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Measuring accountable information in CSR reports: A new operationalization and analysis applied to GHG disclosures
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-2952-7327
Dalarna University, School of Technology and Business Studies, Microdata Analysis.ORCID iD: 0000-0003-2317-9157
2020 (English)Report (Other academic)
Abstract [en]

The paper contributes to the debate if voluntary nonfinancial disclosures, such as greenhouse gas disclosures in corporate social responsibility reporting, exhibit accountability or are merely greenwashing. If firms exhibit accountability, does their actions translate into observable impacts, e.g., as country-level real changes in GHG emissions? How do contextual factors affect accountable disclosures in CSR reporting? To answer these questions, we develop a novel measure to classify accountable information of GHG disclosures in CSR reporting. We operationalize the measure using natural language processing tools, such as collocation analysis, regular expressions, and text mining. Statistical models were used to carry out aggregate analysis to detect real effects in GHG emissions reductions and firm-level analysis to investigate how institutional factors affect accountable GHG disclosures. We find that firms headquartered or reporting in a civil-law legal-environment disclose significantly higher accountable information compared to those in a common-law legal-environment. However, there is a negative trend in accountability worldwide, and firm-level accountability in GHG disclosures is not detectable in a country-level reduction of GHG emissions. The results are robust for various alternative model specifications, and operationalization of the developed measure achieved high concordance when investigated on random samples manually. Compared to most past studies, we work with a significantly larger sample of 4459 reports across 82 countries, thereby dealing with greater complexity and leading to better generalizability. In addition, developing an approach that is many folds scalable and makes replicability straightforward. This is also one of the few studies to move beyond the usual “bag-of-words” approach in classifying voluminous corporate disclosure using NLP techniques.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2020. , p. 40
Series
Working papers in transport, tourism, information technology and microdata analysis, ISSN 1650-5581 ; 2020:02
Keywords [en]
greenhouse gas, accountability, text mining, CSR, sustainability
National Category
Business Administration
Research subject
Research Profiles 2009-2020, Complex Systems – Microdata Analysis
Identifiers
URN: urn:nbn:se:du-34935OAI: oai:DiVA.org:du-34935DiVA, id: diva2:1463555
Available from: 2020-09-02 Created: 2020-09-02 Last updated: 2021-11-12Bibliographically approved
In thesis
1. Corporate Disclosures Regulations: Social Solution or a Problem?
Open this publication in new window or tab >>Corporate Disclosures Regulations: Social Solution or a Problem?
2021 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Regulations are argued to have the answer to solving various social and economic problems that society faces today (e.g., climate change, tax evasion, etc.). However, regulations may instead become the problem (e.g., overregulation). The central research question of this doctoral thesis is “are corporate disclosures regulations a social solution or a problem?” 

To answer the central research question, Papers I and II examine the economic effects of an EU-wide audit reform, the Annual Accounts Directive: 2013/34/EU, on firms and the society. Papers III, IV, and V examine firm behavior to assess the need for public regulation of nonfinancial reporting in the light of an EU-wide reform, the Nonfinancial Reporting Directive: 2014/95/EU, commonly known as the NFRD.

The thesis posits that the current implementations of these reforms in some settings are imperfect and thus costly for the firms and society. It recommends deregulation of the monitoring of financial disclosure, i.e., to allow more small firms the option of deciding if an audit is beneficial for them or not. On the other hand, recommends a different approach for regulating nonfinancial reporting, e.g., sustainability reporting. For instance, regulations that can influence firms’ governance structure, e.g., board diversity. A firm with a diverse board is more likely to adopt a sustainability agenda which is better aligned with the expectations of the EU regulators. 

Stakeholders use firms’ disclosures to evaluate its performance and behavior for various decision making. For example, shareholders, in their investing or divesting decisions; analysts, in making various forecasts and recommendations; or governments, in assessing the need for reforms. Historically, stakeholders commonly used financial information for these types of decision making. Hence, there are well established generic measures to evaluate firms’ financial information (e.g., earnings quality measures and financial-statement ratios). Nowadays, stakeholders are increasingly using firms’ sustainability related information in their decision-making process as well. However, replicable and scalable generic measures to evaluate such information are missing. This thesis develops objective approaches and a generic measure, to evaluate firms’ sustainability related disclosures. The developed approaches for analyzing unstructured text data may be applied to other fields that can benefit from the use of natural language processing tools.

Place, publisher, year, edition, pages
Borlänge: Dalarna University, 2021
Series
Dalarna Doctoral Dissertations ; 17
Keywords
audit choice, audit regulations, corporate governance, corporate sustainability, EU-wide accounting reforms, firm growth, greenhouse gas emissions, machine learning, microdata analysis, natural learning processing, new institutional economics, nonfinancial reporting, survey
National Category
Business Administration Computer and Information Sciences
Identifiers
urn:nbn:se:du-38100 (URN)978-91-88679-16-1 (ISBN)
Public defence
2021-11-12, Room 311, Borlänge, 13:00 (English)
Opponent
Supervisors
Available from: 2021-10-18 Created: 2021-09-14 Last updated: 2023-08-17Bibliographically approved

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Huq, Asif MCarling, Kenneth

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CiteExportLink to record
Permanent link

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Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • chicago-author-date
  • chicago-note-bibliography
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf