Next Article in Journal
A Dermoscopic Inspired System for Localization and Malignancy Classification of Melanocytic Lesions
Next Article in Special Issue
Fusing Local and Global Information for One-Step Multi-View Subspace Clustering
Previous Article in Journal
3He/4He Signature of Magmatic Fluids from Telica (Nicaragua) and Baru (Panama) Volcanoes, Central American Volcanic Arc
Previous Article in Special Issue
Setting Up and Operating Electric City Buses in Harsh Winter Conditions
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Review

A Systematic Review on Blockchain Adoption

by
Mohammed AlShamsi
1,2,
Mostafa Al-Emran
1,3,* and
Khaled Shaalan
1
1
Faculty of Engineering & IT, The British University in Dubai, Dubai P.O. Box 345015, United Arab Emirates
2
Technical Support Section, Emirates Health Authority, Dubai P.O. Box 4545, United Arab Emirates
3
Department of Computer Techniques Engineering, Dijlah University College, Baghdad 00964, Iraq
*
Author to whom correspondence should be addressed.
Appl. Sci. 2022, 12(9), 4245; https://doi.org/10.3390/app12094245
Submission received: 13 March 2022 / Revised: 15 April 2022 / Accepted: 18 April 2022 / Published: 22 April 2022
(This article belongs to the Special Issue Electrification of Smart Cities)

Abstract

:
Blockchain technologies have received considerable attention from academia and industry due to their distinctive characteristics, such as data integrity, security, decentralization, and reliability. However, their adoption rate is still scarce, which is one of the primary reasons behind conducting studies related to users’ satisfaction and adoption. Determining what impacts the use and adoption of Blockchain technologies can efficiently address their adoption challenges. Hence, this systematic review aimed to review studies published on Blockchain technologies to offer a thorough understanding of what impacts their adoption and discuss the main challenges and opportunities across various sectors. From 902 studies collected, 30 empirical studies met the eligibility criteria and were thoroughly analyzed. The results confirmed that the technology acceptance model (TAM) and technology–organization–environment (TOE) were the most common models for studying Blockchain adoption. Apart from the core variables of these two models, the results indicated that trust, perceived cost, social influence, and facilitating conditions were the significant determinants influencing several Blockchain applications. The results also revealed that supply chain management is the main domain in which Blockchain applications were adopted. Further, the results indicated inadequate exposure to studying the actual use of Blockchain technologies and their continued use. It is also essential to report that existing studies have examined the adoption of Blockchain technologies from the lens of the organizational level, with little attention paid to the individual level. This review is believed to improve our understanding by revealing the full potential of Blockchain adoption and opening the door for further research opportunities.

1. Introduction

The creator of Bitcoin, Satoshi Nakamoto, proficiently described the Blockchain technology as a dispersed “peer-to-peer linked-structure” that could be used to resolve the apprehensions of maintaining the transaction order along with dodging double-spending issues [1]. With Bitcoin, transactions are commanded by grouping them into constrained-size structures called blocks, and a similar timestamp is shared between all blocks. In a Blockchain, miners such as network nodes connect the blocks preferably in chronological order, with each block containing a hash of the previous one [2]. Hence, the structure of a Blockchain often succeeds in holding an auditable and robust registry for all the related transactions. Any technology has its negative and positive sides. Negatively, Blockchain technologies have some disadvantages [3]. For example, Blockchains are harder to scale because of their consensus approach. Processing can be slow on Blockchains when many users exist on the network. Some solutions require high energy consumption. It’s challenging to integrate Blockchains with several systems, specifically the legacy ones.
Positively, Blockchain technologies have brought many opportunities for various sectors. For instance, the banking sector can benefit from Blockchain to drive customer transactions under similar Blockchain standards. Blockchain allows for the transparent auditing of transactions. It is the case that different organizations have invested in this technology for a variety of reasons, such as reducing transaction costs and making architectures more transparent, safe, and quick. The significance of the Blockchain is demonstrated by the number of crypto-currencies, which have surpassed 1900 and are still growing [4]. This growth pace often creates interoperability problems due to the assortment of cryptocurrency-related applications [5,6]. Such a landscape is quickly evolving, as Blockchain is being applied in other fields outside of cryptocurrencies, where smart contracts play a primary role. Smart contracts are described as “a computerized transaction protocol that executes the terms of a contract” [7]. These contracts enable an individual to transform contractual clauses into embeddable code [8], consequently limiting the external risks and participation. Thus, a smart contract is an agreement between two parties where the agreed terms and conditions are automatically imposed even if the parties do not trust each other. As such, smart contracts in the context of Blockchains are scripts that run in a decentralized manner and are saved in the Blockchain without relying on third parties [9].
In healthcare, Blockchains can be used to reduce the communication and computational burden in data management [10]. This can be achieved through a secure transaction for a group of networks. Large amounts of healthcare data can also be managed using smart contract systems [11]. Additionally, linking medical devices to a Blockchain platform can connect patients, doctors, and providers to better understand who is complying with treatments and their consequences. In education, Blockchain applications can be used for certificate/degree verification, students’ assessments, credit transfer, data management, and admission purposes. Academic credential verification is essential for employers and other authorities to affirm the validity of an academic degree. Blockchain technologies allow students to access their official certificates under the protection and control of their universities [12].
The significance of Blockchain technology is increasing [13]. IBM added that around 33% of C-suite executives itemized that Blockchain is being discovered by them or were involved actively in the past projects [14]. The research and development community at large is already aware of the potential of upcoming technologies, along with discovering many different applications across a wide array of industries [9]. The development of Blockchain applications can be classified into three generations: (i) Blockchain 1.0 is used for cryptocurrency transactions, (ii) Blockchain 2.0 is used for financial applications, and (iii) Blockchain 3.0 is used for other industrial applications, such as government, health, science, and Internet of Things (IoT) [13].
Blockchain assists in tracing and verifying multistep transactions that require traceability and verification. Blockchains minimize compliance costs and accelerate data transfer processing. Perhaps the worthiest value of adopting Blockchains is the enhanced security provided to users while making transactions. This feature builds confidence between consumers and industry partners, protects privacy, and increases transparency in tracing transactions. Despite the tremendous opportunities of Blockchain technologies, their adoption across many domains is still in short supply [15]. Their low adoption rates stem from inadequate knowledge regarding the factors affecting their use [16]. To draw a holistic view of the factors affecting Blockchain adoption, we need to understand the theories/models through which these factors are derived. Understanding those factors through the lenses of these theories/models would help scholars and practitioners prepare future policies and procedures for effectively employing Blockchain technologies across various sectors. By inspecting the existing reviews on Blockchain, it has been observed that there is inadequate knowledge about the main research methods used in Blockchain adoption and the primary domains involving Blockchain applications. To understand these issues, this systematic review aimed to provide a holistic view of Blockchain adoption through the lenses of technology adoption theories and models, and to identify the main research gaps that would guide future research. Therefore, this review study poses the following research questions:
Q1:
What are the main research methods and domains in the selected studies?
Q2:
What are the main theories/models used for studying the use of Blockchain?
Q3:
What are the most frequent external factors affecting the use of Blockchain?
Q4:
What is the primary purpose of the reviewed studies?
Q5:
Who are the target participants in the selected studies?

