Abstract
Purpose
The study was done to review the existing literature available on the theme using a popular technique known as a bibliometric review. The purpose was to explore important bibliometric trends such as geographical distribution of research; the most relevant countries and institutions and important collaboration networks, frequently published authors, the most relevant topics/research domains and relationships among these, average citations or per year, the most relevant sources, top authors’ production, authors’ impact by H index and the progression of important keywords over a period of time.
Design/methodology/approach
The study analyzed literature published in the English language from 2012 onwards that used the words “cryptocurrency”, “Ethereum” “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY. A specialized approach was followed to retrieve and analyze focused research. The data for analysis was extracted from the Scopus database and was analyzed using Biblioshiny and VOSViewer.
Findings
The study found that the countries such as the UK, Australia, China and the USA have special relevance in terms of the number of citations and collaboration networks. Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. There is a lot of interdisciplinary research on the theme.
Originality/value
The current study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class and not prominently its technological or legal aspects.
Keywords
Citation
Vasudeva, S. (2023), "Cryptocurrency as an investment or speculation: a bibliometric review study", Business Analyst Journal, Vol. 44 No. 1, pp. 34-50. https://doi.org/10.1108/BAJ-07-2022-0008
Publisher
:Emerald Publishing Limited
Copyright © 2023, Sakshi Vasudeva
License
Published in the Business Analyst Journal. Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) license. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this license may be seen at http://creativecommons.org/licences/by/4.0/legalcode
1. Introduction
1.1 Background
Cryptocurrency has been defined using a broad spectrum of technology ranging from virtual complementary currency to electronic currency and its derivative (Dai, 1998). Many cryptocurrencies are based on blockchain technology using a decentralized network of computers. Thus, cryptocurrency is a tradable digital form of money that only exists online and uses encryption to authenticate and secure transactions. Bitcoin was the first currency founded in 2009 by Satoshi Nakamoto to have gained existence in the socio-economic sphere, although the previous attempts such as e-gold in 1996 or liberty reverse in 2006 could not get acceptability (Garcia, Tessone, Mavrodiev, & Perony, 2014). There are currently over a thousand different cryptocurrencies in the world and their supporters see cryptocurrency as the “key to the fairer future economy.” (Inci, & Lagasse, 2019).
Cryptocurrencies are an unconventional/unusual, exceptional type of currency due to their decentralized nature but these cannot be used as a substitute for legal currency (Nakamoto, 2008). Cryptocurrency has become popular because of its features such as anonymity, cheaper and faster money transfers and difficulty to counterfeit or blind spending, but there are various disadvantages such as price volatility. Cryptocurrencies have gained a lot of peculiar and numismatology importance (Chohan, 2022), during their short period of existence and are studied by a wide variety of disciplines such as technical aspects like blockchain, cryptography and smart contracts (Xu, Chen, & Kou, 2019), and also from an investment point of view (Saksonova, & Kuzmina-Merlino, 2019).
The study used a concentrated approach to study the bibliometric literature about the financial implications of cryptocurrency as an asset class. Therefore, while retrieving the results, investments/speculation words have been used to narrow down the results and follow a specialised approach to analyze focused results.
1.2 Cryptocurrency as an investment or speculation
It is only in the last few years mainly towards the end of 2017; momentum was seen in cryptocurrency exchanges as well as Bitcoin and other prominent cryptocurrencies gained much attention. There has been an increase in the investment in cryptocurrency as a source of investment or speculation because of its high growth potential, highly liquid market, volatile returns, where returns may double also in a couple of days or weeks and it is no longer a niche area of investment. Bitcoin is a very volatile currency but has given good returns to investors (Böhme, Christin, Edelman, & Moore, 2015; Fauzi, Paiman, & Othman, 2020).
There is a flip side to it as it is a highly unregulated market and too much volatility may result in returns plunging too low. Not all cryptocurrencies are liquid and there is a lot of risk of money getting locked up. (Chuen, Guo, & Wang, 2017; Thukral, Arora, & Bhandari, 2018; Liew, Li, Budavári, & Sharma, 2019; Saksonova, & Kuzmina-Merlino, 2019; Mikhaylov, 2020). The greatest advantage and limitation also are that investments are anonymous and “individuals can hold, trade, or use cryptocurrencies instruments without having a direct link with a bank account or any particular investment profile” (Lammer, Hanspal, & Hackethal, 2020, p. 1).
