A Journal of University-Industry-Government Innovation and Entrepreneurship
Table 2 Regression results
Binary logistic regression (H1, H2) | ||||
|---|---|---|---|---|
Variables | DV: firm’s propensity to repeat licensing (N = 835) | |||
Model 1 | Model 2 | Model 3 | Model 4 | |
Constant | −0.44(0.37) | 0.47(0.61) | 0.29(0.22) | 0.49(0.65) |
Start-up | 0.13(0.31) | 0.20(0.32) | −0.66***(0.35) | −0.66***(0.35) |
SME | 1.79**(0.30) | 1.74**(0.30) | 0.72*(0.32) | 0.67*(0.33) |
MNC and large enterprises | 1.85**(0.30) | 1.82**(0.30) | 0.91**(0.32) | 0.88*(0.32) |
Biomedical sciences sector | 0.61***(0.31) | 0.42(0.32) | −0.01(0.35) | 0.01(0.35) |
Info-comm sector | 0.49(0.31) | 0.47(0.31) | 0.24(0.33) | 0.24(0.33) |
Engineering and manufacturing sectors | −0.65*(0.30) | −0.64*(0.3) | −0.79*(0.33) | −0.82*(0.33) |
Log_IP licensing costs | −0.23***(0.12) | −1.79**(0.23) | −1.85**(0.24) | |
Log_RICV | 1.67**(0.21) | 1.70**(0.21) | ||
Log_RICV × Log_IP licensing costs | −0.10(0.11) | |||
Cox and Snell R square | 0.08 | 0.08 | 0.17 | 0.17 |
Nagelkerke R square | 0.13 | 0.13 | 0.27 | 0.27 |
Model chi-square | 69.76 | 73.42 | 157.73 | 158.57 |
Significance | 0 | 0 | 0 | 0 |
Classification correct | 78 % | 78 % | 82 % | 83 % |