Status and Hotspots Analysis of Domestic Fintech Empowering Supply Chain Finance Field Based on CiteSpace Knowledge Map ()
1. Introduction
Small and medium-sized enterprises (SMEs) are an important part of the national economy, and their development has attracted much attention. However, SMEs face problems such as difficulty in financing, information asymmetry, and low efficiency of traditional finance during the financing process. To address these issues, supply chain finance emerged. At the same time, fintech plays a crucial role in enhancing the coordination of the supply chain finance system, reducing information asymmetry, and lowering financing risks. Different scholars conduct research from various perspectives. Some representative authors, such as Song Hua, mainly focus on supply chain finance theory, intelligent supply chain finance and enterprise financing (Song & Chen, 2016; Song & Lu, 2017; Song & Yang, 2019; Song & Li, 2020). Lu Minfeng mainly studies risk governance, development and digital integration of supply chain finance (Lu, 2020, 2021, 2022a, 2022b). He Shan and Shen Xiaotong mainly explore the role of blockchain in enabling supply chain finance and how to prevent enterprise asset securitization risks (He & Shen, 2022). Zhang Yu and Zheng Liansheng mainly discuss the efficiency improvement of fintech on supply chain finance from the perspective of information asymmetry and reducing financing risks (Zhang & Zheng, 2021). Zheng Liansheng et al. mainly study platform-led supply chain finance from the perspective of ecological structure (Zheng et al., 2023). Yang Liwen mainly studies fintech enabling supply chain finance from the perspective of enterprise digital transformation and practical application (Yang, 2022; Yang et al., 2024). Cheng Cheng et al. use a sample municipal method to study fintech enabling supply chain finance from the perspective of actual performance (Cheng et al., 2024). Zhao Lihua mainly studies the development path and comprehensive empowerment of fintech enabling supply chain finance (Zhao, 2023). This paper uses the CiteSpace software to conduct a visual analysis of research on fintech enabling supply chain finance from 2015 to 2025, aiming to deeply understand the research topics and development trends in this field, in order to provide certain references and guidance for future research on fintech enabling supply chain finance.
2. Data Sources and Research Methods
2.1. Data Sources
This study uses the China National Knowledge Infrastructure (CNKI) as the database for literature sources. Under the advanced search conditions, the topic is set as “Fintech” and “Supply Chain Finance”, the search condition is set as “exact”, and the literature source category is set as CSSCI source journals. Eventually, a total of 664 relevant journal articles were retrieved.
2.2. Research Methods
This paper uses the visualization software CiteSpace 6.4.R1 to conduct a visual analysis of the relevant literature on fintech empowering supply chain finance. It has drawn the co-occurrence maps of authors, institutions, and keywords. The threshold is set to the default value. A modularity Q value exceeding 0.3 indicates a clear clustering structure in the network, while a silhouette coefficient S value over 0.7 suggests that the clustering results are highly reliable. Selecting the literature indexed by CNKI CSSCI can comprehensively represent the research situation of domestic core journals, thereby reflecting the main development trends in the field of fintech empowering supply chain finance in the academic circle.
3. Research Overview on Financial Technology Empowering Supply Chain Finance
3.1. Descriptive Analysis of the Number of Publications
By organizing the valid literature retrieved from CNKI by publication year, it can be observed that the distribution of the number of papers on fintech empowering supply chain finance each year. The number of published articles showed an increasing trend from 2018 to 2020, and then presented a fluctuating upward trend. Fintech refers to the use of cutting-edge technologies such as artificial intelligence, big data and blockchain to promote the innovation and development of financial services, products and applications. With more and more technological tools being empowered to supply chain finance, such as big data, blockchain, artificial intelligence and other fintech tools. They play a key role in enhancing the credit identification and risk control capabilities of all parties in supply chain finance, alleviating financing constraints and promoting the coordination of supply chain finance (Xie & He, 2013).
