Unravelling the global landscape of Bitcoin research: insights from bibliometric analysis

ABSTRACT

Bitcoin has been gaining increasing attention in academia and industry. This article investigates Bitcoin’s research status and evolution via bibliometrics using a dataset of 3,873 publications between 2012 and 2022 from the Web of Science Core Collection. The findings reveal a significant increase in research on Bitcoin since 2017, coinciding with the cryptocurrency bull market. The article identifies publication trends, influential contributors, collaboration networks, and topics evolution in Bitcoin research. The three Bitcoin research stages are conceptualisation and fundamentals of Bitcoin (2012–2016), cryptocurrency and market efficiency (2017–2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019–2022). The four prominent emerging areas for future Bitcoin research are decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. The article offers valuable insights for researchers, policymakers, and practitioners, facilitating a better understanding of the status quo of Bitcoin research.

KEYWORDS:

• Bitcoin
• blockchain
• cryptocurrency
• bibliometric analysis
• systematic review

JEL classification:

• G1
• O3

1. Introduction

1.1. Background

Bitcoin, the world's first decentralised digital currency, is introduced by an anonymous individual or group using the pseudonym Satoshi Nakamoto (2008). Bitcoin’s first block, or the Genesis Block, is mined on January 3, 2009. Bitcoin received limited attention during its early days, with only a few transactions. Over time, Bitcoin has gained increasing attention from researchers, individual investors, financial institutions, and policymakers, due to its disruptive potential. The interdisciplinary nature of Bitcoin has led to articles from diverse fields, e.g. computer science, business, management, economics, finance, law, and sociology, etc.

Bibliometrics is a quantitative approach that has been widely adopted to assess the development and dynamics in a research field. Bibliometrics assumes publications are indicative of intellectual contributions, and their citation patterns reflect the influence and interconnectedness of ideas within a given field. This means that bibliometrics does not problematise research phenomena as proposed by Alvesson and Sandberg (2011), since assumptions are not challenged and biases are controlled for.

Following a bibliometric analysis approach, the authors formulated the following research questions (Alvesson and Sandberg 2011). Why is Bitcoin research relevant and interesting? What is the distribution pattern of publications and citations in Bitcoin research? What are the impactful countries/regions, institutions, researchers contributing to the Bitcoin research. What is the collaboration network in Bitcoin research? What are the influential publications and who are the influential authors in Bitcoin research? What are the growth patterns, interdisciplinary collaboration, and key thematic trends within the Bitcoin research landscape? How might the findings inform emerging topics and future research directions in the context of the rapidly evolving Bitcoin research? How does Bitcoin research interact with finance research more generally?

1.2. Contribution

The article contributes to the literature by conducting a comprehensive bibliometric analysis of 3,837 publications within the global research landscape on Bitcoin research. This article’s generalizability reflects these 3,837 publications’ breadth, depth and scope. The article’s theorisation and problematization are as follows: Participants of Bitcoin, i.e. researchers, individual Bitcoin holders, financial institutions, and regulators can through this article get a visualised overview of Bitcoin research. First, the article is beneficial for researchers to better understand the evolution and status quo of Bitcoin research and provide insights into the trajectory of choosing valuable research topics. Second, the article illustrates the evolution of Bitcoin publications, the distribution of disciplines and journals, the top cited articles, and the most prolific authors, institutions, and countries. Third, the article presents the Bitcoin research collaboration networks among authors, institutions, and countries/regions through co-authorship analysis. Their geographical distribution. The analysis can serve as a foundation for future research collaborations and knowledge exchange in the field of Bitcoin. Fourth, by analysing the keywords co-occurrence in Bitcoin research, the article identifies under-researched areas and potential avenues for future research, e.g. decentralised finance, non-fungible tokens, clean energy, and monetary policy. Overall, the article provides a comprehensive overview of the global research landscape on Bitcoin, thus contextualising Bitcoin research. It provides insights into the evolution, trend patterns, and future research efforts of Bitcoin research.

2. Literature

The literature commonly explores the economic and financial aspects of Bitcoin, including Bitcoin price dynamics (Wang and Hausken 2022a), the competition between fixed-supply like Bitcoin and variable-supply currency like central bank digital currency (Wang and Hausken 2022b, 2022c), governmental taxation involves Bitcoin and national currency (Wang and Hausken 2021b), market efficiency (Kurihara and Fukushima 2017; Urquhart 2016), and volatility and return (Dyhrberg 2016a; Katsiampa 2017). Researchers investigated the relationship between Bitcoin and traditional financial markets (Matkovskyy and Jalan 2019; Matkovskyy, Jalan, and Dowling 2020), the role of Bitcoin as an alternative investment asset (Al Mamun et al. 2020; Paule-Vianez, Prado-Román, and Gómez-Martínez 2020), and investment method (Mikhaylov 2020; Wang and Hausken 2023a). Furthermore, research has explored the potential of Bitcoin for international trade (Ganne 2018), and financial inclusion (Mavilia and Pisani 2020), and illegal activities like money laundering (Van Wegberg, Oerlemans, and Van Deventer 2018), and regulatory challenges associated with Bitcoin adoption (Cumming, Johan, and Pant 2019), and blockchain technology adoption in developed relative to developing countries (Bhimani, Hausken, and Arif 2021), monetary policy implications (Schilling and Uhlig 2019).

