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  • Published: 04 July 2019

A systematic review of blockchain

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

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

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

Introduction

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

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

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

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

Overview of the current research

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

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

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

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

figure 1

Disciplines in blockchain

Articles in business and economics

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

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

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

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

Cited references

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

Most influential articles

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

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

Research themes

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

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

figure 2

Disciplines and topics

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

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

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

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

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

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

What will be the future research directions for blockchain?

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

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

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

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

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

How can businesses benefit from blockchain?

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

Accounting settlement and crowdfunding

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

Data storage and sharing

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

Supply chain management

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

Smart trading

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

Conclusions

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

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

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

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

Availability of data and materials

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

Abbreviations

Initial coin offering

Web of Science Core Collection

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Acknowledgements

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

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Three generations of blockchain

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

Blockchain 1.0

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

Blockchain 2.0

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

Blockchain 3.0

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

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

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Xu, M., Chen, X. & Kou, G. A systematic review of blockchain. Financ Innov 5 , 27 (2019). https://doi.org/10.1186/s40854-019-0147-z

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What you see is what you get: the impact of blockchain technology transparency on consumers

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  • Matilde Rapezzi   ORCID: orcid.org/0000-0002-3646-2114 1 ,
  • Gabriele Pizzi   ORCID: orcid.org/0000-0002-7531-9003 1 &
  • Gian Luca Marzocchi 1  

Blockchain technology (BT) represents a chance to bolster consumer responses toward retailers due to its ability to ensure transparency in each transaction within supply chain. Relying on signaling theory, we propose and test a theoretical model to examine BT effects. We test our theorizing in three experiments involving a total of 1995 participants. Our results suggest that retailer transparency elicited by BT fosters enhanced quality perceptions and retailer trust. As a result, consumers display higher future intentions toward the retailer. The findings illustrate that information quantity moderates the effects of transparency. Furthermore, the studies rule out interactivity and mental imagery as two possible alternative explanations of the effects of BT transparency. Our findings shed light on the importance of transparency in the supply chain in influencing consumer responses toward retailers and encourage retailers to consider in-store technologies such as BT that enable consumers to access such information.

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1 Introduction

Consumers may face challenges when evaluating product quality, particularly for those products with unobservable quality features. Retailers play a pivotal role in conveying transparent information that can bridge the divide between manufacturers and consumers (Guan & Chen, 2015 ). One may wonder if adopting digital technologies that enhance retailer transparency can reduce the information asymmetry regarding product quality, thereby improving consumer attitudes and behavioral intentions toward the retailer. The current paper revolves around this question.

In doing so, we focus on Blockchain Technology (BT). While BT is predominantly recognized for its role in finance, it has found increasing applications in various sectors. In retailing, one notable use of BT is to ensure supply chain traceability (Iansiti & Lakhani, 2017 ). Platforms like Provenance or IBM Blockchain enable firms to use BT for tracing products. Thus, by leveraging these platforms, retailers now have the opportunity to provide consumers with transparent information about products’ journeys (Gleim & Stevens, 2021 ).

Prior research has provided important insights into the role played by BT in supply chain management (Rejeb et al., 2021 ; Zheng et al., 2018 ). However, with few exceptions (e.g., Cozzio et al., 2023 ; Treiblmaier & Garaus, 2023 ), less is known about the impact of retailer transparency provided by BT on consumer responses. Still, consumers play a central role within the supply chain since they represent its final node. Research has suggested that BT has the potential to alter consumer perceptions due to its transparent transaction mechanism, which allows consumers to access the full set of transactions of supply chains (Gleim & Stevens, 2021 ). With the goal of understanding more about the role of retailer transparency elicited by BT in affecting consumer responses, we conducted three online experiments.

Our findings offer different contributions. First, we respond to the call for research on the effects of transparency in the supply chain on consumers (Gleim & Stevens, 2021 ). Second, we make an original contribution to the literature on quality signals by showing that transparency provided by BT traceability works as an effective quality signal. Third, we identify a boundary condition for the effects of retailer transparency (i.e., information quantity). Finally, our findings encourage retailers to leverage BT to trace supply chains more transparently.