From Figure 1, it can be observed that the research interest in this field has continuously increased and gradually entered the stage of specialization development. From 2015 to 2025, there were approximately 664 research papers on fintech empowering supply chain finance in domestic core journals. The growth trend of the number of core journal publications is clear. Especially since 2022, the number of publications has remained at a relatively high level, indicating that “fintech empowering supply chain finance” has become an important research direction in the academic community. Although the research interest in this field has significantly increased, there is still room for research in specific fields. During the development of supply chain finance, there are still credit risks caused by information asymmetry. The application of fintech is an important means to alleviate information asymmetry and reduce credit risks. By studying the transmission mechanism and system construction of fintech empowering supply chain finance, the research system of supply chain finance can be improved, which has practical significance for the development of fintech empowering supply chain finance. From Figure 2, it can be observed that domestic research on fintech empowering supply chain finance is highly concentrated in the field of finance, accounting for 39.43%. The second is enterprise economy, with 236 papers (28.20%). Then, it is followed by computer software and computer applications with 61 papers (7.29%), information economy and postal economy with 53 papers (6.33%), industrial economy with 35 papers (4.18%), trade economy with 27 papers (3.23%), agricultural economy with 20 papers (2.39%), and accounting with 15 papers (1.79%). The remaining disciplines account for relatively small proportions, mostly around 1% (see Figure 2).
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Figure 1. Number of publications on financial technology enabling supply chain finance.
Figure 2. Distribution of financial technology in supply chain finance disciplines.
3.2. Core Author Analysis
By using the CiteSpace 6.4.R1 software to analyze the knowledge graph of the authors of the literature, it is possible to observe the core author groups and the cooperative relationships within this field. From Figure 3, we can see that there are 99 nodes and 58 connections in the graph, with a network density of 0.012, indicating that the core authors are concentrated, but the cooperation among the authors is limited. Currently, there is room for improvement in academic exchanges and collaborative research. Song Hua, Lu Minfeng, and He Shan have published 7, 5, and 4 papers respectively in the research on fintech enabling supply chain finance. They are the most active researchers among the existing authors. According to Price’s Law, set M = 1.5, that is, authors with more than two published papers are recognized as core authors in this research field. According to this criterion, there are 19 core authors who have published a total of 50 articles, accounting for 7.5% of the total sample literature. The two authors with the most published papers, namely Song Hua and Zhou Lei, have been determined as core authors. Currently, the research on fintech enabling supply chain finance mainly focuses on explaining the role of fintech from the perspective of financing. There is a certain degree of consensus on the role of fintech enabling supply chain finance in credit identification capabilities, enhanced risk control levels, improved financing efficiency, and alleviation of financing constraints for small and medium-sized enterprises.
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Figure 3. Author collaboration network.
3.3. Analysis of Mechanism
The cooperation relationships among institutions were visually analyzed using the CiteSpace software. From Figure 4, it can be observed that there are more research institutions in the field of fintech enabling supply chain finance, but the cooperation among these institutions is relatively scarce. The graph has 226 nodes and 45 connections, with a network density of only 0.0018, indicating that there is no tight and stable research network of cooperative studies. It can be seen from Figure 4 that institutions such as the Business School of Renmin University of China, the Internet Finance Innovation Development Research Center of Nanjing University of Technology, the China Internet Finance Association, and the China Minsheng Bank Research Institute are relatively active, but the scale of their cooperation is small, and the degree of cross-regional cooperation is low. The cooperation among these institutions needs to be further improved. Through the visual analysis of the cooperation network using CiteSpace, it is possible to better reveal the cooperative relationships among institutions in the research field, as well as the core research forces. This provides an important basis for judging the development level, cooperation tightness, and future collaborative direction of this field’s academic community, as well as the key participating institutions and cooperation relationships in the field of fintech enabling supply chain finance in the future. It is recommended that institutions conducting research on fintech enabling supply chain finance in the future should continue to share resources, promote cooperation, and utilize each other’s advantages to drive in-depth research in this field. In addition, efforts should be made to encourage interdisciplinary cooperation and collaborate with industry partners to make the research field more diversified, ensure the relevance and applicability of research results, and enhance the practical application value of research. At the same time, cross-disciplinary cross-research should be actively promoted, and cooperation with industry entities should be strengthened to enhance the diversity, practical relevance, and application value of research.
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Figure 4. Institutional cooperation diagram.
