3.1. Author analysis
First, 4024 publications downloaded by CNKI were converted and imported into CiteSpace.V.6.1r2 (64-bit). Co-occurrence analysis of Author information, the cluster node type attribute was chosen as Cite Author, the threshold value was selected as TOP30, and the primary network cooperation diagram and the output of Author papers were obtained. The overall size of nodes reflects the number of articles published, and the annual rings of nodes represent the number of papers published in different years. The wider the rings in a given year, the more frequently they were cited or appeared in the corresponding year. Figure 1 displays that N = 471, E = 391, and Density = 0.0035; there are many scholars in the field of blockchain. Among them, those with larger nodes are high-producing authors: Feiyue Wang, Yanjun Tang, Dong Yang, Jianming Zhu, Jianpeng Deng, Wei Liu, Wei She, Qiang Li, ZhaoTian, and other scholars in order. Table 3 reveals that the highest number of publications is Feiyue Wang, with 17 related papers, followed by Tang Yanjun, who published 15 articles. The author notes are scattered, and the cooperation among them is poor. Through analyzing their clustering, only Wei She, Wei Liu, Zhao Tian, Feiyue Wang, and Yong Yuan forms the clusters among the high-producing authors.
In Cite Space, freq represents the number of papers published, degree represents the degree of influence of the article, and HalfLife represents the effective value of article. So Table 3 displays that the high-product author Feiyue Wang published 17 papers in the field of blockchain. Second, Yanjun Tang published 15 papers with an impact value of 2 and a half-life of 0.5, indicating that although he published many papers, his influence was not high. Dong Yang published 14 articles with an impact value of 2 and a high half-life of 3.5. Although the number of published papers was not the largest, the influence of the papers was the most effective in this field. Finally, the high-product authors Jianming Zhu, Jianming Deng, Wei Liu, Wei She, Qiang Li, and Zhao Tian published 13,12,12,12,11, and 10 papers, respectively. Blockchain in China’s research field remains a new field. However, there is still much research in this are.
Table 3
Detailed table of Chinese author paper outputs on blockchain.
Author
|
freq
|
Degree
|
Centrality
|
Year
|
Half-Life
|
Feiyue Wang
|
17
|
5
|
0
|
2016
|
2.5
|
Yanjun Tang
|
15
|
2
|
0
|
2020
|
0.5
|
Dong Yang
|
14
|
2
|
0
|
2016
|
3.5
|
Jianming Zhu
|
13
|
2
|
0
|
2017
|
1.5
|
JianmingDeng
|
12
|
1
|
0
|
2017
|
1.5
|
Wei Liu
|
12
|
10
|
0
|
2018
|
2.5
|
Wei She
|
12
|
10
|
0
|
2018
|
2.5
|
Qiang Li
|
11
|
3
|
0
|
2019
|
-0.5
|
Zhao Tian
|
10
|
8
|
0
|
2019
|
1.5
|
Through further analysis of the node information, it was found that Feiyue Wang is a researcher at the State Key Laboratory of Complex System Management and Control, Institute of Automation, Chinese Academy of Sciences, and a professor at the Center for Military Computing Experiments and Parallel System Technology, National University of Defense Technology. His research interests include intelligent systems and complex systems. Among all the papers, the highest index and download of articles were《Development Status and the prospect of blockchain technology》, published in the journal Automatica Sinica in 2016, with 3457 citations and 91,682 downloads. The article pointed out that “the characteristics of blockchain technology, such as decentralized credit, non-tampering and programmability, so that it has a wide range of application prospects in digital cryptocurrency, financial and social systems. However, compared with the successful commercial application of the blockchain, the basic theory and technology research of blockchain remain in their infancy. Many more fundamental scientific issues that are crucial to the development of blockchain industry must be studied and followed up[9].” Yanjun Tang is a lecturer at the college of East China Jiaotong university economic management, Ph.D. research direction for the audit and corporate governance, chain blocks, his paper 《“ blockchain + national audit” power big data research on anti-corruption》was downloaded 2306 times, put forward to block chain make audit data for the underlying technology platform, to promote information sharing and effective use of corruption, On this basis, an institutionalized anti-corruption and anticorrosion mechanism will be established to explore a new effective path for anti-corruption and clean government work in the new era[10]. Prof. Yang Dong is a professor at the Renmin University of China Law School. His research interests include competition law, data governance, blockchain, fintech, and regulation. To cope with the challenges of digital economy, his article pointed out that “to effectively solve the problem of a monopoly of super-platform data, by building blockchain, depending on the technology of big data, and so on to build embedded technology-driven, including the compliance mechanism embedded technology system to solve the organic regulations, both the government and the market failure paths, through real-time transparent sharing books to identify risks in advance.[11]” In the article《B2C E-commerce Platform Product Information Traceability and Anti-counterfeiting Model from the Perspective of Blockchain》, Professor Jianming Zhu pointed out that “given that blockchain technology can rely on distributed storage architecture, blockchain connection, waterfall effect, cryptography, consensus algorithms, smart contracts, and other technologies, Blockchain technology can be used to trace product information and prevent tampering, and then combined with the Internet of Things technology to solve the problem of product anti-counterfeiting.[12]” Jianpeng Deng is a teacher at the Law School of Central University of Finance and Economics. In his paper《Regulation of blockchain: Dilemmas and Solutions section》, it was stated that “using the blockchain to record intermediate parameters of the model training process as evidence, and to motivate collaborative nodes to perform model parameter verification, punishing participating nodes that upload false parameters or low-quality models to constrain their self-interest[13].” Wei Liu, Wei She, and Zhao Tian are researchers in the field of blockchain, and formed a typical network of cooperation, They collaborated on a paper titled《A New Blockchain Technology for Secure Sharing of Medical Big Data》, proposed that “A blockchain-based fully homomorphic medical data security sharing scheme enables the calculation and application of ciphertext-state medical data in a decentralized network, achieving the goal of ensuring the privacy and security of personal data, data authorization distribution, and secure transmission without affecting the analysis and practical application of big medical data[14].”
