Special Issue Editor(s)
Saverio Barabuffi, Scuola Superiore Sant'Anna, Italy
saverio.barabuffi@santannapisa.it
Giulio Ferrigno, Scuola Superiore Sant'Anna, Italy
giulio.ferrigno@santannapisa.it
Letizia Mortara, University of Cambridge, United Kingdom
lm367@cam.ac.uk
Yogesh K. Dwivedi, King Fahd University of Petroleum and Minerals, Saudi Arabia
yogesh.dwivedi@kfupm.edu.sa
Background and Objective
Innovation increasingly emerges from collaboration among a diverse set of actors (i.e., firms, startups, universities, public institutions, and non-profits) who expand and recombine their knowledge bases (Bogers et al., 2017; Zobel, 2017). These collaborative relationships are of utmost importance to open innovation processes (Beck et al., 2022; Chesbrough, 2003; Mortara and Minshall, 2011; West and Bogers, 2014), 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 robustly shown that organizations select partners based on the complementarities or distances between their knowledge structures (Ferrigno et al., 2024; Wang et al., 2014), often 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).
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 (i.e., patents, trademarks, scientific publications, project reports, social media, and online platforms) poses unprecedented opportunities to understand and guide open innovation dynamics (Ferrigno et al., 2025). 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 (Barczak et al., 2022; Cappa et al., 2025). In this context, Artificial Intelligence (AI) technologies provide a method of invention that shifts the boundary between human-led and machine-led knowledge production (Acemoglu and Restrepo, 2019). AI, which relies on tacit knowledge and performs cognitive and non-codifiable tasks (Ide and Talamàs, 2025), is being adopted rapidly and pervasively across industries (Holgersson et al., 2024), generating profound effects on organizations (Holm et al. 2023; Mariani et al., 2023) some of which are already detectable whilst others have just started to manifest.
Recent advancements in AI, such as Large Language Models (LLMs) (Burger et al., 2023; Carlson and Burbano, 2025; Dwivedi et al., 2023; Mastrogiorgio, 2025), 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 (Arts et al., 2021, 2023), detect emerging knowledge fields (Marx and Fuegi, 2020), and support external search strategies through the automation of the analysis of millions of documents and informational signals (Park et al., 2024). 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 (Holgersson et al., 2024), 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 (Montresor and Vezzani, 2023) or on how they help mapping technological landscapes (Capello et al., 2023), while much less evidence and theory is today available about how AI-driven tools intervene upstream in the formation, governance, and evolution of collaborative innovation (Holgersson et al., 2024). For example, 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. Moreover, while AI-driven tools promise to expand collaboration opportunities and improve coordination across heterogeneous actors (Woolley, 2023), 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 (Dwivedi, 2025; Mariani & Dwivedi, 2024; Taherizadeh and Beaudry, 2023). 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.
Against this backdrop, 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 and 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, compatibly with the journal’s guidances, to explore how advances in AI, including Large Language Models (LLMs), 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. Indicative topics of interest include, but are not limited to: AI and Inbound Open Innovation Partner Search and Knowledge Scouting; Orchestration & Governance of Innovation Networks; Knowledge Flows, Spillovers and Innovation Mapping; and AI-Enabled Absorptive Capacity and Human-AI interaction.
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.
Research Topics
1. 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-such as patents, scientific publications, open-source code, or social media-democratize access to innovation ecosystems, or does it favor incumbents with larger digital footprints?
2. 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?
3. Knowledge Flows, Spillovers and Innovation Mapping
- How do generative AI and Natural Language Processing (NLP) 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 (e.g., patents, publications, mobility, digital signals) to track cross-sectoral and cross-regional knowledge diffusion enabled by AI?
4. 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?
References
Acemoglu, D., & Restrepo, P. (2019). Automation and new tasks: How technology displaces and reinstates labor. Journal of Economic Perspectives, 33(2), 3-30.
Arts, S., Cassiman, B., & Hou, J. (2023). Position and differentiation of firms in technology space. Management Science, 69(12), 7253-7265.
Arts, S., Hou, J., & Gomez, J. C. (2021). Natural language processing to identify the creation and impact of new technologies in patent text: Code, data, and new measures. Research Policy, 50(2), 104144.
Barczak, G., Hopp, C., Kaminski, J., Piller, F., & Pruschak, G. (2022). How open is innovation research?–An empirical analysis of data sharing among innovation scholars. Industry and Innovation, 29(2), 186-218.
Beck, S., Bergenholtz, C., Bogers, M., Brasseur, T. M., Conradsen, M. L., Di Marco, D., ... & Xu, S. M. (2022). The Open Innovation in Science research field: a collaborative conceptualisation approach. Industry and Innovation, 29(2), 136-185.
Bogers, M., Zobel, A. K., Afuah, A., Almirall, E., Brunswicker, S., Dahlander, L., ... & Ter Wal, A. L. (2017). The open innovation research landscape: Established perspectives and emerging themes across different levels of analysis. Industry and Innovation, 24(1), 8-40.
