Table 2. Logistic regression model... | Emerald Publishing

Table 2.

Logistic regression model results

	Model 1 PI == 1			Model 2 PI == 1			Model 3 PI == 1			Model 4 PI == 1
Variable name	B	SE	P>\|z\|	B	SE	P>\|z\|	B	SE	P>\|z\|	B	SE	P>\|z\|
Age (log)	−0.02	0.04	0.59	−0.08**	0.03	0.02	−0.07	0.08	0.43	−0.08**	0.03	0.01
Size (log)	0.05	0.10	0.63	−0.03	0.13	0.80	0.03	0.11	0.79	−0.03	0.13	0.80
IIS				4.34***	1.25	0.00				4.63***	1.45	0.00
EIS							1.91**	0.83	0.02	−0.85	0.66	0.20
Constant	−0.18	0.22	0.42	−0.13	0.38	0.74	−0.07	0.29	0.82	−0.12	0.38	0.74
Model information
Type of model	Logistic regression			Logistic regression			Logistic regression			Logistic regression
Observations	294.00			294.00			294.00			294.00
Prob > χ²	0.87			0.00			0.00			0.00
Pseudo R²	0.00			0.17			0.02			0.17
AIC	413.13			343.51			404.57			343.05
BIC	424.18			354.56			415.62			354.10

	Model 1PI == 1			Model 2PI == 1			Model 3PI == 1			Model 4PI == 1
Variable name	B	SE	P>\|z\|	B	SE	P>\|z\|	B	SE	P>\|z\|	B	SE	P>\|z\|
Age (log)	−0.02	0.04	0.59	−0.08**	0.03	0.02	−0.07	0.08	0.43	−0.08**	0.03	0.01
Size (log)	0.05	0.10	0.63	−0.03	0.13	0.80	0.03	0.11	0.79	−0.03	0.13	0.80
IIS				4.34***	1.25	0.00				4.63***	1.45	0.00
EIS							1.91**	0.83	0.02	−0.85	0.66	0.20
Constant	−0.18	0.22	0.42	−0.13	0.38	0.74	−0.07	0.29	0.82	−0.12	0.38	0.74
Model information
Type of model	Logistic regression			Logistic regression			Logistic regression			Logistic regression
Observations	294.00			294.00			294.00			294.00
Prob > χ²	0.87			0.00			0.00			0.00
Pseudo R²	0.00			0.17			0.02			0.17
AIC	413.13			343.51			404.57			343.05
BIC	424.18			354.56			415.62			354.10

Notes:

PI: process innovation, IIS: internal innovation strategy, EIS: external innovation strategy, RIQ: regional institutional quality; *p < 0.10, **p < 0.05, ***p < 0.01