What is the role of relationships between humans and technology in digital ecosystems? Research carried out by leaders of this Innovation Lab shows that for an ecosystem to thrive, it must be intentionally architected to embed trust among its participants. They investigate how different governance models, platform architectures, and protocol implementations may create conditions that either foster or hinder trust development among participants. Second, they analyze how relational coordination functions within digital ecosystems, particularly how communication patterns and interdependence management might influence trust dynamics. This dimension of the research seeks to understand how human relationships persist and evolve when mediated by AI systems. Third, they explore design considerations for agentic AI in ecosystems, focusing on how autonomous systems might be architected to preserve and enhance trust. This includes examining technical factors like explainability and transparency, as well as socio-technical considerations around human-AI interaction patterns and accountability frameworks. By examining governance, relational, and technical dimensions of trust, this research aims to inform more robust approaches to building and maintaining trusted digital environments in an age of increasingly autonomous systems.
Other research by leaders of this Innovation Lab proposes a framework for multi-agent collaboration that emphasizes dynamic coordination among autonomous agents and humans to solve complex, user-driven problems. Each agent operates with its own set of roles, goals, and capabilities but is designed to interact fluidly with other agents and with human users. The system leverages generative AI models embedded within each agent to interpret user intent, articulate subgoals, and collaboratively construct solutions that are aligned with overarching system objectives. At the core of the framework is the integration of coordination mechanisms. Agents synchronize their actions through shared representations of goals and constraints. To enhance decision quality, agents tap into external knowledge sources—databases, APIs, and open datasets—contextualizing their proposals with relevant, real-time information. Human users are active participants in the system, influencing search, guiding priorities, and validating outputs. Through cycles of proposal, negotiation, and refinement, agents and humans iteratively converge on solutions. We discuss the relevance of relational coordination with humans in these complex systems. For instance, agents must interpret human inputs (explicit or implicit), maintain clarity of goals, offer explanations for their actions, and invite human feedback or correction. As such AI agents are not mere elements of the structure —not just tools—but play a role as collaborators who respect shared goals, timeliness, and mutual accountability with human partners.
What Are Ecosystems?
Ecosystems are “relatively self-contained, self- adjusting systems of resource-integrating actors connected by shared institutional arrangements and mutual value creation through service exchange." Ecosystems are composed of diverse actors who interact with each other within and across micro, meso, and macro levels that are nested and complementary to each other. Digital technologies play a growing role in resource integration, thus digital readiness is becoming a key success factor for ecosystems.
How Do Institutions and Relationships Matter for Digital Ecoystems?
But while technology is necessary for coordinating ecosystems, it is likely not sufficient. Scholars have argued that, to co-create mutual economic and social value through their interactions and to avoid value co-destruction, actors in an ecosystem must coordinate with each other. This is crucial given the complexity and dynamic evolution of ecosystems, as well as the potential number and diversity of actors involved, particularly when working to solve grand challenges. Such coordination is supposedly ensured by the existence of shared institutional arrangements among the actors, namely “sets of interrelated institutions” composed of normative (values, social norms), cultural-cognitive (organizational policies), and regulative (laws) institutions. Those institutions enable, guide, and constrain the actors’ behaviors in their resource-integrating interactions.
Relationships are a key ingredient of effective coordination when actors are highly interdependent and when they are carrying out work characterized by high levels of uncertainty and time constraints. Relational coordination is the coordination of work through relationships of shared goals, shared knowledge and mutual respect, supported by frequent, timely, accurate, problem solving communication in the context of interdependence, uncertainty and time constraints. While this theory has most often been applied within organizations, it can be expanded to address cross-organizational and cross-sectoral coordination. Ecosystems may require relational forms of coordination to ensure successful outcomes and to manage conflicting priorities among actors; if so, institutions and digital technologies need to be designed to support relational coordination at and across levels.
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Coordinating Ecosystems at Multiple Levels
Meta level |
Institutional coordination |
Macro level |
Cross-organizational coordination |
Meso level |
Within-organization coordination |
Micro level |
Interpersonal coordination |
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