There are various security and privacy risks of investing in Bitcoin (Conti, Kumar, Lal, & Ruj, 2018). Cryptocurrencies are the most recent financial market innovations in terms of infrastructure and support services (FinTech); the use of blockchain technology makes them distinct from traditional investments (Saksonova, & Kuzmina-Merlino, 2017).
These are considered as promising assets in the portfolio but mostly people prefer to use them for short-term gains, that is speculation (Baek & Elbeck, 2015; Baur, Hong, & Lee, 2018; Zhang, Lu, Tao, & Wang, 2021). Like, too much volatility in Bitcoin prices causes speculative bubbles (Cheah, & Fry, 2015) and thus, it has been considered as inefficient asset (Urquhart, 2016).
There is a lack of government intervention and thus, it is subjected to further price distortions; virtual currencies have also been linked to various unregulated activities, including illegal acts (Foley, Karlsen, & Putniņš, 2019; Griffin & Shams, 2020). Cryptocurrency also impacted investment in other financial assets and some researchers studied dynamic relationships (Corbet, Lucey, & Yarovaya, 2018) and volatility analysis in relation with other assets (Dyhrberg, 2016).
Crypto is a diverse subject area, therefore the current research is focused only on the literature relevant to its being an asset class and not prominently its technological and legal aspects.
2. Methodology
Bibliometric analysis is a popular and meticulous method for analysing large volumes of scientific publications. It is easy to acquire large volumes of bibliometric data using scientific databases, such as Scopus and Web of Science and the availability of various bibliometric software/tools help to perform the analysis in a logical way. The current study used the Scopus database, which is widely recognized as the largest scientific database of academic significance. The current study analysed studies that used “cryptocurrency or bitcoin or Ethereum” and “investment or speculation” in abstracts/keywords or titles. Cryptocurrency/bitcoin/Ethereum has been the oldest and the most popular words to represent “Crypto” in the published literature.
The bibliometric analysis summarizes the social and structural relationships between different research constituents (e.g. authors, countries, institutions and topics). The techniques for bibliometric analysis include performance analysis and science mapping. Performance analysis uses citation and publication-related metrics. Performance analysis studies the contributions of research constituents to a given field (Cobo, López-Herrera, Herrera-Viedma, & Herrera, 2011; Ramos-Rodrígue & Ruíz-Navarro, 2004). The techniques for science mapping include citation analysis, co-citation analysis, bibliographic coupling, co-word analysis and co-authorship analysis (Baker, Kumar, & Pandey, 2021; Cobo et al., 2011; Ramos-Rodrígue & Ruíz-Navarro, 2004).
2.1 Research objectives
The purpose of this research is to study the research articles on the theme to identify the relevant bibliometric trends such as:
To identify the geographical distribution of research and the most relevant countries and institutions researching the theme and their collaboration networks.
To find out the frequently published authors, the most relevant topics, the number of publications by country, average citations per year, the most relevant sources, top authors’ production, authors’ impact by H index and trending topics.
To identify the important research domains/topics and the relationships among them.
To find out the progression of important keywords to understand the impact of changes in economic and political environments on research trends over a period of time.
2.2 Data extraction
Earlier studies have done the bibliometric analysis of CRYPTO from its overall perspective (Dabbagh, Sookhak, & Safa, 2019; Shen, Chang, & Su, 2018; García-Corral, Cordero-García, de Pablo-Valenciano, & Uribe-Toril, 2022). Crypto is a riskier asset class with numerous drawbacks but still, it became popular because of its profitability. The current study used investment or speculation as a part of the search criterion to extract only the relevant literature and to exclude the studies which focused mainly on the other aspects of Crypto.
To perform the bibliometric performance and science mapping analysis, the following steps for data extraction were used (Figure 1):
2.3 Data analysis
Biblioshiny and VOSViewer has been used to analyze the data. Biblioshiny is a web-based graphical interface powered by Bibliometrix (Aria, & Cuccurullo, 2017). Bibliometrics was programmed in the R language and it has interconnection with other R packages. Different mapping techniques have been used (Boyack, Klavans, & Börner, 2005) in Biblioshiny. VOSViewer is a software tool based on Java programming language for creating maps for bibliometric purposes (version 1.6.18 was used). VOSViewer provides three visualizations, referred to as network visualization, overlay visualization and density visualization (Van Eck & Waltman, 2019). The three different indicators have been used for the analysis of documents: quantity, quality, structural form and other bibliographic information of publications. Quantity shows the productivity index in terms of the number of publications. Meanwhile, quality shows the impactful articles on the basis of the number of citations received.
