Decision-making processes have become increasingly critical in the face of complex global dynamics and the era of big data. Advanced analytical methods and multi-criteria decision-making (MCDM) approaches facilitate more informed and optimized decisions across various domains. In this context, the scientific book titled Advancements in Decision-Making and Data Analytics: Case Applications brings together significant studies that explore innovations in decision-making and data analytics.
By focusing on the applied aspects of decision and data analytics, this book serves as a comprehensive resource for academics and professionals. The chapters encompass both theoretical approaches and real-world applications, presenting original research contributions.
The first chapter examines the LOPCOW method and a novel RAM-based MCDM methodology in the context of university rankings. The evaluation of university performance plays a crucial role in shaping educational policies, and this study contributes to the literature by proposing a new integrated model.
The second chapter analyzes the assessment of logistics performance in emerging markets using the CILOS and MOORA methods. Logistics is a fundamental pillar of global trade, and effective decision-support mechanisms are essential for gaining a competitive advantage.
The third chapter focuses on enhancing decision-making processes through machine learning and data analytics techniques. By conducting sentiment analysis on user reviews, this study highlights the role of big data analytics in decision-making.
