1 Introduction
2 Theory background, state of the art, hypotheses
2.1 Theory and concepts
2.2 Prior research on networks and radical innovation
2.3 Hypotheses
-
H1: Direct partners (alters) of a DBF with at least one radical innovation (ego) experience a higher degree of innovativeness, measured by the alters’ number of patent applications in subsequent periods.
-
H2: Direct partners (alters) of a DBF with at least one radical innovation (ego), located in the same region, experience a higher degree of innovativeness, measured by the alters’ number of patent applications in subsequent periods.
-
H3: Non-biotechnology partners (alters) of a DBF with at least one radical innovation (ego) experience a higher degree of innovativeness, measured by the alters’ number of patent applications in subsequent periods.
3 Technological field and data
3.1 Technological field
3.2 Data sources
4 Methodology
4.1 Research design
4.2 Specification of dedicated biotechnology SME’s population
4.3 Identification of radical patents and radical innovators
4.4 Creation of ego networks of radical innovators
4.5 Identification of statistical twins
-
Number of employees that a firm has;
-
Size of the network, measured by the number of unique project partners;
-
Whether the firm was founded as a spin off (1) or not (0);
-
Whether the firm has universities in the network (1) or not (0);
-
Whether firm is situated in a technology center (1) or not (0).
4.6 Econometric approach
Variable | Description | Data source |
---|---|---|
PATENT | Number of patent applications of a firm per year | PATSTAT 2017b |
RADICAL | Firm being partner of radical innovator (1) or non-radical twin | Identification procedure, explained in 4.2, Förderkatalog |
REGION | Partner located in the same NUTS2 region with focal firm (1) or in different region (0) | BICOM AG, Amadeus, WISO-Net |
BIOTECH | Dedicated biotechnology firm (1) or not (0) | BICOM AG, Amadeus |
AGE | Years between founding of the firm and year of observation | BIOCOM AG, Amadeus |
EMPL | Number of employees in a particular year | BICOM AG, Amadeus, WISO-Net |
UNI | Firm having university as a partner in funded project in the year of observation or before (1) or not (0) | Förderkatalog |
SUBS | Firm being subsidiary in the observation year (1) or not (0) | Amadeus |
SPINOFF | Firm being spin-off at founding (1) or not (0). Variable is constant for one firm across all observation periods | Amadeus, Firms´ websites |
5 Results
5.1 Basic descriptive statistics
Variable | Obs | Mean | Std. dev. | Min | Mdn | Max |
---|---|---|---|---|---|---|
Patents, non-radical partners | 21 | 1.90 | 2.93 | 0 | 0 | 10 |
Patents, radical partners | 35 | 8.57 | 22.48 | 0 | 3 | 107 |
Variable | Obs | Mean | Std. dev. | Min | Mdn | Max |
---|---|---|---|---|---|---|
Non-radical partner | ||||||
AGE | 244 | 13.93 | 7.98 | 0 | 11 | 166 |
EMPL | 244 | 13.06 | 11.37 | 1 | 9 | 55 |
UNI | 244 | 0.96 | 0.20 | 0 | 1 | 1 |
SUBS | 244 | 0.18 | 0.39 | 0 | 0 | 1 |
