Poisson regression estimates the effects on I4.0 innovations
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
|---|---|---|---|---|---|---|---|---|
| Breadth | 0.173*** (0.0275) | 0.157*** (0.0286) | 0.620*** (0.131) | 0.715*** (0.131) | 0.101*** (0.0308) | 0.0936*** (0.0275) | 0.0591* (0.0323) | |
| Depth | 0.0570* (0.0336) | 0.0696** (0.0332) | 0.107 (0.0891) | 0.0313 (0.0916) | 0.0347 (0.0266) | 0.0519** (0.0229) | 0.0600** (0.0268) | |
| Partner cluster | 0.0791*** (0.0288) | 0.0305 (0.0216) | 0.0529*** (0.0193) | −0.00277 (0.0183) | 0.0467** (0.0208) | 0.0219 (0.0179) | ||
| Inn. Prod. | 0.318 (0.251) | 0.200 (0.212) | 0.130 (0.191) | 0.216 (0.151) | 0.252 (0.157) | −0.0510 (0.151) | ||
| Inn. Proc. | 0.500** (0.221) | 0.272 (0.190) | 0.421*** (0.146) | 0.286** (0.138) | −0.142 (0.152) | 0.285* (0.153) | ||
| Breadth2 | −0.0427*** (0.0102) | −0.0536*** (0.0102) | ||||||
| Depth2 | −0.00411 (0.00973) | 0.00604 (0.00901) | ||||||
| Large firm | 0.403* (0.227) | −0.0190 (0.146) | 0.187 (0.167) | 0.332*** (0.129) | −0.0361 (0.109) | −0.0719 (0.129) | 0.140 (0.112) | |
| Big data | 1.218*** (0.229) | |||||||
| AI | 1.259*** (0.214) | |||||||
| IoT | 1.494*** (0.351) | |||||||
| Constant | −0.140 (0.218) | 0.256 (0.297) | −0.460* (0.270) | −0.850** (0.374) | −1.328*** (0.388) | −0.743*** (0.220) | −0.631*** (0.225) | −0.753*** (0.239) |
| Observations | 96 | 96 | 96 | 96 | 96 | 96 | 96 | 96 |
| Pseudo R2 | 0.293 | 0.079 | 0.312 | 0.367 | 0.402 | 0.413 | 0.411 | 0.411 |
| AIC | 384.8 | 502.9 | 382.5 | 350.8 | 338.3 | 330.1 | 331.2 | 331.2 |
| BIC | 392.5 | 515.7 | 400.5 | 366.2 | 361.4 | 350.6 | 351.7 | 351.7 |
| Log-likelihood | −189.4 | −246.5 | −184.3 | −169.4 | −160.1 | −157.0 | −157.6 | −157.6 |
| VIF average | 2.08 | 1.01 | 1.46 | 15.52 | 10.76 | 1.57 | 1.58 | 1.69 |
| Model 1 | Model 2 | Model 3 | Model 4 | Model 5 | Model 6 | Model 7 | Model 8 | |
|---|---|---|---|---|---|---|---|---|
| Breadth | 0.173 | 0.157 | 0.620 | 0.715 | 0.101 | 0.0936 | 0.0591 | |
| Depth | 0.0570 | 0.0696 | 0.107 (0.0891) | 0.0313 (0.0916) | 0.0347 (0.0266) | 0.0519 | 0.0600 | |
| Partner cluster | 0.0791 | 0.0305 (0.0216) | 0.0529 | −0.00277 (0.0183) | 0.0467 | 0.0219 (0.0179) | ||
| Inn. Prod. | 0.318 (0.251) | 0.200 (0.212) | 0.130 (0.191) | 0.216 (0.151) | 0.252 (0.157) | −0.0510 (0.151) | ||
| Inn. Proc. | 0.500 | 0.272 (0.190) | 0.421 | 0.286 | −0.142 (0.152) | 0.285 | ||
| Breadth2 | −0.0427 | −0.0536 | ||||||
| Depth2 | −0.00411 (0.00973) | 0.00604 (0.00901) | ||||||
| Large firm | 0.403 | −0.0190 (0.146) | 0.187 (0.167) | 0.332 | −0.0361 (0.109) | −0.0719 (0.129) | 0.140 (0.112) | |
| Big data | 1.218 | |||||||
| AI | 1.259 | |||||||
| IoT | 1.494 | |||||||
| Constant | −0.140 (0.218) | 0.256 (0.297) | −0.460 | −0.850 | −1.328 | −0.743 | −0.631 | −0.753 |
| Observations | 96 | 96 | 96 | 96 | 96 | 96 | 96 | 96 |
| Pseudo | 0.293 | 0.079 | 0.312 | 0.367 | 0.402 | 0.413 | 0.411 | 0.411 |
| AIC | 384.8 | 502.9 | 382.5 | 350.8 | 338.3 | 330.1 | 331.2 | 331.2 |
| BIC | 392.5 | 515.7 | 400.5 | 366.2 | 361.4 | 350.6 | 351.7 | 351.7 |
| Log-likelihood | −189.4 | −246.5 | −184.3 | −169.4 | −160.1 | −157.0 | −157.6 | −157.6 |
| VIF average | 2.08 | 1.01 | 1.46 | 15.52 | 10.76 | 1.57 | 1.58 | 1.69 |
Standard errors in parentheses; *p < 0.1; **p < 0.05; ***p < 0.01
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