Artificial intelligence (AI)-powered technology integration in social fintech has transformative potential to advance social responsibility and support sustainable development. This research examines a Blockchain-based lending mechanism that integrates centralized exchanges (CEX) and decentralized exchanges (DEX) to facilitate seamless financial transactions and equitable resource allocation. AI-driven tools are utilized to enhance transparency, accuracy, and security, while smart contracts facilitate the efficient management and verification of loan distribution. The proposed system focuses on helping underserved communities, poor regions, and green businesses, promoting fair and sustainable finance in line with the Sustainable Development Goals (SDGs). The hybrid ecosystem combines the liquidity and regulatory compliance of centralized exchanges with the autonomy and reduced intermediary involvement of decentralized exchanges. AI enhances loan processing, reducing biases and inefficiencies. This framework with smart contracts is to provide scalable, auditable lending aligned with sustainable goals. Machine Learning (ML) algorithms verified loan eligibility with the borrower dataset. The performance of Random Forest algorithms is good due to their robustness and ensemble learning features. Then, Optuna enhanced model tuning, and SHapley Additive exPlanations (SHAP) identified key parameters. Finally, Smart contracts ensured secure, autonomous execution of green loans based on ML verification and sustainability criteria.
Deddy Rakhmad Hidayat, Dian Parawansa, Jusni Ambo Upe, Idayanti Nursyamsi
This study aims to conduct a bibliometric analysis of purchase intention research indexed in Scopus, focusing on trends and patterns from 2023 to 2025. Using Biblioshiny, the study identifies leading journals, authors, affiliations, countries, collaborations, highly cited articles, and main research themes. The Journal of Retailing and Consumer Services emerges as the most productive journal, with FPT University as the top affiliation and Zhang Y as the most prolific author. The most cited article is Treiblmaier H. (2023), Using blockchain to signal quality in the food supply chain: The impact on consumer purchase intentions and the moderating effect of brand familiarity, published in the International Journal of Information Management. China ranks first for corresponding author contributions. Dominant keywords include “purchase intention,” “sales,” and “purchasing.” Emerging research areas such as the metaverse, NFTs (Non-Fungible Tokens), VIS (Vote to Influence System), skincare, and tactics. This research offers a meaningful contribution that can inform and guide future bibliometric investigations undertaken by scholars within the scope of purchase intention by offering insights into key authors, journals, affiliations, countries, and dominant keywords. Additionally, it supports broader academic collaboration and knowledge development in this research domain.
This study presents a blockchain-based enabling autonomous nursing professional development framework, known as BCeANPDF. The framework aims to enhance transparency, security, and professional autonomy in nursing credential management. It is grounded in the principles of competency-based human resource management. Blockchain and smart contract technologies are integrated to support independent recording, verification, and management of professional and non-professional credentials by nurses. At the same time, hospital human resource administrators continue to have the authority to conduct regulatory oversight and ensure compliance. The framework employs a three-layer architecture that includes controller, service, and repository components. These components coordinate access control, data processing, and blockchain-related operations. Seven smart contracts are designed within the framework. They automate credential ownership verification, credential updates, and compliance review processes. This design strengthens data integrity and reduces administrative workload. A prototype was implemented in a private blockchain environment to evaluate system performance. The results demonstrate stable and efficient operation. The average on-chain processing time per credential was 12.3 s. Median query latency ranged from 5 to 9 ms. These findings confirm that the framework achieves scalability and responsiveness comparable to Ethereum, while preserving data privacy and immutability. By combining decentralized trust mechanisms with credential management practices, the BCeANPDF framework offers a practical approach to supporting autonomous professional development. It also facilitates flexible management of the nursing workforce. Overall, the framework contributes to the development of transparent and competency-oriented healthcare institutions without increasing operational complexity.
