university-related science parks — ‘seedbeds’ or ‘enclaves’ of innovation?

Technovation, 14(2) (1994) 93-110

University-related science parks - ‘seedbeds‘ or ‘enclaves’ of innovation?

Daniel Felsenstein Department of Geography and Institute of Urban and Regional Studies, Hebrew University of Jerusalem, Mount Scopus, Jerusalem 91900, Israel

Abstract

This paper examines the role of science parks as ‘seedbeds’ of innovation. Making the distinction between the spatial and the behavioural conceptions of the seedbed metaphor, the paper surveys the evidence related to the limited interaction effects between science park firms on the one hand and their neighbouring park firms, local universities and off-park firms on the other. This suggests that science parks might be functioning as ‘enclaves’ of innovation rather than seedbeds.

This hypothesis is empirically tested on the basis of a survey of over 160 high-technology firms in Israel located both on and off-park. Specifically, the following questions are addressed: (1) are seedbed eflects important inputs to a firm’s innovation level? and (2) to what extent are these effects contingent on the physical proximity and clustering afforded by science park location? The results indicate that, first, seedbed effects, as indicated by level of interaction with a local university and the entrepreneur’s educational background, are not necessarily related to the firm’s innovative level; second, science park location is shown to have only a weak and indirect relationship with innovation level. It is proposed that the role of the science park is thus innovation-entrenching rather than innovation-inducing. The attraction of science park location could therefore be due to perceived status and prestige conferred rather than benefits in terms of technology transfer and information flow.

1. Introduction

This paper examines science parks as ‘seedbeds’ of innovation. Implicit in the ‘seedbed’ metaphor is the notion of a nurturing process that eventually creates an environment for growth. The science park as a ‘seedbed’ therefore refers to the conditions created to promote innovation. Science parks (and especially those that are university

related) are intuitively conceived as fulfilling such an environment-creating function. They are assumed to play an incubator role, nurturing the development and growth of new, small, high- technology firms, facilitating the transfer of university know-how to tenant companies, encouraging the development of faculty-based spin-offs and stimulating the development of innovative products and processes [l-3]. As such, they are said to create

Technovation Vol. 14 No. 2 0166-4972/94/US$O7.00 0 1994 Elsevier Science Ltd 93

0. Felsenstein

a supportive environment for the development of innovation, creativity and entrepreneurship.

The above description stresses the seedbed role of the science park in a behavioural sense. However, another set of expectations of science parks relates to their function in a regional economic development sense. Science parks are invariably associated with more than just a role in promoting innovation and entrepreneurialism, and a successful science park is viewed as more than an innovation-generating environment. It is often - wishfully - ascribed with the properties of a growth sector leading the area under question into a spiral of propulsive expansion [4, 51. The few success stories notwithstanding, the reality is rather more mundane. The science park as a catalyst in urban and regional growth is not a well- trodden path and, despite public policy rhetoric to the contrary, few examples exist of science park- led local economic development [6].

The common ground between the behavioural and spatial conceptions of the seedbed lies in the notion of the seedbed as creating an environment. This environment, while occupying dimensions in geometric space, is not exclusively spatial. It represents a ‘milieu’ in both the functional and behavioural sense, as well as the geographic. Amongst the principal factors of production in science park development are information and know-how which are inherently unconstrained by spatial boundaries. The science park as a seedbed for innovation is more than just a physical concen- tration of units of production benefiting from the linkages and economies of scale and scope that agglomeration affords. As will be seen below, these advantages are not often realized and the innovation process does not seem to be contingent on them.

There must therefore be a further aspect of the seedbed environment that is related to behavioural factors and is also important in understanding the relationship between science park location and innovation. This relates to the network environment of the firm that comprises the informal and non-institutionalized flows of information, knowledge and collaboration based on contacts,

work experience, education and so on. These factors, while difficult to quantify, add a further dimension to the seedbed function of the science park and also transcend its narrow spatial confines.

This paper examines the relationship between innovation and science park location and, in so doing, highlights these different conceptions of the seedbed function. An empirical analysis, based on evidence from science parks in Israel, is presented. While no attempt is made to determine the direction of causality between level of innovation and location on a science park (does science park location make for more innovative firms, or do the more innovative tend to cluster in science parks?), we are interested in unravelling some of the relationships between innovation, location and the behavioural characteristics of science park company entrepreneurs and managers. This will shed light on the seedbed functions of the science park, both behavioural and spatial.

More specifically, the paper proceeds in the following manner. The main tenets of the seedbed environment, from both a behavioural and a spatial perspective, are highlighted in the next section, and the empirical evidence for science parks as seedbeds is presented. This is followed by a short description of the context within which the empirical work is set, and a formulation of the main hypotheses. Data limitations and issues of method are then discussed and this leads on to the empirical findings. On the basis of a survey of over 160 Israeli high-technology firms and three major university-related science parks, some of the relationships between behavioural characteristics, science park location and innovation level are estimated. The significance of these findings with respect to the role of science parks in the innovation process, and their policy implications, are presented in the concluding section.

2. The seedbed environment

In defining the conditions that give rise to the seedbed environment, a distinction needs to be drawn between the spatial and the behavioural

94 Technovation Vol. 14 No. 2

Science parks - seedbeds or enclaves of innovation?

approaches. From a spatial perspective, the exogenous factors nurturing and promoting innovation at a given location are integral components of the seedbed environment. Much effort has been expended in identifying those areas that have ‘good’ incubator conditions based on city size, level of urbanization, institutional structures and community characteristics [7, 81. While the evidence on the impact of these influences on innovation is mixed, it does seem to suggest that urban areas are not particularly important markets for the development of innovative activity. In fact, a study of the incubator function of the Raandstad cities in Holland has shown these locations to be under-represented in terms of innovative activity [9]. The spatial seedbed hypothesis of an indigenous regional impact supporting innovation could therefore not be supported and the study suggests looking at intra-firm characteristics for further explanation.

While this spatial approach relates to the determinants of innovative activity, other approaches have looked, from this same perspective, at the determinants of the incidence of industrial and science parks. The presence of industrial parks in general has been related to city (or community) size and age, population densities and population growth rates [lo]. Looking more specifically at science parks, Luger and Goldstein [ll] have demonstrated the importance of size of metropolitan area, linkage to a local university and level of service offered, in explaining science park success as measured by employment generation. Con- versely, parks located in small areas and without university connections are the most likely to fail.

This identification of seedbed characteristics has not been without its critics. As an approach to understanding location tendencies it has been termed ‘analytically sterile’ [12 (p. 219)] and it is claimed that innovative places cannot be ‘read-off on the basis of the ‘right’ exogenously determined characteristics. Furthermore, such an approach, it is argued, misses the subtle role played by industrial organizations and overlooks the social divisions of labour that have allowed the seedbed to emerge. In essence, a behavioural component of seedbed

formation is missing. This would include the role of exogenous organizations or institutions such as universities, the role of other firms in promoting or discouraging spin-offs, previous work and educational background of entrepreneurs, and so on.

