Using Probabilistic Topic Models to Study Orientation of Sustainable Supply Chain Research

Even though the notion of sustainable development calls for an equilibrium among social, environmental and economic dimensions, several studies have suggested that an unbalance exists about the attention given to the three dimensions. Nonetheless, few contributions have demonstrated such unbalance. In this article, we propose a method based on LDA Topic Model, conceived to speed up the analysis of the sustainable orientation of a corpus. To test the procedure, we compared the results obtained using our method against those from a manual coding procedure performed on about ten years of literature from top-tier journals dealing with Sustainable Supply Chain issues. Our results confirm unbalance on research in this field, as they were reported previously. They show that most research is oriented to environmental and economic aspects, leaving aside social issues.

[1]  Thomas L. Griffiths,et al.  Probabilistic Topic Models , 2007 .

[2]  Thomas L. Griffiths,et al.  Rational analysis as a link between human memory and information retrieval , 2008 .

[3]  M. Narasimha Murty,et al.  On Finding the Natural Number of Topics with Latent Dirichlet Allocation: Some Observations , 2010, PAKDD.

[4]  Sheng Tang,et al.  A density-based method for adaptive LDA model selection , 2009, Neurocomputing.

[5]  John Elkington,et al.  Partnerships from cannibals with forks: The triple bottom line of 21st‐century business , 1998 .

[6]  S. Seuring,et al.  Core issues in sustainable supply chain management – a Delphi study , 2008 .

[7]  D. Rogers,et al.  A framework of sustainable supply chain management: moving toward new theory , 2008 .

[8]  Janjaap Semeijn,et al.  Issues and initiatives surrounding rail freight transportation in Europe , 2002 .

[9]  S. Vinodh,et al.  Development of checklist for evaluating sustainability characteristics of manufacturing processes , 2013 .

[10]  Mark Steyvers,et al.  Topics in semantic representation. , 2007, Psychological review.

[11]  Mark Steyvers,et al.  Finding scientific topics , 2004, Proceedings of the National Academy of Sciences of the United States of America.

[12]  V. Sloan,et al.  Measuring the Sustainability of Global Supply Chains: Current Practices and Future Directions , 2010 .

[13]  David M. Blei,et al.  Probabilistic topic models , 2012, Commun. ACM.

[14]  Michael I. Jordan,et al.  Latent Dirichlet Allocation , 2001, J. Mach. Learn. Res..

[15]  Samir K. Srivastava,et al.  Green Supply-Chain Management: A State-of-the-Art Literature Review , 2007 .

[16]  Patrice Bellot,et al.  Accurate and effective latent concept modeling for ad hoc information retrieval , 2014, Document Numérique.

[17]  C. Carter,et al.  Social responsibility and supply chain relationships , 2002 .

[18]  Shaowen Yao,et al.  An overview of topic modeling and its current applications in bioinformatics , 2016, SpringerPlus.

[19]  Marco Segura-Morales,et al.  Unveiling Unbalance on Sustainable Supply Chain Research: Did We Forget Something? , 2018, ICITS.

[20]  Jose M. Moneva,et al.  Evaluating sustainability in organisations with a fuzzy logic approach , 2008, Ind. Manag. Data Syst..

[21]  Wenjun Yuan,et al.  Dynamics of the functions $$ f_\mu (z)=z\exp (z+\mu ) $$fμ(z)=zexp(z+μ) with the real parameter , 2016, SpringerPlus.

[22]  Mark Pagell,et al.  Why Research in Sustainable Supply Chain Management Should Have No Future , 2014 .

[23]  C. Carter,et al.  Sustainable supply chain management: Evolution and future directions , 2011 .

[24]  Gregor Heinrich.  Parameter estimation for text analysis , 2009 .