Toward a better understanding of collaborative research, development, and innovation (R&D&I) — exploring virtual, physical, and cognitive structures

Abstract
Objective of the study: The research in this paper contributes to the understanding of how physical, virtual, and cognitive structures support innovation ecosystems’ multi-actor research, development, and innovation (R&D&I) collaboration in its different phases.
Methodology/Approach: The research’s methodological approach is based on a qualitative case study research strategy. It is done by exploring three innovation ecosystem cases. The case data comprises the case ecosystems’ existing documentation that was supplemented with five semi-structured interviews.
Originality/Relevance: Based on the findings of this research, it was possible to explore how industry and academy partners are collaborating through virtual, physical, and cognitive structures. Our cases also provide empirical evidence on how physical industrial sites can be used as environments for collaborative industry-academy R&D&I work.
Main Results: As a result, the paper presents lessons learned from three different innovation ecosystem cases that involve industrial, technology, and academy partners to tackle industrial use cases through virtual, physical, and cognitive structures. An example of such lessons learned is assembling dynamic teams to solve industrial problems.
Theoretical/Methodological Contributions: This article builds an understanding of how virtual, physical, and cognitive structures support collaboration between different participants in their joint R&D&I work covering industry-academy collaboration. The article also explains practical examples of this using innovation ecosystem cases.
Management/Social Contributions: The findings of this study may benefit professionals and managers who have an interest in understanding collaborative R&D&I and how physical, virtual, and cognitive structures can support it. Furthermore, the results provide means and experiences for innovation ecosystem managers to facilitate the definition of operational models suitable for the context of their innovation ecosystems.