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Externalities accounting hexalogy. Multi-entry mechanism model for adjusted integrated reporting

Piotr Staszkiewicz & PAULO ROBERTO B. LUSTOSA

Journal of Applied Accounting Research2026https://doi.org/10.1108/jaar-07-2024-0273article
AJG 2ABDC B
Weight
0.50

Abstract

Purpose This study bridges the financial and non-financial reporting (NFR) gap by incorporating externalities into accounting. It proposes a model to evaluate externalities at the transaction level and integrate them into a modified integrated reporting (IR) framework to mobilise activists' voices. Design/methodology/approach A model is developed to evaluate externalities and integrate them into an adjusted integrated reporting (AIR) framework. Six propositions for externalities accounting and reporting are formulated. Findings The study results found that reducing information asymmetry at the transaction level can foster sustainability-focused decision-making. Research limitations/implications While conceptual, the study requires empirical testing. Findings contribute to theoretical discourse and practical application in accounting and IR. Practical implications The proposed model is feasible to implement. In its simplified form, it requires monitoring input and output externality values. Originality/value This study offers a novel approach to incorporating externalities into accounting, addressing the information gap between financial and NFR.

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https://doi.org/https://doi.org/10.1108/jaar-07-2024-0273

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@article{piotr2026,
  title        = {{Externalities accounting hexalogy. Multi-entry mechanism model for adjusted integrated reporting}},
  author       = {Piotr Staszkiewicz & PAULO ROBERTO B. LUSTOSA},
  journal      = {Journal of Applied Accounting Research},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/jaar-07-2024-0273},
}

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.