This study aims to compare the investment performance of Decentralized Finance (DeFi), equities (IHSG), and gold during the 2021–2024 period, which represents a full market cycle characterized by high volatility and economic uncertainty. The research objective is to evaluate differences in return, risk (volatility), risk adjusted performance, and inter asset correlations to assess portfolio diversification potential. A quantitative comparative approach is employed using monthly secondary data, analyzed through descriptive statistics, non-parametric difference tests, and correlation analysis. The findings indicate statistically significant differences among the three investment instruments. Gold demonstrates the highest risk efficiency and consistently performs as a safe haven asset. Equities show moderate stability but relatively lower risk adjusted performance. In contrast, DeFi records the highest average returns, accompanied by extreme volatility and low efficiency. Correlation results reveal a strong positive relationship between gold and equities, while DeFi exhibits significant negative correlations with both assets, indicating diversification potential despite elevated systemic risk. This study concludes that gold remains the most resilient investment asset, equities serve as a balanced growth instrument, and DeFi should be positioned as a high risk speculative asset rather than a core portfolio component.
Bayan Arab, Maizaitulaidawati Md Husin, Suzilawati Kamarudin
HRMARS - Blockchain is a promising, unique technology that enables decentralized, secure, and tamper-proof transactions. Blockchain technology is rapidly growing and being applied across various fields. Supply chain finance is an emerging financing model that optimizes financial flows between enterprises, as banks connect upstream and downstream entities. Traditional supply chain finance faces numerous challenges, such as double financing fraud and information asymmetry. Blockchain technology enhances the performance of conventional supply chain finance by improving the transparency and security of all financial transactions, thus elevating the quality of supply chain information. This improvement can lead to better overall supply chain performance and sustainability. Scholars have not thoroughly investigated the unique role of Blockchain technology in sustainable supply chain finance practices. This paper examines the effect of Blockchain-based supply chain finance systems on sustainable supply chain performance. The conceptual framework was developed based on the Resource-Based View theory (RBV) to underpin the role of Blockchain technology application in the supply chain finance to improve the supply chain performance. In addition, this paper investigates how Blockchain technology's trust and security features can enhance traditional supply chain finance practices, address challenges, and improve capital flow, ultimately contributing positively to overall supply chain performance. Finally, it emphasizes that the area of research on blockchain-based supply chain finance has potential for exploration.
In view of the core pain points of traditional supply chain finance, such as financing difficulties for secondary and above suppliers, opaque transaction information, and difficulty in confirming accounts receivable of small and medium-sized enterprises, this study proposes a blockchain-based supply chain finance management method and system. The system builds a consortium chain network composed of core enterprise nodes, factoring company nodes, supplier nodes, upstream enterprise nodes and information notary nodes, relying on blockchain core technologies such as distributed ledgers and smart contracts to realize the whole process management of credit line issuance, digital bill issuance, circulation endorsement, maturity redemption and discounting. As the core carrier, digital bills have the characteristics of splitting, circulation and discounting, realizing the cross-level transmission of core enterprise credit; The distributed architecture and information notarization mechanism of the consortium chain ensure that transaction information is transparent, traceable, and cannot be tampered with, effectively reducing information asymmetry and transaction risks. Through multi-node collaboration and automated processes, the system not only alleviates the financing pressure of small and medium-sized enterprises, optimizes the efficiency of supply chain capital flow, but also enhances supply chain synergy and the competitiveness of core enterprises, providing practical solutions for the digital transformation of supply chain finance.
Currently housing finance transaction platforms face challenges of data protection and cybersecurity. Blockchain technology, with its decentralization, non-tampering and high transparency, has become an effective tool for securing transaction data. In this paper, a blockchain-based data protection scheme for housing finance transaction platform is designed, which combines the shared energy storage system and realizes the cyber security protection of the transaction platform by optimizing the PBFT consensus mechanism. Methodologically, distributed file storage technology (IPFS) and smart contracts are adopted to ensure data encryption, storage and transaction transparency. Experimental results show that the proposed scheme excels in smart contract execution time, with a maximum execution time of 0.8ms, and achieves a significant increase in TPS when the concurrent volume of transactions reaches 1,200, and the throughput of the dual-chain architecture is increased by 28% compared to the traditional single-chain architecture. In addition, the system with ITPBFT consensus mechanism reduces the communication overhead by 46.19% compared to the traditional PBFT, and the consensus delay is also significantly reduced, with an efficiency improvement of 53.61%. The study shows that the proposed optimization scheme can enhance the efficiency and reliability of data transactions while improving the security of the system.
