blockchain research papers 2020 pdf

Blockchain in accounting research: current trends and emerging topics

Accounting, Auditing & Accountability Journal

ISSN : 0951-3574

Article publication date: 19 October 2021

Issue publication date: 22 August 2022

This paper provides a structured literature review of blockchain in accounting. The authors identify current trends, analyse and critique the key topics of research and discuss the future of this nascent field of inquiry.

Design/methodology/approach

This study’s analysis combined a structured literature review with citation analysis, topic modelling using a machine learning approach and a manual review of selected articles. The corpus comprised 153 academic papers from two ranked journal lists, the Association of Business Schools (ABS) and the Australian Business Deans Council (ABDC), and from the Social Science Research Network (SSRN). From this, the authors analysed and critiqued the current and future research trends in the four most predominant topics of research in blockchain for accounting.

Blockchain is not yet a mainstream accounting topic, and most of the current literature is normative. The four most commonly discussed areas of blockchain include the changing role of accountants; new challenges for auditors; opportunities and challenges of blockchain technology application; and the regulation of cryptoassets. While blockchain will likely be disruptive to accounting and auditing, there will still be a need for these roles. With the sheer volume of information that blockchain records, both professions may shift out of the back-office toward higher-profile advisory roles where accountants try to align competitive intelligence with business strategy, and auditors are called on ex ante to verify transactions and even whole ecosystems.

Research limitations/implications

The authors identify several challenges that will need to be examined in future research. Challenges include skilling up for a new paradigm, the logistical issues associated with managing and monitoring multiple parties all contributing to various public and private blockchains, and the pressing need for legal frameworks to regulate cryptoassets.

Practical implications

The possibilities that blockchain brings to information disclosure, fraud detection and overcoming the threat of shadow dealings in developing countries all contribute to the importance of further investigation into blockchain in accounting.

Originality/value

The authors’ structured literature review uniquely identifies critical research topics for developing future research directions related to blockchain in accounting.

Literature review
Machine-learning approach
Future trends

Garanina, T. , Ranta, M. and Dumay, J. (2022), "Blockchain in accounting research: current trends and emerging topics", Accounting, Auditing & Accountability Journal , Vol. 35 No. 7, pp. 1507-1533. https://doi.org/10.1108/AAAJ-10-2020-4991

Emerald Publishing Limited

Published by Emerald Publishing Limited. This article is published under the Creative Commons Attribution (CC BY 4.0) licence. Anyone may reproduce, distribute, translate and create derivative works of this article (for both commercial and non-commercial purposes), subject to full attribution to the original publication and authors. The full terms of this licence may be seen at http://creativecommons.org/licences/by/4.0/legalcode

1. Introduction

Blockchain is a technology for storing and verifying transactional records that works by adding “blocks” of data to a ledger, called the blockchain, that is maintained across a network of peer-to-peer computers ( Coyne and McMickle, 2017 ). It is a potentially disruptive technology that has begun to have dramatic impacts on the business models and market structures of many industries ( Casey and Vigna, 2018 ), including accounting ( Bonsón and Bednárová, 2019 ; Deloitte, 2016 ). However, the wealth of information produced about blockchain can make it challenging for researchers to stay up-to-date with the latest developments ( Cai et al. , 2019 ; Linnenluecke et al. , 2020 ). In these circumstances, the role of a structured literature review (SLR) of emerging research of blockchain in accounting should be a helpful tool ( Cai et al. , 2019 ; Moro et al. , 2015 ).

There are published literature reviews on how blockchain might be applied in a wide variety of academic disciplines, including business and management ( Xu et al. , 2019 ), supply chains ( Wang et al. , 2019 ; Gurtu and Johny, 2019 ), FinTech ( Cai, 2018 ; Rabbani et al. , 2020 ), the Internet of things ( Conoscenti et al. , 2016 ), and even cities ( Shen and Pena-Mora, 2018 ) but there has only been one for accounting and it was limited to 16 articles and 20 industry reports/websites ( Schmitz and Leoni, 2019 ). Other authors have also proposed different ways of applying blockchain technology in accounting and auditing (e.g. Yu et al. , 2018 ; Kokina et al. , 2017 ; Faccia and Mosteanu, 2019 ; Bonsón and Bednárová, 2019 ), without offering a comprehensive overview. Similarly, Bonsón and Bednárová (2019 , p. 737) conclude that “blockchain is an under-explored phenomenon, [and] future research is necessary to obtain a full understanding of this emerging technology and its implications for the accounting and auditing sphere”.

What are the current major research trends and topics related to blockchain for accounting?

What is the focus and critique of the key research topics?

What are the future research trends related to blockchain in accounting?

The studies collected for the review were drawn from accounting journals indexed by the Association of Business Schools (ABS), the Australian Business Deans Council (ABDC) and the Social Science Research Network (SSRN). To help analyse the corpus, we enlist the support of machine learning as found in other studies ( Cai et al. , 2019 ; El-Haj et al. , 2019 ; Black et al. , 2020 ; Bentley et al. , 2018 ). From this, we contribute and provide a comprehensive picture and critique of the literature on blockchain in accounting. This includes an analysis of impact; an examination of the four most widely-examined topics, being the changing role of accountants, new challenges for auditors, the opportunities and challenges of blockchain technology application and the regulation of cryptoassets; and a discussion on areas for future research. Identifying emerging topics in the field is an important element in generating insights for future research ( Small et al. , 2014 ) and leading research innovations ( Cozzens et al. , 2010 ). Understanding what we have learnt and how blockchain technology is impacting accounting is of benefit to everyone connected to this area. It may also help to guide future research in this exciting area.

The remainder of the paper is as follows. In Section 2 , we discuss the concept of blockchain as an accounting technology. Section 3 outlines the methodology used for the review, followed by the results in Section 4 . The most representative articles are analysed in Section 5 , with future research directions discussed in Section 6 . Section 7 concludes the paper with the implications of this research for theory, practice and policy, along with the limitations of the study.

2. Blockchain in accounting

The main advantage of blockchain technology is that once a transaction is approved by the nodes in the network, it cannot be reversed or re-sequenced. The inability to modify a transaction is essential for the blockchain's integrity and ensures that all parties have accurate and identical records. Because blockchain is a distributed system, all changes to a ledger are transparent to all the members of a network.

Hence, if transparency is key, implementing blockchain may help to enhance a company's competitive advantage ( Deloitte, 2019 ), and it should certainly help to cultivate trust between market participants ( Yu et al. , 2018 ). In blockchain, the transaction verification process is not managed centrally. Rather, it involves all the computers in the network, so blockchain does not suffer from point of failure events. Nor can individuals collude to override controls or illicitly change or delete official accounting records ( Wang and Kogan, 2018 ). Companies that incorporate blockchain into their accounting systems therefore may reduce their risk of fraud ( Dai et al. , 2017 ). Using blockchain might also mean more transactions can be automated, less data are lost, transactions can be tracked better and users' needs throughout the process can be detected more easily ( Fullana and Ruiz, 2021 ; Bonsón and Bednárová, 2019 ). However, the primary and most valuable difference between traditional databases and blockchain is its novel solution to control whereby transactions cannot be deleted or changed ( Coyne and McMickle, 2017 ; Dai et al. , 2017 ).

Even though, for most industries, blockchain is still a new and not yet well-established technology, the World Economic Forum estimates that, by 2025, at least 10% of global gross domestic product (GDP) will rely on blockchains. And, by 2030, blockchains will have created $3.1tn in business value ( Panetta, 2018 ). It should therefore be unsurprising to consider that this revolution will start to change the nature of accounting and, in turn, the work of its practitioners and theorists (e.g. Yermack, 2017 ; Schmitz and Leoni, 2019 ; Yu et al. , 2018 ).

As such, a literature review on the status of blockchain in accounting is both topical and timely. The insights provided into this emerging technology will have implications for the accounting ecosystem–some beneficial, others challenging. Hopefully, this SLR will serve as a helpful baseline for practitioners, professionals and academics as we navigate the next potential revolution in accounting information systems.

3. Methodology

Massaro et al. (2016 , p. 2) characterise an SLR as “a method for studying a corpus of scholarly literature, to develop insights, critical reflections, future research paths and research questions”. The review process is conducted in several steps.

3.1 The research questions

RQ1. What are the major trends and topics developing within the research related to blockchain in accounting?

RQ2. What is the focus and critique of the key identified research topics?

RQ3. What are the future research trends related to blockchain in accounting?

3.2 Defining a set of articles for further analysis

Phase 1. We first composed a list of all accounting journals from the 2018 Chartered Association of Business Schools rankings (the ABS rankings), which amounted to 87 journals. We did the same for the 2019 Australian Business Deans Council Journal Quality List (the ABDC rankings). This netted 157 journals.

Phase 2. After removing duplicate journals covered in both ranking systems, we were left with 149 journals. In these, we looked for relevant papers published in the period Jan 2008 till June of 2020. We started our search in 2008 as this was when Satoshi Nakamoto first mentioned blockchain in his paper ( Nakamoto, 2008 ). Using the EBSCO, Scopus and Web of Science databases, we searched for any article with the key words “blockchain” or “distributed ledger technology” in the title or abstract. From 2,335 documents, we identified 112 papers that matched our criteria for publication source.

Phase 3. Massaro et al. (2016) outline that when undertaking an SLR, researchers should broaden the boundaries if there is very little published research. They also warn that what is published may already be out of date because of the long lead times involved in publishing academic articles. Massaro et al. (2016) bring clarity to “broadening the boundaries”, arguing that researchers need to search for sources other than academic journals, which may provide valuable insights into emerging research fields. The other sources might include conferences and open-source publishing platforms that offer researchers greater opportunities to disseminate their research to practice ( Massaro et al. , 2015 ).

Since blockchain is just such an emerging topic in the accounting literature ( Schmitz and Leoni, 2019 ; Bonsón and Bednárová, 2019 ; Yu et al. , 2018 ), we decided to add papers not yet published in the accounting journals but uploaded to the SSRN. SSRN is the leading social science and humanities repository and online community that provides “tomorrow's research today” ( Gordon, 2016 ). With more than 950,000 papers from over half a million authors in the e-library, SSRN offers an extensive pool of research ideas that can be tracked before publication to detect emerging research topics and current trends. These papers added an important contribution to our literature review. Here, we searched for “accounting” AND “blockchain” or “accounting AND distributed ledger” over the same period and found 68 papers, some of which overlapped with papers already retrieved. These were excluded, plus we also excluded any of the papers that had subsequently been published in a non-accounting journal or an accounting journal not ranked by ABS or ABDC. This left 41 additional articles to add to the corpus. Thus, our final sample comprised 153 papers on blockchain for accounting.

Portable Document Format (PDF) versions of each of the articles were downloaded and stored in a Mendeley database with full referencing details. The sources and number of papers from each source are given in Table 1 .

