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.
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References
Carter, C.R., Easton, P.L.: Sustainable supply chain management: evolution and future directions. Int. J. Phys. Distr. Log. 41(1), 46–62 (2011)
Pagell, M., Shevchenko, A.: Why research in sustainable supply chain management should have no future. J. Supply Chain Manag. 50(1), 44–55 (2014)
Srivastava, S.K.: Green supply-chain management: a state-of-the-art literature review. Int. J. Manag. Rev. 9(1), 53–80 (2007)
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)
Carter, C.R., Jennings, M.M.: Social responsibility and supply chain relationships. Transport. Res. E-Log. 38(1), 37–52 (2002)
Murphy, P.R., Poist, R.F.: Socially responsible logistics: an exploratory study. Transport. J. 41(4), 23–35 (2002)
Seuring, S., Müller, M.: Core issues in sustainable supply chain management - a Delphi study. Bus. Strateg. Environ. 17(8), 455–466 (2008)
Elkington, J.: Cannibals with Forks: The Triple Bottom Line of 21st Century Business. New Society Publishers, Gabriola Island (1998)
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)
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)
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)
Sloan, T.W.: Measuring the sustainability of global supply chains: current practices and future directions. J. Glob. Bus. Manage. 6(1), 1–16 (2010)
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)
Blei, D., Ng, A., Jordan, M.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3, 993–1022 (2003)
Griffiths, T., Steyvers, M.: Finding scientific topics. PNAS 101(1), 5228–5235 (2004)
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)
Blei, D.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)
Select number of topics for LDA model. https://cran.r-project.org/web/packages/ldatuning/vignettes/topics.html
Griffiths, T., Steyvers, M., Tanenbaum, J.: Topics in semantic representation. Psychol. Rev. 114(2), 211–244 (2007)
Deveaud, R., Sanjuan, E., Bellot, P.: Accurate and effective latent concept for ad hoc information retrieval. Rev. Sci. Tech. Inf. 17, 61–84 (2014)
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)
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)
Parameter estimation for text analysis. http://www.arbylon.net/publications/text-est.pdf
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)
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Montenegro, C., Loza-Aguirre, E., Segura-Morales, M. (2018). Using Probabilistic Topic Models to Study Orientation of Sustainable Supply Chain Research. In: Rocha, Á., Adeli, H., Reis, L.P., Costanzo, S. (eds) Trends and Advances in Information Systems and Technologies. WorldCIST'18 2018. Advances in Intelligent Systems and Computing, vol 745. Springer, Cham. https://doi.org/10.1007/978-3-319-77703-0_57
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DOI: https://doi.org/10.1007/978-3-319-77703-0_57
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