ESG sustainable technologies literature review using cluster-based methods

Abstract

Purpose- This study aims to address the existing gaps in the body of knowledge on sustainable technologies in the Environmental, Social, and Governance (ESG) field. It achieves this by employing a rigorous systematic review methodology and using VOSviewer and Biblioshiny software to analyze a comprehensive dataset of 1,603 papers from the ABS 2023 journal list, covering various knowledge domains. Methodology- This research undertakes a systematic analysis of the multifaceted ESG domain using a cluster-based approach rooted in bibliographic coupling analysis. This methodology reveals intricate network associations within distinct clusters of ESG literature. The study identifies four significant literature clusters: sustainable financial technology (FinTech), sustainable artificial intelligence (AI), sustainable big data, and sustainable cryptocurrency. Findings- The analysis conducted in this study reveals four distinct clusters of literature within the ESG field, shedding light on the interconnectedness of these areas. The identified clusters are sustainable financial technology (FinTech), sustainable artificial intelligence (AI), sustainable big data, and sustainable cryptocurrency. Each cluster represents a specific facet of ESG and provides valuable insights into its individual development and interrelationships. Conclusion- This research offers valuable insights into the state of knowledge in the ESG field, providing a well-structured understanding of the ESG landscape. It not only highlights the key clusters of literature but also offers a qualitative analysis, providing researchers with a roadmap for exploring research opportunities and specific areas within the ESG literature. By identifying these clusters and their relationships, this study contributes to the advancement of sustainable technologies within the ESG domain and sets the stage for future research in this evolving field. Keywords: ESG, sustainability, financial technology, artificial intelligence, big data, cryptocurrency, Cluster analysis. JEL Codes: G34, G38, G39