4. Analysis of Research Hotspots and Development Trends
4.1. Keyword Co-Occurrence Analysis
The keywords were extracted from the literature and are highly generalized terms that help accurately grasp the research focus and themes in a specific field. By analyzing the co-occurrence of keywords in sample documents, the thematic structure and research hotspots in this field can be clearly presented. From the keyword co-occurrence graph (Figure 5), from 2015 to 2025, research on fintech enabling supply chain finance has formed 100 nodes and 471 connections, with a network density of 0.0089, indicating that the keywords in this field are closely linked and have formed a relatively clear research network. In the figure, “supply chain finance” and “fintech” are the two most core keywords, with the largest nodes and the widest connections, indicating that they constitute the core issues of research in this field. Fintech mainly includes technologies such as big data, blockchain, and the Internet of Things. The combination of these two aspects plays a role in risk management, risk control, and alleviating financing constraints for the stakeholders of supply chain finance.
Through the analysis of the keyword co-occurrence graph, it can be observed that the current research in this field mainly focuses on technology empowerment, risk governance, financing model optimization, and digital transformation. The research subjects are mainly commercial banks and small and medium-sized enterprises. It can be seen that the research in this field has a strong practical orientation. Fintech plays an important role in alleviating the financing constraints of small and medium-sized enterprises, optimizing the service model of banks, and improving the operational efficiency of supply chain finance. The keyword co-occurrence network graph provides a visualization tool for researchers, enabling them to more intuitively grasp the relationships and interconnections between the core concepts in the literature.
Figure 5. Keyword co-occurrence graph.
Through the analysis of the keyword co-occurrence matrix, it was found that the centrality of keywords such as blockchain, big data, and digital transformation all exceeded 0.1, indicating that they are in key positions in the network. At the same time, the frequency of occurrence of keywords is positively correlated with their centrality. Generally, high-frequency keywords also have higher centrality, which means that these keywords are research hotspots in this field, receiving extensive attention from scholars and having significant influence. It is worth noting that there are also some keywords with high centrality but low frequency of occurrence. This indicates that they have potential importance in the field of fintech enabling supply chain finance. Although the current attention is limited, they may represent emerging research directions or potential key concepts, such as “financial services”. In-depth exploration of these high centrality and low-frequency keywords is helpful for providing new perspectives and ideas for future research, and promoting the development and innovation of this field. Overall, by analyzing the centrality and frequency of keywords, researchers can more clearly grasp the core concepts and research hotspots, providing directional guidance for subsequent research.
4.2. Keyword Clustering Analysis
Keyword clustering analysis is a method used to group similar data objects together while separating those that are not similar, which helps people better understand the structure and relationships of the data. When drawing the keyword clustering map, the Q value represents the clustering structure value and is an indicator used to evaluate network modularization. When the Q value of the keyword network is higher, it indicates that the clustering structure obtained by the network is better. Additionally, the S value represents the average clustering silhouette value and is an indicator used to measure the homogeneity of the knowledge network. When the S value is above 0.7, it indicates that the clustering results have high persuasiveness. According to the data in Figure 3, the Q value is 0.8304, which is greater than 0.3, indicating a significant clustering structure; while the S value is 0.9601, which exceeds 0.7, suggesting efficient clustering results with high persuasiveness. Based on keyword clustering analysis, a keyword clustering analysis was conducted on the theme of “Fintech Empowering Supply Chain Finance 10”, and the literature was divided into 10 clustering units (see Figure 6), namely Supply Chain Finance #0, Information Asymmetry #1, Fintech #2, Supply Chain Finance Model #3, Blockchain #4, Industrial Chain #5, Digitalization #6, Rural Revitalization #7, Supply Chain #8, and Agricultural Supply Chain Finance #9. According to the results of the clustering study, current scholars’ research in the field of fintech empowering supply chain finance mainly focuses on the following four aspects.
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Figure 6. Keyword clustering diagram
4.2.1. Research on the Foundation and Models of Supply Chain Finance
Supply chain finance is based on the core enterprise and connects the business activities of upstream and downstream enterprises. It customizes financing services for enterprises based on the data from their daily operations (Wang, 2025). Its main models include: the core enterprise-led supply chain finance model, the bank-led model, the platform-led model, and the fintech company-led supply chain finance model (Guan et al., 2025).
