Karine Goglio, Kedge Business School

Marion Neukam, Université de Strasbourg

Florence Crespin-Mazet, Kedge Business School

Patrick Cohendet, HEC Montréal

Eric Davoine, Université de Fribourg

Timeline

• July 30, 2026: Submission deadline

Timeline

• December 31, 2029: Submission deadline

This special issue invites submissions that explore the intersection of data-driven innovation, sustainable product development, and knowledge management. We seek research that examines how organizations can leverage data analytics, big data, machine learning, and knowledge management systems to drive innovation in sustainable product development. The focus is on understanding how data can be collected, analyzed, interpreted, and managed to support decision-making processes that lead to more sustainable products and services.

Potential topics

• Application of data analytics and big data techniques in sustainable product development
• Knowledge management systems for supporting data-driven innovation in sustainability contexts
• Integration of lifecycle assessment (LCA) data with knowledge management platforms
• Machine learning and AI applications for identifying sustainable product innovations
• Data governance frameworks for managing sustainability-related information
• Organizational learning and knowledge sharing in sustainable innovation processes
• Digital transformation enabling sustainable product development
• Real-world case studies of data-driven sustainable product innovation
• Challenges and barriers to implementing data-driven approaches in sustainability
• Tools and technologies for collecting, analyzing, and leveraging sustainability data

Timeline

• October 15, 2026: Submission deadline

Timeline

• September 30, 2026: Submission deadline

Guillermo Antonio Dávila, Universidad de Lima

Susanne Durst, Bangkok University

A growing number of organizations are facing political uncertainty, a knowledge-based economy with heterogeneous development, and technological upheavals that not only pose ethical and social challenges but also demand new business models and ways to maintain competitiveness. Many of these organisations operate in underdeveloped regions, geographically isolated areas, rural communities, conflict zones, post-disaster environments, or informal settlements. Others, like NGOs, operate in sectors with a lack of financial resources. These entrepreneurial contexts share characteristics such as financial constraints, difficulties in accessing and applying technologies, and a shortage of skilled workers. Organisations operating in these contexts must learn to innovate and remain globally competitive despite challenging conditions.

Given limited resources, institutional frameworks, and other uncertainties in today's world, companies should strive to make better use of their knowledge. Targeted and strategic KM enables organizations to maximize the value of their relevant knowledge. Research has shown that KM can mitigate the disadvantages associated with financial and technological constraints. In this sense, it might represent a strategic organisational capability that supports innovation, strengthens resilience, and fosters organisational learning.

Empirical evidence from different contexts supports the critical role of KM in this regard. Regarding innovation, KM practices have been associated with higher innovation performance in firms from Brazil and China. In terms of resilience, studies suggest that organisational resilience can be reinforced through knowledge-based resources and capabilities, including dynamic capabilities in Indonesian firms, the joint action of intellectual capital and generative AI in Turkish organisations, and knowledge transfer, integration, and sharing mechanisms in emergency management systems in China. Likewise, organisational learning has been linked to intellectual capital and knowledge process capability in Taiwanese firms. KM has also proven to be very beneficial for small and medium-sized enterprises and their development. When operating under constraints, it is crucial to understand the qualities of available resources, including knowledge, as it is not automatically an intrinsic asset. Against this backdrop, discussing risks related to knowledge is relevant, allowing the organisation to examine knowledge from both a positive and a negative perspective, which can lead to new ways of thinking and novel solutions for leveraging knowledge.

Although KM has become an established field in recent years, the focus of studies remains on relatively stable and well-institutionalized environments, i.e., developed economies and mature organizational environments. Contexts characterized by institutional weaknesses or resource constraints are comparatively under-researched, although emerging findings suggest that KM could play a crucial role in overcoming such constraints through mechanisms such as tinkering, socialization, and knowledge-sharing routines. Since SMEs constitute the majority of businesses in all economies, and approximately 61 percent of SMEs are from emerging economies, there is a significant need for more context-sensitive KM research that examines the roles KM can play in contexts characterized by resource and institutional scarcity.

This special issue aims to deepen the understanding of KM in contexts with limited resources and institutional frameworks. It seeks to identify mechanisms, capabilities, and theoretical perspectives through which KM enables organisational development and resilience despite such conditions. It welcomes the application or proposal of novel methodological approaches for investigating KM phenomena in such environments. By promoting context-sensitive research from underrepresented areas, this special issue aims to generate both theoretical and practical contributions that help to reduce the existing gap between established and underrepresented contexts.

This Special Issue welcomes a wide range of contributions, including conceptual papers, literature reviews, empirical studies, comparative and cross-country studies, theory-building research, practice-oriented studies, and methodological contributions, that advance understanding of KM in contexts characterised by resource and institutional constraints.

