Focal Interest
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AI Summary of Peer-Reviewed Research

This page presents an AI-generated summary of a published research paper. The original authors did not write or review this article. [See full disclosure ↓]

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Analytics framework targets greenwashing in sustainability claims

Two people in business attire work together at a wooden table with laptops and documents, examining materials in what appears to be a collaborative office or meeting environment.
Research area:Knowledge managementStrategy and ManagementCorporate Social Responsibility Reporting

What the study found

The study proposes an analytics-driven knowledge management framework for detecting and mitigating greenwashing, treating it as a breakdown in how sustainability information is codified, verified, and shared. It also introduces a Greenwashing Index (GWI) as a measurable proxy for credibility erosion.

Why the authors say this matters

The authors conclude that predictive and interpretative analytics can improve transparency, enable early risk detection, and guide governance interventions. The study suggests this is relevant to sustainable knowledge governance and process improvement.

What the researchers tested

The paper combines legitimacy theory, signaling theory, and stakeholder theory with a process-oriented model of knowledge flow from acquisition and structuring to perception and decision-making. It uses digital tools including BERT-based sentiment classification, relational recurrent extreme learning machines (RRELM), Monte Carlo uncertainty modeling, and network diffusion analytics, with empirical analysis based on real-world data.

What worked and what didn't

The abstract says the empirical results demonstrate that the proposed system can support transparency, early risk detection, and governance intervention planning. It does not report any specific failures or negative results.

What to keep in mind

The available summary does not give detailed limitations, effect sizes, or implementation conditions. The abstract also does not provide enough information to assess how the framework performs in settings beyond the real-world data used here.

Key points

  • The study proposes an analytics-driven framework to detect and mitigate greenwashing.
  • Greenwashing is framed as a failure in codification, verification, and dissemination of sustainability information.
  • The paper introduces a Greenwashing Index (GWI) as a proxy for credibility erosion.
  • The framework uses BERT-based sentiment classification, RRELM, Monte Carlo uncertainty modeling, and network diffusion analytics.
  • The abstract says the empirical results support transparency, early risk detection, and governance interventions.

Disclosure

Research title:
Analytics framework targets greenwashing in sustainability claims
Publication date:
2026-02-09
DOI:
10.1002/kpm.70030
OpenAlex record:
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AI provenance: AI provenance information is not available for this post.

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