Targeting Academic Engagement in Open Innovation: Tools, Effects and Challenges for University Management

Abstract

Besides commercialization, university knowledge is commonly transferred through different interactions constituting the so called academic engagement. Very little attention has been paid to professionalizing these various interactions compared to the linear commercialization funnel. In this paper, we conducted a qualitative case study of the innovation support organization at Uppsala University, Sweden, analysing the following: Which mechanisms and tools the university management does apply in order to create targeted open innovation interactions and which effects and challenges emerge when applying these tools? We found that staff with experience of both academia and industry is important for enabling open innovation interactions to evolve and that tools can be used to concretize specific and deeper interactions. However, six challenges was also identified: (1) the intermittent nature of university-industry interactions; (2) lack of codified ways to trace effects; (3) extensive and time consuming preparatory work; (4) the extra resources and low conversion rates in engaging academically unrelated small and medium-sized enterprises (SMEs); (5) high costs of recruiting staff with double competence and running the studied tools; and (6) the interdependencies which makes the system sensitive. Our results indicate that the tools used help the university to follow a mode 3 of knowledge creation. The study addressed the research gap regarding organizational support in academic engagement by indicating which tools can be used by the university management to target and focus industry-academia interactions, their effects and associated challenges. A lot of the effects still need to be codified, and more research is needed to understand their impact over time.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 70 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.