Alberto Di Minin, Scuola Superiore Sant'Anna

Federico Frattini, Politecnico di Milano

Agnieszka Radziwon, University of Southern Denmark

Krithika Randhawa, The University of Sydney

Joel West, Keck Graduate Institute

This special issue celebrates Henry Chesbrough's foundational work on open innovation and explores how organizations are putting these principles into practice within their R&D functions. The editors seek research examining how firms implement and manage open innovation through three interconnected dimensions: the strategic processes and structural changes required, the people and capabilities needed to succeed, and the performance measurement approaches for evaluating OI initiatives.

The journal seeks contributions that address real-world challenges R&D and innovation managers face when transitioning from closed to open processes, including technology strategy, partnership governance, organizational design, and digital transformation. Particularly valued are insights from practitioners and academic-practitioner collaborations that bridge theory and practice, along with research highlighting how open innovation applies across different organizational sizes, industries, and contexts including energy transition and geopolitical shifts.

Appel publié par R and D Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• How do companies build innovation strategies that leverage on OI?
• How are budgets for OI projects established and allocated?
• How do businesses roadmap the development of key technologies by making use of Open Innovation?
• How are new strategies for OI (such as venture clienting and venture building) evaluated and implemented?
• What factors influence the successful implementation of new strategies for OI (such as venture clienting and venture building)?
• What specific search strategies and scouting processes are most effective for identifying valuable external knowledge/technology for R&D?
• How are R&D processes (e.g., stage-gate, agile) adapted to effectively integrate external inputs and manage outbound knowledge flows?
• What digital tools and platforms are used to manage OI processes in R&D, and what is their impact?
• How do firms manage the transition from closed to open R&D processes?
• What are the best practices for managing intellectual property (IP) in collaborative R&D projects under an OI paradigm?
• How do firms effectively evaluate and select external partners for R&D collaboration?
• How does firm size (SME vs. large enterprise) influence organizational design choices for managing OI in R&D?
• How do firms overcome internal resistance (e.g., 'Not-Invented-Here' syndrome) when implementing OI in R&D?
• What is the role of middle managers in facilitating or hindering OI adoption within R&D departments?
• What specific skills, capabilities, and mindsets are required for R&D personnel (researchers, managers) operating in an OI environment?
• How can organizations cultivate an 'open' culture within R&D, that encourages both internal sharing and external engagement?
• Which leadership styles are most effective in managing OI initiatives within R&D teams?
• How are performance management and incentive systems adapted to motivate R&D employees' participation in OI activities?
• What is the role of individual absorptive capacity and boundary-spanning activities of R&D personnel in OI success?
• Which capabilities companies need to develop to successfully work with different types of service providers?
• How can AI enable and support OI projects in R&D and innovation functions?
• What ethical considerations arise in the operational management of OI within R&D (e.g., knowledge appropriation, fairness to external contributors)?
• What metrics do R&D departments use to track the inputs, throughputs, and outputs of their OI activities?
• How can the contribution of OI to R&D project success and overall R&D performance be effectively measured and demonstrated?
• What are the key challenges in measuring the ROI of specific OI tactics within R&D?
• What are the good practices for selecting the right service provider, based on different R&D and innovation needs?

Timeline

• January 2, 2026: Conference submission system opens
• February 1, 2026: Deadline for electronic submission of papers to the conference
• March 1, 2026: Notification of authors regarding acceptance to the conference
• April 20, 2026: Special Issue Conference held at Luiss University
• June 15, 2026: Submission deadline for extended abstracts (max 3 pages) for Academy of Management Researching Open Innovation PDW
• September 1, 2026: Time window to submit full paper through the R&D Management Journal Website
• September 15, 2026: Submission deadline

Joon Mo Ahn, Korea University

Alberto Di Minin, Scuola Superiore Sant'Anna

Sungjoo Lee, Seoul National University

Ahreum Hong, Kyung Hee University

This special issue addresses how artificial intelligence systems function as autonomous innovators rather than tools within R&D management. As AI systems including large language models and physical AI become active participants in innovation processes, existing frameworks assuming human actors must be reconceptualized. The core challenge extends beyond how AI assists humans to how much cognitive work should be delegated to AI and what consequences this has for organizations, ecosystems, and intellectual property.

The special issue examines four interconnected themes: determining optimal levels of AI delegation; understanding how agentic and physical AI reshape innovation ecosystem architectures as autonomous actors; reconceptualizing absorptive capacity for evaluating AI-generated knowledge; and addressing IP ownership and inventorship when non-human systems generate patentable outputs. Collectively, these themes interrogate how the distinction between AI-as-tool and AI-as-innovator requires new governance mechanisms and strategic frameworks for innovation management.