From a behavioural perspective, the seedbed environment is composed of a knowledge infrastructure (such as a university or research institute) that creates positive externalities that become public goods. These institutions can also include local chambers of commerce, banks, venture capital companies and so on. These are all milieu-creating organizations [13] in that, when they occur within a given area, they create an information- and transaction-intensive complex that not only pro- vides economies in scale and scope and cost savings in transactions but also reduces risk [14].

Other firms also have an important role to play in fashioning the behavioural environment. They are sources of knowledge and externalities no less than the institutions mentioned above. As Acs and Audretsch [15] point out, other firms (competitors, collaborators) are repositories of know-how and work experience. Some of this is accessed through formal sources such as contracts and licensing agreements while some is generated through meet- ings, trade fairs, informal contacts and intermediar- ies such as suppliers and consultants. Where all this occurs in a given environment such as a science park, the spatial and behavioural factors interchange. However, as Birley [16] has pointed out, in a local environment one of the main informational shortcomings is the lack of kncwl- edge as to what information is in fact available. This would seem to suggest, for example, that firms choosing to locate on a science park may not necessarily be exercising this choice because of its perceived seedbed function. They often set- up without a proper screening process of the environment, and their location choice may be part of a ‘social signalling’ process [17]. The seedbed properties of the environment (access to information, networks etc.) are then probably only realized at a later stage. This could also seem to suggest that initially the innovative capacities of a new firm in an environment such as a science park

Technovation Vol. 14 No. 2 95

D. Felsenstein

are not fully exploited. This is because innovation is related to the seedbed environment, which is hypothesized to be an unknown quantity at the early stages of firm development.

3. Science parks as seedbeds

Evidence exists suggesting that, in spatial terms at least, universities do have a seedbed effect on their local economies. A series of aggregate analyses on the effects of universities on metropolitan areas or regions in the United States has shown this effect to be wide ranging. Thus Jaffe [18] has found a relationship between firm innovation rates (measured by patents) and the level of local university research. This suggests the existence of technological ‘spillovers’ that benefit firms in proximity to universities. Further evidence of this aggregate spillover effect comes from Bania, Eberts and Fogarty [19], who attribute higher levels of .new firm formation rates to those places with concentrations of highly skilled university labour, and from Beeson and Montgomery [20], who suggest that this spillover effect can also affect occupational composition. They show that the level of R&D funding at a local university increases the odds of being employed locally as a scientist or engineer or being employed in a local high-technology industry. This seedbed function also serves to entrench future rounds of growth as evidenced by universities’ ability to attract scientific infrastructure. For metropolitan areas, it has been shown that industrial R&D labs tend to concentrate in those areas where levels of university research are highest [21]. This relationship has also been found on the basis of case-study analysis [22].1

It should be noted, however, that evidence from outside the US is more equivocal. In the case of Japan, for example, Eto and Fujita [28] reject the hypothesis that universities are instrumental in generating high-tech firm growth. They find strong evidence of the self-entrenching effects of high- tech growth and that scientific-industrial agglomerations will tend to reproduce themselves. This can

often take place in proximity to leading universities, although they find no real causality in this process. Similarly, Florax and Folmer [29] show that, in the case of Holland, the diffusion of knowledge is not necessarily a function of spatial clustering around universities. This seems to imply little spillover effect, in small countries at least.

Overall, however, the claims for a significant university seeding effect on the local economy, with respect to innovation level, new firm start- up rates, occupational composition and so on, seem to be well founded. Evidence attributing a similar role to science parks, however, is rather more mixed. In terms of new firm formation, Massey et al. [6] find mixed evidence for British science parks. On the one hand, new start-ups form a clear minority (less than 30%) of firms on science parks and in much celebrated seedbeds, such as Cambridge, this figure is less than 10%. On the other hand, new start-ups on science parks have a much lower mortality rate than that of new firms in general (less than 2.5% a year). This could however reflect a screening process for science park entry that selects only firms with good survival chances. In the US, case study evidence shows great variation. On the Research Triangle Park, for example, over 70% of firms are part of multi-plant organizations whose existence cannot be attributed to the park. The University of Utah Research Park, on the other hand, has local, single-plant organizations comprising more than half the park population [ll].

Science park-based spin-offs that trace their roots to the university are another seedbed characteristic. While much attention traditionally has been focused on existing firms spawning new firms [30], spin-offs emanating from a university environment have received much less attention [31]. Neverthe- less, accounts of locally based company genealogies nearly always put the local university or science park at the apex of any ‘family tree’ account of seedbed growth.

Survey evidence from firms located on Dutch and Belgian science parks indicates that only 37% of firms in the former and 16% in the latter attribute their origins to universities [32]. In the



US, again the variation across individual parks is very great. Thus, Luger and Goldstein [II] have found that over 120 spin-off companies in the vicinity of the Stanford Research Park have university antecedents whereas for the Research Triangle Park the number of university spin-offs is virtually nil. Massey et al. [6] show that, for UK science parks, on aggregate 25% of firms may have their beginnings in academia although some of these are likely to be analytic service units or technical troubleshooting operations that, prior to the establishment of the science park, were located within the university. Even at the level of the individual park unit, while a location such as the Cambridge Science Park is much heralded as a spin-off incubator and (in 1987) nearly 400 local firms owed their ultimate origin to the local university in one way or another, direct university involvement in their establishment was much more circumspect. Segal Quince Wicksteed [33] reports that less than 20% of new companies were formed by direct university entrants.

If the science park functions as an effective seedbed, it is assumed that the level of R&D conducted by firms located on science parks is generally higher than that of off-park firms and that this reflects the level of (close) interaction presumed to exist between science park firms and the local university. The science park is thus perceived as an important conduit in technology transfer out of the university and into the local economy.

Van Dierdonck et al. [32] present a rather different picture based on science parks in Belgium and Holland. They show rather low levels of interaction performance between park tenants and local universities, with the overall level of R&D activity performed on the park being lower than popularly anticipated. Only 32% of Dutch firms and 57% of Belgian firms surveyed reported in- house R&D, and little R&D interaction was reported with either local (science park) firms or the local university. In fact, for many science park firms, external research linkages were not locally defined and were conducted on an international scale, pointing to the existence of research networks

unconstrained by national boundaries. Similarly, Massey et al. [6] find science park tenant firms in the UK less ‘leading edge’ than generally imagined. On the inputs side, over 40% of employees on parks were scientists and engineers2 and R&D expenditures in relation to sales were very high. On the outputs side, despite a greater propensity to patent amongst on-park than off-park firms, most firms were found to be engaged in modification of existing technologies rather than the development and production of totally new innovations.

