Contingency analysis

In addition to the quantitative analysis in the previous sub-chapter, I have conducted a con-tingency analysis to further substantiate the varying assessments of internal and external enabling channels of knowledge relations to academia in relation to the three different types of knowledge seekers. The contingency analysis examines whether the observed frequencies are statistically significant from the expected frequencies.²⁰⁶ Furthermore, the chi-square test of homogeneity gives an indication whether the variables are equally distributed in the different groups. The phi coefficient and contingency coefficient measure the degree of interaction between the relevant variables. A phi coefficient that is higher than 0.3 indicates a strong interaction of the relevant variables. Moreover, the contingency coefficient is based on the phi coefficient. Its value ranges from 0 to 1 (Backhaus et al.,

206 The contingency analysis enables the consideration of different scales of variables as any kind of variable (categorical, ordinal etc.) can be transformed to the nominal scale. However, it has to be acknowledged that such transformation is related to a loss of information (Backhaus et al., 2008). For the contingency analysis, two categories of the businesses’ evaluations of the internal and external channels and settings have been formed: 1) ratings of very important to important and 2) ratings of average to N/A.

2008). Using a 3x2 contingency table, Table 15 shows the observed frequencies (absolute and relative) of the items examined for the three identified types of knowledge seekers.

Evaluation of internal and external channels for knowledge relations to academia by different types of knowledge seekers (contingency analysis, n=52)

Strong knowledge seekers (n=21)

Moderate knowledge seekers (n=18)

Lame knowledge

seekers (n=13) Total

absolute in % absolute in % absolute in % absolute in %

Internal channels

Personal relations

Very important - important 20 95.2% 18 100% 7 53.8% 45 86.5%

Average – not applicable 1 4.8% 0 0% 6 46.2% 7 13.5%

Requests by scientific institutions

Very important - important 11 52.4% 3 16.7% 0 0% 14 26.9%

Average – not applicable 10 47.6% 15 83.3% 13 100% 38 73.1%

External channels and platforms (KNM instruments)

STP-related knowledge marketing

Very important - important 4 19.0% 3 16.7% 3 23.1% 10 19.2%

Average – not applicable 17 81.0% 15 83.3% 10 76.9% 42 80.8%

Local networking events

Very important – important 12 57.1% 8 44.4% 4 30.8% 24 46.2%

Average – not applicable 9 42.9% 10 55.6% 9 69.2% 28 53.8%

Locally organized conferences

Very important - important 11 52.4% 3 16.7% 1 7.7% 15 28.8%

Strong knowledge seekers (n=21)

Moderate knowledge seekers (n=18)

Lame knowledge

seekers (n=13) Total

absolute in % absolute in % absolute in % absolute in %

Average – not applicable 10 47.6% 15 83.3% 12 92.3% 37 71.2%

Local university TTO

Very important - important 5 23.8% 3 16.7% 1 7.7% 9 17.3%

Average – not applicable 16 76.2% 15 83.3% 12 92.3% 43 82.7%

STP management company

Very important - important 9 42.9% 6 33.3% 5 38.5% 20 38.5%

Average – not applicable 12 57.1% 12 66.7% 8 61.5% 32 61.5%

Regional innovation-promoting

enti-ties

Very important - important 9 42.9% 6 33.3% 3 23.1% 18 34.6%

Average – not applicable 12 57.1% 12 66.7% 10 76.9% 34 65.4%

Local technology networks

Very important – important 13 61.9% 6 33.3% 2 15.4% 21 40.4%

Average – not applicable 8 38.1% 12 66.7% 11 84.6% 31 59.6%

Public support schemes for

indus-try-academia R&D projects

Very important - important 18 85.7% 12 66.7% 4 30.8% 34 65.4%

Average – not applicable 3 14.3% 6 33.3% 9 69.2% 18 34.6%

Total 21 18 13 52

Source: Author

As a result of the contingency analysis, the two internal channels, i.e. personal relation-ships and direct offers by scientific institutions, as well as the three KNM instruments public programmes for joint industry-academia R&D projects, locally organized conferences and local professional technology networks all show a statistically significant distribution among the three types of knowledge seekers that is not homogenous (see Table 16).

