University-related science parks - 'seedbeds' or 'enclaves' of ...

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D. Felsensteincategory of education, rU_ = effect attributableto the jth category of university interaction,yZ, = effect attributable to the kth category ofinnovation level, y,?& = effect attributable to theinteraction between the ith category of educationand the kth category of innovation level, andyEVij = effect attributable to the interactionbetween the ith category of education and the jthcategory of university interaction.Figures in parentheses are chi-square statisticsand degrees of freedom, all significant at thep < O-05 level, except for the TEZik term that wasforced into the model.Likelihood ratio chi-square value for estimatedmodel = 4.64 (p = O-0106, 2 degrees of freedom).This is the most parsimonious model fitted tothe data. While it shows that education, universitylinkage and innovation are all significant in theirown right, for our purposes the interaction effectsare of greater interest. Although the second-orderrelationship between university interaction andeducation level of the entrepreneur/manager issignificant, this does not necessarily lead to innovativeactivity (the E*I relationship is not significant,but was forced into the model). The third-orderinteraction E*Z*U is also not significant. All thiswould seem to suggest that the ability to realizethe commercial potential of innovations (throughestablishing an innovative firm) is not necessarilyrelated to academic education or university linkage.Success in innovation and its commercial exploitation(i.e. the ability to sell the innovation andkeep the firm going on this basis) are probablyrelated to other supply conditions (such as theentrepreneur’s work history and experience) andresult from demand factors such as market structure(foreign or local, barriers to entry and so on).An exhaustive examination of these factors isbeyond the scope of this analysis, but a cursoryexamination of some of these factors does notyield any significant relationships. Supply factors,taken here as entrepreneur’s work experience asmeasured by the relationship between presentproduct and previous product experience (P),show no significant relationship to innovation.This holds true even when this relationship isstratified by previous employment position (R&Dvs. sales/administration/production). This wasexpected to add a further dimension to the depthof work experience, but the x2 statistics are allinsignificant. Market factors, as measured by thelocation of main market (foreign or local) (M),where foreign markets are expected to be moreinnovative, competitive and with higher entrybarriers, also yield no significant relationship toinnovation. However, there is a confounding effecthere with innovation and location, which will bediscussed below.6.2. Seedbed effects and science parklocationNearly half the firms surveyed here are locatedon science parks. This begs the question as towhether there is any significant difference ininnovative activity between on- and off-park firms( i.e., does the park have a seedbed effect?) andwhether this relationship is confounded by anyother factors. In view of the popular perception ofthe science park as facilitating university-industryinteraction, and in the light of the paucity ofempirical evidence supporting this claim, it isimportant to try to gauge the importance of sciencepark location in the innovation process.When turning to the features associated withlocation on a science park (and presumed toenhance innovative activity), the most obviousstarting-point is university interaction. In commonwith the many studies cited earlier, this is foundto be low amongst all science park firms surveyed.High-level interactions (joint research and industryfunding of university research) were reported by13% and 9% of firms respectively. Mid-levelinteractions are not much more prevalent, withreceipt of university consultancy services reportedby less than 20% of science park firms, and keyemployees holding faculty positions reported byonly 8%. As expected, low-level interactions basedon recruitment of local university graduates (28%)and use of university facilities (24%) were moreuniversal. This pattern, while not illustrating particularlyhigh-level interactions, was nevertheless104 Technovation Vol. 14 No. 2
Science parks - seedbeds or enclaves of innovation?significantly different to that observed for nonsciencepark firms (x2 = 3.947, p = O-047).If science park firms have higher level interactionswith universities, does this result in technologytransfer and, as a consequence, high levels ofinnovation (the ‘seedbed’ hypothesis)? This causalrelationship is not particularly significant(x2 = 2.438, p = 0.118). However, the possibilitydoes exist that the relationship is mediated throughthe effect of some other factor. As illustratedabove, innovation is interrelated with informationand much of this flows through channels that aregrounded in work experience, academic educationand the like. In this instance, therefore, we testfor the interrelationship between the factors sciencepark location (L), innovation level of the firm (Z)and work experience of the entrepreneur/manager(w).These relationships are depicted in Fig. 2. Ascan be seen, no clear pattern can be observed forthe relationship between innovation and sciencepark agglomeration. When adding the work experiencedimension, we arrive at a series of profiles.The conventional path is represented by profileA; an entrepreneur with a background in R&Dsets up a high-tech firm producing unique productson a science park. This development trajectory,however, accounts for only 5% of all firmssurveyed. The majority of science park firms(nearly 70%) fall into profile G, which representsthe science park firm engaged in the productionand modification of existing products and foundedby an entrepreneur from a non-R&D background.When stratifying the relationship between sciencepark and innovation by work experience, wefind that the seedbed hypothesis can be upheldindependently of work experience. Thus, forfirm founders with an ‘R&D background, thisrelationship is marginally significant (x’ = 2.93,p = 0.083). For entrepreneurs with technical andproduction backgrounds this relationship is slightlystronger (x2 = 5.99, p = O-013). This suggests thatwork experience might have a direct input intothe innovation capabilities of the firm (i.e. through‘learning by doing’ [48]). If this experience istechnical and managerial, this could lead to morecommercially viable innovative products than thoseproduced by firms where the main entrepreneurshave an R&D orientation. In other words, commerciallyexploitable innovations call for more than justPROFILEABCDEFGHFig. 2. Firm profiles based on entrepreneur’s wbrk background (w), innovation level of the firm (l) and science park location (IL.).Technovation Vol. 14 No. 2 105
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