3. Results
3.1 Preliminary bibliographic information
Scopus database search applied from 2012 to 2021 and sorted out using the criterion mentioned in the data extraction steps (Figure 1). The summary results are presented in Table 1.
We can notice the huge momentum in the overall number of publications from 2019 onwards (Figure 2).
3.2 Documents by the subject area
Our search results show that the articles are distributed across a wider range of subjects.
The subject areas of economics, econometrics and finance have got the highest ranking (29.88%), followed by business, management and accounting (14.6%) and then, followed by computer science (14.04%). The remaining articles are distributed among varied subject areas, that is, engineering, mathematics; the social sciences, decision sciences, physics and astronomy, material sciences, energy, environmental, multi-disciplinary, arts and humanities, chemicals, even agriculture, neuroscience, etc. (Figure 3).
3.3 The most cited articles
Applying the corresponding search results, we found that the top three articles that have been cited the most are on the volatility aspect of the bitcoin. The article “BitCoin meets Google Trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era” (Kristoufek, 2013) is about semantic analysis. The top-cited articles have used “bitcoin” in the title of the papers, which shows the Bitcoin was more popular as compared to the other cryptocurrencies. The most cited topics have been related to volatility/speculation, price formation/trends, security and privacy concerns, and the relationship with other financial assets/hedging capabilities of the Bitcoin (Table 2).
3.4 Geographical distribution
In terms of geographical distribution, an apparent growing trend of research is originating all over the world. (Figures 4 and 5). A minimum of three documents and three citations have been used to draw the map. The UK, Australia and China have been the front-runners in terms of grouping and citations. These countries also account for the largest number of articles and citations together with the highest H indexes. According to the map, India formed a cluster with United Arab Emirates, South Africa, Saudi Arabia, Poland, Indonesia Egypt and Malaysia. The USA and the Czech Republic are the two other notable countries in terms of grouping and citations. The UK, Australia, China and the USA clearly stand out in terms of citations and groupings.
3.5 Pivotal institutions
The highest ranking among the pivotal institutions is of “DCU Business School, Dublin City University, Dublin 9, Ireland" with a total of 14 documents, 992 citations, and 208 link strengths. This institution is followed by "Trinity Business School, Trinity College Dublin, Dublin 2, Ireland" with a total of 10 documents, 890 citations, and 204 link strengths. Again, in terms of the number of published documents, the next ranked institutions are “USEK Business School, Holy Spirit University Of Kaslik, Jounieh, Lebanon” with 11 documents, but has the highest number of citations as 1,032; “Montpellier Business School, Montpellier, France” with a total of ten documents and 737 citations, and “Department Of Economics And Finance, College Of Economics and Political Science, Sultan Qaboos University, Muscat, Oman" with a total of seven documents and 310 citations. A minimum of three documents and three citations have been used to draw the map (Figure 6). Thus, European countries, mainly Ireland and France are doing extensive research.
3.6 Top journals
Table 3 and Figure 7 shows the sources on the basis of citations. To draw the map, the minimum number of documents and citations was fixed at five. Finance Research Letters, Economics Letters, International Review of Financial Analysis and Research in International Business and Finance are the pioneer journals that have worked on the topic.
3.7 Prominent authors
Table 4 and Figure 8 show the author’s order according to the number of articles on the topic. E. Bouri, D. Roubaud and S. Corbet have been leading on the basis of number of publications, H Index, and Total Citations. Y. LI has a large number of articles but the citation is less as compared to the other three authors. Figure 9 shows that both the top authors E. Bouri and D. Roubaud have also collaborated extensively. Similarly, S. Corbet and B. Lucey have good collaboration with each other.
3.8 Trend analysis
Using the VOSViewer software and R (Bibliometrix package), a series of clusters have been identified indicating the frequency and evolution of the author’s keywords (minimum occurrence fixed as ten for identifying more frequent keywords) (Figure 10). Also, three fields plot of top keywords plus, sources and author’s keywords give us an account of trending topics (Figure 11). The results of both graphs show some similarities in terms of key concepts that are maintained over time. Cryptocurrency/Cryptocurrencies, bitcoin has been the base themes with other crucial issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc. Sankey diagram ( Figure 11) shows the relationship between sources (center), keyword plus (left) and author’s keywords (right). The size of the nodes represents the frequency of the item and lines show the connection between them.