Contingency analysis’ results (degrees of freedom= 2, α<0.05), n=52 Chi-square Phi coefficient Contingency

coeffi-cient Internal channels

Personal relationships 16.091 0.556 0.486

Requests by scientific institutions 12.670 0.494 0.443 External channels & platforms (KNM instruments)

Locally organized conferences 9.802 0.434 0.398

Local technology networks 7.786 0.387 0.361

Public support schemes for indus-try-academia R&D projects

10.73 0.454 0.414

Source: Author

In these cases, the relevant Chi-square values show the rejection of the null hypotheses of a homogeneous distribution of the relevant variables in the three groups (degrees of free-dom= 2, α<0.05). With phi coefficients between 0.38 and 0.56 and contingency coefficients between 0.36 and 0.49, the degree of interaction between the internal channels and specific KNM instruments named here and the typology of knowledge seekers can be characterized as relatively strong. The standardized residuals also prove the direction of the relationship of the two independent variables (Bahrenberg et al., 1999; Backhaus et al., 2008).

In this sense, firms that rated the selected two internal channels and three KNM tools as important or very important are clearly over-represented in the group of strong knowledge seekers, while lower evaluations are clearly under-represented.²⁰⁷ In regard to the type of moderate knowledge seekers, related companies with assessments of important or very important are over-represented in regard to the item personal relationships in specifically and, to a smaller degree, concerning the item public support programmes for

207 For the group of strong knowledge seekers, the following standardized residuals are shown for the specific assessments (very important-important | average-N/A) of the five items discussed: personal relationships (0.4 | -1.1), requests by academia (2.2 | -1.4), public support programmes for industry-academia R&D pro-jects (1.6 | -1.2), locally organized conferences (2.0 | -1.3) and local technology networks (1.6 | -1.3).

academia R&D projects.²⁰⁸ In contrast, companies in this group with assessments of im-portant or very imim-portant for the items direct offers by scientific institutions, locally orga-nized conferences and local technology networks are under-represented. In the group of lame knowledge seekers, firms that evaluated all five relevant items strongly are clearly under-represented.²⁰⁹

Consequently, STP resident firms that placed emphasis on the importance of requests by scientific institutions as well as the three KNM tools locally organized conferences, formal networks in the STP and publicly subsidized R&D projects on the regional, national or Eu-ropean scale have the strong tendency to be categorized as strong knowledge seekers.

Thus, these firms tend to have strong and multi-faceted knowledge relations with academia in the STP and external to the STP. Moreover, the STP resident firms that highlighted the fundamental influence of their personal relationships on successful link creation and reali-zation of knowledge interaction with academia tend to be classified as either moderate knowledge seekers or, slightly less likely, as strong knowledge seekers. Thereafter, they also show a higher likelihood to maintain comparatively stronger and versatile local and non-local interactive ties with academia.²¹⁰

At large, the results of the contingency analysis indicate that the use of specific internal and external channels and settings determines the different strengths and geographies of the STP resident firms’ knowledge relations to academia. Accordingly, the multi-faceted, very stable linkages of strong knowledge seekers to academia in the STP and external to the STP are the result of, firstly, the strong exploitation of personal contacts and direct

208 For the group of moderate knowledge seekers, the following standardized residuals are shown for the spe-cific assessments (very important-important | average-N/A) of the five selected items: personal relation-ships (0.6 | -1.6), requests by academia (-0.8 | 0.5), public support programmes for industry-academia R&D projects (0.1 | -0.1), locally organized conferences (-1.0 | 0.6), and local formal networks (-0.5 | 0.4).

209 For the group of lame knowledge seekers, the following standardized residuals are shown for the specific assessments (very important-important | average-N/A) of the five selected items: personal relationships (-1.3| 3.2), requests by academia (1.1 | -1.9), public support programmes for industry-academia R&D projects (-1.5 | 2.1), locally organized conferences (-1.4 | 0.9), and local technology networks (-1.4 | 1.2).

