10
Table 4
marks |
regression |
expected |
significance |
strategic aims of management | |||
• intensifying R&D-activities |
1.926 |
6.859 |
0.007 ** |
• rising the technical level of production |
1.291 |
3.638 |
0.067 |
• introducing new products |
0.735 |
2.086 |
0.041 * |
• improvement in knowledge management |
-0.146 |
0.864 |
0.801 |
• improvement in quality of own products |
-0.245 |
0.783 |
0.663 |
• strengthen the own core competence |
-0.652 |
0.521 |
0.342 |
• develop new markets |
-1.047 |
0.351 |
0.244 |
• increasing usage of outsourcing |
-1.646 |
0.193 |
0.001 *** |
constant factor |
-1.872 |
---- |
0.187 _____© IfM Bonn |
Significant at the 5% (*) , 1% (**) or 0.1% (***) level
Cox & Snell-R2 = 0.393
Number of observations = 383
Log-Likelihood = 501.793
Regional aspects
As the model suggests, regional factors do not contribute significantly to ex-
plain the probability of the observed companies to participate in an R&D co-
operation. Comparing the German federal states with Bavaria - the region in
which the share of firms participating in R&D co-operations almost resembles
the share that has been observed nationwide - only marginal differences in the
commitment to such partnerships can be observed. While the results of the
bivariate analyses have highlighted, at least to some extent, differences be-
tween co-operation activities in Eastern and Western Germany, the multivariate
analysis makes clear that other determinants apart from location specific fac-
tors have to be held responsible for the use of this instrument. Neither regional
barriers nor accumulations in the spread of this instrument can be observed.
Industry
Unlike the firm`s place of residence the branch determines the willingness to
co-operate in R&D. As picture 1 shows, in the building industry the demand for
conjoint R&D is significantly lower than in the reference category, the industry
related services. This is indicated in the negative premise of the regression
coefficient in table 4.
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