Who benefits from SMEs’ radical innovations?—empirical evidence from German biotechnology

Abstract

We analyze transfer channels of radical innovations launched by small and medium-sized firms (SMEs) using an ego network approach. Based on a unique longitudinal dataset covering the observation period 1996–2016, we identify and trace back radical innovations of SMEs in German Biotech in order to analyze the extent to which SMEs themselves—or eventually also other organizations in their direct cooperation surrounding—benefit from radical innovations in terms of subsequent innovation performance. Results from negative binomial panel regressions indicate that direct cooperation partners of radical innovators show higher innovative performance than partners of the control group, i.e., statistical twin firms without radical innovations. Our results confirm that radical innovations initiate a positive impulse for innovation through direct cooperative relationships. Interestingly, geographical proximity does not play a role, and especially technologically different partners benefit from direct cooperation with radical innovators.Plain English Summary Radical innovations are rare but have a great importance for the economy. Our study shows that not only radically innovating biotech SMEs themselves but also various types of directly linked cooperation partners benefit significantly. We investigate whether directly linked cooperation partners of radically innovating firms benefit from knowledge spillover transferred through formal cooperation. A radical innovation represents a novel and often complex combination of already existing knowledge. Thus, knowledge transfer process differs for radical and non-radical innovations. Project-based formal cooperation can support the transfer of radical knowledge. In this context geographical and technological factors matter. Using the example of German small and medium-sized biotechnology firms, we show that collaboration with radical firms is positively related to innovation performance of directly linked project partners. Interestingly, this effect is higher for partners located in other regions as well as for technologically distant partners. Our study extends the research on knowledge transfer by emphasizing the role of formal collaboration projects, especially in knowledge intensive high-tech industries.

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“…3 shows that a size effect can be observed that follows the presumed path of general patent activity across different firm sizes. To address this issue, similar to Shkolnykova & Kudic (2021), we identified statistical twins that are very similar, in a statistical sense, to firms with AI patents, but do not have AI patents themselves. In more concrete terms, we matched each firm that had at least one AI patent in a specific year with one other non-AI patenting firm, starting with the year 2011, where a firm is matched if it has its first AI patent in the dataset 1 year later (2012 in this instance).…”

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“…SprinD 2 ) as well as researchers (e.g. Grashof et al, 2021;Hesse & Fornahl, 2020;Shkolnykova & Kudic, 2021). However, discovering these radical new ideas is anything but easy.…”

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