3.3 Methods of analysis: Latent Dirichlet Allocation combined with manual analysis

In machine learning, there are many different text mining techniques, each designed to suit different types of data and different end purposes (see Wanner et al. , 2014 for a comprehensive review). We used a Latent Dirichlet Allocation (LDA) model, which is well-suited to providing a systematic and non-biased method of investigating a body of literature ( Cai et al. , 2019 ; El-Haj et al. , 2019 ; Black et al. , 2020 ; Bentley et al. , 2018 ; Fligstein et al. , 2017 ). El-Haj et al. (2019 , p. 266) explain that LDA leads to “wider generalizability, greater objectivity, improved replicability, enhanced statistical power, and scope for identifying ‘hidden’ linguistic features”. Research shows LDA to be a relevant and useful tool for working with both big and small literature corpora (e.g. Li, 2010 ; Asmussen and Møller, 2019 ; El-Haj et al. , 2019 ). Asmussen and Møller (2019 , p. 16) highlight that applying LDA to even small sets of papers provides “greater reliability than competing exploratory review methods, as the code can be rerun on the same papers, which will provide identical results”. For these reasons and more, the LDA method is currently one of the most commonly employed topic identification methods that does not simply rely on a static word frequency measure ( Blei et al. , 2003 ). Moreover, El-Haj et al. (2019 , p. 292) recommend employing machine learning methods and high-quality manual analysis in conjunction as they “represent complementary approaches to analyzing financial discourse”. We followed this advice, applying a hybrid approach that comprised LDA analysis, citation analysis and a manual review.

LDA allows us to explore latent relationships between terms and topics in a sample, identify the most representative articles for each topic and identify the trends within the topics. Using LDA helps us capture the idea of a document being composed of a (predetermined) number of topics that represent a probability distribution over a vocabulary. The number of topics is optimised using grid-search and coherence of topics ( Röder et al. , 2015 ). The model also supplies a list of articles that most strongly “belong” to each topic.

The text mining procedure is straightforward. In a Python environment ( www.python.org ), the articles are first converted from PDF documents into text files. The text is then converted into lower case, and all characters other than letters are removed. Next, stop words, such as the , and , but , if , or , are removed, and the remaining words are lemmatised into their dictionary word. Additionally, all words other than nouns are discarded. Finally, the documents are turned into a bag-of-words format and fed into the LDA model.

The results showed that the four topics with the highest marginal distribution accounted for more than half of the overall content of the sample. To test the validity and reliability of this result, we applied several other types of analysis suggested by researchers working with literature reviews. For example, Dumay and Cai (2014) and Jones and Alam (2019) argue that citation impact factors are increasingly important because they identify the most influential articles. Highly cited articles represent a “corpus of scholarly literature” that can help “develop insights, critical reflections, future research paths and research questions” ( Massaro et al. , 2016 , p. 767). To conduct a citation analysis, we use citation counts based on Google Scholar data, based on queries employing Harzing's Publish or Perish software as of 5 March 2021. This step also helped us validate that the papers and topics identified by the LDA analysis were among the most cited.

Although the LDA method helped us to identify past and current trends in the literature, Cai et al. (2019 , p. 710) contend that “the human researcher is potentially better equipped to evaluate future trends in the literature”. Hence, we also manually reviewed the 15 articles identified in the LDA analysis as the most representative of each topic. This review affirmed the results of the LDA analysis and gave us the opportunity to offer a critique and gain more insights while identifying future research directions.

This section provides answers to RQ1 : What are the current research trends and topics in blockchain for accounting?

Figure 1 demonstrates that the volume of articles on the topic is increasing annually. The first articles began to appear in 2015 and, by 2019, 4 articles had increased to 40 papers, with 35 already published just in the first half of 2020.

Of the top-ranked journals–either 4-star ABS or A* ABDC–only two have each published one paper on blockchain. This is a clear indication that the phenomenon has not yet fallen into mainstream research. Given its relatively recent appearance in the literature, this is not surprising. Additionally, most of the articles that have been published are normative in approach and look at the future applications of blockchain in accounting. From this, we can assume that, in future, more cases of blockchain applications in accounting practice will be researched. Once the literati start to read of blockchain having a real influence on the profession, we expect the number of papers published in the leading journals will increase.

4.1 Results of LDA analysis

The LDA analysis unearthed ten topics, which we needed to find appropriate names for. This we did in a two-step procedure. First, we looked at the terms listed against each topic, then we read the most representative articles for each group identified by the model. One author then developed a descriptive title, which was reviewed and perhaps modified before being approved by the remaining authors. The final topic names are listed in Table 2 , along with the 20 most important words for each topic and the marginal distribution of each topic.

As shown in Table 2 , the most widely analysed topics are: the changing role of accountants; new challenges for auditors; the opportunities and challenges of applying blockchain technology and the regulation of cryptoassets. These account for more than half of the papers. No other topic amounts to more than 10% of papers on its own. Figure 2 shows the representation of the different topics from 2016 to 2020. Since there were so few papers in 2015, we did not include this year in the chart.

Two of the most widely discussed topics–“the changing role of accountants” and “the new challenges for auditors”–only seem to be getting more popular. These two subjects account for the highest proportion of the articles. Although “new skills for teams” began to attract attention in 2019, papers on this topic still only account for a small portion of the sample. Interesting, even over such a short period, interest in some topics is already waning, e.g. “FinTech in banking”, “cryptocurrencies and cryptoassets”, and “blockchain and taxation”. With this in mind, and given the overwhelming interest in just a handful of topics, we focused the rest of our analysis on the top four topics.

4.2 Article impact

As mentioned in the methodology, we checked the validity and reliability of the topic results using citation analysis ( Dumay et al. , 2018 ). Table 3 shows the total citation counts for the top 10 articles as listed in Google Scholar citations (5 March 2021).

As shown, all but one of the ten most-cited articles were published in ranked accounting journals. In fact, three were published in the Journal of Emerging Technologies in Accounting. The one exception was found on SSRN. Additionally, the topics cited match the topics revealed by the LDA analysis, particularly new challenges for auditors, opportunities and challenges of blockchain applications, and the regulation of cryptoassets.

Dumay and Cai (2014 , p. 270) note that “One problem with determining the impact from citations alone is that older articles can accumulate more citations”. To overcome this problem and to identify emerging articles, in Table 4 , we also calculated the citations per year (CPY). Six articles are common to both rankings: Kim and Laskowski (2018) , Fanning and Centers (2016) , O'Leary (2017) , La Torre et al. (2018) , Kokina et al. (2017) , Issa et al. (2016) . This offers clear support for the results of the LDA analysis. Further, two of the articles were published in 2019 and are already in the top 10, which is a sign of just how strong the interest in blockchain technology is.

The results of Table 4 allow us to confirm our choice of the topics for further analysis. The top 10 papers with the highest citations per year belong to one of the four research topics that have the marginal distribution over 10% represented in Table 2 and account for more than a half of the overall distribution.

5. Key research topics: focus and critique

In this section, we answer RQ2 : What is the focus and critique of the key identified research topics?

While the LDA analysis revealed ten topics, much of the literature is focussed on four of these: the changing role of accountants, new challenges for auditors, opportunities and challenges of blockchain technology application and the regulation of cryptoassets. In the next sections, we analyse and critique these subject areas in more detail, paying attention to the papers that the model deemed to be strongly representative of each topic.

5.1 The changing role of accountants

Each of the papers on this topic discusses ideas about how the role of accountants and accounting treatments would change if/when blockchain becomes a mainstream technology. For example, several authors discuss the advantages of using blockchain to record transactions on a real-time basis ( Yermack, 2017 ; Dai and Vasarhelyi, 2017 ). Routine accounting data would be recorded permanently with a timestamp, preventing it from being altered ex-post, which Alles (2018) argues would further ensure the reliability of current accounting information systems. Real-time accounting would also reduce the potential opportunities for earnings management ( Yermack, 2017 ). Additionally, using blockchain means anyone can review all transactions, even those that may be suspicious or related to conflicts of interest. Irreversible transactions also mean accountants could not backdate sales or report depreciation expenses in future periods when they should be expensed immediately. As a tool for accuracy and transparency, blockchain places pressure on accountants to justify their accounting choices. It also creates a closer link between accounting and a company's responsibilities to its stakeholders and makes it more challenging for financially-distressed companies to hide their situation ( Smith, 2017 ).

Anyone could aggregate the firm's transactions into the form of an income statement and balance sheet at any time, and they would no longer need to rely on quarterly financial statements prepared by the firm.

We agree that blockchain will impact how accounting information is recorded, but we do not expect that accounting functions will disappear. Rather, accountants will likely retain some old functions, either as-is or modified to suit the new paradigm, and find they have an entirely new set of responsibilities, some of which will require them to develop new skills. For example, well-developed IT competencies may become a prerequisite for the accounting profession, at least in the interim period where firms are prepared to face the changes brought about by integrating blockchain ( Uwizeyemungu et al. , 2020 ; McGuigan and Ghio, 2019 ). That said, we do not think that such changes will happen overnight. It will take time before companies implement blockchain as a ‘foundational technology’, and any disruptions to the profession will take place over years ( Iansiti and Lakhani, 2017 , p. 4).

What could be an even more profound transformation of the profession is how the work of accountants might no longer involve only recording transactions. In future, accountants may need to provide professional judgements during the accounting process ( McGuigan and Ghio, 2019 ; Dai and Vasarhelyi, 2017 ). Even if blockchain takes over the recording and storing of basic accounting transactions, there will be a need to decide on the choice of the most appropriate amortisation and depreciation methods, the length of the useful life of property, plant and equipment, the accounting policies regarding accounting for inventories and fair-value accounting. Moreover, with an increase in the number of cryptoassets and initial coin offerings (ICOs) accountants may also need to develop their skills as advisors and consultants on how to report these kinds of assets and transactions. Further, if blockchain is implemented on a broad scale, accountants will not only have more information for planning and control, they may be required to synthesise it. This, too, will change the role of accountants, particularly management accountants. No longer relegated to the back office, accountants would likely take a much more prominent position as agents of intelligence, advising, communicating and attempting to closely link their firm's activities to strategic decision-making.

Blockchain may also lead to more disclosures of non-financial information, such as that related to sustainability and corporate social responsibility. The transparency of blockchain might prompt companies to do more explaining. They may wish to quantify and make visible “feel-good” information as a counterpart to the financial ( Smith, 2017 ). Additionally, blockchain provides opportunities to collect qualitative social and environmental data, which will continue to require assurance in the future. La Torre et al. (2018) argue that blockchain will generate an automatic assurance system for non-financial information that could substantially modify the current assurance paradigm. Therefore, blockchain may help accountants move away “from traditional accounting assumptions, such as monetary unit[s], economic entit[ies] and time periods, leading organisations more towards holistic views of their relations with the society” ( McGuigan and Ghio, 2019 , p. 800).

Lev and Gu (2016) argue that blockchain may reduce information asymmetry and lead to more effective decision-making. They put forward that the relevance of information disclosed only in financial statements is diminishing because of the growing importance of non-financial information and that blockchain's ability to store quantified non-financial information may see accountants working more closely with other decision-making bodies.

