An investigation of academic perspectives on the 'circular economy' using text mining and a Delphi study

Delphi study

Dr Sampriti Mahanty and co-authors Prof Frank Boons, Prof Julia Handl and Dr Riza Batista Navarro have recently published a journal article titled “An investigation of academic perspectives on the 'circular economy' using text mining and a Delphi study” in the Journal of Cleaner Production.

Highlights of the article include:

  • Assessing the evolution and positioning of the Circular Economy (CE) in academic research;
  • A mixed methods approach combining text mining (topic modelling) and Delphi study;
  • Longitudinal analysis of the literature taking into account 3300 academic articles;
  • Opinion of 68 international experts to investigate the CE's position and evolution;
  • Future research avenues are suggested based on the assessment of the experts.

Visit the ScienceDirect website to access the article:

The authors have also published a book chapter titled “Computation of semantic change in scientific concepts: Case study of the "circular economy” in the book Computational approaches to semantic change.

Here is abstract of the chapter:

In this chapter, we aim to investigate semantic change in a scientific concept underpinned by the evolutionary framework of scientific knowledge production. The aim of this article is threefold. First is to distinguish semantic change computation in scientific concepts from that in core vocabulary and slang. Second is a multi-step analysis combining topic modelling, co-occurrence networks and word embeddings, along with a control condition setup thereby presenting a pipeline to compute semantic change in a scientific concept. Third is an analysis of a popular concept in sustainability studies, i.e., “circular economy”, seeking to advance research on this concept. In order to achieve our objectives, we use topic modelling to detect the point of change in a literature corpus and then we apply two approaches for detecting semantic change: co-occurrence networks and word embeddings. Furthermore, we compare the concept with other related concepts in the same semantic field and use word embeddings to detect if the concept has undergone any changes relative to other concepts.

Find out more about the book: