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Interactive Relations Between Universities andFirms: Empirical Evidence for Austria

Doris Schartinger1

Andreas Schibany2

Helmut Gassler3

ABSTRACT. In recent years interaction between universitiesand the business sector and the role of these collaborations infostering innovation has received greater attention. This paperanalyzes different types of interactions between the two sectors

from the perspectives of universities and firms. The three majorresearch questions address the frequency of different types ofinteractions, the benefits that companies derive from interac-tion with universities and determinants of interaction for bothsectors.

Two different surveys form the empirical base for this paper:One among innovative firms in Austria, one among all Austrianuniversity departments. The methodologies we use are analysesof variance and logistic regressions. Our results demonstratethat the main channel of knowledge transfer from universitiesto the business sector still occurs through the mobility ofhuman capital. The major barriers of interaction lie in thedifferences between cultures of the two spheres as well aslack of information at the side of firms.

JEL Classification:

O31, O32

1. Introduction

The accumulation of knowledge and its spilloverinto new products, new technologies, and produc-tive capacity is considered as the primary engineof economic development in the new growththeories.1 This results in the full recognition of

1Austrian Research Centers Seibersdorf

Division Systems Research Technology-Economy-EnvironmentA-2444 Seibersdorf, Austria

E-mail: [email protected] Research

Institute for Technology and Regional Policy

Wiedner Hauptstrae 76

A-1040 Vienna

E-mail: [email protected] Research

Institute for Technology and Regional Policy

Wiedner Hauptstrae 76

A-1040 Vienna

E-mail: [email protected]

the role of knowledge and technology in eco-nomic growth.2 In the first instance, the accumu-lation of knowledge (i.e. learning) takes place atthe level of the single individual3 through indi-

vidual experientialism4 (experienced-based learn-ing, searching, exploring and organized research)or through interpersonal co-operation (learning-by observing or learning-by-interacting).5 Whereasindividual experientialism restricts the acquisitionof knowledge to one single individual, interper-sonal co-operation enables not only the individualacquisition of external knowledge, but leads alsoto the amplification of knowledge through pro-cesses of communication (multiple processes ofindividual learning).

The focus on the accumulation of knowledge

through dynamic and interactive processes ofknowledge production and diffusion, and therebypromoting technological change, is the core ofthe innovation systems approach.6 Within thisapproach, the focal interest is the innovation per-formance of firms and all relevant determinants ofinnovations. It is not only firms that are involvedin the process of innovation, but also a set ofother actors of various kinds. The interactionsamong them build the backbone of a system ofinnovation. The proposition that firms rarely inno-

vate on their own but involve interactions with

market and non-market institutions is evidencedby several surveys.7 Pivotal for all innovation ori-ented interactions between firms and other actorsis the associated flow of knowledge.

In this context universities as producers of newknowledge may play a crucial role. Universitiescontribute to the production of knowledge andknowledge inputs in the business sector in threemajor ways. First, the bussiness sector receivesinputs from universities in the form of highly edu-cated human capital. Although these individuals

Journal of Technology Transfer, 26, 255268, 20012001 Kluwer Academic Publishers. Manufactured in The Netherlands.

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256 Schartinger, Schibany and Gassler

may require further training, university educationprovides the foundation for subsequent special-ized industrial training. Nevertheless, this influx offresh trained graduates will lead to an inflow ofnew knowledge to the firm. Second, by developingand providing new knowledge through research

which is disseminated through publications andpresentations. Third, by developing and providingnew knowledge through research which is dissem-inated through co-operative research projects orconsultancy for the business sector.

Moreover, the university systems of highly in-dustrialized countries are going through a period

of profound change due to a rise in soci-etys expectations for economic returns of basicresearch. This has led to counteracting pressureson the institutional organization and roles playedby universities within many EU countries: (1) thedifferent impacts of private and public financ-ing, (2) conflicts between the free advancementof basic knowledge and the research frontier andapplied research driven by the needs of industrialfirms. From the early 1980s onwards, policies andpriorities of universities have been increasinglyinfluenced both by the quest for relevance ofuniversity research to national needs and by thepressure of accountability and cost reduction.8

One of the most pertinent indications of theongoing change can be found in the increasedinteractions between university and the businesssector in the European Union. During the 1980sthe share of higher education expenditure onresearch and development (HERD) financed bybusiness enterprises showed positive growth rates

Table IHERD by form of financing in the aggregate for 7 EU countries [in %]

Total publicshare

General universityfunds (OUP)

Directgovernmentfunds Foreign Business

Otherincome NPOa

1983 940 683 257 06 29 11 151985 927 652 275 07 37 13 171989 899 602 297 14 54 12 211991 894 617 277 16 55 12 231993 877 601 276 25 58 14 271995 856 572 284 32 57 18 37

Austria (1993)b 971 827 144 04 19 0 04

Source: OECD (1998). The seven countries are: Denmark, France, Germany, Italy, Ireland, TheNetherlands, UK.aNon-profit organisation.bFor Austria only data for 1993 are available.

in all the EU countries. Although industrial fund-ing of university research showed an indication ofstabilization during the first part of the 1990s, itsshare of total HERD was about 6% in 1995.9

However, in Austria during the last twodecades, there has been no structural change inthe financing of university research. Table I dis-plays the development of financing by share ofcontributor and as part of total university researchcreating an aggregate sample of the seven EUcountries between 1983 and 1995. These sevencountries account for 80% of the total universityresearch in the EU.

