Convenor
Convenor's affiliation
Saverio Barabuffi
Scuola Superiore Sant’Ann
Co-convenors
Giulio Ferrigno, Letizia Mortara
Abstract
Innovation increasingly emerges from collaboration among diverse actors—firms, startups, universities, public institutions, and non-profits—who expand and recombine their knowledge bases. Today, the rapid growth of big data from patents, scientific outputs, and digital platforms, combined with advances in Artificial Intelligence (AI), is reshaping how organisations search for external knowledge, identify complementarities, and govern open innovation processes. AI-driven tools can reveal emerging technological trajectories, support partner discovery and matching, foster cross-sector collaborations, and amplify the impact of ecosystems and mission-oriented initiatives.
This track welcomes conceptual, empirical, and methodological contributions—qualitative, quantitative, mixed-methods or computational—examining how AI transforms collaborative innovation models. We invite studies to advance a multidisciplinary debate on the evolving interplay between AI, collaboration, and open innovation across multiple levels of analysis.
Description
Innovation increasingly depends on the ability of diverse actors—firms, startups, universities, public institutions, non-profits, and ecosystem orchestrators—to expand and recombine their knowledge bases. Collaborative relationships are central to open innovation processes (Chesbrough, 2003; Mortara and Minshall, 2011), enabling the co-creation of product and process innovations (Calvo et al., 2022) and enhancing the impact and diffusion of novel solutions (Hottenrott and Lopes-Bento, 2016). Prior research has shown that organizations select partners based on the complementarities or distances between their knowledge structures (Ferrigno et al., 2023; Wang et al., 2014), leveraging cognitive proximity to drive technological diversification (Boschma, 2017; Breschi et al., 2003) or exploring distant domains to avoid lock-in and enable radical breakthroughs (Castaldi et al., 2015).
Today, vast volumes of structured and unstructured data—from patents, trademarks, scientific publications, project reports, social media, and online platforms—offer unprecedented opportunities to understand and guide collaboration dynamics (Ferrigno et al., 2024). Artificial Intelligence (AI) and data-driven approaches can map technological trajectories (Arts et al., 2021; 2023), detect emerging knowledge fields (Marx and Fuegi, 2020), support external search strategies (Park et al., 2024), and identify new complementarities across sectors, regions, and disciplines. These tools can accelerate not only inter-firm collaborations but also university–industry partnerships, public–private alliances, cross-sector coalitions, and mission-oriented innovation networks.
This special track welcomes conceptual, methodological, and empirical contributions, using qualitative, quantitative, mixed or computational approaches, to explore how AI transforms collaborative innovation models and open innovation processes. We encourage submissions on topics such as: AI-driven partner search and matching, knowledge recombination, platform-mediated collaborations, ecosystems and network analysis, tacit knowledge exchange, absorptive capacity, cross-sector and cross-regional knowledge flows, and AI as an enabler of technological diversification and breakthrough innovation. Our aim is to engage a multidisciplinary audience and stimulate debate at the intersection of AI, collaboration, and open innovation, across multiple levels of analysis—from individuals and teams to organisations, networks, and innovation ecosystems.