Universities and Firms: A Comparative Analysis of the Interactions Between Market Process,...

Universities and Firms: A Comparative Analysis of the Interactions Between Market Process, Organizational

Strategies and Governance

Seminar, September 2-3, 2002

Industry-university S&T transfers: what can Industry-university S&T transfers: what can we learn from Belgian CIS-2 data? we learn from Belgian CIS-2 data?

preliminary draft

Henri Capron and Michele Cincera(DULBEA-ULB)

OBJECTIVES OF THE PAPER:OBJECTIVES OF THE PAPER:

• Role played by universities as a source of innovation information for firms

• Main determinants of industry-university cooperative arrangements in innovation

• Sample of 1205 Belgian manufacturing firms in 1994-96

OUTLINE OF THIS TALK:OUTLINE OF THIS TALK:

• Stylised facts• Data/CIS-2• Econometric framework• Empirical findings• Conclusions

STYLISED FACTS: STYLISED FACTS: interactions between firms & universitiesinteractions between firms & universities

• Focus of R&D policy in Belgium towards a closer collaboration between enterprises and universities

• Large debate about the real effects of universities to permanent interaction with industry

• Important barriers to industry-university collaborations

• Industry and universities are not natural partners

• Firms’ goal is to make profits so that their focus is on marketable outputs

• Universities’ missions are to produce and transmit knowledge with a main stress placed on leading-edge knowledge regardless of its commercial fallout


• Functioning of European universities is not based on the same rules than American ones

• European universities depend to a large extent of public financing while the American ones are privately financed• Rosenberg (2001): American universities are more market-driven than their European counterpart • In the EU, over the last years universities have been increasingly encouraged either directly or indirectly to co-operate with industry in order to alleviate the burdens of public expenditure


University-industry interactions can take several forms (Business-Higher Education Forum, 2001) that are at the source of technology transfers from universities to companies:• industry sponsored research;

• collaborative research that can be encouraged by public funding;

• research consortia that put together some companies and universities engaged in various research efforts of common group interest;

• technology licensing from universities to companies for commercialisation;

• start-up companies involving universities and having licensing agreements to access university technologies;

• exchange of research materials to expedite the performance of research;

• university consultancy and services;

• Graduate and continuing education


Industry-university collaborations can be beneficial for both partners• Hicks (2001): universities can enhance their scientific impact by reinforcing collaborations with the industrial sector. So the counting of the number of papers among the most cited 1,000 papers from 1981 to 1992 puts forward that 3.3 of every 1,000 university-industry collaborative papers were among the most cited papers against 2.2 for university-university collaborative papers and 1.7 for single university papers

• Mansfield (1991, 1992 and 1998): over 10% of the new products and processes introduced by firms could not have been developed without substantial delay in the absence of academic research. The importance of recent research was rated highest by the pharmaceutical industry

•DATA:DATA:

The 2nd Community Innovation survey (CIS-2) database contains about 80 variables regarding innovation and economic activities as well as qualitative information regarding the links between enterprises and the different categories of actors in the innovation system:

• A first type of information deals with the sources of innovation for innovation

• The second bears on the co-operation arrangements on innovation activities of enterprises with other enterprises or institutions

• Both types of information allow one to investigate to what extent beside and compared to other institutions universities can be considered as a privileged source of new innovative ideas for enterprises

DATA:DATA:

• Belgian CIS-2: period 1994-96, 2170 surveyed firms, 1378 answers• OSTC (1998): no selection bias• trimming procedure: all observations with L, X or (L/Y) < –50% or >100%, with R&D/Y >50% and log(L/Y) below the lower centile or beyond the upper centile have been excluded• final sample 1205 manufacturing firms• 290 firms reported >0 R&D expenditures in 1996 • these firms are representative of 40.4% of Belgian total Business Expenditures on Research and Development in 1996

DATA: Sample compositionDATA: Sample composition

• NACECode(Rev.1)

Industry definition Industryabbrev.