Austria shows a very specific picture of theallocation of financial resources for universityresearch, and this picture is very different from theaggregate of EU-7. The share of business financ-ing of university R&D showed positive growthrates during the 1980s in the European Union.

At the beginning of the 1990s, this share on anaggregate scale, remained constant. As a mere2% of university R&D in Austria was financedby the business sector, Austria remained wellbelow the EU-7 average. To enhance the finan-cial contribution by the business sector to uni-

versity R&D is now a stated goal of technologypolicy in Austria. Hence, it is of specific interestto analyze the nature of the interaction processbetween universities and firms in Austria.

Firms in recent years, the focus of most ofthe studies10 on universityfirm interactions waslaid on detailed analysis of universityfirm link-ages in narrowly defined fields of research and

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Interactive Relations Between Universities and Firms 257

technology (so-called high-tech industries),11 onthe aggregate effect of university research onknowledge production in firms,12 or on certaintypes of interactions such as citations of universityresearch in firm patents,13 personnel mobility,14

joint publications15 and spin-off formations of newfirms by university members.16

The aim of this paper is to analyze some ofthe ways knowledge transfer between universitiesand firms may take place. While there is alarge number of different channels of knowledgetransfer,17 this paper restricts to four types ofinteractions between universities and the business

sector: joint research projects, contract research, joint supervision of Ph.D.s and Masters Theses

by university and firm members, the mobility of university researchers into

private firms.

Based on two different surveys one directedat innovative firms, the other one at universitydepartments the paper highlights complemen-tary aspects of universityfirm interactions. Thefollowing sets of research questions are to be

analyzed: What is the frequency of different types of

interactions between universities and firms onthe part of industrial firms and on the part ofuniversity departments?

What are the benefits that firms derive fromuniversities?

What factors determine the intensity ofuniversityfirminteractions on partof industrialfirms and on part of university departments?

The structure of the paper is organized asfollows: first, we briefly discuss the various datasources used an how we proceeded to collect thedata. In Section 3 we give a descriptive overview ofhow firms value the potential benefits gained fromcollaboration with universities. Section 4 discusses

various types of interactions between universitiesand firms and their frequencies. In Section 5 weuse multivariate logistic regression modeling toanalyze interactions between firms and universitiesfrom both points of view, the viewpoint of the firmas well as from the university department. Thepaper ends with a synthesis and main conclusionsin Section 6.

2. Data sources

Data on the various forms of interactions betweenuniversities and firms were collected by distinctsurveys. The first survey aimed at the assessmentof universityfirm linkages from the view of firms.The purpose was to ask innovative firms abouttheir collaboration behavior with the universitysector. Firms were defined as innovative if theyhad developed at least one product innovation

within the last two years.18 To identify innova-tive firms we proceeded as follows:19 Using adata base from a private consulting firm (withabout 40,000 firms listed) we selected 3026 firmsbelonging to the manufacturing sector (NACE20codes 1536) and with more than 10 employees.From this sample space 1006 firms were selectedrandomly. Using a CATI-approach (Computer

Aided Telephone Interview) we then identified443 innovative firms.

These 443 firms were our final sample for apostal survey. The questionnaire for this survey

was designed to get a broader view of the modesof interaction between the university and the busi-ness sector, of the motivations and barriers ofco-operation as well as to provide insight in how

firms value academic knowledge. The question-naire was filled out by the R&D managers ofthe firms and included questions on the generalcharacteristics of the firm (employment, found-ing year, R&D activities, patenting activities) onthe nature of the collaboration with the univer-sity sector (if any) and on perceived barriers tocollaborate with universities.21 99 firms returnedthe questionnaire which results in a response rateof 22.3%. On the side of the university depart-ments, a postal survey was sent to all 834 univer-sity departments in Austria (regardless of facultymembership) in Spring 1999.22 For large depart-

ments that are divided into sub-departments, andthe questionnaire was sent to the sub-departmentsin order to gather more differentiated and reli-able information. The key questions included per-sonnel mobility from universities to the businesssector, spin-off formations of new enterprises, lec-tures by firm members held at universities, train-ing of firm employees by university members,scholarships, postgraduate links and joint publi-cations. 421 questionnaires were returned by 350departments which is a response rate of 37.2%.23

Information on structural characteristics of all

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Austrian university departments is provided by theFederal Ministry for Science and Transport. Thesedata are collected on a biennial basis. Our studycovers the time period 199095.

