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Blockchain@UBC has published a number of research papers, through various academic partners and collobarative efforts. Explore more in detail through the list on this page.

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A survey of blockchain applicability, challenges, and key threats.

1. Introduction

• It emphasizes the different applications that have leveraged blockchain technology across diverse sectors and society in general, showing the main benefits and challenges;
• It offers an identification of the main challenges, and key threats to blockchain technology adoption, and a broad categorization of the challenges, to deliver a clearer overview and better understanding;
• It suggests possible solutions and future research directions for areas that need further exploration.

2. Applicability

2.1. iot environments, 2.2. healthcare, 2.3. cybersecurity and data management, 2.4. supply chain, 2.5. smart transportation, 2.6. education, 2.7. digital and financial management, 2.8. internet of drones, 2.9. maritime shipping, 2.10. distributed agile software development, 3. challenges and key threats, 3.1. technical and performance issues, 3.2. security and protocol integrity, 3.3. operational and global management, 3.4. legal and regulatory compliance, 3.5. adoption and knowledge barriers, 4. discussion and future directions, 5. conclusions, author contributions, data availability statement, conflicts of interest, abbreviations.

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Morar, C.D.; Popescu, D.E. A Survey of Blockchain Applicability, Challenges, and Key Threats. Computers 2024 , 13 , 223. https://doi.org/10.3390/computers13090223

Morar CD, Popescu DE. A Survey of Blockchain Applicability, Challenges, and Key Threats. Computers . 2024; 13(9):223. https://doi.org/10.3390/computers13090223

Morar, Catalin Daniel, and Daniela Elena Popescu. 2024. "A Survey of Blockchain Applicability, Challenges, and Key Threats" Computers 13, no. 9: 223. https://doi.org/10.3390/computers13090223

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IEEE Talks Blockchain

In our latest IEEE Talks Blockchain Q&A session, we spoke with Dr. Ramesh Ramadoss, co-chair of the IEEE Blockchain Initiative. Dr. Ramadoss discusses the evolution of the blockchain ecosystem, including emerging applications, standardization efforts, and opportunities for future growth and development.

IEEE White Papers

This Position Paper describes the basic framework and principles for using blockchain technology in power and energy domains with the emerging participatory grid. A key goal is the development of the most promising global Transactive Energy use cases which can be advanced toward broader commercialization using blockchain technology.

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Reinforcing the Links of the Blockchain IEEE Future Directions - November 2017

The purpose of this white paper is to explore the various ways by which the IEEE can lead and support an initiative on Blockchain while providing educational materials that will foster the next generation of blockchain engineers. This white paper summarizes and expands upon the IEEE Blockchain Incubator Workshop held by IEEE Future Directions at the end of October in 2017.

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IEEE Blockchain Articles

Blockchain for Business Value: A Contract and Work Flow Management to Reduce Disputes Pilot Project IEEE Engineering Management Review - December 2018

By Liang Xi Downey; Frédéric Bauchot; Jos Röling

Blockchain technology has seen significant growth, hype, and potential new developments over the past few years. In this article additional insights into how blockchain can add value to a business process relationship is detailed. Specifically an engineering contract workflow use application pilot including various high level system architectural aspects are presented. This application shows the integration of blockchain technology with existing legacy systems. Some management and technology issues are also overviewed for the reader.

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Blockchain-Enabled E-Voting IEEE Software - July/August 2018

By Nir Kshetri and Jeffrey Voas

Published in the July/August 2018 issue of IEEE Software ; recognized among the top eight winners of the 2018 Most Influential Blockchain Research Papers by the Third Blockchain Connect Conference Awards.

"E-Voting is among the key public sectors that can be disrupted by blockchain technology. The idea in blockchain-enabled e-voting (BEV) is simple. To use a digital-currency analogy, BEV issues each voter a “wallet” containing a user credential. Each voter gets a single “coin” representing one opportunity to vote. Casting a vote transfers the voter’s coin to a candidate’s wallet. A voter can spend his or her coin only once. However, voters can change their vote before a preset deadline."

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By Claudio Lima, Ph.D., Blockchain Engineering Council, BEC Co-Founder; Vice Chair, IEEE Blockchain Standards

"The General Data Protection Regulation (GDPR) that was recently approved for implementation in the EEUU by May 25th, 2018 is already creating some controversies, when confronted with emerging Blockchain technologies, regarding what they have most in common: data privacy and protection. These are two are essential areas where Blockchain shines."

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Read Dr. Lima's article "Adapting Blockchain for GDPR Compliance" at InformationWeek

External Publications

Enhanced Distributed Ledger Technology NIST Computer Security Resource Center - September 2019

The blockchain data structure and proof-of-work protocol were designed to solve the problem of double spending in cryptocurrencies. Although blockchain has found many applications outside of cryptocurrency, many of its features are not well suited to common data management applications. The added trust of distributed ledgers is a valuable feature, providing greatly simplified auditability and verification of actions among multiple parties in applications such as supply chain and others, but there are tradeoffs.

Blockchain's hash-based integrity verification provides trust, at the cost of an inability to delete or update records, leading to design complications that would not arise with conventional database management systems. Similarly, the sequencing guarantees of blockchain consensus protocols are needed for cryptocurrency in the absence of a universal timestamp. Moreover, actions within the distributed ledger must be connected with other actions in the real world, through accurate timestamps. We are developing a new architecture that provides the trust features of blockchains, with characteristics that allow for simpler designs and greater practicality in conventional data management problems. We believe this alternative can lead to new approaches to incorporating trust into distributed systems applications.

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Blockchain and Economic Development: Hype vs. Reality Center for Global Development - July 2017

Increasing attention is being paid to the potential of blockchain technology to address long-standing challenges related to economic development. Blockchain proponents argue that it will expand opportunities for exchange and collaboration by reducing reliance on intermediaries and the frictions associated with them. The purpose of this paper is to provide a clear-eyed view of the technology’s potential in the context of development. In it, we focus on identifying the questions that development practitioners should be asking technologists, and challenges that innovators must address for the technology to meet its potential.

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A Case Study for Blockchain in Healthcare: “MedRec” prototype for electronic health records and medical research data MIT Media Lab, Beth Israel Deaconess Medical Center - August 2016

A decentralized record management system to handle electronic health records, using Blockchain technology that manages authentication, confidentiality, accountability and data sharing.

Download white paper at HealthIT.gov (PDF, 591 KB)

• Open access
• Published: 04 July 2019

A systematic review of blockchain

• Min Xu   ORCID: orcid.org/0000-0002-3929-7759 1 ,
• Xingtong Chen 1 &
• Gang Kou 1  

Financial Innovation volume  5 , Article number:  27 ( 2019 ) Cite this article

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Blockchain is considered by many to be a disruptive core technology. Although many researchers have realized the importance of blockchain, the research of blockchain is still in its infancy. Consequently, this study reviews the current academic research on blockchain, especially in the subject area of business and economics. Based on a systematic review of the literature retrieved from the Web of Science service, we explore the top-cited articles, most productive countries, and most common keywords. Additionally, we conduct a clustering analysis and identify the following five research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.” Recommendations on future research directions and practical applications are also provided in this paper.

Introduction

The concepts of bitcoin and blockchain were first proposed in 2008 by someone using the pseudonym Satoshi Nakamoto, who described how cryptology and an open distributed ledger can be combined into a digital currency application (Nakamoto 2008 ). At first, the extremely high volatility of bitcoin and the attitudes of many countries toward its complexity restrained its development somewhat, but the advantages of blockchain—which is bitcoin’s underlying technology—attracted increasing attention. Some of the advantages of blockchain include its distributed ledger, decentralization, information transparency, tamper-proof construction, and openness. The evolution of blockchain has been a progressive process. Blockchain is currently delimited to Blockchain 1.0, 2.0, and 3.0, based on their applications. We provide more details on the three generations of blockchain in the Appendix . The application of blockchain technology has extended from digital currency and into finance, and it has even gradually extended into health care, supply chain management, market monitoring, smart energy, and copyright protection (Engelhardt 2017 ; Hyvarinen et al. 2017 ; Kim and Laskowski 2018 ; O'Dair and Beaven 2017 ; Radanovic and Likic 2018 ; Savelyev 2018 ).

Blockchain technology has been studied by a wide variety of academic disciplines. For example, some researchers have studied the underlying technology of blockchain, such as distributed storage, peer-to-peer networking, cryptography, smart contracts, and consensus algorithms (Christidis and Devetsikiotis 2016 ; Cruz et al. 2018 ; Kraft 2016 ). Meanwhile, legal researchers are interested in the regulations and laws governing blockchain-related technology (Kiviat 2015 ; Paech 2017 ). As the old saying goes: scholars in different disciplines have many different analytical perspectives and “speak many different languages.” This paper focuses on analyzing and combing papers in the field of business and economics. We aim to identify the key nodes (e.g., the most influential articles and journals) in the related research and to find the main research themes of blockchain in our discipline. In addition, we hope to offer some recommendations for future research and provide some suggestions for businesses that wish to apply blockchain in practice.

This study will conduct a systematic and objective review that is based on data statistics and analysis. We first describe the overall number and discipline distribution of blockchain-related papers. A total of 756 journal articles were retrieved. Subsequently, we refined the subject area to business and economics, and were able to add 119 articles to our further analysis. We then explored the influential countries, journals, articles, and most common keywords. On the basis of a scientific literature analysis tool, we were able to identify five research themes on blockchain. We believe that this data-driven literature review will be able to more objectively present the status of this research.

The rest of this paper is organized as follows. In the next section, we provided an overview of the existing articles in all of the disciplines. We holistically describe the number of papers related to blockchain and discipline distribution of the literature. We then conduct a further analysis in the subject field of business and economics, where we analyze the countries, publications, highly cited papers, and so on. We also point out the main research themes of this paper, based on CiteSpace. This is followed by recommendations for promising research directions and practical applications. In the last section, we discuss the conclusions and limitations.

Overview of the current research

In our research, we first conducted a search on Web of Science Core Collection (WOS), including four online databases: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), and Emerging Sources Citation Index (ESCI). We chose WOS because the papers in these databases can typically reflect scholarly attention towards blockchain. When searching the term “blockchain” as a topic, we found a total of 925 records in these databases. After filtering out the less representative record types, we reduced these papers to 756 articles that were then used for further analysis. We extracted the full bibliographic record of the articles that we identified from WOS, including information on the title, author, keywords, abstract, journal, year, and other publication information. These records were then exported to CiteSpace for subsequent analysis. CiteSpace is a scientific literature analysis tool that enables us to visualize trends and patterns in the scientific literature (Chen 2004 ). In this paper, CiteSpace is used to visually represent complex structures for statistical analysis and to conduct cluster analysis.

Table  1 shows the number of academic papers published per year. We have listed the number of all of the publications in WOS, the number of articles in all of the disciplines, and the number of articles in business and economics subjects. It should be noted that we retrieved the literature on March 25, 2019. Therefore, the number of articles in 2019 is relatively small. The number of papers has continued to grow in recent years, which suggests that there is a growing interest in blockchain. All of the extracted papers in WOS were published after 2015, which is seven years after blockchain and bitcoin was first described by Nakamoto. In these initial seven years, many papers were published online or indexed by other databases. However, we have not discussed these papers here. We only chose WOS, representative high-level literature databases. This is the most common way of doing a literature review (Ipek 2019 ).

In the 756 articles that we managed to retrieve, the three most common keywords besides blockchain are bitcoin, smart contract, and cryptocurrency, with the frequency of 113 times, 72 times, and 61 times, respectively. This shows that the majority of the literature mentions the core technology of blockchain and its most widely known application—bitcoin.

In WOS, each article is assigned to one or more subject categories. Therefore, we use CiteSpace to visualize what research areas are involved in current research on blockchain. Figure  1 shows a network of such subject categories. The most common category is Computer Science, which has the largest circle, followed by Engineering and Telecommunications. Business and Economics is also a common subject area for blockchain. Consequently, in the following session, we will conduct further analysis in this field.

Disciplines in blockchain

Articles in business and economics

Given that the main objective of our research was to understand the research of blockchain in the area of economics and management, we conduct an in-depth analysis on the papers in this field. We refined the research area to Business and Economics, and we finally retrieved 119 articles from WOS. In this session, we analyzed their published journals, research topics, citations, and so on, to depict the research status of blockchain in the field of business and economics more comprehensively.

There are several review papers on blockchain. Each of these paper contains a summary of multiple research topics, instead of a single topic. We do not include these literature reviews in our paper. However, it is undeniable that these articles also play an important role on the study of blockchain. For instance, Wang et al. ( 2019 ) investigate the influence of blockchain on supply chain practices and policies. Zhao et al. ( 2016 ) suggest blockchain will widely adopted in finance and lead to many business innovations and research opportunities.

The United States, the United Kingdom, and Germany are the top three countries by the number of papers published on blockchain; the specific data are shown in Table  2 . The United States released more papers than the other countries and it produced more than one-third of the total articles. As of the time of data collection, China contributed 11 papers, ranking fourth. The 119 papers in total are drawn from 17 countries and regions. In contrast, we searched “big data” and “financial technology” in the same way, and found 286 papers on big data that came from 24 countries, while 779 papers on fintech came from 43 countries. This shows that blockchain is still an emerging research field, and it needs more countries and scholars to join in the research effort.

We counted the journals published in these papers and we found that 44 journals published related papers. Table  3 lists the top 11 journals to have published blockchain research. First is “Strategic Change: Briefings in Entrepreneurial Finance,” followed by “Financial Innovation” and “Asia Pacific Journal of Innovation and Entrepreneurship.” The majority of papers in the journal “Strategic Change” were published in 2017, except for one in 2018 and one in 2019. Papers in the journal “Financial Innovation” were generally published in 2016, with one published in 2017 and one in 2019. All five of the papers in the journal “Asia Pacific Journal of Innovation and Entrepreneurship” were published in 2017.

Cited references

Table  4 presents the top six cited publications, which were cited no less than five times. The list consists of three books and three journal articles. Some of these publications introduce blockchain from a technical perspective and some from an application perspective. Swan’s ( 2015 ) book illustrates the application scenarios of blockchain technology. In this book, the author describes that blockchain is essentially a public ledger with potential as a decentralized digital repository of all assets—not only tangible assets but also intangible assets such as votes, software, health data, and ideas. Tapscott and Tapscott’s ( 2016 ) book explains why blockchain technology will fundamentally change the world. Yermack ( 2017 ) points out that blockchain will have a huge impact and will present many challenges to corporate governance. Böhme et al. ( 2015 ) introduce bitcoin, the first and most famous application of blockchain. Narayanan et al. ( 2016 ) also focus on bitcoin and explain how bitcoin works at a technical level. Lansiti and Lakhani ( 2017 ) argue it will take years to truly transform the blockchain because it is a fundamental rather than destructive technology, which will not drive implementation, and companies will need other incentives to adopt blockchain.

