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Record W3023610231 · doi:10.1108/sampj-10-2018-0272

The greenwashing triangle: adapting tools from fraud to improve CSR reporting

2020· article· en· W3023610231 on OpenAlexaff
John Kurpierz, Ken Smith

Bibliographic record

VenueSustainability Accounting Management and Policy Journal · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicCorporate Social Responsibility Reporting
Canadian institutionsYork University
Fundersnot available
KeywordsGreenwashingCorporate social responsibilityAccountingSustainabilityBusinessIntegrated reportingProcess (computing)Public relationsPolitical scienceComputer science

Abstract

fetched live from OpenAlex

Purpose The purpose of this paper is to show a significant overlap in the models accounting research uses for fraud and the models other research disciplines use for greenwashing, and show how researchers and policymakers interested in the application of effective sustainability policy can draw from fraud accounting literature to better understand, and therefore, combat greenwashing. This is illustrated by showing multi-actor information-asymmetry models from other branches of accounting literature and synthesizing them with the fraud triangle model to suggest new avenues for reducing greenwashing and strengthening corporate social responsibility (CSR). Design/methodology/approach This paper reviews the current literature surrounding the greenwashing aspect of corporate camouflage compares the legal and technical definitions of fraud and synthesizes a new variant fraud triangle that more usefully describes greenwashing. Findings This paper is able to show that other areas of accounting research in North America have already tackled similar systems of multiple actors in an information-asymmetric environment and that a recurring trait is the emergence of a more robust reporting system. CSR reporting is currently in the process of emerging and could develop more swiftly by copying extant fraud-fighting tools. This is particularly salient given the increasing amount of liability legal regimes are giving to both sustainability activities and sustainability reporting from firms, as evidenced in both guidelines and scandals over the past decade. Research limitations/implications Sustainability reporting is not unique in comprising a large number of interrelated entities with non-financial information asymmetry between actors. Previous researchers have encountered similar situations in government accounting and public administration and developed network models to study these relationships as a result. In government accounting, this led to the development both of better diagnostic tools for further research and better models for local governments to use to prevent fraud and malfeasance. This paper suggests that using such research methods in the area of CSR will allow for the development of similarly-useful tools and models. Practical implications Visualizing greenwashing as a form of fraud allows policymakers to use tools from the fraud-fighting literature to improve CSR reporting and produce a more robust regime in the future. As governments increasingly seek to respond effectively to material misstatements with an intent to deceive in sustainability reports, understanding the underlying information asymmetry as it is found in other private-public interfaces is critical. Similarly, researchers can analyze CSR reporting through the lens of fraud researchers to gain novel insights into how information asymmetry in CSR reporting works. Social implications Greenwashing is not traditionally seen as a form of fraudulent reporting, even though it often meets the same technical test used to determine fraudulent reporting. The realization that the two are structurally similar allows the authors to better understand how CSR reporting works and how CSR reporting can be falsified. By understanding the latter, governments, firms and non-governmental organizations (NGOs) can develop tools to prevent CSR reporting from being falsified. Originality/value This paper suggests a new suite of tools with which to study greenwashing, and with which to fight greenwashing in a sustainability accounting context.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.068
metaresearch head score (Gemma)0.192
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: Reporting · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.360

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0680.192
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0140.008
Science and technology studies0.0050.018
Scholarly communication0.0200.035
Open science0.0060.021
Research integrity0.0050.009
Insufficient payload (model declined to judge)0.0090.003

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.036
GPT teacher head0.292
Teacher spread0.256 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

Study designTheoretical or conceptual
DomainReporting
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations135
Published2020
Admission routes1
Has abstractyes

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