3. General estimation of the benefits

from universities

Firms can benefit from universities in variousways. In order to get a more differentiated pictureon how universities contribute to the innovativecapacities of firms, R&D managers of innovativefirms were asked to indicate the importance of

different types of potential benefits from univer-sities. Table II summarizes the results. The sec-ond column of Table II gives the percentage offirms which value the different types of poten-tial benefits with 3 (important) or 4 (very impor-tant). The second part of Table II relates firm size(three size categories) to the mean value of the

valuation of potential benefits. Using analysis ofvariance we test, if there exist significant differ-ences between the three size categories concern-ing their mean values. (The null hypothesis is thatthere is no difference of the mean values betweenthe size categories.) The third part of Table I givesinformation on the valuation of firms with ownR&D departments (labeled as yes) versus thosefirms without own R&D departments (labeled asno). Again we use an analysis of variance to testfor significance.

Table IIGeneral benefits from universities

Mean values on a 14 scale(1= not important, 4= very important)

% of firms Own R&D-answering 3 or 4 By firm size (number of employees) department

Potential types of benefits n= 99 150 51200 201 and more Yes No

Highly skilled personnel 637 222 252 326 305 218

(university graduates)Ideas for new products 472 253 240 204 219 250

and processesGeneral and useful information 427 232 224 233 229 231Direct support in development 411 208 228 267 237 225

processNew instruments and techniques 379 222 220 242 239 217Results of basic research 333 219 204 196 218 198Consulting services 328 186 196 233 202 204

Source: Survey of innovative firms, tip. level of significance (p < 001), level of significance (p < 005).

It can be obtained from Table II that thefollowing four main channels are the most impor-tant types of benefit from universities (in orderof their importance): the employment of edu-cated and highly skilled personnel (universitygraduates), ideas for new products and processes,the provision of general and useful informationand direct support in the development process.Sixty four percent of all firms indicate that theemployment of high skilled, university educatedpersonnel is important or very important for theinnovation process. This result corresponds withmost other studies on this issue24 and confirms the

widely acknowledged importance of availability ofhuman resources for the innovation process andthe role universities are playing in the productionof high-qualified labor.

For almost two thirds of the surveyed firms,highly skilled personnel is either important or

very important. Furthermore, Table II revealsthat firms with an own R&D department valuethe benefits from high-skilled personnel signifi-cantly higher than firms without an own R&Ddepartment (a mean value of 3.05 versus 2.18).The staff at R&D departments is generally dom-inated by employees with university education.Hence, firms have to rely on people with suchqualifications much more extensively than firms

without own R&D departments. Also there is aclear relationship between firm size and the valua-tion of high-skilled, university educated personnel.

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Large firms have a significant higher mean value(3.26) than their smaller counterparts (small firmshave a mean value of only 2.22). Apparently thedemand for qualified R&D-personnel increases

with firm size. This may be the result of largefirms being more likely to have an R&D depart-ment. There are two other types of benefits thatsignificantly increase with firm size: Large firms

value the benefit of universities directly support-ing the development process higher than smallfirms. In addition, large firms value the bene-fit of consulting services by universities higherthan small ones. Somewhat surprising is the fact,

that firm size seems to have no significant impacton how important firms perceive the results ofbasic research to their work. For the majority(66%) of respondents results of basic research areof little or no relevance. Still, about one thirdof the firms find results of basic research rele-

vant to their innovative activities. This is quiteremarkable as in the sample large science basedfirms (e.g. pharmaceutical) seem to be somewhatunderrepresented.

Almost one half of the answering firms indicatethat universities are a significant source of newideas for new products and processes. Concern-ing the other types of benefits no significantdifferences between firm with and without R&Ddepartments can be reported.

4. Types of interactions between universities and

firms and their frequency

Table III shows the prevalence of different typesof interactions according to the two surveyscarried out by the authors. Four questions werecommon to both surveys. These relate to the

joint supervision/financing of Ph.D.s and Masters

Theses, contract research, joint research projectsand the employment of university researchers inthe business sector. For these four types of inter-actions (printed in bold letters) respondents fromeach group indicate the same ranking based uponthe percentage of respondents that engage at leastonce in the corresponding type of interaction.Variation of percentages across surveys are dueto different sample sizes. (421 in the case of uni-

versity departments and 99 in the case of firms.)Variations of percentages within one survey mayprovide insight in the perceived roles of the two

actorsuniversities and firms. The most frequenttypes of interactions are the employment of uni-

versity graduates on the part of firms and thejoint supervision of Ph.D.s and Masters Theses onthe part of the universities. These two types ofinteractions are very related: To educate studentsand prepare them for later employment in theeconomy has always been one of the main func-tions of universities. The supervision of Ph.D.s andMasters Theses is part of this function. Therefore,the most frequent type of interaction in each ofthe surveys allows both actors to behave accordingto their predefined roles: A university supervisor

has to ensure the scientific quality of a Ph.D. ora Masters Thesis whether or not he or she super-vises it jointly with firms. If a university graduateseducational background corresponds to the needsof a firm, the probability for employment is higher.Hence, there are little individual or institutionalbarriers against these types of interactions. In con-trast, the commercialization of university researchresults and knowledge in the form of universityspin offs or license agreements require a reshapein the role of universities traditionally perceivedby firms and by universities themselves. Therefore,these types of interactions seem to be much lessaccepted and less frequently made use of.