# of firmsin thesample

Avera-ge # ofemplo-yees

% ofinno-vators

15-16 Manufacture of food products and beveragesManufacture of tobacco products

IND1 157 167.0 21.0

17-19 Manufacture of textilesManufacture of wearing apparel; dressing and dyeing of furTanning and dressing of leather; manufacture of luggage, …

IND2 141 177.3 19.1

20-22 Manufacture of wood and of products of wood and cork,…Manufacture of paper and paper productsPublishing, printing and reproduction of recorded media

IND3 134 142.2 21.6

23-24 Manufacture of coke, refined petroleum products and nuclear fuelManufacture of chemicals and chemical products

IND4 120 713.4 45.8

25 Manufacture of rubber and plastic products IND5 68 139.9 47.126 Manufacture of other non-metallic mineral products IND6 82 186.8 22.027-28 Manufacture of basic metals

Manufacture of fabricated metal products, except machineryIND7 188 231.7 32.4

29 Manufacture of machinery and equipment, not elsewhere classified IND8 92 217.5 42.430-33 Manufacture of office, accounting and computing machinery

Manufacture of electrical machinery and apparatus, not elsewhere classifiedManufacture of radio, television and communication equipmentManufacture of medical, precision and optical instruments, watches

IND9 90 385.7 53.3

34-35 Manufacture of motor vehicles, trailers and semi-trailersManufacture of other transport equipment

IND10 62 707.2 50.0

36 Manufacture of furniture; manufacturing, not elsewhere classified IND11 71 83.1 16.9

DATA: Sample compositionDATA: Sample composition

• Belgian region Regionabbr.

Brussels Capital REG1 126 666.2 31.7Flanders REG2 799 234.7 30.9Wallonia REG3 280 204.4 35.0Size class (number of employees)

1 < 50 SMALL 423 25.9 20.12 50-150 MEDIUM 391 92.4 30.43 > 150 LARGE 391 720.4 46.3

TOTAL 1205 272.8 31.9

DATA: Descriptive statistics for main variablesDATA: Descriptive statistics for main variables

•

DATA: DATA: Number of innovators with very important sources of Number of innovators with very important sources of information for innovation by country (% and rank), 1996 information for innovation by country (% and rank), 1996

• BE AT DE DK FI FR IE IT NL NO PT SE SP UK EU-15% rk % rk % rk % rk % rk % rk % rk % rk % rk % rk % rk % rk % rk % rk % rk

Internal informationWithin enterprise 44 2 34 2 57 1 29 2 40 2 48 1 56 12 36 1 42 1 51 2 34 2 56 2 73 12 43 2 47 1

Enterprise group 23 3 22 4 38 3 11 4 18 3 24 3 46 10 25 4 14 3 27 4 42 1 17 3 10 19 4 25 3

Market informationCompetitors 23 4 17 5 22 5 17 3 8 6 9 5 29 8 9 6 5 6 19 5 12 7 17 4 22 8 17 5 16 6

Clients or customers 54 1 57 1 45 2 44 1 44 1 32 2 58 11 28 2 14 2 54 1 29 3 69 1 53 11 54 1 42 2

Consultancy enterprises 3 10 1 12 5 9 4 10 3 10 1 9 9 4 8 7 1 10 5 10 15 6 2 10 7 4 2 11 5 8

Suppliers 15 6 7 7 22 6 11 5 13 4 18 6 24 7 21 5 7 5 27 3 28 5 11 6 14 7 23 3 19 5

Publicly available informationPatent disclosures 2 12 2 10 4 11 8 7 1 12 2 11 7 2 2 11 2 8 1 12 3 11 3 9 3 2 4 9 3 11

Professional conferences 5 8 12 6 11 7 2 12 5 8 4 7 14 6 7 8 5 7 8 7 10 8 4 8 8 6 5 7 8 7

Computer based information networks 2 11 5 8 5 10 3 11 3 11 4 10 8 3 4 9 1 11 4 11 1 12 2 11 3 3 10 4 9

Fairs, exhibitions 20 5 31 3 29 4 10 6 11 5 10 4 29 9 26 3 8 4 16 6 29 4 16 5 26 9 15 6 22 4

