
• Culture (more is done in the centre/department
  and more is accepted and more will be done . B.
  Clark entrepreneurial UNI)

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University characteristics

• Quality of the centre/department /-
• Existence of formal infrastructure of KT ?
• Size ?

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Firm Characteristics I

• Quantitative analysis based on surveys Yale,
  Carnegie Mellon, PACE, CIS II-III-IV, KNOW,
  National surveys
• Klevorick et al., 1995 US
• Meyer-Krahmer and Schmoch (1998) and Beise and
  Stahl (1999) national survey Germany
• Arundel and Geuna (2004) PACE EU countries
• Mohnen and Hoareau (2002) CIS II EU countries
• Cohen, Nelson and Walsh (2002) CM USA
• Swann (2002) and Laursen and Salter (2003) CIS
  III UK.

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Firm Characteristics II

• The size of the firm affect collaboration
• The larger the more collaboration.
• but
• Small biotech firms and spin-offs.
• The RD investment and/or RD intensity
• Absorptive capacity.

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Firm Characteristics III

• Openness of the firm ()
• Searching, screening and signalling
• The role of demand !!!
• Product versus process innovation
• Mixed results.
• Independent () versus subsidiaries
• The role of the headquarter.

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Firm Characteristics IV

• Countries differences.
• Technological sector.
• Distance matters (but not always and not for
  all).

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University-industry relationships The importance
of searching, screening and signalling

• Roberto Fontana,
• Aldo Geuna,
• Mireille Matt

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Contribution of the paper

• We want to explain why certain firms do cooperate
  with universities while other dont (probability
  of cooperation yes/no)
• For the sample of firms that cooperated with
  university, we want to explain the number of RD
  JV that firms had (intensity of cooperation how
  many times.
• We want to test if openess of the firm plays a
  role e.g. the role of demand

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Literature and hypotheses (1)

• The degree of openness import external knowledge
  and knowledge disclosure on a voluntary basis
• Search strategy firms look for sources of
  knowledge (number of external knowledge channels)
  (Laursen Salter 2003)
• Screening activity selection of a specific
  relevant source (journals source of open
  science, but also of info about scientists)
• Signalling activity voluntary disclosure (Pénin
  2004) trigger reciprocity, gain feedbacks,
  network, reputation, higher order knowledge,
  attract potential partners.
• H1 Openness should affect positively the
  probability and the intensity (different
  effects).

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Literature and hypotheses (2)

• The size
• Absolute - (Arundel Geuna 2004, Mohnen
  Hoareau 2003, Cohen et al 2002, etc.)
• Relative to RD.
• H2.1 Larger firms should have a higher
  probability to cooperate (internalisation of
  spillovers).
• H2.2. Firms with larger RD investment should be
  involved in a greater of RD projects (spare
  resources).

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Literature and hypotheses (3)

• RD intensity
• Active at the technological frontier more reliant
  on science (Arundel Geuna 2004, Schartinger et
  al. 2001)
• High RD investment gt high absorptive capacity
  (Cohen Levinthal, 1990).
• H3. The higher the RD intensity, the higher the
  probability of cooperating and the greater the
  number of projects.

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Literature and hypotheses (4)

• The legal status of the firm
• RD activities concentrated at a firms
  headquarter
• Independent firms cooperate more with PROs than
  firms belonging to a large group (Mohnen
  Hoareau 2003).
• H4. Within multi-plan firms, headquarters mediate
  collaboration.

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Literature and hypotheses (5)

• Type of innovative activities
• contrasted results
• Positive relation between radical product
  innovation and cooperation with PROs (Mohnen
  Hoareau, 2003)
• Companies involved in process innovation are more
  likely to cooperate with PROs than those engaged
  in product innovation (Swann, 2002).

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DATA SOURCES
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Data sources

• KNOW survey 2000
• 7 EU countries Denmark, France, Germany,
  Greece, Italy, Netherlands, UK
• 5 sectors food and beverages, chemicals
  excluding pharma, communications equipment,
  telecom services and computer services
• 2 size classes (10-249 employees, 250-999
  employees)
• Average response rate 33 (minus UK)
• 50 of innovative firms (222) signed RD
  cooperation with PROs in the 3 years before the
  survey.

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The variables (1)

• Openness of the firm
• Number of external sources (fairs and
  conferences, searching patent db, reverse
  engineering, internet) - SEARCH
• Mean of new innovations introduced in
  collaboration with partners - ExtCOLL
• Screening publications PUBLICATIONS
• Government RD projects SUBSIDIES
• Patents - PATENTS

SEARCHING
SCREENING
SIGNALLING
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The variables (2)

• Firm size
• Number of employees - Employees
• RD employment RD
• Firm RD Activity
• RD intensity RDINT
• Outsourcing RD expenditures ExtRD
• Headquarter - HEADQ

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The variables (3)

• Firm innovative activity
• Process innovation PROCINN
• Product innovation PRODINN
• Country and sector fixed effects
• COUNTRY,
• SECTOR.

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ECONOMETRIC RESULTS
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Estimation models results (1)

• Negative Binomial Models.
• Zero Inflated Negative Binomial
• Number of RD Projects extent of collaboration
• Propensity for firms to engage in RD Project
  existence of a relationship (Logit Selection)

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Estimation models results (2)

• Propensity for firms to engage in RD Projects
  with PROs
• Absolute Size ()
• Openness () screening (publications
  subsidies)
• Absorptive capacity ()
• Headquarter ()

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Estimation models results (3)

• Number of collaborations
• Relative Size RD employment ()
• Openness () signalling (patents), outsourcing
• Absorptive capacity ()

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Estimation models results (4)

• As in previous literature, the type of innovative
  activity (process versus product) does not
  provide any definitive result may be also due to
  the fact that the large majority of respondents
  do both.
• Country dummies are significant to explain the
  number of collaborations, not so much the
  selection.
• Sector dummies are not significant except in the
  case of food and chemicals in the selection
  model.

