Keywords
ESG disclosure intelligence
Intelligence cycle
Dynamic capabilities
Strategic decision-making
Sustainable Competitive Advantage
Natural language processing
Chinese listed companies
How to Cite
Abstract
Purpose: This study aims to establish and validate a Competitive Intelligence (CI)-driven ESG analytics framework capable of transforming ESG disclosure analysis from a purely computational classification exercise into a strategic intelligence mechanism for sustainable decision-making and competitive advantage.
Methodology/approach: The research adopts a strategic-intelligence approach integrating the Competitive Intelligence cycle, dynamic capabilities perspective, and natural language processing (NLP). Using a publicly available dataset containing 8,471 ESG disclosure sentences from Chinese listed companies, the study applies preprocessing, feature engineering, TF-IDF vectorization, and supervised machine learning models, including logistic regression, complement naïve Bayes, and linear support vector machines. A Competitive Intelligence Score was developed to evaluate disclosure maturity and strategic relevance.
Originality/Relevance: The study contributes by reframing ESG analytics as an organizational intelligence capability embedded within intelligence governance and strategic foresight processes. It introduces a maturity-aware CI framework linking ESG disclosure quality to sustainable competitive intelligence and decision-support systems.
Key findings: The proposed models achieved strong classification performance, with disclosure-quality classification reaching 0.882 accuracy and macro-F1 of 0.799. The Competitive Intelligence Score effectively differentiated quantitative, qualitative, and irrelevant disclosures, revealing significant evidence gaps across ESG pillars and supporting managerial benchmarking and strategic responsiveness.
Theoretical/methodological contributions: The article advances the literature by integrating Competitive Intelligence, dynamic capabilities, ESG intelligence governance, and NLP-based analytics into a unified strategic framework. Methodologically, it operationalizes ESG disclosure maturity through a reproducible intelligence-oriented analytical pipeline.
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