Most influential articles

These 119 papers were cited 314 times in total, and 270 times without self-citations. The number of articles that they cited are 221, of which 197 are non-self-citations. The most influential articles with more than 10 citations are listed in Table  5 . The most popular article in our dataset is Lansiti and Lakhani ( 2017 ), with 49 citations in WOS. This suggests that this article has had a strong influence on the research of blockchain. This paper believes there is still a distance to the real application of the blockchain. The other articles describe how blockchain affects and works in various areas, such as financial services, organizational management, and health care. Since blockchain is an emerging technology, it is particularly necessary to explore how to combine blockchains with various industries and fields.

By comparing the journals in Tables 4 and 5 , we find that some journals appeared in both of the lists, such as Financial Innovation. In other words, papers on blockchain are more welcomed in these journals and the journal’s papers are highly recognized by other scholars. Meanwhile, although journals such as Harvard Business Review have only published a few papers related to blockchain, they are highly cited. Consequently, the journals in both of these lists are of great importance.

Research themes

Addressing research themes is crucial to understanding a research field and exploring future research directions. This paper explored the research topic based on keywords. Keywords are representative and concise descriptions of article content. First, we analyzed the most common keywords used by the papers. We find that the top five most frequently used keywords are “blockchain,” “bitcoin,” “cryptocurrency,” “fintech,” and “smart contract.” Although the potential for blockchain applications goes way beyond digital currencies, bitcoin and other cryptocurrencies—as an important blockchain application scenario in the finance industry—were widely discussed in these articles. Smart contracts allow firms to set up automated transactions in blockchains, thus playing a fundamentally supporting role in blockchain applications. Similar to the literature in all of the subject areas, studies in business and economics also frequently use bitcoin, cryptocurrency, and smart contract as their keywords. The difference is that many researchers have combined blockchain with finance, regarding it as an important financial technology.

After analyzing the frequency of keywords, we conducted a keywords clustering analysis to identify the research themes. As shown in Fig.  2 , five clusters were identified through the log-likelihood ratio (LLR) algorithm in Citespace, they are: cluster #0 “economic benefit,” cluster #1 “blockchain technology,” cluster #2 “initial coin offerings,” cluster #3 “fintech revolution,” and cluster #4 “sharing economy.”

Disciplines and topics

Many researchers have studied the economic benefits of blockchain. They suggest the application of blockchain technology to streamline transactions and settlement processes can effectively reduce the costs associated with manual operations. For instance, in the health care sector, blockchain can play an important role in centralizing research data, avoiding prescription drug fraud, and reducing administrative overheads (Engelhardt 2017 ). In the music industry, blockchain could improve the accuracy and availability of copyright data and significantly improve the transparency of the value chain (O'Dair and Beaven 2017 ). Swan ( 2017 ) expound the economic value of block chain through four typical applications, such as digital asset registries, leapfrog technology, long-tail personalized economic services, and payment channels and peer banking services.

The representative paper for cluster “blockchain technology” was published by Lansiti and Lakhani ( 2017 ), who analyze the inherent features of blockchain and pointed out that we still have a lot to do to apply blockchain extensively. Other researchers have explored the characteristics of blockchain technology from multiple perspectives. For example, Xu ( 2016 ) explores the types of fraud and malicious activities that blockchain technology can prevent and identifies attacks to which blockchain remains vulnerable. Meanwhile, Aune et al. ( 2017 ) propose a cryptographic approach to solve information leakage problems on a blockchain.

Initial coin offering (ICO) is also a research topic of great concern to scholars. Many researchers analyze the determinants of the success of initial coin offerings (Adhami et al. 2018 ; Ante et al. 2018 ). For example, Fisch ( 2019 ) assesses the determinants of the amount raised in ICOs and discusses the role of signaling ventures’ technological capabilities in ICOs. Deng et al. ( 2018 ) argue the outright ban on ICOs might hamper revolutionary technological development and they provided some regulatory reform suggestions on the current ICO ban in China.

Many researchers have explored blockchain’s support for various industries. The fintech revolution brought by the blockchain has received extensive attention (Yang and Li 2018 ). Researchers agree that this nascent technology may transform traditional trading methods and practice in financial industry (Ashta and Biot-Paquerot 2018 ; Chen et al. 2017 ; Kim and Sarin 2018 ). For instance, Gomber et al. ( 2018 ) discuss transformations in four areas of financial services: operations management, payments, lending, and deposit services. Dierksmeier and Seele ( 2018 ) address the impact of blockchain technology on the nature of financial transactions from a business ethics perspective.

Another cluster corresponds to the sharing economy. A handful of researchers have focused on this field and they have discussed the supporting role played by blockchain in the sharing economy. Pazaitis et al. ( 2017 ) describe a conceptual economic model of blockchain-based decentralized cooperation that might better support the dynamics of social sharing. Sun et al. ( 2016 ) discuss the contribution of emerging blockchain technologies to the three major factors of the sharing economy (i.e., human, technology, and organization). They also analyze how blockchain-based sharing services contribute to smart cities.

In this section, we will discuss the following issues: (1) What will be the future research directions for blockchain? (2) How can businesses benefit from blockchain? We hope that our discussions will be able to provide guidance for future academic development and social practice.

What will be the future research directions for blockchain?

In view of the five themes mentioned in this paper, we provide some recommendations for future research in this section.

The economic benefits of blockchain have been extensively studied in previous research. For individual businesses, it is important to understand the effects of blockchain applications on the organizational structure, mode of operation, and management model of the business. For the market as a whole, it is important to determine whether blockchain can resolve the market failures that are brought about by information asymmetry, and whether it can increase market efficiency and social welfare. However, understanding the mechanisms through which blockchain influences corporate and market efficiency will require further academic inquiry.

For researchers of blockchain technology, this paper suggests that we should pay more attention to privacy protection and security issues. Despite the fact that all of the blockchain transactions are anonymous and encrypted, there is still a risk of the data being hacked. In the security sector, there is a view that absolute security can never be guaranteed wherever physical contact exists. Consequently, the question of how to share transaction data while also protecting personal data privacy are particularly vital issues for both academic and social practice.

Initial coin offering and cryptocurrency markets have grown rapidly. They bring many interesting questions, such as how to manage digital currencies. Although the majority of the previous research has focused on the determinants of success of initial coin offerings, we believe that future research will discuss how to regulate cryptocurrency and the ICO market. The success of blockchain technology in digital currency applications prior to 2015 caught the attention of many traditional financial institutions. As blockchain has continued to reinvent itself, in 2019 it is now more than capable of meeting the needs of the finance industry. We believe that blockchain is able to achieve large-scale applications in many areas of finance, such as banking, capital markets, Internet finance, and related fields. The deep integration of blockchain technology and fintech will continue to be a promising research direction.

The sharing economy is often defined as a peer-to-peer based activity of sharing goods and services among individuals. In the future, sharing among enterprises may become an important part of the new sharing economy. Consequently, building the interconnection of blockchains may become a distinct trend. These interconnections will facilitate the linkages between processes of identity authentication, supply chain management, and payments in commercial operations. They will also allow for instantaneous information exchange and data coordination among enterprises and industries.

How can businesses benefit from blockchain?

Businesses can leverage blockchains in a variety of ways to gain an advantage over their competitors. They can streamline their core business, reduce transaction costs, and make intellectual property ownership and payments more transparent and automated (Felin and Lakhani 2018 ). Many researchers have discussed the application of blockchain in business. After analyzing these studies, we believe that enterprises can consider applying blockchain technology in the four aspects that follow.

Accounting settlement and crowdfunding

Bitcoin or another virtual currency supported by blockchain technology can help businesses to solve funding-related problems. For instance, cryptocurrencies support companies who wish to implement non-cash payments and accounting settlement. The automation of electronic transaction management accounting improves the level of control of monetary business execution, both internally and externally (Zadorozhnyi et al. 2018 ). In addition, blockchain technology represents an emerging source of venture capital crowdfunding (O'Dair and Owen 2019 ). Investors or founders of enterprises can obtain alternative entrepreneurial finance through token sales or initial coin offerings. Companies can handle financial-related issues more flexibly by holding, transferring, and issuing digital currencies that are based on blockchain technology.

Data storage and sharing

As the most valuable resource, data plays a vital role in every enterprise. Blockchain provide a reliable storage and efficient use of data (Novikov et al. 2018 ). As a decentralized and secure ledger, blockchain can be used to manage digital asset for many kinds of companies (Dutra et al. 2018 ). Decentralized data storage means you do not give the data to a centralized agency but give it instead to people around the world because no one can tamper with the data on the blockchain. Businesses can use blockchain to store data, improve the transparency and security of the data, and prevent the data from being tampered with. At the same time, blockchain also supports data sharing. For instance, all of the key parties in the accounting profession leverage an accountancy blockchain to aggregate and share instances of practitioner misconduct across the country on a nearly real-time basis (Sheldon 2018 ).

Supply chain management

Blockchain technology has the potential to significantly change supply chain management (SCM) (Treiblmaier 2018 ). Recent adoptions of the Internet of Things and blockchain technologies support better supply-chain provenance (Kim and Laskowski 2018 ). When the product goes from the manufacturer to the customer, important data are recorded in the blockchain. Companies can trace products and raw materials to effectively monitor product quality.

Smart trading

Businesses can build smart contracts on blockchain, which is widely used to implement business collaborations in general and inter-organizational business processes in particular. Enterprises can automate transactions based on smart contracts on block chains without manual confirmation. For instance, businesses can file taxes automatically under smart contracts (Vishnevsky and Chekina 2018 ).

Conclusions

This paper reviews 756 articles related to blockchain on the Web of Science Core Collection. It shows that the most common subject area is Computer Science, followed by Engineering, Telecommunications, and Business and Economics. In the research of Business and Economics, several key nodes are identified in the literature, such as the top-cited articles, most productive countries, and most common keywords. After a cluster analysis of the keywords, we identified the five most popular research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.”

As an important emerging technology, blockchain will play a role in many fields. Therefore, we believe that the issues related to commercial applications of blockchain are critical for both academic and social practice. We propose several promising research directions. The first important research direction is understanding the mechanisms through which blockchain influences corporate and market efficiency. The second potential research direction is privacy protection and security issues. The third relates to how to manage digital currencies and how to regulate the cryptocurrency market. The fourth potential research direction is how to deeply integrate blockchain technology and fintech. The final topic is cross-chain technology—if each industry has its own blockchain system, then researchers and developers must discover new ways to exchange data. This is the key to achieving the Internet of Value. Thus, cross-chain technology will become an increasingly important topic as time goes on.

Businesses can benefit considerably from blockchain technology. Therefore, we suggest that the application of blockchain be taken into consideration when businesses have the following requirements: accounting settlement and crowdfunding, data storage and sharing, supply chain management, and smart trading.

Our study has recognized some limitations. First, this paper only analyzes the literature in Web of Science Core Collection databases (WOS), which may lead to the incompleteness of the relevant literature. Second, we filter our literature base on the subject category in WOS. In this process, we may have omitted some relevant research. Third, our recommendations have subjective limitations. We hope to initiate more research and discussions to address these points in the future.

Availability of data and materials

Data used in this paper were collected from Web of Science Core Collection.

Abbreviations

Initial coin offering

Web of Science Core Collection

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This research is supported by grants from National Natural Science Foundation of China (Nos. 71701168 and 71701034).

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Three generations of blockchain

The scope of blockchain applications has increased from virtual currencies to financial applications to the entire social realm. Based on its applications, blockchain is delimited to Blockchain 1.0, 2.0, and 3.0.

Blockchain 1.0

Blockchain 1.0 was related to virtual currencies, such as bitcoin, which was not only the first and most widely used digital currency but it was also the first application of blockchain technology (Mainelli and Smith 2015 ). Digital currencies can reduce many of the costs associated with traditional physical currencies, such as the costs of circulation. Blockchain 1.0 produced a great many applications, one of which was Bitcoin. Most of these applications were digital currencies and tended to be used commercially for small-value payments, foreign exchange, gambling, and money laundering. At this stage, blockchain technology was generally used as a cryptocurrency and for payment systems that relied on cryptocurrency ecosystems.

Blockchain 2.0

Broadly speaking, Blockchain 2.0 includes Bitcoin 2.0, smart-contracts, smart-property, decentralized applications (Dapps), decentralized autonomous organizations (DAOs), and decentralized autonomous corporations (DACs) (Swan 2015 ). However, most people understand Blockchain 2.0 as applications in other areas of finance, where it is mainly used in securities trading, supply chain finance, banking instruments, payment clearing, anti-counterfeiting, establishing credit systems, and mutual insurance. The financial sector requires high levels of security and data integrity, and thus blockchain applications have some inherent advantages. The greatest contribution of Blockchain 2.0 was the idea of using smart-contracts to disrupt traditional currency and payment systems. Recently, the integration of blockchain and smart contract technology has become a popular research topic in problem resolution. For example, Ethereum, Codius, and Hyperledger have established programmable contract language and executable infrastructure to implement smart contracts.

Blockchain 3.0

In ‘Blockchain: Blueprint for a New Economy’, Blockchain 3.0 is described as the application of blockchain in areas other than currency and finance, such as in government, health, science, culture, and the arts (Swan 2015 ). Blockchain 3.0 aims to popularize the technology, and it focuses on the regulation and governance of its decentralization in society. The scope of this type of blockchain and its potential applications suggests that blockchain technology is a moving target (Crosby et al. 2016 ). Blockchain 3.0 envisions a more advanced form of “smart contracts” to establish a distributed organizational unit that makes and is subject to its own laws and which operates with a high degree of autonomy (Pieroni et al. 2018 ).

The integration of blockchain with tokens is an important combination of Blockchain 3.0. Tokens are proofs of digital rights, and blockchain tokens are widely recognized thanks to Ethereum and its ERC20 standard. Based on this standard, anyone can issue a custom token on Ethereum and this token can represent any right or value. Tokens refer to economic activities generated through the creation of encrypted tokens, which are principally but not exclusively based on the ERC20 standard. Tokens can serve as a form of validation of any right, including personal identity, academic diplomas, currency, receipts, keys, event tickets, rebate points, coupons, stocks, and bonds. Consequently, tokens can validate virtually any right that exists within a society. Blockchain is the back-end technology of the new era, while tokens are its front-end economic face. The combination of the two will bring about major societal transformation. Meanwhile, Blockchain 3.0 and its token economy continue to evolve.