According to Zhang et al. (2016), keyword plus is more effective than the author’s keywords in the bibliometric analysis even though these are less representative of the article’s content. Finance Research Letters have been published on varied themes. The publications of the second most influential Node, Physica a: Statistical Mechanics and Applications are closely related to concepts of “Ethereum, bitcoin, and investments” along with author’s keywords “of bitcoin, volatility, COVID-19, hedging, safe haven, gold, blockchain”. Research in International Business and Finance follows the same trend in terms of authors’ keywords. IEEE Access has a wide trend in terms of keywords plus such as “blockchain, investments, bitcoin, Ethereum” and author’s keywords as “cryptocurrency, bitcoin, blockchain, COVID-19”.
The conceptual structure map (Figure 12) Of the MCA keyword plus shows two main clusters in different colors that coincide with the driving theme of these publications. According to the conceptual structure, the research is largely on topics like forecasting, time series analysis, alternative asset classes such as gold, COVID-19, price dynamics/prediction, stock market, empirical analysis, financial system/market, internet and electronic commerce and the technological evolution such as machine learning.
3.9 Thematic map
Porter’s derivation algorithm has been used for thematic maps to reduce the number of words used in a root form. Both show that regardless of the analysis used and keywords, the central topics are bitcoin, cryptocurrency, stock market and investments, and other important themes are Ethereum, blockchain network and electronic money (Figure 13).
Figure 14 shows a thematic division into three different periods on the basis of the title of the articles. Cryptocurrency, bitcoin, network/internet and digital have been the most frequently used words in the titles of articles. From 2012 to 2015, bitcoin was the most important cryptocurrency and most of the work has been done on it during this period. From 2016 to 2020, “blockchain” picked up, and from 2020 to 2022, “COVID-19” has been used frequently in the titles of articles. Figure 15 shows a thematic division into four different periods on the basis of keywords plus.
Since 2016, cryptocurrency and investment have been used as keywords. Then, the environment, crime, blockchain technology, intelligent systems and COVID-19 became popular keywords. Lot of research is also done on crime relate aspects of investments in these assets (Turner, McCombie, & Uhlmann, 2019; Albrecht, Duffin, Hawkins, & Rocha, 2019). Based on the blockchain network analysis, this technology and smart contracts are potential areas in cryptocurrency-related studies (Xu et al., 2019; Chang, Chen, & Lu, 2019).
4. Summary and conclusions
The study analysed the bibliometric trends from 2012 onwards. Bibliometric information was extracted from the Scopus database. The search criterion was “cryptocurrency”, “Ethereum” or “Bitcoin” along with “investment/s” or “speculation/s” in the Title/ABS/KEY to get the concentrated results. The results show a positive evolution in terms of the number of articles published and citations with a growing interest and relevance in recent years (Table 1 and Figure 2). The main topics or research areas that contain concepts related to cryptocurrency are in the subject areas of economics, econometrics and finance (Figure 3).
The countries such as the UK, Australia, China and the USA, stand out in terms of citations and groupings. Asian continent is emerging as a potential researcher on the basis of geographical analysis. A more in-depth analysis shows that the main journals and authors belonging to the ranking also belong to the countries with the highest number of publications and citations (Figures 4 and 5). The pivotal institution that has focused on the theme is “DCU Business School, Dublin City University, Dublin 9, Ireland” followed by “Trinity Business School, Trinity College Dublin, Dublin 2, Ireland” on the basis of the number of documents, citations and link strengths (Figure 6). Finance Research Letters, Economics Letters, International Review of Financial Analysis and Research in International Business and Finance are the pioneer journals that have worked on the topic (Table 3).
E. Bouri, D. Roubaud and S. Corbet have been the most influential authors in terms of the number of publications, H Index and total citations ( Table 4 and Figures 7–9).
Cryptocurrency/Cryptocurrencies, bitcoin have been the base themes along with other critical issues such as volatility, hedging, COVID-19 pandemic, Ethereum, blockchain, co-integration, portfolio diversification/optimization, spillover, safe haven, investor attention, gold, etc (cluster map; Figure 10 and three fields plot; Figure 11). Interdisciplinary studies involving the use of technology, criminology, philosophy and psychology along with basic subject areas of commerce/business and finance have great potential for future researchers (conceptual structure; Figure 12).