The financial services sector is experiencing unprecedented transformation through the adoption of virtualization technologies, encompassing cloud computing and edge computing digitalization initiatives that fundamentally alter operational paradigms and competitive dynamics within the industry. This systematic literature review employed a comprehensive methodology, analyzing peer-reviewed articles, systematic reviews, and industry reports published between 2016 and 2025 across three primary technological domains, utilizing thematic content analysis to synthesize findings and identify key implementation patterns, performance outcomes, and emerging challenges. The analysis reveals consistent evidence of positive long-term performance outcomes from virtualization technology adoption, including average transaction processing time reductions of 69% through edge computing implementations, substantial operational cost savings and efficiency improvements through cloud computing adoption, while simultaneously identifying critical challenges related to regulatory compliance, security management, and organizational transformation requirements. Virtualization technology offers transformative potential for financial services through improved operational efficiency, enhanced customer experience, and competitive advantage creation, though successful implementation requires sophisticated approaches to standardization, regulatory compliance, and change management, with future research needed to develop integrative frameworks addressing technology convergence and emerging applications in decentralized finance and digital currency systems.
The increasing need for sustainable practices has encouraged listed companies to participate in carbon trading markets. Traditional centralized systems for managing carbon trading data often face challenges such as limited transparency, poor traceability, and security risks, leading to inefficiencies and compliance issues. This research proposes a blockchain-based framework with smart contracts to provide a secure, decentralized mechanism for recording and verifying carbon trading data. The system ensures tamper-proof logs of emission allowances, trading transactions, and verification events, enabling real-time access for regulatory authorities. Data preprocessing uses Z-score normalization to standardize inputs, while Kernel Principal Component Analysis (KPCA) reduces dimensionality and extracts relevant features. To improve decision-making and cost-efficiency, a Dynamic Cuckoo Search-mutated Locust Swarm Optimization (DCSLSO) algorithm is embedded within the smart contracts to optimize carbon credit allocation and trading strategies. The framework is evaluated through simulations under varying energy demands, carbon prices, and multi-fuel scenarios, using synthetic datasets from energy-intensive industries. The DCSLSO model is implemented using Python and TensorFlow. This research demonstrates that blockchain technology, combined with intelligent smart contracts, can modernize carbon trading for listed companies, fostering transparency, accountability, and long-term economic sustainability in emissions management. This research highlights the potential of combining blockchain technology with intelligent optimization to modernize carbon markets, promoting transparency, accountability, and sustainable economic growth in emissions management.
This paper explored how digital transformation and the use of blockchain technology influenced supply chain transparency in pharmaceutical companies operating in emerging Chinese markets. The study incorporated the Technology Acceptance Model (TAM), which facilitated the identification of key aspects such as perceived usefulness, perceived ease of use, attitude, and behavioral intention, along with the mediating variable of self-efficacy. Based on these elements, a conceptual framework was developed, which further aided understanding of the hypothesised relationships examined in the study. Accordingly, a quantitative research design was implemented using a primary data collection method. In the Shanghai pharmaceutical industry, data were collected from a sample of 400 managerial employees. The outcomes of technology integration and transparency were quantitatively examined in relation to one another. The results indicated that blockchain technology and digital transformation enhanced supply chain performance through improved traceability, trust, and efficiency. The study shed further light on the main obstacles to implementation and provided insights for policymakers and industry leaders on improving transparency through advanced digital technologies in China’s expanding pharmaceutical market. The findings confirmed that respondents perceived the synergistic effects of digital transformation and blockchain implementation as having the greatest potential to improve supply chain transparency. Blockchain technology enabled real-time, secure, and distributed immutable ledgers that supported product tracking, counterfeiting prevention, verification of authenticity, and enhanced transparency.