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Conclusion (1)

• The role of size and RD activity
• Larger firms have a higher probability to engage
  in formal agreements with PROs but the number of
  RD project signed depends on the size of the RD
  department (do I have enough RD people).
• Firms with important absorptive capacity (being
  near the technological frontier) have a higher
  chance to cooperate and conclude more RD
  projects with PROs.

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Conclusion (2)

• The role of openness of firms
• Acquiring external knowledge via the screening of
  publications and the involvement in public
  policies affects the probability to cooperate
  with PROs.
• Signalling competencies via patenting and RD
  outsourcing affects the level of collaboration.
• Policy implication
• Demand pool policies informed by the idea of firm
  openness (in its various specific aspects) as a
  major driving force.

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The Governance of University-Industry Knowledge
Transfer Are Different Models Coexisting?

• Isabel Bodas Freitas
• Aldo Geuna
• Federica Rossi

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Research questions

• What is the relative importance of the two
  governance models?
• Do firm differ according to the governance model
  they choose?
• Do proximity and collaboration objective explain
  the importance of institutional collaborations?
• None of these questions have yet been addressed
  by the literature in an exhaustive way

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Data Methodology
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Data

• The questionnaire was circulated in
  October/November 2008
• 1052 valid responses (representative sample
  validated by the local Chamber of the Commerce)
• Survey asked about
• whether firms engaged in institutional or
  personal collaborations in the last three years
• for non-collaborators reasons for not
  collaborating
• for institutional collaborators which
  universities they collaborated with, objectives
  of the collaboration, amount of money spent

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Institutional collaborations
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Personal contractual collaborations
Overall 17.5 of the sample has had a
collaboration with at least one univ
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Methodology Models 12

• A firm does not decide to collaborate and then
  select the best governance structure to
  collaborate, institutional or personal.
• A firm may not collaborate (either it has
  internal competences to solve the technological
  problem or does collaborate with other partners)
• Collaborate with a personal contract with a
  researcher
• Develop an institutional collaboration.
• We start by running a series of Logit models (to
  exploit the larger number of observation) then we
  check our results with a Multinomial Logit model.

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Methodology Model 3

• For those firms that engaged in institutional
  collaborations
• factors that explain the financial investment in
  institutional collaborations
• Tobin
• on the logarithm of one plus the total amount
  spent in the collaboration
• Regressors as in Model 2 with the addition of
• The objective of the collaboration (RD, testing,
  organisation, marketing, etc)
• The location of the university (in the region, in
  neighbouring regions, in Italy abroad)

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Results
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Methodology Model 1

• For those firms that did not engage in
  institutional collaborations with universities in
  the last three years
• the choice of establishing personal
  collaborations vs. not collaborating
• Logit model
• dependent variable personal collaboration vs. no
  collaboration at all
• Size, Innovative effort, Sourcing knowledge
  outside, organizational characteristics
  (outsource, multinational, expert)

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Table 5. Reasons for not collaborating with
universities distribution of answers
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Table 6. Rotated Loading factors of reasons for
not having participated in institutional
collaborations with universities in the previous
3 years
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Table 6. Logit Model Estimation of Probability
of non-institutional collaborators to engage in
personal collaborations with Universities
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Methodology Model 2

• Are firms engaging in institutional
  collaborations with universities significantly
  different from those that either do not cooperate
  or cooperate with university researchers through
  personal contract?
• 2 Logit model
• Dependent variable institutional collaboration
  vs. no institutional collaboration,
• Dependent variable institutional collaborations
  vs personal collaboration.

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Table 7. Logit Model Institutional
Collaboration with Universities
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Model 3

• Total investment

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Table 9. Tobit model of the logarithm of total
investment
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Summary of results
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Results

• In line with results from other empirical
  literature
• large firms making innovative efforts (RD or
  design activities) are generally more likely to
  collaborate with universities.
• however, by distinguishing between institutional
  and personal collaborations, we find that
• they are both important channels of knowledge
  transfer
• they seem to involve firms with different
  research strategies

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Results

• Firms that maintained only contractual personal
  collaboration with university researchers were
  found
• to invest more into the acquisition of external
  knowledge than firms that collaborated
  institutionally,
• and to be more likely to rely on external
  sources of technological knowledge than firms
  that did not collaborate at all.
• These firms also tend to be smaller!!
• More open innovation strategies based on multiple
  forms of collaborations with external partners
  and on the integration of internal and external
  RD.

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Results

• Our analysis of the amount of investment in
  institutional collaborations with universities
  suggests that RD and technological development
  activities require the highest investment,
  followed by testing and organizational problem
  solving. The higher the number of objectives,
  which to a certain extent facilitates the
  absorption of knowledge developed, the higher the
  level of investment in the collaboration.

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Results

• The higher the number of geographical areas with
  which the firm maintains university
  collaborations, the more it invests in
  collaborations.
• The number of links with local universities is
  associated with higher levels of investment,
  while collaboration with international
  universities does not significantly affect the
  level of investment in university collaboration.