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

  • Ioanna Kountouri
  • Eleftherios Manousakis
  • Andrianos E. TsekrekosEmail author
Original Article
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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.

Keywords

Quantitative content analysis Textual analysis Latent semantic analysis Corporate social responsibility reports Sample selection bias Greece 

Notes

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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Copyright information

© Springer Nature Limited 2018

Authors and Affiliations

  • Ioanna Kountouri
    • 1
  • Eleftherios Manousakis
    • 1
  • Andrianos E. Tsekrekos
    • 2
    Email author
  1. 1.Department of Management Science and Technology, School of BusinessAthens University of Economics and BusinessAthensGreece
  2. 2.Department of Accounting and Finance, School of BusinessAthens University of Economics and BusinessAthensGreece

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