Knowledge Management for Sustainable Credibility: The Digital Battle Against Greenwashing

Key findings from this study

• The study found that greenwashing operates as a breakdown in organizational knowledge codification, verification, and dissemination rather than solely an intentional deception strategy.
• The authors demonstrate that machine learning and network analytics can quantify greenwashing risk through tracking message propagation and stakeholder trust erosion.
• The framework establishes that early detection of credibility risks requires simultaneous monitoring of information quality, verification gaps, and stakeholder perception dynamics.

Overview

Organizations increasingly face scrutiny over sustainability claims, creating risk to corporate credibility. The study develops an analytics-driven knowledge management framework to detect and mitigate greenwashing. It conceptualizes greenwashing as organizational knowledge process failure, where breakdowns occur in codification, verification, and dissemination of sustainability information. The framework integrates legitimacy theory, signaling theory, and stakeholder theory to explain credibility erosion.

Methods and approach

The framework employs multiple digital tools to trace sustainability information flow from acquisition through stakeholder perception. BERT-based sentiment classification identifies misleading messaging patterns. Relational recurrent extreme learning machines model complex information dynamics. Monte Carlo uncertainty modeling quantifies risk in knowledge processes. Network diffusion analytics reveal how misleading messages propagate across stakeholder networks. A Greenwashing Index operationalizes credibility erosion as a quantifiable metric. Process-oriented analysis maps knowledge from structuring to its influence on decision-making.

Results

Empirical application to real-world data demonstrates the framework's capacity to detect credibility risks and identify process vulnerabilities. The system successfully traces how sustainability messages spread through stakeholder networks and quantifies their influence on trust perceptions. Predictive analytics enabled early detection of greenwashing signals before reputational damage escalates. Interpretative analytics clarified which knowledge governance failures contributed most significantly to credibility erosion, enabling targeted intervention points.

Implications

Organizations can deploy this framework to strengthen internal governance over sustainability claims and reduce reputational risk. Early risk detection capabilities allow management to intervene before misleading information reaches broader stakeholder audiences. The system supports continuous monitoring of knowledge processes, enabling iterative improvements to verification and dissemination protocols. Integration of digital trust monitoring into organizational design shifts knowledge governance from reactive compliance to proactive credibility stewardship.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.