A bibliometric analysis of Bitcoin scientific production
AA bibliometric analysis of Bitcoin scientificproduction
Ignasi Merediz-Sol`a and Aurelio F. Bariviera ∗ Universitat Rovira i Virgili, Department of Business, Av. Universitat 1, 43204 Reus, Spain
June 24, 2019
Abstract
Blockchain technology, and more specifically Bitcoin (one of its fore-most applications), have been receiving increasing attention in the scien-tific community. The first publications with Bitcoin as a topic, can betraced back to 2012. In spite of this short time span, the productionmagnitude (1162 papers) makes it necessary to make a bibliometric studyin order to observe research clusters, emerging topics, and leading schol-ars. Our paper is aimed at studying the scientific production only aroundbitcoin, excluding other blockchain applications. Thus, we restricted oursearch to papers indexed in the Web of Science Core Collection, whosetopic is “bitcoin”. This database is suitable for such diverse disciplinessuch as economics, engineering, mathematics, and computer science. Thisbibliometric study draws the landscape of the current state and trends ofBitcoin-related research in different scientific disciplines.
Keywords:
Bitcoin; bibliometrics; Web of Science; VOSviewer
JEL codes:
G19; E49
Bibliometric studies has become an emergent and buoyant discipline, given theimportance posed on the assessment of scientific production in recent times. Eu-gene Garfield, with the establishment of the Institute for Scientific Information(ISI) in the 1960s, initiated the metrification of papers, journals, researchers, andinstitutions. Scientific papers are now compiled and indexed in large databases,which allow to measure different aspects of such papers, such as number ofauthors, keywords, topic, citations, institutional collaboration etc. The ratio-nale for indexing articles is the following: authors cite other papers due to itsconnection with the core idea of his/her paper. Given that authors must select ∗ [email protected] a r X i v : . [ c s . D L ] J un arefully which papers to cite, including only the most relevant and most closelyrelated to his/her paper, most cited papers could reflect the importance of themwithin its discipline. Institutions can obtain valuable information about indi-vidual and aggregate impact. Therefore it could help in either faculty recruitingprocess or in defining the global research strategy of universities and researchcouncils.However, the importance of bibliometric studies goes beyond the institu-tional level. They could help new researchers of a discipline to understand theextent of a topic, emergent trends, and its evolution through time. In this senseit is different from a traditional literature survey.This kind of analysis is possible due to the availability of big databasessuch as the Web of Science. This indexing service is an important input ofthe evaluation process in academia. The Web of Science is a citation indexingservice, administered by Clarivate Analytics, and constitutes a selective list ofjournals and conference proceedings, with indexing coverage from 1898. It coversmore than 59 million records. The firm produces several impact metrics includedin the Journal Citation Report, e.g. Impact Factor, Eigenfactor, 5-year ImpactFactor, among others. These metrics are available on a subscription basis.Further details can be consulted at its website Clarivate Analytics (2018b).From a macroscopic level, we can obtain certain metrics that are common tomany journals, and they are useful to different stakeholders. However, there aresome features that change from discipline to discipline. There is an uneven num-ber of researchers and journals per discipline. In recent years there has been anexpansion in the number of journals and an increase in its periodicity, probablydue to the expansion of the academic sector in the last decades in several coun-tries. In addition, disciplines have different traditions regarding publication.Some disciplines, such as biomedicine, is prone to ’hyper-authorship’ (Cronin,2001), where a single paper includes massive collaboration, where some of themhave minimal involvement in the production of the paper, and a subgroup ofauthors acts as reporters of the whole group. Therefore, it is important to studythe intrinsic characteristics of homogeneous disciplines and/or topics, in orderto provide for a meaningful classification.Bitcoin research has soared in recent years. From a technological pointof view, blockchain is a disrupting paradigm. It introduced the concept ofdistributed consensus-based validation, instead of centralized validation. Its firstapplication, introduced by Nakamoto (2009), comprises the creation of a sort ofmeans of payment, which runs parallel to the established financial system. Sinceits introduction, bitcoin has been increasingly become an important investmentand speculative device. It got the attention of mass media, as more and moremoney was poured into the cryptocurrency market. At the beginning, mostresearchers were attracted trying to understand how blockchain worked (Zyskindet al., 2015; Zheng et al., 2018). Early research within economics was focusedon the potential of bitcoin as a substitute of national currencies (Yermack, 2013;B¨ohme et al., 2015). Currently, most of the research from an economics andfinance point of view is to analyze all its financial properties for several reasons.Firstly, because of its traded volume. Secondly, its distinct behavior vis-`a-vis Bibliometrics is a research field within library and information sciences thatstudies the bibliographic material by using quantitative methods (Pritchard,1969; Broadus, 1987). Over the years, bibliometrics has become very popular forclassifying bibliography and developing representative summaries of the leadingresults.There are many bibliometric studies of a wide variety of issues. For example,3obo et al. (2011) analyze the thematic evaluation of the Fuzzy Sets Theory.Bonilla et al. (2015) analyze the academic research developed in Latin Americain economics between 1994 and 2013. Cancino et al. (2017) develop a biblio-metric analysis of the publications of the Computers & Industrial Engineeringbetween 1976 and 2015. Related to economics there are several examples. An-drikopoulos et al. (2016) performed a bibliometric analysis in the economics fieldby reviewing the first forty years of the Journal of Econometrics, focusing oncollaboration patterns and the internationalization of research in econometrics.Another example of a bibliometric analysis of economic journals is done by Wei(2019). More specifically, Costa et al. (2019) conduct a bibliometric analysisof the scientific field of Behavioral Economics and Behavioral Finance, provinghow they have turned into an important field of study. Furthermore, Claveauand Gingras (2016) introduce a combination of different bibliometric tools tothe history of economics. So, combining bibliometrics with dynamic networkanalysis, they identified a changing specialty structure in economics from thelate 1950s to 2014. In a different way, Korom (2019) explored the potentialof interdisciplinary perspectives by investigating the thematic overlap betweeneconomic and sociological approaches to wealth inequality.In 2009, an anonymous individual, under the pseudonym “Nakamoto”, pub-lished a white paper setting the grounds for the creation of a decentralized, nongovernment controlled, currency. Those were the times of the global financialcrisis, considered by many specialists to have been the most serious economicdownturn since the Great Depression (Almunia et al., 2009). Many peoplestopped trusting banks. Nakamoto’s idea was timely and rapidly adopted bythe public. Bitcoin allows for peer-to-peer money transfer, avoiding the estab-lished financial system. In addition, transactions are encrypted. Although notanonymous, transactions are blind to national authorities. Bitcoin success hasbeen so great, that it became tantamount to cryptocurrency. The success ofbitcoin encouraged other crypto-entrepreneurs to develop their own currencies.As of February 2019, there are more than 2000 cryptocurrencies, with a totalmarket capitalization of $ $ corpus , in order toprovide a broader view of the scientific production around bitcoin.We also identified some papers dealing, tangentially, with our subject. Zenget al. (2018) present a bibliographic analysis of the blockchain-related literaturebetween January 2011 and September 2017, taking EI Compendex (EI) andChina National Knowledge Infrastructure (CNKI) databases as the literaturesources. Considering that EI is database centered in engineering literature, andCNKI provides mainly China knowledge resources, our scope and coverage isfar broader. Chatterjee et al. (2018), despite not doing a bibliometric analysis,provide a state of-the-art survey over Bitcoin related technologies and making asummary of its evolution. Dabbagh et al. (2019) present a bibliometric analysisof 995 papers dealing with blockchain. Their analysis indicate that researchershave shifted their research interests form bitcoin to blockchain in the recenttwo years. Their study differs from ours in their broader search query, whichincluded, in addition to ‘bitcoin’, the following words: ‘Blockchain’, ‘cryptocur-rency’, ‘ethereum’, and ‘smart contract’. As a consequence, papers in theirsample are more technological-focused. Yli-Huumo et al. (2016) analyzed 41papers, excluding explicitly papers dealing with economic, legal, business andregulation perspectives of blockchain. Their study is conducted using a system-atic mapping process described by Petersen et al. (2008).Consequently, our paper could be considered an expanded contribution tothe literature, providing a full overview of the current trends on Bitcoin research,and identifying top researchers, institutions, and journals in this field. This paper works with data form Web of Science Core Collection (WoS), Clar-ivate Analytics. We selected all indexed papers that contain ’bitcoin’ as topic,which makes a total of 1162 documents, published in 703 sources (journals,books, etc.), during the period 2012-2019. These documents were (co-)authored5y 2293 people. The vast majority of the documents are multi-authored, be-ing only 322 documents single-authored. The average number of authors perdocument is 1.97.Bitcoin as a research topic comprises several disciplines. It is a recent andemerging topic, considering that the first paper is found in 2012, omitting theseminal paper by Nakamoto (2009). Articles cover different aspects of thisnovel product: legal concerns, economic perspectives or computer peculiarities.However, they are concentrated around two main research areas: computerscience and business economics. Web of Science assigns indexed papers to oneor more research areas. The 1162 papers considered in our sample were assigned1543 research areas. The top five research areas are displayed in Table 1Table 1: Main research areas assigned to papers in the sampleResearch Areas Records % of 1543Computer Science 541 35%Business Economics 279 18%Engineering 196 13%Telecommunications 106 7%Science Technology Other Topics 79 5%Total top 5 research areas 1201 78%The detail of yearly publications is displayed in Table 2. We can observethat in the first two years of the sample, the relative increase exceeds 400%.This is produced by the small initial figures. In more recent years the yearlyincrement is around 40%. The expected scientific production for year 2019 is 384, which gives a flat growth rate for the current year. The decreasinggrowth rate could signalize that research in this field is consolidating. In fact,cryptocurrencies emerged as an all-new area in science and technology almostten years ago. During these years, the initial papers were devoted to the studyof the underlying blockchain technology. Soon after it, economic and financialstudies on Bitcoin begun. Table 3 shows that the USA is the country whose authors have published bothmore documents and obtained more total citations, followed by the United King-dom in both aspects. The ten first countries accumulate the 65% of the articlespublished related to Bitcoin.Table 4 displays the main countries, ordered by total citations. The averagenumber of citations per article, which is 4.2. The USA and the United Kingdom,the two countries with more articles published and total citation are above thisfigure, with 5.43 and 5.69 respectively. In spite of the fact that China is thethird country in terms of published articles, it has the lowest average citations The linear forecast for the expected number of papers is 32 ·
12 = 384 * Total 1162 124% ** *The projected Table 5 shows the ten main sources publishing articles related to Bitcoin. Six ofthis sources are journals, and reflect the interdisciplinarity of this research field.
Economics Letters and
Finance Research Letters are two leading economicsjournals.
Economics Letters is, undoubtedly, the main publishing device, with29 published paper on this topic.
Physica A is a physics journal, which is veryfriendly in publishing papers dealing with econophysics and statistical mechan-ics applications to economics.
IEEE Access and
PLOS ONE are two importantmultidisciplinary open access journals. Finally,
Communications of the ACM isleading publication for the computing and information technology fields, whichis very much recognized among industry. Another important source is