Appel publié par R and D Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Is there an inverted-U relationship between the degree of AI delegation in R&D and innovation performance, mirroring the over-search paradox?
• How do firms determine the optimal breadth and depth of AI involvement across different stages of the R&D process? Specifically, under what conditions does algorithmic management enhance or hinder innovation performance?
• What is the AI-era analogue of the NIH syndrome: how does uncritical acceptance of AI-generated knowledge erode internal expertise?
• What governance architectures enable effective human–AI teaming while preserving accountability, creativity, and strategic judgment?
• How do agentic AI systems alter the architecture of knowledge flows in open innovation ecosystems, and what new governance mechanisms are required?
• How does the participation of autonomous AI agents change the logic of platform-based open innovation, including roles, incentives, and boundary conditions?
• How do digital twins and AI-enabled simulation reshape the scope and speed of distributed experimentation across organisational boundaries?
• What new forms of inter-organisational trust, contracting, and coordination are needed when AI agents act as innovation partners?
• How must absorptive capacity (or dynamic capabilities) be reconceptualised when the primary external knowledge source is an AI system rather than a human partner?
• What organisational routines and managerial processes enable firms to transform AI-generated knowledge into innovation value?
• What individual competencies — technical, cognitive, and relational — distinguish high-performing innovators in human–AI contexts?
• What learning mechanisms allow firms to continuously upgrade AI-related innovation capabilities over time, particularly in relation to exploration and exploitation?
• How should inventorship and IP ownership be attributed when agentic AI systems autonomously generate patentable outputs, and what theoretical frameworks from open innovation research best capture this challenge?
• What strategic logic governs firms' decisions to release model weights, training data, or fine-tuned AI capabilities into open-source commons?
• How do trained model weights, fine-tuning data, and emergent AI capabilities constitute a new category of strategic asset, and how do firms govern access to and monetisation of these assets?
• Under what regulatory and institutional conditions does the open-source AI movement accelerate versus impede innovation diffusion?

Timeline

• May 30, 2026: PDW (paper development workshop) at R&D Management Workshop, Seoul, Korea
• July 4, 2026: PDW at KOSIME summer conference, Jeju, Korea
• December 1, 2026: Special Issue Submission Open
• June 30, 2027: Deadline for SI Submission
• July 1, 2028: Publication of SI Articles

Saverio Barabuffi, Scuola Superiore Sant'Anna

Giulio Ferrigno, Scuola Superiore Sant'Anna

Letizia Mortara, University of Cambridge

Yogesh K. Dwivedi, King Fahd University of Petroleum and Minerals

Innovation increasingly depends on collaboration among diverse actors who combine and recombine their knowledge. While organizations traditionally select partners based on complementary knowledge structures, the emergence of large volumes of structured and unstructured data alongside artificial intelligence technologies has fundamentally altered how firms search for external knowledge, identify complementarities, and govern collaborative innovation. Recent advances in AI, particularly Large Language Models, enable systematic analysis of millions of documents to map technological trajectories and detect emerging knowledge fields, potentially reshaping the scope and modalities of knowledge search.

Despite growing interest in AI and innovation, understanding of how AI technologies influence open innovation processes remains fragmented. There is limited evidence on how AI affects partner selection, reconfigures knowledge search strategies, and alters coordination mechanisms within innovation ecosystems. While AI-driven tools promise expanded collaboration opportunities, they also raise challenges including transparency, algorithmic bias, and unequal access to computational capabilities.

Appel publié par Industry and Innovation. Lire l'appel complet sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• AI and Inbound Open Innovation: Partner Search and Knowledge Scouting
• How do AI-based tools reshape the classic trade-off between search breadth and depth in open innovation? Can algorithmic scouting explore distant knowledge domains more efficiently than traditional methods?
• To what extent can AI overcome local search biases, revealing latent complementarities across industries, regions, or technologies that human managers may overlook?
• Can AI-powered analysis of diverse data sources democratize access to innovation ecosystems, or does it favor incumbents with larger digital footprints?
• Orchestration & Governance of Innovation Networks
• How do AI tools enable algorithmic governance of knowledge flows in multi-partner networks?
• How can AI help coordinate heterogeneous actors, including firms, universities, NGOs, and governments, within mission-oriented innovation systems?
• How are platforms leveraging AI-tools to shape technological trajectories and orchestrate complementors in ecosystems?
• What are the implications of AI-mediated orchestration for value capture, appropriation, and transparency in collaborative innovation?
• Knowledge Flows, Spillovers and Innovation Mapping
• How do generative AI and Natural Language Processing techniques uncover tacit knowledge flows and early-stage spillovers invisible to traditional patent- or publication-based metrics?
• How do AI tools improve the mapping of technological landscapes, identify "white spaces", and detect emerging trajectories to inform strategic decisions such as make, buy, or ally?
• What methods best integrate multiple data streams to track cross-sectoral and cross-regional knowledge diffusion enabled by AI?
• AI-Enabled Absorptive Capacity and Human AI interaction
• How should absorptive capacity be reconceptualized when AI tools, such as LLMs, assist in the recognition of external knowledge?
• What is the optimal division of labor between AI systems and human R&D managers in scanning, interpreting, and assimilating external knowledge?
• How can AI support organizational learning while mitigating barriers such as the "Not Invented Here" syndrome, especially when AI identifies previously unknown sources of innovation?

Timeline

• September 1, 2026: Submission window opens
• September 30, 2026: Manuscript deadline