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There is a fairly clear correspondence between the importance and commonness of IP-based and research-related knowledge transfer mechanisms in academic institutes and the importance they attribute to two types of IOs: to IO1 which support spin-offs and IP-based knowledge transfers (such as KTOs, incubators, innovation/science parks, proof-of-concept and commercialisation funds), and to IO2 supporting university-industry cooperation (such as public innovation funders, cooperative research institutes and centres, or thematic networks and cluster organizations). Above all, IO1 and IO2 can support patenting activities, research-based transfers and informal transfers; only IO1 matters for spin-off activities. Type 1 intermediary organizations thus have a slightly broader importance and clearer relationship to knowledge sharing performance than type 2 IOs. Even if a university thus wants to place a focus on research cooperation and contracts with industry, an IO1-type organization might provide more use to scientists than an IO2-type organization, as the former provides valuable complementary knowledge for handling the output of research, e.g., with regard to finding good solutions regarding IP ownership and commercialisation. For industrial doctorates and teaching-based transfer mechanisms none of the IOs provided any noteworthy influence on institutes’ performance. Other factors, matter more, for instance the personal contacts to the private sector via double affiliations or previous positions in industry (not shown in the estimates, see Barjak, Heimsch & Maidl, 2020). Internet-based initiatives (IO3) did not appear as important IOs. However, it must be noted that the survey was conducted before the Covid-19 pandemic and that this might have changed since then. Some observers expect a growing importance of virtual IOs (Albats et al., 2022). Not only the type of IO matters, but also its structures and activities. It is preferable to reduce organizational distance by setting up an internal IO (at least from the academics’ perspective, the industry view might differ on this point). Likewise, cognitive distance is also preferably low (Villani et al., 2017), and IOs in the same discipline and with field knowledge are generally preferred to interdisciplinary IOs. Still, their knowledge should be complementary to the knowledge of academics with regard to aspects of markets and customers. Whereas it does not seem to be crucial whether the service offer of the IOs is flexible and customized to the needs of the scientists on a case-by-case basis, events matter: for important IOs the organization of interesting events was on average higher rated than for unimportant IOs. The findings provide a better understanding of what mechanisms of knowledge sharing are mainly affected by the work of IOs and which of their activities make a difference for their partners in research and higher education. The findings should help IOs to adjust their approaches and services to better meet the needs of a broader range of research institutes which are their main knowledge sources and therefore an important stakeholder in any attempt to increase knowledge sharing. The findings speak to the owners of the IOs as well which often come from the academic sector and need IOs to support them with their third mission activities. Last but not least, innovation policy makers in many countries (definitely in Switzerland) have strong interest in functioning knowledge sharing, as this is one of the main transmission mechanisms for generating an economic and social impact of higher education organizations. |
