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

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.