The above analysis reveals that according to the characteristics of blockchain, various experts and scholars have analyzed the application of blockchain in different fields such as finance, audit, medical treatment, law, data security, and platform governance. Blockchain has great development space in the future, which is conducive to solving normative problems in different fields, it is very important for the sustainability of society. Blockchain is more for commercial applications, and technical research on it remains in its infancy. Some essential scientific issues for the development of the blockchain industry must be further explored and followed up.
3.3. Analysis of research hotspots and frontiers
In CiteSpace, research hotspots refer to topics discussed more recently by experts and scholars in a specific field. Through keyword co-citation analysis, research hotspots and frontiers in this field can be identified. The 4024 literature downloaded from CNKI were converted and imported into CiteSpace. Figure 3 depicts that V.6.1r2 (64-bit), the clustering node type attribute was selected as a keyword, and the threshold value was chosen as TOP30 to obtain the network analysis results for the blockchain keywords and cluster cosine according to the relationship strength between nodes to obtain the keyword knowledge network map, N = 213, E = 390, Density = 0.0173. The ring style figure can analyze some information from keywords and keyword cited situations through a knowledge map of node structure to reflect; the annual rings of different colors represent different years, from far and near years by cold gray to warm red change to reflect, and the control knowledge map can visually identify specific at the top of time of the year. The size of the tree ring represents the citation frequency of a concept. The larger the tree ring indicates the higher the citation frequency, and the smaller the tree ring suggests the lower the citation frequency. The radius of each node corresponds to the total citation number of nodes.
Figure 3 illustrates that, according to the frequency of keywords, the top ten are in turn: blockchain (2829 times), smart contracts (459 times), decentralization (192 times), consensus mechanism (169 times), artificial intelligence (121 times), bitcoin (120 times), big data (118 times), digital currency (118 times ), privacy protection (104 times ), Internet of Things (97 times), and financial technology (94 times).
To further analyze the relationship between keywords, according to all keywords draw clustering timeline map, forming a total of 10 clusters. In keywords clustering, Silhouette is a parameter to evaluate the clustering effect. When Silhouette > 0.7, it means the clustering results are highly reliable. It is more close to 1 ,the higher homogeneity of network. It can be seen from Table 5 that all clusters Sihouette > 0.7,so all of the cluters are valid. From Fig. 4, we can see that, # 0 Blockchain cluster was formed by keywords such as ‘blockchain, technological innovation, de-trust mechanism, challenge, virtual currency, security attack’; #1 Big data cluster was formed by keywords of ‘financial technology, financial supervision, big data, Hewlett-Packard Finance, artificial intelligence, cloud computing, digital economy’; #2 Data sharing cluster was formed by keywords such as ‘data security, digital economy, edge computing, intellectual property, smart grid’; #3 Decentralized cluster was formed by keywords such as ‘commercial banking, non-tampering, digital wallet, de-trust, banking, cryptography, journalism’; #4 Alliance chain cluster was formed by keywords such as ‘trust mechanism, regulation, 5G, sharing economy, power trading ’; #5 Smart contract cluster was formed by keywords such as ‘timestamp, consensus mechanism, consensus algorithm, digital assets, supply chain financing, distributed authentication, electric vehicles, privacy protection’; #6 Digital currency cluster was formed by keywords such as ‘legal tender, cryptocurrency, cyberspace finance, Ethereum, super ledger, digital rights, authentication’; #7 supply chain cluster was formed by keywords such as ‘e-commerce, information security, credit mechanism, file management, traceability, ring signature ’. #8 Bitcoin cluster was formed by keywords such as ‘e-money, value transfer, e-files, technology enable, information sharing, cryptocurrency, bitcoin’; #9 Banking industry cluster was formed by keywords such as ‘financial industry, JPMorgan Chase, direct bank, distributed, financial technology innovation.’