The disruptive potential of accounting technologies can only be fully realised with a similarly profound revolution in accounting thinking. Without an accompanying “mental revolution”, new technologies may result in incremental as opposed to step change.

5.2 New challenges for auditors

Blockchain may also disrupt the auditing profession. With the ability to autonomously execute some audit procedures based on blockchain, smart contracts will provide stakeholders with already partly verified information ( Rozario and Vasarhelyi, 2018 ). La Torre et al. (2018) claim that participants in the accounting ecosystem may act as auditors themselves. Accounting information may be verified by different actors thanks to the assurance abilities of blockchain and because companies can continuously share information. Moreover, there is the possibility to automate some external auditing functions over the blockchain to improve audit quality and narrow the expectation gap between auditors, financial statement users and regulatory bodies ( Rozario and Vasarhelyi, 2018 ). Some authors call for the appearance of a new brand of auditor that can offer attestation services for independent evaluations of blockchain controls ( Canelón et al. , 2019 ; Sheldon, 2019 ).

However, some researchers are not convinced that blockchain will dramatically impact the auditing profession. Rather, they suggest that auditing will take on new features and become more complicated ( Dai et al. , 2019 ; Issa et al. , 2016 ). Distributed public recording on the blockchain will allow real-time audits in many locations and organisations simultaneously ( Issa et al. , 2016 ). These authors argue that auditors will need improved skills to audit the data not only for one company but also for the whole accounting ecosystem.

… continuously collect data from the real world, create a variety of intelligent modules for real-time auditing, monitoring, fraud detection, etc., and thereby improve the effectiveness and efficiency of assurance services.

Blockchain will require auditors to gain new IT skills and technical knowledge as without an improved understanding of blockchain, they will not be able “to design efficient and effective audit processes, to collect accurate audit evidence, and to review the system for potential risks and frauds” ( Dai et al. , 2019 , p. 38). Of course, for blockchain technology to enable continuous auditing and for it to give auditors a better understanding of their clients' businesses, companies will need to record all transactions on the blockchain ( Schmitz and Leoni, 2019 ). After all, “real-time auditing” can only be delivered to the degree that transactions are recorded on the blockchain.

Auditors should be concerned about the risks of privacy breaches deriving not only from both external unauthorised access but also from accessing and using certain corporate and external data to perform audit activities; the latter being a task that needs to engage principles that go beyond legal prohibitions.

Blockchains do not provide a guarantee for transactions taking place in the real world. Even if they are recorded onto blockchains, transactions may still be fraudulent, illegal or unauthorised. Hence, given the need for auditors to detect and investigate transaction errors or fraud, the argument of auditors becoming obsolescent is not evident.

Essential roles for auditors in the future will be assuring the reliability, credibility and authorisation process of blockchain transactions.

Implementing blockchain may benefit most accountants and auditors, but it may be negatively perceived by those who work in the black economy, those who are keen on earnings management, and those who need to manipulate the appearance of illicit transactions. Therefore, we assume that automating data collection and storage using blockchain will not mean the auditing profession disappears. Rather, we see it evolving into a new role within companies and the ecosystem of blockchain accounting.

5.3 Opportunities and challenges of blockchain technology application

Papers on this topic are mostly written from the perspective of a company implementing blockchain. Opportunities range from improved efficiency, transparency and trust to the high potential of new business models and ecosystems that evolve due to blockchain. Challenges include potential risks related to blockchain implementation, the influence of context and a high demand for energy consumption.

Because blockchain eliminates the need to enter and reconcile information in multiple databases, efficiency gains are a key strength. Blockchain also saves time by increasing the speed of transactions, reducing human error and minimising fraud ( Kokina et al. , 2017 ; O'Leary, 2017 ). The use of smart contracts may also improve processes in a range of industries. Smart contracts on the blockchain execute when certain conditions are met without the need for trusted intermediaries to verify the fact ( Coyne and McMickle, 2017 ; Kokina et al. , 2017 ). There is already evidence to show how blockchain may reduce costs in the finance industry (e.g. Fanning and Centers, 2016 ; Kokina et al. , 2017 ).

One of the challenges for implementing blockchain is context ( Stratopoulos and Calderon, 2018 ). It is unlikely that small firms would want to make their transactions publicly available or that they would benefit from blockchain accounting as much as big companies. Distributed ledgers may not be attractive or even needed by every company, so there is a real need to ascertain exactly what the up and downsides of implementing blockchain are. As O'Leary (2019) observes, the opportunities for using blockchain may be limited by the desire and ability of all agents in the ecosystem to implement it. For example, some companies may wish to use a private blockchain, but we do not yet know how to accommodate multiple private blockchains with different levels of secrecy and different kinds of trading partners, some of whom may be members of a public blockchain ( O'Leary, 2019 ; Kim and Laskowski, 2018 ).

It is also important to understand all the advantages and disadvantages of joining a public or a private blockchain ( O'Leary, 2017 ). There are many different configurations of blockchain, e.g. peer-to-peer and public, cloud-based, private and these all need to be analysed before they can be soundly implemented in different settings. Further, those investigations must include analyses at the accounting, auditing and supply chain levels. For example, O'Leary (2017) argues that public blockchains are not the best approach to capturing accounting or supply chain transactions. Instead, he believes private and cloud-based blockchain configurations will dominate the corporate landscape. In a private blockchain, only a preselected number of nodes are authorised to use the ledger. Hence, not everyone has access to all company's data. Yet many researchers speak positively about how blockchain technology will mean provenance in the supply chain that is much more traceable ( Kim and Laskowski, 2018 ). In our opinion, it will be important for all the agents in the ecosystem to understand how blockchain provides similar benefits. For example, due to the potential risks of disclosing information, we assume that blockchain will have a more restrictive effect on business entities than non-profit organisations, because non-profits tend not to hold as many commercial secrets.

Moreover, Kokina et al. (2017) note that the scalability of blockchain is an issue from a technical perspective, as blockchain is computationally intensive and requires a lot of energy. This raises sustainability questions and may not be an issue that gets resolved until renewable energy accounts for most of our energy production ( Coyne and McMickle, 2017 ). Three further risks are often raised, each surrounding changing business processes ( Canelón et al. , 2019 ; Coyne and McMickle, 2017 ; Kokina et al. , 2017 ). The first relates to the centralisation of computing power, also called the “51% attack risk”, which can happen when most of the computing power in a blockchain's network is centralised. In this case, whoever controls that power can, with impunity, discard a valid link in the chain or substitute an invalid block for a valid one. The second risk is transaction malleability, which occurs when an attacker copies a transaction and modifies it to receive tokens (payment) then claims that no tokens were ever received. The third risk relates to flawed smart contracts that can hide malicious code or another contract with a weakness. This risk highlights the need for independent external auditors to approve transactions before the contract enters the blockchain. In short, the ability of blockchain to store records makes it a target for potential cyberattacks. Therefore, to ensure the security of information in a blockchain, there is a need to implement internal and cybersecurity controls that consider privacy preservation issues ( Chohan, 2017 ; Coyne and McMickle, 2017 ; O'Leary, 2017 ).

To gain real efficiencies in the use of blockchain or any technology, there is a need to reengineer, rather than just automate, existing processes. Unfortunately, many of the proposals for the use of blockchain are aimed at automating existing processes, typically in an approach to leverage the immutability and digitisation of paper, but generally do not propose or use changes in the processes.

Unless existing processes and systems are truly scrutinised for their potential to benefit from blockchain technology, the full range of opportunities that blockchain presents will not be realised. Blockchain will only become a “game-changer” if all parties involved in the accounting ecosystem are open to its potential.

5.4 Regulation of cryptoassets

The papers devoted to this topic analyse a variety of questions related to the regulation of cryptoassets (also called tokens), including cryptocurrencies and ICOs (e.g. Gurrea-Martínez and Remolina, 2018 ; Wiśniewska, 2018 ). These assets are not addressed by any accounting standards, that leads to challenges in their classification and measurement and reflects the lack of economic characteristics for a “standard” intangible asset ( Procházka, 2018 ) or a financial asset ( Smith et al. , 2019 ). There are several regulatory issues that need to be solved: classification of cryptoassets in accounting; the kinds of insolvency that affect buyers and sellers of tokens; and the regulation of potential money laundering via blockchain ( Pimentel et al. , 2019 ; Zhang et al. , 2021 ). Moreover, with the increased competitiveness of the market, questions related to data protection and data safety on the blockchain become extremely important for further regulation ( Cai, 2018 ).

The uncertainty linked to valuing cryptoassets is affecting the development of proper regulations, as this issue affects the fundamental qualitative aspects of financial accounting, such as relevance and faithful representation. Moreover, as highlighted in the Conceptual Framework for Financial Reporting , the principles of prudence, neutrality and conservatism continue to pose challenges for properly presenting cryptoassets in financial statements ( FRC, 2018 ; The Interpretations Committee, 2019 ).

There is no commonly shared point of view among researchers on the best way to regulate cryptoassets. Some say that they fit in with the existing accounting standards, while others state there is a need to develop a new regulatory framework that will decrease the probability of fraud ( Auer, 2019 ; Pimentel et al. , 2019 ). For example, there is a high demand for developing regulations for ICOs, cryptoassets that do not offer investors concrete products or services but provide an opportunity for capital gains from reselling cryptocurrencies in the future ( Zhang et al. , 2021 ). In December 2017, SEC Chairman Jay Clayton stated that ICOs are vulnerable to fraud and manipulation because there is less investor protection than in the stock market ( Clayton, 2017 ). We think that as the tokenisation of securities would be a useful tool in capital markets in the future (as already reflected by their fast development in Asian markets) and because ICOs and crowdsourced platforms represent a legitimate means of exchange in ecosystems, the regulatory issues need to be resolved to make this instrument available to wider markets participants ( Gurrea-Martínez and Remolina, 2018 ; Zhang et al. , 2021 ; Sixt and Himmer, 2019 ).

Currently, regulators monitor the field of cryptoassets on a case-by-case basis, but not to the extent that investors, or would-be-investors, could determine with certainty how cryptoassets may be treated ( Smith et al. , 2019 ). Nor are all market participants eager to treat cryptoassets as a security due to their volatility, making it difficult to ascertain an appropriate value to record for income statement and balance sheet purposes ( Smith et al. , 2019 ; Tan and Low, 2019 ). Finally, it is worth noting that financial accounting is characterised by accounting prudence and conservatism, which can lead to differences between a company's market and book value ( Dumay and Guthrie, 2019 ). As cryptoassets are often characterised as a potential future economic benefit, their acquisition may lead to even greater discrepancies between the market and book values of companies, especially in markets with optimistic valuations of intangible assets.