2. Related Work

Blockchain technology is a distributed ledger introduced for cryptocurrency in 2008 by Satoshi Nakamoto. In October 2008, Bitcoin was released [17]. In the second generation, smart contracts were introduced for assets and trust agreements. It was initiated by Ethereum, one of the most renowned Blockchain-based software platforms. The next wave of Blockchain technologies will focus on scaling and addressing transaction processing times and bottlenecking problems. There are three categories of Blockchains, including public, proprietary, and permissioned. As a public Blockchain, anyone can join, leave, contribute, read, and audit the Blockchain network, as it is decentralized, self-governed, and authority-free. Bitcoin represents an instance of public Blockchain. The private Blockchain, in contrast, is a closed network with a verified and authentic invitation that is only available for trusted and selected parties. This means only the Blockchain owner has the authority to edit, delete, or override entries on the Blockchain. The last type is referred to as permissioned Blockchain, which permits anyone to join after their identity is verified. Each individual is given specific permissions on the network to perform specific processes. In the supply chain, suppliers, for instance, could manage a permissioned Blockchain for their business partners and customers with different access rights. On the other hand, customers can only be allowed to read product documents, whereas wholesalers and suppliers have access to edit information about the goods and delivery.
Due to its intrinsic characteristics in maintaining transaction transparency across various entities, Blockchain has received much attention from different industries. The primary example is the use of cryptocurrencies in finance [18,19]. Further, pharmaceutical, transportation, origin-to-consumer, legal, and regulatory areas are other non-financial domains that have witnessed prompt adoption and use of Blockchain applications [20]. Moreover, other applications have emerged regarding the use of Blockchain in the healthcare industry [21,22], the chemical industry [23], and big data [24]. Blockchain technology is viewed as a significant component of the fourth industrial revolution that has facilitated changing the structure of the global economy and enhancing the opportunities for innovation, development, and improved quality of life. Furthermore, a Blockchain-based digital government often streamlines processes, protects data, and reduces abuse and fraud while instantaneously boosting accountability and trust. Governments, businesses, and individuals share resources through a distributed ledger protected by cryptography. By eliminating single points of failure, it protects governments and citizen data.
A synthesis of the previously published reviews was performed to understand the current state-of-the-art of Blockchain technologies. Table 1 shows the earlier review studies conducted on Blockchain technology. This subject has recently gained extensive international interest and attention. It can be noticed that Blockchain technology has been studied across several disciplines, including energy, healthcare, agriculture, education, logistics, and supply chain management. Some reviews have examined the underlying Blockchain technology, such as cryptography, peer-to-peer networking, distributed storage, consensus algorithms, and smart contracts [25,26,27]. Other reviews were interested in highlighting the laws and regulations governing this technology [28]. Some of the reviews focused on the educational applications built using Blockchain technologies, their benefits, and the obstacles to implementation [29]. Another review identified organizational theories and discussed their application in adopting Blockchain technologies in logistics and supply chain management [30]. It can be observed that the existing reviews have neglected to review the factors affecting Blockchain adoption from the perspective of technology adoption theories/models. In addition, there is insufficient knowledge about the main research methods used in Blockchain adoption and the primary domains involving Blockchain applications. Therefore, this systematic review aimed to provide a comprehensive review of Blockchain adoption by examining the main research methods, domains, technology acceptance models/theories, influential factors, research objectives, and target participants.

3. Materials and Methods

This study applied the systematic review approach to review the existing studies on Blockchain adoption. This approach uncovers sources relevant to a research topic and provides a rich synthesis of the subject under examination. This research follows the systematic review guiding principles introduced by Kitchenham and Charters [42] and other related systematic reviews [43,44,45]. The following subsections detail the phases followed during the review process.

3.1. Inclusion and Exclusion Criteria

Table 2 lists the inclusion and exclusion criteria for the publications that were critically evaluated in this review.

3.2. Data Sources and Search Strategies

In this systematic review, the surveyed articles were collected from a wide range of online databases, including Emerald, IEEE, ScienceDirect, Springer, MDPI, and Google Scholar. The search for these studies was undertaken in April 2021. The keywords used in the search include ((“Blockchain”) AND (“adoption” OR “acceptance” OR “use” OR “intention to use” OR “continued use” OR “continuous intention”)). Choosing the keywords is essential since it determines which articles are to be retrieved [46]. Using the above search strategies, the search results retrieved 902 articles. Of those, 218 were marked as duplicates, so we removed them from the analysis. Thus, the overall number of the remaining articles becomes 684. We have applied the inclusion and exclusion criteria for each of these studies. Accordingly, 30 studies met these criteria and were kept for the final analysis. The search and refinement stages were carried out using the “Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA)” [47]. Figure 1 shows the PRISMA flow diagram.

3.3. Quality Assessment

Along with the inclusion and exclusion criteria, quality assessment is another crucial factor to consider [48]. A nine-criteria checklist was adopted from Alqudah et al. [49] and Al-Emran et al. [48] as a quality assessment and used to provide a method for evaluating the quality of the research papers that were kept for the final analysis (n = 30). Table 3 illustrates the quality assessment checklist. The primary purpose of the checklist was not to criticize any scholar’s work, and the checklist was adapted from those suggested by Kitchenham and Charters [42]. Each question in the checklist was scored according to the three-point scale, an answer of “Yes” being worth 1 point, an answer of “No” being worth 0 points, and an answer of “Partially” being worth 0.5 points. Therefore, each study could receive an accumulated score between 0 and 9. The higher the total scores a study attained, the higher the degree to which the study addressed the research questions. This was ensured by assessing each study against the nine quality assessment criteria. For each study, the first and second authors assigned scores to the nine quality assessment criteria independently to ensure accuracy. Differences in assigning scores between the two authors were resolved through discussion and further review of the disputed articles. Table 4 presents the quality assessment results of all 30 studies. It is evident that all studies passed the quality assessment, and they were eligible for final analysis.

3.4. Data Coding and Analysis

For the sake of answering the research questions of this review, we have coded the final list of the remaining articles (n = 30) based on several characteristics, including authors, publication year, methods, countries, factors, domains, theories/models, research aims, and participants.

4. Results

Drawing upon the 30 research studies analyzed in this systematic review, we have reported the findings to answer the formulated research questions. Table A1 (Appendix A) provides a brief description of all the analyzed studies.

4.1. Main Research Methods

Figure 2 depicts the distribution of studies according to the research method used in data collection. It is evident that questionnaire surveys represent the primary research method used in 77% of the analyzed Blockchain adoption studies. However, only 10% of the Blockchain adoption studies relied on interviews in collecting their data.

4.2. Main Domains

Blockchain applications have been extensively used across many domains/sectors. The collected studies were analyzed according to these domains/sectors to provide an overview of the current status of Blockchain applications. Figure 3 depicts the main domains/sectors in which Blockchain applications were adopted. It can be seen that supply chain management dominates the list, with 12 studies. This is followed by education and agriculture, with three studies each. In the supply chain, organizations can automate physical assets and create a decentralized steady record of all transactions, making it possible to track assets from production to delivery or use by end-users. Other applications include maritime shipping [50], organizing decisions [51], and executing operations [52].

4.3. Prevailing Theories/Models in Blockchain Adoption

As we aimed to examine the adoption of Blockchain technologies, the collected articles were analyzed from the perspective of technology adoption theories/models, as shown in Figure 4. It can be seen that the “technology acceptance model (TAM)” is the most common model in studying Blockchain adoption, with 14 studies. This is followed by the “technology-organization-environment (TOE)” (n = 8), “unified theory of acceptance and use of technology (UTAUT)” (n = 7), and “innovation diffusion theory (IDT)” (n = 5). The rest of the theories/models appeared only once in the examined studies (i.e., TRI2, ISS, TTF, TPB, TRA, and TAM3).