4.2.2. Technology-Enabled Research
Supply chain finance, through innovative means, integrates the capital flow, information flow and logistics within the supply chain, creating a brand-new financial ecosystem, thereby optimizing management and enhancing the efficiency of capital utilization throughout the supply chain. Financial technology, by leveraging cutting-edge technologies such as artificial intelligence, big data, and blockchain, enables financial technology to empower supply chain finance. Essentially, this is a process of deep integration and mutual penetration of these cutting-edge technologies (such as big data, cloud computing, and blockchain) with supply chain finance. In the current context, where traditional supply chain finance is facing development bottlenecks and requires new elements to drive innovation, financial technology is precisely meeting this era’s demands. It mainly plays an auxiliary role in enhancing the quality of financial services and operational efficiency (Feng et al., 2024). Most scholars believe that although financial technology continues to evolve and develop, its core remains unchanged in terms of financial attributes (Huang, 2024). At the same time, the urgent need for innovation in traditional supply chain finance has driven the application of financial technology, enabling emerging technologies such as artificial intelligence, cloud computing, blockchain, and big data to be widely adopted in the financial sector. The development of supply chain finance cannot do without the continuous promotion of financial technology.
4.2.3. Research on Risks and Information Asymmetry
Risk management is a systematic framework that includes risk identification, assessment, control and response strategies. Supply chain finance involves multiple links and participants, and its risks mainly stem from a series of liquidity risks and credit risks caused by information asymmetry. Effective risk management can ensure the stability of the supply chain finance ecosystem (He & Ma, 2021).
Information asymmetry refers to the difficulty in obtaining enterprise information, the low quality of the obtained information, and the incompleteness of the information. This leads to the fact that the credit-granting enterprises have great difficulty in comprehensively understanding the actual operating conditions and credit levels of small and medium-sized enterprises, posing challenges to the dimensions of the entire supply chain finance ecosystem. This interferes with the formulation of financial service plans and risk control, and affects the smooth operation of supply chain finance business (Guan et al., 2025).
4.2.4. Application Scenario Study
The research on application scenarios mainly focuses on the practical and innovative applications of fintech in enabling supply chain finance in agriculture, rural industries, and specific sectors. Agricultural supply chain finance provides financing, credit enhancement, risk management, and information services to alleviate the financing constraints of farmers and agricultural enterprises, and enhance the stability and risk-resistance capacity of the supply chain. At the same time, the intervention of fintech, such as big data, blockchain, and digital platforms, can further improve the information transparency, credit assessment accuracy, and business efficiency of agricultural supply chain enterprises, and enhance the operational efficiency of the agricultural supply chain (He & Shen, 2022). Digitalization can significantly reduce the credit risk of agricultural enterprises, and its main mechanisms include improving information transparency, alleviating financing constraints, and optimizing risk control. Agricultural supply chain finance plays a certain intermediary role between digital transformation and credit risk mitigation (Yin, 2025), providing a theoretical basis and practical references for agricultural enterprises to improve their risk management systems and for the government to formulate differentiated support policies.
4.3. Keyword Timeline Analysis
The keyword timeline chart shows that since 2015, research on fintech-enabled supply chain finance has demonstrated a clear dynamic evolution trend. In the early stage (2015-2017), research mainly focused on the basic theories and model exploration of supply chain finance; from 2018 to 2020, with the application of big data, blockchain, and digital technologies, research gradually expanded to the direction of technology empowerment and risk management; and from 2021 to 2025, research further extended to application scenarios such as rural revitalization, agricultural supply chain finance, and industrial chain collaboration, forming a progressive research thread of “theory-technology-application”, reflecting that academic attention is gradually deepening from basic theory to technical practice and actual application scenarios.
The timeline chart based on keyword clustering (see Figure 7) further reveals the trend of research focus evolving over time. It can be observed from the chart that domestic scholars’ research has roughly gone through three stages: the initial stage, the development stage, and the refinement stage. From 2015 to 2017, research was just getting started, focusing on the basic theories and models of supply chain finance; from 2018 to 2020, after the introduction of technology, research gradually focused on the empowerment role of fintech in supply chain finance and risk governance; from 2021 to 2025, research gradually expanded to application scenarios such as agriculture, rural revitalization, and industrial chain collaboration, showing a progressive trend from theoretical discussion to technology application and then to specific scenario practice.