OtherGovernment/PNP research institutes 5 9 1 11 3 12 4 9 5 9 2 12 7 1 2 12 2 9 6 8 6 10 12 5 1 2 12 3 12

Universities 7 7 5 9 7 8 6 8 7 7 3 8 5 5 2 10 1 12 5 9 7 9 5 7 3 5 4 8 4 10

DATA: DATA: Number Number of innovators with innovation collaboration by of innovators with innovation collaboration by country and size (%), 1996 country and size (%), 1996

•

DATA: DATA: Number Number of innovators by type of partners as a share of of innovators by type of partners as a share of innovation co-operators, EEA and Belgium, 1996innovation co-operators, EEA and Belgium, 1996

•

0 10 20 30 40 50 60 70

consultancy enterprises

competitors

government or RTO's

clients

suppliers

universities

enterprises within the group

EEA BE

ECONOMETRIC FRAMEWORKECONOMETRIC FRAMEWORK::

* sources of information for innovation: outcomes of the dependent variable takes values 0(sources not used), 1 (slightly important), 2 (moderately important), 3 (very important)

* ordered probit (or logit) model: natural candidate to account for the ordinal nature of this typeof variable

* but, only innovating firms answer to the sources of information variable, the importance ofinformation sourcing is not observed for the non innovators: selection biais?

* control for selection into the sample: estimate probit model discriminating between innovatorsand non-innovators

* error term jointly distributed with that from the ordered probit model


specification for this two-equations model:

03

02

001

000

01

00

,...,1

*1

*222

*12

*212

*11

*22

*1

*22

2*2

*11

*11

1*1

iii

iii

iii

iii

iii

ii

ii

iii

yandyify

yandyify

yandyify

yandyify

Xy

yify

yify

NiZy

where:*1iy is a binary variable that determines whether a firm innovate or not;

Zi is a set of covariates explaining the decision of firm i to innovate and the are the associatedcoefficients;

*2iy is an ordinal variable concerning different sources of information for innovation; and

Xi represents the main determinants explaining the access to a particular source of informationand the are the associated coefficients.

ECONOMETRIC FRAMEWORKECONOMETRIC FRAMEWORK::Variable abbrev. Variable definitionContinous variablesLEMP Size (log of number of employees (EMP))TURNC Growth of salesEXPC Growth of exportsRDI R&D intensity

(RRDIN/TURN)IEI Other than R&D innovation expenses intensity

(RRDEX+RMAC+ROET+RPRE+RMAR)/TURNOrdered variables

Source of information essential to the design and implementation of innovationprojects:

SFIRM Sources within the enterprise itself or its group of enterprises (SENT+SGRP)SMARKET Market information acquired from customers, competitors, suppliers or consultants

(SCOM+SCLI+SCON+SSUP)SPUBL Publicly available information from trade fairs and exhibitions, journals and computer-

based networks and patent databases(SPAT+SPRO+SNET+SEXB)

SUNI Information supplied by universities and other Higher Education InstitutesSGMT Information supplied by the government or private non-profit research institutesBinary variables

Objectives of innovation activity:OPROD Product oriented objectives: Replace products being phased out, improving product

quality, extend product range or open up new markets or increase market share(OREP+OIMP+OEXT+OOPN)

OPROC Process related objectives: Improve internal business process flexibility, reduce labourcosts, materials consumption or energy consumption(OPCS+OLBR+OMAT+ONRG)

OOTHER Other objectives: Fulfilling regulations or standards or reduce environmental damage(OSTD+OENV)

FGP Part of a foreign groupGMTSUP Firms that received public funds (loans, subsidies) to support their innovation activitiesPAT Firms that applied for at least one patent over 1994-96INMAR Introduction on the market of a product that is new for the market and non only for the

firmCH2 During 1994-1996, firm’s turnover increased due to merger with another enterprise or part

of itCH3 During 1994-1996, firm’s turnover decreased due to sale or closure of part of the

enterpriseIND1-IND11 Industry dummiesREG1-REG3 Regional dummies


If Zis include an intercept: no loss of generality in constraining: 021 ii EE scale of the dependent variables *

