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Corporate social responsibility reports: topic analysis and big data approach

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  • Irina Goloshchapova
  • Ser-Huang Poon
  • Matthew Pritchard
  • Phil Reed

Abstract

This paper performs topic modeling using all publicly available CSR (Corporate Social Responsibility) reports for all constituent firms of the major stock market indices of 15 industrialized countries included in MSCI Europe for the sample period from 1999 to 2016. Our text mining results and LDA analyses indicate that ‘employees safety’, ‘employees training support’, ‘carbon emission’, ‘human right’, ‘efficient power’, and ‘healthcare medicines’ are the common topics reported by publicly listed companies in Europe and the UK. There is a clear sector bias with industrial firms emphasizing ‘employee safety’, Utilities concentrating on ‘efficient power’ while consumer discretionary and consumer staples highlighting ‘food waste’ and ‘food packaging.’ To produce these results, we used a battery of python code to organize the hundreds of reports downloaded from Bloomberg and the internet, the latest R-algorithm to estimate LDA (Latent Dirichlet Allocation) model and the LDAvis interactive tool to visualize and refine the LDA model.

Suggested Citation

  • Irina Goloshchapova & Ser-Huang Poon & Matthew Pritchard & Phil Reed, 2019. "Corporate social responsibility reports: topic analysis and big data approach," The European Journal of Finance, Taylor & Francis Journals, vol. 25(17), pages 1637-1654, November.
    • Handle: RePEc:taf:eurjfi:v:25:y:2019:i:17:p:1637-1654
    DOI: 10.1080/1351847X.2019.1572637
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    Cited by:

    1. Imperiale, Francesca & Pizzi, Simone & Lippolis, Stella, 2023. "Sustainability reporting and ESG performance in the utilities sector," Utilities Policy, Elsevier, vol. 80(C).
    2. Massimiliano Giacalone & Vito Santarcangelo & Vincenzo Donvito & Oriana Schiavone & Emilio Massa, 2021. "Big data for corporate social responsibility: blockchain use in Gioia del Colle DOP," Quality & Quantity: International Journal of Methodology, Springer, vol. 55(6), pages 1945-1971, December.
    3. Claudio Nuber & Patrick Velte, 2021. "Board gender diversity and carbon emissions: European evidence on curvilinear relationships and critical mass," Business Strategy and the Environment, Wiley Blackwell, vol. 30(4), pages 1958-1992, May.
    4. Miao Li, 2023. "Green governance and corporate social responsibility: The role of big data analytics," Sustainable Development, John Wiley & Sons, Ltd., vol. 31(2), pages 773-783, April.
    5. Nandy, Monomita & Lodh, Suman & Kaur, Jaskaran & Wang, Jin, 2020. "Impact of directors' networks on corporate social responsibility: A cross country study," International Review of Financial Analysis, Elsevier, vol. 72(C).
    6. Veltri, Stefania & Bruni, Maria Elena & Iazzolino, Gianpaolo & Morea, Donato & Baldissarro, Giovanni, 2023. "Do ESG factors improve utilities corporate efficiency and reduce the risk perceived by credit lending institutions? An empirical analysis," Utilities Policy, Elsevier, vol. 81(C).
    7. Jong Gyu Park & Kijung Park & Heena Noh & Yong Geun Kim, 2023. "Characterization of CSR, ESG, and Corporate Citizenship through a Text Mining-Based Review of Literature," Sustainability, MDPI, vol. 15(5), pages 1-12, February.
    8. Choi, Hyoung-Yong & Park, Junyoung, 2022. "Do data-driven CSR initiatives improve CSR performance? The importance of big data analytics capability," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
    9. Simona Fiandrino & Alberto Tonelli, 2021. "A Text-Mining Analysis on the Review of the Non-Financial Reporting Directive: Bringing Value Creation for Stakeholders into Accounting," Sustainability, MDPI, vol. 13(2), pages 1-18, January.
    10. Anita Mendiratta & Shveta Singh & Surendra Singh Yadav & Arvind Mahajan, 2023. "Bibliometric and Topic Modeling Analysis of Corporate Social Irresponsibility," Global Journal of Flexible Systems Management, Springer;Global Institute of Flexible Systems Management, vol. 24(3), pages 319-339, September.

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