The current study also found that the earlier studies were mostly related to the speculative nature of cryptocurrency/bitcoin. The trend of the present research is wide and varied such as diversification, linkages with other assets, hedging and investment patterns (thematic maps; Figures 13, 14 and 15). Table 2 (top ten most cited articles) shows research on areas such as risk, volatility, inefficiency, speculative bubbles, security features, price formation and hedging. The trend of the bibliometric research shows there is lot of research on Crypto as a short-term asset and its speculation benefits and other associated risks/linkages, etc. There is hardly any research where in Crypto has been studied as a long-term investment asset. Thus, Crypto might be profitable as a speculative asset but it is not suitable for long-term investment purposes.
The study has some limitations. Firstly, the field of studies is based on only one academic database and that is Scopus. Secondly, the type of document included in the analysis has been limited to articles. The use of other important databases, such as Google Scholar and Web of Sciences, could have been used for wider coverage of the articles.
There is a lot of risk in cryptocurrency as the market is highly volatile and it is subject to bubbles in cryptocurrency markets (Corbet et al., 2018; Cheah and Fry, 2015; Urquhart, 2016). Cryptocurrencies’ basic security model might not be reliable (Auer, 2019). There is ample debate on the legal nature of cryptocurrencies, as well (Fröwis & Böhme, 2017; Walch, 2019). Investors may also consider regular rebalancing of the portfolio, which could help increase its profitability (Saksonova, & Kuzmina-Merlino, 2019).
The research provides an opportunity to explore the important bibliometric trends on the theme. Future researchers can benefit in terms of identifying potential research topics. Cryptocurrency has been challenging to deal with for all the Governments across the world in spite of issuing various advisories. The author recommends that cryptocurrency is a risky asset class as explained in the paper; therefore, avoidance is the best approach for investors seeking a perspective over the long term.
Figures
Figure 1
Data extraction steps
Figure 2
Annual scientific production
Figure 3
Distribution by subject area (Pareto chart)
Figure 4
Geographical distribution
Figure 5
Grouping by country and citations
Figure 6
Cluster map of institutions on the basis of citations and total link strength
Figure 7
Cluster map of journals (sources) on the basis of citations and total link strength
Figure 8
Top author’s production over the time
Figure 9
Author cluster
Figure 10
Cluster map on the basis of the author’s keywords
Figure 11
Three fields plot on the basis of keywords plus, sources and the author’s keywords
Figure 12
Conceptual structure map
Figure 13
Thematic map
Figure 14
Thematic evolution on the basis of titles
Figure 15
Thematic evolution on the basis of keyword plus
Preliminary bibliographic information
| Timespan | 2012:2022 |
| Sources (Journals, Books, etc) | 569 |
| Documents | 1458 |
| Average years from publication | 1.91 |
| Average citations per document | 17.42 |
| Average citations per year per doc | 4.366 |
| References | 54893 |
Top ten the most cited articles
| Year | Title of the article | Authors | Journal name | No of citations |
|---|---|---|---|---|
| 2016 | Bitcoin, gold and the dollar–A GARCH volatility analysis | Dyhrberg A.H. | Finance Research Letters | 559 |
| 2016 | The inefficiency of Bitcoin | Urquhart A. | Economics Letters | 526 |
| 2015 | Speculative bubbles in Bitcoin markets? An empirical investigation into the fundamental value of Bitcoin | Cheah E.-T., Fry J. | Economics Letters | 498 |
| 2018 | Exploring the dynamic relationships between cryptocurrencies and other financial assets | Corbet S., Meegan A., Larkin C., Lucey B., Yarovaya L. | Economics Letters | 450 |
