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Latent semantic analysis of corporate social responsibility reports (with an application to Hellenic firms)

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Abstract

We propose a novel and objective statistical method known as latent semantic analysis (LSA), used in search engine procedures and information retrieval applications, as a methodological alternative for textual analysis in corporate social responsibility (CSR) research. LSA is a language processing technique that allows recognition of textual associative patterns and permits statistical extraction of common textual themes that characterize an entire set of documents, as well as tracking the relative prevalence of each theme over time and across entities. LSA possesses all the advantages of quantitative textual analysis methods (reliability control and bias reduction), is automated (meaning it can process numerous documents in minutes, as opposed to the time and resources needed to perform subjective scoring of text passages) and can be combined in mixed-method research approaches. To demonstrate the method, our empirical application analyzes the CSR reports of Hellenic companies, and first testifies that eight (five) recurring and common textual themes can explain about 50% (40%) of the variation in their CSR reports. We further establish—via cross-sectional regression with selection—that the identified themes have a statistically significant effect on reporting firms’ return-on-assets (ROA), even after controlling for factors known to explain the cross-sectional variation in ROA and the self-selectivity of firms that engage in CSR practices.

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Notes

  1. Excellent reviews of the literature on CSR and financial performance, the different proxies employed and the measurement issues involved are provided, at different points in time, by Aupperle et al. (1985), Griffin and Mahon (1997), Peloza (2009) and Taneja et al. (2011).

  2. Riffe et al. (1998, p. 20) define quantitative content analysis as “the systematic and replicable examination of symbols of communication, which have been assigned numeric values according to valid measurement rules, and the analysis of relationships involving those values using statistical methods, in order to describe the communication, draw inferences about its meaning, or infer from the communication to its context, both of production and consumption”.

  3. As in previous studies, we employ the terms ‘non-financial’ and ‘corporate social responsibility’ disclosure/reporting “synonymously, referring to accountability efforts linked to aspects of business activity that fall beyond the financial domain” (Evangelinos and Skouloudis 2014, p. 49).

  4. Notable examples of country-specific quantitative content analysis studies on CSR reporting include Parsa and Kouhy (2008) for the United Kingdom; Ellerup Nielsen and Thomsen (2007) for Demark; Vuontisjärvi (2006) for Finland; Paul et al. (2006) for Mexico; Thompson and Zakaria (2004) for Malaysia, Frostenson et al. (2011) for Sweden; Mahadeo et al. (2011) for Mauritius; Zhao (2012) for China and Russia; Gao (2011) for China, Campopiano and De Massis (2014) and Satta et al. (2015) for Italy, Saeidi et al. (2015) for Iran, Higgins et al. (2015) for Australia and Alotaibi and Hussainey (2016) for Saudi Arabia.

  5. We failed to locate any CSR report by Hellenic firms prior to 2001. Virtually all firms listed in the ASE that publish CSR reports do so in English, as more that 60% of the ASE’s capitalization is held by foreign (institutional) investors (as of May 2017; see the ASE monthly news releases).

  6. CSR Hellas is a member of the European Business Network for Corporate Social Responsibility (CSR Europe), a platform for businesses looking to enhance sustainable growth and positively contribute to society, with over 10,000 companies as members.

  7. See http://globalsustain.org for more information. The Global Reporting Initiative (GRI, https://www.globalreporting.org) is a leading organization in the sustainability field that promotes the use of sustainability reporting as a way for organizations to become more sustainable and contribute to sustainable development.

  8. The two reports of Sample year 2001 that are not plotted in Fig. 3 for clarity purposes have 6148 and 6678 words/terms respectively. Note that in the remainder, the use of terms Hellenic or Greek sample firms are to be understood as including the sample MNCs that operate in Greece.

  9. See Landauer, Foltz and Laham (1998) for an excellent review.

  10. LSA might not require human-coding or pre-determined categories of interest, but also it does not preclude it, making the method appropriate for mixed (both human and software coding) approaches to quantitative content analysis (see Lock and Seele 2015).