2 Theoretical background

Transparency stands out as a way for companies to enhance practices throughout the supply chain (Fraser & van der Ven, 2022 ). It refers to the practice of making information about the supply chain accessible and understandable to relevant stakeholders, including consumers (Sodhi & Tang, 2019 ). Companies are increasingly encouraged by the law to be transparent about their products (e.g., The California Transparency in Supply Chains Act). However, while many companies pursue transparency by disclosing more information about their products, their efforts may fall short if consumers cannot verify the accuracy of the claims (Reynolds & Yuthas, 2008 ). A technology that should enhance supply chain transparency and, thus, may help companies positively influence consumers, is BT (Gleim & Stevens, 2021 ).

BT is a large distributed ledger that stores a continuously growing set of transaction bundles, called blocks, which are linked and secured cryptographically in a peer-to-peer network (Alkhudary et al., 2023 ). BT has unique features—e.g., transparency and security—compared to other tracking technologies (e.g., labels, barcodes, RFID; Moretto & Macchion, 2022 ). In the area of supply chain traceability, however, the transparency of BT plays the most critical role (Gleim & Stevens, 2021 ). Through this technology, companies can provide consumers with verifiable information about the product’s journey, from source to endpoint. BT decentralizes supply chain information, ensuring universal access instead of storing it in a single location. All transactions occurring along the chain are publicly accessible, and they cannot be altered without consensus.

While BT improves transparency at all stages of production and distribution (Saxena & Sarkar 2023 ), we are specifically interested in the transparency that BT elicits at the end of the supply chain, in the retailing stage, where the interaction with consumers takes place. In other words, we focus on retailer transparency. Footnote 1 We define it as the extent to which retailers are transparent, clear, and upfront in disclosing information about products (Bateman & Bonanni, 2019 ). Building on prior research on transparency and considering the unique features of BT, we advance that the higher transparency provided by BT is related to greater future intentions toward the retailer (i.e., patronage, WOM, and purchase intentions) compared to the lower transparency associated with other tracking technologies. Accordingly.

H1: Higher retailer transparency provided by BT traceability leads consumers to display greater future intentions toward the retailer.

Why may this happen? We suggest that the transparency provided by BT acts as a quality signal compensating for the information asymmetry between retailers and consumers about product quality (Spence, 1973 ). Prior studies have addressed the issue of information asymmetry by proposing cues that retailers can use as quality signals, such as price (Kirmani & Rao, 2000 ). The underlying principle is that consumers cannot observe a product quality directly, and must infer it from other signals. Nonetheless, the issue of information asymmetry may endure even when quality signals are present, as information can be readily forged or manipulated (Treiblmaier & Garaus, 2023 ). One way to overcome this problem may be for retailers to leverage their own verifiable transparency (Bolton, 2019 ).

Research started exploring the role of BT as a quality signal (Xu et al., 2022 ). For instance, in the context of supply chain finance, BT works better than conventional monitoring methods in signaling the firm’s quality (Chod et al., 2020 ). Further, compared to company-owned labels, BT labels act as signals that increase consumers’ quality perceptions of food products (Treiblmaier & Garaus, 2023 ). Consistent with existing theorizing, we advance that retailer higher transparency provided by BT conveys a stronger signal of product quality compared to the lower transparency associated with non-BT traceability.

Indeed, for a signal to be more credible, it should be costlier (e.g., in terms of money, time, risk; Kirmani & Rao, 2000 ). Retailers adopting BT instead of other tracking technologies incur in higher implementation costs (Moretto & Macchion, 2022 ). More importantly from the consumer perspective, using BT means that product information is fully verifiable. The cost of verifiable information is that it entails the risk of immediate identification of any misstep (Chaudhry & Wald 2022). Therefore, transparency enabled by BT should be a stronger signal of product quality than the one provided by non-BT tracking methods.

One condition that is necessary for relational exchanges between retailers and consumers is trust, particularly when the exchange is characterized by information asymmetry (Singh & Sirdeshmukh, 2000 ). We consider trust as consumers’ confidence in the integrity and reliability of retailers (Inman and Nikolova 2017). A solid stream of literature shows that product quality is a significant antecedent of trust toward the retailer (e.g., Rubio et al., 2017 ). Hence, consumers exposed to a more transparent retailer may not only infer higher product quality, but also, as a consequence, place more trust in the retailer. Further, trusting a company drives consumers to be more loyal, more willing to re-purchase, and more inclined to spread positive WOM (Kang and Hustvedt 2014). Building on these findings, we suggest that higher transparency should affect future intentions as mediated by increased product quality and trust. Therefore.

