A Textual Analysis of US Corporate Social Responsibility Reports.
We employ computer‐based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002–2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in...
| Publicado en: | Abacus Vol. 56; no. 1; pp. 3 - 35 |
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| Autores principales: | , , , , , |
| Formato: | Artículo |
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Wiley-Blackwell
Mar2020
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| Materias: | |
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=hlh&AN=142312637&site=ehost-live header: @attributes: shortDbName: hlh uiTerm: 142312637 longDbName: Humanities International Complete uiTag: AN controlInfo: bkinfo: jinfo: jid: 00013072 AUB jtl: Abacus issn: 00013072 maglogo: Y pubinfo: dt: Mar2020 vid: 56 iid: 1 pid: 480 pub: Wiley-Blackwell artinfo: ui: 142312637 10.1111/abac.12182 ppf: 3 ppct: 32 formats: fmt: – @attributes: type: T – @attributes: type: C – @attributes: type: P size: 472KB tig: atl: A Textual Analysis of US Corporate Social Responsibility Reports. aug: au: Clarkson, Peter M. Ponn, Jordan Richardson, Gordon D. Rudzicz, Frank Tsang, Albert Wang, Jingjing affil: UQ Business School, University of Queensland and the Beedie School of Business, Simon Fraser University Department of Computer Science, University of Toronto Joseph Rotman School of Management, University of Toronto Li Ka Shing Knowledge Institute, St Michael's Hospital and Surgical Safety Technologies Incorporated and Department of Computer Science, University of Toronto and Vector Institute for Artificial Intelligence School of Accounting and Finance, Hong Kong Polytechnic University su: Social accounting Content analysis Social responsibility of business Computational linguistics Capital market sug: subj: Social accounting Content analysis Social responsibility of business Computational linguistics Capital market keyword: CSR reports CSR type revelation Textual analysis Valuation ab: We employ computer‐based textual analysis to examine disclosure patterns for a sample of US corporate social responsibility (CSR) reports from the period 2002–2016. Starting from 466 features commonly used in computational linguistics, our results show that the linguistics or disclosure patterns in CSR reports can be used to accurately predict the actual CSR performance type of CSR reporters. Specifically, we find that the two most commonly used disclosure characteristics, number of words and number of sentences, alone can be used to predict reporting firms' CSR performance type with 81% accuracy. The accuracy of prediction increases to 96% when the top 50 linguistics features most relevant to firms' CSR performance are included in the prediction model. In addition, we find that the linguistic features of CSR disclosure identified by our study are incrementally value relevant to investors even after controlling for the actual CSR performance score from the professional CSR rating agencies. This finding suggests that the linguistic features of CSR disclosure can be an important venue for capital market participants in evaluating firms' CSR performance type, especially when professional CSR performance ratings are not available. pubtype: Academic Journal doctype: Article src: R language: English refInfo: copyright: @attributes: flag: Y custom: Copyright of Abacus is the property of Wiley-Blackwell and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. item: Abacus holder: Wiley-Blackwell dt: @attributes: year: 2020 holdings: @attributes: islocal: N |
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