In the survey of university departments, the topis dominated by types of interactions that do notnecessarily include a recurring face-to-face con-tact between university members and firms. In the

joint supervision of Ph.D.s and Masters theses theface-to-face contact is maintained by a third party,the graduate or post-graduate student. Lecturesby firm members at universities may involve anyintensity of interaction between universities andfirms from none at all to regular contacts. Con-tract research in many cases includes face-to-face

contacts at the beginning and the end of the con-tract, but does not have to involve face-to-facecontacts in between.25 Some of the interactionsthat involve a very intensive contact between uni-

versities and firms are performed by about 30%of university departmentsjoint research projects,the permanent mobility of university members tothe business sector, joint publications and thetraining of firm members. Two further types ofinteractions that are associated with a very inten-sive flow of knowledge are carried out by onlya minority of university departmentsacademic

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Table III

Types of interactions between universities and firms in Austria 199598

SURVEY OF UNIVERSITY DEPARTMENTS SURVEY OF INNOVATIVE FIRMS

Percentage of Percentage of responding university responding innovative

Type of interaction departments Type of interaction firms

Supervision/financing of Ph.D.s and 38 Employment of graduates 67Masters theses

Lectures by firm members at universities 35 Supervision/financing of Ph.D.s and 42Contract research 32 Masters theses

Joint research 31 Contract research 32Employment of university 30 Joint research 23

researchers in the business sector International research networks 30

Joint Publications 28 Employment of university 7researchers in the business sector

Training of firm members 27 License agreements 7Spin-off formations of new firms 14Temporary movement of university 9

members to the business sector

n= 421 n = 99

Source: Surveys 1998/99. Percentage of respondents that mentioned to engage at least once in the corresponding type of interaction.

spin-off formations of new enterprises and thetemporary appointment of university members tothe business sector.

The view that the most frequent types ofinteractions are also the ones that involve onlyminor amounts of knowledge flows has to be mod-ified considering the results of the firm survey.Here, most common knowledge flow is derivedfrom the employment of university graduates. Thismay be rated a very intensive flow of knowl-edge as graduates entering the business sectorare equipped with advanced levels of training andexpertise. They bring with them tacit skills, haveexperiences of tackling complex problems and areoften part of networks of researchers.26

The comparison of the two surveys reveals thatthe transfer of human resources in the form ofuniversity graduates seems to be the most favoritetype of interaction. On the part of the firmsemployment of graduates is the most pervasivechannel of knowledge transfer. On the part ofthe universities joint supervision of Ph.D.s andMasters theses is the most pervasive one, whichis likely to prepare university graduates for futureemployment in the co-supervising firm or thebusiness sector of this firm.

5. Determinants of knowledge transfer

between universities and firms

The following section aims at the identificationof factors that enhance the establishment ofuniversityfirm interactions and those that inhibitthe establishment of universityfirm interactions.Various explanatory models are estimated in orderto account for the decision of firms and universitydepartments to engage in interactions with eachother. First, we look on the determinants for inter-action from the firm side and thereafter from theuniversity side. The data used are the same as wasreported in Section 2.

The firm perspective

The following determinants are expected to playa crucial role in affecting the probability for firmsto interact with universities:

Size of the firm. It seems to be a robustempirical pattern that R&D increases with firmsize and therefore enables firms to plug intoexternal sources of scientific and technologicalexpertise.27 This becomes possible because thefirm is equipped with a stock of knowledge ina particular domain that conditions its ability to

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evaluate and exploit external sources of knowl-edge, i.e. its absorption capacity.28 Thus we expectfirm size to have a positive effect on the propensityto interact with universities.

Age of the firm. The possible effect of ageis a priori somewhat unclear. New technology ori-ented start ups (or more generally young firms)are playing an important role in the process oftechnological change.29 These firms are particu-larly dependent on technological innovations andscientific progress and therefore more than othersinclined to engage in interactions with universi-ties. Instead, old firms were able to accumulate a

stock of knowledge within the firm and thus haveincorporated a vast number of fields of knowl-edge throughout their life cycle. Hence, it can beexpected that these firms are less dependent onexternal knowledge generated at universities. Nev-ertheless older firms may have established a set oflinks to universities during their life cycle and thushave more experience in co-operation which maylead to a higher propensity to interact.