Complementing this picture is the evidence pointing to the generally low level of links between science park firms and local universities. Most accessing of academic resources relates to low- level contacts based on recruiting university graduates, or informal contacts [6]. Joint research or subcontracting relations are much less profuse. For UK science parks only 14% of companies reported such links, and a survey of two major parks in Israel reported a similar figure [34]. Firms on US science parks have also indicated that recruitment of graduates and use of university facilities form their main points of contact with universities. Thus, for both the Research Triangle Park and the University of Utah Research Park, technology transfer to local firms (both on- and off-park) was low [ll]. For these firms the local university was not a particularly important source of R&D inputs or innovations. Even for the Stanford Research Park, generally accredited with being an archetypal ‘seedbed’, local firms did not report a significant seeding effect. Thus, while 80% of on-park firms reported links with Stanford, over 70% of off-park firms said they had no connection.

These observed levels of university-science park interaction have led to suggestions that high- technology firms are more dependent on linkages and information flows from other similar firms than on interaction with universities [35]. If this is the case, there should be evidence of science park firms seeding the local economy through their material input-output patterns. However, examining the external purchase and sales linkages

Technovation Vol. 14 No. 2

D. Felsenstein

that science park firms have with local off-park firms, it becomes apparent that in many cases these structures are limited. This is hardly surprising in view of the increasingly national and international markets that high-technology firms are serving [36, 371. When the local market is also a leading market nationally, science park firms are likely to seed the local economy in terms of inputs and outputs. Thus, while firms located on the Stanford Research Park have heavy reliance on California for non-labour inputs, firms in the Research Triangle Park have little interaction with each other or with off-park firms [ll]. Limited sales and purchase links with the local economy have also been reported in the case of Cambridge, with over 70% of local firms reporting either no linkages or interactions of minor significance [38]. Similar evidence with respect to external material linkages exists for science park complexes in both Canada [39] and Australia [40].

The level of inter-firm linkage is of course heavily contingent on factors exogenous to the science park, such as firm organizational structure and market structure. Science parks characterized by branch plants are likely to have less local producer-supplier relations than parks composed of independent, single-facility units [41]. Market structure, similarly, is likely to dictate the level of local linkage. Customized production rather than mass production is more likely to have a local seeding effect in terms of external linkages and spin-offs [42].

It should be noted that, even if local linkage structures are weak, this does not mean that the total impact of the science park on the local economy is negligible. Input-output studies that have tried to capture the induced effect on local incomes, output and employment point to considerable impacts attributable to the science park. In employment terms, Luger and Goldstein [ll], for example, estimate total employment impact (direct and indirect) as ranging from over 4000 jobs for Utah to 75000 jobs in the case of Stanford3. While this represents much more than a seeding effect, it does indicate the growth potential of a process that expands by a fixed multiple of its initial injection.

While the behavioural factors inducing seedbed formation have been examined in relation to innovation and the entrepreneurial process in general [l, 161, they have not been studied with respect to science park firms in particular. If the science park does function as a seedbed for innovation, then we might expect to find differences in these behavioural factors between on- and off- park firms. However, a case study of the Central Florida Science Park and its environs could not support this contention. The behavioural milieu, from which chief executive officers (CEOs) of both science park and non-science park firms emerged, was very similar with respect to work experience, educational background and management skills [37].

4. The setting and hypotheses

University-related science parks in Israel are a result of a government decision taken at the end of the 1960s to improve university-industry interaction through the establishment of technological-industrial complexes in proximity to major universities. Over the 197Os, four such campuses were constructed, associated with the Weizmann Institute of Science at Rehovot (a city south of Tel Aviv but lying within the Tel Aviv metropolitan area), the Technion - Israel Institute of Technology - at Haifa, the Hebrew University of Jerusalem and Tel Aviv University. Firms from three out of these four major parks are surveyed here (the exception being the Technion).

These parks were constructed with the help of liberal government assistance administered under the Law for the Encouragement of Capital Invest- ment , Israel’s principal vehicle for assisting industry [43]. Prospective tenants for the parks were to be screened by the Office of the Chief Scientist at the Ministry of Industry and Trade, and the final decision on acceptance was to be in the hands of the local park authorities. In practice, a lack of clear selection criteria resulted in a more haphazard entry process [43] and each park eventually created its own set of entry qualifications in line with the general objectives and character of the specific park.



Site development and park management vary from case to case. In all parks there is formal association with the university although, in practice, academic involvement in management and development is minimal. Thus, in Rehovot, the university has vested all its authority in a private development company. In Jerusalem, responsibility for the park is divided between local government, a recently privatized city development corporation and the various large tenants in the park who generally deal directly with central government. Needless to say, this situation is not without problems [34] and in recent years the university has reduced its involvement in the park to a minimum. In Haifa, the city’s economic development corporation has set up a subsidiary to manage, develop and promote the park; and in Tel Aviv, park management is similarly in the hands of a joint local authority-university subsidiary.

The parks therefore represent planned environments initiated and originally promoted by central government but whose development trajectory has, over time, devolved to local government and its institutions. Only in one of the four cases have development and management been handed over to private interests.

Physically, all parks have been developed in major urban centres but on sites distinct from existing industrial areas and generally in proximity to residential neighbourhoods.

Turning now to the hypotheses, this paper examines the contention that, in practice, science parks function more as enclaves of innovation than as seedbeds. The empirical evidence reviewed above has served to stress the rather limited seedbed role that most parks play, especially in respect to linkages with local universities, new firm formation, incidence of spin-offs and linkages with the local economy. The performance of parks in all these spheres and their rather constrained interaction patterns suggest that parks may function as ‘islands’ of innovation (see [6], p. 53) or as collections of firms with no real links between them. While we are not proposing that the physical configuration of the park has any role to play in its functional sense as a seedbed, we can hypothes-

ize that the geographic separation that characterizes all parks serves to buttress their enclave-type character. This suggests that if location on a science park is not that important an input for innovation, its importance to tenant firms may lie in the status and prestige effect that it generates.

More specifically, this major hypothesis is tested via an examination of those interactions and relationships (both direct and indirect) that exist between science park location, innovation and various characteristics likely to relate to the firm’s innovation level and associated with the seedbed function in a behavioural sense (for example, relationship with universities, spin-off history of the firm, education and work experience of the entrepreneur or manager). Thus, in relation to the innovation level of the firm, we would expect to find the CEO’s educational background and university linkages related to the level of innovation. This would illustrate the classic human capital influences on innovation. If the science park fulfils a seedbed function for innovative activity, then we would expect to find science park location related directly and indirectly to these characteristics and to others such as CEO work experience, firm’s spin-off history etc. Conversely, absence of these relationships would suggest the enclave function of the science park.

