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

  • Carlos Montenegro
  • Edison Loza-Aguirre
  • Marco Segura-Morales
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 745)

Abstract

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.

Keywords

Sustainable development LDA topic model Content analysis Monte Carlo method Sustainable supply chain 

References

  1. 1.
    Carter, C.R., Easton, P.L.: Sustainable supply chain management: evolution and future directions. Int. J. Phys. Distr. Log. 41(1), 46–62 (2011)CrossRefGoogle Scholar
  2. 2.
    Pagell, M., Shevchenko, A.: Why research in sustainable supply chain management should have no future. J. Supply Chain Manag. 50(1), 44–55 (2014)CrossRefGoogle Scholar
  3. 3.
    Srivastava, S.K.: Green supply-chain management: a state-of-the-art literature review. Int. J. Manag. Rev. 9(1), 53–80 (2007)CrossRefGoogle Scholar
  4. 4.
    Carter, C.R., Rogers, D.S.: A framework of sustainable supply chain management: moving toward new theory. Int. J. Phys. Distr. Log. 38(5), 360–387 (2008)CrossRefGoogle Scholar
  5. 5.
    Carter, C.R., Jennings, M.M.: Social responsibility and supply chain relationships. Transport. Res. E-Log. 38(1), 37–52 (2002)CrossRefGoogle Scholar
  6. 6.
    Murphy, P.R., Poist, R.F.: Socially responsible logistics: an exploratory study. Transport. J. 41(4), 23–35 (2002)Google Scholar
  7. 7.
    Seuring, S., Müller, M.: Core issues in sustainable supply chain management - a Delphi study. Bus. Strateg. Environ. 17(8), 455–466 (2008)CrossRefGoogle Scholar
  8. 8.
    Elkington, J.: Cannibals with Forks: The Triple Bottom Line of 21st Century Business. New Society Publishers, Gabriola Island (1998)Google Scholar
  9. 9.
    Loza-Aguirre, E.F., Segura Morales, M., Roa, H.N., Montenegro, C.: Unveiling unbalance on sustainable supply chain research: did we forget something? In: Rocha, A., Guarda, T. (eds.) International Conference on Information Systems and Technologies 2018, Advances in Intelligent Systems and Computing. Springer, Heidelberg (2018)CrossRefGoogle Scholar
  10. 10.
    Muñoz, M.J., Rivera, J.M., Moneva, J.M.: Evaluating sustainability in organisations with a fuzzy logic approach. Ind. Manage. Data Syst. 108(6), 829–841 (2008)CrossRefGoogle Scholar
  11. 11.
    Vimal, K.E.K., Vinodh, S.: Development of checklist for evaluating sustainability characteristics of manufacturing processes. Int. J. Proc. Manage. Bench. 3(2), 213–232 (2013)Google Scholar
  12. 12.
    Sloan, T.W.: Measuring the sustainability of global supply chains: current practices and future directions. J. Glob. Bus. Manage. 6(1), 1–16 (2010)Google Scholar
  13. 13.
    Steyvers, M., Griffiths, T.: Probabilistic topic models. In: Landauer, T., McNamara, D., Dennis, S., Kintsch, W. (eds.) Handbook of Latent Semantic Analysis. Laurence Erlbaum, Mahwah (2007)Google Scholar
  14. 14.
    Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)MATHGoogle Scholar
  15. 15.
    Griffiths, T., Steyvers, M.: Finding scientific topics. PNAS 101(1), 5228–5235 (2004)CrossRefGoogle Scholar
  16. 16.
    Steyvers, M., Thomas, L.: Griffiths rational analysis as a link between human memory and information retrieval. In: Chater, N., Oaksford, M. (eds.) The Probabilistics Mind: Prospects for Bayesian Cognitive Science, pp. 329–350. Oxford University Press, New York (2008)CrossRefGoogle Scholar
  17. 17.
    Blei, D.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)CrossRefGoogle Scholar
  18. 18.
  19. 19.
    Griffiths, T., Steyvers, M., Tanenbaum, J.: Topics in semantic representation. Psychol. Rev. 114(2), 211–244 (2007)CrossRefGoogle Scholar
  20. 20.
    Deveaud, R., Sanjuan, E., Bellot, P.: Accurate and effective latent concept for ad hoc information retrieval. Rev. Sci. Tech. Inf. 17, 61–84 (2014)Google Scholar
  21. 21.
    Arun, R., Suresh, V., Veni, C., Murthy, M.: On finding the natural number of topics with latent Dirichlet allocation: some observations. In: Zaki, M., Xu, J. (eds.) Advances in Knowledge Discovery and Data Mining, pp. 391–402. Springer, Heidelberg (2010)CrossRefGoogle Scholar
  22. 22.
    Cao, J., Xia, T., Li, J., Zhang, Y., Tang, S.: A density-based method for adaptive LDA model selection. Neurocomputing 72(7–9), 1775–1781 (2009)CrossRefGoogle Scholar
  23. 23.
    Parameter estimation for text analysis. http://www.arbylon.net/publications/text-est.pdf
  24. 24.
    Liu, L., Tang, L., Dong, W., Yao, S., Zhou, W.: An overview of topic modeling and its current applications in bioinformatics. SpringerPlus 5(1608), 1–22 (2016)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  • Carlos Montenegro
    • 1
  • Edison Loza-Aguirre
    • 1
    • 2
  • Marco Segura-Morales
    • 1
  1. 1.Departamento en Informática y Ciencias de la ComputaciónEscuela Politécnica NacionalQuitoEcuador
  2. 2.CERAG FRE 3748, CNRS/UGAGrenoble Cedex 9France

Personalised recommendations