Thesis
Exploring translational assets : a new classification framework
- Creator
- Rights statement
- Awarding institution
- University of Strathclyde
- Date of award
- 2025
- Thesis identifier
- T17299
- Person Identifier (Local)
- 201677995
- Qualification Level
- Qualification Name
- Department, School or Faculty
- Abstract
- This research introduces a novel classification framework for Translational Assets (TAs) within the Scottish Innovation ecosystem. Translational assets, distinct Research and Development (R&D) organisations that bridge academia and industry by facilitating knowledge exchange, driving innovation and promoting technology transfer, play a critical role in enhancing national innovation capabilities. Despite their importance, the existing literature on TAs is notably sparse, particularly concerning qualitative investigations that explore their unique operational modes and contextual adaptability. Moreover, the lack of a comprehensive classification framework has led the challenges in understanding and leveraging these organisations’ diverse roles and contribution within the innovation ecosystem. To address this gap, this study proposes a continuum-based classification framework that captures the multifaced nature of TAs, moving beyond traditional discrete categories to better reflect the diversity of these entities. The research adopts a qualitative methodology comprising a comprehensive literature review, empirical analysis of 19 case studies conducted in Scotland and in-depth interviews with key stakeholders across different TAs. The findings reveal that while TAs in Scotland vary significantly in their size, structure, funding sources, and organisational objectives, certain recurring patterns emerge, allowing for the identification of five distinct TA models. These models range from "Demand-Led" assets responding directly to industry needs to "Advanced Technology and Innovation" assets focusing on high-tech, industry-aligned research. The final classification framework introduces multiple dimensions, including organisational structure, funding model, stakeholder engagement, and research focus, each positioned on a continuum to capture the flexible and evolving nature of TAs. This thesis contributes to both theory and practice. Theoretically, it addresses the need for an adaptable classification framework, overcoming limitations in past approaches, by introducing a multidimensional, continuum-based approach. Practically, it offers a valuable tool for policymakers, researchers, and industry leaders to better understand and strategically engage with TAs, facilitating more effective resource allocation, policy development, and collaborative partnerships within the Scottish innovation landscape. The framework's potential applications beyond Scotland, providing a foundation for comparative research across different regional and national contexts, open up new avenues of exploration and inspire further research.
- Advisor / supervisor
- Mehnen, Jorn
- MacBryde, Jillian
- Resource Type
- DOI
Relations
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PDF of thesis T17299 | 2025-05-27 | Public | Download |