Thus, the uncertainty on measuring cryptoassets leads to the problems of comparability, verifiability, timeliness and understandability in financial accounting ( IASB, 2018 , p. 6). Therefore, in line with Smith et al. (2019 , p. 166), we conclude that for now, “this innovative technology has the potential to change internal management systems …; however, lack of regulation and information makes investment planning for cryptoassets complex and forbidding”. The divergence of crypto classifications means that worldwide regulation and availability of information on cryptoassets will be the most important factors for their spread. As a result, we see the need for a proactive regulatory framework rather than merely reacting to questions regarding the regulation and accountability of cryptoassets.

6. Future research directions

This section answers RQ3 : What are the future research trends related to blockchain in accounting?

The following views regarding the future research trends were framed by the insights in the previous section and reviewing the most representative papers for each topic.

6.1 The changing role of accountants

As discussed in Section 5.1 , most papers on the changing role of accountants are normative. They talk mainly about various assumptions over how blockchain may influence accounting. One of the main changes frequently discussed is how blockchain will change the way accountants collect information. Given this, we think the future will result in more case studies and practically-oriented papers that empirically test blockchain's impact on accounting ( Alles, 2018 ). According to Zhang et al. (2017) , new business reporting models, such as triple-entry accounting, will demand investigations into how blockchain strengthens or alters functions like valuations and contracting. Further, the monitoring role of accountants in managing information for the benefit of stakeholders will need to be established ( Zhang et al. , 2017 ). However, Alles (2018) warns that there is a danger of the “empirical takeover” effect when papers become empirically driven. Thus, there is a need to establish a solid theoretical and conceptual background for how blockchain will disrupt accountancy.

The role of management in implementing blockchain is very important. According to Jarvenpaa and Ives (1991 , p. 205), “Few nostrums have been prescribed so religiously and ignored as regularly as top management support in the development and implementation of IT.” A high degree of support for specific IT innovations is needed to ensure companies hold fast to a long-term vision and optimally manage their resources to see it through. At the same time, these innovations can create a favourable organisational climate that can overcome barriers and resistance to change ( Clohessy and Acton, 2019 ). Future research might therefore investigate the structure of management bodies and the role of top management in blockchain implementation.

Prior research points to a growing trend in the topic of new skills for teams when implementing blockchain and using this technology in day-to-day work ( Changati and Kansal, 2019 ). Fang and Hope (2021) indicate that blockchain is more effectively implemented in teams comprising accountants, managers and experienced analysts as opposed to teams consisting only of highly experienced analysts. We expect that blockchain will involve more multi-tasked teams with diverse knowledge and skills to generate additional synergies. Therefore, future research may analyse the characteristics of teams and government bodies that work better together for the most efficient implementation and decision-making using blockchain.

6.2 New challenges for auditors

In the realm of auditing, future research could explore how different types of blockchain (public, private and permissioned) could be used in accounting and Audit 4.0 to improve the quality of the data collected ( Dai et al. , 2019 ). The dilemma of adopting blockchain in accounting and auditing is in finding the right trade-off between information confidentiality and transparency. The simultaneous protection of data privacy and maintenance of data accuracy is an important area for future research. Further, the ways of creating effective smart audit contracts and smart reporting contracts should also be studied with a special focus on executing traces and enforceability ( Schmitz and Leoni, 2019 ).

More extensive analysis is also needed on the auditing ecosystems based on blockchain ( Smith, 2020 ). For example, if a client is a part of several blockchains, any engagement to audit or attest that information must include an examination of all associated blockchains. In the case of supply chains, cross-border payments, and transfers of intellectual capital, the chains–be they digital or physical in nature–can include dozens, if not hundreds, of organisations. How to conduct an effective and successful audit of such systems should attract the attention of researchers.

Additionally, more real cases will need to be explored to see how technology might disrupt the auditing community ( Marrone and Hazelton, 2019 ). Researchers might also address data protection issues as well as the new skills and competencies needed to remain relevant and add value ( Moll and Yigitbasioglu, 2019 ). Some, like Siew et al. (2020) , argue that, while digitising the validation process will reduce errors, and the immutability of the blockchain will minimise the opportunity to commit fraud, blockchain accounting does not guarantee that financial reports will be true and fair; the processes still need to be tested and the various accounting judgements still need to be reviewed. Moreover, blockchain will not resolve questions over issues like reconciling accounting standards. Hence, accountants will still need to be involved in the process ( Cai, 2018 ). Thus, many of the benefits and challenges of blockchain for auditing still need to be analysed.

6.3 Opportunities and challenges of blockchain technology application

A more fundamental area of future research is the role of financial intermediaries and how their role might change. In the future, we expect to see competition and cooperation among traditional and new intermediaries, and research needs to explore these phenomena to provide guidance to all participants such as incumbents, new entries and regulators ( Cai, 2018 ). The influence of blockchain on risk management and companies' performance indicators is another promising area for future research as there is a need to identify how stakeholders' value creation may be affected by implementing blockchain ( Cai, 2018 ). It would also be worth examining whether the response of managers towards blockchain varies in different industries ( Cao et al. , 2018 ). Burragoni (2017) argues that implementing blockchain in the finance industry might help overcome the threat of a shadow economy, given the improved transparency and legitimacy on offer, but this is an assumption that needs further justification.

Analysing the role of blockchain in changing business models in different industries is sure to be a topic of great interest to researchers ( Johannessen, 2013 ). The efficiency of new business models in comparison to traditional ones may also bring new insights for academics and practitioners. Researchers should test new business models in a market and evaluate transaction efficiency and the degree of novelty in the transaction's content, structure, steering, resource use, network effects and value creation for stakeholders. Researchers can analyse the efficiency of blockchain implementation in different areas and focus on “the benefits of the first-mover advantage” ( Karajovic et al. , 2019 , p. 322). In the future, it will be important to monitor the progress of the implementation of blockchain in different types of organisations ( Gietzmann and Grossetti, 2019 ).

Researchers should analyse how blockchain ecosystems evolve and are applied ( Benjaafar et al. , 2018 ). Blockchain enables real-time, verifiable and transparent accounting, making it reasonable to assume that accounting information systems will become ecosystems. In a data ecosystem that progressively integrates a nearly infinite set of initially disconnected data, the ability to integrate coherently and apply software agents will be of high importance. With an almost infinite supply of new data, novel methods of measuring business performance will inevitably emerge ( Cho et al. , 2019 ). Understanding how blockchain distributes the power of transaction verification and how data are stored and managed to prevent any unauthorised data changes in ecosystems are also key questions in need of investigation.

The challenges of blockchain regarding sustainability and environmental issues should also be a focus in future research. On the one hand, a distributed carbon ledger system based on blockchain technology will not only strengthen the corporate accounting system for carbon asset management but also will fit within existing market-based emissions trading schemes ( Tang and Tang, 2019 ). Blockchain will help integrate national emission trading schemes and corporate carbon asset management into a single synthesised mechanism, making it possible to analyse the overall efficiency of carbon trading markets in some great amount of detail. On the other hand, Nyumbayire (2017) points to environmental sustainability as an issue, explaining that the algorithms that run blockchain require a great deal of electricity. Moreover, as the technology grows, the algorithms become more complicated, and more time and energy are required to validate transactions. We argue that in the future, researchers should investigate the sustainability and environmental issues related to blockchain in more detail.

6.4 Regulation of cryptoassets

To date, the growth of blockchain technology has not led to the building of a corresponding regulatory framework. Thus, there are many questions that need to be resolved surrounding the legal and accounting frameworks for accounting, recognising and valuing cryptoassets. Further, when these frameworks are developed, they will need to be analysed. Researchers will also likely want to determine whether the standard-setting bodies have developed credible reporting conventions over the financial implications of cryptocurrency transactions ( Raiborn and Sivitanides, 2015 ; Tan and Low, 2019 ). Future research could explore whether blockchain has or will have a positive effect on the timeliness of disclosures; how financial reporting standards welcome new types of assets; and how the uncertainty associated with cryptoassets can be overcome.

Academics, together with practitioners, should work on specifying how these regulatory dimensions need to be developed, what type of disclosures are relevant to cryptocurrencies and how disclosure costs may further impact market uncertainty ( Cao et al. , 2018 ). Clarifying the regulatory framework will probably also lead to more ICOs, as initiators will be better prepared and be able to respond to uncertainty in blockchain policy by increasing their voluntary disclosures ( Zhang et al. , 2021 ; Gurrea-Martínez and Remolina, 2018 ). Research on the efficiency and effectiveness of ICOs will be of high interest in the future.

How cryptoassets and cryptocurrencies should be taxed is also open to question ( Ram, 2018 ). Once clarified, researchers will be able to study the taxation policies applicable to this new class of assets in detail. One related research question for the future involves whether blockchain-based instant tax allocation helps to decrease the cost of tax compliance for companies or not ( Karajovic et al. , 2019 ). As the role of external contexts and legal frameworks is highly important to blockchain development ( Allen et al. , 2020 ; Stratopoulos and Calderon, 2018 ), researchers may study the differences in blockchain implementation in environments that are (and are not) “crypto-friendly”.

7. Conclusion

Our aim with this paper was to define the key topics and trends, past, present and future, that concern researchers in blockchain for accounting. Our analysis systematically identified these topics by analysing 153 relevant papers. By combining machine-learning methods with more traditional approaches, we were able to draw a holistic picture of the critical advances and trends in the corpus of literature. The results indicate that the most widely discussed topics are the changing role of accountants, new challenges for auditors, the opportunities and challenges of blockchain technology application, and the regulation of cryptoassets.

This paper provides a compact snapshot of the state of blockchain papers in accounting research. The trends and identified research directions may help predict future citation impact and informed our suggestions for future research. They may also help journal editors decide on calls for special issues as interest in this topic grows.

7.1 Implications for academics

Our analysis reveals that more than two-thirds of the papers under review were published in journals, while less than a third represent works in progress uploaded to SSRN. The top accounting journals from the ABS and ABDC rankings appear to be resistant to the blockchain field of research, as they have published only a few papers devoted to the technology. This could be because those journals are less friendly towards phenomenon-based research ( Von Krogh et al. , 2012 ) than fundamental research or that the publication process takes much longer, and we will see more papers in the upcoming years. Another reason could be that most existing articles are normative and are looking at the future applications of blockchain. We may assume that, in the future, when there will be more cases examining the actual application of blockchain in accounting practices and real examples of the influence of blockchain on the accounting and auditing field, the number of papers in the leading journals may increase. For now, we observe that, with the blockchain landscape changing daily, and ideas and research needing to reach the target audience faster than the traditional journal route allows, researchers are turning to SSRN to share their tentative findings ( Holub and Johnson, 2017 ). We also observe that Australian scholarship is now leading the blockchain research in accounting, as more papers were published in journals included in the ABDC ranking compared to the ABS ranking. Moreover, Australian journals such as the Australian Accounting Review and Meditari Accounting Research are among the top tiers of those who welcome such research.