Potential topics

• KM as an enabler of organizational performance under conditions of resource scarcity
• Knowledge risk management as crucial function in environments characterized by internal and external constraints
• KM in underrepresented organisational settings, including SMEs, family businesses, informal organisations, low-technology firms, social organisations or geographically isolated regions
• Organisational learning processes in contexts characterised by institutional and resource constraints
• The role of KM in strengthening organisational resilience during internal and external crises and disruptions
• Necessary conditions and bottlenecks for organizational development empowered by KM
• The interplay between artificial intelligence and KM in overcoming organisational and institutional constraints
• The role of institutional ecosystems and inter-organisational networks in enhancing the effectiveness of KM
• Novel methodological approaches for studying KM in constrained and underrepresented contexts

Timeline

• March 15, 2027: Manuscript deadline

Giovanni Schiuma, Università LUM

Péter Baranyi, Corvinus University

Francesco Santarsiero, Università della Basilicata

Dagmara Lewicka, AGH University of Science and Technology

The intersection of Knowledge Management (KM) and Artificial Intelligence (AI) is rapidly transforming how organizations create, share, and apply knowledge to pursue sustainability. In this context, 'intelligent knowledge' refers to the management of knowledge systems that dynamically combine human cognitive capacities with advanced AI-driven tools and models to facilitate ethical, inclusive, and purpose-driven innovation. This Special Issue examines how intelligent knowledge management systems can enhance organizations' innovation capacity and align their efforts with environmental, social, and governance (ESG) commitments and the Sustainable Development Goals (SDGs).

The convergence of Knowledge Management (KM) and Artificial Intelligence (AI) is increasingly recognized as a pivotal driver for advancing organizational sustainability in the digital era. In today's complex business landscape, organizations face challenges in developing intelligent knowledge systems—AI-augmented frameworks designed to support sustainable decision-making, foster innovation, and ensure strategic alignment with long-term value creation goals. The emergence of generative AI and large language models (LLMs) is transforming knowledge processes, enabling organizations to dynamically retrieve, synthesize, and apply knowledge to address complex sustainability challenges.

While traditional Knowledge Management (KM) has primarily focused on the systematic processes of creating, storing, sharing, and applying knowledge, the integration of Artificial Intelligence (AI) introduces new dimensions of computational intelligence that enhance and, in some cases, automate these foundational functions. AI-driven technologies are increasingly supporting knowledge-based decision-making, thereby fostering the emergence of intelligent knowledge systems that can reason, learn, and act based on context-aware insights to promote sustainable and purpose-driven outcomes. Within KM theory, the enduring distinction between tacit and explicit knowledge remains highly relevant: while AI excels in processing and managing explicit knowledge, the challenge of capturing and leveraging tacit knowledge persists, particularly in enabling meaningful and ethical human-AI collaboration. Recent studies emphasize that AI should be viewed as augmenting, rather than replacing, human expertise in sustainability-oriented knowledge work, thereby reaffirming the centrality of co-creation within contemporary knowledge management frameworks.

The evolution of KM in the AI era thus calls for new conceptual models and dynamic strategies that can support knowledge environments aligned with long-term sustainability objectives. As organizations strive to integrate human and AI cognition into their knowledge systems, the ability to flexibly interact with and adapt through AI-generated insights becomes a defining capability for intelligent knowledge management. The impact of AI on organizational learning and strategic knowledge flows must be critically assessed from a sustainability perspective, recognizing that AI technologies can either enhance or threaten knowledge-based resilience and responsible innovation. Generative AI and machine learning models are reshaping the landscape of KM, revolutionizing how organizations capture, synthesize, and apply knowledge to create sustainable value.

While large language models (LLMs) and AI-driven knowledge repositories significantly increase the speed and scope of knowledge retrieval and decision support, they also introduce critical risks related to epistemic validity, algorithmic bias, and the sustainability of automated knowledge production. These challenges are especially acute when AI-generated knowledge informs decisions with significant social or environmental implications, where the accuracy, contextual sensitivity, and ethical integrity of outputs are vital for maintaining organizational trust, legitimacy, and long-term innovation capacity.

This Special Issue aims to foster a deeper understanding of how intelligent knowledge systems, powered by the integration of AI technologies, human cognition, and contemporary KM practices, are transforming the pursuit of sustainability, innovation, and responsible governance within organizations. We invite contributions that critically explore how human-AI collaboration can be harnessed to develop resilient, ethical, and sustainability-oriented knowledge ecosystems. Beyond conceptual and theoretical contributions, we particularly encourage empirical studies that provide evidence-based insights into how intelligent knowledge processes enhance sustainability performance, strengthen ESG strategies, and promote adaptive, long-term value creation. Given the transformative impact of AI on knowledge flows and strategic decision-making, special attention will be devoted to research that examines the capabilities, limitations, and practical applications of AI-enabled knowledge systems, including generative AI tools and large language models (LLMs), in advancing sustainable organizational practices.

