Call for Chapters

AI-Driven Knowledge Management: Strategies for the Modern Business Landscape

Important Dates:

Submission of Chapter Proposals: Ocotber 30, 2025 Full Chapter Submission Due: December, 10, 2025 Revisions Due: February 15, 2025 Publication: Q4 2025

Editors:

Professor Meir Russ Professor Emeritus, Cofrin School of Business, University of Wisconsin - Green Bay, USA Research Fellow, Dept. of Information Science, Stellenbosch University, South Africa Email: [email protected]

Professor Miltiadis D. Lytras Visiting Researcher, Effat University, Kingdom of Saudi Arabia Deree College- The American College of Greece, Greece Email: [email protected]

Scopus: https://www.scopus.com/authid/detail.uri?authorId=55830169000

Google Scholar: https://scholar.google.com/citations?user=oA2FOOAAAAAJ&hl=el

Introduction to the Theme:

The rapid integration of AI into business processes has revolutionized the way organizations manage, process, and leverage knowledge. This book aims to explore the intersection of AI and Knowledge Management (KM), offering insights into how AI-driven solutions can enhance organizational learning, decision-making, and competitive advantage for sustainable business success. The book, “AI-Driven Knowledge Management: Strategies for the Modern Business Landscape,” aims to serve as a comprehensive guide, providing both theoretical and practical insights on how AI technologies are transforming KM practices across industries in a continuously changing market landscape and commercial contexts.

Objectives of the Book:

1. Understanding AI in Knowledge Management: Analyze how AI technologies such as machine learning, natural language processing, and intelligent data analytics can enhance KM processes.

2. Exploring AI-Driven Business Strategies: Examine how AI-driven KM strategies can impact organizational decision-making, innovation, and business performance.

3. Case Studies and Practical Applications: Highlight successful implementations of AI in KM, drawing lessons from real-world examples.

4. Ethical and Managerial Implications: Discuss the ethical, managerial, and societal considerations of integrating AI into KM systems.

Indicative Topics:

We welcome chapters that address, but are not limited to, the following topics:

• The Role of AI in Enhancing Knowledge Management Processes
• AI and Knowledge Discovery: Techniques and Applications
• Machine Learning for Organizational Learning and Knowledge Sharing
• AI-Driven Decision Support Systems in Business
• Ethical Considerations in AI-Driven Knowledge Management
• The Impact of AI on Knowledge Retention and Transfer
• Implementing AI for Knowledge Management in SMEs
• AI Applications in Data Analytics for Knowledge Management
• Case Studies on AI-Driven Knowledge Management Systems
• The Future of AI in Knowledge Management: Challenges and Opportunities

Section 1: Foundations of Knowledge Management Theory

This section lays the theoretical groundwork for understanding knowledge management, covering the evolution, key theories, and foundational concepts that underpin KM practices in modern enterprises. - Evolution of KM theories - Key concepts in KM - Models of KM - KM life cycles - Theoretical frameworks for KM analysis - The role of organizational culture in KM success - Psychological foundations of knowledge sharing - Ethical considerations in KM

Section 2: Knowledge Management Strategies: Integrating KM into Business Strategy

This section focuses on the strategic integration of KM into business practices, discussing how KM aligns with overall business objectives to enhance organizational effectiveness and decision-making. - Strategic alignment of KM - KM and corporate governance - Role of KM in competitive advantage - Integrating KM into business models - KM for innovation and creativity - KM in change management - KM in crisis management - Measuring the impact of KM on business performance

Section 3: Best Practices and Case Studies: KM at Team, Organizational, and Inter-organizational Levels

Presents practical applications and real-world case studies of KM across different levels of organizations. This section showcases successful implementations and lessons learned from diverse industries. - KM in small teams - Organizational knowledge networks - Cross-organizational KM projects - Sector-specific KM case studies (e.g., healthcare, finance, manufacturing) - Challenges in implementing KM - KM in non-profit organizations - Cross-border KM strategies and challenges

Section 4: Tacit Knowledge Management

Explores the complexities of managing tacit knowledge—knowledge that is unspoken and unwritten, residing in the minds of employees and in the culture of the organization. - Identification of tacit knowledge - Techniques for capturing tacit knowledge - Tools to disseminate tacit knowledge - Tacit knowledge and organizational culture - Case studies on tacit knowledge conversion - Storytelling as a tool for tacit knowledge transfer - Mentorship programs as a means of managing tacit knowledge

Section 5: Foundations of AI in Business

Introduces AI fundamentals relevant to business contexts, explaining how AI technologies are developed and applied to support business functions and enhance operational efficiencies. - AI technologies in business - Developing AI solutions for KM - AI in decision support systems - Machine learning models for KM - Ethical considerations in AI applications - AI and Big Data: Opportunities for KM - Implementing AI projects: From planning to execution

Section 6: AI as a Catalyst for KM Business Performance

Discusses how AI acts as a catalyst for enhancing KM processes, focusing on AI-driven innovations that transform how organizations capture, process, and utilize knowledge for business performance. - AI-driven KM tools - Impact of AI on KM efficiency - AI in knowledge discovery and generation - Integrating AI with existing KM systems - Future trends in AI for KM - AI in knowledge dissemination and collaboration - AI-powered decision-making and analytics for KM

Submission Guidelines:

Interested authors are invited to submit a two-page chapter proposal outlining the chapter's goals, methodology, and expected outcomes. Proposals should be submitted via email to both editors by October 30, 2024. Upon acceptance, full chapters will be due by December 10, 2024. All submissions will undergo a double-blind peer-review process.

Contact Information:

Professor Meir Russ: [email protected]

Professor Miltiadis Lytras: [email protected]