5. Data and method

5.1. Data

The data source for the empirical analysis that follows is a questionnaire survey of 162 Israeli high-technology firms. The survey endeavoured to cover all firms located on the three target science parks (some 110 firms). In practice, responses were received from 73 firms (66% of the target population). The remainder of the firms were drawn on a stratified basis from off-park locations in the vicinity of the science parks (the Herzliya, Petach Tikva and Holon industrial zones for science parks in the Tel Aviv metropolitan area), the various industrial zones in Jerusalem for the


0. Felsenstein

park in that city and a further group of ‘other’ (generally metropolitan) locations (Table 1).

On a sectoral basis, we can point to a certain level of local specialization (Table 1). Thus, firms in the Tel Aviv area would seem to be concentrated in the electronics and software sectors while the Rehovot area is characterized by a large collection of biotechnology and chemical firms and Jerusalem by chemicals, electronics and electro-optics. In each location, the sectoral composition of on- and off-park firms is very similar, as are their industrial and employment characteristics.

The industrial and employment profile of the surveyed firms is outlined in Table 2. As the average figures belie the importance of the small firm sector in the sample, the means for this sub- group are presented separately. Small independent firms form the vast majority of the sample (65% of firms surveyed have less than 50 employees and 67% are of independent ownership). In general they are younger and more technology intensive in terms of inputs (percentage of R&D employees, R&D expenditures etc.) but, as expected, less capital intensive and production oriented.

TABLE 1. Location and sectoral composition of firms surveyed

In the present analysis, approximately 12% of responses were incomplete in one way or another, leaving a total of 142 usable responses. These provided data relating to (a) firm characteristics and (b) the characteristics of the entrepreneurs or managers of the firms surveyed. In most cases the owner-manager of the firm was interviewed. Failing that, the data relate to the characteristics of the senior company executive that responded to the questionnaire. All the data used are categorical and the variables are defined in Table 3.

Firm characteristics refer here to the firm’s location, innovation level, location of main markets, level of interaction with university and spin- off antecedents. Entrepreneur/manager characteristics describe the education level and work experience of the firm’s owner-manager and also the extent to which the entrepreneur or manager is engaged in production of products with which he has prior experience.

In view of the relatively limited number ‘of observations and the prospect of empty cells when using n-dimensional cross-tabulations, the variables above have been coded into a minimal number of

n % % located % % % % optical % % Total on chemicals and electrical and transportation and software other

science pharma- electronic equipment3 precision engineering’ branches

park ceuticals’ equipment* equipment’

Tel Aviv Metropolitan Area 62 39.0 24.0 2.3 52.3 4.6 20.5 20.3 100 (Herzliya, TA, Petach Tikva)

Rehovot 36 22.0 91.0 39.3 35.1 10.7 10.7 3.6 100 (Nes Ziona and Rehovot)

Jerusalem 35 21.0 52.0 30.5 30.8 3.8 23.1 7.7 4.1 loo

elsewhere 29 18.0 15.8 45.3 4.5 2.3 12.4 19.7 loo

TOTAL 162 100

‘Includes Israeli 3-digit SIC codes: 200 (basic chemicals), 201 (pharmaceuticals), 204 & 205 (paints, varnishes, insecticides, fungicides). -‘Includes Israeli 3digit SIC codes: 250 (electrical motors, transformers), 251 (electrical equipment), 255 & 256 (communications equipment, electronic equipment for control, scientific and medical uses). ‘Includes Israeli Migit SIC codes: 262 & 263 (aircraft parts and Sight control equipment). ‘Includes Israeli 3-digit SIC codes: 280 & 2SfJ (scientific measuring and controlling instruments, optical instruments and photographic equipment). 51ncludes Israeli 3-digit SIC codes: 733 & 738 (data processing and research consultancies).



TABLE 2. Industrial and employment characteristics of surveyed firms

Firm characteristics (alI firms)

N Mean C.V.’ Mean of smaU firms (<50 employees)

(n = 105)

age of 8rm (years) no. of employees % academics % technicians % skilled labour % non-skilled labour % female labour % R&D employees % production

revenue per employee (ThS) % exports capital stock per employee (ThS) wages as % total expenditure raw materials as % total expenditure R&D expenditure as % of sales

160 12 0.14 10 162 % 2.21 159 31.1 0.80 36.2 159 16.2 0.77 17.0 155 22.0 0.96 17.5 145 24.1 1.06 21.9 156 27.0 0.69 29.1 156 28.6 0.97 34.0 156 51.1 0.56 44.5

112 5637 1.18 1077 114 58.1 1.32 60.3 % 1%3 084 431

136 47.4 0.42 49.9 137 31.0 0.59 28.8 108 27.6 1.14 34.3

Note ‘Calculated as (S.D./J?).

TABLE 3. Description of categorical variables

Firm charactetitics 1. Firm location (L):

coded as 1 if science park; 0 if other (industrial zone etc.). 2. Firm innovation level (I):

coded as 1 if ‘significant’ (i.e. unique product); 0 if ‘incremental’ (modification of existing products, similar product produced by other firms).

3. Location of major market (M): coded as 1 if mainly local; 0 if otherwise.

4. Intensity of university connection (U): coded as 1 if high (joint research and staff, funding of university research); 0 if low (employ university graduates).

5. Relationship of firm’s present major product to previous products with which owner/manager was associated (P): coded as 1 if similar to previous product; 0 if completely different.

Entrepreneur/manager charactetitics 1. Academic education level (E):

coded as 1 if PhD or beyond; 0 if otherwise. 2. Work experience (W); previous area of work activity:

coded as 1 if R&D related; 0 if related to production, sales or administration. 3. Spin-off (5):

coded as 1 if former place of employment was Israeli (civilian sector) company; 0 if otherwise (foreign company, defence sector or university).

categories. While this involves a certain loss of tables. As the aim of this section is to unravel some information, it also avoids the problem of multiple of the relationships between innovation, science park cells with low observation counts which can hamper location and characteristics of firms’ entrepreneurs or the interpretation of multi-dimensional contingency managers, this aggregation seems justified.


D. Felsenstein

5.2. Method

In order to identify and test the relationships between the categorical variables outlined above, we use log-linear modelling. This involves developing a model that will predict the number of cases in a cell of a multi-dimensional contingency table. In this case we are dealing with a 2 x 2 x 2 contingency table (basically a form of multi- dimensional x’). The model predicts the log of the frequencies in each cell as a function of the values of the various combinations of categorical variables (marginal distributions) and the inter- active effects between them [44]. As such, the dependent variable is the number of cases in a cell of the contingency table, while the independent variables are those variables used for the cross- classification.

