GENERATING ARCHITECTURAL KNOWLEDGE IN DIGITAL INNOVATION NETWORKS
Doctoral thesis, 2024
This thesis investigates the generation of architectural knowledge within digital innovation networks, focusing on the collaborative integration of diverse component knowledge to create new digital products. Architectural knowledge, or the understanding of how individual components are integrated into a coherent whole, is particularly important to align and coordinate actors in interorganizational networks with highly heterogeneous knowledge bases. However, little research has investigated how such knowledge is generated when the innovation involves digital components that are inherently malleable.
Through qualitative, longitudinal single-case studies of two distinct networks, this thesis investigates how architectural knowledge is generated across different innovation strategies. Findings indicate that given the malleable nature of digital components, architectural knowledge is highly dynamic, as desirable component interactions, contextual requirements, and new value opportunities may only emerge during the innovation process. Specifically, this thesis offers a refined definition of architectural knowledge in digital innovation as knowledge of how digital and physical components synergistically contribute to overarching architectural goals within specific use contexts to create current and future value opportunities.
The required capabilities for innovation networks to generate such knowledge are fundamentally similar to those in non-digital innovation. However, they need to facilitate knowledge integration across more disparate component knowledge domains, higher paces of architectural knowledge refinement, and increased design flexibility. Findings are synthesized in a theoretical framework on architectural knowledge generation in digital innovation networks.
The thesis contributes to the literature on digital innovation, innovation networks, and organizational design by explaining knowledge dynamics in digital innovation networks and offering practical implications for actors in these networks and other actors such as supporting funding agencies.
Through qualitative, longitudinal single-case studies of two distinct networks, this thesis investigates how architectural knowledge is generated across different innovation strategies. Findings indicate that given the malleable nature of digital components, architectural knowledge is highly dynamic, as desirable component interactions, contextual requirements, and new value opportunities may only emerge during the innovation process. Specifically, this thesis offers a refined definition of architectural knowledge in digital innovation as knowledge of how digital and physical components synergistically contribute to overarching architectural goals within specific use contexts to create current and future value opportunities.
The required capabilities for innovation networks to generate such knowledge are fundamentally similar to those in non-digital innovation. However, they need to facilitate knowledge integration across more disparate component knowledge domains, higher paces of architectural knowledge refinement, and increased design flexibility. Findings are synthesized in a theoretical framework on architectural knowledge generation in digital innovation networks.
The thesis contributes to the literature on digital innovation, innovation networks, and organizational design by explaining knowledge dynamics in digital innovation networks and offering practical implications for actors in these networks and other actors such as supporting funding agencies.
combinative capabilities
innovation networks
architectural knowledge
layered modular architecture
case study
architectural innovation
radical innovation
digital innovation
Author
Adrian Bumann
Chalmers, Technology Management and Economics, Entrepreneurship and Strategy
The challenges of knowledge combination in ML-based crowdsourcing - The ODF Killer shrimp challenge using ML and kaggle
Proceedings of the Annual Hawaii International Conference on System Sciences,;Vol. 2020-January(2021)p. 4930-4939
Paper in proceeding
No Ground Truth at Sea - Developing High-Accuracy AI Decision-Support for Complex Environments
Proceedings of the Annual Hawaii International Conference on System Sciences,;Vol. 2023-January(2023)p. 4566-4575
Paper in proceeding
Bumann, A., Sandberg, J., Teigland, R. Theorizing Digital Innovation Network Orchestration: Navigating the Tension Between Leveraging Generativity and Bounding Innovation.
Bumann, A., Mansoori, Y. Generating architectural knowledge in interindustry digital innovation: the case of a maritime navigation decision-support system.
In an increasingly digital world, solving complex societal and environmental challenges requires collaboration across diverse fields. My research focuses on how organizations work together in digital innovation networks to create novel digital products. These networks bring together actors from various sectors, such as shipping, telecommunications, or data science, who combine their specialized technological knowledge to create a new architecture—a technical product that is greater than the sum of its parts. The collective knowledge developed during these collaborations is known as "architectural knowledge."
I studied two case networks, each involving actors from different sectors collaborating to develop new AI systems: one for monitoring marine species and the other for supporting maritime navigation. Through qualitative research methods, including observations and interviews, I explored how knowledge was shared and utilized in these networks, and the challenges and capabilities required for their success.
Compared to traditional innovation, a key difference in digital innovation is that it involves digital components that are highly flexible because they can be easily reprogrammed and recombined. This flexibility allows for dynamic problem-solving but also introduces new complexities. To address these, organizations need to better understand how to coordinate their knowledge and technological resources effectively.
Four practical takeaways:
❖ A digital component can serve multiple purposes beyond its original intent. Organizations should regularly reassess components to identify potential new uses or synergies with other technologies.
❖ Even when creating new products, it is not necessary to start from scratch. Learn from what has been done in other contexts, codify best practices, and apply existing knowledge to reduce uncertainty.
❖ No single organization can know everything. When collaborating across different fields, it is important to balance acquiring enough knowledge to manage interdependencies and build a cohesive technical architecture, without getting bogged down by unnecessary technical details.
❖ Collaboration can be challenging when diverse interests are involved. Focusing on achievable short-term goals can provide immediate value and a shared direction, while broader long-term objectives ensure that the innovation remains adaptable and relevant as new opportunities emerge.
The findings offer new insights for academics studying digital innovation, as well as practical guidance for organizations and funding agencies involved in collaborative projects. The lessons learned can help foster more successful partnerships across domains to help tackle urgent issues, from environmental challenges to improved industry practices.
I studied two case networks, each involving actors from different sectors collaborating to develop new AI systems: one for monitoring marine species and the other for supporting maritime navigation. Through qualitative research methods, including observations and interviews, I explored how knowledge was shared and utilized in these networks, and the challenges and capabilities required for their success.
Compared to traditional innovation, a key difference in digital innovation is that it involves digital components that are highly flexible because they can be easily reprogrammed and recombined. This flexibility allows for dynamic problem-solving but also introduces new complexities. To address these, organizations need to better understand how to coordinate their knowledge and technological resources effectively.
Four practical takeaways:
❖ A digital component can serve multiple purposes beyond its original intent. Organizations should regularly reassess components to identify potential new uses or synergies with other technologies.
❖ Even when creating new products, it is not necessary to start from scratch. Learn from what has been done in other contexts, codify best practices, and apply existing knowledge to reduce uncertainty.
❖ No single organization can know everything. When collaborating across different fields, it is important to balance acquiring enough knowledge to manage interdependencies and build a cohesive technical architecture, without getting bogged down by unnecessary technical details.
❖ Collaboration can be challenging when diverse interests are involved. Focusing on achievable short-term goals can provide immediate value and a shared direction, while broader long-term objectives ensure that the innovation remains adaptable and relevant as new opportunities emerge.
The findings offer new insights for academics studying digital innovation, as well as practical guidance for organizations and funding agencies involved in collaborative projects. The lessons learned can help foster more successful partnerships across domains to help tackle urgent issues, from environmental challenges to improved industry practices.
Areas of Advance
Information and Communication Technology
Subject Categories
Information Studies
Business Administration
Information Systemes, Social aspects
Driving Forces
Innovation and entrepreneurship
Infrastructure
Chalmers Maritime Simulators
ISBN
978-91-8103-106-5
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 5564
Publisher
Chalmers