1iy and *2iy undefined: 121 ii VarVar

iiCov 21 , .

parameters ,, estimated by maximum likelihood

3,1 22

2,1 1222

1,1 212

0,1 2

1 1

21

21

21

21

1

,,1log

,,log,,log

,,log,,log

,,log

log,,log

ii

ii

ii

ii

i

yy ii

yy iiii

yy iiii

yy ii

y i

ZX

ZXZX

ZXZX

ZX

ZL

1 is the univariate normal cdf and 2 is the bivariate normal cdf with correlation coefficient

log-likelihood function not globally concavemay be difficult to estimate both because starting values hard to obtain dependent variabletypically rather coarse and do not provide much identifying power for the coefficientswhen is 0: log-likelihood = sum of the log-likelihoods of the probit model controlling forselection and the ordered probit model estimated on the sub-sample of innovating firms


CIS-2 questions as regards collaborative agreements are asked in a sequential way(1) are you innovative or not?(2) If yes, have you any co-operation arrangements with any type of partners?(3) If yes, with which type of partner, e.g. universities?

trivariate probit model with censoring (Mohnen and Hoareau,2002)

01

3,2,100

,...,1

*

*

322*3

211*2

1*1

kiki

kiki

iii

iii

iii

yify

kyify

Xy

Xy

NiZy

*2iy is a binary variable that determines whether a firm collaborate or not;*3iy is a binary variable that determines whether a firm collaborate with a particular partner, e.g.

universities; andX1i and X2i represent the determinants explaining the decision to collaborate.


erro r term s 321 ,, iii fo llow a trivariate standard no rmal d istribut ion

1,1,1 23131222113

0,1,1 23131222113

0,1 12222

0 1

321

321

21

1

,,,,,log

,,,,,log

,,log

log,,log

iii

iii

ii

i

yyy iii

yyy iii

yy ii

y iij

XXZ

XXZ

XZ

ZL

321 ,, are the univariate, bivariate, trivariate no rmal cd f

ij contemporaneous correlat ion coeffic ients betw een the erro r term s

if 321 ,, iii are uncorrelated: sequent ia l model o f innovat ion

bivariate o r trivariate no rmal cd f = products o f univariate no rmal cd f

EMPIRICAL FINDINGS:EMPIRICAL FINDINGS:

Role of universities as sources of information Role of universities as sources of information for manufacturing innovatorsfor manufacturing innovators::







Table 4. Comparison of universities and other types of partners as sources of information forinnovation– results of the probit model

SUNI SFIRM SMARKET SGMT SPUBLest. s.e. p-val. est. s.e. p-val. est. s.e. p-val. est. s.e. p-val. est. s.e. p-val.

LEMPM 0.306 0.113 [.007] 0.317 0.116 [.006] 0.097 0.118 [.411] 0.545 0.123 [.000] 0.292 0.117 [.012]

TURNCM 0.249 0.263 [.345] -0.144 0.262 [.582] -0.079 0.270 [.770] 0.763 0.304 [.012] 0.059 0.269 [.827]

EXPCM -0.040 0.037 [.280] 0.022 0.026 [.392] -0.029 0.025 [.241] -0.162 0.107 [.129] -0.048 0.032 [.132]

GMTSUP 0.531 0.137 [.000] -0.195 0.142 [.169] -0.102 0.145 [.481] 0.561 0.144 [.000] 0.324 0.143 [.023]

PAT 0.201 0.148 [.174] 0.152 0.153 [.318] -0.134 0.157 [.392] -0.116 0.159 [.465] 0.325 0.153 [.034]

RDI 0.018 0.015 [.223] 0.019 0.016 [.235] 0.001 0.016 [.949] 0.012 0.015 [.414] -0.017 0.016 [.273]

IEI 0.000 0.001 [.499] 0.000 0.000 [.489] 0.000 0.000 [.693] 0.000 0.001 [.661] 0.000 0.000 [.903]

INMAR 0.005 0.122 [.966] 0.168 0.124 [.174] 0.034 0.127 [.788] 0.060 0.129 [.644] 0.109 0.125 [.386]

