Generative AI‑Empowered Knowledge Management Systems for Sustainable Governance

Call for Papers – Generative AI‑Empowered Knowledge Management Systems for Sustainable Governance

Journal: Journal of Innovation & Knowledge
Publisher: Elsevier
Journal metrics: Impact Factor 15.5, CiteScore 20.5

Submission window: 1 April 2026 – 30 September 2026

This special issue explores how Generative AI‑empowered Knowledge Management Systems (KMSs) can support sustainable governance by embedding accountability, ethics, and long‑term value creation into knowledge processes. It invites conceptual, empirical, and methodological contributions that examine how GenAI reshapes knowledge discovery, codification, sharing, and utilization across organisations and public‑sector settings, and how governance mechanisms can ensure these systems remain reliable, transparent, and socially responsible.

Why this issue matters

• Generative AI (GenAI)—including large‑language models, retrieval‑augmented generation (RAG), and autonomous knowledge agents—is transforming the entire knowledge lifecycle, from retrieval and synthesis to decision‑support and learning.

• At the same time, GenAI‑driven KMSs raise sustainability concerns around environmental resources, cognitive load, deskilling, bias, and trust, making governance and accountability central to their long‑term deployment.

• Sustainable governance requires KMSs that balance technological performance with human‑centred, ethical, and policy‑aware practices, turning AI‑enhanced knowledge into shared value for organisations and society.

This SI explicitly links innovation, knowledge management, AI, and governance, and targets both theoretical advances and managerially relevant insights for designing, assessing, and regulating AI‑empowered knowledge infrastructures.

Core focus and research question

The central research question is:
“How can KMSs empowered through GenAI support sustainable governance?”

The special issue therefore seeks work that either:

• Shows how GenAI‑enhanced KMSs improve efficiency, accountability, and long‑term value creation, or

• Proposes governance frameworks, architectures, and metrics to ensure responsible and sustainable operation of AI‑driven knowledge systems.

Key themes and topics

Submissions may be theoretical, empirical, or methodological, and should clearly connect GenAI, KMSs, and governance.

• Integration of KMSs and GenAI

  • How GenAI enhances knowledge discovery, codification, search, and synthesis in organisations and public institutions.

  • Design and assessment of AI‑empowered KMS architectures (e.g., knowledge graphs, RAG‑based systems, agent‑driven platforms).

• Governance, accountability, and ethics

  • Governance models and accountability frameworks for AI‑enabled KMSs, covering transparency, auditability, and human oversight.

  • Policy, regulation, and standards (e.g., ISO 30401‑aligned KM practices) shaping the design and use of GenAI‑empowered KMSs.

• Sustainable and responsible KM in AI‑driven environments

  • Lean and efficient knowledge processes that reduce waste and improve resource use while leveraging GenAI.

  • Business‑model innovation and knowledge‑based value creation through AI‑enhanced KMSs (e.g., platform‑enabled knowledge ecosystems, open‑innovation infrastructures).

• Human–AI collaboration and socio‑cognitive sustainability

  • Human–AI collaboration in knowledge work, including impacts on innovation, stress, deskilling, and cognitive load.

  • Managing emotive, tacit, and socially embedded knowledge in AI‑augmented environments.

• Metrics, maturity, and impact assessment

  • Maturity models, KPIs, and impact assessments for sustainable KMS initiatives (e.g., innovation outcomes, efficiency gains, equity, and trust).

  • Metrics for responsible AI use (bias, fairness, inclusiveness, safety) within KM contexts.

• Design patterns and socio‑technical artefacts

  • Modular and reusable artefacts, toolkits, and design patterns for building and deploying AI‑empowered KMSs.

  • Cross‑sector and comparative studies (e.g., public‑sector vs private‑sector, healthcare vs education) on GenAI‑driven KMSs.

Guest editors

• Prof. Giovanni Schiuma, LUM University / Corvinus University of Budapest (Italy/Hungary)

• Prof. Dmitry Kudryavtsev, Haaga‑Helia University of Applied Sciences, Finland

• Prof. Gustavo Morales‑Alonso, Universidad Politécnica de Madrid, Spain

• Dr. Francesco Santarsiero (Contact Guest Editor), University of Basilicata, Department of Engineering, Italy

Submission details

• Submission portal: Elsevier Editorial Manager for Journal of Innovation & Knowledge:
  https://submit.elsevier.com/JIK

• Submission window:

  • Submissions open: 1 April 2026

  • Submissions close: 30 September 2026

• All submissions will be reviewed on a rolling basis and should follow the journal’s Guide for Authors.

• Authors must explicitly disclose the use of AI and AI‑assisted technologies in their manuscripts, including a statement in the published work (as required by the journal).

• Accepted papers will be published online immediately and included in the next available issue.

Papers should show clear conceptual or empirical contributions to how GenAI‑empowered KMSs enable sustainable governance, and discuss implications for theory, management practice, and public policy.

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