Palavras-chave
Inteligência de disclosure ESG
Ciclo de inteligência
Capacidades dinâmicas
Tomada de decisão estratégica
Vantagem competitiva sustentável
Processamento de linguagem natural
Empresas chinesas listadas
Como Citar
Resumo
Objetivo: Este estudo tem como objetivo estabelecer e validar um framework de analytics ESG orientado por Competitive Intelligence (CI), capaz de transformar a análise de disclosure ESG de um exercício puramente computacional em um mecanismo estratégico de inteligência para suporte à decisão sustentável e vantagem competitiva.
Metodologia/abordagem: A pesquisa adota uma abordagem de inteligência estratégica integrando o ciclo de Competitive Intelligence, a perspectiva de capacidades dinâmicas e técnicas de processamento de linguagem natural (NLP). Utilizando um dataset público contendo 8.471 sentenças ESG de empresas chinesas listadas, o estudo aplica pré-processamento, engenharia de atributos, vetorização TF-IDF e modelos supervisionados de machine learning, incluindo regressão logística, complement naïve Bayes e máquinas de vetor de suporte lineares. Foi desenvolvido um Competitive Intelligence Score para avaliar maturidade e relevância estratégica dos disclosures.
Originalidade/Relevância: O estudo contribui ao reposicionar ESG analytics como capability organizacional de inteligência integrada à governança da inteligência e aos processos de strategic foresight. O framework proposto conecta qualidade de disclosure ESG à inteligência competitiva sustentável e aos sistemas de suporte à decisão.
Principais resultados: Os modelos apresentaram desempenho robusto, alcançando acurácia de 0.882 e macro-F1 de 0.799 na classificação da qualidade dos disclosures. O Competitive Intelligence Score diferenciou efetivamente disclosures quantitativos, qualitativos e irrelevantes, evidenciando gaps de maturidade informacional entre os pilares ESG.
Contribuições teóricas/metodológicas: O artigo integra Competitive Intelligence, capacidades dinâmicas, governança da inteligência ESG e analytics baseados em NLP em um framework estratégico unificado. Metodologicamente, operacionaliza a maturidade dos disclosures ESG por meio de um pipeline analítico reproduzível orientado ao ciclo de inteligência.
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