Intellectual Property Management in Innovation Ecosystems: Developing Visual Methods for Mapping Dynamics, Uncertainties, and Risks

Abstract

Innovation plays a key role in addressing society’s grand challenges, such as poverty, climate change, and pandemics. To be effective, it requires the collaboration of heterogenous actors, the orchestration of dynamic innovation systems, and openness towards sharing and accessing knowledge and intellectual property (IP) from external sources. The innovation ecosystem and open innovation paradigm offer powerful approaches to navigating these multi-actor, multi-technology, and systemic innovation contexts, but also confront researchers and practitioners with a variety of interrelated concepts and dynamic complexity that can overwhelm and compromise their sense-making and decision-making abilities. Visual methods have seen increasing popularity in the management sciences for the capture and analysis of complexity and dynamic phenomena. However, a systematic review of the current state-of-the-art of visual methods for ecosystem research uncovered a gap for the visual capture and analysis of dynamics, uncertainties, and risks when managing IP in innovation ecosystems. This study addresses this gap by proposing and demonstrating three new visual methods and practice-oriented tools, namely the Standardised Visual Ecosystem Language (SVEL), its IP-specific modular extension (SVEL+IP), and the SVEL+IP Actionable Process. This study adopts an empirical and qualitative multiple-case study approach in three parts to develop these new visual methods and tools. In part I, 18 semi-structured interviews and ecosystem value mapping are conducted at one established manufacturer-independent commercial aircraft maintenance, repair, and overhaul service provider to capture and analyse three servitization-triggered dynamic processes in this sector. The analysis of primary empirical data follows an inductive graphical approach and leads to the emergence of the SVEL syntax. Part II applies the design science research methodology to visually integrate relevant IP management concepts in the previously developed SVEL syntax. The resulting SVEL+IP modular extension is iteratively evaluated and developed using the Nominal Group Technique by mapping IP dynamics in three entry strategy scenarios pursued by industrial firms during the COVID-19 pandemic to scale up manufacturing capacities for crisis-critical products. Finally, part III follows the design thinking approach to develop the SVEL+IP Actionable Process based on practitioner feedback captured during the 2022 programme of the Strategic Technology & Innovation Management Consortium. The resulting prototype is tested in a real-world business setting to collect feedback on the SVEL+IP Actionable Process’ feasibility, usability, and utility. The results from this study consist of the SVEL method for capturing and analysing static structure, dynamics processes, and co-evolution in innovation ecosystem, the SVEL+IP modular extension for integrating relevant IP management concepts in the analysis of dynamic process and co-evolution in innovation ecosystems, and the SVEL+IP Actionable Process for managing IP risks and opportunities in innovation ecosystems. The visual methods and tools developed and demonstrated in this study contribute to the advancement of methods, theory, and practice of IP management in innovation ecosystems. The SVEL method and the SVEL+IP modular extension narrow the identified methods gap for visualising dynamic processes, co-evolution, and IP management concepts in multi-actor, multi-technology, and systemic innovation contexts. This study furthermore contributes to theory by suggesting IP management as a stimulus for dynamic processes and proposing paraground IP as a new IP type relevant to collaborative innovation. Regarding IP management practice, the SVEL+IP Actionable Process provides a structured process and collaborative tools for the identification, assessment, and treatment of IP risks and opportunities in competitive / collaborative relationships in innovation ecosystems. Future research needs to demonstrate and test the SVEL method and the SVEL+IP modular extension in diverse empirical contexts to refine its syntax and establish its generalisability. Furthermore, the SVEL+IP Actionable Process should be adopted more widely to develop a practice-oriented framework of IP specific risk and opportunity treatment measures. This study ultimately exemplifies the potential of visual methods for IP management in innovation ecosystems and for addressing societal grand challenges.