ESG sustainable technologies literature review using cluster-based methods

Pressacademia Pub Date : 2024-02-01 DOI:10.17261/pressacademia.2023.1862

M. Alqudah, Laura Sierra‐García, M. García-Benau

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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

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使用基于群集的方法对 ESG 可持续技术进行文献审查

目的--本研究旨在解决环境、社会和治理（ESG）领域可持续技术知识体系中的现有差距。为此，本研究采用了严格的系统性综述方法，并使用 VOSviewer 和 Biblioshiny 软件分析了 ABS 2023 期刊列表中的 1603 篇论文的综合数据集，这些论文涵盖了各个知识领域。方法论--本研究采用基于书目耦合分析的聚类方法，对多方面的 ESG 领域进行了系统分析。这种方法揭示了 ESG 文献独特集群中错综复杂的网络关联。研究发现了四个重要的文献集群：可持续金融技术（FinTech）、可持续人工智能（AI）、可持续大数据和可持续加密货币。这些集群分别是可持续金融技术（FinTech）、可持续人工智能（AI）、可持续大数据和可持续加密货币。每个集群代表了环境、社会和公司治理的一个特定方面，并为其各自的发展和相互关系提供了宝贵的见解。它不仅突出了主要的文献集群，还提供了定性分析，为研究人员探索 ESG 文献中的研究机会和特定领域提供了路线图。通过确定这些文献集群及其关系，本研究有助于在 ESG 领域内推动可持续技术的发展，并为这一不断发展的领域的未来研究奠定基础：ESG、可持续性、金融技术、人工智能、大数据、加密货币、聚类分析：G34, G38, G39

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