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Research Article

A look into the future of blockchain technology

Roles Conceptualization, Data curation, Investigation, Methodology

Affiliation Groupe ALTEN, France

Contributed equally to this work with: Francesco Fontana, Elisa Ughetto

Roles Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, Italy

Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation Politecnico di Torino & Bureau of Entrepreneurial Finance, Corso Duca degli Abruzzi 24, Turin, Italy

• Daniel Levis, 
• Francesco Fontana, 
• Elisa Ughetto

• Published: November 17, 2021
• https://doi.org/10.1371/journal.pone.0258995
• Reader Comments

In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications might affect firms’ organizations, innovations, and strategies by 2030, and, consequently, which societal areas may be mainly affected. We provide a deep understanding of how the adoption of this technology could lead to changes in Europe over multiple dimensions, ranging from business to culture and society, policy and regulation, economy, and technology. From the projections that reached a significant consensus and were given a high probability of occurrence by the experts, we derive four scenarios built around two main dimensions: the digitization of assets and the change in business models.

Citation: Levis D, Fontana F, Ughetto E (2021) A look into the future of blockchain technology. PLoS ONE 16(11): e0258995. https://doi.org/10.1371/journal.pone.0258995

Editor: Alessandro Margherita, University of Salento, ITALY

Received: June 1, 2021; Accepted: October 9, 2021; Published: November 17, 2021

Copyright: © 2021 Levis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Over the last few years, the hype and interest around blockchain technology have consistently increased. Practitioners from many industries and sectors have joined an open, yet mainly unstructured, discussion on the potential disruptive capabilities of this newly born technology [ 1 – 3 ]. In principle, the size of the phenomenon could be huge, with latest estimates predicting blockchain to store, by 2025, the 10 per cent of the world’s GDP (about $88tn in 2019) [ 4 ]. However, the complexity of the technology itself and the difficulties in assessing its impact across the different application fields have prevented the social, industrial and scientific communities to agree upon a shared vision of future blockchain-based scenarios. Very fundamental questions are still to be answered. Which blockchain-enabled applications will see the light in the next few years? Which industrial sectors will be mainly affected? How will companies react to potential industry-disruptors? How will the current societal paradigm shift? Which role will policy makers play in enhancing this new paradigm?

Despite the great and undoubted technological innovation linked to this technology, uncertainties and speculation on the potential scenarios still animate the industrial and scientific dialogue [ 5 ]. In particular, it is not yet clear which applications will see the light, and, eventually, what effects these changes will have at a societal level.

In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications will affect firms’ organizations, innovations and strategies by 2030, and, consequently, which societal areas will be mainly affected. With this methodology, we aim at reaching experts’ consensus to gain new insights and assess the likelihood about the future of the technology. This is a relevant issue, as blockchain technology applications cover a wide spectrum of areas. Blockchain can be applied vertically within an industry (e.g. disrupting its supply chain) or horizontally across different industries or within single companies (e.g. modifying the internal structures and the modus operandi of the different company functions). Given the number of potential applications and the complexity of the technology, stakeholders are divided into skeptics, who believe the technology is still too immature to become a paradigm in the near future, and enthusiasts, who instead believe that this radical innovation will disrupt many industries and completely change business models and people’s behaviors, like internet did during the 90s.

The literature on blockchain is also widely fragmented. Different works have investigated possible blockchain applications within specific domains, such as finance [ 6 – 8 ], logistics [ 9 ], healthcare [ 10 , 11 ] and education [ 12 ]. However, a holistic approach on possible blockchain-enabled future scenarios is still missing. To our knowledge, the only contribution in this direction is the one by White [ 13 ], who explores blockchain as a source of disruptive innovation exclusively with regard to the business field. We depart from his work to adopt a much wider perspective in this study. In fact, our aim is to obtain a deep understanding on how the adoption of this technology in Europe will lead to changes over multiple dimensions, ranging from business to culture and society, policy and regulation, economy and technology. Thus, our research aims at exploring if a convergence between the two divergent perspectives on blockchain can be found, bringing together experts currently working on blockchain projects to explore the possible changes that the technology will bring to the society by 2030.

Our study outlines an overall agreement among experts that the blockchain technology will have a deep impact on multiple dimensions. In the near future people will likely start using and exploit the blockchain technology potential, without really knowing how the technology behind works, in the same way as they send emails today, ignoring how the digital architecture that allows to exchange bytes of information works. Policy makers and governments will play a crucial role in this respect, by enabling productivity boosts and competitive gains from the companies operating under their jurisdictions. As such, a tight and cooperative relationship between industrial actors and regulatory bodies will be extremely important and auspicial. To this aim, it will be of key importance for all players to understand the real competitive advantage that blockchain can bring to their own industry and market.

This work aims at contributing to the raising blockchain literature by offering a holistic view on possible blockchain-enabled future scenarios in Europe, and to investigate which of the proposed scenarios is more likely to occur. As widely agreed by the academic literature, technological developments dictate the speed and pace at which societies change [ 14 ]. Under this assumption, technological forecasting appears to be a method of fundamental importance to understand “ex-ante” the potential development of technological changes, and their impact on different societal aspects [ 15 ]. Foreseeing future technological trends could help society in understanding possible future scenarios, thus contributing to a better knowledge of the new paradigms our society is heading towards. The work is structured as follows. Section 2 provides an overview on the main research streams upon which this work is based. Section 3 presents the methodology. Results are described in Section 4 and Section 5 concludes the work.

2 Background literature

2.1 the blockchain technology.

As defined by Crosby et al. [ 3 ] a blockchain can be conceptualized as a shared and decentralized ledger of transactions. This chain grows as new blocks (i.e. read transactions or digital events) are appended to it continuously [ 16 , 17 ]. Each transaction in the ledger must be confirmed by the majority of the participants in the system [ 3 , 18 – 21 ]. This means for the community to verify the truthfulness of the new piece of information and to keep the blockchain copies synchronized between all the nodes (i.e. between all the participants to the network) in such a way that everybody agrees which is the chain of blocks to follow [ 19 ]. Thus, when a client executes a transaction (e.g. when it sends some value to another client), it broadcasts the transaction encrypted with a specific technique to the entire network, so that all users in the system receive a notification of the transaction in a few seconds. At that moment, the transaction is “unconfirmed”, since it has not yet been validated by the community. Once the users verify the transaction with a process called mining, a new block is added to the chain. Usually, the miner (i.e. the user participating to the verification process) receives a reward under the form of virtual coins, called cryptocurrencies. Examples of cryptocurrencies are Bitcoins, Ether, Stellar Lumens and many others. Virtual coins can then be used on the blockchain platform to transfer value between users [ 17 – 19 ].

Thanks to a combination of mathematics and cryptography, the transactions between users (i.e. exchange of data and value), once verified by the network and added to the chain, are “almost” unmodifiable and can be considered true with a reasonable level of confidence [ 17 , 19 , 22 ]. These attributes of the technology make it extremely efficient in transferring value between users, solving the problem of trust and thus potentially eliminating the need of a central authority (e.g. a bank) that authorizes and certifies the transactions [ 7 , 23 , 24 ].

The technology can be easily applied to form legally binding agreements among individuals. The digitalized asset, which is the underlying asset of the contract, is called token. A token can be a digitalized share of a company, as well as a real estate property or a car. Through the setting of smart contracts (i.e. digitalized contracts between two parties), the blockchain technology allows users to freely trade digital tokens, and consequently to trade their underling physical assets without the need of a central authority to certify the transaction (OECD, 2020).

2.2 Blockchain technology applications

The academic literature has investigated a wide range of possible blockchain applications within specific domains, such as finance [ 6 – 8 ], logistics [ 9 ], healthcare [ 10 , 11 ] and education [ 12 ].

As mentioned, one of the undoubted advantages of the blockchain technology is the possibility to overcome the problem of trust while transferring value [ 25 ]. Not surprisingly, the technology seems to find more applications in markets where intermediation is currently high, like the financial sector, and in particular the FinTech sector, that has recently experienced a consistent make-over thanks to the diffusion of digital technologies [ 7 , 26 , 27 ]. The implementation of the blockchain technology in the financial markets could provide investors and entrepreneurs with new tools to successfully exchange value and capitals without relying on central authorities, ideally solving the problem of trust. This is among the reasons why many observers believe that the blockchain would become a potential mainstream financial technology in the future [ 28 ]. Blockchain represents an innovation able to completely remodel our current financial system, breaking the old paradigm requiring trusted centralized parties [ 6 – 8 ]. With new blockchain-based automated forms of peer-to-peer lending, individuals having limited or no access to formal financial services could gain access to basic financial services previously reserved to individuals with certified financial records [ 29 ]. Indeed, blockchain technology can provide value across multiple dimensions, by decreasing information asymmetries and reducing related transactional costs [ 30 ]. Initial coin offerings (ICOs) represent one of the most successful blockchain-based applications for financing which has been currently developed. Virtual currencies like Bitcoins can disruptively change the way in which players active in the business of financing new ventures operate [ 7 , 30 – 33 ]. Through an ICO, a company in need of new capital offers digital stocks (named token) to the public. These digital tokens will then be used by investors to pay the future products developed by the financed company [ 30 , 34 , 35 ]. ICOs represents a disruptive tool: entrepreneurs can now finance their ventures without intermediaries and consequently lower the cost of the capital raised [ 31 , 36 ]. However, some threats coming from the technology adoption can also be identified, as blockchain can also lead to higher risks related to the lower level of control intrinsically connected to the technology, especially in the case of asymmetric information between the parties involved.

Disintermediation plays a key role in the healthcare sector as well, where blockchain has recently found numerous applications. Indeed, many players currently need to exchange a huge amount of information to effectively manage the whole sector: from hospitals, to physicians, to patients. The ability to trustfully exchange data and information becomes of undoubted value in this context [ 10 , 11 ]. It should not be difficult to envision blockchain applications in other fields as well. In every sector in which information, value, or goods are supposed to flow between parties, blockchain can enable a trustful connection between the players, with the need of a central body entrusting the transaction. Within supply chain, it can increase security and traceability of goods [ 9 , 37 ]. Within education, it can help in certifying students’ acquired skills, reducing, for example, degree fraud [ 12 ]. To conclude, a recent work from Lumineau et al. [ 38 ] highlights possible implications of the technology in the way collaborations are ruled and executed, shading light on new organizational paradigms. Indeed, the authors show how the intrinsically diverse nature of the technology could strongly affect organizational outcomes, heavily influencing and modifying (possibly improving) the way in which different entities cooperate and collaborate.

3 Research methodology

3.1 forecasting technique: the delphi method.

In the past decade, an increasing number of forecasting techniques has been employed in the academic literature to predict the potential developments induced by technological changes. In particular, the Delphi method, whose term derives from the Greek oracle Delphos, is a systematic and interactive method of prediction, which is based on a panel of experts and is carried out through a series of iterations, called rounds. Many academic works have adopted this method since its development [ 14 , 39 – 44 ]. As the core of the Delphi approach, experts are required to evaluate projections (representations of possible futures) and assess their societal impact and the likelihood that they will occur within a specific time horizon.

While the majority of forecasting methods does not account for the technological implications on the social, economic and political contexts, the Delphi technique allows subjective consideration of changes in interrelated contexts [ 45 ]. Many different variants of the Delphi methodology have been developed according to the needs and goals of each research. For the purpose of this research, we decided to follow the four-steps procedure suggested by Heiko and Darkow [ 46 ] ( Fig 1 ).

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The first step of the method requires to develop and envisage projections and possible scenarios that might arise through the adoption of the technology. These projections must be short, unequivocal, and concise [ 14 ]. This phase requires researchers to deeply understand the technology by analyzing the existing literature, attending courses and workshops and conducting a number of face-to-face interviews with experts ( Fig 2 ). Once the insights are gathered, the results are synthetized in future projections that will help develop the survey. The second step consists in presenting the study to the panel of selected experts who will take part in the first round of the survey. The main challenge during this phase is to select an appropriate panel of experts and maintain their commitment and response rate. The third step consists in a statistical and quantitative analysis of the answers received and in the selection of the second-round scenarios that experts will need to evaluate again. Through the analysis of the second round of answers, updated scenarios are developed adding to the projections the qualitative and quantitative insights provided by the research. The ultimate goal of this iterative process is to reach consensus among the experts on the scenarios that are most likely to happen in the future.

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3.2 Formulation of the Delphi projections

The formulation of the projections represents a key aspect of the methodology and requires a particular attention and effort. In this phase, the projections that are later tested by the panel of experts are generated. Vagueness and inaccuracy might generate confusion in experts, leading to less meaningful results. To avoid this situation, we developed the projections by means of triangulation: literature review, interviews with experts and participation to workshops and conferences. The analysis of the literature on blockchain technology (and its benefits) allowed us to understand which industries and businesses will be mainly impacted by the technology.

We chose 2030 as a time horizon for the generation of the scenarios. This is a recommended time span for a Delphi study, since a superior period would have become unmanageable to provide relevant advice for strategic development. As reported in Table 1 , projections span among different areas. To the scope of the work, i.e. to grasp a holistic view of the most likely scenarios, it was necessary to investigate a number of multiple dimensions. Projections are related to socio-cultural, policy and regulations, economic, technological and business aspects. As it can be noticed, projections are all structured in the same way, to facilitate their understanding by experts.

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3.2.1 Interviews with experts.

Twelve blockchain experts were interviewed among academics, startups’ founders and professionals working in consultancy firms, banks and legal institutions. The selection of the experts was made in order to get different points of view and a high level of expertise, as provided by the Delphi method guidelines. We conducted interviews that took between thirty and forty-five minutes on average, according to the interviewee’s availability. Each single interview was tailored for each participant by providing guidelines and reflection tips to encourage discussion. However, a certain degree of freedom was given to the expert to allow his/her spontaneous contribution and to gain some original insights that helped in the final design of the future scenarios. Some common aspects were discussed in all interviews generating redundancy and repetition of already emerged scenarios (e.g. ICOs, business model evolution, security and utility tokens, and legal issues). This is one of the reasons why twelve interviews were considered to be sufficient for the purposes of our research.

3.2.2 Conferences.

One of the authors attended three main events in order to strengthen the knowledge about blockchain and have a broader view of its implications in different fields and industries: one in Milan and two in Paris. Of particular notice, the Community Blockchain Week, a blockchain tech-focused initiative organized voluntarily by actors engaged into the technology and with the will and vision to spread the knowledge among citizens. Thanks to various workshops and speeches during the week, it was possible to dive deeper into many aspects of the technology, as well as to meet some knowledgeable experts of various fields, some of which agreed in participating to the research. The event was extremely useful not only to understand how the technology is evolving, but also to see how the community engages itself to spread the knowledge in order to generate more and more interest around it.