H2 : The effect of retailer transparency on future intentions is serially mediated by perceived product quality and trust toward the retailer.

If consumers perceive higher transparency as a signal of product quality, then its beneficial impact may disappear in the presence of other quality signals. Thus, if consumers already perceive product quality as high, tracing the supply chain by means of BT could be less beneficial for retailers. We focus on information quantity as an alternative quality signal (Chang & Wildt, 1994 ).

Quantity is one of the cues that make information diagnostic (Andrews, 2013 ). Literature on crowdfunding shows that when creators provide extensive information about their projects, they signal higher quality to funders as they are perceived as more prepared (Wessel et al., 2017 ). Indeed, offering a detailed description not only diminishes information asymmetry between parties, but also signals the costs invested by creators in terms of time and effort (Moradi & Badrinarayanan, 2021 ). Larger amounts of information offer a meaningful product quality cue even when the information provided is not highly informative, by serving a compensatory function (Keller & Staelin, 1987 ).

Accordingly, if retailers provide consumers with more information about the product’s journey (i.e., more details about each step of the supply chain), the latter should infer higher product quality. Hence, we propose that, when product information quantity is high, consumers should already perceive product quality as high, mitigating the positive effect of BT traceability. Formally.

H3 : Information quantity moderates the relationship between retailer transparency and perceived product quality: The effect of retailer transparency on perceived quality disappears when information quantity is high.

3 Experiments

To test the above hypotheses, we conducted three online experiments. Data and materials of the studies can be found here.

3.1 Study 1

Study 1 had two main goals. First, to test the main effect of BT transparency on future intentions (H1) and the sequential mediation by perceived quality and trust (H2). Second, to rule out alternative explanations: interactivity and mental imagery. We manipulate interactivity—i.e., the level of interactivity with which participants can access information about the product—to exclude the possibility that it is this technology feature, rather than the transparency elicited by it, to positively affect consumers (Fiore et al., 2005 ). Furthermore, consumers provided with more vivid product information may engage in higher mental imagery which could lead to positive outcomes (Babin & Burns, 1998 ). Thus, we include mental imagery as a parallel mediator in the model.

3.1.1 Method

We recruited a total of 400 UK participants on Prolific (71% female; Median age  = 44). Participants were randomly assigned to one of four experimental conditions resulting from a 2 (transparency: low vs. high) × 2 (interactivity: low vs. high) between-subjects design. First, we asked them to imagine themselves engaging in the purchase of a sweater from an apparel clothing store. All the participants saw the picture of a tag that simulated the label placed on the sweater. In the high transparency condition, the label explicitly reported that the product was tracked using BT. To make sure that participants understood what BT was, we provided them with a short definition of the technology (see Appendix A ). In the low transparency condition, the label did not mention BT. In the low interactivity condition, the tag reported the list of steps that the product followed. In the high interactivity condition, the tag included a QR code that participants had to scan to obtain the same information on their phones. Examples of experimental stimuli used in Study 1 are displayed in Fig.  1 (see also Appendix A ).

figure 1

Sample experimental stimuli (Study 1). Note. Left: Fictitious product label with the QR code participants had to scan to obtain traceability information. Right: Screenshot of what participants saw on their phones after scanning the QR code on the label

Next, we asked participants to evaluate the perceived information interactivity on three items ( α  = 0.61), and retailer transparency on five items ( α  = 0.82). Then, we presented them with a four-item mental imagery measure ( α  = 0.88), a five-item perceived quality measure ( α  = 0.90), a two-item trust toward the retailer measure ( α  = 0.93), and a three-item future intentions measure ( α  = 0.95). Finally, we asked participants to rate their knowledge of BT, and to indicate their gender, age, and level of education (see Appendix B for scale items and sources).

3.1.2 Results

Manipulation checks.

A two-way MANCOVA with BT knowledge as a covariate Footnote 2 showed that participants perceived the retailer using BT as more transparent ( M HT  = 5.13, SD  = 0.90) than the retailer not using BT ( M LT  = 4.33, SD  = 1.10; p  < 0.001). Additionally, participants perceived the information accessible through the QR code as more interactive ( M HI  = 4.66, SD  = 1.24) than the information reported directly on the label ( M LI  = 4.18, SD  = 1.12; p  < 0.001). The interactions between the two manipulations on transparency and interactivity were non-significant.

Perceived quality, trust, and future intentions

As predicted, a two-way MANCOVA with BT knowledge as a covariate showed a significant effect of transparency on perceived quality ( F (1, 395) = 26.83, p  < 0.001, \({h}_{p}^{2}\) = 0.064), trust ( F (1, 395) = 34.64, p  < 0.001, \({h}_{p}^{2}\) = 0.081), and future intentions ( F (1, 395) = 27.02, p  < 0.001, \({h}_{p}^{2}\) = 0.064), but a non-significant effect of interactivity, and a non-significant interaction between transparency and interactivity on perceived quality, trust, and future intentions. Given the non-significant interaction, we collapsed the high and low interactivity conditions. Participants in the high (vs. low) transparency condition perceived the product to be of higher quality ( M HT  = 4.92 vs. M LT  = 4.47), showed a greater trust toward the retailer ( M HT  = 5.14 vs. M LT  = 4.54), and displayed greater future intentions ( M HT  = 4.97 vs. M LT  = 4.44).

Mediation analysis

To test whether perceived quality and trust sequentially mediate the relationship between transparency and future intentions, we run a mediation analysis using PROCESS Model 6 (Hayes, 2017 ). As in Study 1, we included BT knowledge as a covariate. A 5000-sample bootstrap analysis found a 95% confidence interval that excluded zero, indicating a significant indirect effect of transparency on future intentions through perceived quality and trust (indirect effect = 0.26, SE  = 0.12, 95% CI = [0.04, 0.5]).

Next, we ran an additional (customized) mediation model with mental imagery as a parallel mediator. Results yielded a significant indirect effect of transparency on future intentions through mental imagery (indirect effect = 0.11, SE  = 0.03, 95% CI = [0.06, 0.17]). Nonetheless, the indirect effect through perceived quality and trust remained significant (indirect effect = 0.16, SE  = 0.04, 95% CI = [0.10, 0.23]). This finding rules out the possibility that mental imagery overshadows the signaling effect of transparency on future intentions.

3.2 Study 2

Study 2 replicated the findings of the previous study, and tested the moderation by information quantity (H3).

3.2.1 Method

A total of 400 UK participants were recruited on Prolific (76% female; Median age  = 45) participated in the study. No respondent has been excluded from the analyses. Participants were randomly assigned to one of four experimental conditions in a 2 (transparency: low vs. high) × 2 (amount of information: low vs. high) between-subjects design. We used the same scenarios and manipulation of transparency as in Study 1. Differently from Study 1, we maintained the same level of interactivity across all conditions—each label featured a QR code that all participants had to scan to read traceability information. Additionally, we manipulated information quantity by providing more or less detailed descriptions of the product’s journey (see Appendix A ).

To follow, we asked participants to evaluate the perceived information quantity on three items ( α  = 0.85), and the set of scales from Study 1 (see Appendix B for items and internal consistencies).

3.2.2 Results

We conducted a two-way MANCOVA with BT knowledge as a covariate and found that participants perceived the retailer using BT as more transparent ( M HT  = 5.25, SD  = 1.09) than the retailer not using BT ( M LT  = 4.56, SD  = 1.09; p  < 0.001). Participants perceived the information to be in higher quantity in the high quantity condition ( M HQ  = 5.76, SD  = 1.15) compared to the low quantity condition ( M LQ  = 5.04, SD  = 1.24; p  < 0.001). The interactions between the two manipulations on transparency and information quantity were non-significant.

A two-way MANCOVA with BT knowledge as a covariate yielded a significant effect of transparency on perceived quality ( F (1, 395) = 15.10, p  < 0.001, \({h}_{p}^{2}\) = 0.037), trust ( F (1, 395) = 15.58, p  < 0.001, \({h}_{p}^{2}\) = 0.038), and future intentions ( F (1, 395) = 7.95, p  = 0.005, \({h}_{p}^{2}\) = 0.020). Similarly, we found a significant effect of information quantity on perceived quality ( F (1, 395) = 8.90, p  = 0.003, \({h}_{p}^{2}\) = 0.022), trust ( F (1, 395) = 5.58, p  = 0.019, \({h}_{p}^{2}\) = 0.014), but a non-significant effect on future intentions. Finally, we found a significant interaction between transparency and information quantity on perceived quality ( F (1, 395) = 4.57, p  = 0.033, \({h}_{p}^{2}\) = 0.011), consistent with H3 (Fig.  2 ). Participants in the low quantity condition perceived a higher product quality when the retailer was perceived as more transparent ( M HT  = 5.45 vs. M LT  = 4.91, p  < 0.001). However, for participants in the high quantity condition, this difference disappeared ( M HT  = 5.53 vs. M LT  = 5.38, p  = 0.215).

figure 2

Results (Study 2). Note. Error bars: 95% CI

Moderated mediation analysis

To test whether perceived quality and trust sequentially mediate the interaction effect of transparency and information quantity, we conducted a moderated mediation analysis using Model 83 of the PROCESS macro (Hayes, 2017 ). As in the previous studies, we included BT knowledge as a covariate. As expected, a 5000-sample bootstrap analysis found a 95% confidence interval that excluded zero, indicating a significant moderated mediation effect (index of moderated mediation =  − 0.17, SE  = 0.08, 95% CI = [− 0.33, − 0.02]). The overall model can be found in Fig.  3 .

figure 3

Full model estimates (Study 2). Note. *** p  < 0.001

3.3 Study 3

Study 3 aims at addressing the following questions: Are the effects found in the previous studies attributable to the specific transparency provided by BT, or are they due to transparency in general? In other words, is there something special about BT-elicited transparency, or could we observe similar effects if the retailer used another approach to increase transparency? We posit that, given its unique features (e.g., security), it is BT-elicited transparency to drive the previously found effects rather than transparency more in general. To test this prediction, we manipulate both the type of traceability technology used by the retailer and the level of retailer transparency.

3.3.1 Method

We gathered 395 UK participants on Prolific (58.1% female; M age  = 46.17, SD  = 14.03). We excluded a total of 42 participants who failed an attention check. Footnote 3 Participants were randomly assigned to one of four experimental conditions resulting from a 2 (traceability technology: BT vs. company-owned) × 2 (transparency: low vs. high) between-subjects design. First, we asked them to state their level of knowledge of BT technology. Then, we explained them what product traceability is, and that such traceability can be provided by different technologies, including BT and company-owned technologies (COT). We provided them with a short definition of both technologies, following Cozzio et al., 2023 (see Appendix A ). Next, we instructed participants to imagine themselves searching for a sweater online, and landing on the website of a fictitious clothing store called Envant, specifically in the product traceability section. We showed them the screenshot of this section.

To manipulate the traceability technology, the text on the fictious webpage reported that products were tracked using either BT or COT. In the high transparency condition, the website reported different transparency cues. In the low transparency condition, the website did not include any transparency cues (see Appendix A ).

Then, we asked participants to evaluate the perceived security of traceability information as a check for the manipulation of type of technology ( α  = 0.96), and the same retailer transparency, perceived quality, trust toward the retailer, and future intentions measures from Study 1. Finally, we asked participants to provide demographic information (see Appendix B ).

3.3.2 Results

A two-way MANCOVA with BT knowledge as a covariate showed that participants perceived traceability information as more secure when the retailer used BT ( M BT  = 5.15, SD  = 1.19) rather than COT ( M COT  = 4.50, SD  = 1.23; p  < 0.001). Participants perceived the retailer reporting transparency cues on the website as more transparent ( M HT  = 4.90, SD  = 1.34) than the retailer not reporting transparency cues ( M LT  = 4.56, SD  = 1.33; p  = 0.011). The interactions between the two manipulations on security and transparency were non-significant. However, participants perceived the retailer using BT as more transparent ( M BT  = 5.01, SD  = 1.30) than the retailer using COT ( M COT  = 4.38, SD  = 1.33; p  < 0.001), in line with the findings from previous studies.

