IMPACT RESEARCH NETWORKS: LIVING LAB AND RESILIENCE IN NETWORKED SYSTEMS
IMPACT RESEARCH NETWORKS: LIVING LAB AND RESILIENCE IN NETWORKED SYSTEMS
Author: Sebastiano Martignano Research strategy advisor -CRF
Monica Bianco, Ecosystems Cooperation advisor -CRF Italy
Abstract
Contemporary societal challenges increasingly manifest as wicked problems — complex, evolving, and stakeholder-intensive issues that defy traditional linear approaches. Addressing these challenges demands a fundamental shift towards networked innovation ecosystems grounded in cooperation, adaptability, and territorial anchoring.
This article explores how Living Labs, as dynamic nodes within impact-oriented research networks, can act as infrastructures for systemic innovation and regional regeneration. Drawing from multilevel network theory and empirical studies on cooperative ecosystems, we analyze the design principles necessary for resilient network architectures, including specialization, complementarity, and trust. Particular attention is given to the systemic risks posed by toxic nodes and parasitic hubs that undermine network vitality, proposing strategies for governance, resilience, and inclusive growth. Ultimately, building resilient, impact-driven research networks represents a strategic imperative for regenerating territories and fostering sustainable futures across complex socio-technical systems.
Navigating Complexity: Wicked Problems and Networked Innovation
Contemporary societies are increasingly confronted with wicked problems, complex challenges that resist clear definitions and definitive solutions. Rittel and Webber (1973) introduced this concept to highlight the nature of issues whose formulation and resolution evolve dynamically, influenced by conflicting stakeholder perspectives and shifting contexts [1]. Unlike technical problems with a clear cause-and-effect chain, wicked problems demand adaptive, participatory, and systemic approaches.
The linear model of innovation, where knowledge flows from research to application in predictable steps, proves insufficient in the face of such complexity. As Head and Alford (2015) emphasize, wicked problems require collaborative governance, interdisciplinary knowledge integration, and iterative learning processes [2]. Networked innovation ecosystems emerge as critical infrastructures, linking diverse actors and facilitating collective sense-making, experimentation, and co-evolution.
Living Labs, as participatory, real-world experimentation environments, can function as dynamic nodes within these ecosystems, operationalizing cooperation and anchoring innovation within territorial realities. Their embeddedness and adaptability offer a strategic advantage in tackling complexity at regional scales.
Living Labs as Nodes in Regional Innovation Networks
Living Labs have evolved from isolated experimental spaces to become key enablers of regional innovation systems. As formalized by the European Network of Living Labs (ENoLL), they are open, user-centered ecosystems where multiple stakeholders co-create solutions in real-world settings [3].
Within regional innovation networks, Living Labs serve as interfaces between research institutions, enterprises, public authorities, and civil society. Their strength lies in facilitating iterative prototyping, feedback loops, and adaptation, essential capabilities when navigating the uncertainty and volatility inherent in wicked problems.
Rather than acting as isolated pilots, Living Labs must be integrated into broader, mission-driven strategies for territorial development. Leminen and Westerlund (2017) demonstrate that Living Labs succeed when embedded within regional policies, supported by stable cooperation structures, and linked through multilevel governance frameworks [4].
They are not mere facilitators of innovation but critical organizational nodes that sustain systemic learning, collective intelligence, and adaptive capacity across territories.
Designing Cooperative Network Architectures: Specialization, Complementarity, and Trust
Building effective impact research networks requires intentional design based on three interrelated principles: specialization, complementarity, and trust.
Specialization ensures that each node contributes unique competences and resources, avoiding redundancy and enabling knowledge recombination. Complementarity allows these specialized competences to synergize, creating a functional whole greater than the sum of its parts.
However, as Powell (1990) emphasized, specialization and complementarity alone are insufficient without trust [5]. Trust acts as the invisible infrastructure that sustains collaboration, especially under conditions of uncertainty, asymmetry, and dynamic evolution.
In wicked contexts, where problems cannot be fully defined or predicted, trust enables actors to experiment without fear, to admit and learn from failures, and to adapt strategies without blame. As Sabel (1993) argues, “learning by monitoring” — the iterative co-evolution of strategies through mutual evaluation — requires an environment of trust where actors are willing to expose vulnerabilities and share incomplete knowledge [6].