Digital transformation is reshaping healthcare systems worldwide, with nursing practice positioned at the forefront of technology-driven innovation. As nurses increasingly engage in data management, coordination of care, and decision-making across complex health systems, the need for secure, transparent, and trustworthy digital infrastructures has become paramount (Khezr et al., 2019). Among emerging technologies, blockchain, a decentralised and tamper-resistant ledger system, and smart contracts, which enable automated and verifiable transactions, present promising opportunities to enhance trust, accountability, and interoperability within nursing workflows (Khezr et al., 2019; Saeed et al., 2022). Smart contract’s core features: immutability, transparency, and decentralisation, make it particularly suited for addressing long-standing challenges in healthcare data management and nursing administration. In nursing contexts, potential applications include secure sharing of patient records, real-time tracking of clinical documentation, automated credential verification, and protection of consent and privacy (Kuo et al., 2017; Naresh et al., 2025). It may further facilitate process automation in areas such as nursing resource allocation, performance auditing, and continuing education accreditation. Despite its promise, the adoption of blockchain technology in nursing remains in its infancy. Most studies have centered on technical or conceptual models rather than empirical evaluations, and few have examined the direct or indirect impacts on nursing efficiency, patient safety, or care coordination (Hasselgren et al., 2020). Implementation challenges, such as scalability, interoperability with existing hospital information systems, regulatory ambiguity, and user acceptance also persist (Saeed et al., 2022). Moreover, the ethical implications related to data ownership and governance in decentralised environments warrant careful consideration in nursing settings. Given these gaps, a scoping review is needed to synthesise current evidence on the applications and impacts of blockchain and smart contract technologies in nursing practice. This review will explore their roles across nursing service delivery, management, and education, with particular attention to how these technologies influence efficiency, trust, accountability, and data security. By consolidating interdisciplinary insights, this study seeks to guide future research and inform policy and practice frameworks for integrating blockchain and smart contract technologies into the nursing profession.
Prof. M. A. Sayyad, Veerendra Yadav, Dr. Geetika M. Patel, Dr. Prakash Deep · 8 authors
The problem of data privacy, interoperability, cyberattacks, and unauthorized changes of sensitive medical records are becoming critical issues in healthcare information exchange systems. The conventional centralized healthcare designs have single-point failures, inadequate transparency, sluggish data synchronization, and insufficient trust management among dispersed medical organizations. In order to overcome these shortcomings, this paper suggests a Blockchain-Assisted Distributed Artificial Intelligence Framework to Secure Healthcare Information Exchange and Data Integrity. The suggested architecture combines a distributed AI-based healthcare analytics system with blockchain-based immutable ledger systems to provide secure, open, and alteration-free medical data exchange among various healthcare nodes. The automated access control and the secure management of authorization is applied using smart contracts, and intelligent anomaly detection and integrity verification of healthcare transactions are implemented using distributed AI modules. The framework also includes encrypted communication and decentralized consensus systems to promote security and reliability in the context of multi-institutional healthcare settings. Simulated healthcare data based on experimentation shows that the proposed framework has a data integrity verification accuracy of 96.4, anomaly detection accuracy of 92.7 and offers both efficient and secure transaction validation performance at a ratio of 41.3 lower than traditional centralized healthcare systems. The suggested architecture enhances the security of healthcare data and trust management, scalability and interoperability of the next-generation intelligent healthcare ecosystems significantly.
With its decentralized, tamper proof, transparent and traceable characteristics, blockchain technology has shown great potential in fields such as finance, supply chain, and the Internet of Things. However, the public transparency of its ledger poses a serious challenge to user transaction privacy. Traditional privacy protection schemes such as homomorphic encryption and zero knowledge proofs can enhance privacy, but often struggle to balance computational overhead, communication costs, and data availability. This article explores the innovative application of neural networks in blockchain privacy protection and proposes a transaction obfuscation model based on generative adversarial networks. This model utilizes a generator to learn the statistical features of raw transactions and generate difficult to track obfuscated transactions, while ensuring the validity and compliance of obfuscated transactions through discriminators and blockchain verification contracts. The experimental results show that compared with traditional obfuscation methods and differential privacy methods, the proposed model significantly reduces the consumption of privacy budget and computation delay while ensuring high transaction utility (such as reducing address correlation by more than 85%), achieving a better balance between privacy and utility. This study provides new ideas for building efficient and practical blockchain privacy enhancement solutions.
