Steven Parker, Open University

Kate Broadhurst, Loughborough University

Public service hubs are organizational arrangements bringing together multiple services and professionals to address complex social challenges. Despite their growing adoption, these hubs remain poorly conceptualized and theoretically underdeveloped. The special issue seeks to advance understanding of what constitutes a hub, how it differs from other collaborative forms, and what value it creates.

The call particularly welcomes research examining financial and resourcing dimensions of hub working, including funding mechanisms, commissioning, and value-for-money evaluations. Submissions should critically explore hub design, implementation, governance, and evaluation, acknowledging the diversity of models ranging from specialist to inclusive arrangements, and including emerging digital variants. Contributions may draw on various theoretical frameworks and examine hubs internationally using alternative terminology.

The journal invites practitioner-academic collaborations and welcomes debate and new development formats alongside research articles.

Appel publié par Public Money & Management. Voir l'appel sur le site de l'éditeur. Fiche de l'appel sur kerostig.

Potential topics

• Conceptualizing and defining public service delivery hubs
• Evolution of hub-based approaches across public services
• Hub forms, hybridity, and access
• Governance, leadership, and management of hubs
• Information sharing and inter-professional working in hubs
• Funding, commissioning and financial sustainability of hub models
• Hub user experiences, accessibility, and equity
• Public value creation in hub models
• Evaluating hub outcomes and value for money
• Challenges and unintended consequences of hubs
• Global hub perspectives

Timeline

• December 31, 2026: Extended abstract submission deadline
• March 31, 2027: Research articles submission deadline
• June 30, 2027: Debate articles and new development articles 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 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