| 2018 | Bitcoin: medium of exchange or speculative assets? | Baur D.G., Hong K., Lee A.D. | Journal of International Financial Markets, Institutions and Money | 396 |
| 2018 | A survey on security and privacy issues of Bitcoin | Conti M., Sandeep K.E., Lal C., Ruj S. | IEEE Communications Surveys and Tutorials | 384 |
| 2015 | What are the main drivers of the Bitcoin price? Evidence from wavelet coherence analysis | Kristoufek L. | PLoS ONE | 382 |
| 2013 | Bitcoin meets Google Trends and Wikipedia: quantifying the relationship between phenomena of the Internet era | Kristoufek L. | Scientific Reports | 380 |
| 2016 | The economics of Bitcoin price formation | Ciaian P., Rajcaniova M., Kancs D. | Applied Economics | 379 |
| 2016 | Hedging capabilities of Bitcoin. Is it the virtual gold? | Dyhrberg A.H. | Finance Research Letters | 344 |
Source(s): Author’s own compilation from the Scopus database
Sources on the basis of total citations (top ten journals)
| Sr. no | Element | h_index | g_index | m_index | TC | NP | PY_start |
|---|---|---|---|---|---|---|---|
| 1 | FINANCE RESEARCH LETTERS | 27 | 57 | 3.857142857 | 3419 | 89 | 2016 |
| 2 | ECONOMICS LETTERS | 17 | 26 | 2.125 | 2937 | 26 | 2015 |
| 3 | INTERNATIONAL REVIEW OF FINANCIAL ANALYSIS | 18 | 35 | 3.6 | 1696 | 35 | 2018 |
| 4 | RESEARCH IN INTERNATIONAL BUSINESS AND FINANCE | 17 | 32 | 2.833333333 | 1085 | 42 | 2017 |
| 5 | PHYSICA A: STATISTICAL MECHANICS AND ITS APPLICATIONS | 16 | 29 | 2.666666667 | 874 | 31 | 2017 |
| 6 | JOURNAL OF INTERNATIONAL FINANCIAL MARKETS, INSTITUTIONS, and MONEY | 8 | 13 | 1.6 | 834 | 13 | 2018 |
| 7 | PLOS ONE | 7 | 13 | 0.777777778 | 730 | 13 | 2014 |
| 8 | APPLIED ECONOMICS | 6 | 16 | 0.75 | 707 | 16 | 2015 |
| 9 | IEEE COMMUNICATIONS SURVEYS AND TUTORIALS | 2 | 2 | 0.4 | 503 | 2 | 2018 |
| 10 | TECHNOLOGICAL FORECASTING AND SOCIAL CHANGE | 11 | 16 | 2.75 | 468 | 16 | 2019 |
Source(s): Author’s own compilation from the Scopus database using Biblioshiny
Distribution by the author
| Authors | Number of articles | Articles fractionalized | TC | H index | PY_start |
|---|---|---|---|---|---|
| BOURI E | 26 | 7.35 | 1,476 | 16 | 2017 |
| LI Y | 23 | 6.10 | 250 | 7 | 2018 |
| CORBET S | 20 | 5.50 | 1,076 | 13 | 2017 |
| ROUBAUD D | 15 | 4.07 | 1,345 | 13 | 2017 |
| ZHANG Y | 13 | 4.12 | 85 | 4 | 2019 |
| KANG SH | 12 | 3.30 | 227 | 9 | 2019 |
| TIWARI AK | 12 | 3.58 | 537 | 8 | 2015 |
| URQUHART A | 12 | 6.42 | 1,264 | 9 | 2016 |
| GUPTA R | 11 | 3.17 | 635 | 8 | 2017 |
| WANG P | 11 | 3.17 | 267 | 7 | 2018 |
| LI X | 10 | 2.59 | 154 | 6 | 2018 |
| LUCEY B | 10 | 2.47 | 980 | 8 | 2018 |
| SENSOY A | 10 | 2.72 | 193 | 7 | 2019 |
| BEKIROS S | 9 | 3.92 | 309 | 8 | 2018 |
| REHMAN MU | 9 | 3.78 | 168 | 7 | 2019 |
| SU C-W | 9 | 2.15 | 307 | 7 | 2019 |
| TAO R | 9 | 2.15 | 267 | 6 | 2019 |
| WANG S | 9 | 2.51 | 95 | 4 | 2017 |
| WANG Y | 9 | 2.64 | 220 | 5 | 2018 |
Source(s): Scopus database: author’s own compilation using R studio
References
Albrecht, C., Duffin, K. M., Hawkins, S., & Rocha, V. M. M. (2019). The use of cryptocurrencies in the money laundering process. Journal of Money Laundering Control, 22(2), 210–216. doi: 10.1108/JMLC-12-2017-0074.
Aria, M., & Cuccurullo, C. (2017). bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics, 11(4), 959–975. doi: 10.1016/j.joi.2017.08.007.
Auer, R. (2019). Beyond the doomsday economics of “proof-of-work” in cryptocurrencies. (No. 765). Bank for International Settlements. SSRN. Available from: https://ssrn.com/abstract=3331413 (accessed February 2022)
Baek, C., & Elbeck, M. (2015). Bitcoins as an investment or speculative vehicle? A first look. Applied Economics Letters, 22(1), 30–34. doi: 10.1080/13504851.2014.916379.
Baker, H. K., Kumar, S., & Pandey, N. (2021). Forty years of the journal of futures markets: A bibliometric overview. Journal of Futures Markets, 41(7), 1027–1054. doi: 10.1002/fut.22211.
