Olasiuk, Hanna Petrivna, Kumar, Sanjeev, Singh, Sudhanshu, Nagpal, Gaurav and Ganushchak, Tetiana (2024) Thematic Clustering of Green Banking Research Using Topic Modelling and Text Mining: A Machine Learning Approach. In: 2023 Fourth International Conference on Smart Technologies in Computing, Electrical and Electronics (ICSTCEE), 08-09 December 2023, Bengaluru, India.
Full text not available from this repository. (Request a copy)Abstract
Green banking is pivotal in aligning the financial sector with sustainability objectives. It plays a crucial role in mitigating environmental risks and fostering responsible banking practices with the potential for long-term economic benefits. In the last decade, there has been substantial growth in research on green banking. However, a notable gap exists in comprehensive reviews that consolidate and clarify the core themes in this field. To bridge this gap, the study combines bibliometric analysis and structural topic modelling, delving into a dataset of 169 articles from Elsevier’s Scopus database, covering 2012 to 2023. This study focuses on green banking, identifying key themes, scrutinising publication trends, and recognising influential journals. Notably, "Sustainability (Switzerland)" stands out as the leading source, contributing 9 articles, equivalent to around 5.3% of the total articles. These research findings offer valuable insights into the evolving landscape of green banking research. The outcomes of structural topic modelling reveal three distinct thematic clusters: i) Green banking and sustainability, ii) The role of technology and customer perceptions in green banking, and iii) Financial performance and regulations in green banking. Future green banking research will centre on assessing economic effects, enhancing transparency, and promoting sustainability through new financial tools and technology like blockchain and AI. The emphasis will be on policy frameworks, standardised metrics, and interdisciplinary research to encourage eco-friendly banking practices. These results enrich the comprehension of green banking by highlighting vital themes and crucial keywords. They hold significant relevance for diverse audiences, encompassing scholars, policymakers, financial institutions, and professionals in the industry.
Item Type: | Conference or Workshop Item (Paper) |
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Keywords: | Machine Learning | Text Mining | Structural Topic Modelling | Green Banking | Sustainability |
Subjects: | Social Sciences and humanities > Business, Management and Accounting > Management of Technology and Innovation Social Sciences and humanities > Economics, Econometrics and Finance > Banking and Finance Social Sciences and humanities > Social Sciences > Social Sciences (General) |
JGU School/Centre: | Jindal Global Business School |
Depositing User: | Subhajit Bhattacharjee |
Date Deposited: | 16 Aug 2024 07:22 |
Last Modified: | 17 Aug 2024 10:22 |
Official URL: | https://doi.org/10.1109/ICSTCEE60504.2023.10585113 |
URI: | https://pure.jgu.edu.in/id/eprint/8279 |
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