NewScientist , which is a popular weekly science and technology magazine, foundedin 1956. There is also one book title “The Digital Currency Challenge” andpublished by Palgrave Macmillan US. This book details legal issues and tech-nological developments of digital currencies in the US. Finally, there are twoconference proceedings, providing a significant number of papers. The diver-sity of the sources, in type and discipline, reflects the multidisciplinarity in thisresearch topic. 7able 3: Top ten corresponding author’s countriesCountry Articles Frequency Single country Multiple country MCP Ratiopublications publicationsUSA 249 23% 214 35 14%United Kingdom 100 9% 67 33 33%China 99 9% 66 33 33%Germany 57 5% 37 20 35%Australia 41 4% 30 11 27%Italy 40 4% 25 15 38%India 31 3% 28 3 10%Switzerland 31 3% 21 10 32%France 30 3% 13 17 57%Spain 26 2% 14 12 46%Total 10 countries 704 65% 515 189 27%Table 4: Top ten total citations per countryCountry Total Citations Average Article CitationsUSA 1353 5.4United Kingdom 569 5.7Australia 247 6.0Germany 222 3.9Ireland 187 15.6China 180 1.8Spain 170 6.5Switzerland 150 4.8Israel 146 13.3Austria 138 11.5Total (all countries) 5019 4.2Table 5: Top ten most relevant sources
Sources
Author(year) Title Source
Table 6 shows the ten most used keyword in the Bitcoin articles. Web of Scienceprovides two types of keywords: (a) Author Keywords, which are those providedby the original authors, and (b) Keywords-Plus, which are those extracted fromthe titles of the cited references by Thomson Reuters (the company maintainingWoS). Keyword Plus are automatically generated by a computer algorithm.The two more frequent Author Keywords are ’Bitcoin’ and ’Blockchain’. Itis remarkable that the word ’Economics’ is the second most frequent Keyword-Plus, but it does not appear as an Author Keyword. It is clear that ’Economics’is too general to describe an article; authors do not used it as keyword, butthe algorithm used to find Keyword-Plus does not discriminate such a uselesskeyword. On contrary, Keyword-Plus is precise at identifying keywords such as’Volatility’, ’Inefficiency’, or ’Returns’, as many economics papers are focusedon these aspects of Bitcoin. It is also relevant to notice that Blockchain is thesecond more used Authors Keywords as it is gaining a lot of attention amongresearchers but it is not in the ten more used Keywords-Plus.9 .4 Highly cited papers
Table 7 shows the list of the 17 articles categorized as a highly cited paper.According to the Clarivate Analytics (2018a), “Highly cited papers are the topone percent in each of the 22 ESI subject areas per year. They are based onthe most recent 10 years of publications. Highly Cited Papers are consideredto be indicators of scientific excellence and top performance and can be used tobenchmark research performance against field baselines worldwide”. This mea-sure is useful in the sense that separates each article depending on its field andit is a known fact that depending on the field, the number of citations used perarticle is different. So, it is good way to highlight important articles from differ-ent fields. These papers signalize, in some way, research paths in the literature.Ciaian et al. (2016) study bitcoin price formation following the methodology byBarro (1979). They find that bitcoin price was mainly influenced by demandand supply (partly also by speculative investors), but that that macroeconomicshas no significant effect. Urquhart (2016) develops a methodology to measureinefficiency in bitcoin using six tests. His methodology has been subsequentlyused in several articles. This paper concludes (and it was tested by other papers)that bitcoin was, initially, an inefficient market; but it could be in the processof moving towards a more efficient market. Dyhrberg (2016a,b) were amongthe first applying GARCH models to cryptocurrencies. Moreover, it was on thefirst articles to compare Bitcoin with Gold in order to classify bitcoin as anasset due to its characteristics. Their papers concluded that Bitcoin has a placeon the financial markets and in portfolio management as it can be classified assomething in between gold and the American dollar. Nadarajah and Chu (2017)followed the methodology by Urquhart (2016), and find that a power transfor-mation of bitcoin returns could be weakly informationally efficient. Katsiampa(2017) focus her attention on time series volatility, and detects short and long-run components in bitcoin conditional variance. Bariviera (2017) also finds highpersistence in daily variance, which makes GARCH models suitable for variancemodelization. Bouri