Wang et al. (2021) apply bibliometric analysis to examine blockchain-related research. They identify the evolution of blockchain research and propose emerging topics for future research. Yu, Yin, and Wang (2023) investigate the status quo and dynamic of quantitative finance research using a dataset of 2,723 publications. Merediz-Solà and Bariviera (2019) conduct a bibliometric analysis of Bitcoin based on 1,162 articles during 2012–2019.

There are ongoing debates on review research. For example, Kunisch et al. (2023) introduces review research as a distinct category of research inquiries that leverages prior research as data sources to generate knowledge contributions for academia, practice, and policy. They emphasise the significance of aligning the purpose and methods in various forms of review research for ensuring high quality. Antons et al. (2023) emphasise the need for systematic and rigorous literature reviews while recognising the limitations of human researchers’ processing capabilities. They introduce and define computational literature reviews (CLRs) as a novel approach for conducting review research. This article contributes to the literature by conducting a comprehensive bibliometric analysis to provide insights into the Bitcoin research.

3. Data and methodology

Humankind deserves Bitcoin research, and finance research more generally, to create sound foundations for a healthy financial system. The extensive publications on Bitcoin research deserve a systematic analysis to present the status quo of the Bitcoin research and find directions for future research. For this purpose, we first proceed with data collection.

3.1. Data collection

This article employs the Web of Science Core Collection (WoS) to retrieve publication records. This article adopts a topic search (TS), i.e. the query formula TS = ‘Bitcoin’ is applied in the WoS, where TS covers title, abstract, author keywords, and keyword plus. A broad search query is preferable in bibliometric analysis for two reasons. First, it encompasses potentially relevant publications that may challenge or expand the existing knowledge on the topic. Second, irrelevant topics can be reduced or removed in bibliometric analysis using CiteSpace. The following steps are taken to refine the dataset. First, the publication date range is set from January 1, 2,008 to December 31, 2022. Second, the document type is limited to article, early access, and proceeding paper. Third, the duplicate removal function in CiteSpace is applied to eliminate duplications. Finally, a dataset of 3,837 publications about Bitcoin (downloaded on February 15, 2023) is collected.

3.2 . Methodology

The article begins with a statistical analysis to explore the dataset. Statistical analysis provides an overview of constituents, e.g. authors, institutions, countries, and their contributions within a research field. Thereafter, the article proceeds with bibliometric analysis, which focuses on the relationships and connections among research elements, e.g. authors, institutions, countries, topics.

Alternatives to bibliometric analysis are altmetrics, expert opinions, patent analysis, meta-analysis, text mining, machine learning, and artificial intelligence analysis, computational literature reviews, and various quantitative analysis and usage statistics. This article focuses on bibliometric analysis because of its common use and authors believe its explanatory power. Undertaking a systematic literature review on an individual basis for such a vast dataset would be practically unfeasible. Bibliometrics helps to examine the evolution, trends, and interdisciplinary collaborations within the vast and rapidly evolving Bitcoin field. The choice aligns with the article's objective of providing a comprehensive overview, as bibliometric analysis enables the assessment of publication patterns, citation networks, and thematic trends on a global scale. The article applies a bibliometric analysis approach due to its inherent capacity to systematically map and analyse tons of publications in the Bitcoin research filed.

4. Descriptive analysis

4.1. Overall growth

The dataset contains 3,837 publications, which are distributed in 1,024 journals. 8,390 authors from 7,503 institutions contribute to the publications in Bitcoin. The Bitcoin white paper, i.e. Bitcoin: A peer-to-peer electronic cash system was written by Nakamoto (2008). Notably, the WoS does not include the Bitcoin white paper. The Bitcoin publications increase steadily during 2013–2017 and explode during 2017–2019, with publications increasing from 63 to 591. The trend continues after 2019 and reaches 1,032 publications in 2022. Correspondingly, the citations of Bitcoin-related publications have a similar patten. The Bitcoin publications’ citation increases from 435 in 2017 to 26,381 in 2022. The consistent upward trajectory of Bitcoin publications and citations demonstrates that academic interest in Bitcoin is exploding (Figure 1).

Figure 1. Bitcoin publications and citations by year.

4.2. Discipline categories

Bitcoin has garnered interest from various fields of research. In Figure 2, most Bitcoin publications come from Business Finance, Economics, Computer Science Information Systems, Engineering Electrical Electronic, and Telecommunications. These areas have produced 973, 782, 677, 402, and 370 publications, representing percentages of 25.34%, 20.37%, 17.63%, 10.74%, and 9.64%, respectively. This is consistent with the nature of Bitcoin, which involves concepts such as peer-to-peer networks, cryptography, mining, proof of work, economics, and currency, etc. Additionally, researchers in other areas, e.g. Computer Science Theory Methods, Business, Management, and Multidisciplinary Sciences, have also been exploring Bitcoin. The wider range of research fields indicates the expanding impacts of Bitcoin.