In the process of building materials supply chain management, there are problems such as information opacity, low logistics coordination efficiency, difficulty in material quality traceability, and weak trust mechanism among supply chain entities, which lead to rising costs, low efficiency, and waste of resources. In addition, the construction industry has a large amount of carbon emissions, and the impact of supply chain management on carbon emission reduction cannot be ignored. To this end, this paper introduces blockchain technology to improve supply chain transparency, optimize logistics management, enhance material quality traceability, and explore its role in carbon emission reduction. This paper constructs a blockchain-based building materials supply chain management system, using distributed ledgers to ensure data transparency, smart contracts to automate procurement, acceptance and payment, which is a material traceability system to ensure quality control, the Internet of Things combined with blockchain to optimize logistics management, and establish a carbon emission monitoring and optimization mechanism to achieve real-time data recording and low-carbon scheduling. The system built in this study shows significant advantages in multiple key indicators. The overall carbon emissions of the supply chain in the experimental group are 88 tons of CO2, a 12% decrease compared to 100 tons of CO2 in the control group. The average transportation time in the experimental group is 4.5 hours, while that in the control group is 8.2 hours, a 45.1% decrease. The application of blockchain technology has effectively improved the efficiency and transparency of building materials supply chain management, optimized logistics and material quality control, and played a positive role in carbon emission reduction.
Spa services in wellness tourism often face limitations in transparency, service integration, and customer trust in operational flows. This study develops a blockchain-based smart contract model that integrates five key indicators: reservations, cancellations, customer satisfaction, inventory, scheduling, and finance. A literature review of 113 articles yielded 25 key references, with significant trends such as the occurrence of the keyword “customer reservation” 10,100 times (2020–2024). Linear regression, correlation analysis, and ANOVA methods were used to test the research results. Linear regression predicts the relationship between variables, while correlation measures the strength of the relationship. The calculation results show a Pearson correlation coefficient of 0.93 (α = 0.05), indicating a very strong linear relationship. ANOVA shows significant differences between groups. These findings confirm that blockchain-based smart contracts are effective in digitally automating spa service workflows, strengthening transparency, and improving customer satisfaction
This paper aims to carry out a systematic study on the application of blockchain technology in the field of Accounts Receivable Financing (ARF). The report first peels apart the main pain points of the traditional ARF model (factoring) from the aspect of information asymmetry, transmission of credit and confirmation of rights. Then the report does a thorough analysis on how blockchain technology (especially the characteristics of unchangeable nature, smart contracts, and asset digitization) theoretically solves these pain points, emphasizing the elaboration on the realization path of "penetration of credit". The core of this report is the in-depth study and comparison of four important cases of significance, namely, the "Dual-Chain Connect" of Ant Group, "Yi Enterprise Chain" (YQLink) of Ping An OneConnect, WeBank (based on FISCO BCOS), and "Jing Bao Bei" of JD Technology. Through the comparison of these cases in terms of their business models, technical architecture and risk control mechanism, this report summarizes three mainstream realization mode: "central enterprise-led", "fintech platform-led", and "(digital) bank-led", and reveals their basic difference regarding "source of credit". Finally, the study talks about the common problems confronting the field, such as data silo, interoperability and regulatory uncertainty, and gives its optimistic outlook regarding its future trends of integration into Artificial Intelligence (AI), Internet of Things (IoT), and evolution towards Decentralized Finance (DeFi).
The in-depth application of blockchain technology in the financial sector has made smart contracts the core execution carrier for various decentralized financial businesses. Their security performance is directly related to the safety of financial assets and the stable development of the blockchain financial ecosystem. The immutability of smart contract code makes it difficult to fix vulnerabilities once they occur, which can easily lead to serious risks such as the theft of financial assets and transaction defaults. Moreover, the severity of different vulnerabilities varies significantly. Therefore, accurately defining the risk level of vulnerabilities and predicting the risk level in advance have become the core requirements for the security protection of blockchain applications in the financial field. This paper first explores the distribution patterns and correlation characteristics of the vulnerability features of smart contracts through correlation analysis and violin graph analysis. Then, multiple mainstream machine learning algorithms are introduced to conduct comparative experiments. The results show that the Transformer-LSTM-KELM algorithm proposed in this paper has the best comprehensive performance, with an accuracy rate of 71%. It is 5 percentage points higher than the suboptimal CatBoost and 25 percentage points higher than AdaBoost. With an precision rate of 77%, it is significantly better than all comparison algorithms. Its F1 value of 70% and recall rate of 71% are both at the leading level. This algorithm provides an efficient solution for the precise prevention and control of vulnerability risks in smart contracts in financial scenarios, and has significant practical value in ensuring the safe and compliant operation of blockchain financial business.