Motivations of interactions. Firms pursue objec-tives that motivate the establishment of interac-tions with universities. Possible motivations are

the access to problem solving capacities of univer-sities, access to the state of the art science and tocomplementary know-how, outsourcing of R&Dand cost reduction, as well as access to researchnetworks or building up new research areas.

Among the barriers of interactions the lack ofresources on both sides, various measures of cul-tural differences, lack of information, lack ofsecrecy, spatial distance between interaction part-ners are likely to play a major role.

The description of variables used in the logisticregression model are given in Table IV, the resultsof logistic regression in Table V. The dependent

variable was dichotomous and calculated as fol-lows: if any type of interaction activities occurredin the firm within the last two years than its

value was 1, otherwise 0. The different types ofinteraction activities were contract research, jointresearch, joint supervision of Ph.D.s and MastersTheses, employment of university researchers.

The independent variables of Table IV aredefined as follows: Size of firm is measured asnumber of employees, age of firm as 1999 minus

year of foundation. All other variables of Table Vare measured on a ranking scale.

Table V reveals that the probability to interactwith university departments is growing signifi-cantly with the firm size as was expected.30 Onemay argue that this relationship varies over dif-ferent business sectors. However, we tried somemodels including a dummy for high-tech sectors(based upon the well known OECD high-tech defi-nition). This variable turned out to be insignificantin all model variants.31

The age of the firm plays a significant rolefor determining interaction with universities.Younger firms tend to rely on external sourcesof knowledge to a greater extent than their older

counterparts.The crucial motivation for firms to interact withuniversities is to get direct support in the innova-tion process. During the innovation process firmsare confronted with a wide range of possible prob-lems and difficulties which may be beyond thefirms own problem solving capacity. Hence, theyrely on external sources and are demanding con-crete support from universities for their innovativeactivities. This goes hand in hand with the fact thatthe basic research capabilities of universities donot play a significant role in enhancing the inter-actions between firms and universities (the respec-tive variable was insignificant in all types of modelscalculated).

On the other hand, from Table V some distinctbarriers can be obtained. Firms which considercommon projects with universities as difficult tomanage have a significant lower probability tointeract with universities. This points to the fact,that there exist somewhat different culturesbetween the two spheres. The main goal of univer-sities (beside teaching) is to produce knowledge inthe form of public goods and thus to enhance thestock of knowledge open to the society as a whole.

On the contrary, profit maximizing firms seek toappropriate the results of the innovation processand often try to keep the results secret.32 Addi-tionally, university research often is more longterm oriented while firms are mostly looking fordirect and short run effects.

Lack of information or poor communicationabout what universities actually do (and whatmight be the benefits for the firm) is also reduc-ing the probability for cooperation significantly.To acquire relevant information about universi-ties is associated with high search costs for firms.

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Table IV

Description of variables used in logistic regression analysis

Variable Question in survey

Direct support in development process What renders universities useful for your firm? Direct support in the developmentprocess: not relevant, , very relevant (four categories)

Results of basic research What renders universities useful for your firm? Results of basic research: notrelevant, , very relevant (four categories)

Lack of information on university research Barriers of research cooperation. Lack on information on relevant research atuniversities: high barrier, , low barrier (five categories)

Cultural differences Barriers of research cooperation. Cooperation with universities is difficult tomanage: high barrier, , low barrier (five categories)

Table VLogistic regression results:determinants of interactions on the level of firms

Dependent variableIndependent variables sum interactionsa c

Structural variablesSize of the firm 0003

Age of the firm 0052

Motivations and BarriersDirect support in 1586

development processResults of basic research 0227Lack of information on 0393

university researchCultural differences 0923

Constant 1328Number of observationsb 76Cox & Snell R2 047Nagelkerke R2 063Prediction success 842

aIf (sum(contract research, joint research, joint supervisionof Ph.D.s and Masters Theses, employment of universityresearchers) > 0;1; otherwise 0).bDue to missing values only 76 out of original sample ( n= 99)

were selected.cSignificant at 01 at 005 at 001 level, respectively.

Since the output of this search process is by nomeans clear and quite uncertain, firms may haveno incentive to undertake this process at all.33

The university perspective

The following variables are assumed to play a sig-nificant role in determining various types of uni-

versity firm interactions:The size of a university department may strongly

affect the resources available for R&D projects.

Large departments are expected to own a largerstock of transfer resources (personnel, physicalcapital and technical equipment, knowledge, expe-rience), which enables them to engage in interac-tions with the business sector and at the same timehandle all the other tasks of a university depart-ment. (teaching, publishing etc.) Small depart-ments are likely to be more flexible and special-ized on a narrowly demarcated field of research,

which may render them attractive partners for pri-vate firms. Medium-sized departments are viewedas being less flexible than small ones while provid-ing less infrastructure. Thus, a U-shaped curve ofsize effects interactions is expected. In the logisticregression model this variable is measured by thelogarithmic number of academic researchers.