3.2.3 Desk research.

We performed desk research to formulate the initial set of projections. Through the survey of the literature, we gained a comprehensive view of all the potential scenarios of the technology. The analysis of consulting companies’ reports also offered a broader vision of future scenarios, thanks to their strategic rather than technical approach [ 1 , 2 ]. This process led to identify 76 projections that represented the basis for a reflection during the expert face-to-face interviews. After screening the relevant articles and reports, a first filtering of the identified 76 projections was made in order to dismiss redundant or incomplete projections, and to keep only the most complete and varied ones. This process reduced the number of projections to 33 and to 20 after the review of two experts.

3.3 The Delphi projections

The formulation of the projections represents the most sensitive part of the research since it influences the whole study. A detailed analysis was carried out in order to avoid mistakes and confusion. In order to facilitate the respondents filling the questionnaire and to avoid any kind of ambiguity, an introduction explaining the meaning of the terminology used in the questionnaire was presented before starting the survey. The developed scenarios were broken down into six macro categories (the same as proposed by Heiko and Darkow [ 46 ]) to guarantee a more complete and systemic view of how the blockchain ecosystem and community can change and shape the future. The choice of 20 projections to be evaluated by experts is in line with prior studies exploiting the Delphi method [ 46 , 47 ]. Parente and Anderson-Parente [ 47 ] have proposed to limit the number of Delphi questions (e.g. to 25 questions) in order to guarantee a high response rate and properly filled-in questionnaires, including only closed answers. We decided to add the possibility to comment the given answers in order to gather additional qualitative data to improve the quality of the results, in line with the methodology proposed by Heiko and Darkow [ 46 ].

3.4 Selection of the panel of experts

As blockchain experts that took part to the survey, we selected individuals working in companies and institutions on the basis of their experience and knowledge of the field. Following Adler and Ziglio [ 48 ] and Heiko and Darkow [ 46 ] four requirements for “expertise” were considered:

• knowledge and experience on blockchain technology;
• capacity and willingness to participate to the Delphi study;
• sufficient time to participate to the Delphi study;
• effective communication skills.

A minimum panel size of 15–25 participants is often required to lead to consistent results. In our case, a panel of 35 experts was reached for the first round. For the reliability of the study the panelists were selected with different backgrounds and profiles. To be aligned with the European focus of the study, we considered experts working in twelve European countries, being France and Italy the ones with the highest number of respondents. The panel characteristics are reported in Figs 3 , 4 and 5 .

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3.5 Execution of the Delphi surveys

In line with the methodology proposed by Heiko and Darkow [ 46 ], two rounds of surveys were executed. We decided to carry no more than two rounds because participating to a Delphi study requires a lot of effort and is a time-consuming task for panelists. By limiting the rounds to two, we reached a sufficient number of respondents that led to have valuable results and consistent conclusions. Moreover, since for each scenario the possibility to include a qualitative argumentation was included, the smaller number of iterations worked as a stimulus for the experts to explain the reasons of their quantitative answers.

The survey was carried out following the standards of the Internet-based Delphi, also called e-Delphi [ 39 , 40 ]. Giving the possibility to respondents to answer digitally allowed experts to be more flexible in responding to the survey, ensuring a greater participation. The way the questionnaire was structured was exactly as the e-Delphi website suggests, but for practical reasons we edited the survey using Google Form. Other standards, such as the real-time Delphi solution proposed by several studies [ 14 , 42 , 43 , 49 ] could have led to a better comparison among experts, but would have likely caused more withdraws to the survey.

3.5.1 First round.

In the first round of the survey, the experts assessed the expected probability and impact of the twenty outlined projections. Some Delphi studies [ 50 , 51 ] include a third factor that helps to assess the desirability of a scenario (i.e. how much an expert is in favour of the realization of a prediction). However, we decided not to include this last aspect to make the questionnaire lighter and faster to be filled in, and to reduce drop-outs ( Table 2 ).

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Impact, evaluated at the industry level, was measured on a five-point Likert scale [ 52 ]. Since there is not a general consensus among experts regarding the number of points the scale should have, and due to the general nature of the scenarios, we preferred to use a five-point Likert scale. The corresponding probabilities are: 0%, 25%, 50%, 75% and 100%. Gathering quantitative data allowed to perform a first set of analyses based on descriptive statistics (e.g. mean, median and interquartile range-IQR). We used qualitative data, instead, to build the final scenarios during the fourth step of the forecasting technique. Even though the literature regarding the Delphi method does not suggest a standardized way to analyze consensus, central tendency measures, such as median and mean values, are useful to grasp a first understanding and are frequently accepted and adopted ( Table 3 ). Scenarios with an IQR equal or lower than 1.5 were considered as having reached an acceptable degree of consensus. It should be noticed that most of the projections that achieved the highest probability, having a median value of 75% achieved also the consensus, i.e. IQR below 1.5. This was the case for projections 3, 4, 8, 9, 10, 13, 15, 19, 20.

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These results show that it was easier for experts to find a consensus over the projections that resulted as very likely to occur. Only projection number 18 achieved a high probability score but could not reach a consensus.

3.5.2 Second round.

During the Delphi’s second round only the projections with an IQR above 1.5 (i.e. which did not reach consensus in the first round) were tested. In order to allow the respondents to easily understand the answers that the panel gave as a whole in round one, for each projection a quantitative report was provided. This report was made of a bar chart with the distribution of the first round’s answers and the correspondent qualitative details, i.e. some of the argumentations provided by some of the panelists. Experts were asked to reconsider the likelihood of occurrence of the projections number 1, 5, 7, 11, 12, 14 and 18. The second round was again structured using Google Form. Following the Delphi’s approach, we did not ask again to estimate the impact for each projection, since this would have presumably been not subject to any change. Moreover, we decided to leave the opportunity to offer again some qualitative comments in support of the answers for a better analysis of the results. The number of experts who successfully completed the second round of the survey dropped to 28, i.e. the 80% of the experts that completed Round 1 and 56% of the selected initial panel. Again, we evaluated the central tendency measures for the projections tested during the second round ( Table 4 ).

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In order to provide a more effective and structured analysis of the results, we first report the final summary table of the Delphi survey and then describe the insights obtained from the analysis. It has to be noticed that Table 5 reports quantitative data only, while during the survey qualitative data were collected as well. In presenting the results of this research, both quantitative and qualitative data are used to provide the best possible picture of what the blockchain-based future will look like. Alongside with standard statistics, we build on qualitative insights obtained during the interviews carried on with experts.

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Firstly, it is interesting to analyze which projections, out of the initial 20, reached a significant consensus (IQR <1.5 after the two rounds of the surveys) and were given a high probability of occurrence by the experts. We can summarize the findings in this domain around three major axes: efficiency, security, and innovation.

By 2030, it will be easier, faster and leaner to exchange value and data among users, institutions and countries. Efficiency will boost and uncover innovation potential within companies and societies if these latter will be able to exploit such a new opportunity. Policies will be a necessary pre-requisite for companies to be able to build a competitive edge globally. From this perspective, the capability of central governments to spur innovation with lean and flexible regulations will be a key driver in explaining the ex-post productivity differential among companies belonging to different countries. From the interview with an investment banker part of the BPCE French group (one of the largest banks in France), it emerged how efficiency is often hampered by the lack of an equally efficient regulation. To provide the reader with an interesting example, in 2018, Natixis, the international corporate and investment banking, asset management, insurance and financial services arm of BPCE, entered the Marco Polo consortium, an initiative born to provide a newly conceived trade and supply chain finance platform, leveraging Application Programming Interfaces (APIs) and blockchain technology. Many other leading banks joined the consortium as well. However, as highlighted by the investment banker, the main limiting factor of the consortium, strongly hampering its efficiency and ability to provide a competitive edge, was the “old-style” bureaucracy linked to it. Although transactions were in principle to be executed smoothly, a bulk of legal paperwork was required to approve them formally. In this case, it appears evident that technology often runs faster than policy, consistently lowering its potential. Interestingly, this view is also shared by regulatory bodies. An experienced lawyer and notary, also member of a panel of experts elected by the Italian government to define the national strategy on blockchain, highlighted that, sometimes, regulators working on blockchain-related policies are trying to adapt existing regulations to the new paradigm. Due to the intrinsically different nature of the technology, this could represent a wrong approach. At the same time, building a new set of policies from scratches could represent a challenging task. From this perspective, projections 4 and 5 confirm this insight: policy and technology should come hand in hand to synergically boost productivity. The three projections reached consensus after the two rounds and were assigned a high probability of occurrence. Overall, it is evident that regulatory aspects linked to the adoption of this new technology shall not be underestimated.

As previously mentioned, security, and specifically cybersecurity, is another dimension around which blockchain could bring consistent advantages, as projections 3, 10, 11 and 15 suggest. On this specific aspect, we interviewed a project leader of the World Economic Forum who previously worked for the United Nations for more than ten years. She dealt specifically with digital regulations, justice, and cybersecurity, and in the last three years before the interview, she specifically worked on blockchain implications and how the technology could be implemented in existing ecosystems. Thanks to her experience in the domain, she clearly explained how the blockchain represents a meaningful technology to avoid cyberattacks to sensitive data and digital files. In her opinion, the avoidance of a single point of failure is the main reason behind a possible blockchain adoption over the next years, since cyberattacks are becoming more frequent and dangerous and related costs for companies are exponentially increasing (e.g. 2020 has been a record year for cyber attacks). Consequently, companies will be increasingly investing in distributed ledgers as a form of contingency budget to lower the cybersecurity risk and its related cost. Given the centrality of data in today’s businesses, serious attacks and loss of data could represent a serious threat to business long-term sustainability.

The third relevant aspect on which blockchain will have a strong impact is, not surprisingly, innovation. Although regulation could represent a non-negligible limiting factor, experts foresee many sectors to be impacted by the technology adoption. For example, the financial sector could be heavily affected by this new paradigm. Particularly, companies’ capital structures and their strategic interlink with business models will drive a differential competitive power. Most likely, enterprises will have to rethink their business models to account for the possibility to digitize/tokenize their assets (Projections 8 and 18). The capability in flexibly adapting their service offerings to the new opportunity and the ability to raise, and re-invest, new capitals will shape the global competition landscape across different industrial sectors and geographies. From one side, blockchain will enable new strategic decisions, from the other side, it will be of fundamental importance to build technological capabilities to enable these decisions. The underlying technology behind transactions, equity offering and equity share transfers will most likely be the blockchain (Projections 13 and 16). Disintermediation and the ability to exchange value, information, and data trustfully without a central authority will enable a new way of funding and cooperation on open-source projects (Projection 19). Most likely, people will refer to blockchain systems as they now refer to browsers such as Chrome, Firefox or Internet Explorer. Many blockchains are already available and are constantly improved and developed, and it is foreseeable that this will remain the case in the future. Users will just need to know the characteristics that a blockchain provides to choose the most suitable one for their business and purposes. Blockchain-based systems will require new skills and knowledge that developers and engineers will need to develop. Big efforts will be needed to make the blockchain more and more user friendly and attractive for those who just want to benefit from the immutability, traceability, and security that it intrinsically brings. At the time of the writing and in line with the Abernathy and Utterback model [ 53 ] many players are currently investing and innovating on blockchain to provide services that will satisfy the new market needs.

The opportunity for people to deal freely will in fact generate opportunities that were unforeseeable before. Self-enforcing smart contracts (Projection 20) will let parties to buy and sell products or to rent them with pay-for-use schemes in an automated way, the digitization of shares and assets will allow companies to raise capital in new ways, without the need to rely on banks, venture capitals or traditional IPOs. Indeed, it is important to understand how the digitization of assets can challenge existing investments and the funding industry represented by traditional private equity firms and banks. Blockchain could allow the creation of platforms for the issuance of traditional financial products on a tokenized nature, making it easier, more transparent and cheaper to manage and access these tools for everyone, including both individual savers and SMEs. Two different types of companies can and will operate in the market: those which have blockchain at their core since their foundation, and those which have (or will have) to embark in a digital transformation process to reconvert themselves into blockchain-based enterprises. In both cases, companies are investing to get a competitive advantage over competitors, betting on the technology that is promising to reduce costs and increase efficiency. Once a dominant design in product and services will be achieved, companies that took a different path will likely exit the market, letting firms following the dominant design to gain market shares.

To conclude and to conceptualize the insights we obtained from both quantitative and qualitative data, we derived four scenarios that we organized in a matrix framework, reported in Table 6 . The framework was built around two main dimensions: on one hand the digitization of assets, and on the other hand the change in business models. The proposed framework leads to the identification of four quadrants: scenarios which envision both the digitization of assets and business model changes and scenarios which do not foresee neither of these two changes. These four main development scenarios were completed and analyzed in the light of the conducted interviews and of the quantitative and qualitative data gathered through the Delphi survey. Each quadrant was given a label: Internal Processes, Flow-less Coopetition, Suppliers Potential and Investment Opportunities. When discussing the quadrants, we try to highlight which of the three improvement areas previously identified (efficiency, security, and innovation) are exploited in the discussed scenario.

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To derive relevant insights from the framework, it is useful to start from the bottom left quadrant, Internal Processes. This name was chosen to highlight the absence of any particular evolution for the company at a strategic level through the blockchain adoption. In this case, it is conceivable to use the technology to incrementally improve firms’ operation performances. Blockchain’s main benefits are to increase traceability of transactions and guarantee their immutability. All these characteristics adopted on today’s processes will result in an automation of routine business functions, such as settlements and reconciliation, customs clearance, heavy payments, invoicing, and documentation, boosting operational efficiency and cost performance. In this scenario, security and efficiency will see a consistent improvement.

The top-left scenario shows instead a different perspective, by considering a broader adoption of blockchain that generates new cooperative business models among different stakeholders, potentially even among competitors. This is why it is called Flow-Less Coopetition. In this case, the benefits of blockchain will help at generating a more democratic ecosystem in terms of information. Those actors that base their business models on information asymmetry, having access to key information before others, will need to revisit their business models if they want to stay competitive. It is of interest to notice how big financial institutions, traditionally competing, are now exploring potential collaboration models in the light of this new technology (e.g. JP Morgan Chase, Morgan Stanley). This quadrant envisages an advance in all three blockchain-enabled dimensions: efficiency, security, and innovation.

The bottom-right scenario, called Suppliers Potential, highlights how, thanks to the digitization that blockchain allows, many actors could jump in the market providing solutions to those companies that would like to benefit from the advantages of digitizing their assets, but are lacking means and competences to internally develop them. Those companies would rather outsource the development of blockchain-based solutions. For this reason, the potential for the creation of a remunerative B2B market exists. Even though there are already protocols that are leaders in the market (Hyperledger Fabric and Ethereum), new solutions with different configurations will likely be needed to support different industries and use case solutions. As for the first scenario, also in this context efficiency and security will be mainly affected.