SPINOFF | 244 | 0.30 | 0.46 | 0 | 0 | 1 |
Radical partner | ||||||
AGE | 288 | 10.56 | 8.23 | 0 | 8 | 39 |
EMPL | 288 | 38.51 | 60.12 | 2 | 20 | 329 |
UNI | 288 | 0.90 | 0.31 | 0 | 1 | 1 |
SUBS | 288 | 0.34 | 0.48 | 0 | 0 | 1 |
SPINOFF | 288 | 0.27 | 0.44 | 0 | 0 | 1 |
RADICAL | AGE | EMPL | UNI | SUBS | SPINOFF | |
---|---|---|---|---|---|---|
RADICAL | 1.000 | |||||
AGE | −0.203 | 1.000 | ||||
EMPL | 0.272 | 0.387 | 1.000 | |||
UNI | −0.119 | 0.125 | −0.182 | 1.000 | ||
SUBS | 0.184 | −0.107 | 0.245 | 0.012 | 1.000 | |
SPINOFF | −0.035 | −0.176 | −0.067 | −0.217 | −0.078 | 1.000 |
5.2 Results of the panel regression analysis
Baseline model (1) | Intra vs. interregional partners (2) | Biotech vs. non-biotech partners (3) | Intra vs. interregional and biotech vs. non-biotech partners (4) | |||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 year patent lag | 1 year patent lag | 2 years patent lag | 0 year patent lag | 1 year patent lag | 2 years patent lag | 0 year patent lag | 1 year patent lag | 2 years patent lag | 0 year patent lag | 1 year patent lag | 2 years patent lag | |
RADICAL | 1.045** (0.430) | 0.855** (0.395) | 0.593 (0.384) | - | - | - | - | - | - | - | - | - |
AGE | −0.089*** (0.024) | –0.068*** (0.021) | −0.049** (0.020) | −0.129*** (0.034) | −0.097*** (0.030) | −0.079*** (0.282) | −0.162*** (0.0364) | −0.098*** (0.033) | −0.075** (0.030) | −0.164*** (0.036) | −0.107*** (0.032) | −0.085*** (0.029) |
EMPL | 0.012** (0.005) | 0.016*** (0.005) | 0.018*** (0.005) | 0.010** (0.005) | 0.139*** (0.005) | 0.014*** (0.005) | 0.010* (0.005) | 0.012** (0.006) | 0.013** (0.005) | 0.009* (0.005) | 0.013** (0.006) | 0.014*** (0.005) |
UNI | 0.651 (0.571) | 0.860 (0.548) | 1.610** (0.683) | 0.806 (0.571) | 0.962* (0.556) | 1.800** (0.712) | 0.994* (0.553) | 0.916 (0.560) | 1.729** (0.705) | 1.015* (0.550) | 1.002* (0.557) | 1.816** (0.711) |
SUBS | −0.703* (0.362) | −0.401 (0.325) | −0.174 (0.315) | −0.399 (0.394) | −0.106 (0.350) | 0.172 (0.340) | −0.273 (0.361) | −0.017 (0.344) | 0.264 (0.343) | −0.215 (0.364) | −0.010 (0.352) | 0.232 (0.349) |
SPINOFF | −0.305 (0.429) | −0.222 (0.405) | 0.173 (0.391) | 0.336 (0.631) | 0.294 (0.557) | 0.349 (0.514) | 0.437 (0.710) | 0.239 (0.596) | 0.175 (0.514) | 0.662 (0.722) | 0.387 (0.604) | 0.350 (0.530) |
REGION | − | – | – | −1.680** (0.672) | −1.247** (0.577) | −1.045** (0.515) | – | – | – | −0.924 (0.665) | −0.888 (0.635) | −0.855 (0.577) |
BIOTECH | – | – | – | – | – | – | −2.519*** (0.901) | −1.389* (0.755) | −0.941 (0.618) | −1.975** (0.997) | −0.814 (0.833) | −0.430 (0.684) |
CONSTANT | −0.672 (0.760) | −1.272* (0.691) | −2.228*** (0.792) | 1.544* (0.891) | 0.407 (0.790) | −1.086 (0.882) | 2.410** (1.012) | 0.731 (0.948) | −0.984 (0.977) | 2.610** (1.016) | 0.826 (0.911) | −0.854 (0.953) |
Log likelihood | −394.931 | −422.021 | −436.373 | −294.085 | −312.022 | −319.750 | −296.483 | −313.579 | −320.882 | −291.991 | −311.496 | −319.545 |
Observations | 532 | 532 | 532 | 288 | 288 | 288 | 288 | 288 | 288 | 288 | 288 | 288 |