Knowledge Management for Sustainable Credibility: The Digital Battle Against Greenwashing

ABSTRACT Organizations face growing scrutiny over their sustainability claims, making knowledge governance a critical concern in the management of corporate credibility. This study develops an analytics‐driven knowledge management framework designed to detect and mitigate greenwashing by modeling the breakdowns in codification, verification, and dissemination of sustainability‐related information. Drawing on legitimacy theory, signaling theory, and stakeholder theory, the paper conceptualizes greenwashing as a failure in organizational knowledge processes and introduces a Greenwashing Index (GWI) as a quantifiable proxy for credibility erosion. The proposed system integrates advanced digital tools—BERT‐based sentiment classification, relational recurrent extreme learning machines (RRELM), Monte Carlo uncertainty modeling, and network diffusion analytics—to capture how misleading sustainability messages spread and influence stakeholder trust. A process‐oriented approach is used to trace knowledge from acquisition and structuring to its influence on perception and decision‐making. Empirical results from real‐world data demonstrate how predictive and interpretative analytics can improve transparency, enable early risk detection, and guide governance interventions. The study contributes to the field by bridging process‐based knowledge management and digital trust monitoring, offering both a conceptual model and a practical decision‐support system. Implications for sustainable knowledge governance and process improvement are outlined for managerial practice and organizational design.