Abstract
University spin-offs (USOs) are increasingly recognised as a prime mechanism to generate technological, economic and societal impact on regional and national levels. Yet, despite favourable institutional and policy arrangements fostering academic entrepreneurship, many USOs face major issues to overcome the liability of newness and smallness in the initial venturing phases. In result, existing research commercialisation practices remain ineffective with the majority of USOs failing to reach the expected objectives. While this problem has garnered attention both in research and practice, there is a lack of comprehensive understanding of USOs at the early-stage of development, hindering more impactful research commercialisation.
To address this problem and to develop new actionable insights, this dissertation employs robust multi-disciplinary, mixed-method techniques. First, this dissertation consolidates and synthesises the existing knowledge on university-industry collaborations and academic entrepreneurship, and presents these concepts as interconnected, multi-layered ecosystems at the individual, organisational and institutional levels. Second, this dissertation examines how early-stage USO characteristics need to be shaped to overcome the initial venturing phases and acquire funding as a leverage for long-term survival. Third, by employing robust text mining and unsupervised machine learning techniques, this dissertation presents a novel USO typology with different venture development trajectories in relation to exploitative and explorative technology development and technology commercialisation activities.
The findings of this dissertation foster a comprehensive understanding of university-industry collaborations and academic entrepreneurship research field. Additionally, this dissertation presents new actionable insights for academic entrepreneurs with regards to key determinants of USO development, and stimulates a development of effective government-based support mechanisms of research commercialisation.
To address this problem and to develop new actionable insights, this dissertation employs robust multi-disciplinary, mixed-method techniques. First, this dissertation consolidates and synthesises the existing knowledge on university-industry collaborations and academic entrepreneurship, and presents these concepts as interconnected, multi-layered ecosystems at the individual, organisational and institutional levels. Second, this dissertation examines how early-stage USO characteristics need to be shaped to overcome the initial venturing phases and acquire funding as a leverage for long-term survival. Third, by employing robust text mining and unsupervised machine learning techniques, this dissertation presents a novel USO typology with different venture development trajectories in relation to exploitative and explorative technology development and technology commercialisation activities.
The findings of this dissertation foster a comprehensive understanding of university-industry collaborations and academic entrepreneurship research field. Additionally, this dissertation presents new actionable insights for academic entrepreneurs with regards to key determinants of USO development, and stimulates a development of effective government-based support mechanisms of research commercialisation.
Original language | English |
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Qualification | Doctor of Philosophy |
Awarding Institution |
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Supervisors/Advisors |
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Award date | 11 Sept 2020 |
Place of Publication | Enschede |
Publisher | |
Print ISBNs | 978-94-640-2498-2 |
Electronic ISBNs | 978-94-640-2498-2 |
DOIs | |
Publication status | Published - 11 Sept 2020 |