Core Topics Discovery in Sustainable Supply Chain Literature: An Automatic Approach

The study of Sustainable Supply Chain (SSC) has evolved and expanded over the last two decades. This study uses text mining and machine learning methods for automatically identify and classify the topics that permeate a collection of documents. The topics of SSC research were identified, using the Latent Dirichlet Allocation model, from 684 articles published between 2001 and 2017 in 13 top journals. Then, we explored trends by examining changes in the classification of topics in different periods and by identifying the hot and cold topics of SSC research. The relationships of these topics with the journals were also determined. Finally, applying the Competitive Neural Network learning model, the topics were classified according to the Elkington's Triple Bottom Line precepts. The findings of this study are expected to provide clues for researchers and policymakers in the field of SSC.