Deriving Corporate Social Responsibility Patterns in the MSCI Data

  • Zina TaranEmail author
  • Boris Mirkin
Conference paper
Part of the Lecture Notes in Business Information Processing book series (LNBIP, volume 353)


Empirical research effort over Corporate Social Responsibility (CSR) is typically concentrated on a limited number of aspects. We focus on the whole set of CSR activities to find out if there is a structure in those. We take data on the four major dimensions of CSR: environment, social & stakeholder, labor, and governance, from the MSCI database. To find out the structure hidden under almost constant average values, we apply a modification of K-means clustering with its complementary criterion. This method leads us to discover an impressive process of change in patterns that we predict will continue in the future.


Corporate social responsibility Pattern K-means Anomalous clusters 


  1. 1.
    Adam, A.M., Shavit, T.: How can a ratings-based method for assessing corporate social responsibility (CSR) provide an incentive to firms excluded from socially responsible investment indices to invest in CSR? J. Bus. Ethics 82(4), 899–905 (2008)CrossRefGoogle Scholar
  2. 2.
    Albinger, H.S., Freeman, S.J.: Corporate social performance and attractiveness as an employer to different job seeking populations. J. Bus. Ethics 28(3), 243–254 (2000)CrossRefGoogle Scholar
  3. 3.
    Arthur, D., Vassilvitskii, S.: K-means++: the advantages of careful seeding. In: Proceedings of the Eighteenth Annual ACM-SIAM Symposium on Discrete Algorithms. Society for Industrial and Applied Mathematics, pp. 1027–1035 (2007)Google Scholar
  4. 4.
    de Amorim, R.C., Hennig, C.: Recovering the number of clusters in data sets with noise features using feature rescaling factors. Inf. Sci. 324, 126–145 (2015)MathSciNetCrossRefGoogle Scholar
  5. 5.
    de Amorim, R.C., Makarenkov, V., Mirkin, B.: A-Wardpβ: effective hierarchical clustering using the Minkowski metric and a fast K-means initialisation. Inf. Sci. 370, 343–354 (2016)CrossRefGoogle Scholar
  6. 6.
    Betts, S.C., Taran, Z.: Conflicting issues and corporate social responsibility: aligning organizational efforts with stakeholder interests. J. Int. Manag. Stud. 11(3), 39–46 (2011)Google Scholar
  7. 7.
    Block, J.H., Wagner, M.: The effect of family ownership on different dimensions of corporate social responsibility: evidence from large us firms. Bus. Strategy Environ. 23(7), 475–492 (2014)CrossRefGoogle Scholar
  8. 8.
    Bosch-Badia, M.T., Montllor-Serrats, J., Tarrazon, M.A.: Corporate social responsibility from Friedman to Porter and Kramer. Theor. Econ. Lett. 3(3A), 11–15 (2013)CrossRefGoogle Scholar
  9. 9.
    Carroll, A.B.: Corporate social responsibility: evolution of a definitional construct. Bus. Soc. 38(3), 268–295 (1999)CrossRefGoogle Scholar
  10. 10.
    Clarkson, M.B.E.: A stakeholder framework for analyzing and evaluating corporate social performance. Acad. Manag. Rev. 20(1), 92–117 (1995)CrossRefGoogle Scholar
  11. 11.
    Chen, R.Y., Chen-Hsun, L.: Assessing whether corporate social responsibility influence corporate value. Appl. Econ. 49(54), 5547–5557 (2017). Scholar
  12. 12.
    Chiang, M., Mirkin, B.: Intelligent choice of the number of clusters in K-Means clustering: an experimental study with different cluster spreads. J. Classif. 27(1), 3–40 (2010)MathSciNetzbMATHCrossRefGoogle Scholar
  13. 13.
    Cochran, P.L.: The evolution of corporate social responsibility. Bus. Horiz. 50(3), 449–454 (2007)CrossRefGoogle Scholar
  14. 14.
    Fassin, Y.: The stakeholder model refined. J. Bus. Ethics 84(1), 113–135 (2009)CrossRefGoogle Scholar