For a two-variable analysis (A, B), the general model, in log-transformed terms, takes the form

In(M,J = In(a) + ln(yAJ + ln(rBj) + ln(yABij)

where ln(Mij) = the expected cell frequency, cx = overall mean of the log of expected cell frequencies, and y = parameters to be estimated; i.e., the effect on the anchor cell resulting from changes in the individual or interaction values of the variables.

The general model described above is ‘saturated’ in that observed and predicted cell frequencies are the same and error expectancies sum to zero. This model would therefore fit the data perfectly but may not represent the most efficient or parsimonious set of relationships. As the present analysis is not concerned with describing all possible relationships but rather with unravelling the most significant amongst them, we fit the reduced form (main effects) model to the data, rather than the saturated model. The test statistic for the reduced form model is the likelihood ratio,4 which has a x2 distribution.

In the analysis that follows this model is fitted for two sets of relationships: (1) the relationship between the innovation level of the firm (I), interaction with a university (v) and educational background of the entrepreneur/manager (E); and (2) the relationship between the innovation level

of the firm (I), science park location (L) and prior work position (W) of the entrepreneur/manager. While both sets of relationships are based on the 142 observations, firms are stratified only by science park location in the second model.

6. Results

6.1. Paths to innovation and the role of the seedbed

Figure 1 depicts an ‘innovation profile’ for the firms surveyed. While the figure is presented in a hierarchial form that suggests some kind of struc- tural ordering, we cannot in fact validate this proposition (and neither is this the object of this analysis). Rather, what we can suggest is the different innovative profiles of different firms, and we try to examine the seedbed interactions in these. This is, of course, a very preparatory and exploratory analysis but it is nevertheless instructive for establishing further hypotheses as to the way seedbeds of innovation operate and the way in which science park location augments the seedbed effect.

The profiles described in Fig. 1 suggest that the stereotype of the high-tech firm established by highly educated scientists with intensive university connections (profile A) is by no means the only path, or even the most common one, to innovation. Profile G, which represents innovative firm development without the university and high-level education background, is a more prevalent form amongst those firms surveyed. In fact, the largest individual grouping of innovative companies comprises those without the traditional research- oriented background (Profile G, which accounts for 50% of all innovative firms). It is also interesting to note that close to 70% of surveyed firms are engaged in activities classified as incremental-level innovation, i.e. the reproduction and modification of products (albeit sophisticated) developed and produced by other companies.

The intensity of university connection does not seem to be related to innovative production (x2



PROFILE

A

F

G

H

Fig. 1. Innovation profiles based on entrepreneurs’ education level (E), university linkage ((I) and innovation level of firm (I).

not significant). Seventy-five percent of firms have no high-level linkage to universities at all, although a PhD-level background does seem to breed some form of interaction (over half the companies founded by PhDs have intensive connections with universities).

This low-key relationship, however, could be indicative of the generally low-level interaction between university and industry existing in Israel that has been alluded to in other studies [45, 461. This is grounded in the fact that university research (even in an applied form) is often not ‘ripe’ enough to be taken up by industry. In addition, academic research is unaware of the true problems facing industry, and industry does not always want to reveal these problems to academia. Only when the university engages in industrial ‘troubleshooting’ (generally in the field of applied scientific services) does it meet industry’s needs. Industry, for its part, has a conservative attitude to funding university research (conditioned by a shortage of capital) and wants immediate results. The upshot is that the university-industry relations that do exist are rather limited, especially in terms of intensive relations such as joint research and funding [47].

In general, the simple distribution outlined in Fig. 1 seems to suggest that high-level educational background does not say very much about the ability to exploit innovations commercially. Fur- thermore, the level of interaction with universities is generally low, and where this interaction does exist, it results in only marginally more innovative firms; one-quarter of all firms with intense university connections are ‘significant’ innovators as against 21% of firms with low-level connections.

Putting these relationships in a log-linear frame- work, we arrive at similar conclusions. The reduced-form model of the 3-way cross-tabulation between the variables in Fig. 1 results in the following equation

ln(M,J = a + YEi + Y”j + ?lk

(30*09/l) (7*51/l) (9-39/l)

+ YEI,, f YEUij

(1*96/l) (544/l)

(i, j, k = 1,0)

where ln(M& = the expected cell frequency, a = overall mean of the log of expected cell frequencies, YEi = effect attributable to the ith


D. Felsenstein

category of education, rU_ = effect attributable to the jth category of university interaction, yZ, = effect attributable to the kth category of innovation level, y,?& = effect attributable to the interaction between the ith category of education and the kth category of innovation level, and yEVij = effect attributable to the interaction between the ith category of education and the jth category of university interaction.

Figures in parentheses are chi-square statistics and degrees of freedom, all significant at the p < O-05 level, except for the TEZik term that was forced into the model.

Likelihood ratio chi-square value for estimated model = 4.64 (p = O-0106, 2 degrees of freedom).

This is the most parsimonious model fitted to the data. While it shows that education, university linkage and innovation are all significant in their own right, for our purposes the interaction effects are of greater interest. Although the second-order relationship between university interaction and education level of the entrepreneur/manager is significant, this does not necessarily lead to innovative activity (the E*I relationship is not significant, but was forced into the model). The third-order interaction E*Z*U is also not significant. All this would seem to suggest that the ability to realize the commercial potential of innovations (through establishing an innovative firm) is not necessarily related to academic education or university linkage. Success in innovation and its commercial exploi- tation (i.e. the ability to sell the innovation and keep the firm going on this basis) are probably related to other supply conditions (such as the entrepreneur’s work history and experience) and result from demand factors such as market structure (foreign or local, barriers to entry and so on).

An exhaustive examination of these factors is beyond the scope of this analysis, but a cursory examination of some of these factors does not yield any significant relationships. Supply factors, taken here as entrepreneur’s work experience as measured by the relationship between present product and previous product experience (P), show no significant relationship to innovation. This holds true even when this relationship is

stratified by previous employment position (R&D vs. sales/administration/production). This was expected to add a further dimension to the depth of work experience, but the x2 statistics are all insignificant. Market factors, as measured by the location of main market (foreign or local) (M), where foreign markets are expected to be more innovative, competitive and with higher entry barriers, also yield no significant relationship to innovation. However, there is a confounding effect here with innovation and location, which will be discussed below.

6.2. Seedbed effects and science park location

Nearly half the firms surveyed here are located on science parks. This begs the question as to whether there is any significant difference in innovative activity between on- and off-park firms

( i.e., does the park have a seedbed effect?) and whether this relationship is confounded by any other factors. In view of the popular perception of the science park as facilitating university-industry interaction, and in the light of the paucity of empirical evidence supporting this claim, it is important to try to gauge the importance of science park location in the innovation process.