In CiteSpace, burst keywords represent a frontier of recent research in this field. Based on the knowledge map of keyword research hotspots, the burst detection of keywords in time series was obtained, and a total of 18 burst keywords were obtained. From 2014 to 2018, the burst keyword was ‘Bitcoin’,with a burst intensity of 15.19. From 2016 to 2017, the burst keywords were ‘digital currency’, ‘commercial bank’, and ‘cryptography’,with burst intensities of 8.11, 3.54, and 2.95, respectively. From 2016 to 2018, the burst keywords were ‘distributed’,‘business model’, ‘financial regulation’, ‘financial innovation’,and the intensity of emergent was 5.27, 3.83, 3.57, and 3.52, respectively. From 2017 to 2019, the burst keywords were ‘information security’, ’regulatory technology’, with burst intensities of 7 and 5.23; From 2017–2018, the burst keywords were ‘digital assets’,‘regulatory sandbox’,‘inclusive finance’, with burst intensity of 4.43, 4.11, 3.08; In 2018–2019, the burst keywords were ‘sharing economy’,‘regulation’, with burst intensity of 5.83 and 4.16; From 2019 to 2020, burst keywords were ‘library’,‘ digital publishing’, with burst intensity of 3.77 and 2.8; In 2020–2022, the burst keyword was ‘technology enabling’, with burst intensity of 2.83.
Blockchain technology originated from the groundbreaking paper ‘Bitcoin: a peer-to-peer electronic cash system’, published in 2008 by Satoshi Nakamoto, a scholar in the cryptography email group. It is a chain structure that combines data blocks in chronological order with a distributed data ledger shared and maintained by each node in a decentralized system[15]. With the substantial rise in digital cryptocurrencies represented by Bitcoin, the emerging blockchain technology has gradually become a hot research topic in academia and industry.
Table 5
The blockchain research hotspots and trend information in China
Keywords
|
Freq
|
Cluster
|
Cluster Size
|
Silhouette
|
Bust Words
|
Strength
|
Begin Time
|
Blockchain
|
2829
|
# 0 Blockchain
|
32
|
0.91
|
Bitcoin
|
15.19
|
2014
|
Smart contracts
|
459
|
#1 Big data
|
31
|
0.955
|
Digital currency
|
8.11
|
2016
|
Decentralization
|
192
|
#2 Data sharing
|
24
|
0.741
|
Distributed
|
5.27
|
2016
|
Consensus mechanism
|
169
|
#3 Decentralized
|
23
|
0.877
|
Business model
|
3.83
|
2016
|
Artificial intelligence
|
121
|
#4 Alliance chain
|
18
|
0.843
|
Financial regulation
|
3.57
|
2016
|
Bitcoin
|
120
|
#5 Smart contract
|
16
|
0.765
|
Commercial bank
|
3.54
|
2017
|
Big data
|
118
|
#6 Digital currency
|
14
|
0.872
|
Financial innovation
|
3.52
|
2018
|
Digital currency
|
118
|
#7 Supply chain
|
12
|
0.935
|
Regulatory technology
|
5.23
|
2019
|
Privacy protection
|
104
|
#8 Bitcoin
|
11
|
0.939
|
Sharing economy
|
5.83
|
2018
|
Internet of Things
|
97
|
#9 Banking industry
|
9
|
0.845
|
Finance
|
4.16
|
2019
|
Financial technology
|
94
|
|
|
|
Technology enabling
|
2.83
|
2020
|
(1)Smart contract: As the underlying technology of blockchain, it currently plays an essential role in asset management, contract management, supervision, and law enforcement, and its security issues, performance issues, privacy issues, are of concern to many experts and scholars. Aiming at the security problem of smart contracts, Luu proposed a symbolic execution tool, to detect potential vulnerabilities such as transaction order dependence, timestamp dependence, re-entry, and exception handling in Ethereum smart contracts
[16].To address performance issues, Dickerson proposed a smart contract parallel execution framework that allows independent, non-conflicting contracts to run simultaneously, thereby increasing system throughput and contract execution efficiency
[17]. In response to privacy issues, Qiang Miao
[18] pointed out that using of blockchain smart contracts can solve the problem of data privacy in large-scale manufacturing. He designed the core alliance chain and value chain alliance chain, developed smart contracts between core enterprises and upstream and downstream enterprises, and realized the trusted and traceable operation of cross-chains, so as to build a multivalue chain collaborative ecosystem with mutual trust, resource sharing, win-win cooperation, and sustainability .
