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Table 2 Regression results

From: Assessing economic impact of research and innovation originating from public research institutions and universities—case of Singapore PRIs

  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 %
  1. *p < 0.05; **p < 0.01; ***p < 0.10; robust standard errors are in parentheses