Blockchain technology is considered a transformative innovation, offering decentralized, secure, and transparent solutions to various industries, with cryptocurrencies being its most famous application. The volatility and non-linear behavior of cryptocurrency markets pose significant challenges for predicting their prices accurately. Predicting cryptocurrencies prices based on traditional statistical methods often fail to capture the market complex dynamics. Therefore, the recent developments in Artificial Intelligence, especially in deep learning and ensemble-based approaches have presented promising results. This study delivers a comprehensive literature review focusing on applying deep learning and ensemble deep learning algorithms in cryptocurrency time series price prediction. The main deep learning models such as Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), Convolutional Neural Network (CNN), and Recurrent Neural Network (RNN) are examined with a variety of time intervals and cryptocurrency types. The findings present that deep learning models, especially when used in hybrid or ensemble configurations, have obtained promising results. This review highlights the efficacy and significant potential of ensemble deep learning and its capabilities in cryptocurrencies price trend forecasting offering valuable insights for investors and researchers.
With the increasing maturity of blockchain technology, its characteristics such as decentralization, data immutability, and consensus mechanisms can effectively address issues in supply chain finance, including high risk control costs, difficulties in credit endorsement for small and medium-sized enterprises, and cumbersome operational processes. By synthesizing research on the integration of blockchain technology into supply chain financial services and analyzing a case study of JD.com’s application of blockchain technology in supply chain finance ABS business, this paper proposes future development prospects for “blockchain technology + financial services”. The aim is to provide decision-making references for the modern financial services industry to expand operations, improve service performance, and reduce financial risks.
The aim of the study is to focus on the marketing communication strategies in the banking and finance sector from past to present, and to detail the concepts of phygital banking and metaverse banking in terms of both usage and the advantages and disadvantages it brings from the perspective of industry professionals. In-depth interviews were conducted with a total of 6 expert bankers from 3 different banks, which constitute the universe of the research while providing sample criteria. The data transcripts created with participant statements were divided into six themes and forty-three sub-codes and presented to expert opinion to ensure the external control of the research. The data were subjected to content analysis using the MAXQDA 2022 qualitative analysis program. Based on the findings, answers were sought to the following questions: (1) What are the definition, scope, and application areas of digital marketing communication in the banking and finance sector? (2) What are the elements of digital marketing communication used in the banking and finance sector? (3) What are the advantages and disadvantages of the digital marketing communication era in the banking and finance sector compared to the traditional marketing communication era shopping experience? According to the data analysis results, participants define digital marketing as a new marketing strategy that enhances consumer experience by combining traditional financial services with digital technologies. In addition, digital applications in the banking and finance sector are concentrated in areas such as application processes, marketing activities, payment systems, and smart voice systems. While the most commonly used digital elements are artificial intelligence (AI) and QR code, augmented reality (AR), virtual reality (VR), and blockchain are following these digital elements. According to the research results, the prominent advantage of digital marketing is experience-orientedness, while it is observed that digital spaces such as metaverse, with their decentralized and anonymous structure, also bring some privacy and security disadvantages. Concepts such as digital and metaverse are important innovative concepts that shape the future understanding of marketing communication. In the study, focusing on the digital marketing strategies used in the banking and finance sector, their characteristic features and technological components were evaluated from the perspective of industry professionals, and recommendations were made to the banking and finance sector based on the findings.