Finally, the last scenario (Investment Opportunities) focuses on the combination between the complete digitization of the assets of a company and the new business models that this major change could generate. As already mentioned in previous paragraphs, industries are experimenting many ways to facilitate the access to capital. Since the explosion of ICOs in 2017, new and easier ways to access capital have become possible and achievable. However, due to their unregulated nature, ICOs still present numerous potential threats (Projection 14 did not reach consensus). For this reason, other solutions, such as STOs (Security Token Offerings), are on the way of being tested. Bringing a higher degree of freedom to investments will allow companies to receive funds from diverse and non-traditional investors, and it will also boost investments by private individuals into early-stage companies. Efficiency and innovation will be at the core of this last scenario.

5 Conclusions

In this paper, we studied different blockchain-based projections and we assessed their likelihood and impact thanks to the participation of a pool of experts. We built our findings around three dimensions (efficiency, security, and innovation) and we derived four scenarios based on experts’ shared vision. Being the current literature widely fragmented, we believe this research represents a useful starting for conceptualizing blockchain potential and implications. While many research papers focus on blockchain specific applications or general reviews of the state of the art, we try to propose a unifying framework building on different typologies of insights and analyses. We merged quantitative observations derived from standard statistics with qualitative insights obtained directly from experts’ opinions.

Overall, we believe our research can constitute a useful tool for many practitioners involved in the innovation ecosystem and for managers of small, medium and large enterprises to look at future possible scenarios in a more rational and systematic way. From one side, a company’s management can use these forecasts as a starting point for the implementation of new strategies. As previously highlighted, blockchain offers endless possibilities. However, the ability to focus on activities and projects with a positive return on investment will be crucial. Firstly, managers will face the choice between insourcing or outsourcing the technological development of the platform. While the former choice ensures higher flexibility, it also generates high development and maintenance costs. Companies which will identify blockchain as their core service will be entitled to adopt this first strategy, while the majority of the enterprises will probably gain better competitive advantages adopting Blockchain as a Service (BaaS) solution. This latter approach will boost companies’ performances, by enhancing new service offerings as well as a new level of operational efficiency, without carrying the burden and costs of technological complexity.

As mentioned, we believe this research provides useful insights for policy makers as well. The adoption of blockchain represents a tremendous technological change bringing along interesting and tangible opportunities. However, different threats can be foreseen. Central authorities do not only solve the problem of trust in certifying value transactions. They also provide essential supervision on the process itself, for example ensuring that information asymmetry is kept at reasonable levels between parties engaging in any sort of contracts, especially in the financial world. Letting people directly exchange value between themselves or allowing companies to easily raise capitals can boost financial efficiency, but also provides room for frauds and ambiguous behaviours. Today, companies which are interested in raising capitals both through innovative tools such as crowdfunding or through traditional entities such as public financial markets, have the duty to disclose relevant information and usually go through a deep process of due diligence. Regulators should ensure the same level of control on companies that will raise money through Initial Coin Offerings or other sort of blockchain-enabled offerings. We believe that the first step towards a fair regulation of this newly born technology is the understanding of its foreseeable impact on the society in the near future. This work aims to be a precious enabler in this direction. As highlighted in the body of this research, it appears fundamental for policy makers, regulators and government to deeply understand the potential upsides and threats of this new technology, and to correctly navigate the different possible blockchain-enabled scenarios. The successful cooperation between companies’ management and regulators could enable significant productivity shifts in the economic tissue of many countries. Failing in efficiently grasping and enhancing these new paradigms from a regulatory perspective could result into a heavy deficit for the competitive edge and productivity of the industrial sectors of the governments’ respective countries, potentially leading to macroeconomic differentials in productivity.

To conclude, this research could be a useful reference for orienting into this complex and dynamic environment, reducing the perceived uncertainty associated to such a new technology. Thanks to the experts’ advice, it is now possible to have a clearer picture of the evolution of blockchain technologies and of the opportunities and threats that the technology will generate. Certain limitations and characteristics of this study must be considered to correctly and effectively take advantage of its results. The main objective of this work was to examine the most disrupting aspects that are likely to occur in Europe by 2030, with a particular focus on how the technology will facilitate financing, reduce costs, increase transparency and, in general, influence firms’ business models. From this point of view, the objectives and assumptions presented at the beginning of this paper can be considered as fully achieved, but further works exploring other industries and geographies are required to get an organic understanding of the new enhanced paradigms.

Our research only paves the way for a better understanding of what a blockchain-based future will look like, as the differences between industries are too large to be analyzed in a single work. Organizations and businesses in the financial world are consistently changing, but it will be necessary also for companies belonging to different sectors to completely rethink their core activities. From this perspective, we believe further works are needed in these directions. We hope researchers will use and explode our framework to further characterize and meticulously describe the new possible paradigms around the multiple dimensions examined in this work.

Supporting information

https://doi.org/10.1371/journal.pone.0258995.s001

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A Systematic Overview of Blockchain Research

Blockchain has been receiving growing attention from both academia and practices. This paper aims to investigate the research status of blockchain-related studies and to analyze the development and evolution of this latest hot area via bibliometric analysis. We selected and explored 2451 papers published between 2013 and 2019 from the Web of Science Core Collection database. The analysis considers different dimensions, including annual publications and citation trends, author distribution, popular research themes, collaboration of countries (regions) and institutions, top papers, major publication journals (conferences), supportive funding agencies, and emerging research trends. The results show that the number of blockchain literature is still increasing, and the research priorities in blockchain-related research shift during the observation period from bitcoin, cryptocurrency, blockchain, smart contract, internet of thing, to the distributed ledger, and challenge and the inefficiency of blockchain. The findings of this research deliver a holistic picture of blockchain research, which illuminates the future direction of research, and provides implications for both academic research and enterprise practice.

1 Introduction

With the era of bitcoin, digital cash denoted as BTC makes it possible to store and transmit value through the bitcoin network [ 1 ] . And therewith, blockchain, the technology underlying bitcoin, which adopts a peer-to-peer network to authenticate transactions, has been gaining growing attention from practices, especially Libra, a global currency and financial infrastructure launched by Facebook, and digital currency electronic payment. Currently, blockchain is also an increasingly important topic in the academic field. Blockchain research has considerably progressed, attracting attention from researchers, practitioners, and policy-makers [ 2 , 3 , 4 , 5 , 6 , 7 , 8 , 9 ] .

Considering the huge potential benefits that blockchain would bring in various aspects of industries, for instance, finance and economy [ 10 , 11 , 12 ] , internet of things [ 13 , 14 , 15 ] , energy [ 16 , 17 ] , supply chain [ 18 , 19 ] , and other areas. It is often compared with the Internet and is even referred to as a new form of the Internet. As a result, the number of publications in the blockchain is growing rapidly. According to an initial search on the Web of Science Core Collection, over 2000 scientific papers published are related to blockchain.

Under the circumstances where the number of research publications in the blockchain is quickly increasing, although studies have tried to provide some insights into the blockchain research via literature reviews [ 20 , 21 , 22 , 23 , 24 ] . Comprehensive scientometric analysis of academic articles published in influential journals are beneficial to the further development of blockchain research. This research conducts a bibliometric visualization review and attempts to deliver an overview of the research in this fast-growing field.

The objectives of this research are as follows. First, we intend to build an overview of the distribution of blockchain-related research by time, authors, journals, institutions, countries (regions), and areas in the blockchain academic community. Second, we probe the key research topics of blockchain study, for which purpose, we conduct keyword co-occurrence analysis. Third, we picture the intellectual structure of blockchain study based on co-citation analysis of articles and author co-citation analysis. Finally, we identify the direction for the evolution of blockchain study. We adopt Citespace to detect and visualize emerging trends in blockchain study. To achieve these targets, we posed the following research questions:

Q1: What is the distribution pattern of blockchain publications and citations over recent years? Q2: Which are the main international contributing countries (regions) and institutions in blockchain research, and the collaboration network among them? Q3: What are the characteristics of the authorship distribution pattern? Q4: What are the key blockchain subjects based on the number of publications? Q5: Which are the major journals or conferences for blockchain-related research? Q6: Which are the most influential papers in blockchain research based on the number of citations? Q7: Who are the most influential authors in blockchain research according to the author co-citation network? Q8: What are the research trends in blockchain? Q9: What are the most supportive funding agencies for blockchain research?

Our intended contributions in this research are twofold. First, it is an attempt of adopting co-citation analysis to provide comprehensive and up-to-date developing trends in the lasted hot area, blockchain. Second, this study depicts a state-of-the-art blockchain research development and gives enlightenment on the evolution of blockchain. The findings of this research will be illuminating for both academic researchers, entrepreneurs, as well as policymakers.

The rest of the article is organized as follows. The literature review mainly summarizes related work. The “Data and methodology” section describes the data source and methodological process. The “Results” section presents the main results based on the bibliometric analysis as well as statistical analysis. “Conclusions and implications” conclude this research provides answers to the aforementioned research questions and poses directions for further work.

2 Literature Review

Scientometric analysis, also known as bibliometric network visualization analysis has been widely adopted in numerous areas to identify and visualize the trends in certain fields. For instance, Bonilla, et al. analyzed the development of academic research in economics in Latin America based on a scientometric analysis [ 25 ] . Li, et al. conducted research on emerging trends in the business model study using co-citation analysis [ 26 ] . Gaviriamarin, et al. applied bibliometric analysis to analyze the publications on the Journal of Knowledge Management [ 27 ] .

Since the birth of bitcoin, as the foundation of which, blockchain has gained an increasing amount of attention in academic research and among practices. The research papers focus on the blockchain are quite abundant and are continuing to emerge. Among a host of papers, a few studies investigate the research trend of blockchain-based on a bibliometric analysis [ 22 , 23 , 28 , 29 , 30 ] .

Table 1 presents a summary of these bibliometric studies that summarized some findings on blockchain research, yet very few investigated the co-citation network and the evolution of popular topics in a timeline view. The number of papers these articles analyzed is relatively small, which may be because they used simple retrieval formula in searching blockchain-related articles, and it could pose a threat to bibliometric analysis. Therefore, this research aims to conduct a comprehensive analysis of the status of blockchain research, which is beneficial to future research and practices.

An overview of existing bibliometric studies on blockchain research

Note: NP = number of publications; WOS = Web of Science Core Collection; CNKI = China National Knowledge Infrastructure Databases; EI = EI Compendex, an engineering bibliographic database published by Elsevier; Scopus = Elsevier’s abstract and citation database.

3 Data and Methodology

This section elaborates steps to conduct a comprehensive bibliometric-based analysis: 1) data collection, 2) methodological process. The overall approach and methodology are shown in Figure 1 , the details could be seen as follows.

Research methodology

3.1 Data and Collection

As the leading database for science and literature, the Web of Science Core Collection has been widely used in bibliometrics analysis. It gives access to multidisciplinary information from over 18,000 high impact journals and over 180,000 conference proceedings, which allows for in-depth exploration of the complete network of citations in any field.

For the sake of acquiring enough articles that are relative to the blockchain, we select keywords from Wikipedia and industry information of blockchain, and some existing research literature [ 1 , 20 , 23 , 30 ] . Moreover, in consideration of that, there are a host of blockchain research papers in various fields, in fact, although some papers use keywords in abstract or the main body, blockchain is not the emphasis of the researches. Therefore, in order to get more accurate research results, we choose to conduct a title search instead of a topic search. Table 2 presents the retrieval results with different keywords in the titles, we find that among publications that are relative to the blockchain, the number of Proceeding Papers is the biggest, which is closely followed by articles, and a few reviews. Based on the comparison of five search results in Table 2 . In addition, for accuracy and comprehensiveness, we manually go through the abstract of all the papers form conducting a title search, and choose papers that are related to blockchain. Finally, a dataset with 2451 articles is used in the subsequent analysis.

The dataset we choose has good representativeness, although it may not completely cover all papers on the blockchain, it contains core papers, and in bibliometric analysis, core papers are enough to provide a holistic view for a comprehensive overview of blockchain research.

Blockchain research article characteristics by year from 2013 to 2019

Note: Document type include: Article(A), Proceedings Paper(P), Review(R); Timespan = 2013 ∼ 2019, download in May 31, 2019; Indexes = SCI-EXPANDED, SSCI, A&HCI, CPCI-S, CPCI-SSH, ESCI, CCR-EXPANDED, IC.

3.2 Methodological Process

The bibliometric approach has received increasing attention in many research domains. In this study, the methodological process mainly includes three methods: 1) descriptive statistical analysis, 2) article co-citation, author co-citation, and cluster analysis on co-cited articles; 3) time-zone analysis on co-cited keywords.

Descriptive statistical analysis displays an overall status of the research development in the target field, which mainly presents an overview by publication years, document types, the research area of published journals, number of citations, and in terms of most cited paper, influential author, institutions and countries. Co-citation analysis helps to identify the frequency of co-cited papers and authors and provides crucial insights into the intellectual structure of certain research fields [ 31 ] . Time-zone analysis helps to understand the flow of information and research trends in the target area [ 32 ] .

Various visualization tools have been designed and developed as computer software such as Citespace and VOSviewer. In this study, we use Citespace for co-citation analysis and timezone analysis, VOSviewer is adopted for social network analysis and visualization, we also apply other tools such as Excel and Tableau for basic statistical analysis and the visualization of the bibliometric results. Notably, in Citespace, core nodes are displayed as “citation tree-rings”, which contain abundant information of an article, for instance, the color of a citation ring denotes the year of corresponding citations, and the rule of colors in Citespace is the oldest in dark blue and newest in light orange with a spectrum of colors in between, the thickness of a ring is proportional to the number of citations in a time slice [ 33 ] . Figure 2 illustrates the details of the citation tree-rings. In addition, Citespace adopts a time-slicing mechanism to produce a synthesized network visualization [ 34 ] .

Citation tree-rings [ 33 ]

4.1 Distribution by Publication Year

Table 3 illustrates several characteristics of blockchain-related publications sorted by the year of publication. The annual number of articles and countries has been growing continuously since the proposing of Nakamoto’s paper in 2008 [ 1 ] , and the first blockchain research paper was published in 2013. By examining the published papers over time, there were only eight articles published in 2013. Afterward, with a continuous increase, a peak of 1,148 articles was published in 2018, and the number of publications is likely to grow ever since. Meanwhile, the annual number of countries taking part in blockchain research has also rapidly increased from 6 to 93 between 2013 and 2017, whereas the average number of Times Cited for single articles declined from 34.00 to 1.73 between 2013 and 2018. Over the observation period, 97 countries took part in the research on the blockchain with a sample of 44 in the H-index of our paper.

Statistical description of Blockchain research article from 2013 to 2019

Note: NP = number of publications; No.CO = number of countries; AV.TC = average number of Times Cited.

Figure 3 presents the cumulative numbers of published articles and citations from 2013 to 2019. There was a drastic increase in the number of papers published annually after 2016. As for the cumulative number of citations, there was no citation of blockchain literature before 2013, and 272 citations in 2013. By 2018, this number has grown over 10,000, which implies a widespread influence and attention of blockchain study in recent years.