It will be important to monitor the progress in the take-up of blockchain in the future ( Bonsón et al. , 2019 ; Gietzmann and Grossetti, 2019 ; Bonsón and Bednárová, 2019 ). More papers applying machine learning techniques will help to gather information from reports, and web crawlers will be able to discover new aspects of how blockchain technologies have been implemented in practice. Combined with manual analysis, these data will help to chart new paths forward for researchers.

7.2 Implications for accounting practice

Even though we anticipate that blockchain will influence accounting and auditing, we do not assume they will be totally replaced. Most expect that these professions will be augmented rather than fully automated, and the need for accountants and auditors will not disappear ( Agnew, 2016 ; Marrone and Hazelton, 2019 ). There will still be a need for professional judgement, and, further, issues such as reconciliation are almost impossible to perform at the current stage of blockchain's development. In line with McGuigan and Ghio (2019) , we argue that accountants will not only have to understand the data on blockchain, they will also have to interpret and explain the implications of this information to management and other decision-makers. As a result, accountancy is likely to become a much more strategically oriented profession.

However, the skills required of accountants are likely to change, and there may be a need for fewer entry-level accountants ( Kokina and Davenport, 2017 ; Marrone and Hazelton, 2019 ). There may be a shift towards notions such as creativity, innovation, holistic thinking, complex decision-making and sense-making. The ability to adapt to keep pace with an increasingly evolving business environment and technological context will also be important. Addressing such changes in education through content and delivery is necessary to ensure that graduates have up-to-date and workplace-relevant knowledge and can keep up with global accreditation standards and professional qualifications ( Al-Htaybat et al. , 2018 ). Teams, management and government bodies implementing blockchain and making decisions based on data obtained from blockchain will also need new skills to adapt to the changing environment ( Pimentel et al. , 2019 ; Siew et al. , 2020 ). Therefore, we propose that universities and higher education institutions should change and improve the curriculum of accounting and finance programmes to help students develop the above-mentioned skills. It is essential to start making the changes now as current students will soon become accounting and auditing practitioners as well as managers working with blockchain and other disruptive technologies.

7.3 Implications for policy

The literature review reveals a pressing need for legal frameworks to govern blockchain technologies and regulate cryptoassets. Comprehensive work by regulators and policymakers may help implement and spread these technological innovations further, opening new sources of financing for companies. There is also a need to work on legal and taxation policies for tokens, bitcoins and other cryptocurrencies so that they become valuable tools and stable assets in capital markets. With the improved regulatory framework, we also propose that in the future governments may develop national cryptocurrencies, e.g. crypto-euros or crypto dollars, that will be easier and faster to use compared to existing currencies. A well-developed regulatory framework may help tokens become a legitimate means of exchange in ecosystems that will start growing in the future. Further work is required from accounting bodies to accept new types of digital assets and develop standards that will solve the issues related to their recognition, measurement and disclosure. In the future, the implementation of blockchain may also raise questions related to the regulation of social and environmental accounting that becomes possible with this technology. All this will help to improve transparency further and decrease information asymmetry in the market.

7.4 Limitations

This study has several limitations. First, the sample only covers the period till June 2020. Extending this timeline could be an option for future research. Second, other machine learning techniques could be applied while working with the corpus of literature. Although our LDA approach is much more advanced than mere word count or word cloud methods, it still models documents using a bag-of-words representation. A similar topic model using more advanced neural natural language processing (NLP) architectures like Bidirectional Encoder Representations from Transformers (BERT) ( Devlin et al. , 2018 ) or Generative Pre-trained Transformer 3 (GPT-3) ( Brown et al. , 2020 ) that also consider the context and semantics of words might result in different fields of inquiry or a more revealing combination of topics. Third, we included articles uploaded to the SSRN database as well as published articles in ranked journals. We are aware that the peer-review process is accepted as a proxy for the quality of published works, especially with respect to academic journal articles ( Hart, 1999 ; Massaro et al. , 2015 ). However, we believe that, given the speed of new knowledge development, especially in the areas of disruptive technologies like blockchain, papers from SSRN added an important contribution to the topics identified. Finally, the validity of the results can only be considered at the time of the analysis, as literature reviews “are not a panacea” ( Massaro et al. , 2015 , p. 546). They only identify the current state of the field, and they only offer pathways for future research directions at a particular point in time.

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Blockchain in Education: A Systematic Review and Practical Case Studies

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A critical analysis of the integration of blockchain and artificial intelligence for supply chain

Original Research
Published: 25 January 2023
Volume 327 , pages 7–47, ( 2023 )

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Vincent Charles ORCID: orcid.org/0000-0001-8943-5681 1 , 2 ,
Ali Emrouznejad 3 &
Tatiana Gherman 4

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The integration between blockchain and artificial intelligence (AI) has gained a lot of attention in recent years, especially since such integration can improve security, efficiency, and productivity of applications in business environments characterised by volatility, uncertainty, complexity, and ambiguity. In particular, supply chain is one of the areas that have been shown to benefit tremendously from blockchain and AI, by enhancing information and process resilience, enabling faster and more cost-efficient delivery of products, and augmenting products’ traceability, among others. This paper performs a state-of-the-art review of blockchain and AI in the field of supply chains. More specifically, we sought to answer the following three principal questions: Q1—What are the current studies on the integration of blockchain and AI in supply chain?, Q2—What are the current blockchain and AI use cases in supply chain?, and Q3—What are the potential research directions for future studies involving the integration of blockchain and AI? The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI.

A Systematic Literature Review of Integration of Blockchain and Artificial Intelligence

Blockchain Technology Solutions for Supply Chains

Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

Satish Kumar, Weng Marc Lim, … Jaspreet Kaur

Avoid common mistakes on your manuscript.

1 Introduction

Traditionally dispersed geographically, supply chains have always been difficult to manage. Supply chain complexity is caused by a variety of factors and its long-term viability necessitates effective maintenance, repair, and operations management, among others. In supply chain networks, everything from link maintenance and regulatory policies to cultural norms and human behaviour makes evaluating information and managing risk a difficult task (Ivanov et al., 2019 ). Trust can be easily undermined by inefficient transactions, fraud, theft, and weak supply chains, which highlights the need for better information sharing and verifiability (Saberi et al., 2019 ).

In today’s business environment, traceability is becoming a necessity and a competitive advantage in many supply chain industries. Without transparency in the supply chain, stakeholders cannot properly assess and validate the true value of items. The cost of dealing with intermediaries, as well as their dependability and transparency, make managing supply chain traceability even more difficult, leading to strategic and reputational competitive issues (Saberi et al., 2019 ).

There are a number of issues with today's supply chains because they rely so heavily on central, sometimes disparate, and stand-alone systems of information management, such as enterprise resource planning systems (Saberi et al., 2019 ). The single point failure of centralised information systems is a drawback of such systems, which in turn makes the entire system vulnerable to error, hacking, corruption, or attack (Dong et al., 2017 ). Without doubt, there must be a high level of trust present for supply chain entities to entrust their sensitive and valuable data to a single organisation or broker (Abeyratne & Monfared, 2016 ).

In addition, there are continuous pressures on supply chain practice to recognise and certify the sustainability of supply chains. Environmental, social, and business aspects must all be considered in order to achieve sustainability, as part of the triple-bottom-line concept (Seuring et al., 2008 ). As a strategic and competitive issue, supply chain sustainability requires confirming and verifying that supply chain processes, products, and activities meet certain sustainability criteria and certifications (Grimm et al., 2016 ).

Existing supply chain information systems must be examined to determine if they can provide the secure, transparent, and reliable data needed to track the timely origin of goods and services. The key to resolving these difficult matters is to improve supply chain security, transparency, long-term viability, and process integrity. Blockchain technology could be the solution to this problem. New technological breakthroughs and applications based on the blockchain concept have made these objectives more attainable from an organisational, technological, and financial standpoint (Abeyratne & Monfared, 2016 ). With its decentralised ‘trustless’ database characteristics, blockchain technology can facilitate global-scale transaction and process disintermediation and decentralisation among a variety of different stakeholders (Crosby et al., 2016 ; Saberi et al., 2019 ).

As Saberi et al. ( 2019 ) noted, although the number of blockchain use cases has grown over time, blockchain, like any potentially disruptive system or technology, faces a number of challenges and barriers in terms of adoption and implementation by supply chain networks. Blockchain is still in its early stages of development, posing a number of challenges in terms of behavioural, organisational, technological, and policy-related issues.

Artificial intelligence (AI) promises to solve some of the above-mentioned problems. As a matter of fact, the integration of blockchain and AI is estimated to bring a number of significant various advantages, such as more robust deliverables (Odekanle et al., 2022 ). With such integration, parties can share massive amounts of data for the purposes of analysis, learning, and decision-making without the need for a central authority or third-party intermediaries.

By automating the entire workflow, the use of AI technology in the blockchain system has the potential to redefine the supply chain. Using a combined AI and blockchain approach, useful information can be extracted from historical purchase data and other sources, allowing for the identification of data characteristics and the performance of predictive analysis tasks such as future demand and sales forecasting (Zhang et al., 2021a , b ).

In spite of its importance and relevance, to the best of our knowledge, there is, at the time of conducting this research, no systematic literature review of studies on the integration of blockchain and AI for supply chain. Our research contributes to the operations management field by addressing this gap through a critical evaluation of such studies. The three principal questions we aim to answer are:

What are the current studies on the integration of blockchain and AI in supply chain?

What are the current blockchain and AI use cases in supply chain?

What are the potential research directions for future studies involving the integration of blockchain and AI?

As such, the aim is to not only investigate the current state of studies focusing on the integration of both technologies, but also to highlight how such integration can revolutionise “business-as-usual” practices in supply chain management. The analysis performed in this paper has identified relevant research studies that have contributed both conceptually and empirically to the expansion and accumulation of intellectual wealth in the supply chain discipline through the integration of blockchain and AI. As we will show, the majority of the literature is conceptual rather than empirical in nature, indicating that blockchain and AI integration is still in its early stages. Nonetheless, by highlighting current trends and interests in the field, it is expected that this study will assist academics, practitioners, and policymakers in understanding the current state-of-the-art on the topic and aid their decision-making to engage in empirical studies focusing on the actual deployment of AI-driven blockchain technology for supply chain and its implications for long-term performance. As a result, the current work will serve as a foundation for future research studies.

The remainder of the paper is organised as follows. In Sect. 2 , we lay out the theoretical framework by defining our key terms, namely blockchain, artificial intelligence, and supply chain. In Sect. 3 , we first detail the steps followed in identifying relevant studies (Sect. 3.1 ). The results of the bibliometric analysis are presented in Sect. 3.2 in order to obtain an overall view of the publications available in the literature on the topic. In Sect. 3.3 , we then narrow down the pool of studies identified by means of defining inclusion and exclusion criteria. In Sect. 4 , we discuss the identified thematic clusters, which are indicative of the current research trends on blockchain and AI integration in supply chain. Section 5 concludes the paper with final thoughts, limitations, and future research directions.

