Maksim Belitski, Loyola University New Orleans

James Cunningham, Newcastle University

Riccardo Fini, University of Bologna

Simon Mosey, University of Nottingham

Phillip Phan, Johns Hopkins University

The Journal of Technology Transfer is pleased to introduce a new Special Collection on Technology Transfer through Collaboration: Bridging Academia, Policy, and Practice. The collection invites submissions that examine technology transfer, entrepreneurship, and innovation through collaborations between academic researchers and non-academic actors in policy and practice.

Timely and high-quality research on technology transfer often emerges from close interaction between academics, practitioners, and policymakers. Such collaborations can provide access to emerging phenomena, reveal underexplored mechanisms, and generate insights that may not be visible through conventional data sources or established theoretical lenses alone. The aim of this Special Collection is to support rigorous research that draws on boundary-spanning collaborations to advance scholarship on technology transfer while also generating meaningful implications for policy and practice.

The Special Collection particularly welcomes papers co-authored by academics and non-academic actors, including but not limited to technology transfer professionals, policymakers, entrepreneurs, innovators, investors, industry experts, platform managers, R&D managers, and innovation intermediaries. Such collaborations provide unique insights to policy and practice environments, enhance access to data, expand the contextual reach of empirical work, and address fundamental questions through a diversity of collaborative perspectives. Submissions should use collaborative engagement to develop new theory, enrich empirical analysis, access distinctive data, and address important questions in technology transfer, entrepreneurship, and innovation. Contributions should combine methodological rigor with practical and policy-relevant insight and demonstrate novel, impactful contributions to technology transfer scholarship.

The Special Collection is intended to create a lasting foundation for cumulative scholarly development rather than a one-off set of papers. The Journal of Technology Transfer remains firmly committed to its identity as a double-blind, rigorously peer-reviewed academic journal. Submissions must meet the journal's standards for theoretical contribution, methodological rigor, empirical credibility, and clarity of argument. The goal of this Special Collection is not to relax scholarly standards, but to broaden the range of relevant questions, data sources, contexts, and perspectives that inform high-quality research in technology transfer.

Potential topics

• Papers co-authored by academics and non-academic actors, including technology transfer professionals, policymakers, entrepreneurs, innovators, investors, industry experts, platform managers, R&D managers, and innovation intermediaries
• Technology transfer, entrepreneurship, and innovation through boundary-spanning collaborations
• Development of new theory through collaborative engagement
• Enrichment of empirical analysis using collaborative perspectives
• Access to distinctive data through academic-non-academic partnerships
• Addressing important questions in technology transfer, entrepreneurship, and innovation from multiple collaborative perspectives

Timeline

• Invalid DateTime: Submission deadline

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 emerges from collaboration among a diverse set of actors (firms, startups, universities, public institutions, and non-profits) who expand and recombine their knowledge bases. These collaborative relationships are of utmost importance to open innovation processes, enabling the co-creation of product and process innovations and enhancing the impact and diffusion of novel solutions.

Prior research has robustly shown that organizations select partners based on the complementarities or distances between their knowledge structures, often leveraging cognitive proximity to drive technological diversification or exploring distant domains to avoid lock-in and enable radical breakthroughs.

However, open innovation has acquired new and largely unexplored facets in recent years. The rapid growth of the availability of a large volume of structured and unstructured data poses unprecedented opportunities to understand and guide open innovation dynamics. This massive amount of data, often referred to as Big Data, is fundamentally reshaping how firms search for external knowledge, identify complementarities, and govern open innovation processes. In this context, Artificial Intelligence technologies provide a method of invention that shifts the boundary between human-led and machine-led knowledge production. AI is being adopted rapidly and pervasively across industries, generating profound effects on organizations.

Recent advancements in AI, such as Large Language Models, represent a leap forward in this transformation. When embedded in data-driven approaches, these powerful tools are expected to allow organizations to systematically map technological trajectories, detect emerging knowledge fields, and support external search strategies through the automation of the analysis of millions of documents and informational signals. As a result, AI will not simply enhance analytical efficiency, but actively contribute to redefining the scope, boundaries, and modalities of knowledge search within open innovation processes.

Despite some recent investigations in the innovation field, our understanding of how recent advancements and adoption of AI technologies can promote and shape open innovation processes remains fragmented and incomplete. Existing studies have largely focused on the role of AI and Big Data in relation to innovation outcomes or on how they help mapping technological landscapes, while much less evidence and theory is available about how AI-driven tools intervene upstream in the formation, governance, and evolution of collaborative innovation. We still lack insights into how AI influences partner selection, reconfigures knowledge search strategies, alters power and coordination mechanisms within innovation ecosystems, and reshapes the roles of firms, universities, and public actors in open innovation settings.

While AI-driven tools promise to expand collaboration opportunities and improve coordination across heterogeneous actors, they also raise new organizational, strategic, and governance challenges, including issues of transparency, algorithmic bias, control over decision-making, and unequal access to data and computational capabilities. Addressing these open questions is crucial to understanding when, how, and under what conditions advances in AI can effectively promote open innovation, rather than merely optimize existing practices.

This Special Issue seeks to advance theory based on empirical research on the role of AI in enabling, shaping, and governing open innovation processes across firms, industries, and innovation systems. It calls for works which support an understanding of the role of AI technologies in creating new opportunities for firms to innovate, to redesign industry boundaries, and generate new value systems and partnership networks.

We invite scholars to move beyond the what digital tools question to engage with the how and why they alter open innovation dynamics. We welcome conceptual, methodological, and empirical contributions, using qualitative, quantitative, mixed or computational approaches, to explore how advances in AI, including Large Language Models, generative AI, and other advanced machine learning techniques, actively enable, reshape, and govern collaborative innovation and open innovation processes. We particularly encourage submissions that move beyond descriptive applications of AI to investigate its role as a driver of partner selection, coordination, and knowledge integration.

The Special Issue aims to engage a multidisciplinary audience and stimulate scholarly debate at the intersection of AI, collaboration, and open innovation, across multiple levels of analysis, ranging from individuals and teams to organisations, inter-organizational networks, ecosystems, and innovation ecosystems.

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

• Invalid DateTime: Expected publication of the Special Issue
• September 1, 2026: Submission window opens
• September 30, 2026: Full paper submission deadline