Designing a data-driven methodology for academic analysis: a case study in the Industrial Systems Engineering community

Understanding the behaviors and performance metrics of academic researchers is crucial, particularly in contexts where such metrics influence recruitment and funding policies. The paper presents the design and implementation of a data-driven methodology to study the dynamics of a large community of researchers, starting with the setting of specific elements to measure, to identify proper indicators and datasets to feed the analysis tools. The main source of the study is a comprehensive dataset comprised of performance indices, co-authorship information, and data obtained from the official list of academic researchers by the Italian Ministry of University and Research, as well as Elsevier’s Scopus database. The application of the methodology is tested on the community of Italian academic researchers within the specific disciplinary field of industrial systems engineering, with 280 researchers and 42 universities. The analysis not only provides insights into the evolution of research topics over time but also evaluates the prevalence and impact of declaratory themes that define the sector. Questions addressed include the existence of citing clusters, the extent of international collaboration, publication trends across various journals, and the emergence of predatory journals. Through detailed examination, this paper provides a systematic methodology to reveal significant patterns in publication and collaboration, shedding light on the underlying factors that may influence academic performance and career progression. The findings propose a future research agenda aimed at enhancing understanding of research dynamics, fostering increased collaboration, and supporting career advancement in academia.