2 Theoretical framework: blockchain and artificial intelligence for supply chain

From the procurement stage to the product stage, the supply chain encompasses all activities involving the production of goods and the delivery of finished goods. Or, in the words of Pimenidis et al. ( 2021 ), “in the realm of manufacturing, a supply chain is the process of the flow of goods from the upper echelons of value creation to the end customer consumption. It is a form of symbiotic connection in which customers and suppliers work together to achieve the best interests of each other, buying, converting, distributing and selling goods and services to create specific final products and to add value to their organisations” (p. 369). In this sense then, regardless of industry, the supply chain has a complex architecture, on which a significant portion of business productivity and profits is based.

Sharing information securely, effectively, and efficiently is critical to running supply chains smoothly. A good supply chain requires efficiency and transparency at every level of the supply chain, as well as trust among stakeholders. Moreover, supply chains must become more adaptable and responsive, while also increasing their resilience and traceability, in order to be sustainable. Yet again, innovation and technology will be central to supply chain success (Baucherel, 2018 ). It is in this context that blockchain has emerged as a critical technology that has the potential to improve supply chain operations’ flexibility and agility (Cole et al., 2019 ). Through the use of blockchain technology, all stakeholders in the ecosystem can actively engage, share, and verify all types of information and data (Gohil & Thakker, 2021 ).

Satoshi Nakamoto is credited with the invention of the concept of “blockchain”. In 2008, Nakamoto ( 2008 ) published a paper wherein he presented the concept of the first peer-to-peer electronic cash system aided by digital currency, dubbed as “bit-coin”. Transactions are recorded as interlinked blocks that are linked to one another. In this context, the term “blockchain” refers to a chain of interconnected transactions. Blockchain enables parties who do not know each other to deal securely without the need for a centrally trusted middleman, lowering legal and transaction expenses (Pilkington, 2016 ). The name “distributed ledger” comes from the fact that records can be shared with different parties and maintained in multiple locations.

In other words, blockchain technology is a decentralised, distributed database of records or shared public/private digital ledgers that exists across a network and that is used to record transactions that have been executed and shared among participating agents (Crosby et al., 2016 ). Four key characteristics of blockchain technology distinguish it from most existing information system designs: non-localisation (decentralisation), security, auditability, and smart execution (Saberi et al., 2019 ; Steiner & Baker, 2015 ).

The way that the blockchain works is that a blockchain agent creates a new transaction to be added to the blockchain. This new transaction is transmitted to the network for auditing, and once it has been approved by a majority of nodes, it is added to the chain as a new block. It is further saved in several distributed nodes for security reasons. The smart contract, a key feature of blockchain technology, allows for trustworthy transactions to be executed without third-party involvement. Real-time visibility of transactions throughout the supply chain and a reduced risk of data manipulation and fraud are thus among the many advantages of blockchain technology (Cottrill, 2018 ; Partida, 2018 ).

The key properties of this technology that make it a valuable proposition to handle transactions, according to Rodríguez-Espíndola et al. ( 2020 ), are:

Immutable: All transactions will be recorded and stored by blockchain, and each transaction will be protected against deletion, tampering, and revision. The software code in the blocks automatically records any changes as new transactions that are linked to the previous transaction.

Distributed: Each of the blockchain network’s participants has an identical copy of the ledger on their computer (frequently called nodes). Cash management and the parties involved in the transaction are more easily spotted as a result of the increased visibility provided by this.

Decentralised: Transactions between the blockchain network entities will be easier with this property, as it eliminates the need for a central intermediary and speeds up processing time.

Automated: The code running in the blockchain automatically records and cryptographically verifies each transaction, ensuring transaction authenticity. This renders the entire process incorruptible and error-free. Transaction times are reduced as a result of the automation.

A single unified ledger: If a permissionless blockchain network is used and all transactions are recorded in a single immutable public ledger, grouping them together will be easier.

Self-reviewing: When a transaction occurs, the blockchain automatically updates and records the information, ensuring that every node (network participant) has an up-to-date ledger copy of the information available.

AI is a way of getting a machine or programme to perform tasks that would normally be performed by human intelligence; hence, it is used to develop intelligent computers and machines that behave like humans. There are two types of AI, namely General AI and Narrow AI. Narrow AI, or AI programmes capable of solving a single problem or task, is still the prevalent type of AI today. General AI, on the other hand, which can be described as an AI programme that can solve any problem it is given, is still a work in progress.

When integrated with AI, blockchain technology can assist the supply chain in various ways. A supply chain powered by blockchain and AI could be the answer to securing the operations of a local or regional supply chain and providing the intelligence required to enhance operational efficacy (Pimenidis et al., 2021 ). It can increase data security, data efficiency, and contribute to making smart decisions (Banerjee et al., 2018 ). Blockchain securely stores a large amount of data, while AI helps analyse and produce insights from data and generates new scenarios and patterns based on data behaviour (Ahmed et al., 2022 ). In order to make supply chains more adaptable, responsive, and efficient, while also maintaining transaction transparency for the benefit of all members, blockchain and AI algorithms can help (Pimenidis et al., 2021 ). All in all, the integration between blockchain technology and AI can strengthen applications in terms of reliability, security, transparency, and trust. To the best of our knowledge, papers have looked into the use of blockchain and AI in supply chains, but they have generally treated the two technologies separately, as if they were on opposite ends of a spectrum. The current paper, on the other hand, examines the integration of the two concepts.

3 Methods and materials

3.1 extracting relevant literature.

To address our research questions, a systematic literature review was carried out. To this aim, the Scopus search database, which is the abstract and citation database of Elsevier, was used as the main scientific database. Scopus is well-known and widely used for literature mapping (e.g., Fahimnia et al., 2019 ). Despite the fact that there are other databases available, Scopus was chosen as one of the best options because of its consistent citation metrics and precision in locating authors and institutions (Charles et al., 2022a , b ). Scopus also provides a more comprehensive coverage of articles in Business, Economics, Management, and Social Sciences in general (Martín-Martín et al., 2018 ). Hence, it was deemed that the works identified in the Scopus database can provide a reasonably good overview of what has been published on the topic of blockchain and artificial intelligence integration for supply chain that are relevant to this study.

Figure 1 depicts the flowchart of the approach followed in this paper. In the first phase, to conduct the search for relevant literature, we devised a set of keywords that would cover all relevant publications in the Scopus database to the greatest extent possible. The Scopus search algorithm was TITLE-ABS-KEY (“blockchain” OR “block chain”) AND (“artificial intelligence” OR “machine learning” OR “neural network” OR “deep learning”) AND “supply chain”). These keywords were looked up in each publication’s title, abstract, and keyword list. The search returned 280 document results, all published between 2017 and 2022. Data were downloaded on 27 March 2022. These documents were analysed using bibliometric analysis.

Flowchart of the systematic literature review with bibliometric analysis and thematic analysis

The application of the above criteria for identifying relevant material was left intentionally broad, so that we could get a sense of what researchers in the field are interested in right now and in the future. Moreover, only 280 results were returned, which is a relatively low number. On the other hand, we considered excluding conference papers from our analysis because they are frequently written to present preliminary findings and thus constitute works in progress rather than complete papers (Mubin et al., 2018 ). A similar consideration was made with regards to book chapters, conference reviews, and reviews, as they serve a different purpose and do different work than journal articles. However, a decision was made not to exclude such work. The reasoning was that, because the field is still young, it is understandable that more research is ongoing rather than finished; also, that more work is conceptual or theoretical in nature rather than empirical. As a result, it would be ideal at this point to provide an overview of the entire pool of documents that we came across in order to better capture all ongoing research endeavours. The 280 records were subjected to a co-occurrence analysis using the VOSviewer software for bibliometric analysis (Sect. 3.2 ).

In the subsequent phase (Sect. 3.3 ), the 280 records were screened, and only journal articles (as complete, published, and peer-reviewed research work) were selected for further analysis; this yielded 75 research articles. Then, a set of inclusion and exclusion criteria was developed, and a manual review of the 75 research articles was conducted to determine whether or not they complied with the set criteria and clearly addressed the integration of blockchain and AI for supply chain. This step resulted in a final pool of 42 research articles, which was subjected to a thematic analysis.

3.2 Bibliometric analysis of the results

Bibliometric analysis is a useful research technique for identifying global research trends and predicting future research directions (Liu et al., 2011 ). Through bibliometric analysis, this paper reveals the characteristics and trends in studies integrating blockchain and AI in the field of supply chain. The results are analysed using summary statistics.

The number of publications on blockchain and AI for supply chain has increased over time, as shown in Fig. 2 . It is worth noting that all 280 documents identified were published after 2017, implying that there were no publications combining the three keywords before this year, at least according to the Scopus database. Second, in recent years, there has been an increase in interest in the topic, with a peak in the year 2021 (124 records).

(Source: Scopus 2022)

Annual scientific production.

Figure 3 depicts the annual document volume broken down by information source. It can be observed that top five preferred outlets for publications on blockchain and artificial intelligence for supply chain are IFIP Advances in Information and Communication Technology (17 documents), Advances in Intelligent Systems and Computing (11 documents), Communications in Computer and Information Science (10 documents), Lecture Notes in Computer Science including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics (10 documents), and Sustainability Switzerland (9 documents).

Documents per year per source.

Figure 4 shows the documents by affiliation, with the first 12 affiliations compared to the total number of documents. Affiliation-wise, the University of the West of England (Great Britain) leads the ranking with 5 publications and is closely followed by Hong Kong Polytechnic University (Hong Kong) and Peter the Great St. Petersburg Polytechnic University (Russia), each with 4 publications. The subsequent 9 institutions shown in the figure account for 3 publications each. It is interesting to note how these institutions reflect a mix of countries from around the world.

Documents by affiliation

Figure 5 compares the document counts for 13 countries/territories to show which have the most publications. The countries of origin were determined by the corresponding author’s country. It can be easily observed that India dominates the ranking, with 51 publications, followed by China and the United States, with 38 and 34 publications, respectively. A notable observation is that these first three countries together account for almost half (i.e., 44%) of the total number of publications. The fourth position is held by the United Kingdom, with 22 publications and the fifth place belongs to Australia, with 12 publications. France and Germany occupy the sixth and seventh place, respectively, both with 10 publications, followed by Hong Kong and South Korea (each with 7 publications), Italy and Taiwan (each with 6 publications) and Finland, Greece, and Iran (each with 5 publications).

Documents by country or territory.

Table 1 shows the number of documents by publication type. In this regard, we can observe that conference papers predominate in the literature (86 documents, which constitute approx. 30% of the publications). The high number of conference papers in the literature could be attributed to the novelty of the research topic as well as the fact that the field is still in its early stages. Then there are articles (75 documents, representing approx. 27% of the publications). Conference reviews comprise 68 documents, or approximately 24% of the publications. Book chapters and reviews each have 22 documents, while books and editorials have the fewest (4 and 3 documents, respectively).