Figure 2. The top 10 WoS categories of Bitcoin publications.

4.3. Productive journals

Table 1 lists the top 10 journals ranked by publications in the dataset. The total link strength indicates how strong the links are between a constituent, e.g. author, institution, country, and journal, with other constituents (Van Eck and Waltman 2013). Finance Research Letters ranks first with 222 publications and 9,772 citations. It has the highest total link strength, i.e. 6,348. Thereafter comes IEEE Access with 177 publications and 3,117 citations. Its total link strength is relatively low at 741. The third journal is Journal of Risk and Financial Management, with 79 publications and 706 citations. Notably, Economics Letters, International Review of Financial Analysis rank second and third in the total link strength, i.e. 3,845 and 2,697, respectively. Overall, most journals in the list pertain to economics and finance, exceptions, i.e. Physica A-Statistical Mechanics and Its Applications, IEEE Access.

Table 1. The top 10 journals ranked by publications in Bitcoin.

Rank	Journal	Publications	Citations	Citations per publication	Total link strength
1	Finance Research Letters	222	9,772	44.0	6,348
2	IEEE Access	177	3,117	17.6	741
3	Journal of Risk and Financial Management	79	706	8.9	1,148
4	Research In International Business and Finance	78	1,982	25.4	2,255
5	Physica A-Statistical Mechanics and Its Applications	75	2,228	29.7	1,835
6	Economics Letters	72	6,079	84.4	3,845
7	International Review of Financial Analysis	60	3,096	51.6	2,697
8	North American Journal of Economics and Finance	48	602	12.5	1,126
9	Applied Economics Letters	47	667	14.2	730
10	Mathematics, MDPI	40	292	7.3	499

4.4. Highly cited articles

Table 2 shows the top 10 cited articles in the dataset. The article by Tschorsch and Scheuermann (2016) has the highest citations. They examine the Bitcoin protocol and its impact on digital currencies field while highlighting key concepts and structures. Thereafter comes the article by Dyhrberg (2016a) that explores the financial asset features of Bitcoin. The article suggests that Bitcoin falls somewhere between gold and the US dollar in terms of classification. The third article by Böhme et al. (2015) explores the benefits and challenges of Bitcoin and highlights its potential impact on traditional financial system. The other top 10 articles mainly explore Bitcoin market behaviour, speculation, and price volatility, and compare it with other financial assets.

Table 2. Top 10 cited articles in Bitcoin research based on the dataset.

Rank	Article	Title	Journal	Citations
1	Tschorsch and Scheuermann (2016)	Bitcoin and Beyond: A Technical Survey on Decentralised Digital Currencies	IEEE Communications Surveys & Tutorials	737
2	Dyhrberg (2016a)	Bitcoin, Gold and The Dollar–A GARCH Volatility Analysis	Finance Research Letters	645
3	Böhme et al. (2015)	Bitcoin: Economics, Technology, and Governance	Journal of Economic Perspectives	587
4	Urquhart (2016)	The Inefficiency of Bitcoin	Economics Letters	575
5	Cheah and Fry (2015)	Speculative Bubbles in Bitcoin Markets? An Empirical Investigation into The Fundamental Value of Bitcoin	Economics Letters	565
6	Corbet et al. (2018)	Exploring The Dynamic Relationships Between Cryptocurrencies and Other Financial Assets	Economics Letters	542
7	Bouri et al. (2017)	On The Hedge and Safe Haven Properties of Bitcoin: Is It Really More Than a Diversifier?	Finance Research Letters	573
8	Baur, Hong, and Lee (2018)	Bitcoin: Medium of Exchange or Speculative Assets?	Journal of International Financial Markets, Institutions and Money	477
9	Katsiampa (2017)	Volatility Estimation for Bitcoin: A Comparison of GARCH Models	Economics Letters	469
10	Ciaian, Rajcaniova, and Kancs (2016)	The Economics of Bitcoin Price Formation	Applied Economics	420

5. Collaboration network analysis

5.1. Authors 

5.1.1. Productive authors

Table 3 presents the top 10 authors ranked by publications in the dataset. The most productive authors are Bouri, Elie, Corbet, Shaen, and Roubaud, David, with publications 67, 33, and 28, respectively. Other productive authors include Yarovaya, Larisa, Lucey, Brian, Sensoy, Ahmet, Shahzad, Syed Jawad Hussain, Gupta, Rangan, and Urquhart, Andrew.

Table 3. The top 10 authors ranked by publications in Bitcoin.