4.4. Most Frequent External Factors Affecting the Use of Blockchain

The low adoption rates of many technologies, with no exceptions to Blockchain, stem from the inadequate knowledge regarding the factors affecting their use. Therefore, we have analyzed the collected studies to identify the most common external factors affecting the adoption of Blockchain technologies, as shown in Figure 5. Trust appeared to be the most common factor affecting the adoption of Blockchain technologies (n = 17). This is followed by the perceived cost and social influence with 11 studies each, then by facilitating conditions (n = 10), performance expectancy, effort expectancy, and information security, with seven studies each.
Apart from the collected studies, we have also analyzed the existing literature on Blockchain to determine the barriers affecting its adoption. Security risk [53,54] and privacy risk [53,55,56] were among the main risks that negatively affect the use of Blockchain technologies. High energy costs [53,54,57] and investment costs [58,59,60] represent the main costs of using Blockchain technologies. Organizations also impose some barriers to using Blockchain technologies, such as organizational policies [61,62,63], organizational culture [55,61,64,65], lack of knowledge and management support [63,65,66], and lack of collaboration and coordination [63,64,67]. It is also imperative to mention that adopting Blockchain is hindered by some technological barriers, such as technological immaturity [53,54,65], reluctance to change [60,68,69], interoperability issues [60,63,65,70,71], and scalability issues [53,56,63]. Cultural differences [61,64,72] are also considered a barrier to Blockchain adoption. This is mainly because users rely on themselves when seeking advice related to using Blockchains in individualistic societies, while they rely on others in collectivistic cultures.

4.5. Primary Purpose of the Reviewed Studies

There are three different concepts within the technology adoption domain, including adoption, acceptance, and post-adoption/continuous intention. The adoption is usually measured by potential users who have not yet used the technology, whereas actual users measure the acceptance. The post-adoption/continuous intention measures the continued use of the technology after a sufficient period of users’ experience. It is essential to understand the purpose of the analyzed studies concerning the previously mentioned concepts to understand where we stand on Blockchain adoption. It has been noticed that 80% of the analyzed studies concentrated on measuring Blockchain adoption, followed by 10% for both acceptance and continuous intention.

4.6. Target Participants in the Selected Studies

To understand who evaluated the use of Blockchain technologies, we have classified the analyzed studies in terms of participants, as depicted in Figure 6. Fifty percent of the analyzed studies relied on the top management to evaluate the use of Blockchain technologies. This was followed by experts and consultants (28%), academics (13%), and students (5%).

5. Discussion

It is imperative to understand what impacts the adoption of new technologies, such as Blockchain. Adopting any technology relies on the determinants affecting its users [73,74,75]. A forecasting report illustrates the positive evolution of the Blockchain market between 2017 and 2024 [76]. While this magnitude was USD 800 million in 2017, it is estimated to reach USD 20,550 million in 2024. Therefore, it is vital to gain more insights into what impacts the adoption of Blockchain technologies across many sectors to improve and sustain their usage. Hence, this systematic review was carried out to analyze the adoption of Blockchain technologies from the lenses of technology adoption theories/models.
The results showed that questionnaire surveys represent the primary research method used in 77% of the analyzed Blockchain adoption studies. These outcomes agree with some of the earlier systematic reviews in the technology adoption domain [77,78,79,80], which concluded that questionnaire surveys were the most common data collection method. In terms of the Blockchain, these results contradict what Frizzo-Barker et al. [81] reported, in which comparative studies were the primary method used in most of the analyzed articles. Drawing upon the findings of this systematic review, it is suggested that further research would consider the mixed-research approach by involving interviews or focus groups besides using questionnaire surveys. This is because qualitative approaches provide more insights into the cause–effect relationships among the factors affecting Blockchain adoption.
Regarding the main domains/sectors through which the analyzed studies were carried out, supply chain management dominates the list with 12 studies, followed by education and agriculture, with three studies each. While our findings agree with [82], who found that supply chain management is the dominating sector concerning studies related to Blockchain adoption, it contradicts what is reported by [81], who suggested that banking and finance was the primary sector. Regardless of the differences between this review and the previously conducted reviews, understanding Blockchain adoption across many domains is still in short supply due to the limited number of applications.
Concerning the prevailing technology adoption theories/models, the results pointed out that the TAM is the most common model in studying Blockchain adoption, with 14 studies. This is followed by the TOE (n = 8), UTAUT (n = 7), and IDT (n = 5). In the same vein, Taherdoost [82] found that TAM and TOE were the most dominating models in studying Blockchain adoption. TAM is still a valid model to evaluate large-scale emerging technologies [83,84], where Blockchain is not an exception. These results indicate that studies focusing on the individual level have mainly relied on TAM, while those examining the organizational level have relied on TOE. Further research might consider other adoption models that have yet to be used in the existing literature, such as UTAUT2, ECM, PMT, etc.
For the influential factors affecting Blockchain adoption, trust was seen to be the most common factor affecting the adoption of Blockchain technologies (n = 17). This was followed by the perceived cost and social influence, with 11 studies each, as well as facilitating conditions (n = 10), performance expectancy, effort expectancy, and information security, with seven studies each. Studies on the individual level have examined trust, social influence, performance expectancy, effort expectancy, and information security. In contrast, those focusing on the organizational level studied mainly the organizational perspective’s factors in delivering Blockchain-based services, such as trust, perceived cost, and facilitating conditions. Still, there is abundant room for other factors to be investigated from the perspective of other technology adoption theories/models and Blockchain-related specific characteristics. On the other side, we have also analyzed the existing literature on Blockchain to determine the barriers affecting its adoption. Being aware of these barriers and considering them when implementing Blockchains would improve their adoption rate. Security risk and privacy risk were among the main risks that negatively affect the use of Blockchain technologies. High energy and investment costs represent the main costs of using Blockchain technologies. Organizations also impose some barriers to using Blockchain technologies, such as organizational policies, organizational culture, lack of knowledge and management support, and lack of collaboration and coordination. It is also imperative to mention that adopting Blockchain is hindered by some technological barriers, such as technological immaturity, reluctance to change, interoperability issues, and scalability issues. Cultural differences are also considered a barrier to Blockchain adoption. This is mainly because users rely on themselves when seeking advice related to using Blockchains in individualistic societies, while they rely on others in collectivistic cultures.
To understand the primary purpose of the analyzed studies, the results showed that 80% of those studies concentrated on measuring Blockchain adoption, followed by 10% for both acceptance and continuous intention. These results clearly indicate that the majority of existing studies have examined the adoption stage of Blockchain technologies, the step that precedes the actual use of the technology. The results provided evidence that there is inadequate exposure to studying the actual use of Blockchain technologies and their continued use, which furnish a good space for further research.
The results reported that 50% of the analyzed studies relied on the top management to evaluate the use of Blockchain technologies, followed by experts and consultants (28%), academics (13%), and students (5%). This shows that most of the existing studies have examined the adoption of Blockchain technologies from the lens of the organizational level, with little attention paid to the individual level.
This systematic review differs from previous reviews in several ways. This review did not limit the data collection to a specific domain, while most of the earlier reviews did. Most of the earlier reviews concentrated on using Blockchain applications in healthcare [26,31,32,36] and supply chain and logistics [28,30,33,39]. While some of the previously conducted reviews were general in the domain, their aims and scope were entirely different from the current study. For instance, Lu [35] investigated the Blockchain and its essential features, concerns (IoT, security, and data management), and industrial applications. Karger [38] offered the most recent research on the potential combination of AI and Blockchain technologies and discussed the possible advantages of such a combination. Besides, Namasudra et al. [41] discussed the fundamentals of Blockchain technologies and their technical details. To make it distinct, this systematic review provided a thorough review of Blockchain adoption by examining the main research methods, domains, influential factors, research objectives, and target participants through the lenses of technology acceptance models/theories.