Finally, an analysis of published documents by subject area (Table 2 ) reveals that the subject area of “computer science” concentrates most of the research on the topic, with a whopping 171 documents or 29% of the publications. This is followed by the area of “engineering”, with 114 documents or approx. 19% of the publications, and the area of “decision sciences”, with 67 documents or approx. 11% of the publications. The areas of “business, management, and accounting” and “social sciences” occupies the fourth position, with 47 documents, or 8% the publications.

The VOSviewer software was used to create maps of co-authorship and keyword co-occurrence based on bibliographic data. The co-authorship analysis required at least five documents per country (Fig. 6 ); 14 of the 63 countries identified met this threshold. In this regard, Fig. 6 depicts the network map of international collaboration among major countries (those with the highest total link strength) engaged in blockchain and AI for supply chain research. The colours denote the clusters to which countries are assigned according to the strength of their relationships, while the surface area of the circles indicates the number of publications secured by each country. We can see that there are a total of six clusters.

Co-authorship network of countries

Using index keywords as the unit of analysis, a minimum of six occurrences per keyword and full counting as the counting method, a keyword co-occurrence analysis (Fig. 7 ) was performed. Of the 1210 keywords, 35 met the threshold. The total strength of the co-occurrence links with other keywords was calculated for each of the 35 keywords. We chose the keywords with the most total link strength. Keywords are denoted by coloured frames, the size of which is proportional to the number of times the keyword appears in the document.

Network map showing the relations between various topics in the literature on blockchain and AI for supply chain (280 documents)

Moreover, these keywords are grouped into four clusters that seem thus to assume a prominent role vis-à-vis “food and agriculture” (green cluster), “risk, resilience, and sustainability” (blue cluster), “security and ethical governance” (red cluster), and “emerging technologies and their benefits” (yellow cluster). This is indicative of the thematic areas that have captured the interest of researchers in the area of blockchain and AI for supply chains.

3.3 Pool of research articles on the integration of blockchain and AI

After obtaining an overall view of “what is out there” in terms of existing literature on the topic, that is, the 280 records previously discussed in Sect. 3.2 , we added, as previously mentioned, a second stage in which we narrowed our search results in order to bring additional accuracy and identify only those studies that specifically dealt with the integration of blockchain and AI for supply chain. On this occasion, it was decided to look only at research articles in order to clearly identify complete and published research work that had also been peer-reviewed. This consideration resulted in the selection of 75 research articles for further analysis (see previous Table 1 ). Following that, the authors manually reviewed these 75 research articles to determine whether or not they dealt with the integration of blockchain and AI for supply chain.

Studies were considered for further analysis if they met the following criteria:

The articles treated the topics of “blockchain”, “AI” (or any of its proxies, “machine learning”, “neural network”, or “deep learning”), and “supply chain” as a core question.

Regardless of whether the studies were literature reviews, conceptual works, empirical, case studies, or experimental, the studies clearly focused on the integration of blockchain and AI for supply chain.

The studies included research that was published in English, regardless of the year of publication.

Articles were excluded if:

The topics of “blockchain” and “AI” (or any of its proxies, “machine learning”, “neural network”, and “deep learning”) were treated separately, with no emphasis on their integration.

Because the full text was not available, we were unable to verify the content.

The journal publication was discontinued, which also meant that the full text was no longer available (hence, we were unable to verify the content).

Despite being labelled as research articles, the records were discovered to be other types of publications, such as editorials.

The articles used the terms “blockchain” and/or “AI” (or any of its proxies, “machine learning”, “neural network”, or “deep learning”) to analyse empirical data in the context of a supply chain, which was outside the scope of our focus.

The use of the aforementioned criteria resulted in a final pool of 42 research articles. Table 3 summarises the characteristics of the studies examined. Surprisingly, a manual content analysis of these 42 studies revealed that the previously identified thematic clusters can still be used to categorise this pool of studies. In this regard, it was discovered that 18 studies could be classified as belonging to the “Emerging Technologies and Their Benefits” cluster, 13 studies as belonging to the “Food and Agriculture” cluster, 8 studies as belonging to the “Risk, Resilience, and Sustainability” cluster, and 3 as belonging to the “Security and Ethical Governance” cluster.

It is also worth noting that the vast majority of these studies (30 articles) are conceptual in nature, followed by experimental (15 articles), literature reviews (8 articles), case studies (6 articles), and, finally, empirical studies (5 articles). Of course, with the caveat that some articles combine conceptual and literature review, conceptual and empirical, and so on (see Table 3 ). Such findings emphasise the still-emerging nature of studies focusing on the integration of blockchain and AI for supply chain, indicating that more empirically completed studies are still required to fully understand what such a field has to offer from a very practical standpoint. In the next section, we proceed to interpret the identified thematic clusters. To be noted that additional searches for materials relevant to our discussion were further conducted using the referenced works of the main pool of 42 articles (snowball effect) and included herein. Section 4 thus complements the information presented in Table 3 .

4 Interpreting clusters

4.1 cluster 1: food and agriculture.

AI and machine learning (ML), big data analytics, cloud computing, IoT, robotics, and blockchain are examples of disruptive information and communication technologies that can help to solve problems like increasing productivity and yield, conserving water, ensuring soil and plant health, and improving environmental stewardship. According to Lezoche et al. ( 2020 ), these technologies “will let the agriculture to evolve in a data-driven, intelligent, agile and autonomous connected system of systems”.

Apart from the sustainability benefits, AI-ML applications in particular can help to improve supply chain transparency, visibility, and product traceability (Sharma et al., 2020a , b ). However, all these are ongoing efforts and practitioners will need to investigate the possibilities of combining various sources of AI-ML data with blockchain technology and other technologies to a greater extent (Kamble et al., 2018 ; Sharma et al., 2018 ). Studies have started to emerge. Using RFID (Radio-Frequency IDentification) and Blockchain technology, Tian ( 2016 ), for example, gathered and transferred trusted information to reduce agri-food risk in order to build a supply chain traceability system. It is worth noting that our review revealed that building product traceability systems is one of the most common research endeavours that researchers are currently engaged in.

Smart contracts and cybersecurity are two critical applications of blockchain in the domain of agriculture. The Internet of Things enables precise sensing throughout the supply chain. With smart contracts, all transactions are documented in a distributed manner. Immutable transaction histories from raw material suppliers to consumers would aid in food quality control, increase traceability, and, ultimately, resolve food safety concerns. Blockchain-based smart contract technologies are expected to enable the agri-food supply chain's digital transformation, resulting in a “traceable, transparent, trustful, and intelligent ecosystem” (Liu et al., 2021a , b , p. 4330).

Tripoli and Schmidhuber ( 2018 ) highlighted how data generated by the IoT can be used to enhance the details of blockchain transactions. Then, the precise data provided by blockchain technology can be used to power AI applications (Rabah et al., 2018 ). Moreover, AI can enhance IoT by developing applications that use machine learning algorithms to analyse data captured by sensors in real time (Reshma & Pillai, 2018 ).

Sharma et al., ( 2020a , b ) performed a systematic literature review of machine learning applications in agricultural supply chains and found that all three ML algorithms, that is, supervised, unsupervised, and reinforcement learning, are used to develop sustainable agricultural supply chains. However, the study does not discuss the integration between ML algorithms and blockchain. On the other hand, Putri et al. ( 2020 ) noted that in the future, hyperledger blockchains can be developed and implemented using AI, allowing for the prediction, classification, and clustering of existing data and transactions based on their time period.

Although most of materials identified represent a theoretical treatment of the subject, there are a few studies that demonstrate successful integration of blockchain and AI techniques for supply chain traceability improvement, such as the study by Chen ( 2018 ). In this sense, Chen ( 2018 ) proposed a novel approach called Takagi–Sugeno Fuzzy cognitive maps artificial neural network as a traceability chain algorithm.

4.2 Cluster 2: Risk, resilience, and sustainability

Supply chain network complexity, combined with external factors, has resulted in supply chain disruptions over the last few years (Fan & Stevenson, 2018 ). Most recently, the pandemic has focused renewed attention on sustainability. As a matter of fact, no recent event has highlighted the vulnerability of supply chains as much as the COVID-19 outbreak in early 2020 (Pournader et al., 2020 ; Spieske & Birkel, 2021 ).

The medical supply chain, especially for pharmaceutical drugs, has been particularly affected. COVID-19’s sudden emergence and uncontrolled global spread has exposed the inadequacies of the current healthcare systems across the world in responding to public health emergencies in a timely manner. Breakthrough technologies like blockchain and AI have emerged as viable and sustainable solutions for combating the pandemic in such circumstances (Baz et al., , 2021 ) and this has evolved into a common research endeavour that has recently piqued the interest of researchers. In this context, research has emerged looking at medical supply chain and drugs and pharmaceutical supply chain management.

The COVID-19 outbreak has the potential to strain existing healthcare systems. At the moment, there is a dearth of a reliable data surveillance system capable of providing relevant healthcare organisations with real-time information about potential outbreaks. Indeed, the majority of existing Covid-19 data originates from a wide range of sources, including the general public, hospitals, and clinical laboratories, and contains a substantial amount of incorrect information that has not been thoroughly monitored (Nguyen et al., 2021 ).

Blockchain technology can assist in combating the COVID-19 pandemic by facilitating early detection of outbreaks, ensuring a secure drug supply chain and expediting drug delivery, and establishing consensus on the ordering of Covid-19 data records (Baz et al . , 2021 ). Additionally, AI-based supervised and unsupervised ML techniques enable the development of intelligent solutions for real-time epidemic outbreak monitoring, pandemic trend forecasting, coronavirus symptom identification for treatment, and drug manufacturing support.

Nguyen et al. ( 2021 ) conducted a comprehensive survey on the use of blockchain and AI in the fight against the recent pandemic. The authors introduced a novel conceptual architecture that integrates blockchain and AI to combat Covid-19 and reviewed recent studies on the use of blockchain and AI to combat Covid-19 in a multitude of scenarios, including the medical supply chain.

Blockchain-based smart contracts are computer programmes that carry out the terms of a contract when certain objectives are satisfied (Griggs et al., 2018 ). Automating auditing processes, medical supply chain management, outbreak tracking, and remote patient monitoring could all be done using smart contracts based on blockchain technology (Griggs et al., 2018 ; Roosan et al., 2022 ). Using blockchain-based smart contracts, pharmaceutical supply chains can be improved, and their quality and regulatory compliance can be verified, all while automating auditing processes (Angeles, 2018 ).

These combined technologies were further examined by Jabarulla and Lee ( 2021 ) to see if they could be used to help with the conventional public health strategies for combating Covid-19, such as contact tracing, outbreak estimation, coronavirus detection and analysis, as well as the management of clinical data and the supply chain, among others.

The patient-centred approach to healthcare could be reshaped by integrating blockchain and AI technology (Chen et al., 2019 ; Jabarulla & Lee, 2021 ; Ploug & Holm, 2020 ). In turn, it is possible that a patient-centred approach to dealing with the coronavirus pandemic could be useful in distributing treatment and managing pandemics (Jabarulla & Lee, 2021 ).