Personnel structure. A high share of seniorresearchers (measured as researchers having a socalled Habilitation34) signals the predominanceof more experienced researchers with an estab-lished university career over young researchersat the beginning of a university career. Seniorresearchers are assumed to be socialized accord-ing to the traditional role of universities whichcomprises the education of students and the com-

munication of research results through scientificpublications. The cultivation of universityfirminteractions is not traditionally part of the role.These different types of researchers may there-fore show differences in their interaction behav-ior and in their previous experiences in differ-ent types of interactions. This independent vari-able is measured by the relation of researchers

with habilitation to researchers without habili-tation. The hypothesis is that a relatively highshare of researchers with habilitation decreasesuniversityfirm interactions.

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International publications. Collaboration withdepartments that provide high quality of researchresults and reputation may reduce risks of collab-oration and therefore reduce costs. As universitydepartments tend to communicate their researchresults through publications, a critical indicatorfor the quality of output may be the numberof publications per researcher published in inter-national top-level scholarly journals (Qualityoutput factor). We thereby assume a positiverelationship between the number of publicationsand universityfirm interactions. The variable ismeasured by the number of international publica-tions per researcher at the university department.

Experience in contract research projects with the

business sector. If a university department hasalready gathered experiences in carrying out var-ious kinds of research projects with firms, institu-tional and individual barriers are likely to be lessimportant than in the case of a department with-out any relevant experience so far. This variableis introduced into the logistic regression modelas a dummy variable, taking one if the universitydepartment has already gathered experiences in

contract research with the business sector.Experience in contract research projects with

public authorities. University departments withcontract research projects with public authoritiesdo also show experience in the competitive acqui-sition of external funds, i.e. experience in mar-keting competencies and knowledge available atthe university department. We therefore assumethat the number of contract research projects withpublic authorities per researcher has a positiveimpact on a university departments interactions

with the business sector.A high intensity of supervising Ph.D. and Mastersstudents, measured in terms of a high numberof supervised Ph.D.s and Masters Theses perresearcher, indicates a high teaching orientation.This is likely to detract resources from other activ-ities of a university department, such as researchand research cooperation. We therefore assume anegative relationship between a university depart-ments intensity of supervising Ph.D. and Mastersstudents and its decision to establish interactions

with private firms.

Frequency of public presence. Lengthy pro-cesses of search for an adequate interaction part-ner tend to be omitted because of opportunitycosts. It seems likely that every way of inform-ing a broader public of research activities andresults is conducive in order for interactions to beestablished. University departments are publiclypresent, whenever they present research results inall kinds of mass media. The amount of presenta-tions of a university department in mass media isintroduced into the logistic regression models as acategorial variable with four categories.

Table VI summarizes the variables defined in

the logistic regression models.The model results in Table VII suggest uni-formly that it is university departments in tech-nical sciences that interact with industrial firms.This is no surprise considering the sector-specificR&D intensities in Austria. It is mostly tech-nical and engineering sciences that have thehighest R&D intensities in Austria, particularlyelectronics and telecommunications, pharmaceuti-cal industry, office machinery and computers andtransport technologies.35 University departmentsin humanities apparently tend not to interact withthe business sector, dummy variables for other

university fields of research are mostly insignif-icant. The structural variables included in thelogistic regression models partly show the impactson the dependent variables according to the pre-

vious assumptions. The size of a university depart-ment has a significantly positive impact in four ofthe five models. Instead, a high share of experi-enced researchers with an established universitycareer, does not have any significant influence onthe dependent variables.

In contrast to our previous assumptions, thepublic presence in the form of presentations inmass media has a significantly negative impacton the extent of interactions between universi-ties and firms in almost all of the models. Appar-ently, university departments and industrial firmsestablish their contacts via occasions other thanthe signaling of university departments via presen-tations for a broader public on TV, radio or inprint media. A possible explanation may be thatuniversity researchers in mass media usually com-ment on issues on a more aggregate level than thefirm level (for example National economic devel-opment, genetics, BSE crisis etc.). Another expla-nation may be that university researchers that

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Table VI

Description of variables used in logistic regression analysis

Names of variables Definition of variables

Dependent variablesPh.D.s/M.M. If (joint supervision of Ph.D.s and Masters Theses between firms and

university departments) > 0 1; otherwise 0)Researcher mobility If (employment of university researchers in the business sector) > 0 1;

otherwise 0)Joint research If (joint research of firms and university departments) > 0 1; other-

wise 0).Contract research If (contract research of university departments for firms) > 0 1;

otherwise 0).Sum interactions If (sum(contract research, joint research, joint supervision of Ph.D.s

and Masters Theses, employment of university researchers) > 0;1;

otherwise 0).Independent variables

log (Size) log (number of researchers (full professors, associate professors,research assistants).

Percentage of researchers with Habilitation (Full professors + associate professors)/research assistants withouthabilitation.

International publications number of publications in foreign scholarly journals per researcher atuniversity department.