Potential topics

• The design of intelligent knowledge systems integrating AI technologies and human cognitive capacities
• Reimagining knowledge management (KM) practices to align with ESG commitments and sustainable development goals (SDGs)
• Human-AI collaboration models to augment human expertise in knowledge work
• The transformative impact of generative AI and large language models (LLMs) on knowledge processes
• Integrating Human cognitive systems with AI-enabled technologies
• Human-based emotional knowledge and AI-based rational knowledge
• Challenges in Managing Tacit and Explicit Knowledge in AI-Enhanced Knowledge Management Environments
• Development of transformative leadership competencies for navigating digital complexity and fostering sustainable innovation
• Strategic knowledge intelligence approaches for building resilience, responsible innovation, and adaptive capabilities
• Democratization of knowledge access and support for inclusive value co-creation through intelligent knowledge systems
• Risks and challenges of automated knowledge production and ensuring the sustainability of AI-generated knowledge
• Governance models and policy frameworks to guide the ethical evolution of intelligent knowledge systems

Timeline

• January 15, 2026: Submissions open
• June 1, 2026: Review process on a rolling basis
• August 16, 2026: Manuscript submission deadline

Asha Thomas, Wrocław University of Science and Technology

Moreno Frau, Corvinus University of Budapest

Dominyka Venciūtė, ISM University of Management and Economics

Across contemporary organizations, advances in artificial intelligence (AI) are transforming AI from a discrete technological resource into a systemic organizational capability that actively shapes decision-making, business model innovation, and competitive advantage. Traditionally, AI interfaces have largely been reactive, responding to human prompts and predefined inputs. The emergence of Agentic Artificial Intelligence represents a fundamental shift, as agentic systems are designed to operate with increasing autonomy, enabling goal-driven planning, workflow orchestration, coordination across systems, and machine-initiated action with limited human intervention.

For organizations, this growing autonomy presents both significant opportunities and substantial risks. Agentic AI promises new forms of value creation by enhancing efficiency, scalability, personalization, and decision quality across organizational functions such as human resource management, marketing, customer engagement, knowledge management, and operations. At the same time, the delegation of agency to autonomous systems heightens concerns related to governance, transparency, accountability, and oversight, particularly when organizations have limited visibility into how agentic systems reason, learn, and act. Moreover, misaligned interactions and problematic resource integration may produce unintended negative outcomes, underscoring the coexistence of value creation and value co-destruction in AI-enabled organizational processes.

Despite these unresolved challenges, agentic AI is no longer a speculative phenomenon. Organizations have already begun embedding agentic systems into core practices, including recruitment, onboarding, performance management, customer service, marketing operations, and knowledge-intensive work. This diffusion reflects a broader shift in which AI is increasingly understood as a normalized and enduring component of contemporary organizational and marketing systems rather than a temporary technological trend. In parallel, early implementations in knowledge management demonstrate how agentic systems can unify fragmented knowledge bases, dynamically adapt insights, and support continuous organizational learning.

From an academic standpoint, these developments challenge existing organizational and information management theories. While socio-technical systems theory, agency theory, and organizational learning have traditionally conceptualized AI as a support tool within human-centric systems, they offer limited explanatory power for autonomous, multi-agent systems capable of independent coordination and action. In marketing and customer engagement contexts, interactive value formation is increasingly shaped by emotional and relational dynamics emerging from human–AI interactions, further complicating assumptions about control and responsibility. As such, new theoretical perspectives are needed to capture agency, accountability, and knowledge dynamics in AI-enabled organizations.

Potential topics

• How does Agentic AI enable new forms of value creation, capture, and measurement in organizations? Under what conditions can agentic systems also lead to value co-destruction or unintended negative outcomes due to misaligned autonomy, resource integration, or decision logic?
• How do organizations design governance, accountability, trust, and regulatory compliance mechanisms for Agentic AI systems operating with increasing autonomy? What challenges arise when human oversight is limited or distributed across functions?
• How do ethical considerations, responsibility, and moral agency evolve when AI systems act as semi-autonomous organizational actors rather than decision-support tools?
• How does Agentic AI reshape business process redesign, orchestration, and automation across organizational functions such as marketing, human resource management, operations, finance, and customer engagement?
• In what ways is artificial intelligence becoming normalized within organizational and marketing practice, shifting from experimental adoption to routinized, AI-embedded decision-making and workflows?
• How does Agentic AI influence workforce transformation, the future of work, and human resource management practices, including recruitment, performance evaluation, learning, and employee autonomy?
• How does the adoption of Agentic AI differ across organizational contexts, such as small and medium-sized enterprises versus large corporations, and what factors shape successful implementation and impact?
• How do emotional, relational, and interactional dynamics shape human–AI engagement in Agentic AI–driven sales, marketing, and customer experience contexts?
• How can human–AI collaboration and human-in-the-loop design be sustained when AI systems increasingly initiate actions, coordinate tasks, and learn autonomously?
• How do multi-agent systems, coordination mechanisms, and organizational architectures evolve as multiple human and artificial agents interact within complex socio-technical environments?
• How can existing theories of agency, organizational learning, and socio-technical systems be extended or reconfigured to explain machine agency and autonomous action in Agentic AI–enabled organizations?
• How does Agentic AI transform knowledge management, organizational learning, and decision support by enabling systems that not only retrieve and integrate knowledge but also reason, adapt, and act upon it?

Timeline

• March 1, 2026: Opening date for manuscript submissions
• September 30, 2026: Closing date for manuscript submissions