The greenwashing triangle: adapting tools from fraud to improve CSR reporting
Notice bibliographique
Résumé
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.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,010 | 0,084 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,001 | 0,000 |
| Bibliométrie | 0,000 | 0,002 |
| Études des sciences et des technologies | 0,003 | 0,000 |
| Communication savante | 0,007 | 0,003 |
| Science ouverte | 0,001 | 0,002 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».