Baur, D. G., Hong, K., & Lee, A. D. (2018). Bitcoin: Medium of exchange or speculative assets?. Journal of International Financial Markets, Institutions, and Money, 54, 177–189. doi: 10.1016/j.intfin.2017.12.004.
Böhme, R., Christin, N., Edelman, B., & Moore, T. (2015). Bitcoin: Economics, technology, and governance. Journal of Economic Perspectives, 29(2), 213–38. doi: 10.1257/jep.29.2.213.
Boyack, K. W., Klavans, R., & Börner, K. (2005). Mapping the backbone of science. Scientometrics, 64(3), 351–374. doi: 10.1007/s11192-005-0255-6.
Chang, S. E., Chen, Y., & Lu, M. (2019). C, Lu M‐F. Supply Chain Re-engineering using blockchain technology: A case of smart Contract-based tracking process. Technological Forecasting and Social Change, 144, 1–11. doi: 10.1016/j.techfore.2019.03.015.
Cheah, E. T., & Fry, J. (2015). Speculative bubbles in bitcoin markets? An empirical investigation into the fundamental value of bitcoin. Economics letters, 130, 32–36. doi: 10.1016/j.econlet.2015.02.029.
Chohan, U. W. (2022). Cryptocurrencies: A brief thematic review. doi: 10.2139/ssrn.3024330. SSRN. Available from: https://ssrn.com/abstract=3024330 (accessed January 2022)
Chuen, D. L. K., Guo, L., & Wang, Y. (2017). Cryptocurrency: A new investment opportunity?. The Journal of Alternative Investments, 20(3), 16–40. doi: 10.3905/jai.2018.20.3.016.
Cobo, M. J., López-Herrera, A. G., Herrera-Viedma, E., & Herrera, F. (2011). An approach for detecting, quantifying, and visualizing the evolution of a research field: A practical application to the fuzzy sets theory field. Journal of Informetrics, 5(1), 146–166. doi: 10.1016/j.joi.2010.10.002.
Conti, M., Kumar, E. S., Lal, C., & Ruj, S. (2018). A survey on security and privacy issues of bitcoin. IEEE Communications surveys & tutorials, 20(4), 3416–3452. doi: 10.1109/COMST.2018.2842460.
Corbet, S., Lucey, B., & Yarovaya, L. (2018). Datestamping the bitcoin and Ethereum bubbles. Finance Research Letters, 26, 81–88. doi: 10.1016/j.frl.2017.12.006.
Dabbagh, M., Sookhak, M., & Safa, N. S. (2019). The evolution of blockchain: A bibliometric study. IEEE Access, 7, 19212–19221. doi: 10.1109/ACCESS.2019.2895646.
Dai, W. (1998). B-money. DIALOG. Available from: http://www.weidai.com/bmoney.txt (accessed 14 January 2022).
Dyhrberg, A. H. (2016). Bitcoin, gold and the dollar–A GARCH volatility analysis. Finance Research Letters, 16, 85–92. doi: 10.1016/j.frl.2015.10.008.
Fauzi, M. A., Paiman, N., & Othman, Z. (2020). Bitcoin and cryptocurrency: Challenges, opportunities and future works. The Journal of Asian Finance, Economics and Business (JAFEB), 7(8), 695–704. doi: 10.13106/jafeb.2020.vol7.no8.695.
Foley, S., Karlsen, J. R., & Putniņš, T. J. (2019). Sex, drugs, and bitcoin: How much illegal activity is financed through cryptocurrencies?. The Review of Financial Studies, 32(5), 1798–1853. doi: 10.1093/rfs/hhz015.
Fröwis, M., & Böhme, R. (2017). In code we trust? Measuring the control flow immutability of all smart contracts deployed on Ethereum. In Data Privacy Management, Cryptocurrencies and Blockchain Technology: ESORICS 2017 International Workshops, DPM 2017 and CBT 2017, Oslo, September 14-15, 2017 (pp. 357–372). Proceedings, Springer International Publishing. doi: 10.1007/978-3-319-67816-0_20.
García-Corral, F. J., Cordero-García, J. A., de Pablo-Valenciano, J., & Uribe-Toril, J. (2022). A bibliometric review of cryptocurrencies: How have they grown?. Financial Innovation, 8(1), 1–31. doi: 10.1186/s40854-021-00306-5.
Garcia, D., Tessone, C. J., Mavrodiev, P., & Perony, N. (2014). The digital traces of bubbles: Feedback cycles between socio-economic signals in the bitcoin economy. Journal of the Royal Society Interface, 11(99), 20140623. doi: 10.1098/rsif.2014.0623.
