What we know and what should we know about the future of blockchain in finance

2.1 Database & tools

The Dimensions database stands out as one of the largest accessible resources within the academic and research community, offering free access to users. In recent years, it has been extensively utilized across various research fields (Jaiswal et al., 2022a, 2023, 2024d; Cortés et al., 2022). Its inclusion of the most influential journals and publishing houses across diverse academic disciplines makes the Dimensions database particularly attractive for bibliometric analysis.

For the construction and visualization of bibliometric networks involving journals, researchers, citations, bibliographic coupling, co-citation, or co-authorship, we employ two software applications: VOSviewer (Van Eck & Waltman, 2010) and RStudio Bibliometrix (Aria & Cuccurullo, 2017). These tools offer robust capabilities for visualizing and analyzing bibliometric data (Jaiswal and Gupta, 2023a, 2023b; Prakhar et al., 2024a; Gupta et al., 2024; Jaiswal et al., 2022b). Notably, VOSviewer comes equipped with text mining features, allowing the construction and visualization of co-occurrence networks of keywords extracted from a body of literature (Jaiswal and Kumar, 2023; Jaiswal et al., 2024a, 2024b, 2024c, 2024d). These applications have gained widespread use in recent bibliometric research studies (Jaiswal et al., 2022a, 2024a).

Bibliometric Analysis was performed using VOSViewer (https://www.vosviewer.com/) and RStudio Biblioshiny (https://www.bibliometrix.org/home/index.php/layout/biblioshiny).

2.2 Article search and selection

The first step in retrieving data is identifying the proper collection of blockchain-related articles published in finance research, as outlined in Table 1. An initial search performed over the period 2018-2022 using the search criteria ((“blockchain” OR “blockchain technology” OR “blockchain technologies”) AND (“finance” OR “banking” OR “financial” OR “bank”)) results in 5,561 documents. These are filtered into the fields “commerce, management and tourism and services”, “business and management”, and “banking, finance, and investment”, producing 1,198 publications. When further screened to exclude conference papers, books, chapter summaries, and other non-articles, the final corpus includes 500 articles, which are examined using bibliometric analysis, which has the capacity to manage vast corpora, to reveal fundamental characteristics, identify publication trends, distinguish advanced topics and construct visualizations of thematic evolution. Both retrospective analysis and future research directions are considered (Ibrahim & Truby, 2021).

Data can be obtained from the Dimensions AI database or can be directly downloaded from here: FigShare, “Dimensions-Publication-2022-07-09_04-12-30.csv” (Dataset. https://doi.org/10.6084/m9.figshare.26364760.v1).

3.1 Publication output and growth trend

Blockchain technology has emerged as a recent development in the field of finance. Despite the growing number of financial journal publications, it is noteworthy that only one paper from 2014 has specifically delved into the realm of blockchain within finance literature. Consequently, the analysis primarily focuses on post-2018 publications, as this marks the period when blockchain research in finance began gaining momentum in addressing practical, real-world issues.

In 2022, a total of 162 articles were published on the subject, demonstrating a substantial increase in scholarly interest. This trend is consistent with the preceding years, where 2021 saw 158 articles, 2020 had 144, 2019 featured 73, and 2018 recorded 43. The sheer volume of work dedicated to blockchain technology in finance research underscores the escalating interest among researchers, highlighting a growing inclination towards publishing and contributing to the evolving discourse in this domain.

3.2 Productive institutions based on total publications and citations

Table 2 compares the top ten affiliations and institutions based on the total number of citations and publications. The Royal Institute of Technology has the most citations (349), and Hong Kong Polytechnic University has the second- most (307), but for total publications, Hong Kong Polytechnic University has the most (7), followed by the University of Luxembourg (6).

The VOSviewer user manual specifies that the strength of a link is consistently positive, with a higher value indicating increased reliability. The Total Link Strength (TLS) is determined by the frequency of a specific combination of items (such as keywords, authors, sources, or institutions) appearing in scholarly articles. Figures 1 and 2 illustrate the network visualization of institutions based on their total publications and total citations, respectively. In terms of publications, the Hong Kong Polytechnic University has a Total Link Strength of 15, Dublin City University has a TLS of 17, and ETH Zurich has a TLS of 10. In the context of total citations, the Royal Institute of Technology boasts a TLS of 10, Worcester Polytechnic Institute has a TLS of 11, and Stevens Institute of Technology has a TLS of 8.

Figure 1. Collaborative networks of publications with institutions.

Figure 2. Collaborative networks of citations with institutions.

3.3 Productive journals, countries, and authors in the field of blockchain technology

Table 3 shows that Business Horizons is top of the list for total citations, with 643 citations and 3 publications, followed by Technological Forecasting and Social Change with 512 citations and 14 publications. Notably, the latter journal has a TLS of 40, which is better than any of the journals listed in the top 10.