et al. (2017) use a dynamic conditional correlation modelin order to study the potential use of bitcoin as a safe-haven asset. Their mainfindings are that bitcoin is acts as a poor hedge, but it is suitable for diver-sification purposes. Balcilar et al. (2017) perform a non-parametric quantileanalysis in order to analyze causal relation between trading volume and bit-coin returns and volatility. Their study reveal that volume has some predictivepower on returns, but not in volatility. Lahmiri and Bekiros (2018) also detectlong-range correlations in returns, and a dynamical nonlinear behavior in thetime series. In addition, prices and returns exhibits multifractality probablydue to the fat-tailed distributions.Table 8 show the most productive authors of Bitcoin related articles. Thefirst one is P. Carl Mullan with 17 articles published followed by Elli Androulaki,Elie Bouri and David Roubaud, all of them with 10 articles published.10able 8: Most productive authorsAuthors Institution
In this subsection we analyze globally several bibliometric variables, in orderto show the degree of concentration of them. On important characteristic inbibliometric studies is the evenness of the contribution of authors, countries,and journals to a research topic. Information theory provides alternative metricsto traditional statistical measures of concentration, such as standard deviation,skewness or kurtosis. In particular, Shannon and Weaver (1949) developedthe celebrated Shannon entropy. Given a discrete distribution probability P = { p j ; j = 1 , . . . , N } , with (cid:80) Nj =1 p j = 1, Shannon entropy is defined as: S [ P ] = − N (cid:88) j =1 p j ln( p j ) (1)This formula could be interpreted from different points of view. Within thedata communication realm, it can be seen as the average rate of informationproduced by a stochastic source, and it is frequently used in data compression(Huffman, 1952). From a statistical mechanics point of view, it represents thedegree of order/disorder of a physical system (Lamberti et al., 2004; Rossoet al., 2007). It is used in economics in order to construct measures of businessconcentration (Horowitz, 1970; Hart, 1971). In biological sciences, it is usedas as a measure of the diversity at the species level (Pielou, 1966; Fedor andSpellerberg, 2013). Finally, it is used in bibliometric studies in order to studythe evenness/concentration distribution of different important variables such asresearch topics or authors, among others (Polyakov et al., 2017).In order to make interpretation easier, it is better to normalize Shannonentropy, dividing by its maximum value. Thus, the normalized entropic concen-tration index reads: H [ P ] = S [ P ] S max = − (cid:80) Nj =1 p j ln( p j )ln N (2)11able 9: Entropic concentration index ( H ) of selected variablesVariable H Authors 0.9432Sources 0.8817Countries 0.4595Research areas 0.3195Papers Citations 0.3042Under this configuration 0 ≤ H ≤
1, were 1 means that all categories are evenlyrepresented. In other words, H = 1 means an absence of concentration, and H = 0 a concentrated distribution at one single point.We calculate the normalized entropic concentration index for the distribu-tion of authors, sources, countries, research areas and citations. The resultsare displayed in Table 9. We observe that papers citations are very concen-trated ( H = 0 . Economics Letters , Finance Research Letters and
IEEE Access not only are among the journalsthat have published many articles on Bitcoin, but also have several highly citedpapers.An alternative measure of authorship concentration is Lotka’s law. Accord-ing to Lotka (1926) empirical finding, authors’ productivity follows a form ofZipf’s law. The original finding, based on a restricted database of physics andchemistry, can be summarized by the equation: a n = a n , n = 1 , , · · · , N (3)where a n is the number of authors publishing n papers, and a is the numberof authors publishing one paper. Power laws, as measures of concentration,are also used firms demography (Zambrano et al., 2015), population studies(Hernando and Plastino, 2012), and other social applications (Hernando andPlastino, 2013).Considering that Lotka (1926) deduced his empirical law from a very specific12able 10: Observed distribution of the number of authors who wrote a givennumber of papers, and Lotka’s law fitted values a n = a n c , n = 1 , , · · · , N (4)where c is a parameter that should be estimated, so that it best fit data.In our sample c = 2 .
70, with an R = 0 .
