Table 3

Logistic regression results for product–service innovation

Logistic regression results
Dep. Variable: Product-service InnovationObservations: 177 servitized manufacturers
Model: Logit (Method: MLE)Pseudo R-squ.: 0.488
AIC: 3669.284BIC.: 3773.938
converged: True (Interactions = 8)Log-Likelihood: −1818.6
LL-Null: −3548.9LLR p-value: 0.00
CoefStd. errzP>|z|[0.0250.975]OR
Intercept−4.4660.212−21.0740.000−4.882−4.0510.011
IPO cooperation2.9880.4586.5200.0002.0903.88619.849
Competitors cooperation0.0510.114−0.4500.653−0.2750.1720.950
University cooperation0.5360.2162.4880.0130.1140.9591.709
Total percentage of funding0.0500.00412.4970.0000.0410.0581.051
Government cooperation0.9090.2743.3130.0010.3711.4472.482
Clients cooperation0.3730.0934.0170.0000.1910.5551.452
Other firms cooperation0.1610.1311.2330.217−0.0950.4181.175
Labs cooperation−0.7950.203−3.9090.000−1.194−0.3960.451
Headquarter cooperation0.9830.1486.6490.0000.6941.2742.674
Customers cooperation0.5980.1015.9240.0000.4000.7961.819
Suppliers cooperation−0.6590.094−7.0080.000−0.843−0.4750.517
Obj.: Increase quality1.0900.1248.7940.0000.9461.2342.974
Objective: Incr. flex0.0640.0471.3810.167−0.0270.1561.066
Obj.: Incr. mark. share0.0730.0471.5460.122−0.0200.1661.076
Obj.: Entry new markets0.2280.0445.1680.0000.1410.3141.256
Obj.: Red. env. impact0.4060.03710.8540.0000.3330.4791.501

Source(s): Authors’ own work

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