6. Conclusions and Future Work

Despite the immense opportunities of Blockchain technologies, their adoption across many domains is still in short supply [15]. This is one of the main reasons behind conducting studies related to users’ satisfaction and adoption. Determining what impacts the use and adoption of Blockchain technologies can efficiently address their adoption challenges. Therefore, we have reviewed the Blockchain adoption studies from the perspective of technology adoption theories/models to identify the most influential factors, main research methods, domains/sectors, research objectives, and target participants. It is believed that this systematic review would be a valuable guide for scholars and practitioners seeking to understand the challenges and opportunities related to the adoption of Blockchain technologies across various sectors.
This review shed light on several gaps in research. First, the TAM and TOE were the most common models for understanding the factors affecting the use and adoption of Blockchain technologies. Little attention has been paid to the role of technical, social, and psychological elements in understanding the adoption of Blockchain applications. This gap requires further research by considering other adoption theories/models such as UTAUT2, ECM, PMT, etc. Second, although Blockchain adoption is still in short supply, the findings showed that supply chain management was the dominating sector among others in the examined studies. We found a dearth of empirical research in the other domains, which necessitates the need for future research to look at how Blockchain technologies are adopted. Third, trust, perceived cost, and social influence were the most common factors affecting the adoption of Blockchain technologies. The other factors were mainly adapted from the most common theories, such as TAM and TOE. By involving other theories/models, understanding what impacts the use and adoption of Blockchain technologies would be enlightened, specifically when the factors are related to Blockchain-specific characteristics.
Fourth, 77% of the analyzed Blockchain adoption studies relied on questionnaire surveys for data collection. Hence, it is suggested that further research would consider the mixed-research approach by involving interviews or focus groups, besides using questionnaire surveys. This is because qualitative methods can explain the interrelationships among the factors affecting the adoption of Blockchain. Fifth, unlike the previous systematic reviews that analyzed conceptual and empirical studies, it is imperative to mention that this review has concentrated only on empirical Blockchain studies. Since there is still a limited number of studies across the world, more empirical research is required to examine the users’ maturity levels and capabilities of adopting Blockchain applications across many collectivistic and individualistic societies. The implications of Blockchain applications, with their negative or positive sides, in certain cultural environments would assist in developing these applications both socially and economically.
Sixth, the findings showed that 80% of the examined studies concentrated on measuring Blockchain adoption, with a limited number of studies focusing on the acceptance and continuous intention perspectives. There is insufficient knowledge of what impacts the actual use of Blockchain technologies and their continued use, which opens the door for further research trials. Seventh, 50% of the analyzed studies relied on the top management, experts, and consultants to evaluate the use of Blockchain technologies. This shows that most of the existing studies have examined the adoption of Blockchain technologies from the organizational level perspective, with little attention paid to the individual level.
This review is limited in two ways. First, we focused on specific online databases to collect articles, such as Emerald, IEEE, ScienceDirect, Springer, MDPI, and Google Scholar. However, these online databases do not represent the entire literature published on Blockchain adoption. Further reviews might thus extend this review by involving studies indexed in other databases, such as Scopus and Web of Science. Second, this systematic review involved analyzing only empirical quantitative studies. Considering qualitative studies in future reviews would add more insights into the observed results.

Author Contributions

Conceptualization, M.A., M.A.-E. and K.S.; methodology, M.A. and M.A.-E.; validation, M.A. and M.A.-E.; formal analysis, M.A.; investigation, M.A.; resources, M.A.; writing—original draft preparation, M.A.; writing—review and editing, M.A.-E. and K.S.; supervision, M.A.-E. and K.S.; project administration, M.A.-E. and K.S. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the authors.

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. List of analyzed studies.
Table A1. List of analyzed studies.
#SourceYearMethodCountryDomainTheories/Models
S1[85]2020SurveyNot specifiedFinanceTAM
S2[51]2021SurveyIndiaSupply chain managementTAM and TOE
S3[52]2020SurveyMalaysiaSupply chain managementTOE
S4[86]2020SurveyIndiaSupply chain managementTAM and IDT
S5[87]2020SurveyIndiaAgriculture supply chainISM-DEMATEL
S6[50]2019Survey and interviewsTaiwanMaritime shippingTAM
S7[88]2020Not specifiedMalaysiaWarehouse industryUTAUT
S8[89]2020SurveyNigeriaLogisticsTOE
S9[90]2019SurveyBrazilSupply chain managementUTAUT
S10[91]2019SurveyIndonesiaGamingTAM
S11[92]2017SurveyTaiwanFinanceIDT and TAM
S12[93]2019SurveyUSAAcademiaUTAUT
S13[94]2019SurveyUSA and IndiaLogistics and supply chain managementTAM and UTAUT
S14[95]2018Not specifiedNot specifiedSupply chain managementUTAUT
S15[96]2021SurveyKenyaFinanceTAM and IDT
S16[97]2019SurveyCanadaResearch communityTAM
S17[98]2019SurveyTaiwanTourism and hospitalityTAM
S18[99]2020SurveyDeveloped countriesEnergyTAM and DOI
S19[100]2021SurveyMalaysiaEducationTAM and DOI
S20[101]2020SurveyMalaysiaIntelligence communityTAM 3 and TRI 2
S21[102]2021Semi-structured interviewsMiddle East and North AfricaN/ADOI and TOE
S22[103]2021SurveyIndiaAgri-food supply chainISM and DEMATEL
S23[61]2021SurveyNot specifiedSupply chain managementTOE
S24[104]2021SurveyMalaysiaManufacturingTOE
S25[105]2021SurveyAustraliaSupply chain managementUTAUT, TTF, and ISS
S26[106]2018SurveyIndiaSupply chain managementTAM, TRI, and TPB
S27[17]2019InterviewsIrelandMixed contextsTOE
S28[107]2020Not specifiedNot specifiedFinanceTAM
S29[108]2020SurveyBrazilSupply chain managementUTAUT
S30[109]2020Case studyIndonesiaAgricultureTOE and the theory of mindfulness of adoption