210 Note: In an additional contingency analysis, I tested the influence of the specific firm characteristics on the derived typology of knowledge seekers: location (STP case study), firm age, firm size (in terms of employ-ment), R&D expenditures, duration of STP residency and entrepreneurial origin. The contingency analysis reveals a significant inhomogeneous distribution of the two different categories of firms’ employment: 1)

<10 employees and 2) ≥10 employees (Chi-square= 6.692, degrees of freedom= 2, α<0.05). Thus, the lat-ter category is over-represented in cluslat-ter one (strong knowledge seekers) and cluslat-ter two (moderate knowledge seekers), while it is underrepresented in the group of unscalable companies (lame knowledge seekers). Consequently, companies with ten and more employees tend to be classified as strong or moder-ate knowledge seekers and, thus, tend to have proportionmoder-ately stronger interactive ties with academia.

These findings coincide with results of previous studies (e.g. Cohen et al., 2002; Fontana et al., 2006). Us-ing a 3x2 contUs-ingency table, Table A8 in the Appendix shows the frequencies of the selected firm character-istics for the three groups of knowledge seekers. However, this thesis only focuses on the analysis of inter-nal as well as exterinter-nal enabling channels and related underlying mechanisms as influencing factors of the industry-academia knowledge relations.

quiries of scientific actors, as well as, secondly, the thoroughly planned use of specific KNM instruments, namely publicly subsidized industry-academia R&D projects, confer-ences and local technology networks. Due to the characteristics and geographical focus of the different channels, assumptions concerning their specific influence on local and/or ex-tra-local linkages can be made. Amongst other channels, the two internal channels named both enable strong, multifaceted local and non-local knowledge relations in general, whereas publicly subsidized industry-academia R&D projects to non-local academia and conferences primarily are instrumental for the development of the strong linkages to non-local academia (regional, national and European pipelines). The participation in non-local tech-nology networks primarily contributes to the multi-faceted and strong linkages to co-located academia in the STPs.

Box 10: Preliminary summary of enabling channels and platforms

In sum, the firms specified as strong knowledge seekers strongly take advantage of a large variety of enabling channels to build and maintain knowledge ties to academia; on the one hand, in terms of internal sources, i.e. personal relations and requests by academia, and, on the other hand, specific KNM instruments, namely publicly subsidized R&D projects, networking events, conferences, technology networks, the STP management firms and regional innovation promoting entities. With the exception of inquiries of scientific institu-tions, the same applies to moderate knowledge seekers, although for most relevant chan-nels to a rather moderate degree. In contrast, the companies categorized as lame knowledge seekers primarily only utilize personal contacts and individual KNM tools, also to a comparatively much lower degree.

From the contingency analysis, it seems fair to conclude that strong, multi-faceted linkages to scientific institutions both in the STP and external to the STP primarily are a result of a firm’s strong social and structural embeddedness, as well as a firm’s keen use of specific KNM tools, namely publicly subsidized industry-academia R&D projects, conferences and local technology networks. It is assumed that the KNM tools highlighted affect a firm’s em-beddedness.

The strong significance of non-local knowledge relations with academia in all three types of knowledge seekers reaffirms the notion put forward in the recent academic discussion that geographical proximity alone is not a necessary and sufficient criterion for knowledge inter-action to take place. Instead, this aspect, as well as the importance of personal relations and other internal and external channels points to other forms of non-spatial proximity as critical criteria of successful link creation and knowledge interaction with academic institu-tions. In this respect, I examine the relation of non-spatial forms of proximity, in addition to geographical proximity, to the STP resident firms’ knowledge relations to academia in more detail in Chapter 4.5. Also in regard to the systematic organization of proximity I analyse the underlying mechanisms of specific KNM instruments in Chapter 4.6.

Im Dokument Knowledge network management and territorial innovation systems – a comparative analysis of science parks (Seite 161-168)