FGP 0.329 0.126 [.009] 1.017 0.137 [.000] 0.157 0.132 [.236] 0.351 0.134 [.009] -0.029 0.131 [.827]

CHG2 0.228 0.238 [.337] -0.324 0.248 [.191] 0.750 0.257 [.004] 0.162 0.250 [.517] 0.289 0.249 [.245]

CHG3 0.200 0.398 [.615] 0.436 0.443 [.325] -0.512 0.436 [.241] -0.069 0.424 [.870] 0.316 0.425 [.457]

OPROD 0.094 0.094 [.317] 0.104 0.095 [.276] 0.385 0.098 [.000] 0.168 0.102 [.099] 0.322 0.098 [.001]

OPROC 0.138 0.084 [.101] 0.127 0.084 [.129] 0.459 0.088 [.000] 0.041 0.090 [.647] 0.239 0.085 [.005]

OOTHER 0.174 0.078 [.026] 0.002 0.078 [.977] 0.150 0.080 [.062] 0.214 0.085 [.012] 0.240 0.080 [.003]

MU2 1.061 0.082 [.000] 1.676 0.170 [.000] 1.785 0.151 [.000] 0.984 0.084 [.000] 2.016 0.117 [.000]

MU3 2.202 0.125 [.000] 3.418 0.193 [.000] 3.989 0.194 [.000] 2.013 0.141 [.000] 3.747 0.194 [.000]

reg. dumm. x x x X xind. dumm. x X X X xlog-likel. -421 -359 -323 -364 -337


Collaborations between universities and industryCollaborations between universities and industry::

Table 5. Innovation collaborations between firms, universities and other types of partners –results of the probit model

Collaboration withuniversities (‘CO6’)

Collaboration withother firms within the

group (‘CO1’)

Collaboration withcompetitors (‘CO2’)

Collaboration withclients or customers

(‘CO3’)

Collaboration withconsultancy firms

(‘CO4’)

Collaboration withsuppliers of

equipment (‘CO5’)

Collaboration withgovernment or

private non-profitinstitutions (‘CO7’)

est. s.e. p-val. est. s.e. p-val. est. s.e. p-val. est. s.e. p-val. Est. s.e. p-val. est. s.e. p-val. est. s.e. p-val.C -1.966 0.532 [.000] -2.217 0.730 [.002] -80.158 582346 [1.00] -2.927 0.945 [.002] -2.074 1.078 [.054] -4.124 1.344 [.002] -9.307 0.959 [.000]

LEMPM 0.881 0.180 [.000] 0.963 0.191 [.000] 0.837 0.255 [.001] 0.270 0.168 [.108] 0.272 0.272 [.318] 0.635 0.174 [.000] 1.031 0.238 [.000]

TURNCM 0.168 0.416 [.687] -0.310 0.488 [.526] -0.834 0.664 [.209] -0.103 0.484 [.831] 0.787 0.708 [.267] 0.069 0.517 [.894] 0.553 0.569 [.331]

EXPCM -0.007 0.076 [.931] -0.002 0.038 [.960] -0.013 0.145 [.929] -0.061 0.143 [.670] -0.025 0.100 [.802] -0.118 0.260 [.649] -0.011 0.165 [.949]

GMTSUP 0.831 0.191 [.000] 0.274 0.217 [.207] 0.490 0.294 [.096] 0.451 0.198 [.023] 0.228 0.299 [.446] 0.551 0.197 [.005] 0.681 0.232 [.003]

PAT 0.406 0.198 [.041] 0.202 0.219 [.356] 0.457 0.325 [.160] 0.550 0.204 [.007] 0.238 0.320 [.458] 0.321 0.211 [.128] 0.317 0.258 [.220]

RDI -0.002 0.023 [.943] 0.022 0.028 [.436] -0.011 0.035 [.752] -0.034 0.027 [.212] -0.046 0.056 [.414] -0.026 0.028 [.360] 0.034 0.025 [.174]

IEI -0.009 0.018 [.622] -0.013 0.021 [.546] 0.019 0.019 [.307] -0.001 0.015 [.956] -0.052 0.048 [.277] -0.006 0.015 [.710] -0.060 0.043 [.169]

