Convenor
Convenor's affiliation
Beatrice Orlando
University of Ferrara
Co-convenors
N/A
Abstract
The rapid development of Large Language Models (LLMs) and autonomous AI agents is reshaping how innovation is generated, shared, and governed across organizations and ecosystems. This track invites conceptual, empirical, and practice-based contributions that explore how AI-driven knowledge recombination, automated problem-solving, and emerging forms of machine agency are reconfiguring open innovation paradigms. The objective is to understand how firms, research institutions, public bodies, and entrepreneurial ventures adapt their strategies, governance structures, and collaboration models in environments where human and artificial intelligence co-create. We particularly welcome research addressing strategic decision-making, intellectual property logic, R&D collaboration, ecosystem orchestration, and ethical implications in the transition toward possible forms of collective or superintelligent problem-solving. The track aims to foster interdisciplinary dialogue linking management, innovation studies, computational social science, AI policy, and organizational theory.
Description
Open Innovation has traditionally relied on human collaboration, networks of knowledge exchange, and the orchestration of inter-organizational relationships. However, the diffusion of generative Artificial Intelligence — and specifically Large Language Models (LLMs) capable of autonomous reasoning, content production, experimentation, and simulation — introduces a profound transformation in how innovation is sourced, evaluated, and scaled. AI is no longer merely a tool: it is increasingly a co-creator in ideation, R&D discovery, strategic decision-making, and ecosystem coordination.
This track explores how the transition from human-centric open innovation to hybrid human–AI innovation reshapes foundational premises in R&D management. In particular, LLM-based agents are altering:
• Knowledge Recombination: AI systems enable the integration of tacit, distributed, and heterogeneous knowledge across boundaries, accelerating solution search and reducing cognitive constraints.
• Collaborative Structures: Machine-mediated collaboration introduces new forms of partnership, where organizations interact with AI entities as strategic actors rather than mere computational instruments.
• Strategic Decision-Making and Bias: The delegation of exploratory search and evaluation to AI systems challenges managerial agency, risk perceptions, and behavioral heuristics.
• Innovation Governance and IP: Generative innovation accelerates experimentation while raising new questions around authorship, ownership, and data ethics.
• Ecosystems and Platform Dynamics: AI-driven innovation networks reshape power asymmetries between established firms, startups, research institutions, and platform orchestrators.
The track also addresses the theoretical and practical implications of emerging superintelligent coordination — where interconnected AI systems may exceed human capacity in complex problem-solving domains (e.g., biotechnology, climate modeling, supply chain resilience).
This development invites a reevaluation of core frameworks in innovation management:
• How do organizations balance AI-accelerated exploration with responsible governance?
• Are traditional boundaries between firm-internal and ecosystem knowledge still meaningful?
• How should R&D management adapt evaluation, rewards, and learning systems when innovation becomes partially autonomous?
We welcome diverse methodologies, including qualitative case studies, ethnographies of AI adoption, computational simulation, experimental behavioral analysis, survey-based empirical research, and theory-building contributions. Cross-disciplinary collaborations (management, computer science, cognitive psychology, ethics, law, philosophy of technology) are highly encouraged.
By reconceptualizing innovation as a human–AI co-evolutionary process, this track aims to define the next generation of research in R&D management.