Boschma, R. (2017). Relatedness as driver of regional diversification: A research agenda. Regional Studies, 51(3), 351–364.
Breschi, S., Lissoni, F., & Malerba, F. (2003). Knowledge-relatedness in firm technological diversification. Research Policy, 32(1), 69-87.
Burger, B., Kanbach, D. K., Kraus, S., Breier, M., & Corvello, V. (2023). On the use of AI-based tools like ChatGPT to support management research. European Journal of Innovation Management, 26(7), 233-241.
Calvo, N., Fernández-López, S., Rodríguez-Gulías, M. J., & Rodeiro-Pazos, D. (2022). The effect of population size and technological collaboration on firms' innovation. Technological Forecasting and Social Change, 183, 121905.
Capello, R., Lenzi, C., & Panzera, E. (2023). The rise of the digital service economy in European regions. Industry and Innovation, 30(6), 637-663.
Cappa, F., Ardito, L., Oriani, R., Peruffo, E., & Ritala, P. (2025). Leveraging Big Data and Open Data “From” and “For” Open Innovation: Expanding the New Digital Frontier. R&D Management. DoI: https://doi.org/10.1111/radm.70033
Carlson, N. A., & Burbano, V. (2025). The use of LLMs to annotate data in management research: Foundational guidelines and warnings. Strategic Management Journal. DoI: https://doi.org/10.1002/smj.70023
Castaldi, C., Frenken, K., & Los, B. (2015). Related variety, unrelated variety and technological breakthroughs: An analysis of U.S. state‐level patenting. Regional Studies, 49(5), 767–781. https://doi.org/10.1080/00343404.2014.940305
Chesbrough, H., 2003. Open Innovation: The New Imperative for Creating and Profiting from Technology. Harvard Business School Press, Boston, USA.
Dwivedi, Y. K. (2025). Generative Artificial Intelligence (GenAI) in entrepreneurial education and practice: emerging insights, the GAIN Framework, and research agenda. International Entrepreneurship and Management Journal, 21(1), 1-21.
Dwivedi, Y. K., Kshetri, N., Hughes, L., Slade, E. L., Jeyaraj, A., Kar, A. K., ... & Wright, R. (2023). “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642.
Ferrigno G., Barabuffi S., Marcazzan E., Piccaluga A. (2025). “What “v” of the big data influence open innovation breadth and depth? An empirical analysis on italian SMES”. R&D Management, 55(3), 795-816.
Ferrigno G., Martin X., Dagnino G.B. (2024). “Explaining the interplay of value creation and value appropriation in strategic alliances: a developmental perspective”. International Journal of Management Reviews, 26(2), 232-253.
Holgersson, M., Dahlander, L., Chesbrough, H., & Bogers, M. L. (2024). Open Innovation in the Age of AI. California Management Review, 67(1), 5-20.
Hottenrott, H., & Lopes‐Bento, C. (2016). R&D partnerships and innovation performance: Can there be too much of a good thing? Journal of Product Innovation Management, 33(6), 773-794.
Ide, E., Talamàs, E., 2025. Artificial intelligence in the knowledge economy. J. Political Econ. 133 (12). https://doi.org/10.1086/737233
Mariani, M., & Dwivedi, Y. K. (2024). Generative artificial intelligence in innovation management: A preview of future research developments. Journal of Business Research, 175, 114542.
Mariani, M. M., Machado, I., Magrelli, V., & Dwivedi, Y. K. (2023). Artificial intelligence in innovation research: A systematic review, conceptual framework, and future research directions. Technovation, 122, 102623.
Marx, M., & Fuegi, A. (2020). Reliance on science: Worldwide front‐page patent citations to scientific articles. Strategic Management Journal, 41(9), 1572-1594.
Mastrogiorgio, M. (2025). AI in innovation research: an overview of transformers. Industry and Innovation, 32(10), 1204-1227.
Montresor, S., & Vezzani, A. (2023). Digital technologies and eco-innovation. Evidence of the twin transition from Italian firms. Industry and Innovation, 30(7), 766-800.
Mortara, L., & Minshall, T. (2011). How do large multinational companies implement open innovation?. Technovation, 31(10-11), 586-597.
Park, S., Piezunka, H., & Dahlander, L. (2024). Coevolutionary lock-in in external search. Academy of Management Journal, 67(1), 262-288.
Taherizadeh, A., & Beaudry, C. (2023). An emergent grounded theory of AI-driven digital transformation: Canadian SMEs’ perspectives. Industry and Innovation, 30(9), 1244-1273.
West, J., & Bogers, M. (2014). Leveraging external sources of innovation: A review of research on open innovation. Journal of Product Innovation Management, 31(4), 814-831.
Woolley, J. L. (2023). Getting along with frenemies: enhancing multi-competitor coopetition governance through artificial intelligence and blockchain. Industry and Innovation, 30(9), 1156-1189.
Zobel, A. K. (2017). Benefiting from open innovation: A multidimensional model of absorptive capacity. Journal of Product Innovation Management, 34(3), 269-288.