References

  1. Nakamoto, S. RAIN: A Bio-Inspired Communication and Data Storage Infrastructure. Artif. Life 2008, 23, 552–557. [Google Scholar] [CrossRef]
  2. Crosby, M.; Pattanayak, P.; Verma, S.; Kalyanaraman, V. Integration von Big Data-Komponenten in die Business Intelligence. Controlling 2016, 27, 222–228. [Google Scholar] [CrossRef] [Green Version]
  3. Iredale, G. Top Disadvantages of Blockchain Technology. Available online: https://101blockchains.com/disadvantages-of-blockchain/ (accessed on 1 April 2022).
  4. CoinMarketCap Cryptocurrency Prices, Charts And Market Capitalizations. Available online: https://coinmarketcap.com/ (accessed on 20 December 2021).
  5. Haferkorn, M.; Diaz, J.M.Q. Seasonality and interconnectivity within cryptocurrencies—An analysis on the basis of bitcoin, litecoin and namecoin. In Lecture Notes in Business Information Processing; Springer: Cham, Switzerland, 2015; Volume 217, pp. 106–120. [Google Scholar] [CrossRef]
  6. Tschorsch, F.; Scheuermann, B. Bitcoin and beyond: A technical survey on decentralized digital currencies. IEEE Commun. Surv. Tutor. 2016, 18, 2084–2123. [Google Scholar] [CrossRef]
  7. Nick Szabo Smart Contracts. Available online: http://szabo.best.vwh.net/smart.contracts.html (accessed on 15 December 2021).
  8. Szabo, N. Smart Contracts: Formalizing and Securing Relationships on Public Networks. First Monday 1997, 2, 9. [Google Scholar] [CrossRef]
  9. Christidis, K.; Devetsikiotis, M. Blockchains and Smart Contracts for the Internet of Things. IEEE Access 2016, 4, 2292–2303. [Google Scholar] [CrossRef]
  10. Ismail, L.; Materwala, H.; Zeadally, S. Lightweight Blockchain for Healthcare. IEEE Access 2019, 7, 149935–149951. [Google Scholar] [CrossRef]
  11. Khatoon, A. A Blockchain-Based Smart Contract System for Healthcare Management. Electronics 2020, 9, 94. [Google Scholar] [CrossRef] [Green Version]
  12. Vidal, F.R.; Soares, C.; Blockchain, A. Analysis of Blockchain Technology for Higher Education. In Proceedings of the 2019 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Guilin, China, 17–19 October 2019; pp. 28–33. [Google Scholar] [CrossRef]
  13. Zhao, J.L.; Fan, S.; Yan, J. Overview of business innovations and research opportunities in blockchain and introduction to the special issue. Financ. Innov. 2016, 2, 28. [Google Scholar] [CrossRef] [Green Version]
  14. IBM Blockchain for Business. Enterprise Adoption Patterns Use Case Examples from Practice Into Hyperledger Fabric v1; IBM Blockchain for Business: Frankfurt, Germany, 2017. [Google Scholar]
  15. Agi, M.A.; Jha, A.K. Blockchain technology in the supply chain: An integrated theoretical perspective of organizational adoption. Int. J. Prod. Econ. 2022, 247, 108458. [Google Scholar] [CrossRef]
  16. Lu, L.; Liang, C.; Gu, D.; Ma, Y.; Xie, Y.; Zhao, S. What advantages of blockchain affect its adoption in the elderly care industry? A study based on the technology–organisation–environment framework. Technol. Soc. 2021, 67, 101786. [Google Scholar] [CrossRef]
  17. Clohessy, T.; Acton, T. Investigating the influence of organizational factors on blockchain adoption: An innovation theory perspective. Ind. Manag. Data Syst. 2019, 119, 1457–1491. [Google Scholar] [CrossRef]
  18. Tapscott, T. How Blockchain is Changing Insurance. Itnow 2017, 60, 16–17. [Google Scholar] [CrossRef]
  19. Fosso Wamba, S.; Kala Kamdjoug, J.R.; Epie Bawack, R.; Keogh, J.G. Bitcoin, Blockchain and Fintech: A systematic review and case studies in the supply chain. Prod. Plan. Control 2018, 31, 115–142. [Google Scholar] [CrossRef]
  20. Perez, G. Blockchain: A Study Rooted in Reality. 2018. Available online: https://news.sap.com/2018/04/blockchain-a-study-rooted-in-reality/ (accessed on 20 December 2021).
  21. Grover, P.; Kar, A.K.; Davies, G. “Technology enabled Health”—Insights from twitter analytics with a socio-technical perspective. Int. J. Inf. Manag. 2018, 43, 85–97. [Google Scholar] [CrossRef]
  22. Sodhro, A.H.; Luo, Z.; Sangaiah, A.K.; Baik, S.W. Mobile edge computing based QoS optimization in medical healthcare applications. Int. J. Inf. Manag. 2019, 45, 308–318. [Google Scholar] [CrossRef]
  23. Takhar, S.S.; Liyanage, K. Blockchain application in supply chain chemical substance reporting—A Delphi study. Int. J. Internet Technol. Secur. Trans. 2018, 11, 75–107. [Google Scholar] [CrossRef]
  24. Chae, B. A General framework for studying the evolution of the digital innovation ecosystem: The case of big data. Int. J. Inf. Manag. 2019, 45, 83–94. [Google Scholar] [CrossRef]
  25. Andoni, M.; Robu, V.; Flynn, D.; Abram, S.; Geach, D.; Jenkins, D.; McCallum, P.; Peacock, A. Blockchain technology in the energy sector: A systematic review of challenges and opportunities. Renew. Sustain. Energy Rev. 2019, 100, 143–174. [Google Scholar] [CrossRef]
  26. Chukwu, E.; Garg, L. A systematic review of blockchain in healthcare: Frameworks, prototypes, and implementations. IEEE Access 2020, 8, 21196–21214. [Google Scholar] [CrossRef]
  27. Li, J.; Greenwood, D.; Kassem, M. Blockchain in the built environment and construction industry: A systematic review, conceptual models and practical use cases. Autom. Constr. 2019, 102, 288–307. [Google Scholar] [CrossRef]
  28. Wang, Y.; Han, J.H.; Beynon-Davies, P. Understanding blockchain technology for future supply chains: A systematic literature review and research agenda. Supply Chain Manag. 2018, 24, 62–84. [Google Scholar] [CrossRef]
  29. Alammary, A.; Alhazmi, S.; Almasri, M.; Gillani, S. Blockchain-based applications in education: A systematic review. Appl. Sci. 2019, 9, 2400. [Google Scholar] [CrossRef] [Green Version]
  30. Kummer, S.; Herold, D.M.; Dobrovnik, M.; Mikl, J.; Schäfer, N. A systematic review of blockchain literature in logistics and supply chain management: Identifying research questions and future directions. Futur. Internet 2020, 12, 60. [Google Scholar] [CrossRef] [Green Version]
  31. Agbo, C.; Mahmoud, Q.; Eklund, J. Blockchain Technology in Healthcare: A Systematic Review. Healthcare 2019, 7, 56. [Google Scholar] [CrossRef] [Green Version]
  32. Hölbl, M.; Kompara, M.; Kamišalić, A.; Zlatolas, L.N. A systematic review of the use of blockchain in healthcare. Symmetry 2018, 10, 470. [Google Scholar] [CrossRef] [Green Version]
  33. Queiroz, M.M.; Ivanov, D.; Dolgui, A.; Fosso Wamba, S. Impacts of epidemic outbreaks on supply chains: mapping a research agenda amid the COVID-19 pandemic through a structured literature review. Ann. Oper. Res. 2020, 1–38. [Google Scholar] [CrossRef]
  34. Bermeo-Almeida, O.; Cardenas-Rodriguez, M.; Samaniego-Cobo, T.; Ferruzola-Gómez, E.; Cabezas-Cabezas, R.; Bazán-Vera, W. Blockchain in agriculture: A systematic literature review. Commun. Comput. Inf. Sci. 2018, 883, 44–56. [Google Scholar] [CrossRef]
  35. Lu, Y. The blockchain: State-of-the-art and research challenges. J. Ind. Inf. Integr. 2019, 15, 80–90. [Google Scholar] [CrossRef]
  36. Tandon, A.; Dhir, A.; Islam, N.; Mäntymäki, M. Blockchain in healthcare: A systematic literature review, synthesizing framework and future research agenda. Comput. Ind. 2020, 122, 103290. [Google Scholar] [CrossRef]
  37. Liu, Y.; Lu, Q.; Zhu, L.; Paik, H.-Y.; Staples, M. A Systematic Literature Review on Blockchain Governance. SSRN Electron. J. 2021. [Google Scholar] [CrossRef]