Griffin, J. M., & Shams, A. (2020). Is Bitcoin really untethered?. The Journal of Finance, 75(4), 1913–1964. doi: 10.1111/jofi.12903.
Inci, A. C., & Lagasse, R. (2019). Cryptocurrencies: Applications and investment opportunities. Journal of Capital Markets Studies, 3(2), 98–112. doi: 10.1108/JCMS-05-2019-0032.
Kristoufek, L. (2013). BitCoin meets Google trends and Wikipedia: Quantifying the relationship between phenomena of the Internet era. Scientific Reports, 3(1), 1–7. doi: 10.1038/srep03415.
Lammer, D. M., Hanspal, T., & Hackethal, A. (2020). Who are the Bitcoin investors? Evidence from indirect cryptocurrency investments. SAFE Working Paper No. 277. Frankfurt: Leibniz Institute for Financial Research SAFE. doi: 10.2139/ssrn.3501549.
Liew, J., Li, R. Z., Budavári, T., & Sharma, A. (2019). Cryptocurrency investing examined. The Journal of The British Blockchain Association, 2(2), 1–12. doi:10.31585/jbba-2-2-(2)2019.
Mikhaylov, A. (2020). Cryptocurrency market development: Hurst method. Finance: Theory and Practice, 24(3), 81–91. doi: 10.26794/2587-5671-2020-24-3-81-91.
Nakamoto, S. (2008). Bitcoin: A peer-to-peer electronic cash system, DIALOG. Available from: http://bitcoin.org/bitcoin.pdf (accessed January 2022).
Ramos‐Rodríguez, A. R., & Ruíz‐Navarro, J. (2004). Changes in the intellectual structure of strategic management research: A bibliometric study of the strategic management journal, 1980–2000. Strategic Management Journal, 25(10), 981–1004. doi: 10.1002/smj.397.
Saksonova, S., & Kuzmina-Merlino, I. (2017). Fintech as financial innovation — the possibilities and problems of implementation. Journal of European Research Studies, 20(3), 961–973. Available from: https://econpapers.repec.org/article/ersjournl/v_3axx_3ay_3a2017_3ai_3a3a_3ap_3a961-973.htm (accessed December 2021).
Saksonova, S., & Kuzmina-Merlino, I. (2019). Cryptocurrency as an investment instrument in a modern financial market. Journal of Economic Studies, 35(2), 269–282. doi: 10.21638/spbu05.2019.20.
Shen, C. W., Chang, L. C., & Su, T. C. (2018). Research development of bitcoin: A network and concept linking analysis. Library Hi Tech, 39(2), 488–505. doi: 10.1108/LHT-10-2019-0210.
Thukral, S., Arora, A., & Bhandari, V. (2018). When economies Jitter, bitcoin flutters: Evidence from the impact of macroeconomic factors. Business Analyst SRCC, 39(1), 3–25.
Turner, A. B., McCombie, S., & Uhlmann, A. J. (2019). A target-centric intelligence approach to WannaCry 2.0. Journal of Money Laundering Control, 22(4), 646–665. doi: 10.1108/JMLC-01-2019-0005.
Urquhart, A. (2016). The inefficiency of Bitcoin. Economics Letters, 148, 80–82. doi: 10.1016/j.econlet.2016.09.019.
Van, N. J., & Waltman, L. (2019). VOSviewer manual. Leiden: Univeristeit Leiden.
Walch, A. (2019). Deconstructing “decentralization”: Exploring the core claim of cryptosystems. In Brummer, C. (Ed.), Crypto assets: Legal, regulatory, and monetary perspectives: 39-68. New York: Oxford University Press, SSRN. Available from: https://ssrn.com/abstract=3326244 (accessed January 2022).
Xu, M., Chen, X., & Kou, G. (2019). A systematic review of blockchain. Financial Innovation, 5(1), 1–14. doi: 10.1186/s40854-019-0147-z.
Zhang, J., Yu, Q., Zheng, F., Long, C., Lu, Z., & Duan, Z. (2016). Comparing keywords plus of WOS and author keywords: A case study of patient adherence research. Journal of the Association for Information Science and Technology, 67(4), 967–972. doi: 10.1002/asi.23437.
Zhang, X., Lu, F., Tao, R., & Wang, S. (2021). The time-varying causal relationship between the Bitcoin market and internet attention. Financial Innovation, 7(1), 1–19. doi: 10.1186/s40854-021-00275-9.