Abstract:
The study provides a review of sustainable supplier selection (SSS), which has proceed to emerge and expand along with greater speed over the last decades. This research aims to identify and explore the use of machine learning and text mining methods to automatically identify relevant core topics in sustainable supplier selection. The traditional approach by personal judgments with predetermined categories, cannot adequately take latent topics from large volumes of research data. Therefore, this study selects the topic modelling approach. which automatically identify topics that extend a large and unstructured collection of documents to uncover research topics in sustainable supplier selection research. The papers for the literature review were collected from SCOPUS database. The model with 20 topics was selected through the Latent Dirichlet Allocation (LDA) model from selected articles published from 2010 to 2020 in associated with various journals, and the top 5 most popular topics in sustainable supplier selection research are reviewed in a nutshell. We then explore topic trends by considering the transformation over different topics of sustainable supplier selection research over the last few decades. For each of the top 10 journals, the areas of subspecialty and the effects of editor changes on the topic portfolios are also investigated. The findings of this study are expected to provide implications for researchers, journal editors, and policy makers in the field of sustainable supplier selection.
Description:
Proceedings of the 8th National and International Conference on "Research to Serve Society" "Research to Serve Society", 25 June 2021 at Huachiew Chalermprakiet University, Bangphli District, Samutprakarn, Thailand p. 375-388.