  38. Karger, E. Combining blockchain and artificial intelligence—Literature review and state of the art. In Proceedings of the 41st International Conference on Information Systems, ICIS 2020, Making Digital Inclusive: Blending the Locak and the Global, Hyderabad, India, 13–16 December 2020. [Google Scholar]
  39. Dujak, D.; Sajter, D. Blockchain Applications in Supply Chain; Springer International Publishing: Berlin/Heidelberg, Germany, 2021; ISBN 9783319916682. [Google Scholar]
  40. Lo, S.K.; Liu, Y.; Chia, S.Y.; Xu, X.; Lu, Q.; Zhu, L.; Ning, H. Analysis of Blockchain Solutions for IoT: A Systematic Literature Review. IEEE Access 2019, 7, 58822–58835. [Google Scholar] [CrossRef]
  41. Namasudra, S.; Deka, G.C.; Johri, P.; Hosseinpour, M.; Gandomi, A.H. The Revolution of Blockchain: State-of-the-Art and Research Challenges. Arch. Comput. Methods Eng. 2021, 28, 1497–1515. [Google Scholar] [CrossRef]
  42. Kitchenham, B.; Charters, S. Guidelines for Performing Systematic Literature Reviews in Software Engineering; Keele University: Keele, UK, 2007; Volume 4, pp. 5356–5373. [Google Scholar]
  43. Al-Qaysi, N.; Mohamad-Nordin, N.; Al-Emran, M. Employing the technology acceptance model in social media: A systematic review. Educ. Inf. Technol. 2020, 25, 4961–5002. [Google Scholar] [CrossRef]
  44. Al-Saedi, K.; Al-Emran, M.; Abusham, E.; El-Rahman, S.A. Mobile Payment Adoption: A Systematic Review of the UTAUT Model. In Proceedings of the 2019 International Conference on Fourth Industrial Revolution, ICFIR 2019, Manama, Bahrain, 19–21 February 2019. [Google Scholar]
  45. Alsharida, R.A.; Hammood, M.M.; Al-Emran, M. Mobile Learning Adoption: A Systematic Review of the Technology Acceptance Model from 2017 to 2020. Int. J. Emerg. Technol. Learn. 2021, 16, 147–162. [Google Scholar] [CrossRef]
  46. Costa, V.; Monteiro, S. Key knowledge management processes for innovation: A systematic literature review. VINE J. Inf. Knowl. Manag. Syst. 2016, 46, 386–410. [Google Scholar]
  47. Moher, D.; Liberati, A.; Tetzlaff, J.; Altman, D.G.; Altman, D.; Antes, G.; Atkins, D.; Barbour, V.; Barrowman, N.; Berlin, J.A.; et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med. 2009, 6, e1000097. [Google Scholar] [CrossRef] [Green Version]
  48. Al-Emran, M.; Mezhuyev, V.; Kamaludin, A.; Shaalan, K. The impact of knowledge management processes on information systems: A systematic review. Int. J. Inf. Manag. 2018, 43, 173–187. [Google Scholar] [CrossRef]
  49. Alqudah, A.A.; Al-Emran, M.; Shaalan, K. Technology Acceptance in Healthcare: A Systematic Review. Appl. Sci. 2021, 11, 10537. [Google Scholar] [CrossRef]
  50. Yang, C. Maritime shipping digitalization: Blockchain-based technology applications, future improvements, and intention to use. Transp. Res. Part E 2019, 131, 108–117. [Google Scholar] [CrossRef]
  51. Kamble, S.S.; Gunasekaran, A.; Kumar, V.; Belhadi, A.; Foropon, C. A machine learning based approach for predicting blockchain adoption in supply Chain. Technol. Forecast. Soc. Chang. 2021, 163, 120465. [Google Scholar] [CrossRef]
  52. Wong, L.W.; Leong, L.Y.; Hew, J.J.; Tan, G.W.H.; Ooi, K.B. Time to seize the digital evolution: Adoption of blockchain in operations and supply chain management among Malaysian SMEs. Int. J. Inf. Manag. 2020, 52, 101997. [Google Scholar] [CrossRef]
  53. Esmaeilian, B.; Sarkis, J.; Lewis, K.; Behdad, S. Blockchain for the future of sustainable supply chain management in Industry 4.0. Resour. Conserv. Recycl. 2020, 163, 105064. [Google Scholar] [CrossRef]
  54. Böckel, A.; Nuzum, A.K.; Weissbrod, I. Blockchain for the Circular Economy: Analysis of the Research-Practice Gap. Sustain. Prod. Consum. 2021, 25, 525–539. [Google Scholar] [CrossRef]
  55. Hatzivasilis, G.; Ioannidis, S.; Fysarakis, K.; Spanoudakis, G.; Papadakis, N. The green blockchains of circular economy. Electron. 2021, 10, 2008. [Google Scholar] [CrossRef]
  56. Kouhizadeh, M.; Sarkis, J.; Zhu, Q. At the nexus of blockchain technology, the circular economy, and product deletion. Appl. Sci. 2019, 9, 1712. [Google Scholar] [CrossRef] [Green Version]
  57. Ajwani-Ramchandani, R.; Figueira, S.; Torres de Oliveira, R.; Jha, S.; Ramchandani, A.; Schuricht, L. Towards a circular economy for packaging waste by using new technologies: The case of large multinationals in emerging economies. J. Clean. Prod. 2021, 281, 125139. [Google Scholar] [CrossRef]
  58. Demestichas, K.; Daskalakis, E. Information and communication technology solutions for the circular economy. Sustainability 2020, 12, 7272. [Google Scholar] [CrossRef]
  59. Upadhyay, A.; Mukhuty, S.; Kumar, V.; Kazancoglu, Y. Blockchain technology and the circular economy: Implications for sustainability and social responsibility. J. Clean. Prod. 2021, 293, 126130. [Google Scholar] [CrossRef]
  60. Yildizbasi, A. Blockchain and renewable energy: Integration challenges in circular economy era. Renew. Energy 2021, 176, 183–197. [Google Scholar] [CrossRef]
  61. Kouhizadeh, M.; Saberi, S.; Sarkis, J. Blockchain technology and the sustainable supply chain: Theoretically exploring adoption barriers. Int. J. Prod. Econ. 2021, 231, 107831. [Google Scholar] [CrossRef]
  62. Hew, J.J.; Wong, L.W.; Tan, G.W.H.; Ooi, K.B.; Lin, B. The blockchain-based Halal traceability systems: A hype or reality? Supply Chain Manag. 2020, 25, 863–879. [Google Scholar] [CrossRef]
  63. Erol, I.; Peker, I.; Ar, I.M.; Turan, İ.; Searcy, C. Towards a circular economy: Investigating the critical success factors for a blockchain-based solar photovoltaic energy ecosystem in Turkey. Energy Sustain. Dev. 2021, 65, 130–143. [Google Scholar] [CrossRef]
  64. Ada, N.; Kazancoglu, Y.; Sezer, M.D.; Ede-Senturk, C.; Ozer, I.; Ram, M. Analyzing barriers of circular food supply chains and proposing industry 4.0 solutions. Sustainability 2021, 13, 6812. [Google Scholar] [CrossRef]
  65. Bekrar, A.; El Cadi, A.A.; Todosijevic, R.; Sarkis, J. Digitalizing the closing-of-the-loop for supply chains: A transportation and blockchain perspective. Sustainability 2021, 13, 2895. [Google Scholar] [CrossRef]
  66. Gopalakrishnan, P.K.; Hall, J.; Behdad, S. Cost analysis and optimization of Blockchain-based solid waste management traceability system. Waste Manag. 2021, 120, 594–607. [Google Scholar] [CrossRef]
  67. Çetin, S.; De Wolf, C.; Bocken, N. Circular digital built environment: An emerging framework. Sustainability 2021, 13, 6348. [Google Scholar] [CrossRef]
  68. Kouhizadeh, M.; Zhu, Q.; Sarkis, J. Blockchain and the circular economy: Potential tensions and critical reflections from practice. Prod. Plan. Control 2020, 31, 950–966. [Google Scholar] [CrossRef]
  69. Shojaei, A.; Ketabi, R.; Razkenari, M.; Hakim, H.; Wang, J. Enabling a circular economy in the built environment sector through blockchain technology. J. Clean. Prod. 2021, 294, 126352. [Google Scholar] [CrossRef]
  70. Bressanelli, G.; Pigosso, D.C.A.; Saccani, N.; Perona, M. Enablers, levers and benefits of Circular Economy in the Electrical and Electronic Equipment supply chain: A literature review. J. Clean. Prod. 2021, 298, 126819. [Google Scholar] [CrossRef]
  71. Wang, B.; Luo, W.; Zhang, A.; Tian, Z.; Li, Z. Blockchain-enabled circular supply chain management: A system architecture for fast fashion. Comput. Ind. 2020, 123, 103324. [Google Scholar] [CrossRef]
  72. Romero-Hernández, O.; Romero, S. Maximizing the value of waste: From waste management to the circular economy. Thunderbird Int. Bus. Rev. 2018, 60, 757–764. [Google Scholar] [CrossRef]