Cumulative growth in blockchain publications and citations, 2013–2019

The exponential growth is a typical characteristic of the development of research fields [ 35 ] . The model can be expressed as:

where C is the cumulative number of articles or citations, Y is the publication or citation year, α , and β are parameters. In this study period, the cumulative articles and citations in the filed grow exponentially by R articles  2 = 0.9463 and R citations  2 = 0.8691 respectively. This shows that the research quantity curve of the blockchain is like an exponential function, which means the attention of academic circles on the blockchain has been increasing in recent years.

4.2 Distribution and International Collaboration Among Countries/Regions

A total of 97 countries/areas have participated in blockchain research during the observation period. Table 4 shows the number of articles for each country (region) contributing to publications. Remarkably, an article may be written by several authors from different countries/areas, therefore, the sum of articles published by each country is large than the total number of articles. As can be seen from Table 4 , the USA and China play leading roles amongst all countries/areas observed, with publications of 532 (20.94%) and 489 (19.24%) articles respectively, followed by the UK, which published 214 (8.42%) articles.

Blockchain research country (region) ranked by number of articles (top 25)

Note: NP = number of publications; No.TC = number of total Times Cited; AV.TC = average number of Times Cited; No.CA = number of Citing Articles.

From the perspective of citations, according to country/area distribution in Table 4 , we also find that USA-authored papers were cited by 1,810 papers with 3,709 (36.57%) citations, accounting for 36.57% of total citations. Meanwhile, articles from the USA also have a very high average number of citations per paper with a frequency of 6.97, which ranks third among the top 25 countries/ areas. Interestingly, the articles from Austria and Singapore appeared with the highest average number of citations per paper, with a frequency of 7.44 and 7.16 respectively, whereas the number of publications from these two countries was relatively low compared with the USA. The second was China, following the USA, papers were cited by 753 articles with 1,357 (13.38%) citations. Although the number of articles from China is close to the USA, the average number of citations per paper is lower with a frequency of 2.78. The subsequent countries include the UK, Germany, and Italy. The results indicate that the USA is the most influential country in blockchain.

International collaboration in science research is both a reality and a necessity [ 36 ] . A network consisting of nodes with the collaborating countries (regions) during the observation period is shown in Figure 4 . The network is created with the VOS viewer in which the thickness of the linking lines between two countries (regions) is directly proportional to their collaboration frequency. We can see from Figure 4 that the USA has the closest collaborative relationships with China, the UK, Australia, Germany, and Canada. China has the closest collaborative relationships with the USA, Australia, Singapore, UK, and South Korea. UK has the closest collaborative relationships with the USA, China, France, and Switzerland. Overall, based on the collaboration network, collaboration mainly emerges in highly productive countries (regions).

International collaboration network of the top 25 countries (territories), 2013–2019

4.3 Institution Distribution and Collaboration

A total of 2,190 institutions participated in blockchain-related research, and based on the number of publications, the top 25 of the most productive institutions are shown in Table 5 . Chinese Academy of Sciences had the highest number of publications with 43 papers, followed by the University of London with 42 papers, and Beijing University of Posts Telecommunications ranked third with 36 papers. The subsequent institutions included the University of California System and the Commonwealth Scientific Industrial Research Organization (CSIRO). In terms of the number of total Times Cited, Cornell University is cited most with 499 citations, and the average number of Times Cited is 20.79. Massachusetts Institute of Technology followed closely with 407 citations and with an average number of Times Cited of 22.61. The University of California System ranks third with 258 citations and an average number of Times Cited of 8.06. ETH Zurich ranked fourth with 257 citations and an average number of Times Cited of 10.28. It is notable that the National University of Singapore also had a high average number of Times Cited of 12.56. These results indicate that most of the influential institutions are mainly in the USA and Europe and Singapore. The number of publications from institutions in China is large, whereas few of the papers are highly recorded in average Times Cited. Papers from the National University of Defense Technology China took the highest of average Times Cited of 7.79.

Blockchain research country (territory) ranked by number of articles (top 25)

To further explore data, the top 186 institutions with at least 5 articles each are chosen for collaboration network analysis. The collaboration network map is shown in Figure 5 , the thickness of linking lines between two institutions is directly proportional to their collaboration frequency. As seen from the cooperation network in the Chinese Academy of Sciences, Cornell University, Commonwealth Scientific Industrial Research Organization (CSIRO), University of Sydney, and ETH Zurich cooperated widely with other institutions. This shows that collaboration between institutions may boost the research of blockchain which echoes with extant research that proposes with-institution collaboration and international collaboration may all contribute to article quality [ 37 ] .

Collaboration network for institutions, 2013–2019

4.4 Authorship Distribution

The total number of authors who contribute to the publications of blockchain is 5,862. Remarkably, an article may be written by several authors from different countries (regions) or institutions. Therefore, the total number of authors is bigger than the total number of articles. In fact, during the observation period, the average number of authors per paper is 2.4 articles. Reveals the distribution of the number of authors with different numbers of papers. As seen from the results, most of the authors had a tiny number of papers, i.e., among 5,862 authors, 4,808 authors have only one paper, 662 authors have two papers, and 213 authors have three papers.

According to the participation number of articles, the most productive author in the blockchain is Choo, Kim-Kwang Raymond from Univ Texas San Antonio, who took part in 14 articles in blockchain, followed by Marchesi, Michele from Univ of Cagliari, who took part in 13 articles related to blockchain. The third most productive author is Bouri, Elie from the Holy Spirit University of Kaslik, and David Roubaud from Montpellier Business School. Miller, Andrew, Shetty, Sachin, and Xu, Xiwei ranked fourth, who took part in 10 articles related to blockchain.

The distribution of number of author with different numbers of articles

Note: No.AU = number of author; No.AR = number of articles.

Figure 6 displays the collaboration network for authors. The thickness of the linking lines between the two authors is directly proportional to their collaboration frequency. As we can see from Figure 6 , it indicates the most productive authors cooperate widely with others.

Collaboration network for authors, 2013–2019

4.5 Distribution of Subject Categories

Table 7 presents the top 25 blockchain categories ranked in terms of the number of articles published. As can be seen from Table 7 , among the top 10 categories, six are related to the Computer Science field, which indicates that blockchain-related researches are more abundant in the field of Computer Science compared with other research fields. Besides, there are also publications in the category of Business & Economics with 385 records.

The top 25 blockchain categories ranked by the number of publications

Figure 7 illustrates the betweenness centrality network of papers of the above categories by using Citespace after being simplified with Minimum Spanning Tree network scaling, which remains the most prominent connections. We can see from Figure 7 , the centrality of Computer Science, Engineering Electrical Electronic, Telecommunications, Engineering, and Business & Economics are notable.

Categories involved in blockchain, 2013–2019

4.6 Journal Distribution

The research of blockchain is published in 1,206 journals (conferences), the top 25 journals (conferences) are displayed in Table 8 . Blockchain research papers are concentrated in these top journals (conferences) and with a concentration ratio of nearly 20%. The major blockchain research journals include Lecture Notes in Computer Science, IEEE Access, Economics Letters, Future Generation Computer Systems, and Finance Research Letters, with more than 20 articles in each one. Meanwhile, the major blockchain research conferences include IEEE International Conference on Hot Information-Centric Networking, International Conference on Parallel and Distributed Systems Proceedings, International Conference on New Technologies Mobility, and Security, and Financial Cryptography and Data Security, with at least 14 articles published in each of these.

The top 25 blockchain publication journals (conferences)

Note: NP = number of papers; No.TC = number of total Times Cited; Italic represents conference.

4.7 Intellectual Structure of Blockchain

Since the notion of co-citation was introduced, there are a host of researchers have adopted the visualization of co-citation relationships. The work is followed by White and Griffith [ 38 ] , who identified the intellectual structure of science, researches then broaden the unit of analysis from articles to authors [ 39 , 40 ] . There are two major types of co-citation analysis, namely, article cocitation analysis and author co-citation analysis, which are commonly adopted to visualize the intellectual structure of the research field. In this study, we explore the intellectual structure of blockchain by using both article co-citation analysis and author co-citation analysis. We apply Citespace to analyze and visualize the intellectual structure [ 41 ] .

In this study, mining spanning trees was adopted to present the patterns in the author cocitation network, a visualization of the network of author co-citation is demonstrated in Figure 8 . In the visualization of the co-citation network, pivot points are highlighted with a purple ring, and landmark nodes are identified with a large radius. From Figure 8 , there are six pivot nodes and landmark nodes: Nakamoto S, Buterin V, Eyal I, Wood G, Swan M, Christidis K. These authors truly played crucial roles during the development of blockchain research. Table 9 shows the ranking of author citation counts, as well as their prominent publications.

Network of author co-citation, 2013–2019

The top 15 co-cited author ranked by citation counts

Nakamoto S, as the creator of bitcoin, authored the bitcoin white paper, created and deployed bitcoin’s original reference implementation, is not surprised at the top of the co-citation count ranking, and has 1,202 citations in our dataset. Buterin V, a Russian-Canadian programmer, and writer primarily are known as a co-founder of ethereum and as a co-founder of Bitcoin Magazine, follows Nakamoto S, receives 257 citations. Eyal I, an assistant professor in technion, is a third of the ranking, with a representative article is “majority is not enough: Bitcoin mining is vulnerable”. Wood G, the ethereum founder, and free-trust technologist ranks fourth with 244 citations. The other core author with high citations includes Swan M, Christidis K, Bonneau J, Szabo N, Zyskind G, Castro M, and Meiklejohn S, with more than 150 citations of each person, and the typical publications of there are present in Table 9 .

To further investigate the features of the intellectual structure of blockchain research, we conducted an article co-citation analysis, using cluster mapping of co-citation articles networks to complete a visualization analysis of the evolution in the research field of blockchain. According to the article co-citation network, we adopted Citespace to divide the co-citation network into several clusters of co-cited articles. The visualization of clusters of co-cited articles is displayed in Figure 9 .

Clusters of co-cited articles, 2013–2019

As we mentioned earlier in the “Data and Methodology” section, the colors of citation rings and links are corresponding to the different time slices. Therefore, the deeper purple cluster (Cluster #1) is relatively old, and the prominent clusters (Cluster #0 and #2) are more recent. Cluster #0 is the youngest and Cluster #1 is the oldest. Cluster labels are identified based on burst terms extracted from titles, abstracts, keywords of bibliographic records [ 26 , 41 ] . Table 10 demonstrates six predominant clusters by the number of members in each cluster.

Results show that the research priorities of the clusters keep changing during the observation period. From the earlier time (Cluster # 1), bitcoin and bitcoin network are the major priorities of researchers, then some researchers changed the focuses onto cryptocurrency in blockchain research. Notably, more researchers are most interested in blockchain technology and public ledger recently.

According to the characteristics of pivot nodes and landmark nodes in the co-citation article network. The landmark and pivot nodes in co-citation articles are shown in Figure 10 , Five pivot nodes are Nakamoto S [ 1 ] , Wood G [ 44 ] , Kosba A [ 51 ] , Eyal I [ 12 ] and Maurer B [ 55 ] . The main landmark nodes are Christidis K [ 45 ] . Swan M [ 2 ] , Zyskind G [ 48 ] Nakamoto S [ 1 ] , Kosba A [ 51 ] , Notably, some nodes can be landmark and pivot at the same time.

Landmark and pivot nodes, 2013–2019

Summary of the largest 6 blockchain clusters

Details of the largest cluster (Cluster #0, top10)

Details of the largest cluster (Cluster #1, top10)

Details of the largest cluster (Cluster #2, top10)

As seen from Table 10 , Cluster #0 is the largest cluster, containing 36 nodes, for the sake of obtaining more information about these clusters, we explored the details of the largest clusters. Table 11 illustrates the details of the Cluster 0#.

We also explored Cluster #1 and #2 in more detail. Table 12 and Table 13 present the details of Cluster #1 and Cluster #2 respectively, it is notable that the most active citation in Cluster #1 is “bitcoin: A peer-to-peer electronic cash system”, and the most active citation in Cluster #2 is “bitcoin: Economics, technology, and governance”. The core members of Cluster #1 and Cluster #2 deliver milestones of blockchain research related to the bitcoin system and cryptocurrency.

Table 14 lists the first 10 most cited blockchain research articles indexed by the Web of Science. These articles are ranked according to the total number of citations during the observation period. Among these articles, the publication of “blockchains and smart contracts for the internet of things” by Christidis is identified as the most cited paper of 266 citations. The paper also has the highest average number of citations per year.

The top 10 cited blockchain articles

4.8 Keywords Co-Citation Analysis

According to Callon, et al. [ 77 ] co-word analysis is a useful way of examining the evolution of science. In our study, among 2,451 articles related to blockchain, we obtained 4,834 keywords, 594 keywords appeared 3 times, 315 keywords appeared 5 times, and 130 keywords appeared 10 times. Table 15 presents the most important keywords according to frequency. As seen, ‘blockchain’ ranks first with an occurrence frequency of 1,105, followed by ‘bitcoin’ of 606. The other high occurrence frequency keywords include: ‘cryptocurrency’, ‘smart contract’, and ‘iot’ (internet of thing).

The top 25 keywords ranked by frequency

For the sake of further exploration of the relation amongst the major keywords in blockchain research papers, we adopted the top 315 keywords with a frequency no less than 5 times for co-occurrence network analysis. The keywords co-occurrence network is illustrated in Figure 11 . In a co-occurrence network, the size of the node represents the frequency of the keywords co-occurrence with other keywords. The higher the co-occurrence frequency of the two keywords, the closer the relationship between them.

The keywords co-occurrence network, 2013–2019

We can see from Figure 11 , the size of blockchain and bitcoin are the largest among all keywords. This means, in general, blockchain and bitcoin have more chances to co-occurrence with other keywords. Besides, blockchain is closer with a smart contract, iot, Ethereum, security, internet, and privacy, whereas bitcoin is closer with digital currency and cryptocurrency.

Figure 12 displays the time-zone view of co-cited keywords, which puts nodes in order from left to right according to their years being published. The left-sided nodes were published in the last five years, and on the right-hand side, they were published in recent two years. Correspondingly, some pivot nodes of keywords are listed in the boxes. We hope to show the evolution of blockchain in general and the changes of focuses in blockchain study.