98. Table 10 summarizes theactual and fitted distribution of the number of authors publishing n papers. Weobserve that the actual number of authors publishing only 1 paper is greater ofwhat predicted by Lotka’s law, which confirms that authorship is widely andmore evenly distributed. Comparing our results to those by Chung and Cox(1990), we detect that author concentration in Bitcoin is lower than in severaltop financial journals (for any topic). The following figures were generated using VOS viewer software, which allowsto count the words which appear in the title, abstract and keywords to build allthe relations which appear between different documents published in the Webof Science (van Eck and Waltman, 2010).Figure 1 represents the cloud map with relevant words of the article. Thismap shows how many times the words appear in the articles and how relatedare between them. The main finding is that the cloud could be divided into twoparts. The right side is more related to economics and finance issues (blue andred) and the left side is more related to engineering and computer science (greenand yellow). In the economics and finance part, we can distinguish between: (i)the blue part, more focused in the finance part of Bitcoin; and (ii) the red area,more specific in topics related to monetary economics. The engineering andcomputer science side also offers two distinctive subareas. One is more relatedmore specifically to blockchain technology and smart contracts. The other is13ore related to mining protocols, security and cyber attacks. It is also relevantto observe that the expressions ’blockchain technology’, ’money’, and ’protocol’act as a nexus among different clusters.Figure 2 represents something similar to Figure 1, with the slight differencethat it is binary counted. This means that when a word appears, it is onlycounted once independently from how many times it appears in the document.This can change the results in the sense that if a word is very repeated in adocument, it does not overestimate the results. In this cloud map, we can seethat with the binary counting, there are three main clusters. The two economicsand finance clusters merged into one bigger cluster. This difference suggests thatthe difference which appeared in Figure 1 could be because some specific wordsfrom economics or finance are enough repeated in the same article to create thisdifference. The main keywords used are: blockchain and blockchain technology(blue), protocol (green), study and money (red).In terms of the source of the articles, we observe in Figure 3 again two welldifferentiated journals types: economics journals, and computer science andengineering journals. This result is consistent with those from Figure 1 and2, in the sense that Bitcoin is a multidisciplinary field, where economists andcomputer scientists are the most interested in it. In agreement with Table 5,we detect several journals that are highly connected among them. In addition,
PLOS ONE occupies a position in the middle of the graph, which is expectedas it is a multidisciplinary journal. We should also highlight that
Physica A is located in the economics area. In spite of the fact that it is a statisticalphysics journal, it publishes many papers on quantitative finance and is a highlyrespected journal within the econophysics community.Figure 4 show all the articles and the size of the node depends on the numberof citations. In this figure, we can observe that the most cited article from 2012is the one written by Zyskind et al. (2015), published in
IEEE Security andPrivacy Workshops (SPW) , and dealing with the technological part of Bitcoin.The second one, which is the one written by B¨ohme et al. (2015) and publishedin the
Journal of Economic Perspectives , is more related to economics studies.There is a group on the right of the map which are closely related papers, mostlypublished in
Economics Letters and
Finance Research Letters , such as Urquhart(2016), Cheah and Fry (2015), Dyhrberg (2016a) and Bariviera (2017).There are some other outstanding articles in terms of number of citationssuch as the one by Van Hout and Bingham (2014) published in the
InternationalJournal of Drug Policy or the one written by Miers et al. (2013) and publishedin the . Figure 2 signalizes a clear separation between technology-oriented and economic-oriented papers. Considering the intrinsic technological component of bitcoinand its strong economic impact, we detect a lack of interdisciplinary works. Forexample, despite the correlation found among cryptocurrencies (Zhang et al.,14igure 1: Cloud map of words in titles and abstracts (full counting), generated with VOSviewer ( ) igure 2: Cloud map of words in titles and abstracts (binary counting), generated with VOSviewer( ) igure 3: Cloud map of journals where papers on Bitcoin were published, generated with VOSviewer( ) igure 4: Cloud map of journals of authors with papers on Bitcoin were published, generated with VOSviewer( ) This study shows that cryptocurrencies’ literature comprises mainly combina-tion computer science and economics. Even though it is originally a technolog-ically product, the foremost blockchain applications are the cryptocurrencies.Among them, Bitcoin is the dominant actor, both in the market and in theliterature interest. The number of documents published on this topic has beenincreasing at a yearly rate of 124%, albeit diminishing over the years. This pa-per constitutes the first comprehensive bibliometric study of Bitcoin literature,comprising all the papers indexed in the Web of Science since 2012. The largeamount of data (1162 papers) allows to find significant results regarding topscholars, main journals and keywords of this multidisciplinary research field.We detected a high concentration in publishing countries. However, authors arediverse, and less concentrated than in leading finance journals. Additionally,citations are concentrated among a few papers. In future works we would liketo study the temporal evolution of keywords. Additionally, we would like to testif the quick growth and the subsequent fall in Bitcoin price in 2017 have affectedthe Bitcoin related research. Finally, related words such as “cryptocurrencies”,“fintech”, and “peer-to-peer lending” will be addressed in further research.
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