Blockchain has become peer-to-peer immutable distributed ledger technology network, and its consensus protocol is essential to the management of decentralized data. The consensus algorithm, at core of blockchain technology (BCT), has direct impact on blockchain's security, stability, decentralization, and many other crucial features. A key problem in development of blockchain applications is selecting the right consensus algorithm for various scenarios. Ensuring scalability is the most significant drawback of BCT. The industry has been rejuvenated and new architectures have been sparked by the usage of consensus protocols for blockchains(BC). Researchers analyzed shortcomings of proof of work (PoW) consensus process and subsequently, alternative protocols like proof of stake (PoS) arose. PoS, together with other improvements, lowers the unimaginably high energy usage of PoW, making it protocol of time. In PoS, only the user with highest stake becomes the validator. To overcome this, we propose Scalable Proof of Stake (SPoS), a novel consensus protocol, which is an enhancement of PoS protocol. In the proposed algorithm, each stakeholder based on the stake gets a chance to become the validator and can mine blocks in the blockchain. Clustering of the stakeholders is done using mean shift algorithm. Each cluster gets a different number of blocks to mine in BC. Cluster with highest stake will get a greater number of blocks to mine when compared to other groups and the cluster with the least stake gets least number of blocks to mine when compared to other groups. To mine the blocks, validator is chosen based on the cluster in which he is present. Fair mining is ensured for all stakeholders based on number of stakes. Mining is distributed among all the stakeholders. Since the validators are chosen fast, the transaction rate is high in the network. Validators in PoS are selected according to the quantity of cryptocurrency they stake. More stakeholders will get chance of validating blocks and receiving rewards. Over time, this reduces fairness and decentralization by concentrating on wealth and power. This is addressed in SPoS using clustering-based validator assignment.
This comprehensive technical specification details the ADN-NChain protocol, a first-of-its-kind Distributed Ledger Technology (DLT). It introduces a revolutionary self-repairing neural blockchain architecture, leveraging biologically-inspired genetic algorithms for state management and unprecedented network resilience to systemic and quantum threats. The whitepaper thoroughly covers the robust system design, the novel Proof-of-Resonance (PoR) consensus mechanism, and a future-proof post-quantum cryptography implementation (CRYSTALS-Dilithium) essential for securing the next generation of crypto-assets and decentralized applications (dApps). Performance metrics confirm high scalability (12,000+ TPS) and exceptional data integrity with a 99.9% automatic corruption repair success rate. This work is critical for researchers, investors, and developers focused on next-generation blockchain, Web3, cryptocurrency security, and Decentralized Finance (DeFi) solutions.
In the contemporary global job market, the secure and efficient verification of a candidate’s academic qualifications presents a significant challenge, particularly across international boundaries. Conventional techniques frequently necessitate physical documents or PDF scans, rendering them inefficient, susceptible to falsification, and hazardous about privacy. This study presents a contemporary, scalable framework that integrates Zero-Knowledge Proofs (ZKPs), blockchain technology, and decentralized storage (IPFS) to establish a secure, privacy-oriented method for candidate verification. In this proposed system, candidates submit their academic documents, which are digitally signed by the issuing universities using cryptographic methods. The signed files are preserved on IPFS, guaranteeing their integrity and accessibility. The hash of each document is then stored on a blockchain, either Ethereum or Polygon, offering a public and immutable reference. Zero-Knowledge Proofs enable candidates to validate the legitimacy of their credentials while safeguarding sensitive information. Human Resources teams can authenticate these documents in real time, validating their integrity against the blockchain hash while preserving the candidate’s confidentiality. The evaluation results demonstrate that Ethereum offers robust decentralization and trust; nevertheless, Polygon proved to be more pragmatic because to its reduced gas price and expedited transaction times, making it suitable for high-volume recruitment. This proposed initiative addresses weaknesses in digital recruitment by guaranteeing trust, privacy, and automated credential verification procedure. It provides a customized approach for present recruitment requirements, particularly for organizations engaged in cross-border hiring, where security, scalability and protection of candidate information are paramount.
