Sustainable project management for humanitarian disasters and refugee crisis management: Bibliometric analysis

Abstract

This study conducted a bibliometric analysis of the literature on sustainable project management in crisis management, particularly in humanitarian disasters, using the Scopus database. After filtration and abstract screening of 115,737 (1980–2022), only 544 related articles were identified and analyzed graphically using VoSviewer (visualization of similarities) to create maps of co-occurrence, co-authorship, and citations. The analysis provided an understanding of the most frequent keywords, key authors, journals, affiliations, and contributing countries in the literature on project management and crisis management. The trends observed in this study’s results highlight the significance of sustainable project management within the realm of disaster management. This study examines the bibliometric parameters and indices as measures of research success and their potential implications for grant funding sources, universities, governments, and organizations. The findings suggest that bibliometric parameters and indices could be increasingly utilized by grant funding sources as metrics to evaluate research success. Universities and research institutions may consider incorporating benchmarking standards based on bibliometric data into promotion and tenure guidelines to determine academic achievement. Furthermore, governments and organizations may also benefit from establishing benchmark standards to enhance sustainability in disaster management projects. Although Scopus has limitations that prefer certain fields, this study provides avenues for future research, including using other databases and conducting a comparative study between Scopus and WoS (Web of Science) to determine their strengths and weaknesses.

Insurance market transparency research trends: Bibliometric analysis

“…A bibliometric analysis of trends in the use of big data in insurance (Ellili et al, 2023;Mall et al, 2023;Shamsuddin et al, 2023;Saadi et al, 2023;Gonzalez-Samaniego et al, 2023) shows that the implementation of machine learning and artificial intelligence technologies contributes to increased transparency in the insurance sector. This ensures more efficient processing and analysis of large volumes of data, which is important for increasing the transparency of insurance products and services.…”