Table 2

Logistic regression results for product innovation

Logistic regression results
Dep. Variable: product innovationObservations: 1,412 pure manufacturers
Model: Logit (Method: MLE)Pseudo R-squ.: 0.664
AIC: 2956.710BIC.: 3064.617
converged: True (Interactions = 9)Log-Likelihood: −1462.4
LL-Null: −4348.8LLR p-value: 0.00
CoefStd. errzP>|z|[0.0250.975]OR
Intercept−7.4830.246−30.3810.000−7.966−7.0010.001
Headquarter cooperation0.3520.1702.0730.0380.0190.6861.422
Other firms cooperation0.4380.1642.6660.0080.1160.7611.550
Government cooperation0.5320.2402.2130.0270.0611.0031.702
Suppliers cooperation−0.1440.114−1.2650.206−0.3680.0790.866
Total perc. of funding0.0040.0012.6930.0070.0010.0061.004
IPO cooperation0.0930.2920.3180.750−0.4800.6661.097
Ind. Manuf. sector4.1060.12233.5420.0003.8664.34660.703
University cooperation0.6740.2392.8210.0050.2061.1431.962
Labs cooperation0.6590.1893.4800.0010.2881.0301.933
Customers cooperation1.2210.09313.1260.0181.0381.4043.391
Competitors cooperation0.4570.1373.3520.0010.1900.7251.579
Clients cooperation0.5670.1115.1020.0000.3500.7861.763
Obj.: Incr. mark. share−0.0390.060−0.6480.517−0.1570.0790.962
Obj.: Reduce envi. impact1.0030.09111.0190.0021.0011.0052,726
R&D Department1.4840.10613.9810.0001.2761.6924.411
Obj.: Entry new mark0.4390.0567.8900.0000.3300.5491.551

Source(s): Authors’ own work

or Create an Account

Close subscription notice
Close access options