Contract research with the business sector If (contract research projects with the business sector > 0;1;otherwise 0).

Contract research with public authorities Number of contract research projects with public authorities perresearcher at university department.

Public presence Categorial variable for the amount of presentations in mass media(4 categories: 0, 15, 620, more than 21).

Intensity of supervision of Ph.D.s/M.M.s Number of supervised Ph.D.s and Masters Theses per researcher.

appear in mass media more frequently than aver-age do not behave as it is expected from universityresearchers and therefore seem less trustworthy ascooperation partners.36

As for the determinants of the individual typesof interactions, Table VII demonstrates clearlythat different types of interactions are determinedby different factors. If a university department hasalready gathered experience in contract research

with the business sector and at the same time

supervises a high overall number of Ph.D.s andMasters Theses per researcher, it is more likelyto co-supervise Ph.D.s and Masters Theses jointly

with private firms. This indicates that universityresearchers initiate jointly with employees (or themanagement) of the firm the interaction via suc-cessful past contacts, rather than the graduatestudent himself. In all other logistic regressionmodels the intensity of supervisions of Ph.D.s andMasters Theses has no significant impact.

In the contrast, it seems that the vast majorityof university researchers getting employed by

industrial firms are graduates and experts comingfrom big departments in technical sciences. Theonly variables that exert a significantly positiveinfluence on the mobility of university researchersare the size of a university department and itsbeing a technical science. This confirms ear-lier findings that private firms rely on universityknowledge in order to increase their technologi-cal problem solving capacities. The recruitment ofuniversity researchers from technical sciences maybe considered as one such strategy. As for the size,it may be an explanation that in big departments itis unlikely that every researcher is able to pursuethe career he/she would like to, whereas a changeto another employer might increase the chancesof the desired career.

A university departments propensity to carryout joint research projects is to some extentdetermined by a high number of publicationsin international scholarly journals per researcher.This indicates that a high quality of research

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Table VII

Determinants of interactions on the level of university departments: parameter estimates of logistic regressions

Ph.D.s/M.M.Researchersmobility

Jointresearch

Contractresearch

Suminteractions

Structural variableslog (Size) 1079 1224 1638 0438 1176

Percentage of researchers with Habilitation 0183 0123 0018 0090 0016Research characteristics

International publications 0131 0019 0405 0195 0042Contract research with the business sector 0946 0564 0519 0886 0832

Contract research with public authorities 0710 0260 0853 0202 0321Public presence 0525 0623 0590 0261 0447

Intensity of supervision of Ph.D.s/ M.M.s 0058 0012 0011 0009 0024Dummy variables for fields of research

Technical sciences 1664 2091 1268 1982 2591Natural sciences 0453 0045 0859 0332 0015Human medicine 0068 0915 0764 0523 0041Humanities 1226 1830 3164 2869 1830

Constant 1735 0987 2050 1930 0844

Number of observations 309 309 309 309 309Prediction Success 751 757 731 741 751Log-Likelihood at constant 422362 406893 390535 402384 424834Log-Likelihood at maximum 322832 298613 294239 303039 311390Chi-Square 99530 108269 96296 99324 113444

Significance level of coefficients: 0 01: 0 05: 0 1:

results close to the scientific frontier of the dis-

cipline. It is striking that the type of interac-tion joint research projects is the only one wherethe quality of a university departments researchresults seems to matter. In order for indus-trial researchers to be worth to invest time andresources in joint research activities, the universitydepartment has to be at the edge of the respectivediscipline. But it is not only quality that deter-mines joint research projects between universitydepartments and firms, it is also experience in con-tract research. Past contract research with pub-lic authorities significantly influences a universitydepartments propensity to carry out joint research

projects with private firms.The only variable among the characteristics

of a university department which has a statis-tically significant effect upon the propensity tocarry out contract research with the business sec-tor is a university departments experience in con-tract research with the business sector in the past.Excellence of research results at the edge of thediscipline and international research networks donot play a significant role. If private firms lookfor the technological problem solving capacity ofuniversity departments, it is not the quality of

research that matters but the quality of past inter-

action. If interaction has been successful on atechnological as well as on a personal level, futurecontract research with the business sector is morelikely to take place.37

A logistic regression model where all presentedtypes of interaction are combined (i.e. summedup, and dependent variable equals one, if thesum is greater than zero) confirms what seem tobe the main determinants of interaction on partof the university departments: It is mainly size,past experience in contract research with the busi-ness sector and the dummy variable for technicalsciences that have a significantly positive effect thepropensity of university departments to interact

with the business sector.

6. Synthesis and conclusion

The main transfer of knowledge between theindustrial and the university sector still occursthrough the mobility of people equipped with sci-entific knowledge. Asked for the general bene-fits from universities, a vast majority of the firms

values highly skilled personnel as the main output

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from universities and considers the employment ofgraduates as important access to academic knowl-edge. Furthermore, the joint supervision of Ph.D.sand Masters Theses which results in graduatesbeing not only equipped with scientific knowledgebut also acquainted with the needs of the firm orthe business sector of the firmis one of the mostfrequent types of interaction between universitiesand the business sector.