  73. Al-Emran, M.; Mezhuyev, V.; Kamaludin, A. Students’ Perceptions towards the Integration of Knowledge Management Processes in M-learning Systems: A Preliminary Study. Int. J. Eng. Educ. 2018, 34, 371–380. [Google Scholar]
  74. Al-Emran, M.; Mezhuyev, V.; Kamaludin, A.; AlSinani, M. Development of M-learning Application based on Knowledge Management Processes. In Proceedings of the 2018 7th International conference on Software and Computer Applications (ICSCA 2018), Kuantan, Malaysia, 8–10 February 2018; pp. 248–253. [Google Scholar]
  75. Al Shamsi, J.H.; Al-Emran, M.; Shaalan, K. Understanding key drivers affecting students’ use of artificial intelligence-based voice assistants. Educ. Inf. Technol. 2022, 1–21. [Google Scholar] [CrossRef]
  76. Chernov, A.; Chernova, V. Global blockchain technology market analysis-current situations and forecast. In Proceedings of the Economic and Social Development: Book of Proceedings, Warsaw, Poland, 26–27 September 2018; pp. 143–152. [Google Scholar]
  77. Wu, W.-H.; Wu, Y.-C.J.; Chen, C.-Y.; Kao, H.-Y.; Lin, C.-H.; Huang, S.-H. Review of trends from mobile learning studies: A meta-analysis. Comput. Educ. 2012, 59, 817–827. [Google Scholar] [CrossRef]
  78. Crompton, H.; Burke, D.; Gregory, K.H. The use of mobile learning in PK-12 education: A systematic review. Comput. Educ. 2017, 110, 51–63. [Google Scholar] [CrossRef]
  79. Al-Saedi, K.; Al-Emran, M. A Systematic Review of Mobile Payment Studies from the Lens of the UTAUT Model. In Recent Advances in Technology Acceptance Models and Theories; Springer: Cham, Switzerland, 2021; Volume 335, pp. 79–106. [Google Scholar]
  80. Al-Maroof, R.A.; Al-Emran, M. Research Trends in Flipped Classroom: A Systematic Review. In Recent Advances in Intelligent Systems and Smart Applications; Springer: Berlin/Heidelberg, Germany, 2021; pp. 253–275. [Google Scholar]
  81. Frizzo-Barker, J.; Chow-White, P.A.; Adams, P.R.; Mentanko, J.; Ha, D.; Green, S. Blockchain as a disruptive technology for business: A systematic review. Int. J. Inf. Manag. 2020, 51, 102029. [Google Scholar] [CrossRef]
  82. Taherdoost, H. A Critical Review of Blockchain Acceptance Models—Blockchain Technology Adoption Frameworks and Applications. Computers 2022, 11, 24. [Google Scholar] [CrossRef]
  83. Al-Emran, M.; Granić, A. Is It Still Valid or Outdated? A Bibliometric Analysis of the Technology Acceptance Model and Its Applications From 2010 to 2020. In Recent Advances in Technology Acceptance Models and Theories; Springer: Cham, Germany, 2021. [Google Scholar]
  84. Al-Emran, M.; Al-Maroof, R.; Al-Sharafi, M.A.; Arpaci, I. What impacts learning with wearables? An integrated theoretical model. Interact. Learn. Environ. 2020. [Google Scholar] [CrossRef]
  85. Albayati, H.; Kyoung, S.; Jeung, J. Technology in Society Accepting financial transactions using blockchain technology and cryptocurrency: A customer perspective approach. Technol. Soc. 2020, 62, 101320. [Google Scholar] [CrossRef]
  86. Karamchandani, A.; Srivastava, S.K.; Srivastava, R.K. Perception-based model for analyzing the impact of enterprise blockchain adoption on SCM in the Indian service industry. Int. J. Inf. Manag. 2020, 52, 102019. [Google Scholar] [CrossRef]
  87. Yadav, V.S.; Singh, A.R.; Raut, R.D.; Govindarajan, U.H. Blockchain technology adoption barriers in the Indian agricultural supply chain: An integrated approach. Resour. Conserv. Recycl. 2020, 161, 104877. [Google Scholar] [CrossRef]
  88. Wahab, S.N.; Loo, Y.M.; Say, C.S. Antecedents of Blockchain Technology Application among Malaysian Warehouse Industry. Int. J. Logist. Syst. Manag. 2020, 1, 1. [Google Scholar] [CrossRef]
  89. Orji, I.J.; Kusi-Sarpong, S.; Huang, S.; Vazquez-Brust, D. Evaluating the factors that influence blockchain adoption in the freight logistics industry. Transp. Res. Part E Logist. Transp. Rev. 2020, 141, 102025. [Google Scholar] [CrossRef]
  90. Wamba, S.F.; Queiroz, M.M. The role of social influence in blockchain adoption: The Brazilian supply chain case. IFAC-PapersOnLine 2019, 52, 1715–1720. [Google Scholar] [CrossRef]
  91. Pantouw, R.T.; Aruan, D.T.H. Influence of Game Design and Playability Toward Continuance Intention Using TAM Framework. IPTEK J. Proc. Ser. 2019, 307. [Google Scholar] [CrossRef]
  92. Lou, A.T.F.; Li, E.Y. Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective. In Proceedings of the International Conference on Electronic Business, Dubai, United Arab Emirates, 4–8 December 2017; pp. 299–302. [Google Scholar]
  93. Lee, C.C.; Kriscenski, J.C.; Lim, H.S. An empirical study of behavioral intention to use blockchain technology. J. Int. Bus. Discip. 2019, 14, 1–21. [Google Scholar]
  94. Queiroz, M.M.; Fosso Wamba, S. Blockchain adoption challenges in supply chain: An empirical investigation of the main drivers in India and the USA. Int. J. Inf. Manag. 2019, 46, 70–82. [Google Scholar] [CrossRef]
  95. Francisco, K.; Swanson, D. The Supply Chain Has No Clothes: Technology Adoption of Blockchain for Supply Chain Transparency. Logistics 2018, 2, 2. [Google Scholar] [CrossRef] [Green Version]
  96. Aketch, S.; Mwambia, F.; Baimwera, B. Effects of Blockchain Technology on Performance of Financial Markets in Kenya. Int. J. Financ. Account. 2021, 6, 1–15. [Google Scholar] [CrossRef]
  97. Shrestha, A.K.; Vassileva, J. User Acceptance of Usable Blockchain-Based Research Data Sharing System: An Extended TAM- Based Study. In Proceedings of the 2019 First IEEE International Conference on Trust, Privacy and Security in Intelligent Systems and Applications (TPS-ISA), Los Angeles, CA, USA, 12–14 December 2019. [Google Scholar]
  98. Nuryyev, G.; Wang, Y.P.; Achyldurdyyeva, J.; Jaw, B.S.; Yeh, Y.S.; Lin, H.T.; Wu, L.F. Blockchain technology adoption behavior and sustainability of the business in tourism and hospitality SMEs: An empirical study. Sustainability 2019, 12, 1256. [Google Scholar] [CrossRef]
  99. Ullah, N.; Alnumay, W.S.; Al-Rahmi, W.M.; Alzahrani, A.I.; Al-Samarraie, H. Modeling cost saving and innovativeness for blockchain technology adoption by energy management. Energies 2020, 13, 4783. [Google Scholar] [CrossRef]
  100. Ullah, N.; Al-Rahmi, W.M.; Alzahrani, A.I.; Alfarraj, O.; Alblehai, F.M. Blockchain technology adoption in smart learning environments. Sustainability 2021, 13, 1801. [Google Scholar] [CrossRef]
  101. Muhamad, W.N.W.; Razali, N.A.M.; Wook, M.; Ishak, K.K.; Zainudin, N.M.; Hasbullah, N.A.; Ramli, S. Evaluation of Blockchain-based Data Sharing Acceptance among Intelligence Community. Int. J. Adv. Comput. Sci. Appl. 2020, 11, 597–606. [Google Scholar] [CrossRef]
  102. Toufaily, E.; Zalan, T.; Dhaou, S. Ben A framework of blockchain technology adoption: An investigation of challenges and expected value. Inf. Manag. 2021, 58, 103444. [Google Scholar] [CrossRef]
  103. Saurabh, S.; Dey, K. Blockchain technology adoption, architecture, and sustainable agri-food supply chains. J. Clean. Prod. 2021, 284, 124731. [Google Scholar] [CrossRef]
  104. Fernando, Y.; Rozuar, N.H.M.; Mergeresa, F. The blockchain-enabled technology and carbon performance: Insights from early adopters. Technol. Soc. 2021, 64, 101507. [Google Scholar] [CrossRef]
  105. Alazab, M.; Alhyari, S.; Awajan, A.; Abdallah, A.B. Blockchain technology in supply chain management: An empirical study of the factors affecting user adoption/acceptance. Clust. Comput. 2021, 24, 83–101. [Google Scholar] [CrossRef]
  106. Kamble, S.; Gunasekaran, A.; Arha, H. Understanding the Blockchain technology adoption in supply chains-Indian context. Int. J. Prod. Res. 2018, 57, 2009–2033. [Google Scholar] [CrossRef]