Rank	Author	Publications	Citations	Citations per publication	Total link strength
1	Bouri, Elie	67	4,596	68.6	116
2	Corbet, Shaen	33	2,500	75.8	62
3	Roubaud, David	28	3,869	138.2	66
4	Yarovaya, Larisa	23	1,346	58.5	43
5	Lucey, Brian	22	2,437	110.8	50
6	Sensoy, Ahmet	22	796	36.2	34
7	Shahzad, Syed Jawad Hussain	22	1,190	54.1	43
8	Gupta, Rangan	21	1,649	78.5	37
9	Urquhart, Andrew	21	1,694	80.7	17
10	Kang, Sang Hoon	20	593	29.7	34
10	Kristoufek, Ladislav	20	1,797	89.9	22

5.1.2. Co-authorship network

Figure 3 shows the co-collaboration network with a minimal of five articles in Bitcoin. Bouri, Elie, Roubaud, David, Gupta, Rangan collaborate closely. Authors, i.e. Corbet, Shaen, Shahzad, Syed Jawad Hussain, Sensoy, Ahmet and Naeem, Muhammad Abubakr, also have their good collaboration networks. Bouri Elie's strong collaborations with other researchers contribute to his research output, visibility, and impact.

Figure 3. Co-authorship among authors in Bitcoin research with a minimal of five articles.

5.2. Institutions

5.2.1. Productive institutions

Table 4 presents the top 15 institutions ranked by publications in the dataset. The three most productive institutions are the University of Economics Ho Chi Minh City, the Chinese Academy of Sciences and Montpellier Business School. They have published 56, 52 and 48 papers, respectively, with citation counts of 1,968, 1,965 and 4,546. Univ Econ Ho Chi Minh City also has the highest total link strength, followed by Trinity Coll Dublin and Montpellier Business Sch. Notably, University of Economics Ho Chi Minh City takes the lead.

Table 4. The top 15 institutions ranked by publications in Bitcoin.

Rank	Institution	Publications	Citations	Citations per publication	Total link strength
1	Univ Econ Ho Chi Minh City	56	1,968	35.1	159
2	Chinese Acad Sci	52	1,965	37.8	88
3	Montpellier Business Sch	48	4,546	94.7	125
4	Lebanese Amer Univ	40	536	13.4	92
5	Trinity Coll Dublin	39	2,993	76.7	129
6	Univ Sydney	36	2,091	58.1	94
7	South Ural State Univ	35	868	24.8	106
8	Univ Southampton	35	1,754	50.1	71
9	Dublin City Univ	34	2,502	73.6	113
10	Univ Pretoria	33	1,734	52.5	54
11	Holy Spirit Univ Kaslik	32	3,275	102.3	75
12	Peking Univ	32	480	15.0	33
13	Univ Chinese Acad Sci	32	1,257	39.3	60
14	Univ Coll Dublin	31	2,179	70.3	44
15	UCL	30	796	26.5	33

5.2.2. Network of co-author’s institutions

Figure 4 shows the collaboration network of institutions represented by co-authorships’ institutions (with a minimal of ten articles). Productive institutions usually have good collaboration networks.

Figure 4. Institutional collaboration network in Bitcoin research with a minimal of ten articles.

5.3. Countries

5.3.1. Productive countries

Table 5 shows the top 15 countries by publications in the dataset. The three most productive countries are China, USA and England, with 760, 624, 392 publications, 12,589, 15,533, 14,090 citations, respectively. China has most publications, while the citations per publication are relatively low at 16.6, compared to the USA’s 24.9 and England’s 35.9. The result suggests that both developed and developing countries are actively researching Bitcoin.

Table 5. The top 15 countries and regions ranked by publications in Bitcoin.

Rank	Country	Publications	Citations	Citations per publication	Total link strength
1	China	760	12,589	16.6	525
2	USA	624	15,533	24.9	439
3	England	392	14,090	35.9	498
4	India	233	3,262	14.0	200
5	Australia	226	6,988	30.9	299
6	France	213	7,616	35.8	343
7	Italy	203	4,158	20.5	179
8	South Korea	174	3,866	22.2	159
9	Germany	164	4,673	28.5	174
10	Spain	161	4,016	24.9	127
11	Canada	160	3,930	24.6	190
12	Turkey	144	2,769	19.2	172
13	Pakistan	122	1,812	14.9	223
14	Vietnam	117	2,653	22.7	241
15	Saudi Arabia	115	2,002	17.4	181

5.3.2. Network of co-authors’ countries/regions

Figure 5 depicts the collaboration network among the countries/regions represented by co-authorships’ countries and regions (with a minimal of ten articles). International collaboration in Bitcoin research is prevalent. The USA is the most influential country by citations and engages in active collaborations with England, Australia, Canada, and China. England exhibits a high frequency of cooperation with Germany, Italy, and Spain, three prominent countries in the network. Notably, The US, England, Gemeny, and Australia are identified as pioneers in exploring the field of Bitcoin. Asian countries, e.g. China, India, Pakistan, and Saudi Arabia, started to investigate Bitcoin began exploring Bitcoin comparatively later than these early adopters.

Figure 5. International collaboration network in Bitcoin research with a minimal of ten articles, 2012–2022.

5.4. Analysis of global Bitcoin research collaboration: Google Earth Visualisation

Figure 6 depicts the evolution of the global Bitcoin research collaboration network from 2015 to 2022. It initially begins in the US and European countries like UK, Spain, France, etc. in 2015. It extends to Austria in 2016 and further expands to Asian countries like China, South Korea, India, etc. in 2018, when the global Bitcoin research collaboration network gains strength.

Figure 6. The evolution of a geographic cooperation network in Bitcoin research based on the dataset.

6. Science mapping analysis

6.1. Co-citation analysis

6.1.1. Cluster analysis of co-cited articles

Co-citation analysis is used to identify relationships and similarities between articles based on their co-citation patterns. Cluster analysis helps to identify research themes or topics. The article applies lag-likelihood ratio (LLR) method to obtain the cluster labels from keywords (Chen 2006). Figure 7 shows the result of cluster analysis for the co-citation network in the Bitcoin research.

Figure 7. Clusters of co-cited articles in Bitcoin research based on the dataset.

Table 6 summarises the co-cited articles in Bitcoin research. Silhouette indicates cluster homogeneity and Mean (Year) represents the average publication year in each cluster. The top 13 clusters with more than 20 publications are: Blockchain, market efficiency, bitcoin, connectedness, deep learning, Covid-19, privacy, GARCH (Generalised Autoregressive Conditional Heteroskedasticity), economic policy uncertainty, energy consumption, stakeholders, economic regulation, herding, and money. The publication years vary across clusters, with most falling between 2012 and 2017.

Table 6. Summary of co-cited articles in Bitcoin research.

Cluster ID	Size	Silhouette	Mean (Year)	Cluster labels (LLR)
#0	164	0.788	2014	blockchain, smart contract, cryptocurrency, security, scalability
#1	111	0.761	2012	blockchain, market efficiency, hurst exponent, price discovery, efficiency
#2	101	0.844	2015	blockchain, bitcoin, cryptocurrency, security, smart contract
#3	100	0.722	2016	blockchain, connectedness, tvp-var, volatility spillover, spillovers
#4	94	0.858	2013	deep learning, machine learning, investor attention, price prediction, sentiment analysis
#5	91	0.854	2017	Covid-19, blockchain, gold, safe haven, safe haven
#6	66	0.908	2009	privacy, blockchain, anonymity, Covid-19, public key
#7	56	0.825	2009	blockchain, GARCH, realised volatility, volatility, volatility forecasting
#8	45	0.848	2016	economic policy uncertainty, blockchain, epu, geopolitical risks, policy uncertainty
#9	44	0.93	2017	energy consumption, initial coin offering, cryptocurrency mining, bitcoin mining, proof-of-work
#11	25	0.932	2001	blockchain, Covid-19, stakeholders, production, principal component analysis
#12	25	0.985	2009	economic regulation, social web, negative bubbles, law, pow
#13	24	0.967	2016	herding, blockchain, non-fungible tokens, nfts, herding behaviour

6.1.2. Landmark nodes and pivot nodes

Landmark nodes are highly cited in a co-citation network. Pivot nodes have high betweenness centrality scores and function as central linking points within a co-citation network. Table 7 summarises the details of landmark nodes and pivot nodes in the co-citation network of Bitcoin research. The major 10 landmark nodes are Eyal and Sirer (2014), Tschorsch and Scheuermann (2016), Bariviera et al. (2017), Bariviera (2017), Dyhrberg (2016b), Urquhart (2016), Ji et al. (2019), Urquhart (2018), Guesmi et al. (2019), and Reid and Harrigan (2011). Notably, Eyal and Sirer (2014) is also a pivotal node that proposes modifications to Bitcoin mining protocol. Selgin (2015) introduces the concept of synthetic commodity money, which combines features of both commodity and fiat money. Bouoiyour, Selmi, and Tiwari (2015) analyse Granger causality between Bitcoin price, trade transactions, and investors’ attractiveness, confirming Bitcoin’s speculative nature but recognising its economic utility.

Table 7. Landmark nodes and pivot nodes in Bitcoin research.

Article	Title	Source	Cluster ID
Landmark nodes
Eyal and Sirer (2014)	Majority Is Not Enough: Bitcoin Mining Is Vulnerable	International Conference on Financial Cryptography and Data Security	#0
Tschorsch and Scheuermann (2016)	Bitcoin and Beyond: A Technical Survey on Decentralised Digital Currencies.	IEEE Communications Surveys & Tutorials	#0
Bariviera et al. (2017)	Some Stylized Facts of the Bitcoin Market	Physica A: Statistical Mechanics and its Applications	#1
Bariviera (2017)	The Inefficiency of Bitcoin Revisited: A Dynamic Approach	Economics Letters	#1
Dyhrberg (2016b)	Hedging Capabilities of Bitcoin. Is It the Virtual Gold?	Finance Research Letters	#2
Urquhart (2016)	The Inefficiency of Bitcoin	Economics Letters	#2
Ji et al. (2019)	Dynamic Connectedness and Integration in Cryptocurrency Markets	International Review of Financial Analysis	#3
Urquhart (2018)	What Causes the Attention of Bitcoin?	Economics Letters	#4
Guesmi et al. (2019)	Portfolio Diversification with Virtual Currency: Evidence from Bitcoin	International Review of Financial Analysis	#5
Reid and Harrigan (2011)	An Analysis of Anonymity in the Bitcoin System	2011 IEEE Third International Conference on Privacy, Security, Risk and Trust (PASSAT)	#6
Bonneau et al. (2015)	SoK: Research Perspectives and Challenges for Bitcoin and Cryptocurrencies	2015 IEEE Symposium on Security and Privacy	#6
Bollerslev (1986)	Generalised Autoregressive Conditional Heteroskedasticity	Journal of Econometrics	#7
Demir et al. (2018)	Does Economic Policy Uncertainty Predict the Bitcoin Returns? An Empirical Investigation	Finance Research Letters	#8
Baker, Bloom, and Davis (2016)	Measuring Economic Policy Uncertainty	The Quarterly Journal of Economics	#8
Foley, Karlsen, and Putniņš (2019)	Sex, Drugs, and Bitcoin: How Much Illegal Activity Is Financed through Cryptocurrencies?	The Review of Financial Studies	#9
Narayanan et al. (2016)	Bitcoin and Cryptocurrency Technologies: A Comprehensive Introduction	Princeton University Press	#9
Nakamoto (2008)	A Peer-to-peer Electronic Cash System	Decentralised Business Review	#11
Kondor et al. (2014)	Do the Rich Get Richer? An Empirical Analysis of the Bitcoin Transaction Network	Plos One	#11
Grinberg (2012)	Bitcoin: An Innovative Alternative Digital Currency	Hastings Sci. & Tech	#12
McMillan (2013)	Ex-googler Gives the World a Better Bitcoin	Wired Magazine	#12
Bouri, Gupta, and Roubaud (2019)	Herding Behaviour in Cryptocurrencies	Finance Research Letters	#13
Phillips, Shi, and Yu (2015)	Testing For Multiple Bubbles: Historical Episodes of Exuberance and Collapse in the S&P 500	International Economic Review	#13
Pivot nodes
Eyal and Sirer (2014)	Majority Is Not Enough: Bitcoin Mining Is Vulnerable	International Conference on Financial Cryptography and Data Security	#0
Selgin (2015)	Synthetic Commodity Money	Journal of Financial Stability	#2
Bouoiyour, Selmi, and Tiwari (2015)	Is Bitcoin Business Income or Speculative Foolery? New Ideas Through an Improved Frequency Domain Analysis	Annals of Financial Economics	#2

6.1.3. Network of author co-citation

Table 8 presents the top 10 most cited authors ranked by citations (the total citation within the dataset). Nakamoto (2008) creates Bitcoin. Elie Bouri is well recognised for the empirical work on Bitcoin hedge and volatility (Balcilar et al. 2017; Bouri et al. 2017). Shaen Corbet contributes to the understanding of the connections between cryptocurrencies and other financial assets (Corbet et al. 2018; Corbet et al. 2019). Dirk Baur primarily focuses on the speculative features of Bitcoin (Baur, Hong, and Lee 2018).

Table 8. The top ten most cited authors in Bitcoin research based on the dataset.

Rank	Author	Citations	Institution	Research area
1	Satoshi Nakamoto	1276	/	/
2	Elie Bouri	1053	Lebanese American University	Financial Economics, Energy Finance, Empirical Finance, Bitcoin, and Cryptoassets
3	Shaen Corbet	955	Dublin City University	Financial Econometrics, Cryptocurrencies
4	Dirk Baur	836	University of Western Australia	Gold, Safe Haven, Bitcoin, Contagion
5	Anne Haubo Dyhrberg	746	Wilfrid Laurier University	Blockchain, Cryptocurrencies, Market Microstructure
6	Andrew Urquhart	742	University of Reading	Cryptocurrencies, Fintech, Bitcoin, High Frequency Trading
7	Ladislav Kristoufek	604	Charles University	Financial Econometrics, Cryptoassets, Cryptocurrencies
8	Paraskevi Katsiampa	549	Sheffield University	Financial Econometrics, Financial Markets
9	Jeremy Eng Tuck Cheah	506	Nottingham Trent University	Cryptocurrency, Bitcoin
10	Pavel Ciaian	479	European Commission	Institutional Economics, Transition Economics, Policy Analysis

6.2. Topic trends in Bitcoin research

6.2.1. Keyword co-occurrence analysis

Figure 8 shows the keyword co-occurrence network in Bitcoin research using the dataset, with a minimal of 15 co-occurrences. The top ten keywords with highest co-occurrence are Bitcoin, cryptocurrency, blockchain, Covid-19, Ethereum, smart contract, volatility, security, gold, and distributed ledger. Bitcoin, cryptocurrency, and blockchain are three fundamental concepts in the network. Other main topics are peer-to-peer computing, machine learning, digital currency, safe haven, privacy, Fintech, market efficiency, GARCH, money, and proof-of-work.

Figure 8. The keyword co-occurrence network in Bitcoin research based on the dataset.

