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Table 1. Characteristics of the data
Firms with IPRs Firms with no IPRs Total
N % N % N %
Sample
Branch of industry
28 8.0 320 92.0 348 100
Construction 2 0.6 34 9.8 36 10.3
Trade 6 1.7 80 23.0 86 24.7
Transport and
1 0.3 12 3.4 13 3.7
telecommunication
Services 10 2.9 160 46.0 170 48.9
Manufacturing 9 2.6 34 9.8 43 12.4
Location
Rural area 6 1.7 80 23.0 86 24.7
Urban area 22 6.3 240 69.0 262 75.3
Size (personnel)
10-19 14 4.0 126 36.2 140 40.2
20-49 7 2.0 126 36.2 133 38.2
50-99 5 1.4 11 3.2 16 4.6
100-249 1 0.3 46 13.2 47 13.5
250-500 1 0.3 11 3.2 12 3.4
Age (years)
< 5 4 1.1 49 14.1 53 15.2
5-9 10 2.9 85 24.4 95 27.3
10-19 9 2.6 116 33.3 125 35.9
20-29 3 0.9 52 14.9 55 15.8
30 2 0.6 18 5.2 20 5.7
A stepwise logistic regression (LRA) analysis was applied to estimate the best-performing
model. In the LRA model IPR was as a dichotomic dependent variable (0=firms with no IPRs,
1=firms with IPRs). The independent variables included location, size, age, success index, public
R&D funding, turnover/employee ratio, which could be interpreted as a measure of productivity.
The branch of industry and its annual growth as well as the amount of personnel and its annual
growth were also tested as independent variables. These variables did not have any statistical
significance or they did not improve the overall classification rates of the estimated model, thus they
were left out of the model.
It may be concluded that location, size and age will have no statistical significance in
differentiating growing innovative SMEs from their non-innovative counterparts (Table 2, Model 1).
Moreover, the overall classification rate improves when these variables are excluded from the
analysis. Thus the hypotheses 1, 2 and 3 should be rejected.