Without trust, networks tend to collapse into bureaucratic formalism or disintegrate into competitive fragmentation, losing their systemic coherence. Provan and Kenis (2008) show that network governance models succeed only when trust levels among participants are sufficient to sustain decentralized collaboration and adaptive capacity [7].
Thus, trust is not an ancillary cultural trait but a strategic asset that enables networks to remain resilient, innovative, and mission-driven even amidst turbulence.
Trust as an Enabler of Cooperative Networks
Trust operates simultaneously at interpersonal, interorganizational, and systemic levels. At the interpersonal level, trust reduces transaction costs and facilitates knowledge sharing. At the interorganizational level, it enables resource pooling, risk-sharing, and long-term commitment beyond contractual obligations. At the systemic level, trust in the governance structures of the network sustains its resilience and capacity for coordinated action.
In the context of wicked problems, where solutions are provisional and dynamic, trust becomes the condition that allows iterative experimentation and collective learning to flourish. Actors engaged in networks characterized by high trust are more willing to co-invest in infrastructure, to engage in transparent communication, and to sustain cooperation even when immediate returns are uncertain.
Living Labs, by virtue of their participatory ethos and territorial anchoring, are natural trust builders. Their practice of co-design, open experimentation, and shared governance fosters the types of relational bonds necessary for cooperative resilience.
Building and maintaining trust, therefore, is not optional; it is a fundamental design requirement for any impact-oriented research network capable of addressing societal complexity.
Dynamics of Cooperation in Networks: Managing Resource Drain and Ensuring Resilience
Despite best intentions, networks are vulnerable to internal distortions. Among the most critical threats are the emergence of toxic nodes — actors that extract more resources than they contribute, distort information flows, and undermine trust.
As Provan and Kenis (2008) highlight, networks often fragment not because of external pressures but due to internal governance failures and asymmetries of power [7]. Toxic nodes can trigger cascading failures by disrupting reciprocity, monopolizing opportunities, or hijacking decision-making processes.
Detecting and neutralizing parasitic hubs requires dynamic monitoring, participatory governance structures, and mechanisms for sanctioning opportunistic behavior. It also demands a culture of collective responsibility, where the health of the network is understood as a shared asset.
Living Labs, due to their embeddedness in local ecosystems and commitment to transparency, are well positioned to act as early warning systems for emerging dysfunctions.
Their capacity to foster mutual accountability, inclusive governance, and adaptive feedback makes them crucial infrastructures for maintaining network vitality and territorial resilience.
Multilevel Network Structures: Theoretical Foundations and Practical Applications
Multilevel network theory, as articulated by Lazega et al. (2016), provides a powerful lens for understanding how different types of actors and interactions are embedded across scales [7]. In regional innovation ecosystems, actors operate simultaneously at local, regional, national, and transnational levels, requiring coordination across formal and informal structures.
Living Labs, positioned at the intersection of different governance levels, can function as connective tissues that mediate knowledge flows, translate policies into local action, and channel grassroots innovation into systemic change.
As Carayannis and Campbell (2012) argue in their quadruple and quintuple helix models, sustainable innovation ecosystems require the integration of academia, industry, government, civil society, and the environment [8].
Multilevel networks enable the alignment of these helices, fostering territorial resilience and systemic impact.
Conclusion: Toward Resilient and Impact-Oriented Research Ecosystems
Impact research networks structured around Living Labs and governed by cooperative architectures offer a transformative pathway to address wicked problems. They embody a shift from siloed, linear models of innovation towards adaptive, systemic, and territorially anchored infrastructures capable of fostering societal resilience.
Their success, however, hinges on the deliberate cultivation of specialization, complementarity, and trust, as well as the vigilant management of toxic dynamics that can fragment networks from within.
In a context where societal challenges are increasingly complex and urgent, building resilient, impact-oriented ecosystems is not merely an academic exercise: it is a strategic necessity for regenerating territories, empowering communities, and sustaining futures across the Euro-Mediterranean and beyond.
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