Emerging Technologies for Socioenvironmental Auditing: Identification of Factors, Challenges and Technologies Using Text Mining and Analysis

• Autor: Musunuru, Kamakshaiah
• Formato: artículo
• Fecha de Publicación: 2025
• Institución: Pontificia Universidad Católica del Perú
• Repositorio: Revistas - Pontificia Universidad Católica del Perú
• Lenguaje: inglés
• OAI Identifier: oai:revistaspuc:article/29424
• Enlace del recurso: http://revistas.pucp.edu.pe/index.php/Kawsaypacha/article/view/29424
• Nivel de acceso: acceso abierto
• Materia: Socioenvironmental auditing; Information systems; Big data technology; Text mining and analysis; Global challenges; Auditoría socioambiental; Sistemas de información; Tecnología de big data; Minería y análisis de texto; Desafíos globales

Both the environment and society are fundamentalaspects for businesses. Companies should consider these aspects while addressing challenges like transparency, accountability, sustainability and governance. This study aims at assessing the impact of information systems on Socioenvironmental Auditing Practices (SAP). For that purpose,it assumes that the impact of business activities on society and the environment can be audited, and the efficiency of such auditing practices depends on information systems. Datasets were mined from Scopus and Web of Science (WoS) using text mining methodology and were analyzed using the following tools: Correspondence Analysis (CA) to identify factors, challenges and technologies; Exploratory Factor Analysis (EFA) to identifyunderlying factor structures; Confirmatory Factor Analysis (CFA) to evaluate cause-and-effect relationships among factors, technologies and challenges. A few emerging technologies, viz., big data, blockchain, cloud computing solutions, machine learning, appear to have both a positive and a negative influence on SAP, but these are mostly insignificant. Challenges such as transparency, accountability, and sustainability were found to be associated with SAP. Blockchain technologiesinfluence social and environmental factors positively, whilemachine learning influencesEFAnegativelyyet its impact on CFA is positive and significant