A Diagnostic Model for Assessing Class and Cultural Dynamics in Corporate Green Claims

• Sunday Sunday

  Ladoke Akintola University of Technology

  Author

Corporate green claims have proliferated amid tightening ESG disclosure regimes, yet existing diagnostic tools treat firms as homogeneous entities, overlooking how social class position and cultural context shape both claim production and audience reception. This study addresses that gap by developing and validating a diagnostic model that integrates class-based and cultural variables into green claim assessment. Using a design-based research approach, we constructed a composite index from firm-level disclosure data (n = 412) and culturally coded claim samples drawn from sustainability reports across three institutional contexts. The model combines natural language processing with weighted socio-structural indicators, including income-tier market exposure, cultural framing alignment, and discursive costliness. Validation against expert-coded benchmarks yielded 89.4% classification accuracy (F1 = 0.87), outperforming disclosure-only baselines by 14.2 percentage points. Feature importance analysis identified cultural-semantic misalignment and class-targeted vagueness as the strongest predictors of problematic claims. The study contributes a replicable diagnostic framework that moves beyond textual greenwashing detection toward a socio-culturally situated model of claim credibility. Practically, it offers regulators and practitioners a structured instrument for identifying claims whose risk profile is elevated by class and cultural dynamics rather than by linguistic markers alone.

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