The time-zone view of co-cited keywords, 2013–2019

The results suggest that, in 2013, when blockchain research begins to surface, bitcoin dominated the blockchain research field. Reasonably, the bitcoin is the first cryptocurrency based on blockchain technology, and the influential essays include quantitative analysis of the full bitcoin transaction graph [ 54 ] ; a fistful of bitcoins: Characterizing payments among men with no

names [ 50 ] ; and bitcoin meets google trends and Wikipedia: Quantifying the relationship between phenomena of the internet era [ 69 ] . Afterward, as various altcoins appeared, cryptocurrency and digital currency are widely discussed in blockchain-related research. The high-citation article is Zerocash: Decentralized anonymous payments from bitcoin [ 74 ] and privacy, which is the prominent characteristic of cryptocurrency. In 2015, blockchain and smart contract become a hotspot, the core publications include blockchain: A blueprint for a new economy [ 2 ] ; decentralizing privacy: Using blockchain to protect personal data [ 48 ] ; at the same time, some researchers also focus on the volatility and mining of cryptocurrency. In 2016, a growing number of researchers focus on the internet of things. The most popular article is blockchains and smart contracts for the internet of things [ 45 ] . In 2017, distributed ledger and blockchain technology become a research focus point. From 2018 onward, research focus on the challenge, and the inefficiency of blockchain appear.

4.9 Funding Agencies of Blockchain-Related Research

Based on all 2451 funding sources we analyzed in this study, the National Natural Science Foundation of China (NSFC) has supported the biggest number of publications with 231 papers, followed by the National Key Research and Development Program of China, which supported the publication of 88 papers. Comparatively, the National Science Foundation of the USA has only supported 46 papers. It is remarkable that the “Ministry of Science and Technology Taiwan” supported 22 papers, which is more than the European Union. Table 16 illustrates the top 20 funding agencies for blockchain research ranked by the number of supported papers. The results indicate that China is one of the major investing countries in Blockchain research with the biggest number of supporting articles.

The top 20 funding agencies of blockchain-related research

5 Conclusions and Implications

5.1 conclusions.

This research comprehensively investigates blockchain-related publications based on the Web of Science Core Collection and provides a quick overview of blockchain research. In this study, a coherent comprehensive bibliometric evaluation framework is adopted to investigate the hot and promising blockchain domain. We outline the core development landscape of blockchain, including the distribution of publications over time, by authors, journals, categories, institutions, countries (territories), intellectual structure, and research trends in the blockchain academic community. Combining the results of statistical analysis and co-cited articles, authors, and keywords, we formulate the answers to the following research questions:

RQ1 What is the distribution pattern of blockchain publications and citations over recent years?

The published blockchain papers significantly increased since 2013, when the first blockchain paper was published. An increasing number of articles were published since. In 2018, 1,148 articles were published at the peak, and the number of publications is likely to continuously grow. As for the cumulative number of citations, there were only 272 citations in 2013. By 2018 this number has grown to more than 10,000, which implies a widespread influence and attention attracted by blockchain study in recent years.

RQ2 Which are the main international contributing countries (regions) and institutions in blockchain research, as well as collaboration networks among them?

A total of 97 countries (regions) participated in blockchain research during the observation period. USA and China play the leading roles among all countries (regions), with publications of 532 (20.94%) and 489 (19.24%) articles respectively, followed by the UK, Germany, Italy, and Australia. From the aspect of citations, USA-authored papers were cited by 1,810 papers with 3,709 (36.57%) citations, accounting for 36.57% of total citations. Articles from the USA also have a very high average number of citations per paper with a frequency of 6.97. Although the number of articles from China is close to the USA, the average number of citations per paper is lower with a frequency of 2.78. The results indicate that the USA is the most influential country in the field of blockchain.

A total of 2,190 institutions participated in blockchain-related research. Among them, the Chinese Academy of Sciences has the highest number of publications with 43 papers, followed by the University of London, Beijing University of Posts Telecommunications, University of California System, Commonwealth Scientific Industrial Research Organization (CSIRO), Beihang University, University of Texas System, ETH Zurich. In respect of the number of total Times Cited and the average number of Times Cited, Cornell University is cited the most with 499 citations, and the average number of Times Cited is 20.79. followed by the Massachusetts Institute of Technology, University of California System, and ETH Zurich. The number of publications forms institutions in China is large, whereas few papers own high average Times Cited.

In terms of collaboration networks among different institutions, we found that the Chinese Academy of Sciences, Cornell University, Commonwealth Scientific Industrial Research Organization (CSIRO), University of Sydney, and ETH Zurich cooperated widely with other institutions.

RQ3 What are the characteristics of the authorship distribution?

The total number of authors who contribute to the publications of blockchain is 5,862. the average number of authors per paper is 2.4. Among 5,862 authors, 4,808 authors have only one paper, 662 authors have two papers, and 213 authors have three papers. Based on the number of participated papers, the most productive author in the field of blockchain is Choo, Kim-Kwang Raymond from Univ Texas San Antonio, who participated in 14 articles in the field of blockchain, followed by Marchesi M, Bouri E, David R, Miller A, Shetty S and Xu X.

RQ4 What are the core blockchain subjects and journals based on the number of publications?

Blockchain-related researches are more abundant in the field of Computer Science compared with other categories. Other major fields include Engineering, Business & Economics, Telecommunications, and Business & Economics.

RQ5 What are the major journals or conferences for blockchain-related research?

The research of blockchain is published in 1,206 journals (conferences), the major blockchain research journals include Lecture Notes In Computer Science, IEEE Access, Economics Letters, Future Generation Computer Systems, and Finance Research Letters. Meanwhile, the major blockchain research conferences include IEEE International Conference on Hot Information-Centric Networking, International Conference on Parallel and Distributed Systems Proceedings, International Conference on New Technologies Mobility and Security, and Financial Cryptography and Data Security.

RQ6 What are the most influential papers in blockchain research based on the number of citations?

Ranked by the total number of citations during the observation period, the publication: “blockchains and smart contracts for the internet of things” by Christidis and Devetsikiotis [ 45 ] is identified as the most cited paper with 266 citations, which also has a highest average number of citation per year, followed by decentralizing privacy: Using blockchain to protect personal data [ 48 ] with 169 citations and 33.80 average number of citations per year.

According to the number of times co-cited, the top five influential publications are as follows: Bitcoin: A peer-to-peer electronic cash system [ 1 ] , A next-generation smart contract and decentralized application platform [ 42 ] , Majority is not enough: Bitcoin mining is vulnerable [ 12 ] , Ethereum: A secure decentralised generalised transaction ledger [ 44 ] , Blockchain: Blueprint for a new economy [ 2 ] .

RQ7 Who are the most influential authors in blockchain research according to the author co-citation network?

Some authors played a crucial role during the development of blockchain research, Nakamoto S, as the creator of Bitcoin, and the author of the bitcoin white paper, created and deployed bitcoin’s original reference, therefore is not surprised at the top of the co-citation count ranking and got 1,202 citations in our dataset. Buterin V, a Russian-Canadian, programmer, and writer, primarily known as a co-founder of Ethereum and as a co-founder of Bitcoin Magazine who follows Nakamoto S and receives 257 citations. Other core authors with high citations include Eyal I, Wood G, Swan M, Christidis K, Bonneau J, Szabo N, Zyskind G, Castro M, and Meiklejohn S.

According to co-cited articles clusters, the research priorities in blockchain-related research keep changing during the observation period. Bitcoin and bitcoin network are the main priorities of researchers, then some researchers changed to focus on cryptocurrency in blockchain research.

RQ8 What are the research trends of blockchain?

The research priorities in blockchain-related research evolve during the observation period. As early as 2013, when the research on blockchain first appears, bitcoin dominated the blockchain research field. Then only one year later, as various altcoins begin to appear, cryptocurrency and digital currency are widely discussed in blockchain-related research. In 2015, blockchain and smart contracts become a hotspot till 2016 when a growing body of researches begin to focus on the internet of things. In 2017, distributed ledger and blockchain technology become the research focal point. From 2018 onward, research focus on the challenge and inefficiency of blockchain.

RQ9 What are the most supportive funding agencies of blockchain research?

The most supportive funding agency of blockchain research is the National Natural Science Foundation of China (NSFC) which has supported the publication of 231 papers. The results indicate that China is one of the major investing countries in Blockchain research with the biggest number of supporting articles.

Given the potential power of blockchain, it is noticeable that governments, enterprises, and researchers all pay increasing attention to this field. The application of blockchain in various industries, the supervision of cryptocurrencies, the newly rising central bank digital currency and Libra, are becoming the central issues of the whole society.

In our research, we conducted a comprehensive exploration of blockchain-related research via a bibliometrics analysis, our results provide guidance and implications for academic research and practices. First, the findings present a holistic view of research in the blockchain domain which benefits researchers and practitioners wanting to quickly obtain a visualized overview of blockchain research. Second, according to our findings of the evolution and trends in blockchain research, researchers could better understand the development and status of blockchain, which is helpful in choosing valuable research topics, the distributed ledger, the discussions on the inefficiency and challenges of blockchain technology, the supervision of cryptocurrencies, the central bank digital currency are emerging research topics, which deserve more attention from the academic community.

5.2 Limitations and Future Work

As with any research, the design employed incorporates limitations that open avenues for future research. First, this study is based on 2,451 articles retrieved from the Web of Science of Core Collection, although the Web of Science of Core Collection is truly a powerful database for bibliometric analysis, we can’t ignore the limitation brought by a unique data source. Future research can deal with this limitation by merging the publications from other sources, for instance, Scopus, CNKI, as well as patent database and investment data of blockchain, and it could help to validate the conclusion. Second, we mainly adopt the frequency indicator to outline the state-of-the art of blockchain research, although the frequency is most commonly used in the bibliometric analysis, and we also used H-index, citation to improve our analysis, some other valuable indicators are ignored, such as sigma and between centrality, therefore, it’s beneficial to combine those indicators in future research. Besides, it should be noted that, in co-citation analysis, a paper should be published for a certain period before it is cited by enough authors [ 26 ] , the newest published papers may not include in co-citation analysis, it’s also an intrinsic drawback of bibliometric methods.

Supported by the National Natural Science Foundation of China (71872171), and the Open Project of Key

Laboratory of Big Data Mining and Knowledge Management, Chinese Academy of Sciences

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Research on the Application of Blockchain Technology in Digital Asset Management

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With the rapid development of the digital era, digital asset management has become an important issue. This article takes an in-depth look at the application of blockchain technology in digital asset management and explores how it can solve challenges in key areas such as identity verification, copyright management, digital currency and smart contracts. By analyzing two specific cases of a digital identity verification platform and a digital rights management system, this article reveals the potential of blockchain technology to enhance security, transparency, and efficiency. Research has found that blockchain technology can provide secure, tamper-proof solutions, effectively protect the rights and interests of creators and users, while promoting fairness and efficient operation of the market. Although there are some research limitations, such as the number and scope of case studies, and the rapid changes in technological development, future research is expected to further explore the application of blockchain technology in digital technology through more extensive case analysis, technology impact assessment and quantitative analysis methods. Application potential and practical strategies in asset management.

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Department of Economics, University of Pretoria, Pretoria, South Africa

Rangan Gupta

Department of Economics, University of Perugia, Perugia, Italy

Francesco Bartolucci

Department of Economics, National and Kapodistrian University of Athens, Athens, Greece

Vasilios N. Katsikis

Interscience Institute of Management and Technology, Bhubaneswar, Odisha, India

Srikanta Patnaik

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Deng, L. (2024). Research on the Application of Blockchain Technology in Digital Asset Management. In: Gupta, R., Bartolucci, F., Katsikis, V.N., Patnaik, S. (eds) Recent Advancements in Computational Finance and Business Analytics. CFBA 2024. Learning and Analytics in Intelligent Systems, vol 42. Springer, Cham. https://doi.org/10.1007/978-3-031-70598-4_19

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How to Use ChatGPT to Write a Research Paper: Prompts & Ideas 

If you’re a student, you’ve probably heard of ChatGPT. This advanced AI model can handle the task of writing an entire research paper from scratch. Many scholars use the chatbot to create texts with minimum effort. However, most of the time, the results are far from impressive. Plus, students risk getting punished for unethical AI use.

But what if we told you there are ways of using ChatGPT to create excellent papers and save time on the steps that would take ages to do manually?

In this article, our experts will discuss how to use the chatbot to make research writing easier. We will also explain what to avoid and suggest what custom GPTs you may use to make the writing process more efficient.

🔍 Is ChatGPT Good for Research?

• ✍️ Limitations of ChatGPT
• 🎯 How to Use AI to Write a Research Paper
• 🤔 Aspects AI Can’t Help You With
• 🚀 Best Custom GPTs for Research
• 💡 Key Takeaways

🔗 References

In general, it’s possible to use ChatGPT as a research assistant. But the question is, for what end can you use it?

Well, if your aim is not to generate an entire paper but rather to make some of the routine writing steps easier, then using an AI tool can be ethical and permissible.

For instance, you can use ChatGPT to:

• Search for information,
• Rewrite complex ideas,
• Summarize longer texts,
• Develop research questions ,
• Create research paper abstracts,
• Generate sample outlines.

ChatGPT is very effective when it comes to handling such tasks. It’s also absolutely legal to use it for these purposes.

All you need to do is write a very clear prompt . Otherwise, the chatbot might give you an irrelevant answer. We’re going to share the most effective prompts for various research purposes in the following sections.

But first, an important disclaimer:

We’re firmly against AI cheating , and we don’t encourage it in any shape or form. That’s why this guide will talk about legal and honest ways to use AI for research assistance.

✍️ Limitations of Using ChatGPT for Research

If you decide to use the chatbot for your academic work, it’s essential to consider its limitations . This will help you focus on what AI does best and avoid trusting it with things that can ruin your research.

Here are the main limitations you want to consider:

• It’s limited by the datasets . Because ChatGPT is not trained on recent data, it has no knowledge of the most up-to-date information or findings in specific fields.
• It lacks depth . While the chatbot can effectively summarize information, it may fail to fully grasp complex theories and arguments of a research topic.
• It tends to plagiarize . When ChatGPT generates its texts, it rehashes the information from its database. So, even if a text seems original, it contains other people’s ideas.
• It struggles with contextualizing and interpreting data . The chatbot lacks the human ability to analyze and contextualize information critically. It needs guidance through concrete, well-written prompts.
• It can present incorrect answers . Sometimes, the chatbot simply lacks information in a specific research field. This leads to inconsistencies and inaccuracies in the generated content.
• It’s prone to bias . Since ChatGPT is trained on online data that often includes biased and offensive information, it can generate statements expressing bias towards specific ideas or people.
• It’s associated with cheating . Some unethical researches generate entire papers without making any real effort. If students do the same, they will be caught with the help of AI essay checkers and punished for cheating.

Even though it may be tempting, it’s best not to rely on AI too much if you want to conduct good research. You can use some of the chatbot’s functions to make the process easier, but you still have to do most of the work yourself.

🎯 How to Use ChatGPT to Write a Research Paper: Top 11 Ideas

So, do you want to know how to use ChatGPT to write a paper? If you do, look no further!