The global Web3 Payments Market is projected to grow from USD 2.83 billion in 2023 to USD 44.98 billion by 2030, driven by decentralized blockchain adoption and AI integration. Web3 payments enable secure, transparent, and cost-efficient financial transactions for businesses worldwide. To access the full report, visit - https://www.nextmsc.com/report/web3-payments-market
HOT Protocol provides the infrastructure that allows smart contracts to securely own and manage private keys. The Multi-Party Computation (MPC) Network manages signing keys. By running an MPC node inside a Trusted Execution Environment (TEE), the protocol achieves stronger security guarantees while lowering economic requirements for participants. The NEAR Protocol provides a decentralized and efficient state layer. Key management can be integrated with any smart contract across Stellar, TON, Solana, and EVM-compatible networks.
In 2025, the blockchain network Qubic conducted a mining campaign on Monero, a privacy-focused cryptocurrency. It publicly presented the campaign as a ``51\% takeover'' and a demonstration of selfish mining. The episode provides a rare opportunity to test such claims in a privacy-preserving proof-of-work network, where pool attribution and private release decisions are difficult to observe. We combine Monero node measurements, Qubic pool observations, community-shared artifacts, and disclosed view keys to attribute blocks and identify candidate withholding periods. Rather than sustained majority control, we observe elevated orphaning and deeper reorganizations. Qubic did not follow a single optimized selfish mining strategy, instead varying between standard selfish mining and more defensive block-release decisions. Across the campaign, it gained no reward advantage over honest mining. Community monitoring and Qubic's countermeasures reveal how real-world mining campaigns evolve in response to an active ecosystem. Overall, the campaign disrupted Monero without exhibiting a stable profit-maximizing strategy, leaving open disruptive incentives beyond direct mining rewards.
Large Language Models (LLMs) have demonstrated remarkable potential as autonomous agents, approaching human-expert performance through advanced reasoning and tool orchestration. However, decision-making in fully dynamic and live environments remains highly challenging, requiring real-time information integration and adaptive responses. While existing efforts have explored live evaluation mechanisms in structured tasks, a critical gap remains in systematic benchmarking for real-world applications, particularly in finance where stringent requirements exist for live strategic responsiveness. To address this gap, we introduce AI-Trader, the first fully-automated, live, and data-uncontaminated evaluation benchmark for LLM agents in financial decision-making. AI-Trader spans three major financial markets: U.S. stocks, A-shares, and cryptocurrencies, with multiple trading granularities to simulate live financial environments. Our benchmark implements a revolutionary fully autonomous minimal information paradigm where agents receive only essential context and must independently search, verify, and synthesize live market information without human intervention. We evaluate six mainstream LLMs across three markets and multiple trading frequencies. Our analysis reveals striking findings: general intelligence does not automatically translate to effective trading capability, with most agents exhibiting poor returns and weak risk management. We demonstrate that risk control capability determines cross-market robustness, and that AI trading strategies achieve excess returns more readily in highly liquid markets than policy-driven environments. These findings expose critical limitations in current autonomous agents and provide clear directions for future improvements. The code and evaluation data are open-sourced to foster community research: https://github.com/HKUDS/AI-Trader.
In recent years,the security issues of smart contracts have become increasingly prominent,and vulnerability detection has become a key challenge.In scenarios where source code is not publicly available,bytecode-based detection methods have attracted significant attention.However,existing deep learning methods typically rely solely on sequences or graph structures,which makes it difficult to fully capture vulnerability features.To address this,this paper proposes a smart contract bytecode vulnerability detection method based on heterogeneous graphs and instruction sequences,called RGCN-ResNet1D(Relational Graph Convolutional Network and ResNet-based 1D Convolutional Network).This method models bytecode as a heterogeneous graph and instruction sequence,using a Relational Graph Convolutional Network(RGCN) to extract structural features and a ResNet-based 1D Convolutional Network(ResNet1D) to extract sequential features,and then fuses the two types of features for vulnerability detection.A cross-entropy loss function is also designed,which dynamically adjusts the weight based on the number of misclassified samples,effectively alleviating the class imbalance problem in the training set.Experimental results show that RGCN-ResNet1D achieves F1 scores of 95.43%,90.67%,and 92.31% for detecting integer overflow,timestamp dependency,and self-destruct vulnerabilities,respectively,significantly outperforming the comparison methods.