When turning to the features associated with location on a science park (and presumed to enhance innovative activity), the most obvious starting-point is university interaction. In common with the many studies cited earlier, this is found to be low amongst all science park firms surveyed. High-level interactions (joint research and industry funding of university research) were reported by 13% and 9% of firms respectively. Mid-level interactions are not much more prevalent, with receipt of university consultancy services reported by less than 20% of science park firms, and key employees holding faculty positions reported by only 8%. As expected, low-level interactions based on recruitment of local university graduates (28%) and use of university facilities (24%) were more universal. This pattern, while not illustrating particularly high-level interactions, was nevertheless



significantly different to that observed for non- science park firms (x2 = 3.947, p = O-047).

If science park firms have higher level interactions with universities, does this result in technology transfer and, as a consequence, high levels of innovation (the ‘seedbed’ hypothesis)? This causal relationship is not particularly significant (x2 = 2.438, p = 0.118). However, the possibility does exist that the relationship is mediated through the effect of some other factor. As illustrated above, innovation is interrelated with information and much of this flows through channels that are grounded in work experience, academic education and the like. In this instance, therefore, we test for the interrelationship between the factors science park location (L), innovation level of the firm (Z) and work experience of the entrepreneur/manager

(w). These relationships are depicted in Fig. 2. As

can be seen, no clear pattern can be observed for the relationship between innovation and science park agglomeration. When adding the work experience dimension, we arrive at a series of profiles. The conventional path is represented by profile A; an entrepreneur with a background in R&D

sets up a high-tech firm producing unique products on a science park. This development trajectory, however, accounts for only 5% of all firms surveyed. The majority of science park firms (nearly 70%) fall into profile G, which represents the science park firm engaged in the production and modification of existing products and founded by an entrepreneur from a non-R&D background.

When stratifying the relationship between science park and innovation by work experience, we find that the seedbed hypothesis can be upheld independently of work experience. Thus, for firm founders with an ‘R&D background, this relationship is marginally significant (x’ = 2.93, p = 0.083). For entrepreneurs with technical and production backgrounds this relationship is slightly stronger (x2 = 5.99, p = O-013). This suggests that work experience might have a direct input into the innovation capabilities of the firm (i.e. through ‘learning by doing’ [48]). If this experience is technical and managerial, this could lead to more commercially viable innovative products than those produced by firms where the main entrepreneurs have an R&D orientation. In other words, commercially exploitable innovations call for more than just

PROFILE

A

B

C

D

E

F

G

H

Fig. 2. Firm profiles based on entrepreneur’s wbrk background (w), innovation level of the firm (l) and science park location (IL.).


D. Felsenstein

technological prowess and innovation prompted by science park location, this latter factor can serve supply-push conditions. to entrench existing seedbed interactions.

The complete system of these interrelationships is examined more formally in the log-linear model. The reduced-form model of the relationships between W, I and L takes the form

ln(M,iJ = (Y + YWi t YZj + rWZij

(18*18/l) (853/l) (4.5411)

+ YWZLij~ (8*14/l)

(i,Z, k = 1,O)

The question now arises as to whether other factors exist that could impact on location. Are other seedbed-promoting processes related to science park location? As noted earlier, the spin-off process is often associated with spatial clustering, the assumption being that spin-off firms remain in the local area once they have broken away and continue to interact formally (e.g. subcontracting) or informally (social exchange) with their former places of employment [49].

where ln(Mij,) = the expected cell frequency, OL = overall mean of the log of expected cell frequencies, yWi = effect attributable to the ith category of work experience, YZj = effect attributable to the jth category of innovation level, YWZij = effect attributable to the interaction between the ith category of work experience and the jth category of innovation level, and YWZL,, = effect attributable to the third-order interaction between the ith category of work experience, the jth category of innovation and the kth category of location.

Figures in parentheses are chi-square statistics and degrees of freedom, all significant at the p < O-05 level.

Likelihood ratio chi-square value for estimated model = 5.76 (p = 0.0461,3 degrees of freedom).

Amongst the firms surveyed, the pattern of spin-off was distributed across: (1) local firms, both Israeli and local foreign subsidiaries (36% of firms attributable to this source); (2) foreign companies abroad (22%); (3) universities (nearly all local) (26%); and (4) the defence industry (including former army officers) and a ‘miscel- laneous’ category (16%). This distribution highlighted the relatively large number of firms that have spun-out of foreign companies abroad. Work experience abroad would seem to expose the potential entrepreneur to the possibilities of setting up an independent operation. It could also be that new firms starting up in this way have a guaranteed market in the form of their former company. This mitigates some of the risk generally involved in spinning-off [49].

The results show that only one second-order and one third-order interaction term are significant. Both these terms express the interaction effects of work experience with innovation. There would thus seem to be an information flow based on employment background that is related to innovation level. This also interacts with location, suggesting that while work experience is related to innovation this may be contingent on location. However, this latter three-way interaction is the only way in which location shows up in the results. All the two-way interactions that include location are not ‘significant, and even the direct effect of science park location by itself is not included in the model. All this would seem to imply that while the seedbed effect is not necessarily contingent on

A significant relationship is found to exist between spin-off (P) and science park location (L) (x2 = 4@07, p = 0X)451). However, when stratifying the relationship between innovation and location by type of spin-off, no significant relationship exists. This would seem to show that the (rather weak) relationship between innovation and location, outlined above, is not contingent on the type of spin-off. Thus, unlike the case for work experience, we cannot conclusively say that former companies are an apparent source of input that will influence the firm’s innovation level. As such, there does not seem to be any reason for conscious science-park clustering of spin-offs around ‘incubator’ organizations. They might cluster due to inertia, prestige considerations associated with the location or simply through lack of

106 Technovation Vol. ?4 No. 2


information about alternative locations. However, we have little evidence to suggest that they cluster for information and interaction purposes or that the incubator organization is an inherent component of the seedbed effect. In addition, the surprisingly high number of spin-offs attributable to foreign firms, thereby discounting the option of locational clustering around incubators, would seem to support this contention.

Type of market (M) (export or local) was hypothesized as exerting an influence on innovation level. Here we address the issue of whether firms with similar markets are likely to choose a science park for its seedbed effect. On the basis of firms surveyed, there is no evidence of a direct relationship between science park location (L) and market orientation (M). However, the relationship between type of location and innovation level is again mediated by type of market. Thus, for firms operating in local markets this relationship exists (x2 = 6.397, p = O-012), while it is not apparent for firms serving export markets. In view of the relationship between market and innovation level, this might suggest that for those firms operating in local markets science park clustering as an interaction strategy is more important than for firms operating abroad. The latter probably have alternative information networks connected with their markets and are thus less contingent on the science park and its seedbed effects.

