The Special Issue accepts research on how GenAI-empowered Knowledge Management Systems enable sustainable governance through accountable, ethical, and value-creating knowledge processes.

Guest editors:

Guest Editors:

Name: Giovanni Schiuma

LUM University, Department of Engineering, IT

Corvinus University of Budapest, Hungary

Name: Dmitry Kudryavtsev

Haaga-Helia University of Applied Sciences, FI

Name: Gustavo Morales-Alonso

Universidad Politécnica de Madrid, ES

Contact Guest Editor: Francesco Santarsiero

University of Basilicata, Department of Engineering, IT

Special issue information:

Background and Motivation

Generative Artificial Intelligence (GenAI), including large-language models (LLMs), retrieval-augmented generation (RAG) pipelines, and autonomous knowledge agents, has started to transform every stage of the knowledge lifecycle, from discovery and codification to sharing and utilization (Alavi and Westerman, 2023; Jarrahi et al., 2023). In this fast-changing landscape, Knowledge Management Systems (KMSs) have become essential infrastructures for capturing, organizing, and applying knowledge across organizational boundaries (Cerchione et al., 2026; Raina et al., 2026).

While early empirical studies document significant productivity and innovation gains (Noy & Zhang, 2023), two interconnected challenges now dominate both managerial and academic discussions: the long-term sustainability of AI-driven knowledge processes and the governance mechanisms needed to ensure reliability, transparency, and ethical legitimacy.

On the sustainability front, recent studies highlight the significant resource demands and organizational effects involved in developing and deploying generative AI systems (Raisch & Krakowski, 2025). However, from a managerial perspective, sustainability goes far beyond environmental factors to include economic resilience and competitive edge. Organizations need to design lean, resource-efficient knowledge systems that turn GenAI capabilities into lasting value for organizations and their stakeholders (Porter & Kramer, 2018; Bocken et al., 2014). Using GenAI requires rethinking business models to create knowledge-based value, reduce waste, and support circular flows of data, talent, and innovation (Teece, 2018). Issues related to social and cognitive sustainability, like deskilling, technostress, and biased knowledge representations, are equally important for organizational legitimacy and long-term success (Dong et al., 2025).

Building on these managerial and sustainability considerations, the theoretical lens on KM has also expanded. The introduction of ISO 30401 Knowledge Management Systems has provided a formal quality standard for KM practices (Easterby-Smith & Prieto, 2008). Research on KM maturity stresses the need for creativity, novelty, relevance, and impact to ensure both the sustainability and the effectiveness of knowledge processes (Cristache et al., 2025; Lönnqvist et al., 2022). Moreover, the concept of emotive knowledge emphasizes the affective and social dimensions of knowledge-based value creation (Schiuma & Lerro, 2011), underscoring the importance of balancing tacit and explicit knowledge within AI-enhanced environments. Recent research also highlights the value of modular, reusable artefacts and toolkits that can facilitate the design and deployment of AI‑enabled knowledge systems (Faraj et al., 2018). These approaches emphasize scalability, adaptability, and the co-creation of socio-technical solutions capable of enhancing knowledge capture, synthesis, and access within organizations. These insights underline the need for KMSs to incorporate configurable components and reusable knowledge assets that foster sustainability, governance, and organizational learning. Together, these insights confirm that KM frameworks must evolve toward human-centric, dynamic configurations capable of orchestrating AI-driven tools without compromising contextual judgment or ethical responsibility (Jarrahi et al., 2023). This evolution demands attention not only to theoretical advancement but also to managerial and policy implications, ensuring that both scholars and practitioners can operationalize sustainable governance through GenAI-enabled KMSs.

Against this backdrop, this Special Issue addresses the central research question: How can KMSs empowered through GenAI support sustainable governance?

This question requires a deeper understanding of how GenAI can augment KMSs to promote sustainable governance, which is defined as the capacity to integrate efficiency, accountability, ethical conduct, and long-term value creation into organizational and societal decision-making.

The aim is to explore conceptual and empirical approaches that illustrate how the interplay between KMSs and GenAI can foster responsible innovation, improve organizational resilience, and contribute to the broader sustainability agenda.

This Special Issue, therefore, seeks contributions that develop frameworks, case studies, and analytical models clarifying how AI-empowered KMSs can act as enablers of sustainable governance, balancing technological performance with ethical, managerial, and societal objectives.

Topics and Research Questions

We welcome original research contributions, including conceptual, empirical, and methodological studies, addressing, but not limited to, the following topics:

• Integration of Knowledge Management Systems and Generative AI for sustainable governance and decision-making
• Governance models and accountability frameworks for AI-enabled knowledge systems
• Design and assessment of AI-empowered KMS architectures
• Sustainable and responsible knowledge management in AI-driven environments
• Lean and efficient knowledge processes and operations
• Business model innovation and sustainable value creation through knowledge
• Human–AI collaboration and socio-cognitive sustainability in knowledge work
• Policy, regulation, and standards shaping the governance of KMSs and AI
• Metrics, maturity models, and impact assessment of sustainable KMS initiatives
• Modular and reusable artefacts, toolkits, and socio-technical solutions for designing and deploying AI-enabled KMSs
• Cross-sector and comparative studies on KMSs empowered by GenAI

Manuscript submission information:

The timeline of this special issue is as follows:

Submission dates:

From 01/04/2026 to 30/09/2026

Review process dates:

On a rolling basis from 01/04/2026

Publication process dates:

Accepted papers will be published online immediately once accepted and will be included in the next available issue of the journal.