Loess, which stands for “locally estimated scatterplot smoothing”, is used for multivariate data. Source dynamics apply loess to the number of documents published from 2018 to 2022 (Table 4). The Loess procedure is a robust, nonparametric method for smoothing empirical data that contains outliers, offering flexibility, because no assumptions are made regarding the parametric form of the regression surface (Cleveland and Devlin, 1988). In 2020 and 2021, we observe a sharp increase in the number of publications on blockchain research in finance (see Table 4 and Figure 3). However, as we approach 2022, the loess method moderates this anomaly with a gradual decline.

Figure 3. Collaborative networks of productive journals.

Gilbert (1977) demonstrates that scientists use citations as a persuasive tool. The age of an article is a factor in its citations (Dang & Li, 2020). According to Schwert (1993), affiliation with the top contributing institutions and nations positively influences citations. Authors affiliated with the most prolific contributing institutions publish more and cite more of their prior work (Donthu et al., 2020). Table 5 indicates that the United States is the leading contributor to blockchain research in finance in terms of total citations and total publications, followed by China for total publications and the United Kingdom for total citations. Although the United Kingdom has produced fewer publications (42) than China (55), it has received slightly more citations (1,101 for the United Kingdom versus 1,082 for China).

Figure 4 provides insight into clusters of countries. China, the United States, United Arab Emirates, Pakistan, South Korea, Malaysia, and Canada fall into the same cluster, shown in red, while the United Kingdom, India, France, and Turkey belong to the cluster shown in blue. The third cluster, in green, consists of Germany, Italy, Luxembourg, and Belgium.

Figure 4. Collaborative networks of countries for blockchain research in finance.

Lotka (1926) uses a variation of the inverse square law to describe the publication frequency of researchers in a specific field. As a general rule, according to this law, the number of authors who make multiple contributions during a given period is smaller than the number of authors who make just one. The actual number of publications in a given period is compared to the theoretical Lotka curve for the same field to determine an author’s productivity. The author productivity for blockchain research in finance is shown in Table 6, which shows that the blockchain research corpus is dominated by occasional authors with less than five publications, as opposed to core authors who each contribute multiple papers to the body of knowledge. The Lotka curve theory supports this.

An author may produce less influential work but cite the work of authors from other countries, thereby indirectly increasing the citation score of locally cited documents in the corpus. Table 7 lists the 15 most-cited articles from 2018 to 2022, while Figure 5 provides the network collaboration among the top documents. Supply chain, the fintech revolution, and financial services are the most frequently cited study areas in the blockchain research in finance (Moosavi et al., 2021). Most of these studies are published by Elsevier, Taylor & Francis, Springer, and MDPI. Logistics, Journal of Management and Information Systems, Business Horizons, International Journal of Production Economics, Journal of Financial Intermediation, Technological Forecasting and Social Change, Journal of Business Venturing, and The Electricity Journal are among the leading journals that publish such articles.

Figure 5. Network collaboration among the top documents.

4.1 Document-level analysis of blockchain research in finance

According to the existing literature, a scientific document’s topicality, methodological rigor, design, core content, and quality should be the primary determinants of citations rather than the individual authors. It is often argued that the contents and quality of an article affect its citations. Valtakoski (2020) refers to scholarly content in a piece of literature, whereas Stremersch et al. (2007) argue that longer articles tend to receive more citations. Furthermore, it seems easier to track down an article with more keywords, leading to more citations (Valtakoski, 2020), whereas it is difficult to search for a lengthy title, which can hurt citations. When analysing the corpus, we consider three elements: trends in the words used, a word tree map, and a word cloud.

Trending topics based on bigrams applied to the abstracts reveal that Islamic fintech, fintech companies, collaborative innovation, smart contract, and financial innovations are top trending topics for blockchain research in finance. Researchers frequently include a variety of keywords, each of which has the potential to be incorporated into a graphical illustration of the data. The results of applying a tree map to the top fifty keywords in the extracted corpus to construct a proportional map are displayed in Figure 6. The term “blockchain” is by far the most common, but the research is heavily influenced by concepts such as supply chain, initial coin offering (ICO), and financial services.

Figure 6. Word tree map based on title frequency using bigrams.

The title and abstract unigram frequency distribution of the corpus is used to generate a word cloud (see Figure 7). The word “blockchain” is used most frequently (title 996, abstract 1,514), followed by “financial” (title 108, abstract 1,058), “technology” (title 104, abstract 978), and “finance” (title 104, abstract 978), as highlighted with thick font densities.

Figure 7. Word cloud based on title and abstract frequency.