Past experience in interaction with the businesssector are crucial for university departments to getinvolved in interactive relations with the businesssector. Satisfaction with past interactions on a per-

sonal, technological and on a research level lowersindividual and institutional barriers and rendersuniversityfirm interactions more likely. Appar-ently, the quality of research does not count asmuch as the quality of the past relationship.

For all types of interaction, apart from humancapital mobility, direct support in the innova-tion process forms the main motivation on partof the firms. Path-dependence and the localizednature of firm-specific knowledge result in con-strained technological capabilities of firms. Theseconstraints of technological capabilities entail thatfirms attempting to innovate are very likely to run

into problems which lie outside their existing capa-bilities and knowledge base. This implies a need toimport externally-developed technological knowl-edge in order to find a solution for innovationproblems which crucially motivates universityfirminteraction on the part of the firms. As for themain barriers to universityfirm interaction, thisapplication-orientation of firms is in strong con-trast with university objectives, pace and methodsof validation and reward. Universities as well asprivate firms follow their individual rationality indeciding whether to establish inter-organizationalrelationships. Hence, cultural differences seemto be the main barriers in the creation ofuniversityfirm links.

Furthermore, the lack of information is abarrier for universityfirm interactions. Hence,one bottleneck for improving the interactionbetween universities and the business sector ispoor communication about what universities actu-ally do and what might be relevant for firms.Marketing instruments as public presence in massmedia do not seem to be an appropriate strat-egy to decrease the informational mismatch. Obvi-ously, the results presented by university members

in mass media apply for aggregated levels of theeconomy and do not seem useful for problem-solving on the level of the individual firm.However, the question if this informational mis-match between universities and the business sectorshould be interpreted as an obligation for deliv-ery on part of the universities or an obligation forcollection on part of the firms, could not be solved.

Acknowledgments

The authors would like to thank ChristianRammer and Wolfgang Polt for valuable com-

ments on earlier version of this paper. In addi-tion we have greatly benefited from the commentsby Jerry Thursby and Albert Link. However, theusual caveats apply.

Notes

1. Romer (1986, 1990, 1994), Grossman and Helpman (1991,1994).2. OECD (1996).3. Fischer (2000).4. Lundvall (1988).5. Lundvall (1988).

6. Freeman (1987), Lundvall (1992), Nelson (1993), Edquist(1997).7. For example the so called Community Innovation Surveys

(CIS-I and CIS-II) performed by the member states of theEuropean Union or the survey carried out by the OECDfocus group on Innovative Firm Networks (Christensen et al.,1999).8. See Geuna (1999) for a discussion of this empirical trend

as well as for a critical assessment of the changing rationalefor European university research funding.9. OECD (1998).

10. See Varga (2000) for an overview.11. Bania et al. (1993), Acs et al. (1994).12. Jaffe (1989), Varga (2000) and Anselin et al. (1997).13. Jaffe et al. (1993), Almeida and Kogut (1995).14. Bania et al. (1992), Almeida and Kogut (1995).15. Hicks et al. (1993).16. Parker and Zilberman (1993), Kelly et al. (1992).17. Acs et al. (1994), Schartinger et al. (2000).18. We based our definition on innovation rather than onthe existence of an own R&D department because in Austriathere are many firms which are highly innovative but do nothave a formal R&D department. This definition was also usedby the European Commission in defining the concept of theCommunity Innovation Survey.19. Schibany (1998).20. Classification of business sectors by the European Union.21. The detailed questionnaire is available from the authorson request. However it is in German only.

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22. This survey was financed by the Austrian Science Fund

(FWF).23. Schartinger et al. (2000).24. Martin et al. (1996).25. Schmoch (1999).26. Martin and Salter (1996).27. Cohen (1995)28. Cohen and Levinthal (1989).29. Storey and Tether (1998).30. Some of the above mentioned independent variables(for example, spatial distance between the actors) have beenomitted during the modeling steps because they proved to beinsignificant throughout the various modeling steps.31. This might be due to the small sample size.32. Hall et al. (2000).33. Interestingly, lack of interest from the university is notconsidered as a barrier of interaction. This variable proved tobe insignificant in various model variants.34. The so called Habiliation is required to gain theposition of an associate professor. It consists of originalscientific research (usually published as a book or as collectedpapers) and a public lecture which is evaluated by a scientificcommission. The Habilitation is a specific requirement inGerman speaking countries.35. Statistics Austria (ISIS data base).36. Comment by Jason Owen Smith at the Purdue UniversityWorkshop on Organizational Issues in University-IndustryTechnology Transfer.37. Comment by Elaine Brock at the Purdue UniversityWorkshop on Organizational Issues in University-IndustryTechnology Transfer.

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