(2)Decentralization: Due to the fairness and justice of decentralization, it solved the long-standing trust problem in human interaction, and it will enhance the sustainability of industry. Liu Tao and Yuan Yi
[19] proposed that decentralized self-organization (DAO) based on blockchain technology is emerging globally, using rules and mechanisms such as notification, voting, incentives, and evaluation to make it a new form of human collaborative management, which may become the cornerstone of organizational structure of enterprises in the development of Yuan universe industry. Nana Shi and Shijiang Xie
[20] pointed out that the construction of a decentralized agricultural product circulation system by blockchain technology is an effective way to reshape the trust mechanism of agricultural product circulation industry chain and improve the efficiency of agricultural product circulation. Shuihai Zhang, Haoyi Sun, Yiwei Sun, and Bei Pei
[21] proposed a decentralized storage space trading system applied to the blockchain network environment, which effectively improves the space utilization and data recovery efficiency of storage nodes while ensuring the security of user data, and enhances the adaptability to the complex environment of distributed storage. Lei Zheng
[22] proposed that with the development of financial technology, decentralized finance has emerged in digital finance. Decentralized finance is committed to improving the scope, quality, and efficiency of financial services so that financial resources can better serve society.
(3)Regulatory sandbox: In the wave of a new scientific and technological revolution, financial technology, as a product of the deep integration of technological innovation and industrial development, has rapidly risen worldwide
[23]. Yudong Qi and Huanhuan Liu
[24] pointed out that the regulatory sandbox, as a practical form of adaptive regulation, can bring experience-intensive benefits to all kinds of subjects in the market: reducing technical uncertainty and information asymmetry through real-world test results, effectively resolving the contradictions between regulators and regulators, new entrants and late entrants, and maximizing innovation based on controlling risks. A regulatory sandbox is an innovative tool and a necessary support for promoting the transformation of regulation from theory to practice. Jian Wang and Bingyuan Zhao
[25] suggested that by reasonably determining the sample size of test users, strengthening macro-prudential supervision and strengthening cross-border supervision mechanisms, and completing the shortcomings of regulatory sandbox system, we can promote the innovation of internet financial market while resisting market risks, thus creating an excellent legal atmosphere for the sustainable and healthy development of Internet finance.
(4)Big data: To ensure the correctness, security, and effectiveness of big data, many scholars have attempted the fusion of blockchain and big data in many fields and conducted corresponding research. Xuguang Zhu, Chunxiao Xing, Wenqing Li, and Yingting Hao
[26] proposed a privacy protection evaluation method based on blockchain technology for the whole life cycle of transaction data and provided a comprehensive blockchain privacy protection ability evaluation method for blockchain system users, developers, and regulators. Qiang Ye, Yue Ye, and Guangxin Ye
[27] designed a blockchain system architecture for the carbon market in a future big data environment and proposed a price-driven mechanism model of individual carbon assets in the blockchain carbon market. Fang Hu
[28] pointed out that for sustainability, blockchain can solve the problems of generation, collection and use of information related to sustainability. It is the information technology foundation for sustainability and it is also very important for sustainability. Integrating blockchain technology and big data requires in-depth research and mining by relevant experts and scholars to make considerable and more data resources better serve society.
(5)Technology enabling: With globalization and the intensification of international competition, science and technology play an increasingly important role in economic development. Yiyan Chen
[29] pointed out that using the decentralization and traceability characteristics of blockchain to build a digital circular agriculture framework, and integrating the 'digital' empowerment of blockchain with the 'sustainability ' connotation of circular agriculture, can solve the problems of keeping private organic vegetables in good condition, pesticide residues and irregular production processes. Chaoqun Sun
[30] has broad application prospects in empowering urban governance with blockchain technology, which can make the governance tools more intelligent, the governance subjects more equal, and the governance behaviors more standardized. However, the urban governance empowered by blockchain technology faces governance challenges posed by landing obstruction, perceived limitations, and potential risks. Shun Yao and Bin Zhang
[31] pointed out that the book publishing industry can use blockchain technology to better mine reader data, improve the viscosity of readers and publishing institutions, enhance the internal management performance of publishing institutions, and innovate management models.
Since the advent of blockchain technology, the Chinese government has attached great importance to the technological empowerment brought by blockchain and has encouraged all walks of life to tap the potential of blockchain technology to boost economic construction. Chinese experts and scholars have conducted extensive study on the blockchain applications. We put forward the application idea and conducted a model design and application in finance, education, government, medical care, big data, supervision, information storage, and other aspects. The problems in the application process and whether the specific application effect has reached the envisaged goal require further study.