6.2.2. Bursts analysis of keyword co-occurrence

Table 9 shows the keywords with the highest citation bursts in Bitcoin research. The evolution of the Bitcoin research field can be divided into three stages. The first stage (2012-2016) revolves around the conceptualisation and fundamentals of Bitcoin, with keywords such as digital currency and electronic money dominating the research landscape. The second stage (2017-2018) witnesses the expansion of research focus to encompass cryptocurrency and market efficiency, as reflected by the emerging keywords like Bitcoin market, cryptocurrency market and market efficiency. The third stage (2019-2022) witnesses a shift towards technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets, as evidenced by keywords such as technical analysis, big data, and data privacy. These three stages may be compared against Bitcoin maximalists (e.g. Mark Yusko), who commonly cite Gandhi (2023) as follows: First they ignore you 2009-2015, then they laugh at you 2015-2019, then they fight you 2019-2027, then you win 2027-$\mathrm{\infty }$.

Table 9. Keywords with the strongest citation bursts in Bitcoin research.

Keyword	Year	Strength	Begin	End	1993–2022
Digital currency	2015	7.27	2015	2018
Electronic money	2016	2.53	2016	2018
Crypto currency	2018	6.6	2018	2019
Virtual currency	2018	4.53	2018	2019
Hurst exponent	2018	4.29	2018	2020
Efficient market hypothesis	2018	3.6	2018	2019
Graph analysis	2018	3.29	2018	2019
Bitcoin market	2018	3	2018	2019
Market efficiency	2018	2.96	2018	2019
Technical analysis	2019	3.65	2019	2020
Initial coin offering	2019	3.39	2019	2020
Game theory	2019	3.28	2019	2019
Internet of things (iot)	2019	2.66	2019	2020
Big data	2019	2.55	2019	2020
Data privacy	2020	2.58	2020	2020
Stock markets	2021	3.3	2021	2022

6.2.3. Time-zone view of keyword co-occurrence

Figure 9 shows the evolution of keyword co-occurrence in Bitcoin research. The primary early keywords are digital currency, internet of things, Bitcoin mining, and electronic. In 2017, with the rise of the cryptocurrency bull market, the main keywords are safe haven, cryptocurrency market, and cloud computing. Keywords such as smart contract, distributed ledger, market efficiency, blockchain technology, data mining, and artificial intelligence show up in 2018. In 2019, the focus expands to-peer-to-peer computing, machine learning, deep learning, portfolio diversification, and energy consumption. More recently, hot keywords are cryptocurrency market, stock market, network analysis, prediction models, price prediction, and reinforcement learning. Decentralised finance, non-fungible tokens, clean energy and mining, and monetary policy.

Figure 9. The time-zone view of co-occurrence keyword in Bitcoin research based on the dataset. The circles with higher centrality (>0.1) are displayed with purple rings.

7. Future research, limitations and how society may benefit

The article identifies four prominent emerging areas for future Bitcoin research: Decentralised finance (DeFi), non-fungible tokens (NFTs), clean energy and mining, and monetary policy. First, the emergency of decentralised exchanges, lending protocols, stablecoins, and the impact of DeFi on financial system. Second, the NFTs’ role in digital ownerships, marketing implications, integration with the Bitcoin blockchain, dynamics of NFT markets. Third, the environmental impact of Bitcoin mining, the clean energy solutions and strategies to mitigate the carbon footprint. Fourth, the impacts of Bitcoin on monetary policies, central banking systems, and currencies competition.

The authors suggest interdisciplinary collaboration between the Bitcoin field and fields such as economics, finance, computer science, energy, management, and environmental studies, and practically any fields. As Bitcoin evolves, with ebbs and flows, within global finance, one new area for exploration is ethics associated with Bitcoin, including issues related to privacy, security, financial inclusion, and illegal activities. Another area of exploration pertains to the regulation challenges. This includes assessing the legal and jurisdictional challenges associated with Bitcoin transactions, and potential strategies for balancing innovation with regulatory compliance. Moreover, the societal implications of Bitcoin, investigating its impact on, for example, financial inclusion, economic stability, investment and wealth distribution. Future endeavours may explore how Bitcoin intersects with broader societal trends, e.g. democracy, organisation and management, innovation, etc.

No classification exists for actors which we consider to be a limitation of bibliometric analysis. We would prefer to classify the literature according to who benefit and do not benefit from the various kinds of Bitcoin research, how Bitcoin research benefits groups potentially differentially, for whom Bitcoin research is important, which actors and groups are potentially overrepresented versus underrepresented or not represented in Bitcoin research, etc. Some literature explores different actors in economies involving Bitcoin. For example, Wang and Hausken (2023b) show that a borrower and buyer, a seller and multiple non-traders benefit differently from the availability of hard and fiat money. Wang and Hausken (2021a) show that conventionalists, pioneers and criminals evolve[open-strick]s[close-strick] in different fractions in a system with a global currency such as Bitcoin approximated as hard money and a national fiat currency such as a central bank digital currency. Future research could assess how different societal actors such as individual users, developers, firms, commercial banks, central banks, regulators, governments and the broader society benefit or do not benefit differentially from changes in the financial system with and without Bitcoin. Future research can also delve into a more meticulous review of the existing Bitcoin publications. By connecting the dots between what has been studied and what remains unexplored, researchers can identify the significance of specific topics and ascertain their relevance to academia, systems, societies, and various stakeholders.