  107. Singh, H.; Jain, G.; Munjal, A.; Rakesh, S. Blockchain technology in corporate governance: Disrupting chain reaction or not? Corp. Gov. 2020, 20, 67–86. [Google Scholar] [CrossRef]
  108. Queiroz, M.M.; Fosso Wamba, S.; De Bourmont, M.; Telles, R. Blockchain adoption in operations and supply chain management: Empirical evidence from an emerging economy. Int. J. Prod. Res. 2021, 59, 6087–6103. [Google Scholar] [CrossRef]
  109. Rijanto, A. Business financing and blockchain technology adoption in agroindustry. J. Sci. Technol. Policy Manag. 2020, 12, 215–235. [Google Scholar] [CrossRef]
Figure 1. PRISMA flowchart.
Figure 1. PRISMA flowchart.
Applsci 12 04245 g001
Figure 2. Distribution of studies by research methods.
Figure 2. Distribution of studies by research methods.
Applsci 12 04245 g002
Figure 3. Main domains in Blockchain adoption.
Figure 3. Main domains in Blockchain adoption.
Applsci 12 04245 g003
Figure 4. Distribution of studies by technology adoption models/theories.
Figure 4. Distribution of studies by technology adoption models/theories.
Applsci 12 04245 g004
Figure 5. Most common external factors affecting Blockchain adoption.
Figure 5. Most common external factors affecting Blockchain adoption.
Applsci 12 04245 g005
Figure 6. Distribution of studies by participants.
Figure 6. Distribution of studies by participants.
Applsci 12 04245 g006
Table 1. Previous review studies on Blockchain technologies.
Table 1. Previous review studies on Blockchain technologies.
SourceReview TypeNumber of Reviewed StudiesDomainAim
[31]Systematic review65 studiesHealthcareTo review the use of Blockchain technology in healthcare.
[25]Systematic review140 studiesEnergyTo review and examine the basic ideas that drive Blockchain technology, such as systems design and distributed consensus methods. It also concentrated on Blockchain solutions for the energy industry and enlightened the state-of-the-art issues by extensively analyzing the literature and existing business cases.
[32]Systematic review33 studiesHealthcareTo demonstrate the potential use of Blockchain technologies, their obstacles, and future research directions in healthcare.
[33]Systematic review27 studiesSupply chain managementTo explain the most common Blockchain applications in supply chain management (SCM). It also covered the critical disruptions and problems resulting from Blockchain adoption in SCM, and how the future of Blockchains in SCM holds.
[29]Systematic review31 studiesEducationTo review the applications of Blockchain in education and provide an insight into the main benefits and obstacles of implementation.
[28]Systematic review29 studiesSupply chainTo evaluate how Blockchain technologies would affect supply chain practices and policies in the future.
[26]Systematic review61 studiesHealthcareTo review the prototypes, frameworks, and implementations of Blockchain in healthcare.
[34]Systematic review10 studiesAgricultureTo review current research subjects, significant contributions, and benefits of using Blockchain technologies in agriculture.
[30]Systematic review22 studiesLogistics and supply chain managementTo identify the most relevant organizational theories used in Blockchain literature in the context of logistics and supply chain management (LSCM). It also examined the content of those organizational theories to formulate relevant research questions for investigating the adoption of Blockchain technologies in LSCM.
[35]Review-General (not specific to a particular domain)To examine the Blockchain and its related essential features, concerns (IoT, security, and data management), and industrial applications. It also provides potential difficulties and future directions.
[36]Systematic review42 studiesHealthcareTo figure out how Blockchain technologies can be used in the healthcare domain.
[37]Systematic review35 studiesGovernanceTo provide scholars and practitioners with directions on using Blockchain applications in governance research.
[38]Systematic review32 studiesGeneral (not specific to a particular domain)To offer the most recent state of research on the potential combination of AI and Blockchain technologies and discuss the possible advantages of such a combination.
[39]Review-Supply chain and logisticsTo explain and describe the idea of Blockchain and its use in logistics and supply chains.
[40]Systematic review35 studiesIoTTo evaluate academic solutions and approaches of integrating Blockchain with IoT.
[41]Review-General (not specific to a particular domain)To discuss the fundamentals of Blockchain technologies and their technical details.
This studySystematic review30 studiesGeneral (not specific to a particular domain)To provide a thorough review of Blockchain adoption by examining the main research methods, domains, technology acceptance models/theories, influential factors, research objectives, and target participants.
Table 2. Inclusion and exclusion criteria.
Table 2. Inclusion and exclusion criteria.
Inclusion CriteriaExclusion Criteria
Should be published between 2010 and 2021.Studies involving Blockchain but without a theoretical model.
Should involve a theoretical model for evaluating Blockchain.Studies involving a theoretical model but without a Blockchain.
Should measure the adoption, acceptance, or continued use of Blockchain.Studies written in languages other than English.
Should be written in English language.
Table 3. Quality assessment checklist.
Table 3. Quality assessment checklist.
#Questions
1Is the research aim specified clearly?
2Did the study achieve its aim?
3Are the variables considered by the study clearly indicated?
4Is the context/discipline of the study clearly defined?
5Are the data collection methods sufficiently detailed?
6Are the measures’ reliability and validity clearly described?
7Are the statistical techniques used to analyze the data sufficiently described?
8Do the findings add to the literature?
9Does the study add to your knowledge or understanding?
Table 4. Quality assessment results.
Table 4. Quality assessment results.
StudyQ1Q2Q3Q4Q5Q6Q7Q8Q9TotalPercentage
S11110.5111118.594.44%
S21111111119100%
S310.5111110.50.57.583.33%
S41111111119100%
S510.5111110.50.57.583.33%
S61111111119100%
S7111101010.56.572.22%
S810.510.510.510.50.56.572.22%
S91111111119100%
S10111111110.58.594.44%
S11110.50.50.50.50.510.5666.66%
S121111111119100%
S131111111119100%
S14111100.5010.5666.66%
S151111111119100%
S161111110.5118.594.44%
S171111111119100%
S181111111119100%
S19111110.50.511888.88%
S201111111119100%
S2110.51110.510.517.583.33%
S221111111119100%
S2311110.510.511888.88%
S241111111119100%
S2511110.511118.594.44%
S261111111119100%
S271111110.510.5888.88%
S2811110.511118.594.44%
S291111110.5118.594.44%
S3011110.511118.594.44%
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

AlShamsi, M.; Al-Emran, M.; Shaalan, K. A Systematic Review on Blockchain Adoption. Appl. Sci. 2022, 12, 4245. https://doi.org/10.3390/app12094245

AMA Style

AlShamsi M, Al-Emran M, Shaalan K. A Systematic Review on Blockchain Adoption. Applied Sciences. 2022; 12(9):4245. https://doi.org/10.3390/app12094245

Chicago/Turabian Style

AlShamsi, Mohammed, Mostafa Al-Emran, and Khaled Shaalan. 2022. "A Systematic Review on Blockchain Adoption" Applied Sciences 12, no. 9: 4245. https://doi.org/10.3390/app12094245

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop