CHAPTER 5: DIFFUSION OF RADICAL INNOVATION AMONG
3. D ATA AND RESEARCH FIELD
5.1 Descriptive statistics
In order to perform the analysis, an unbalanced panel reflecting the dependent and independent variables for each citing SME was created. For each firm, the observation period begins in the year of a citation and ends in 2016, except in the case of a citing firm or radical innovator dissolving before 2016.93 The year of dissolution was identified using the Orbis database.94 Our panel includes 678 observations drawn from our sample of 78 citing firms.
Table 4 presents descriptive statistics for the patent variable, and the proximity dimensions.
Tab. 4 Descriptive statistics, dependent variable and proximity measures
Variable Obs Mean Std. Dev. Min Mdn Max
PAT 678 1.73 3.57 0 0 39
GEO 678 5.03 1.47 -2.73 5.53 6.37
COG 678 0.45 0.22 0.00 0.46 0.90
ORG 678 0.07 0.26 0.00 0.00 1.00
SOC 678 0.12 0.30 0.00 0.00 1.00
It can be seen that the number of patents filed by a firm each year varies greatly. Most of the firms have zero to one application each year, however, there are several firms in the sample that have a relatively high number of patents applications. Additional checks of these firms did not show any peculiarity of their characteristics that would suggest deleting these firms from the sample.
The measurement of our proximity dimensions also show different tendencies. Whereas the mean values of social and organizational proximity are on rather low, cognitive proximity shows a generally high to medium level. A significant degree of variation can also be seen in the measurement of geographical proximity. We will explore these variables in more detail later in the paper.
93 If after a successful exit (e.g., through acquisition) a firm stays in the market, the end observation year was not affected. However, the value of subsidiary variable for that firm might change (e.g., in the case of acquisition).
94 Last retrieved in January 2020.
187 Fig. 2 Location of citing and radical firms
As can be seen in Figure 2,95 not all firms apply for patents in their respective regions. There are some small citation distances in western (North Rhine-Westphalia, Hessen) and southern Germany (Bavaria – Munich, Nuremberg; Heidelberg region). It can also be seen that while radical innovators are mostly located close to traditional biotechnology clusters (especially the regions, funded by BioRegio), citing firms are spread across Germany.
It can also be seen that, on average, there is medium cognitive proximity between cited and citing firms. Moreover, the most popular classes along citing and cited patents do not differ much. The most popular technological class of citing papers is C07K 14 (“Peptides having more than 20 amino acids; Gastrins; Somatostatins; Melanotropins; Derivatives thereof”), and is also common in radical patents. The same can be said about the most popular classes across radical innovators: C12N 15 (“Mutation or genetic engineering…”) and C12Q 1 (“Measuring or testing processes involving enzymes, nucleic acids or microorganisms”). The only class that is present in citing sample but not in the radical sample is C12N 9 (“Enzymes, e.g. ligases (6.);
Proenzymes; Compositions thereof”). The only class that is present in the radical and not in the citing sample is A61K 31 (“Medicinal preparations containing organic active ingredients”).
It should be noted that these two classes share certain common characteristics. This might
95 Here, only those radical innovators that are cited by SMEs are presented.
188 indicate that either the least popular classes among radical and non-radical innovators are different, or that of the cited and citing firms have different areas of specialization.
Technological classes, radical firms Technological classes, citing firms
Fig. 3 Location of citing and radical firms
On another note, only six firms appear to be in the same corporate group with a cited radical innovator, and 15 firms exhibit positive social proximity, based on having a common patent with the radical innovator. Being connected to a radical innovator on the corporative level, however, does not necessarily indicate social proximity. Only three out of six firms from the same corporate group show positive social proximity.
Tab. 5 Descriptive statistics for control variables*
Variable Obs Mean Std. Dev. Min Mdn Max EMPL 678 2.81 1.33 0.00 3.26 5.26
AGE 678 2.17 1.25 -2.30 2.31 5.38
TECHPARK 678 1.77 2.10 -3.17 2.57 4.84
UNI 678 0.30 0.46 0.00 0.00 1.00
SUBS 678 0.16 0.37 0.00 0.00 1.00 BIGCOMP 678 0.82 0.38 0.00 1.00 1.00 FEREG 678 6.90 0.66 5.19 7.01 8.35 HUMRES 678 6.66 0.51 5.37 6.89 7.37 BIOREGIO 678 0.38 0.49 0.00 0.00 1.00
*EMPL, AGE, TECHPARK FEREG and HUMREG present logarithm variables
189 Table 5 shows the descriptive statistics for our control variables. As can be seen, the number of employees and firm age vary greatly. The largest firm in the sample has 192 employees, whereas the smallest has only one employee. Firm age also differs between being founded in the year of citation, and a family firm that was found in the 19th century. The distance to a technology park also has a high variation, from firms being less than one kilometer from a technology park to being more than 500 km away from a technology park.
More than 80% of the firms are located in the same regions as the biggest pharma and biotech corporations. There may be several reasons for this. First, large pharma and biotech corporations are spread across most of the regions in Germany. Second, it is possible that citing firms want to be close to these large corporations, either hoping to acquire knowledge, or to provide services (e.g. Kahl 2015).
About one third of the citing firms have connections to universities and are located in BioRegio funded regions. This indicates that citing firms stick less to the standard biotechnology clusters than do radical firms. The fact that there is a relatively low number of patents resulting from university collaboration may show the higher industrial orientation of citing firms. The fact that only one sixth of the firms in our sample are subsidiaries, a common characteristic of firm structures in the biotech field, may indicate that the SMEs in our sample are quite independent and rely on the knowledge of their peers. SMEs in our sample that are subsidiaries might be more inclined to engage in corporate relations and communicate with parent companies.
We next calculate the correlations between our variables to determine if any of the variables in the model might cause multicollinearity problems. Results of our correlation analysis are presented in the Table 6.96 As can be seen from the table, the coefficients vary from low to moderate, implying that no variable needs to be excluded from the model.
96 Polychoric correlations were alternatively calculated for binary variables, however, they do not differ significantly from Pearson correlations. In addition, variance VIFs were calculated and did not show the multicollinearity potential for any of the variables.
190 Tab. 6 Correlation coefficients
GEO COG ORG SOC EMPL AGE TECHPARK UNI SUBS BIGCOMP FEREG HUMRES BIOREGIO
GEO 1.000
COG 0.027 1.000
ORG -0.392
*** -0.007 1.000
SOC -0.262
*** -0.032 -0.246
*** 1.000 EMPL 0.090
** 0.015 -0.171
*** -0.031 1.000
AGE 0.133
*** -0.100
** -0.161
*** -0.121
*** 0.212
*** 1.000
TECHPARK 0.148
*** -0.090
** -0.204
*** 0.187
*** -0.090
** 0.112
*** 1.000
UNI 0.167
*** -0.009 -0.185
*** -0.195
*** 0.213
*** 0.080
** -0.052
* 1.000
SUBS -0.045 -0.185
*** 0.195
*** 0.052 0.174
*** 0.035 -0.052 0.075
* 1.000
BIGCOMP -0.052 -0.150
*** 0.103
*** 0.160
*** 0.007 0.004 -0.022 -0.287
*** 0.062 1.000
FEREG -0.096
** 0.219
*** -0.113
*** -0.221
*** -0.002 0.269
*** 0.097
** 0.055 0.026 -0.017 1.000
HUMRES 0.003 0.053 -0.019 -0.078
** -0.267
*** 0.153
*** -0.115
*** 0.097
** -0.040 0.055 0.511
*** 1.000
BIOREGIO 0.076 -0.138
*** -0.091
** 0.027 -0.019 0.022 0.008 0.135
*** -0.014 0.039 0.070
* 0.163
*** 1.000
***- significance at 0.01 level, ** - significance at 0.05 level, * - significance at 0.1 level
191