Bibliometric Analyses in Data-Driven Decision-Making

Bibliometric Analyses in Data-Driven Decision-Making is a comprehensive guide that will provide insights into the growth, impact, and trends within bibliometric tools and methodologies. The handbook will adopt a pragmatic approach, balancing theoretical concepts with practical applications of data-driven decision-making models through the perspectives of bibliometric analyses. It's a cohesive guide to navigate the complexities of bibliometric analysis. This book will accommodate a specific audience interested in utilizing bibliometric analysis to understand and navigate the dynamic landscape of the increasingly vital field of data-driven decision-making and its applications in a range of areas.

The book is organized into six different sections. Each chapter will include real-world examples, case studies, and step-by-step guides for applying bibliometric analysis. Section one begins with an overview of bibliometric analysis and methodologies, laying the foundation for the detailed discussion that follows. Subsequent sections explore bibliometric analysis in logistics and supply chain management, healthcare, and medicine, and its integration with multi-criteria decision-making. The book also addresses advances in artificial intelligence (AI) and machine learning and concludes with a focus on technology, sustainability, and innovation. Each section provides a thorough analysis of the literature and emerging trends within these fields.