We conducted a two-way MANCOVA with BT knowledge as a covariate and found a significant effect of traceability technology on perceived quality ( F (1, 348) = 9.29, p  = 0.002, \({h}_{p}^{2}\) = 0.026), trust ( F (1, 348) = 21.82, p  < 0.001, \({h}_{p}^{2}\) = 0.059), and future intentions ( F (1, 348) = 13.09, p  < 0.001, \({h}_{p}^{2}\) = 0.036), a significant effect of transparency on perceived quality ( F (1, 348) = 3.93, p  = 0.048, \({h}_{p}^{2}\) = 0.011), but a non-significant interaction between technology and transparency on the dependent variables. Thus, we collapsed the high and low transparency conditions. Participants in the BT (vs. COT) condition perceived the product to be of higher quality ( M BT  = 4.70 vs. M COT  = 4.38), showed a greater trust toward the retailer ( M BT  = 4.95 vs. M COT  = 4.35), and displayed greater future intentions ( M BT  = 4.71 vs. M COT  = 4.23).

We run a mediation analysis using PROCESS Model 6 with traceability technology as the independent variable, perceived quality and trust as sequential mediators, future intentions as the dependent variable, and BT knowledge as a covariate (Hayes, 2017 ). Consistently with the previous studies, we found a significant indirect effect of traceability technology on future intentions through perceived quality and trust (indirect effect = 0.19, SE  = 0.06, 95% CI = [0.06, 0.32]).

4 Discussion

4.1 theoretical and managerial implications.

This research makes different theoretical contributions. First, we respond to the call for research on the effects of transparency in the supply chain on consumers (Gleim & Stevens, 2021 ). Our research provides evidence that BT can benefit retailers through greater transparency, adding also to the literature on in-store technologies. Such technologies have the potential to increase value perceptions (Pizzi and Scarpi 2020), but could also induce distrust (Darke et al., 2016 ). We found that consumers trust retailers more and display increased future intentions thanks to higher transparency enabled by BT. Second, we uncovered an underlying mechanism for these effects. We found that transparency provided by BT traceability works as a product quality signal that increases trust toward the retailer. Third, we identified a boundary condition of these effects—i.e., information quantity. Our findings suggest that transparency and information quantity may work as substitute quality signals.

Our research also has managerial implications. Our results encourage businesses to leverage BT in order to more transparently trace the supply chain. Since retailers play a pivotal role between manufacturers and consumers, our results prompt them to weigh the cost of investing in the technological infrastructure needed to provide consumers with BT traceability against the higher levels of future intentions expressed by consumers in response to such traceability. Our results are relevant for manufacturers as well, as sharing transparency data with retailers could become an excellent trade marketing tool. Complete disclosure of product information is a necessary condition for successful implementations of transparency strategies and alignments between supply chain businesses. BT-induced transparency may not only boost retailers’ profits, but also generate backward advantages for all the suppliers involved with retailers.

4.2 Limitations and future research

Our research is not without limitations. First, drawing on existing literature and real-world evidence, we regarded BT as a fully secure technology, without questioning the assumption that the data recorded in the ledger could potentially be falsified or incorrect. However, wrong data may indeed be recorded by data providers, a misbehavior that could endanger consumer trust. This may represent a relevant avenue for future research.

Second, we assumed consumers view retailers as impartial verification agents (i.e., they adopt BT to solve an information asymmetry problem), but this may not always be the case. For example, what could happen if BT shows that the supply chain of the retailer’s private label is inferior to that of brands available in store? Would the retailer be an impartial verification agent in that scenario too? Future research could consider this alternative view and investigate the effect of BT traceability on channel relationships (e.g., channel conflict).

Finally, while we focused on the signaling power of BT-induced transparency, we recognized that other quality signals (e.g., WOM) may be stronger. We examined information quantity as an alternative quality signal. Future research could explore other quality signals that may act as boundary conditions for the positive effects of BT.

Data Availability

The data that support the findings of this study are openly available in a project directory on the Open Science Framework at https://osf.io/dr5au/?view_only=59cb096ed2f44f8dbfd8a6c8f5e8726b .

Hereafter, we use “transparency” to refer to “retailer transparency.”.

We conducted all the analyses of the studies with and without BT knowledge as a covariate. Results are consistent. The main text includes the findings related to the inclusion of the covariate, while additional results can be found in Appendix C .

Inclusion of these participants in the analyses does not change the results (details in Appendix D ).

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Matilde Rapezzi, Gabriele Pizzi & Gian Luca Marzocchi

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Rapezzi, M., Pizzi, G. & Marzocchi, G.L. What you see is what you get: the impact of blockchain technology transparency on consumers. Mark Lett (2024). https://doi.org/10.1007/s11002-024-09723-9

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