Bibliometric Characteristics of Cryptocurrency through Citation Network Analysis


  • Reganti Lavanya Vellore Institute of Technology Author
  • Rajesh Mamilla Vellore Institute of Technology Corresponding Author



Bibliometric analysis, Cryptocurrency, Blockchain, Electronic money, Internet of things


Purpose: This research aims to investigate the bibliometric characteristics of existing studies published in Scopus-indexed journals to comprehend the most recent movements in Cryptocurrency publishing, the primary themes in cryptocurrency, and factors impacting them, and analyze the Citation network of the cryptocurrency based on the previously published Scopus indexed journals.

Cryptocurrencies have emerged as a new asset class, and individuals and organizations often face investment decisions related to cryptocurrencies. Decision science techniques, such as risk analysis, portfolio optimization, and behavioral economics, can be applied to assess the risks and potential returns associated with cryptocurrency investments. These techniques help investors make informed decisions based on risk tolerance, financial goals, and market conditions.

Design/Methodology: Both citation network analysis and author co-citation analysis are used in our analysis based on Cryptocurrency articles written in 2019 from 2013 to 2022 with top titles including Current Issues in Auditing and International Review of Financial Analysis to find both keywords and track Bitcoin-related cited works. The research also uses VOS Viewer and R in the analysis to visualize both data networks and spread

Findings: In our analysis, we find that Cryptocurrency, Cryptocurrency, Blockchain, and Distributed Ledger Technology were the most commonly-used terms, studying Cryptocurrency in emerging nations is difficult due to significant social disparities and poor reading levels, Guglielmo Maria Caporale is the most cited author in this area with three works and 147 Citations up to the moment of our study, the most Cryptocurrency-focused institution is the Sumy State institution in Ukraine, which has produced two papers up to the moment of our study, and India gets the most publications in the area, followed by the United States, China, the United Kingdom, Russia, and so on, and the United Arab Emirates comes in last place regarding cryptocurrency-related publications. Our study claims no consensus on defining cryptocurrency, leading authors to use varying terms, words such as cryptocurrency, blockchain, cryptocurrency, and distributed ledger technology are often used equally, and cryptocurrency research has had a significant uptick over the past decade and is increasing at an impressive rate.

Practical Implications: The research findings have several practical implications for cryptocurrency, specifically regarding Bitcoin patterns that benefit researchers, policymakers, and stakeholders. For instance, researchers can utilize the study's information to explore new areas and understand the evolving landscape of cryptocurrency research. Policymakers can leverage the insights to develop effective policies and initiatives, enhancing regulation, risk assessment, and consumer protection in cryptocurrency ecosystems. The research contributes to a broader understanding of cryptocurrency's dynamics, societal impact, and implications for finance, economy, and society. This knowledge aids informed decision-making for individuals, businesses, and organizations involved with cryptocurrency.

Originality: Cryptocurrency citation network analysis has been used in this study. It explains why some writers are cited extensively around the globe while others are cited so rarely locally. Indications are that the worldwide citation is much greater. However, the number of local to worldwide citations is low and could be improved by prioritizing better local citations. Therefore, the study concludes that scholarly interest in cryptocurrency is new and has excellent potential for future research.


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How to Cite

LAVANYA, R., & Mamilla, R. (2023). Bibliometric Characteristics of Cryptocurrency through Citation Network Analysis. Advances in Decision Sciences, 27(2), 46-74.