This paper discusses the obstacles to the capitalization of data elements, such as the difficulties in confirming data ownership, trust deficit, privacy breaches, and inefficiency of transactions, through a distributed solution based on blockchain technology. First, a data ownership confirmation mechanism based on a consortium blockchain is established by using the Merkle tree and PBFT (Practical Byzantine Fault Tolerance) consensus algorithm to achieve transparency and traceability of data ownership. Second, a multi-dimensional data value evaluation and RF-BP (Random Forest-Back Propagation) dynamic pricing mechanism are established by using machine learning algorithms to evaluate the value of data assets in a scientific manner. Third, a smart contract is established for pricing and payment, in order to achieve transaction automation and clearing and settlement. Finally, ZKP (Zero-Knowledge Proof) technology is applied to develop a mechanism for verifying compliance and privacy of data under the proposition of public review and "visible, invisible". Experimental results show that the proposed method reduces the average leakage risk and defense success rate under various attacks to 8.57 % and 97.1%, respectively. In terms of transaction efficiency, the proposed method achieves a throughput of 1250 TPS (Transactions Per Second) with a latency of 120 milliseconds at a 50-node scale. Overall performance is demonstrated with a confirmation and transaction success rate of 99.2% and 97.8%, respectively. The suggested framework provides reliable confirmation of data elements, scientific pricing, efficient trading and transaction processes, and privacy protection. It can support the vision of developing a secure, transparent and efficient market for data element circulation with technical feasibility and performance.
Md Imran Khan, Ahmad Raza, Abdulrahman Alomair, Abdulaziz S. Al Naim
This study provides a comprehensive contribution to the current understanding of blockchain technology and non-fungible token (NFTs). Blockchain technology is a revolutionary data storage and management tool that records data shared across a network of computers globally, making it safe, transparent, and decentralized. Non-fungible token is a specific type of token built on a blockchain, enabling the authentication of digital assets and safeguarding them against copying or fabrication. The research employed information on 3760 abstract data collected for the period from January 1, 2017, to June 03, 2025. The search criteria for data retrieval are based on the following keywords: “blockchain”, “non-fungible token”, and “token”. The data sources are Scopus and Web of Science. The study highlights the multi-dimensional and evolving discussion around blockchain and NFTs, including elements of technology, security, money, digital rights, and decentralization. The Wordcloud indicates a strong and growing innovation ecosystem, proposing new study avenues in trust mechanisms, smart contract development, and tokenized economies. The correlation graph visualizes that AI, data, finance, and blockchain show their mutual dependence has revolutionized our view of autonomy and governance. The study highlights authors who actively research blockchain and NFTs, as well as correlations between them. China leads the way in this research area and USA leads in terms of citation. The study’s finding informs evidence-based decision-making regarding the regulation and governance of blockchain and NFT technologies. For industry practitioners, the study’s insights can guide the development of innovative applications and solutions leveraging blockchain and NFTs. By examining a vast dataset of academic papers, the inquiry illuminates the key themes, emerging trends, and potential research gaps within this rapidly evolving field.
This study addresses key challenges in tax governance for multinational enterprises, including data silos, delayed risk identification, and insufficient privacy protection. It proposes a collaborative governance framework that integrates blockchain-based smart contracts and multimodal machine learning, termed the Blockchain-Enhanced Multimodal Risk Assessment Framework. The framework employs a dual-layer architecture that combines blockchain technology with federated feature engineering. At the data processing layer, it utilizes a multi-chain coordination mechanism based on Adaptive Proof of Stake, enhanced by a node reputation-based dynamic evaluation algorithm to significantly improve consensus efficiency and data transmission security. At the risk prediction layer, it integrates temporal dependency mining with heterogeneous graph neural networks, applying a composite embedding method to extract 89 highly discriminative features from an initial 1,423-dimensional feature space.
Abstract In digital higher education, digital transformation is mandatory. Blockchain technology, with its unique features of distributed ledgers, consensus mechanisms, smart contracts, and traceability, provides a new perspective for digital educational resource platforms. In this study, a blockchain-based design was proposed for an open service platform for digital education resources in universities. The platform addresses challenges such as limited openness, complex resource copyright certification, and difficulty in effectively ensuring resource security and quality. The platform offers resource publishing, resource trading, operation management, and interface management ensuring data security using the distributed ledger of blockchain. Consensus mechanisms and smart contracts are used to ensure fairness and efficiency in platform operation and automate resource transactions. Traceability is utilized to ensure the certification and protection of resource copyrights.