  11. See http://provalisresearch.com/ for details regarding the software; a recent paper by Satta et al. (2015) employs the same software in the context of voluntary corporate disclosure.

  12. For interested readers, we make our complete, modified dictionary employed in this study available upon request.

  13. The Porter (1980) stemming algorithm is inbuilt in WordStat®. More details regarding the algorithm, as well as a great stemming vocabulary can be found at http://tartarus.org/martin/PorterStemmer/.

  14. Employing common term-weighting schemes like the tf-idf (term frequency-inverse document frequency) considered in Loughran and McDonald (2011) does not affect the nature of the study’s empirical findings.

  15. Singular value decomposition is a generalized form of principal component analysis that can be used on arbitrary rectangular matrices. See Wall et al. (2003) for a thorough review, or Golub and Reinsch (1970) for a commonly used computation algorithm.

  16. It is usual in PCA or similar factor-decomposition applications to only retain the factors/themes that pass certain ‘rules of thumb’. One such ‘rule of thumb’ is to only retain the factors that individually account for 5%, and above, of the variation; another (known as the Guttmann–Kaiser criterion) is to retain any factors with eigenvalue greater than the average of all eigenvalues.

  17. The supplementary use of more than one databases is required for sample size purposes, given the small and fragmented coverage of Hellenic firms by any one database in isolation. In a few cases, values for our variables employed had to be manually collected from the annual reports.

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Acknowledgements

We are grateful to Professor Michael Alles (the editor) for insightful comments and suggestions that have greatly improved the paper. We are also grateful to Fotios Kitsios for providing detailed comments on an earlier version of the paper and to Panagiotis Mazis for discussions and suggestions on earlier drafts. The paper has benefited from comments and suggestions by participants of the 14th annual conference of the Hellenic Finance and Accounting Association (HFAA) and of the 2nd FSTEP conference at the University of Macedonia. The last author gratefully acknowledges the financial support provided by the Basic Research Funding Program of the A.U.E.B. research center (project EP–1990–01) and by project EP–1688–01 of the A.U.E.B. research center. Any errors are the authors’ sole responsibility.

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  1. Department of Management Science and Technology, School of Business, Athens University of Economics and Business, Athens, Greece

    Ioanna Kountouri & Eleftherios Manousakis

  2. Department of Accounting and Finance, School of Business, Athens University of Economics and Business, 76 Patision Str, GR10434, Athens, Greece

    Andrianos E. Tsekrekos

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  1. Ioanna Kountouri
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Appendix 1: Example of the document filtering process

Appendix 1: Example of the document filtering process

This Appendix demonstrates the CSR reports’ text preprocessing that is discussed in "Formation of the corpus and text preprocessing" section of the text, by using the 2009 report by Alpha Bank as an example.

  1. 1.

    Raw data (edited exert from Alpha Bank’s CSR report 2009)

The Bank’s guiding principles are integrity, honesty, fairness, and independence. With these values firmly in mind, we consistently strive to ensure that our transparency and operations support the comprehensive implementation of the financial sector regulatory framework.

  1. 2.

    Remove formatting and delete stop words (stop words seem marked-up)

The Bank’s guiding principles are integrity, honesty, fairness, and independence. With these values firmly in mind, we consistently strive to ensure that our transparency and operations support the comprehensive implementation of the financial sector regulatory framework.

  1. 3.

    Lemmatization

Guide principle integrity honesty fairness independence value mind strive ensure transparency operation support comprehensive implementation financial sector regulatory framework

  1. 4.

    Stemming and list the unique words in alphabetic order

Comprehens ensur fair finance framework guid honesti implement independ integr mind oper principl regulatori sector strive support transpar valu

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Kountouri, I., Manousakis, E. & Tsekrekos, A.E. Latent semantic analysis of corporate social responsibility reports (with an application to Hellenic firms). Int J Discl Gov 16, 1–19 (2019). https://doi.org/10.1057/s41310-018-0053-z

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