A vulnerability in smart contracts refers to weaknesses in the code that can be exploited by attackers, leading to security breaches and unintended behavior. With the growing use of smart contracts in decentralized blockchain systems, particularly in internet of things (IoT) environments, ensuring their security has become increasingly critical. Traditional vulnerability detection techniques, such as formal verification and symbolic execution, face significant limitations, including high rates of false positives and negatives, scalability issues, and difficulty in detecting complex vulnerabilities. To address these challenges, this paper proposes semantic contract flow analysis and deep learning ensemble (SCADE) for smart contract vulnerability detection. SCADE leverages semantic flow analysis combined with an ensemble of deep learning models, including convolutional neural networks (CNN), bidirectional sequence encoder (BSE), layered probabilistic neural network (LPNN), and adaptive context learning network (ACLN), to detect vulnerabilities effectively. The methodology breaks down the smart contract code into structured components through a contract structure mapper, followed by extracting semantic paths and converting them into sequential vector representations. These representations are then processed through a deep learning ensemble to identify potential vulnerabilities such as reentrancy, timestamp dependency, code injection, and hardcoded gas amounts.
Herman Herman, Rinday Zildjiani Salji, Herman Yuliansyah
This research studies implementation of decentralized applications (DApps) that are combined with blockchain technology and IPFS for storing patient medical data. The goal of this research is to increase the security, transparency, and access control of stored medical data to make sure only legitimate users can access the data. The proposed system uses smart contracts on the Ethereum network to handle user rights of access (doctors, patients, and admins) and ensure data integrity through the blockchain immutability feature. Patient medical records are retained in IPFS and traced using the Content Identifier (CID). Implementation outcome reveals that the system can safely process medical information, keeping patients in full control of their information, and restricting data access only to scheduled time. This system also shows the potential of blockchain and IPFS technology-based applications in achieving a more efficient health ecosystem focused on safeguarding people's data.
It is worth noting that the topic of cryptocurrencies is characterized by modernity, and the resulting vacuum exists for many of them, and this is entirely the result of the failure of the vast majority of countries and international organizations to analyze them, to distinguish the topic as virgin, especially since it has been digital, so it pushes modern and innovative has become at the forefront. Details list Controversy around the world, as cryptocurrencies represented a dangerous stage in the development of currencies that we witness today in different eras, especially in light of the noticeable spread of these currencies, whether in the present or in the future One of the most important problems resulting from dealing in cryptocurrencies has become the lack of legislative texts that address disputes arising from the trading of digital currency in most countries and international organizations. We also did not find legal legislation for cryptocurrencies in Iraqi legislation that regulates them, and the issue of Research is considered a virgin topic in private international law. Being a complex and thorny subject that includes technical rules, technical complexities and multiple inter-related relationships involving multiple legal systems, which requires an integrated legal system.
Open access
Blockchain Technology Applications and Security
Security, Politics, and Digital Transformation
Innovations and Analysis in Business and Education
Purpose – The purpose of this research is to explore the opportunities and barriers related to the use of cryptocurrencies in tourism from the local community’s perspective. Cryptocurrencies are increasingly accepted worldwide, yet their use in tourism consumption remains limited. Evaluating the attitudes and readiness of residents in urban areas, particularly in Zagreb, is essential for assessing the sustainability of digital payment technologies in tourism. Methodology – The research was conducted in Zagreb and its surroundings, with a sample of 484 respondents. A structured questionnaire was used to assess knowledge, perceived security, intention to use, and perceived barriers and incentives regarding cryptocurrency usage in tourism. Data analysis involved descriptive statistics and Pearson’s Chi-square test to examine relationships between key variables and sociodemographic factors. Findings – The results indicate limited awareness about cryptocurrencies, with more than 75% of respondents being completely unfamiliar or only superficially familiar with the topic. A small percentage currently uses cryptocurrencies, but there is substantial conditional willingness for future usage, particularly if regulatory, educational, and security issues are addressed. Statistically significant gender differences were observed in perceived awareness and trust in Bitcoin systems, with men exhibiting higher levels of awareness and trust compared to women. Contribution – This study provides valuable insights into local community readiness for cryptocurrency usage in tourism, highlighting the significance of education, trust, and regulatory frameworks. The findings can serve as a foundation for policymakers, tourism stakeholders, and digital innovators to develop strategies for the effective integration of cryptocurrencies into tourism economies.
This paper introduces the Universal Turing Market Machine (UTMM): a unified, neuromorphic market infrastructure designed to compute, adapt, and coordinate economic activity autonomously. Building on Hayek’s theory of spontaneous order and Ashby’s Law of Requisite Variety, the paper argues that while markets themselves emerge naturally, the computational substrate that supports them can be intentionally designed. The UTMM integrates sensory inputs (e.g., IoT data), distributed ledger signaling, evolutionary compute layers, and real-world actuators to form an adaptive, nervous-system-like architecture for market coordination. This framework enables transparent, auditable, self-organizing market processes capable of discovering their own requisite dimensionality. The paper formalizes these systems under the term Adaptive Resource-Coordinated Organisms (ARCOs), digital-economic organisms that merge machine learning, blockchain, and adaptive market solvers into a cohesive evolutionary market machine.