The following list of tips will explain to you how to:

• Brainstorm topics,
• Create a roadmap,
• Refine your research topic,
• Find gaps in your outline,
• Locate sources,
• Find cases to explore,
• Summarize texts,
• Analyze statistical data,
• Edit as you write,
• Create an abstract,
• Format the references.

We’ve also included examples of the most effective prompts for each research aspect. Feel free to try them out!

1. Brainstorm Topics with ChatGPT

For many students, the most challenging part of research is the beginning. Sometimes, you just don’t know what to focus on. In situations like this, ChatGPT can help you brainstorm ideas and collect information.

Let’s assume you need to write an essay about pop culture and modern art, but you need help figuring out where to start.

The first step would be to create a good prompt for the chatbot. For instance, you can phrase your request like this:

The second step will be to choose one topic from the generated list and ask ChatGPT to tell you more about it.

For instance:

You’ll get a detailed answer on the subject.

Lastly, you can use the most interesting points from the last reply to narrow down your topic even further or to get more ideas for the direction of your paper.

For example:

And now you have fresh, focused topics that you can research.

2. Use AI to Create a Roadmap

Once you’ve chosen a topic, you can use ChatGPT to create a detailed roadmap that will guide your research and help you stay on track.

As we’ve mentioned before, when you work with ChatGPT, a good prompt is the key to getting a relevant answer. This is especially true for generating outlines and roadmaps. The more detailed and clear your prompt is, the better outcome you will get.

Here’s an example of what your request might look like:

The roadmap also included suggestions for a literature review, methodology, case studies, discussion, and other essential research components. You can use all this information to create an outline. To streamline this task, try using our free research paper outline generator .

We also suggest you research the topic before using AI. That way, you’ll have a general understanding of the subject and will be able to judge the quality of the generated roadmap.

3. Refine Your Research Question with AI

Any research needs a central question. Sometimes, it may be hard to collect your thoughts and come up with an interesting issue to focus on. In this case, you can use ChatGPT to assist you.

Here’s how:

• Describe the topic of your paper clearly and concisely.
• Ask the chatbot to suggest several research questions based on your requirements.
• Choose the best one.

Your prompt can look like this:

Tip : We also recommend using our free question generator from text . It will easily turn your research idea into a question without you having to come up with elaborate prompts.

4. Ask ChatGPT to Identify Gaps in Your Outline

Suppose you’re done with the outline and have the research question, but you feel something needs to be added to it. In that case, you can use ChatGPT to identify the gaps in your research plan.

Here’s what the prompt may look like:

5. Use the Chatbot to Find Sources

Once you have completed the preparation stage, it’s time to find suitable sources and start researching.

How can ChatGPT help you with it?

Well, one way is to ask ChatGPT to create a list of references on the subject. However, this is a bad idea since you’ll only get a list of nonexistent sources vaguely related to the central theme of your research.

Instead, we suggest you ask ChatGPT to locate relevant works on separate points from your outline. This will allow you to get a comprehensive overview of diverse aspects related to your topic and not just the central theme itself.

For instance, if the topic of your paper is the semiotics of memes used in modern art, one of the points in your outline can be dedicated to the visual language in memetic social commentary. Suppose this part of your research will be about 3 pages long.

So, you can ask the chatbot the following:

One thing to note is that ChatGPT can’t provide you with internet links, so you’ll have to look for sources on your own. And while you do that, you may also come across interesting articles or get new ideas that you can use to perfect your outline.

Tip : To ensure that your text doesn’t contain any plagiarized fragments from the sources, you may run it through our free plagiarism checker . And if you need additional clarifications regarding the meaning of complicated passages, feel free to use a quote explainer .

6. Find Specific Cases to Explore

Think your research lacks depth? Try adding to it real-life cases or specific historical incidents. They will help you support your statement, better convey your ideas, and add credibility to your paper.

If you want to include an example in your paper but struggle to think of one, you can use ChatGPT to look for a specific event or real-life instance related to your topic.

Here’s what the prompt can look like:

The results also included Brazilian, South Korean, and French memes with explanations. We’d say that’s enough to create a comprehensive overview. Just make sure to fact-check the generated examples and see if they actually exist.

7. Use AI to Summarize Lengthy Sources

If you need to learn the main idea of a lengthy passage quickly, you can use ChatGPT as a summarizer. This can be especially helpful when you’re working on a literature review and need brief descriptions of your sources.

Let’s try it with an article on the semiotics of memes :

When you need a summary, it’s best to provide the chatbot with the whole text by copy-pasting it. It’s important because if you use vague prompts with only the work’s title, the tool will either refer to a nonexistent work or refuse to answer the request:

Tip : In addition to ChatGPT, try using our key points maker . It will present all the necessary information from the text in a convenient list format. And if you need to create an overview of previous research for your paper’s introduction, use our background of the study generator .

8. Analyze Statistical Data with ChatGPT

Now, what should you do if your research includes statistical data? Sometimes, it can be hard to interpret numbers and connections between them. Luckily, ChatGPT can help you analyze and organize it, isolate the most critical pieces of data, and create a comprehensive summary.

To make it work, ensure that your prompt is as detailed as possible. You will also need to include the entire dataset in it.

Here’s an example:

The chatbot can also use inferential statistics methods such as chi-square tests or ANOVA , but that would require large, concrete numbers.

Otherwise, the chatbot will get stuck repeating the same formula:

Another important thing is not to rely on AI entirely when it comes to statistical analysis. The reason is that the chatbot can overlook certain things or give biased results. In some cases, ChatGPT usage for data analysis can lead to p- hacking , which is highly unethical.

What does this mean, exactly?

Well, p-hacking is a way of misusing data to create a statistically significant result when, in fact, it doesn’t exist. This leads to false conclusions and undermines the reliability of the study. For that reason, it’s crucial to analyze the data yourself and carefully double-check the results if you decide to delegate the task to AI.

Still, ChatGPT works effectively when it is necessary to summarize data or conduct a preliminary analysis of the statistical data and make conclusions depending on the study findings.

9. Use the Chatbot to Edit as You Write

While working on your paper, you may want to improve or edit its parts. That’s another aspect where ChatGPT will come in handy.

Here’s what it can do for you:

• Simplify the language . Some people use the tool to make their text more complex and formal, but more often than not, it only makes it unreadable. Simplifying it is a much better option.
• Shorten long or complex sentences . Using short sentences will help you create a more cohesive and reader-friendly paper.
• Substitute certain words with synonyms . This is especially useful when you want to say something complicated in a few words.
• Check your grammar . It’s always a good idea to proofread your paper to make sure everything runs smoothly.

Take a look at this text:

Within the contemporary art landscape, memes are used as agents of cultural discourse, where their inherent capacity for humor and cultural resonance help address the challenges of modern society. Memes fuse visual and textual elements, which allows them transcend traditional artistic mediums, thus offering artists a dynamic platform for critiquing current socio-political events. This combination of digital vernacular and artistic expression not only blurs the boundaries between high and low culture but also reflects the evolving nature of expression in the digital age.

It sounds overcomplicated, but we can improve it with the help of Chat GPT:

Want to know more? Check out our comprehensive guide on using ChatGPT to edit essays .

Tip : We recommend listening to the text of your research paper with our essay reader tool. This will help you effectively notice any inconsistencies, difficult wording, or confusing grammar patterns.

10. Create an Abstract with AI

When you’re done with the bulk of your research and have little time to work on your abstract, ChatGPT can be a real lifesaver.

Copying and pasting the whole paper into the chatbot may not be the best idea since the AI can miss important information and not include it in the abstract. Instead, it’s best to ask the tool to create short summaries for each section of your work.

What you need to do is start a new chat and use the following prompt with each portion of your text:

After that, you simply combine the summaries to create an abstract. You use ChatGPT to help you structure it with this prompt:

Note that the generated abstract contains phrases typical for ChatGPT, like “delves into.” If your professor decides to check such a text for AI, it will most probably be marked as suspicious. That’s why it is not a good idea to submit it as it is. Consider rewriting such an abstract manually or using an AI humanizer to quickly add a human touch.

Tip : Another easy way to create an abstract is to use our free online abstract generator .

11. Use ChatGPT to Format Your References

Creating a list of references is an essential part of any research. It’s also one of its most challenging and time-consuming aspects. After all, each formatting style has numerous specific characteristics that can easily get confused.

ChatGPT can save your time and help avoid unnecessary mistakes while citing the sources. The most important thing is not to ask the AI tool to generate references from scratch.

Instead, we recommend you find all the necessary data for the references yourself. Then, manually input these data into ChatGPT and ask it to create a reference for you. That way, you’ll ensure everything is formatted correctly and avoid citing nonexistent works in your paper.

🤔 Research Aspects AI Can’t Help You With

The list of research aspects ChatGPT can help you with may look very impressive, but don’t let it fool you! There are still plenty of things that shouldn’t be entrusted to AI tools . It’s essential to know about them since ChatGPT can easily make its users overestimate its abilities regarding research, writing, and editing.

Let’s see why you shouldn’t trust AI with the following:

• Experimental design and methodology,
• Moral reasoning,
• In-depth data analysis,
• Comprehensive literature review,
• Peer review and personalized guidance.

Experimental Design & Methodology

ChatGPT can do many things, but it still lacks the nuanced understanding and the expertise to design experiments and choose the correct methodology. The chatbot can suggest some general ideas or set the direction for your research, but you’ll have to work on your own to achieve the most accurate results.

Moral Reasoning

AI tools like ChatGPT lack moral reasoning and ethical judgment. They can write about ethical principles if you ask them to, but they don’t know how to apply these principles in their writing.

When you work on your research, considering small nuances and adhering to guidelines is very important, especially if your paper involves human subjects. For that reason, you have to rely on yourself to navigate ethical issues in your research.

In-Depth Data Analysis

To analyze or interpret the research data, you need to be well-versed in the subject of your study. While AI models work with a vast amount of data, they still lack in-depth information in certain study fields.

ChatGPT can be helpful if you want to analyze a small amount of data, but for more detailed and nuanced conclusions, it’s best to rely on yourself.

Comprehensive Literature Review

You can use ChatGPT to generate small summaries and lay the basis for your literature review. However, to make your research work, you’ll need an in-depth analysis that discusses the source’s relevance, credibility, and importance in the context of your research. This task is something ChatGPT can’t handle.

Peer Review & Personalized Guidance

Collaboration with other researchers in the same field is a chance to improve your knowledge, learn more about different perspectives, and receive valuable feedback. Surely, ChatGPT can check your paper for mistakes and evaluate it, but it can never replace a real peer review.

Remember to refrain from delegating these aspects to AI. This will help you avoid problems and make the most out of your research experience.

🚀 Use ChatGPT for Research Effectively with Custom GPTs

For a more effective AI-assisted writing process, we suggest you use custom models designed for specific research areas. Check out the best GPTs that can serve as your personal assistants.

Note : To use these custom models, you need a subscription to ChatGPT Plus. For $20 a month, you will access the most advanced LLM to date and enjoy many additional functions.

• SciSpace GPT . With this handy tool, you’ll have access to over 200 million academic articles right at your fingertips. SciSpace will give an accurate response to any scientific question and provide digestible summaries of research papers for a deeper study.
• Consensus . You can use this custom GPT to get an evidence-based answer to your question and conduct an in-depth literature search. Besides, Consensus can give you a proper bibliography list in the APA format.
• Scholar GPT . With this AI model, you can research millions of academic sources quickly and efficiently. Feel free to ask it for graphics, charts, and even real-time updates. It will also help you with problem-solving and NLP tasks.
• Research-Paper Analyzer . Want to have access to simplified summaries of complicated research papers? Try this GPT. It instantly extracts the main points of a study to facilitate easy comprehension. It can also analyze the credibility and relevance of a chosen paper.
• Academic Research Reviewer . Working with this GPT is like having a seasoned professor to help you refine your research paper. It will examine the depth of your literature review, underline the strengths and weaknesses of your paper, highlight gaps, and suggest areas for further research.
• PubMed Buddy . This GPT allows you to search for information in PubMed and UnPaywall databases. With PubMed Buddy, you’ll avoid going through countless irrelevant studies and have more time to spend on the more exciting research aspects.
• Chemistry Lab Partner . Get specific help on difficult chemistry questions with this tool. Analyzing data and explaining concepts are not the only functions of Chemistry Lab Partner. It can also assist with your lab work and even enable virtual experiments.
• Graduate Level Physics GPT . Try this custom GPT to make the advanced physics more understandable. It’s a practical tool that breaks complicated topics into brief and digestible explanations. Besides, it assists with physics problems and research.
• Physics Tutor . Do physics concepts seem too complex and intimidating to you? This GPT is here to sort things out. It will give simple and clear explanations understandable to physics students and laypeople who are just curious about how this world works.
• Math Expert . Working with this GPT is like having a helpful math tutor available anytime. It not only gives you a correct solution to any problem or equation but also provides a step-by-step explanation of how to get there.

💡 How to Use ChatGPT for Research: Key Takeaways

ChatGPT is a smart and helpful tool that can help you simplify some of the steps of your research process. You can use it to:

• Look for fresh and exciting topics,
• Create roadmaps,
• Find sources,
• Find real-life examples,
• Create research questions,
• Identify gaps in your outline,
• Analyze statistics,
• Edit your writing,
• Create abstracts,

One thing to remember is that relying on yourself is always a better option. Even though AI can save you lots of time, it still lacks ethical judgment, critical thinking, and expert knowledge in your field. If you decide to use ChatGPT in your research, make sure to double-check everything it produces and never use generated replies in your texts.

We hope you’ve found this article helpful and learned something new. Now, you know how to use ChatgGPT ethically, so you don’t have to worry about being caught. We suggest you check out your school’s guidelines before using the chatbot. Feel free to share your experiences working with AI tools in the comments below!

Want to know more about using ChatGPT in your studies? Check out our in-depth articles with tips on how to generate longer essays and make AI texts undetectable .

• How to Use ChatGPT to Do Research for Papers, Presentations, Studies, and More: ZDNet
• Three Ways to Leverage ChatGPT and Other Generative AI in Research: Times Higher Education
• The Top 10 Limitations of ChatGPT – Forbes
• Top 10 Drawbacks of Using ChatGPT in Academics: Analytics Insight
• The Best Custom GPTs to Make ChatGPT Even More Powerful: Digital Trends
• How to Use ChatGPT for Research and Essays: MakeUseOf
• Increase Your Creativity with ChatGPT – Psychology Today
• How Can I Use ChatGPT in My Research Work?: GitHub
• How to Use ChatGPT as a Research Tool: LinkedIn
• The Best AI Tools to Power Your Academic Research: Euronews
• ChatGPT and Fake Citations: Duke University
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