Clustering abstracts from the literature on Quality Management (1980–2020)

Abstract

The research analyses the intellectual structure of research publications on Quality Movement (1980–2020), indexed in the Scopus database. It examines how themes have evolved, and it seeks to plan an editorial agenda. The research assumes a content-related method to reveal conceptual relationships in abstracts and conclude thematic trends, gaps, and weaknesses in the Quality Management domain and its implementation frameworks. In particular, the analysis is based on the BERTopic approach, i.e. it employs machine learning algorithms based on text summarisation and c-TF-IDF to create dense clusters using UMAP and hDBSCAN clustering. Although keywords are helpful in knowledge extraction, identifying hidden topics and their associations is a more robust approach to understanding the proper context of the analysed articles. As a result, the study identifies 48 topics and 13 metatopics for Quality Movement. In addition, the paper shows the temporal evolution of the topics, and identifies the topics and metatopics of growing interest in the emerging literature in QM.