The combination of blockchain and AI technologies enables the development of an all-encompassing predictive system that could help to keep the threat of a pandemic at bay within a country’s borders (Fusco et al., 2020 ). Moreover, the public surveillance system can be made more effective and robust by combining blockchain technology with AI and geographic information systems (Sharma et al., 2020a , b ).

Another research strand that has been gaining momentum is the use of blockchain-AI integration for circular economy and sustainability. Studies like Ebinger and Omondi’s ( 2020 ) report on the current use of digital technologies (such as blockchain, cloud computing, and AI) in sustainable supply chain management provide further evidence of the progress being made in this area. A blockchain-enabled and Intelligent Agent-supported supply chain community that is intelligent and responsive, as well as secure and sustainable has been proposed by Pimenidis et al. ( 2021 ). Chidepatil et al. ( 2020 ) discussed how blockchain and multi-sensor-driven AI can transform the circular economy of plastic waste. When it comes to the protection and preservation of the global environment, including life on land, life underground, and climate change, Sivarethinamohan and Sujatha ( 2021 ) looked at how AI-driven blockchain technology could be put to use for this purpose.

4.3 Cluster 3: Security and ethical governance

Recent surges in security breaches and digital surveillance have highlighted the need for enhanced privacy and security, particularly with regard to users’ personal data (Heister & Yuthas, 2021 ). Blockchain technologies enable novel methods of protecting user data through decentralised identity and other privacy mechanisms. These systems can empower users by providing tools that enable them to own and handle their data. On the other hand, AI opens up new avenues for improving system and user security.

As Heister and Yuthas ( 2021 ) highlighted, “ blockchain provides new mechanisms, such as decentralized identities and zero-knowledge proofs, that enable data to be shared in ways that maintain the privacy of the individual and allow users to maintain control over their own data. These advances can provide both increased cybersecurity and more ethical use of personal data. Blockchain participants can realize these outcomes through careful development of governance frameworks and mechanisms ”. As a result, advancements in these technologies open up new avenues for the ethical use of data.

Furthermore, by improving the quality, transparency, traceability, and security of data, blockchain can improve the capabilities of other technologies and techniques such as AI and IoT in-process monitoring, trend prediction and decision-making, among others (Sun & Zhang, 2020 ). The combination of blockchain, AI, and supply chains may result in an improvement in information management for interconnected devices. A recent study by Unal et al. ( 2021 ) presented a practical approach for integrating blockchain with federated learning in order to provide private and secure big data analytics services.

One of the driving forces behind blockchain and AI in supply chains is information management. Supply chains can use blockchain technology to organise a reliable and secure digital transition in which all operations can potentially be digitised. Implementing blockchain and AI on information management tasks can result in administrative benefits such as time savings, improved information quality, and increased security. All of these developments are currently the subject of ongoing research efforts.

4.4 Cluster 4: Emerging technologies and their benefits

Blockchain, AI, cloud computing, IoT, cyber-physical systems, and robotics are examples of recent technological developments (components of Industry 4.0) that enable the integration of disparate supply chain advancements into intelligent and connected Systems of Systems. These technologies have the capability for strengthening the supply chain management and will assist the relevant sectors in becoming data-driven, agile, intelligent, and automated. Table 3 showcases recent studies, with applications ranging from healthcare, business, and financial services to the automobile industry and humanitarian logistics.

Rodríguez-Espíndola et al. ( 2020 ), for example, argued that integrating disparate technologies is critical for the humanitarian supply chain to reap real gains. To accomplish this, the authors proposed a framework for enhancing the flow of information, products, and financial resources in humanitarian supply chains through the integration of three emerging disruptive technologies: blockchain, AI, and 3D printing. Their analysis demonstrates that the framework has the potential to alleviate supply chain congestion, improve simultaneous collaboration among various stakeholders, reduce lead times, improve accountability, traceability, and transparency of material and financial resources, and empower victims to contribute to their own needs fulfilment.

Applications can also combine blockchain with machine learning (Ramezani & Camarinha-Matos, 2020 ). This combination of technologies would enable systems to improve continuously, respond more quickly and effectively to disruptions, and predict potential failures.

Given recent advancements in these fields, the adoption of smart products based on IoT and other technologies may necessitate systems with high data integrity and user privacy, which can only be furnished by blockchain technology (Chanson et al., 2019 ). Moreover, by integrating AI and blockchain with IoT sensors and edge devices, supplies chains can work toward becoming “smart supply chains”.

These technologies also make intelligent transportation systems possible. Self-driving cars can use IoT sensors to constantly monitor and, in some cases, predict developments using AI, while also incorporating blockchain wallets that allow passengers to pay for rides, rentals, tolls, and other services without revealing personal information (Heister & Yuthas, 2021 ).

5 Conclusions and future research directions

Blockchain and AI technologies are rapidly evolving, opening up new avenues for working with data that were previously unimaginable. They are setting the pace of innovation and introducing a radical shift in almost every industry. On their own, blockchain and AI are cutting-edge technologies, but when combined, they can be truly revolutionary, of course, as long as such integration is underpinned by a problem-centric thinking approach (Charles et al., 2022a , b ). Each of them has the potential to improve the other’s capabilities, allowing for better oversight and accountability.

Although blockchain and AI have been explored in the extant literature on operations management, no state-of-the-art review of blockchain and AI integration in the field of supply chain was identified. This article is, therefore, the first to provide an overview and assessment of such studies. More specifically, we sought to answer the following three principal research questions: Q1—What are the studies on the integration of blockchain and AI in supply chain?, Q2—What are the blockchain and AI use cases in supply chain?, and Q3—What are the potential research directions for future studies involving the integration of blockchain and AI?

To answer the first two questions, we queried the SCOPUS database. Our search turned up 280 results, all of which were published between 2017 and 27 March 2022. Early on, it was noted that the majority of the material published was conceptual in nature rather than empirical. This indicated that the field of blockchain and AI convergence is still in its infancy. Given the field’s nascent status, as evidenced also by the large number of publications other than completed research articles, we decided not to remove any of the materials we came across. This was done so that we could get a sense of what researchers in the field are currently interested in. A bibliometric analysis of the materials revealed four main themes that have piqued researchers’ interest. These themes are “Emerging Technologies and Their Benefits”, “Food and Agriculture”, “Risk, Resilience, and Sustainability”, and “Security and Ethical Governance”, which are also indicative of the most significant use cases. Based on the findings of this study, a number of future research directions have been outlined below, which helps us to answer the third research question.

First, as previously mentioned, most of the works reviewed are conceptual rather than empirical in nature. In other words, empirical studies aimed at implementing blockchain-AI applications in real life are still lacking. This, in turn, limits the full validity of conversations on the topic. Blockchain is still in its infancy, and so is the integration of blockchain with other emerging technologies such as AI. Therefore, many flaws must be addressed before any benefits and long-term effects can be observed in practice. Empirical studies focusing on the actual deployment of AI-driven blockchain technology for supply chain and its implications on long-term performance make thus an interesting and promising future research direction.

Second, the integration between blockchain and AI can prove to be highly advantageous especially for the healthcare sector, while at the same time tackling existing ethical issues. The Covid-19 pandemic has compelled medical researchers and technologists alike to look into methods of rapidly collecting information about virus exposure and transmission while maintaining user privacy. Testing patients who may have been exposed to the virus quickly with point-of-care diagnostics has proven effective in tracing its spread and mitigating its effects. Blockchain and AI can help by collecting the data on blockchain infrastructure and then quickly analysing it using AI. Moreover, blockchain and AI can be combined with other technologies, such as cloud computing, to create more comprehensive and efficient systems. High computational power and resourceful storage are two of the most important aspects of the cloud that can aid in AI analytics. The integration of these promising technologies can be further explored in the near future to create highly advanced medical systems to combat future coronavirus-like pandemics.

Third, our analysis reveals that more studies are needed to explore the integration of blockchain and AI from an environmental sustainability, social, and economic points of view (triple bottom line perspective). In the end, blockchain and AI are merely tools, and their long-term effects are largely determined by the underlying vision and strategies that companies choose to govern their daily operations. As a result, more research is needed to optimise the range of business initiatives and strategies as well as the technology choices in order to achieve the United Nation’s Sustainable Development Goals. The needs of multiple stakeholders and the complexity of multi-layered supply chains should be taken into account in future analyses.

Finally, because the integration of blockchain and AI for supply chain is still in its early stages, more cross- and inter-disciplinary empirically grounded research, particularly research approaches to study practice in truly insightful and impactful ways, is required (Charles & Gherman, 2018 ). As the concept of distributed ledger technologies evolves over time, new AI-based emerging technologies surface, and new sustainability debates emerge, such endeavours will necessitate the participation of a wide range of stakeholders, from researchers to practitioners and policymakers (Charles et al., 2019 ).

The present paper is not without limitations. Our analysis and findings are limited to the Scopus scientific database, which was chosen for its consistent citation metrics and precision in locating authors and institutions. Despite this, the Scopus database is the largest database of peer-reviewed literature and further offers a comprehensive coverage of articles in Business, Economics, Management, and Social Sciences in general. As a result, we are confident that the works identified and included in this paper provide a reasonably good overview of what has been published on the topic of blockchain and artificial intelligence integration for supply chain, as well as current and future interests on the topic. Future studies can, of course, complement the current study by including other databases. Additionally, future studies can expand the scope of the present study by detailing the challenges and limitations of existing researches. We hope, however, that the current work serves as a foundation for future research studies.

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Blockchain in accounting research: current trends and emerging topics

Design/methodology/approach

Research limitations/implications

Practical implications

Originality/value

1. Introduction

2. Blockchain in accounting

3. Methodology

3.1 The research questions

3.2 Defining a set of articles for further analysis

3.3 Methods of analysis: Latent Dirichlet Allocation combined with manual analysis

4.1 Results of LDA analysis

4.2 Article impact

5. Key research topics: focus and critique

5.1 The changing role of accountants

5.2 New challenges for auditors

5.3 Opportunities and challenges of blockchain technology application

5.4 Regulation of cryptoassets

6. Future research directions

6.1 The changing role of accountants

6.2 New challenges for auditors

6.3 Opportunities and challenges of blockchain technology application

6.4 Regulation of cryptoassets

7. Conclusion

7.1 Implications for academics

7.2 Implications for accounting practice

7.3 Implications for policy

7.4 Limitations

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Blockchain in Education: A Systematic Review and Practical Case Studies

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Artificial Intelligence and Blockchain Integration in Business: Trends from a Bibliometric-Content Analysis

1 Introduction

2 Theoretical framework: blockchain and artificial intelligence for supply chain

3 Methods and materials

3.2 Bibliometric analysis of the results

3.3 Pool of research articles on the integration of blockchain and AI

4 Interpreting clusters

4.2 Cluster 2: Risk, resilience, and sustainability

4.3 Cluster 3: Security and ethical governance

4.4 Cluster 4: Emerging technologies and their benefits

5 Conclusions and future research directions

Acknowledgements

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