One limitation is that the article is based on a dataset of 3,837 articles. Future research may expand the data sources by combining articles from Scopus, and patent databases. Additionally, examining strategies of renowned Bitcoin institutional investors like MicroStrategy Inc.. Second, an intrinsic limitation is bibliometrics fails to capture the latest research in Bitcoin, as new articles are in the pipeline and may take time to gain attention in the academic field. Future research could explore alternative approaches to investigate the most recent publications in Bitcoin. Third, bibliometrics may not capture the full complexity of scientific research. Future research may consider combining other techniques, such as meta-analysis, case studies, content analysis, to better explore the Bitcoin research landscape and gain a deeper understanding of the field.

8. Conclusion

This article presents a comprehensive bibliometric analysis of Bitcoin research, utilising a dataset of 3,837 articles retrieved from the Web of Science Core Collection. The analysis encompasses multiple dimensions of Bitcoin research dynamics, including the overall growth of publications and citations, discipline categories, productive journals, distribution patterns, and identification of highly cited articles. The article identifies the most prolific contributors in the field of Bitcoin research, including authors, institutions, and countries/regions, and elucidates their collaboration networks through co-authorship analysis.

The main findings of this study are as follows. First, there has been a significant increase in Bitcoin publications since 2017, indicating that Bitcoin research has emerged as a hot topic. The most productive journals in Bitcoin pertain to economics and finance. Second, the most productive authors in this field are Bouri, Elie; Corbet, Shaen; and Roubaud, David. The most productive institutions contributing to Bitcoin research are the University of Economics Ho Chi Minh City, the Chinese Academy of Sciences, and Montpellier Business School. The most productive countries are China, US and UK.

Third, the global Bitcoin research collaboration network originated in the US and several European countries such as the UK, Spain, and France in 2015. The global Bitcoin research collaboration network has gained strength since 2018. Fourth, the top four clusters of co-cited articles with more than 100 articles in Bitcoin research are blockchain, market efficiency, bitcoin, and connectedness. The authors with the most citations are Satoshi Nakamoto, Elie Bouri, Shaen Corbet, Dirk Baur, and Anne Haubo Dyhrberg.

This article delineates Bitcoin research into three stages, i.e. conceptualisation and fundamentals of Bitcoin (2012-2016), cryptocurrency and market efficiency (2017-2018), and technical analysis, big data, data privacy, and the connection between Bitcoin and financial markets (2019-2022).

The findings of this bibliometric analysis offer valuable insights for researchers, policymakers, and practitioners in the field of Bitcoin. The article contributes both theoretically and practically to the Bitcoin field. Theoretically, the article contributes by offering a comprehensive understanding of the evolving global landscape of Bitcoin research through a rigorous bibliometric analysis. It identifies growth patterns, interdisciplinary collaborations, and key thematic trends, emerging research topics, providing valuable insights for scholars in the field. The article contributes to a deeper understanding of the evolving dynamics within Bitcoin research. Practically, the findings serve as a valuable resource for researchers, policymakers, and industry professionals, offering insights that can inform future research directions and potential applications in the rapidly evolving realm of Bitcoin.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

Data associated with this article is straightforwardly downloadable from the Web of Science.

Additional information

Notes on contributors

Guizhou Wang

Guizhou Wang is a PhD candidate at the University of Stavanger, Norway, since 2020-06. He has published 25 articles in peer reviewed journals. His research fields are digital currencies, game theory, risk analysis, cryptocurrencies, central bank digital currencies, and econometrics. He holds a MSc degree in financial economics from the University of Chinese Academy of Sciences (Beijing, China), 2016-09–2019-06, focusing on mathematical finance, econometrics, venture capital, and cryptocurrency. He holds a BSc degree in finance from the Jinan University (Guangzhou, China), 2010-09–2014-06, focusing on finance, derivatives, and mathematical modelling. Email: [email protected].

Kjell Hausken

Kjell Hausken is a professor of economics and societal safety at the University of Stavanger, Norway, since 1999. His research fields are terrorism, societal safety, economics, economic risk management, economics and safety, political economy, information security, public choice, conflict, game theory, reliability, war, crime, risk analysis, disaster prevention, stochastic theory, dynamics, petroleum economics, resilience management. He holds a PhD from the University of Chicago (1990–1994), was a postdoc at the Max Planck Institute for the Studies of Societies (Cologne) 1995–1998, and a visiting scholar at Yale School of Management 1989–1990. He holds a Doctorate Program Degree (HAE) (‘Philosophical, Behavioural, and Gametheoretic Negotiation Theory’) in Administration from the Norwegian School of Economics and Business Administration (NHH), a MSc degree in electrical engineering, cybernetics, from the Norwegian Institute of Technology (NTNU), focusing on mathematics and statistics, and a minor in Public Law from the University of Oslo. He has published 280 articles in peer reviewed journals, one book, edited two books, is/was on the Editorial Board for Theory and Decision (May 20, 2007), Reliability Engineering & System Safety (January 17, 2012), and Defence and Peace Economics (December 4, 2007–December 31, 2015), has refereed 400 submissions for 85 journals, and advises and has advised seven PhD students. Email: [email protected].