INMAR 0.169 0.180 [.345] 0.177 0.179 [.323] -0.023 0.274 [.934] 0.315 0.177 [.075] 0.739 0.292 [.011] 0.355 0.173 [.040] -0.129 0.223 [.563]

FGP 0.115 0.201 [.568] 0.723 0.194 [.000] -0.321 0.294 [.274] 0.174 0.196 [.375] -0.057 0.295 [.847] -0.127 0.194 [.511] -0.194 0.245 [.428]

CHG2 0.246 0.340 [.469] -0.188 0.364 [.606] -0.151 0.540 [.779] 0.384 0.344 [.264] 0.299 0.448 [.504] -0.210 0.391 [.591] 0.264 0.418 [.528]

CHG3 -0.405 0.515 [.432] -0.126 0.554 [.820] 0.264 0.521 [.612]

SFIRM 0.028 0.141 [.841] 0.827 0.154 [.000] -0.157 0.205 [.443] 0.294 0.140 [.035] 0.035 0.211 [.869] 0.343 0.137 [.012] 0.154 0.175 [.380]

SMARKET -0.041 0.162 [.798] -0.059 0.161 [.716] 0.207 0.234 [.376] 0.036 0.162 [.822] 0.311 0.239 [.193] 0.232 0.160 [.148] -0.029 0.203 [.887]

SUNI 0.525 0.127 [.000] -0.183 0.140 [.191] 0.172 0.179 [.338] 0.007 0.125 [.956] -0.053 0.211 [.800] -0.163 0.133 [.219] -0.175 0.176 [.318]

SGMT -0.130 0.133 [.327] 0.000 0.138 [.999] 0.104 0.197 [.598] -0.045 0.134 [.738] 0.258 0.201 [.199] 0.262 0.133 [.049] 0.676 0.163 [.000]

SPUBL -0.028 0.158 [.861] 0.023 0.163 [.889] -0.345 0.231 [.135] 0.164 0.153 [.284] -0.179 0.234 [.444] -0.064 0.155 [.679] 0.054 0.192 [.777]

OPROD -0.003 0.146 [.981] 0.217 0.153 [.156] 0.242 0.236 [.305] 0.108 0.147 [.463] 0.016 0.217 [.942] 0.363 0.153 [.017] 0.027 0.185 [.882]

OPROC 0.084 0.130 [.518] -0.157 0.126 [.211] 0.079 0.191 [.678] 0.171 0.129 [.182] -0.137 0.190 [.473] 0.031 0.127 [.806] 0.069 0.161 [.668]

OOTHER -0.163 0.116 [.158] -0.180 0.120 [.132] -0.317 0.170 [.063] -0.181 0.117 [.120] 0.039 0.175 [.824] -0.076 0.116 [.511] -0.256 0.150 [.089]

reg. dumm. x x x x x x xind. dumm. x x x x x x xlog-l -135 -142 -67 -147 -64 -149 -96

CONCLUSIONS:CONCLUSIONS:

Main findings:Main findings:

• factors explaining the use of a particular source of information for innovation not the same according to the type of source

• Access to university-based information positively influenced by size of firms, public support to innovation and membership to foreign group

• Among the objectives to innovate, only the fulfilling of regulations and standards and the reduction of environmental damage positively affect firms to access to university-based information.

• In terms of industry-universities S&T collaborations, size, government support, and patent applications tend to increase the probability to innovate


Main findings:Main findings:

• At European level, universities do not appear to be the most important source of information for firms’ innovative activities

• The most important sources are internal, i.e. within the enterprise or with other firms of the group and with clients or customers

• Information from universities appears however to be more important in Belgium as compared to the European average

• A different picture emerges when we look at university-industry collaborations:

• In Belgium, universities are the second most important types of collaborators (after the firms within the group) and 53% of Belgian companies claim having co-operative agreements with universities, which is significantly above the European average of 38%


Policy recommendations:Policy recommendations:


Policy recommendations:Policy recommendations:

THANK YOU!THANK YOU!

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