Empirical research on fintech-enabled supply chain finance in China has gradually increased, with research perspectives expanding from enterprise value and financing efficiency to risk management and the effectiveness of technology application. For instance, based on China’s A-share data, Cheng et al. found that engaging in supply chain finance business helps enhance enterprise value, and the effect is more significant when enterprises disclose fintech-related information, suggesting that fintech can improve enterprise market performance by enhancing information transparency and financing support (Cheng et al., 2024). Guan et al., using “specialized, refined, distinctive, and innovative” small and medium-sized enterprises listed on the Shanghai and Shenzhen stock exchanges as samples, further verified the positive role of supply chain finance in improving the financing efficiency of small and medium-sized enterprises, and pointed out that the development of fintech has enhanced this promoting effect (Guan et al., 2025). Additionally, Yin (2025), from the perspective of digitalization and innovation mechanisms, analyzed that in the context of the digital economy, supply chain finance promotes enterprise digital technology innovation and sustainable development through the coordinated operation of capital flow and information flow, and by optimizing credit assessment and risk control mechanisms with digital technology, providing theoretical support for the deep integration of fintech and supply chain finance.
Based on the analysis of the sample literature, some empirical studies show that research directions are gradually extending from superficial description to mechanism analysis and quantitative testing. Although the sample size is limited, these studies provide preliminary observations and references for understanding the operational path and potential economic effects of fintech-enabled supply chain finance. It should be noted that this observation is mainly based on CNKI CSSCI literature from 2015 to 2025 and is not sufficient to infer the complete development trend of the entire field.
Figure 7. Timeline of keywords
5. Conclusion
Based on the journal literature indexed by CNKI CSSCI from 2015 to 2025 and combined with the visualization analysis results of CiteSpace, the study found that fintech empowering supply chain finance is one of the hot topics in domestic academic circles and is currently in a stage of rapid development. From the perspective of the number of publications, the related research has significantly increased in the past five years, especially showing a rapid growth from 2018 to 2020, followed by slight fluctuations, indicating that the research interest has generally increased but the growth rate is uneven.
From the analysis of the author and institution collaboration network, the overall cooperation relationship is relatively loose, and the core author group and institution collaboration network have not yet been fully formed. This suggests that in the future, a collaborative cooperation model can be established to further promote academic exchanges and research collaboration.
The co-occurrence, clustering and timeline analysis of keywords show that the research in this field is centered on “supply chain finance-fintech”, and is carried out around topics such as big data, blockchain, digital transformation, risk management and financing for small and medium-sized enterprises. The research presents a progressive structure from basic theory to technology empowerment and then to application scenarios. In recent years, application scenarios such as rural revitalization and agricultural supply chain finance have gradually become the focus of attention, indicating that the research is developing towards a practice-oriented and diversified direction.
From the perspective of discipline distribution, the research is mainly in the field of finance (accounting for approximately 39%), and also involves interdisciplinary fields such as enterprise economy, information economy and agricultural economy, reflecting the characteristics of multi-disciplinary integration in this field. The study found that fintech empowering supply chain finance has played a positive role in enhancing information transparency, optimizing risk management, improving financing efficiency, supporting the implementation of application scenarios and promoting digital and intelligent development, providing references for theoretical research and practical application.
Domestic scholars’ research in this field mainly focuses on four directions: first, theoretical framework and model innovation; second, risk governance and technology empowerment; third, technology application and empirical effects; fourth, specific application scenarios. Some studies focus on the basic theory of supply chain finance and smart/digital supply chain models, emphasizing the financing mechanism for small and medium-sized enterprises; other studies focus on the role of fintech in risk management, credit identification and business process optimization; and some studies take blockchain, big data and digital platforms as the core, conducting empirical analysis of their impact on enterprise value, financing efficiency and risk assumption. At the same time, rural revitalization, agricultural supply chain finance and industry collaboration have gradually become research hotspots.
Although the research hotspots are clear and technology application is prominent, there are still shortcomings such as sparse cooperation networks, unstable core author groups, limited research depth, insufficient cross-disciplinary integration, and some potential high centrality issues not being fully explored. In the future, it is possible to further strengthen the collaborative cooperation among universities, financial institutions and technology companies, conduct in-depth analysis of the fintech empowerment mechanism, expand industry and application scenario research, and pay attention to potential emerging themes such as financial service innovation and supply chain digital ecology, to provide references and inspirations for academic research and practical exploration in this field.