7. Conclusions

This paper has examined the case for the existence of seedbed conditions on science parks that promote innovation. The basic questions that the paper attempts to answer are, first, whether these effects are an important input to the firm’s innovation level and, second, the extent to which these effects are contingent on the physical proximity and clustering afforded by the science park.

The evidence seems to indicate that the information flows and knowledge networks associated with university interaction and an entrepreneur’s education level do not necessarily translate into

innovation. We suggest that the influences on innovation might lie somewhere else: in both supply conditions (such as the work experience of the entrepreneur) and the structure of demand (market conditions). Thus, the results presented in this paper provide further support for the argument in much of the business literature to the effect that technical knowledge without business skill does not necessarily make for innovatively successful products or firms [l, 2, 501. To this we must add that the particular nature of high- technology markets (competition, imperfect knowledge at the early stages and high barriers to entry at later stages) also means that the information networks likely to arise on the basis of university interaction and educational level are not necessarily those needed for innovation (in contrast to invention).

The ‘seedbed’ hypothesis, that forms the central focus of this paper, is supported only under certain conditions. In common with other empirical evidence [6, 35, 39, 401, the level of interaction between firms located on science parks and local universities is generally low. It is higher, however, than the level of interaction exhibited by companies that are not science park tenants. This of itself, however, is no indication of a seedbed effect arising from science park location. Testing this proposition more directly, we find that the location-innovation connection is strengthened when stratified by work experience. This would seem to indicate that science park location, rather than being seedbed-inducing, could be seedbed- entrenching. In common with other studies, we have suggested that the choice of a science park location is due as much to the status and prestige effect that these exclusive locations confer [5, 11, 351, as it is to the perceived benefits in terms of innovative edge. Science park location could therefore be the outcome of a process of ‘social signalling’ and ‘swarming’ [ 171.

The policy implications of these findings suggest that alternative vehicles for technology transfer, university-industry interaction and inter-firm flows of information and materials need to be developed. Universities are slowly beginning to develop


D. Felsenstein

innovative new forms of relations with industry, based on alternative technology transfer mechan- isms and synergistic relationships such as limited partnerships, R&D seed funds and generic research collaboration [31, 461. Firms are exploring new channels for cooperation, alliances, network formation and diagonal integration in an attempt to disperse risk, cope with technological complexity and in reaction to perceived external threats [51]. Faced with the magnitude of the task of innovation, appendixing to the science park the role of innovation ‘seedbed’ or regional development ‘growth pole’ may be an unrealistic expectation from a project that in many instances is not much more than a real-estate development. Thus, if there is some disappointment in the fact that science parks are often not much more than high-tech ‘islands’ with minimal interactions both between themselves and with their neighbouring universities, it could be that hopes were pitched too high in the first place.

From the public policy perspective, the ‘seedbed’ metaphor allows officials and policy makers to operationalize their expectations of science parks. Therein lies its appeal. The science park is, of course, just one of a collection of policy instruments that aim to encourage the development of seedbeds of innovation. However, it is often uncritically accepted as such - a position that this paper has sought to re-evaluate. While, for didactic purposes, we have characterized science parks in terms of a an enclave/seedbed dichotomy, in reality most of them probably lie somewhere along the continuum running between these two polar positions. The challenge therefore lies in the development of policy tools that will encourage them to develop into more than just a ‘collection of firms’.

Acknowledgements

Funding for this research was supported in part by a grant from the Israel Trustees Foundation. The work was completed while the author was a visiting scholar at the Center for Urban Affairs and Policy Research, Northwestern University,

108

Evanston, Illinois, USA, and in receipt of a grant from the Lady Davis Fellowship Trust.

Notes

’ All these effects exist, of course, over and above the regular income, employment and output effects of universities on local economic growth. On this issue, research has shown a particularly profound effect on local service sectors induced by university purchasing, staff and student expenditures [23,24]. Impact studies of individual universities have reached similar conclusions although methodologies and consequently outputs vary greatly (see for example [25-271).

2 In contrast, scientists and engineers as a percentage of the total science park labour force are reported as 23% in the case of the University of Utah Research park, 31% for Research Triangle Park and 33% for Stanford Research Park [ll].

3 Braun and McHone [37] report income, output, employment and value-added multipliers for science park firms located on the Central Florida Research Park. These lie in the range of 1~59-1~87. Interestingly, multipliers for Central Florida firms off-park are generally higher, ranging from 1.65 to 2.07.

4 This ratio (~5~) is defined as

where h4, = observed frequencies in each cell, and ticijkj = expected frequencies in each cell.

References

A.C. Cooper, The role of incubator organizations in the founding of growth-oriented firms. Journal of Business Venturing, 1 (1985) 75-86. A.C. Cooper and W.C. Dunkelberg, Entrepre- neurship and paths to business ownership. Strategic Management Journal, 7 (1986) 53-68. R.N. Cox, Lessons from 30 years of science parks in the USA. In: J.M. Gibb (ed.), Science Parks and Innovation Centres: Their Economic and Social Impact. Elsevier Science Publications, Amsterdam, 1985, pp. 17-25. R. Miller and M. Cote, Growing the next Silicon Valley, Harvard Business Review, 63(4) (1985) 114-123.

Technovation Vol. 14 No. 2


5 R.A. Joseph, Silicon Valley myth and the origins of REI Working Papers Series, Center for Regional technology parks in Australia. Science and Public Economic Issues, Case Western University, Cleve- Policy, 16 (1989) 353-365. land, OH, 1990.

6 D. Massey, P. Quintas and D. Wield, High Tech Fantasies: Science Parks in Society, Science and Space. Routledge, London, 1992.

7 R. Struyk and F. James, Intra-Metropolitan Industrial Location. Lexington Books, Lexington, MA, 1975.

8 I.G. Begg and G.C. Cameron, High technology location and the urban areas of Great Britain. Urban Studies, 25 (1988) 361-379.

20 P. Beeson and E. Montgomery, The Effects of Colleges and Universities on Local Labor Markets. NBER Working Paper No. 3280, Cambridge, MA, 1990.

9 E.J. Davelaar and P. Nijkamp, The role of the metropolitan milieu as an incubation centre for technological innovations: a Dutch case-study. Urban Studies, 26 (1989) 517-525.

21 N. Bania, L.N. Calkins and D.R. Dalenberg, The effects of regional science and technology policy on the geographical distribution of industrial R&D laboratories. Journal of Regional Science, 32(2) (1992) 209-228.

10 M.T. Peddle, An empirical investigation of the relationship between community characteristics and the presence of industrial parks. Regional Science Perspectives, 18(2) (1988) 14-28.