Uhryn A.P. FINANCIAL SUPPORT FOR THE DEVELOPMENT OF LOCAL COMMUNITIES IN DEVELOPED COUNTRIES: IMPLEMENTATION IN DOMESTIC PRACTICE Purpose. The aim of the article is to summarise and systematise international experience in financial support for the development of local communities in developed countries and to substantiate the directions for its implementation in domestic practice, taking into account the tasks of strengthening the revenue base of local budgets in Ukraine, increasing the institutional stability of communities and ensuring their sustainable development in the context of post-war recovery and budget decentralization. Methodology of research. The methodological basis of the study is the systemic and institutional approaches, which made it possible to consider local finances as a complex multi-level system in which budgetary, tax, and transfer mechanisms interact within the current institutional environment. In the process of research, comparative analysis methods were used to compare models of local finance organization and mechanisms of fiscal decentralization in different countries, logical generalization to form theoretical conclusions and conceptual provisions, as well as structural and functional analysis to identify the role of individual elements of the budget system in ensuring the financial autonomy of local self-government. Thus, the research methodology is based on a comprehensive combination of modern theoretical and methodological approaches to analysing the functioning of the public finance system in decentralized conditions. The theoretical basis of the work is formed by the provisions of the theory of fiscal federalism, which reveal the patterns of distribution of revenue and expenditure powers between levels of government, the concept of tax autonomy of territorial communities, as well as scientific approaches to inter-budgetary equalization as a tool for ensuring financial capacity and balanced regional development. Findings. It has been established that the effectiveness of financial support for local communities is determined by a balanced combination of tax autonomy, stable own and fixed revenues, as well as effective mechanisms of vertical and horizontal financial equalisation. The institutional features of decentralised, cooperative and centralised models of local finance, their impact on the financial stability of communities and the quality of public services are revealed. It is substantiated that Ukraine's priorities are: strengthening the role of personal income tax and property tax in local budget revenues, improving formula equalisation based on European models, developing municipal investment and project financing instruments, and integrating international aid into the local finance system while maintaining budgetary discipline. The obtained results correspond to the tasks of improving the effectiveness of community’s information and communication resources and strengthening the financial capacity of territories. Originality. International models of financial support for territorial communities have been systematised through the prism of combining tax autonomy and solidarity mechanisms; adaptation guidelines for Ukraine have been substantiated, combining the expansion of communities' own revenue base with the improvement of inter-budgetary relations and the strengthening of the financial responsibility of local self-government bodies. Practical value. The proposed approaches can be used by public authorities and territorial communities of Ukraine to form a sustainable revenue policy, optimise the financial equalisation system, plan development investments and improve the quality of budget management. The research results are relevant to scientific and practical tasks of improving local budget revenue management and introducing strategic risk management into community activities. Key words: financial support for local communities, fiscal decentralization, local budgets, tax autonomy, inter-budgetary relations, financial equalisation, post-war recovery.
Dec 1, 2025·International journal of intelligent computing and information sciences/International Journal of Intelligent Computing and Information Sciences
sarah Osama anis, Mohammed Mabrouk Morsey, Mostafa Aref
In the rapidly evolving landscape of cryptocurrency, gaining a deep understanding of public sentiment has become increasingly essential, especially given the significant impact of social media platforms on market perceptions and trends. This paper introduces a sophisticated sentiment classification model that utilizes a Bi-LSTM architecture to analyse over one million tweets related to Bitcoin. By integrating Explainable AI techniques, particularly LIME (Local Interpretable Model-agnostic Explanations) framework, our model not only achieves an impressive test accuracy of 98% but also offers valuable insights into its decision-making process, making the results more interpretable for users Our findings highlight robust performance metrics across precision, recall, and F1-scores, which collectively underscore the model's reliability and effectiveness in real-world applications. Furthermore, we delve into the opaque nature of the Bi-LSTM model through the application of LIME, which sheds light on how particular words and phrases have a strong impact on sentiment predictions. This research equips future investigations with conceptual frameworks and analytical tools that can be customized to study a broader range of cryptocurrencies. Through this work, we aim to foster a more nuanced comprehension of how public sentiment shapes market behaviour and decision-making in the digital currency space.
