BIG DATA ANALYTICS TO SHAPE INNOVATIVE ESG SOLUTIONS THROUGH HUMAN-GENERATIVE-AI LENSES

Resumo

Are companies led by big data analytics capabilities likely to improve environmental, social, and governance (ESG) issues? This study suggests big data analytics capabilities as a booster to enhance ESG performance and disclosure – that is, we introduce and examine the strength of big data analytics capabilities to enhance ESG performance. We hypothesize and find robust evidence for a positive relationship between big data analytics capabilities and ESG performance and disclosure through the lens of generative artificial intelligence (GEN-AI) and managers of innovative multinational companies from three sectors of an emerging economy. The main findings of our study suggest that big data analytics capabilities are substantive and enhance environmental, social, and governance performance and disclosure. The results also imply that governance is more responsive to the adoption of big data analytics; while social performance was more muted. We shed new light on the relevance of big data analytics capabilities to shape ESG under a human-machine lens. Our findings therefore contribute to the conversation on the role of big data analytics capabilities in improving ESG issues in organizations by unearthing the organizational implications of big data analytics capabilities for ESG. As organizations increasingly develop analytics capabilities, ESG becomes more powerful. Our study offers a timely warning to companies that aim to make progress on ESG but remain inactive regarding big data analytics capabilities.

Biografia do Autor

Selma Regina Martins Oliveira , Universidade Federal Fluminense (UFF)

Pós-Doutorado, Faculdade de Engenharia e Ciências, Universidade de Aaborg, Dinamarca. Doutorado pela Escola de Engenharia de São Carlos/USP. Docente Associada da Universidade Federal Fluminense, Volta Redonda, Departamento de Ciências Contábeis.

Edson Walmir Cazarini, Universidade de São Paulo (USP)

Doutor em Engenharia Mecânica pela Universidade de São Paulo. Docente e pesquisador no Programa de Pós-Graduação em Engenharia de Produção da Escola de Engenharia de São Carlos, Universidade de São Paulo.

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Publicado
2024-11-12