Learning Organizations and Sustainable Value Creation in Changing Environments

Dear Colleagues,

Organizations increasingly operate in dynamic, uncertain, technology-driven, and knowledge-intensive environments. In this context, the ability to learn continuously, transform knowledge into actionable insights, and adapt to emerging challenges becomes a critical strategic capability. The concept of the learning organization has therefore gained renewed relevance, emphasizing processes such as knowledge acquisition, knowledge sharing, unlearning, and organizational renewal.

At the same time, the rapid diffusion of Artificial Intelligence (AI), including machine learning, advanced analytics, and generative AI, has profoundly reshaped how knowledge is created, processed, and leveraged within organizations. AI-enabled systems are increasingly complementing and augmenting human learning, enabling real-time data interpretation, pattern recognition, and informed decision-making. As a result, learning processes are becoming more intertwined with digital and algorithmic capabilities, raising both new opportunities and challenges related to ethics, transparency, autonomy, and the reconfiguration of work and organizational routines.

Scholars and practitioners are paying growing attention to how these evolving learning mechanisms—now shaped by hybrid human–AI interactions—contribute to value creation, whether economic, social, environmental, or relational. Understanding how AI is integrated into learning practices, knowledge structures, organizational culture, and governance systems is essential for capturing its transformative potential while ensuring responsible and sustainable outcomes.

This Special Issue aims to explore the multifaceted relationship between organizational learning, AI-enabled knowledge dynamics, and value creation from both theoretical and empirical perspectives. We welcome contributions that advance conceptual understanding, propose new frameworks, examine micro- and macro-level learning processes, or provide evidence on how learning practices—supported or reshaped by AI—enhance performance, innovation, resilience, and long-term sustainability. Submissions adopting interdisciplinary approaches, novel methodologies (including AI-driven research methods), or comparative analyses across industries and sectors are particularly encouraged.

By bringing together diverse viewpoints, this Special Issue aims to deepen our understanding of how organizations can effectively nurture learning capabilities, responsibly leverage AI, and translate these combined strengths into meaningful, measurable, and sustainable forms of value.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

• Organizational learning processes (knowledge acquisition, knowledge sharing, unlearning, organizational renewal) and their impact on value creation.
• Human–AI interaction and its implications for learning capabilities, decision-making, and knowledge development.
• AI-enabled learning mechanisms, including machine learning, predictive analytics, and generative AI for organizational transformation.
• The role of AI in fostering innovation, creativity, and continuous improvement within learning organizations.
• Ethical, transparent, and responsible AI adoption as part of organizational learning and governance.
• Digitalization and socio-technical approaches to learning and sustainable value creation.
• Learning cultures and dynamic capabilities for sustainability and long-term competitiveness.
• Organizational resilience, adaptability, and strategic renewal in rapidly changing and technology-intensive contexts.
• Learning-led sustainable value creation, including economic, social, environmental, and relational dimensions.
• Methodological advancements, such as AI-supported research designs, digital trace data, and mixed-method approaches to studying learning processes.
• Comparative studies across industries and sectors, with a focus on different maturity levels in AI integration and organizational learning.

We look forward to receiving your contributions.

Prof. Dr. Francesca Di Virgilio
Prof. Dr. Gianluigi Mangia
Dr. Muhammad Waseem Bari
Dr. Angelo Rosa
Guest Editors

Manuscript Submission Information

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Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a double-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Administrative Sciences is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

• learning organizations
• organizational learning
• dynamic capabilities
• augmenting tacit knowledge
• human–AI collaboration
• sustainable value creation
• micro-learning
• workforce reskilling
• human-AI workflow design
• measurement: hybrid knowledge metrics