4.2 Conceptual knowledge structure

To decipher the data contained within a body of accumulated literature, bibliometrics are used, most frequently based on citations. However, new studies having low citations does not necessarily indicate that they are without significance (Bhukya et al., 2022). When new conceptual knowledge structures (CKSs) are researched in the context of their temporal evolution, as cited events, it is much simpler to comprehend the beginnings and development of these structures over time. In order to locate the CKS within this corpus, we make use of a topic dendrogram.

This section deals with topic grouping and clustering. We analyse the network diagram with blockchain as the central theme. The surrounding clusters give us an idea of blockchain research in finance. We identify eight distinct clusters with simple manual interpretation and analysis of the dendrogram (Figure 8). These 8 clusters are labelled (Table 8) smart contracts, supply chain, financial services, financial institutions, financial market, financial technologies, initial coin offerings, and business model. We provide notable publications with more than 50 citations. It is evident that most of the work is in the area of smart contracts, followed by supply chain, and financial institutions.

Figure 8. Clustering of blockchain research in finance for topic grouping (Generated using RStudio – Biblioshiny).

After compiling the content of the existing literature and organizing the data from the clusters, we find that the most active areas of blockchain research are in the following two domains: financial risk management and digital transformation; and big data analytics, coupled with machine learning and artificial intelligence (AI).

4.2.1 Financial risk management and digital transformation

The financial sector pioneered and ultimately made the most effective use of blockchain technology. Many academics study the blockchain for its possible financial uses (Ali et al., 2020; Osmani et al., 2020). For example, Cong et al. (2021) argue that centralized mining pools for risk sharing would not disrupt decentralized mining based on blockchain technology due to cost settings and energy consumption, providing valuable insight into other consensus protocols and organizations. Distributed ledger technology (DLT) and smart contracts are cited by Pal et al. (2021) as the two main aspects of blockchain that can streamline banking and corporate operations. Blockchain technology’s rapid development and updates are set to profoundly impact the nature of businesses and economic and social progress. Blockchain could damage the traditional and centralized business management and organization model because it could lower transaction costs and make it easier to acquire external resources. Treiblmaier (2018) develops a research framework based on four economic theories (principal-agent theory, transaction cost analysis, resource-based view, and network theory) to investigate how blockchain has modified supply chain management.

4.2.2 Big data analytics coupled with artificial intelligence and machine learning

The theory and technology behind machine learning and its potential uses have advanced rapidly since its inception (Jaiswal and Gupta, 2024; Jaiswal et al., 2023; Jaiswal and Kumar, 2023; Jaiswal and Medhavi, 2018). Much research attention is focused on the intersection of machine learning, AI, and blockchain technology. Tanwar et al. (2019) combine machine learning algorithms with blockchain to ensure accurate data analysis results. Outchakoucht et al. (2017) propose a distributed and dynamic policy to control access to the internet of things (IoT). Meanwhile, dynamic optimization and self-adjustment of policy are made possible by the combination of machine learning and reinforcement learning algorithms. A blockchain and AI-based architecture for the IoT has been developed by Singh et al. (2020), representing a powerful approach to integrating the two technologies. The authors conduct a qualitative and quantitative evaluation of the proposed architecture, showing that it is superior to current IoT architectures.

There are several challenges to the adoption of blockchain in finance, including regulatory challenges (Guo & Liang, 2016), security and privacy of data (Deshpande et al., 2017), interoperability (Kshetri, 2018), the energy cost of the technology (Hassani et al., 2018), uncertainty about how to govern technology (Attaran & Gunasekaran, 2019), and technical and scalability challenges.

4.3 Future research avenues

In this section, while briefly discussing the eight thematic clusters, we provide some future research questions (FRQs) based on the content analysis of the articles in each cluster.

4.3.1 Smart contracts

Smart contracts can help with finance in a variety of ways. They facilitate secure and automated transactions, reduce costs and paperwork, streamline processes, and make payments more efficient (Rozario & Thomas, 2019). Smart contracts can also be used for the digital tokenization of assets such as stocks, bonds, and real estate as well as for automated loan management, escrow services, and trading (Cai, 2018). Additionally, smart contracts can facilitate payment processing, verify identity, and authenticate documents (Dubey et al., 2020). Future research should explore the following FRQs:

4.3.2 Supply chains

Blockchain technology can revolutionize the way supply chain finance is conducted. By using a distributed ledger system, businesses can securely and transparently track the movement of goods and money through the supply chain. This would allow for improved visibility and streamline the process of financial transactions. Blockchain technology could also be used to securely store data related to invoices, payments, and contracts, making it easier to access and update information in real- time. This would help improve efficiency and reduce the costs associated with supply chain finance (Kurpjuweit et al., 2021). Furthermore, blockchain technology can facilitate the emergence of decentralized, peer-to-peer financing models, which could open the door to more efficient and cost- effective financing options (Kouhizadeh et al., 2021; Dubey et al., 2020). Future research should explore the following FRQs:

4.3.3 Financial services

Blockchain technology can improve many aspects of the financial services industry by providing more transparency and faster, more secure, efficient, and cheaper transactions, including international payments (Meng & Du, 2021). Blockchain can also reduce the risk of fraud and help combat money laundering (Chang et al., 2020). Additionally, it can provide access to financial services to those who previously did not have access. Blockchain can facilitate the transfer of assets, such as stocks and bonds, and the trading of digital assets, such as cryptocurrencies. Future research should explore the following FRQs:

4.3.4 Financial institutions

Blockchain technology has the potential to provide financial institutions with a variety of benefits, including improved security, lower costs, faster transactions, and greater transparency (Sun et al., 2021). With blockchain, financial institutions can increase the security of their transactions by using distributed ledgers and smart contracts that are cryptographically secure. This technology can also reduce the costs associated with verifying transactions, since a network of computers automatically validates them. Blockchain transactions are faster than traditional methods, as they are conducted directly between two parties in a matter of minutes, compared to days or weeks with traditional methods (Basori & Ariffin, 2022). Notably, blockchain technology can increase transparency by providing an immutable record of all transactions that can be accessed by all parties, allowing for greater trust in the system. Future research should explore the following FRQs:

4.3.5 Financial market

Blockchain technology has the potential to revolutionize the financial market. It can be used to store and share data, facilitate transactions, and provide access to financial services for individuals and businesses. Blockchain technology can also improve transparency and security in the financial market by providing a secure, distributed ledger that records all transactions. This can help reduce the risk of fraud and cyber threats (Somin et al., 2020). Blockchain technology can also be used to develop new products and services, such as smart contracts and decentralized exchanges, that benefit investors and other market participants (Gorkhali & Chowdhury, 2022). Future research should explore the following FRQs:

4.3.6 Financial technologies

Blockchain technology has the potential to revolutionize the financial services industry by providing a secure, efficient, and cost-effective way to move money, store data, and facilitate transactions between parties (Bataev et al., 2020). Blockchain allows for high levels of transparency and trust as all records are immutable, meaning they cannot be changed or altered in any way. This technology can also offer greater security for financial transactions as all records are stored on a distributed ledger that is viewable by all participants. Blockchain can also streamline processes such as digital payments, smart contracts, and more. By leveraging blockchain, financial institutions can reduce costs, increase efficiency, and open up new business opportunities. It can revolutionize financial services by enabling transactions to be processed more quickly and securely, with higher transparency and traceability (Sun et al., 2022). Companies are already using blockchain technology to improve the efficiency of payments, transfers, and other financial processes. Blockchain can also be used to facilitate faster and more secure access to financial data, as well as to provide more efficient recordkeeping for financial institutions. Additionally, blockchain could be used to build digital currencies, such as cryptocurrencies, that could be used for payments and other financial activities (Brus, 2020). Future research should explore the following FRQs:

4.3.7 Initial coin offerings

Blockchain technology can be used for initial coin offering (ICO) fundraising. ICOs are a form of crowdfunding that involves the sale of digital tokens, usually based on blockchain technology. Using blockchain technology, ICOs can provide investors with a more secure, transparent, and efficient way to participate in fundraising. This can be done by providing a platform for project creators to issue digital tokens and for investors to purchase them using cryptocurrency (Momtaz, 2021). Once the tokens are issued, the proceeds from the sale can be used for project development, marketing, and other related expenses. Additionally, blockchain technology can provide an immutable ledger to track the ownership of the tokens and facilitate the transfer of tokens between investors (Colombo et al., 2022). Future research should explore the following FRQs:

4.3.8 Business models

Blockchain technology has become a significant focus of business-minded individuals due to its potential to revolutionize business operations. The technology can create new business models and transform existing ones (Ioannou & Demirel, 2022). For example, it can create a secure and transparent transaction environment and provide a shared data record across multiple organizations (Suwanposri et al., 2021). It also has the potential to automate processes such as payments, contracts, and other transactions. Using blockchain technology, businesses can benefit from cost savings, improved efficiency, and enhanced trust and security. Additionally, the technology can create new business models based on a distributed ledger, allowing multiple parties to participate in transactions without needing a centralized authority (Baba et al., 2021; Khan et al., 2021). Future research should explore the following FRQs:

Ethics and consent

This study did not involve human participants or animals, and thus did not require informed consent. The analysis was conducted using publicly available data from published articles in the Dimensions database. All sources of data are properly cited, and no personal or sensitive information was used. The study adheres to the ethical standards of research integrity and academic honesty. All data sources are publicly accessible and properly cited.