  15. 15.
    Harjoto, M.A., Jo, H.: Corporate governance and CSR nexus. J. Bus. Ethics 100(1), 45–67 (2011)CrossRefGoogle Scholar
  16. 16.
    Hartigan, J.A., Wong, M.A.: Algorithm AS 136: a K-means clustering algorithm. J. Roy. Stat. Soc.: Ser. C (Appl. Stat.) 28(1), 100–108 (1979)zbMATHGoogle Scholar
  17. 17.
    Jones, E.: Bridging the gap between ethical consumers and corporate social responsibility: an international comparison of consumer-oriented CSR rating systems. J. Corp. Citizsh. 2016(65), 30–55 (2017)Google Scholar
  18. 18.
    Krüger, P.: Corporate goodness and shareholder wealth. J. Financ. Econ. 115(2), 304–329 (2015)CrossRefGoogle Scholar
  19. 19.
    Lord, E., Willems, M., Lapointe, F.J., Makarenkov, V.: Using the stability of objects to determine the number of clusters in datasets. Inf. Sci. 393, 29–46 (2017)CrossRefGoogle Scholar
  20. 20.
    Martinez, F.: Corporate strategy and the environment: towards a four-dimensional compatibility model for fostering green management decisions. Corp. Gov. 14(5), 607–636 (2014)CrossRefGoogle Scholar
  21. 21.
    Matlab: kmeans. (2018). Accessed 26 July 2018
  22. 22.
    McWilliams, A., Siegel, D., Wright, P.M.: Corporate social responsibility: a theory of the firm perspective. Acad. Manag. Rev. 26(1), 117–127 (2011)CrossRefGoogle Scholar
  23. 23.
    Mirkin, B.G.: A sequential fitting procedure for linear data analysis models. J. Classif. 7(2), 167–195 (1990)MathSciNetzbMATHCrossRefGoogle Scholar
  24. 24.
    Mirkin, B.: Choosing the number of clusters. WIREs Data Min. Knowl. Discov. 1(3), 252–260 (2011)MathSciNetCrossRefGoogle Scholar
  25. 25.
    MSCI: User Guide and ESG Ratings Definition (2011). Accessed 25 Oct 2015
  26. 26.
    Mur, A., Dormido, R., Duro, N., Dormido-Canto, S., Vega, J.: Determination of the optimal number of clusters using a spectral clustering optimization. Expert Syst. Appl. 65, 304–314 (2016)CrossRefGoogle Scholar
  27. 27.
    Peloza, J., Shang, J.: How can corporate social responsibility activities create value for stakeholders? A systematic review. Acad. Mark. Sci. J. 39(1), 117–135 (2011)CrossRefGoogle Scholar
  28. 28.
    Porter, M.E., Kramer, M.R.: Creating shared value. Harvard Bus. Rev. 89(1), 2–17 (2011)Google Scholar
  29. 29.
    Rodriguez, A., Laio, A.: Clustering by fast search and find of density peaks. Science 344(6191), 1492–1496 (2014)CrossRefGoogle Scholar
  30. 30.
    Schendler, A., Toffel, M.: The factor environmental ratings miss. MIT Sloan Manag. Rev. 53(1), 17–18 (2011)Google Scholar
  31. 31.
    Schreck, P.: Reviewing the business case for corporate social responsibility: new evidence and analysis. J. Bus. Ethics 103(2), 167–188 (2011)CrossRefGoogle Scholar
  32. 32.
    Sen, S., Bhattacharya, C.B.: Does doing good always lead to doing better? Consumer reactions to corporate social responsibility. J. Mark. Res. 38(2), 225–243 (2001)CrossRefGoogle Scholar
  33. 33.
    Weber, J., Gladstone, J.: Rethinking the corporate financial–social performance relationship: examining the complex, multistakeholder notion of corporate social performance. Bus. Soc. Rev. 119(3), 297–336 (2014)CrossRefGoogle Scholar
  34. 34.
    Zhou, S., Xu, Z., Liu, F.: Method for determining the optimal number of clusters based on agglomerative hierarchical clustering. IEEE Trans. Neural Netw. Learn. Syst. 28(12), 3007–3017 (2017). Scholar

Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  1. 1.Delta State UniversityClevelandUSA
  2. 2.Birkbeck University of LondonLondonUK
  3. 3.National Research University Higher School of EconomicsMoscowRussian Federation

Personalised recommendations