Background and motivation

In an era of unprecedented technological advancement, the convergence of artificial intelligence (AI), cutting-edge technologies, and knowledge management (KM) has become important for organizational success (Cheng et al., 2025; Iaia et al., 2024). As organizations transition into knowledge-intensive business models, scholars and managers are increasingly interested in how AI and emerging technologies reshape various aspects of KM. This includes the handling of explicit and tacit knowledge, the development of organizational knowledge bases, and practices related to knowledge sharing and protection (Massa et al., 2023).

The power of AI and cutting-edge technologies is already known. The integration of AI into KM processes is redefining how organizations create, share, and utilize knowledge. For instance, smart documentation platforms using natural language processing (NLP) can automatically extract and categorize policies, procedures, and best practices from vast document repositories. While this enhances transparency and efficiency, it also raises social concerns about equitable access, especially in multilingual or culturally diverse workplaces where such systems may not be equally effective, potentially marginalizing certain employee groups (Lim and Hwang, 2024). AI can foster more efficient collaboration through intelligent knowledge-sharing platforms (Sumbal et al., 2024). Recommender systems and smart assistants help employees access relevant information, facilitating the knowledge flow across different organizational units. This can help organizations in improving knowledge-sharing practices and reducing knowledge-hiding behaviors (Deng et al., 2022). AI’s ability to process vast amounts of data can lead to new knowledge generation, especially in complex, data-rich environments. NLP, machine learning, and predictive analytics enable organizations to detect patterns, forecast trends, and make more informed decisions (Chowdhury et al., 2022; Leal-Rodríguez et al., 2023).

However, while AI and cutting-edge technologies bring significant benefits, they also pose substantial risks, particularly in the field of knowledge management. Ethical decisions related to data privacy and security are significant challenges. AI systems rely on vast datasets; therefore, organizations should ensure the privacy and security of this knowledge. The integration of AI into KM processes raises concerns about who has access to sensitive knowledge and how this knowledge can be protected from cyber threats (Rahman and Islam, 2024). As organizations increasingly rely on AI for decision-making and knowledge creation, questions of accountability, transparency, and human oversight become paramount (Dwivedi et al., 2021). Moreover, AI-driven automation in knowledge processes may lead to job displacement, especially among knowledge workers performing routine cognitive tasks (AlQershi et al., 2023). This will require organizations to rethink workforce development strategies and invest in reskilling programs to ensure humans remain relevant in this rapidly evolving landscape. In developing these strategies, organizations should consider how intellectual capital (in terms of human, relational, and structural) interacts with KM practices related to AI or vice-versa (Mei et al., 2023; Wu et al., 2024).

Crucially, the integration of AI in KM holds significant potential for advancing social sustainability and inclusivity. The International Monetary Fund (2024) highlights how AI, if appropriately regulated, can increase labor productivity and, therefore, stimulate sustainable and inclusive economic growth, central to achieving SDG 8. Furthermore, by breaking down language barriers and optimizing knowledge dissemination, AI has the potential to reduce global inequalities in access to information (Dubey, 2025).

However, realizing these benefits requires a concerted effort to design inclusive and sustainable knowledge ecosystems. This involves not only technological innovation but also the development of appropriate governance structures, ethical frameworks, and policies that ensure equitable participation in the knowledge economy (Janssen et al., 2020).

As organizations continue to adapt to the new business paradigm, it is essential to predict the future of knowledge management in the technology-driven era (Song and Wu, 2021). AI and cutting-edge technologies present new opportunities for continuous learning ecosystems that evolve in response to organizational needs, enabling firms to adapt and grow in knowledge-intensive industries and enhance the awareness of the importance of intellectual capital.

Objectives and themes

This Special Issue aims to explore how AI and a wider ecosystem of cutting-edge technologies, including blockchain, IoT, big data analytics, extended reality, and cloud-based infrastructures, are transforming KM and IC in contemporary organizations. We seek to advance theoretical understanding and empirical insights into how these technologies are reshaping knowledge flows, collaboration, decision-making, organizational learning, and social sustainability.

In particular, this track emphasizes bridging theoretical innovation with practical application, encouraging contributions that integrate multiple disciplines, such as information systems, organizational behavior, ethics, and digital policy, to enrich the conversation on technology-enabled knowledge ecosystems.

By bridging macro-level digital innovation strategies with micro-level organizational and human practices, this issue seeks to promote cross-disciplinary research that deepens theoretical insights and informs actionable strategies across sectors and regions. It encourages contributions that align technological innovation with social value creation and sustainability, especially in the context of the UN Sustainable Development Goals (e.g., SDG 8, 9, 10, and 16).

We invite high-quality research that investigates, but is not limited to, the following themes:

1. Digital Transformation, Knowledge Flows, and Social Impact

• How do technologies such as blockchain and IoT contribute to secure, decentralized, and transparent knowledge sharing?
• What is the role of extended reality (AR/VR) in preserving and transferring tacit knowledge in training and onboarding?
• How can digital twins support the development of real-time organizational memory?
• What is the impact of cloud-based infrastructures and 5G on cross-functional knowledge collaboration in distributed organizations?

2. Technological Innovation in Knowledge Dynamics and Organizational Learning

• How do different technologies (e.g., RPA, big data analytics, digital platforms) reshape the creation, storage, and diffusion of explicit knowledge?
• In what ways can immersive technologies (e.g., VR) foster richer tacit knowledge sharing and experiential learning?
• How can IoT-enabled environments and smart systems facilitate continuous organizational learning in real-time?

3. Emerging Technologies and the Evolution of Intellectual Capital

• How do blockchain and decentralized technologies affect the management of structural capital and intellectual property?
• How can organizations leverage digital platforms to enhance relational capital and stakeholder engagement?
• What role do wearable technologies or digital interfaces play in augmenting human capital and well-being in intensive work?
• How do technology ecosystems influence the development and measurement of intellectual capital at the organizational level?

4. Governance, Ethics, and Risks in Technology-Augmented KM

• What are the data privacy and knowledge security implications of IoT, cloud storage, and interconnected platforms?
• How can organizations govern hybrid knowledge systems that involve both human and machine-generated knowledge?
• What are the ethical and legal implications of using extended reality for knowledge training and surveillance?
• How can transparency and trust be maintained in blockchain-based knowledge certification or reputation systems?

5. Inclusive and Sustainable Knowledge Systems Enabled by Technology

• How can digital technologies support inclusion in global knowledge networks, especially in developing regions?
• How can organizations use technology to promote knowledge equity and intergenerational knowledge transfer?
• How can technology infrastructures be designed to support sustainable, human-centered knowledge ecosystems?

By fostering dialogue between technology-focused and socially-oriented perspectives, this Special Issue aims to contribute to a more holistic understanding of KM and IC in the age of AI, paving the way for more inclusive and sustainable knowledge practices in organizations and society at large.

Submissions may include empirical studies based on quantitative, qualitative, or mixed-method approaches that explore real-world applications and implications of emerging technologies in knowledge management and intellectual capital. Conceptual and theoretical papers that offer novel frameworks or challenge existing paradigms are equally encouraged. We are also interested in interdisciplinary case studies and policy-oriented research that provide practical insights across diverse contexts, as well as design science research that proposes and tests innovative socio-technical solutions. In addition, systematic literature reviews that critically assess the current state of research and identify future directions are welcome.