Prioritization methods play a crucial role in decision-making processes across various contexts, industries, and sectors. These methods are widely applied in project management, policy development, resource allocation, and strategic planning, among other domains. In particular, when budgets and resources are limited, prioritization methods are essential for ensuring that they are allocated in the most effective way. By evaluating various aspects, which may sometimes be in conflict, these methods help decision-makers in maximizing value and direct resources toward the most critical and high-impact initiatives, thereby enhancing efficiency and achieving the best possible outcomes within constraints. Despite their broad applicability, a comprehensive bibliometric review that spans all possible prioritization contexts is currently lacking in the literature, as existing reviews tend to focus on specific, specialized areas. As a result, there is not a unified analysis of how prioritization methods have evolved over time across different domains. The present research aims to bridge this gap through a bibliometric analysis of the scientific literature on prioritization methods. The research was conducted using the Scopus database, employing a systematic selection process based on the PRISMA method. Bibliometric analysis was performed using various tools to extract meaningful insights, including temporal and geographic distribution of publications, keywords co-occurrence analysis with VOSviewer, and topic categorization with Bibliometrix. This approach enabled the identification of key research themes, interconnections, and evolving trends in prioritization studies. The findings reveal that current research is largely driven by the integration of MCDM techniques, uncertainty management, and sector-specific applications such as transport planning, sustainability, and AI-supported decision-making. Moreover, by identifying key trends and research gaps, this work offers valuable insights for both scholars and practitioners looking to refine existing methods or develop new strategies for prioritization across a variety of applications.
F. et al. (Wed,) studied this question.