11 M.I. Luger and H.A. Goldstein, Technology in the Garden, Research Parks and Regional Economic Development. University of North Carolina Press, Chapel Hill, NC, 1991.

22 N. Bania, M.S. Fogarty and S. Kaufman, An Assessment of Cleveland’s Scientific and Engineering Base and its Role in the Economy. Center for Regional Economic Issues, Case Western Reserve University, Cleveland, OH, 1987.

23 D. Steinnes, On understanding and evaluating the university’s evolving economic development policy. Economic Development Quarterly, l(2) (1987) 214-223.

12 A.J. Scott and M. Storper, High technology industry and regional development: a theoretical critique and reconstruction. International Social Science Journal, 112 (1987) 215-233.

13 P. Aydalot and D. Keeble, High Technology Industry and Innovative Environments: The European Experi- ence. Routledge, London, 1988.

14 R. Camagni, Local ‘milieu’, uncertainty and innovation networks: towards a new dynamic theory of economic space. In: R. Camagni (ed.), Innovation Networks; Spatial Perspectives. Belhaven, London, 1991, pp. 121-144.

24 D.W. Hedrick, S.E. Henson and R.S. Mack, The effects of universities on local retail, service and FIRE employment: some cross-sectional evidence. Growth and Change, 21(3) (1990) 9-20.

25 M. Brownrigg, The economic impact of a new university. Scottish Journal of Political Economy, 20(20) (1974) 1234-1239.

26 H.A. Goldstein, Estimating the regional economic impact of universities: an application of input-output analysis. Planning for Higher Education, 18(l) (1989-90) 51-63.

15 Z.J. Acs and D.B. Audretsch, Innovation and Small Firms, MIT Press, Cambridge, MA, 1990.

16 S. Birley, The role of networks in the entrepreneurial process. Journal of Business Venturing, 1 (1985) 107-117.

27 M.F. Bleaney, M.R. Binks, D. Greenaway, G.V. Reed and D.K. Whynes, What does a university add to its local economy? Applied Economics, 24 (1992) 305-311.

28 H. Eto and M. Fujita, Regularities in the growth of high technology industries in regions. Research Policy, 18 (1989) 135-153.

17 S.J. Appold, The location process of industrial research laboratories. Annals of Regional Science, 25 (1991) 137-144.

29 R. Florax and H. Folmer, Knowledge impacts of universities on industry: an aggregate simultaneous investment model. Journal of Regional Science, 32(4) (1992) 437-466.

18 A. Jaffe, Technological opportunity and spillovers of R&D: evidence from firms’ patents, profits and market value. American Economic Review, 76 (1986) 984-1001..

30 D.A. Garvin, Spin-offs and the new firm formation process. California Management Review, 25(2) (1983) 3-20.

19 N. Bania, R. Eberts and M.S. Fogarty, Universities and rhe Start-Up of New Companies: Can we Generalize from Route 128 and Silicon Valley?

31 A.M. Brett, D.V. Gibson and R.W. Smilor (eds.), University Spin-Off Companies: Economic Develop- ment, Faculty Entrepreneurs and Technology Trans- fer. Rowan and Littlefield, Savage, MA, 1991.


0. Felsenstein

32

33

34

35

36

37

38

39

40

41

42

43

44

R. Van Dierdonck, K. Debackere and M.A. Rappa, An assessment of science parks: towards a better understanding of their role in the diffusion of technological knowledge. R&D Management, 21(2) (1991) 109-122. S.Q. Wicksteed, Universities, Enterprise and Local Economic Development: An Exploration of Links. Manpower Services Commission, HMSO, London, 1988. A. Shachar and D. Felsenstein, Urban economic development and high technology industry. Urban Studies, 29(6) (1992) 839-855. S. Macdonald, British science parks: reflections on the politics of high technology. R&D Management, 17(l) (1987) 25-37. R. Rothwell and M. Dodgson, External linkages and innovation in small and medium sized enterprises.

R&D Management, 21(2) (1991) 125-137. B.M. Braun and W.W. McHone, Science parks as economic development policy: a case study approach. Economic Development Quarterly, 6(2) (1992) 135-147. S.Q. Wicksteed, The Cambridge Phenomenon: The Growth of High Technology Industry in a University Town. Segal Quince Wicksteed, Cambridge, 1985. G. Steed and D. De Genoa, Ottawa’s technology- oriented complex. Canadian Geographer, 27(l) (1983) 263-278. R.A. Joseph, Technology parks and their contri- bution to the development of technology oriented complexes in Australia. Environment and Planning C, 7 (1989) 173-192. M.J. Hagey and E.J. Malecki, Linkages in high technology industries: a Florida case study. Environ- ment and Planning A, 18 (1986) 1477-1498. A. Glasmeier, Factors governing the development of high tech industry agglomerations: a tale of three cities. Regional Studies, 22(4) (1988) 287-301. State Comptroller, Annual Report No. 32. Office of the State Comptroller, Government Printing Office, Jerusalem, 1982 (in Hebrew). D. Knoke and P. Burke, Log-Linear Models. Sage, Beverly Hills, CA, 1980.

45

46

47

48

49

50

51

A. Keynan, Science and Israel’s Future: A Blueprint for Revitalizing Basic Research and Strengthening

Science-Based Industry. Israel Academy of Science and Humanities and the Jerusalem Institute for Israel Studies, Jerusalem, 1988. D. Felsenstein, The Role of Catalytic Funding

in Promoting University-Zndustry Collaboration in Applied Research. Research Report No. 6. Jerusalem Institute for Israel Studies, Jerusalem, 1990. I. Bijawi and Y. Raban, The Research System and Science Based Industry in Israel. Research Report. Interdisciplinary Centre for Technological Fore- casting and Analysis, Tel Aviv University, Tel Aviv, 1988 (in Hebrew). M.A. Maidique and D.J. Birger, The new product learning cycle. Research Policy, 14(6) (1985) 299-313. R.W. Smilor, D.V. Gibson and G.B. Dietrich, University spin-out companies: technology start-ups from UT-Austin. Journal of Business Venturing, 5 (1990) 63-76. M. Cross, New Firm Formation and Regional Devel- opment. Gower, Aldershot, UK, 1981. C. De Bresson and F. Amese, Networks of innovators: a review and introduction to the issue. Research Policy, 20(5) (1991) 363-380.

appeared in journals such as -_.

Research Policy, World Development, Regional Studies, Urban Studies, Small Business Economics and Entrepreneurship and Regional Development. At present he is working on a method- ology for estimating the knowledge impacts generated by universities on their urban and regional economies, and on a study of the network potential arising from new forms of university-industry collaboration.


university-related science parks — ‘seedbeds’ or ‘enclaves’ of innovation?

Documents