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Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach

1
Koppelman School of Business, Brookyn College of the City University of New York, Brookyn, NY 11210, USA
2
Gabelli School of Business, Fordham University, New York, NY 10023, USA
*
Author to whom correspondence should be addressed.
Sustainability 2020, 12(11), 4753; https://doi.org/10.3390/su12114753
Received: 7 May 2020 / Revised: 7 June 2020 / Accepted: 8 June 2020 / Published: 10 June 2020
(This article belongs to the Section Economic and Business Aspects of Sustainability)
Corporations have embraced the idea of corporate environmental, social, and governance (ESG) under the general framework of sustainability. Studies have measured and analyzed the impact of internal sustainability efforts on the performance of individual companies, policies, and projects. This exploratory study attempts to extract useful insight from shareholder sustainability resolutions using machine learning-based text analytics. Prior research has studied corporate sustainability disclosures from public reports. By studying shareholder resolutions, we gain insight into the shareholders’ perspectives and objectives. The primary source for this study is the Ceres sustainability shareholder resolution database, with 1737 records spanning 2009–2019. The study utilizes a combination of text analytic approaches (i.e., word cloud, co-occurrence, row-similarities, clustering, classification, etc.) to extract insights. These are novel methods of transforming textual data into useful knowledge about corporate sustainability endeavors. This study demonstrates that stakeholders, such as shareholders, can influence corporate sustainability via resolutions. The incorporation of text analytic techniques offers insight to researchers who study vast collections of unstructured bodies of text, improving the understanding of shareholder resolutions and reaching a wider audience. View Full-Text
Keywords: machine learning; shareholder resolution; sustainability; sustainability reporting; text analytics machine learning; shareholder resolution; sustainability; sustainability reporting; text analytics
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Raghupathi, V.; Ren, J.; Raghupathi, W. Identifying Corporate Sustainability Issues by Analyzing Shareholder Resolutions: A Machine-Learning Text Analytics Approach. Sustainability 2020, 12, 4753.

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