Before submitting a manuscript, please read carefully the Journal of Innovation & Knowledge Guide for authors.

In particular, authors should disclose in their manuscript the use of AI and AI-assisted technologies and a statement will appear in the published work. Declaring the use of these technologies supports transparency and trust between authors, readers, reviewers, editors, and contributors and facilitates compliance with the terms of use of the relevant tool or technology.

Plagiarism in all its forms constitutes unethical behaviour and is unacceptable.

More information can be found at the following link:

https://www.sciencedirect.com/journal/journal-of-innovation-and-knowledge/about/call-for-papers

References:

Alavi, M., & Westerman, G. (2023). How generative AI will transform knowledge work. Harvard Business Review. https://hbr.org/2023/11/how-generative-ai-will-transform-knowledge-work. Accessed 20 Nov 2025.

Bocken, N. M. P., Short, S. W., Rana, P., & Evans, S. (2014). A literature and practice review to develop sustainable business model archetypes. Journal of Cleaner Production, 65, 42‑56. https://doi.org/10.1016/j.jclepro.2013.11.039

Carlucci, D., Kudryavtsev, D., Santarsiero, F., Lagrutta, R., & Garavelli, A. C. (2022). The ISO 30401 Knowledge Management Systems: A new frame for managing knowledge. Conceptualisation and practice. Knowledge Management Research & Practice, 20(6), 975–986. https://doi.org/10.1080/14778238.2022.2118637.

Cerchione, R., Liccardo, G., Passaro, R. (2026). Artificial knowledge generation: investigating the revolutionary role of generative AI in knowledge management. Journal of Innovation & Knowledge, Vol. 11, 100866, ISSN 2444-569X, https://doi.org/10.1016/j.jik.2025.100866.

Cristache, N., Croitoru, G., Florea, N.V. (2025). The influence of knowledge management on innovation and organizational performance. Journal of Innovation & Knowledge, Vol. 10, Issue 5, 100793, ISSN 2444-569X, https://doi.org/10.1016/j.jik.2025.100793.

Dong X, Tian Y, He M, Wang T (2025), "When knowledge workers meet AI? The double-edged sword effects of AI adoption on innovative work behavior". Journal of Knowledge Management, Vol. 29 No. 1 pp. 113–147, doi: https://doi.org/10.1108/JKM-02-2024-0222

Easterby-Smith, M., & Prieto, I.M. (2008). Dynamic capabilities and knowledge management: An integrative role for learning? British Journal of Management, 19(3), 235–249. https://doi.org/10.1111/j.1467-8551.2007.00543.x

Faraj, S., Pachidi, S., & Sayegh, K. (2018). Working and organizing in the age of the learning algorithm. Information and Organization, 28(1), 62–70. https://doi.org/10.1016/j.infoandorg.2018.02.005

Jarrahi, M.H., Askay,D., Eshraghi,A., & Smith,P. (2023). Artificial intelligence and knowledge management: A partnership between human and AI. Business Horizons, 66(1), 87-99. https://doi.org/10.1016/j.bushor.2022.03.002

Kaplan, A., & Haenlein, M. (2019). Siri, Siri in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business Horizons, 62(1), 15-25. https://doi.org/10.1016/j.bushor.2018.08.004

Lönnqvist, A., Jackson, T., & Schiuma, G. (2022). Key development areas for the growing and maturing knowledge management research field: Creativity, novelty, relevance, and impact. Knowledge Management Research & Practice, 20(2), 175–176. https://doi.org/10.1080/14778238.2022.2064606

Noy, S., & Zhang, W. (2023). Experimental evidence on the productivity effects of generative artificial intelligence. Science, 381(6654), 187-192. DOI: 10.1126/science.adh2586

Porter, M. E., & Kramer, M. R. (2018). Creating shared value: How to reinvent capitalism—And unleash a wave of innovation and growth. In Managing sustainable business: An executive education case and textbook (pp. 323-346). Dordrecht: Springer Netherlands. https://doi.org/10.1007/978-94-024-1144-7_16

Raina, K., Sharma, G. D., Taheri, B., Dev, D., & Chavriya, S. (2026). Artificial intelligence-driven management: Bridging innovation, knowledge creation, and sustainable business practices. Journal of Innovation & Knowledge, Vol. 11, 100860, ISSN 2444-569X, https://doi.org/10.1016/j.jik.2025.100860

Raisch, S., & Krakowski, S. (2025). Combining human and artificial intelligence: Hybrid problem-solving in organizations. Academy of Management Review, 50(2), 441–464. https://doi.org/10.5465/amr.2021.0421

Schiuma, G., & Lerro, A. (2011). Managing knowledge assets in a complex business landscape: The relevance of emotive knowledge. Knowledge Management Research & Practice, 9(4), 279–285. https://doi.org/10.1057/kmrp.2011.32

Teece, D. J. (2018). Business models and dynamic capabilities. Long Range Planning, 51(1), 40‑49. https://doi.org/10.1016/j.lrp.2017.06.007

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