A Sowmiya, Kavitha Muthukumaran, V Jhansi, Jesus Milton Rousseau S. · 6 authors
Decentralized Finance (DeFi) represents a transformative shift in the financial landscape by using blockchain technology to enable peer-to-peer services without traditional intermediaries. This study adopts a socio-cultural lens to examine the key factors that influence individuals’ intentions to adopt DeFi technologies. In particular, we explore how performance expectancy (perceived usefulness), effort expectancy (perceived ease of use), social influence, and innovativeness drive user adoption, and how these relationships are moderated by demographic factors such as age, gender, education, and income. Drawing on survey data (N = 425) collected in India (an emerging market context), the research employs Structural Equation Modeling (SEM) to test the proposed framework. Results indicate that perceived usefulness and ease of use are significant positive predictors of DeFi adoption. Social influence and individual innovativeness also encourage adoption, especially among younger and more educated users. Moreover, demographic characteristics shape the strength of these effects: for instance, younger users find DeFi more useful and easier to use, women are more impacted by social recommendations, and higher-income individuals are more inclined to adopt innovative financial solutions. These findings underscore that DeFi adoption is not just a technical or economic process, but a culturally situated phenomenon influenced by social dynamics and user diversity. The paper discusses implications for improving digital financial inclusion and strategies for stakeholders to foster broader DeFi acceptance across different social groups
Tengku Mohd Diansyah, Nuraminah Ramli, Muzammil Jusoh
This study addresses the limitations of existing decentralized e-voting systems, particularly their reliance on public distributed infrastructures, limited real-world deployment feasibility, and lack of comprehensive evaluation. Previous studies have demonstrated the potential of distributed ledger-based voting mechanisms; however, most focus on conceptual designs or small-scale prototypes without detailed performance and usability validation. To address this gap, this research proposes and implements a decentralized e-voting system deployed on a local server infrastructure using distributed ledger technology and automated validation mechanisms for vote integrity. The system is designed to reduce dependency on external networks while maintaining transparency, immutability, and operational efficiency. The system was evaluated through functional testing, performance analysis, and user acceptance testing involving 30 participants in a controlled environment with 20 simulated voters. The results show that the system achieved a functional accuracy of 96% across 25 test scenarios. The average transaction response time ranged between 0.6 and 1.6 seconds, indicating efficient processing under moderate load conditions. However, the evaluation is limited to small-scale simulations and does not include stress testing, large-scale scalability analysis, or advanced security validation. Therefore, the findings demonstrate system feasibility rather than fully validated effectiveness. These results suggest that decentralized e-voting systems deployed on local infrastructures can provide a practical and efficient solution for controlled election environments, while further research is required to evaluate scalability, security robustness, and real-world deployment readiness.
This paper challenges the prevailing assumption in Central Bank Digital Currency (CBDC) design that comprehensive transaction surveillance is necessary for financial stability and crime prevention. We propose an alternative privacy-preserving architecture that achieves equivalent or superior fraud detection through mechanism design rather than identity monitoring. Key contributions: Separation of pattern detection from identity: Transaction graph analysis identifies structural anomalies without accessing participant identities Transaction-level intervention: Suspicious activity flags individual transactions, not accounts or users Opt-in deanonymization: Identity revelation is always voluntary; users may abandon flagged transactions without consequence Architectural enforcement: Privacy guarantees are structural, not policy-dependent The framework inverts the burden of proof in financial surveillance. Rather than requiring users to demonstrate legitimacy, it requires the system to demonstrate suspicion—and even then, users retain the option to walk away. This creates a game-theoretic deterrent where illicit actors cannot complete transactions, while legitimate users experience minimal friction. We demonstrate that privacy-preserving CBDC architecture is technically feasible using established cryptographic primitives (zero-knowledge proofs, secure multi-party computation, threshold cryptography) and that the choice to implement surveillance infrastructure represents a policy decision rather than technical necessity. Part of the Adversarial Systems Research program investigating friction dynamics in complex systems where competing interests generate structural conflict.
The growth of cloud computing in the healthcare field has led to significant developments, but ensuring the confidentiality and protection of medical records such as electronic health records (EHRs) remains a major concern for healthcare service applications. In cloud computing, the basic authentication provided by most service providers is insufficient to ensure secure access to critical or sensitive resources. Moreover, most of the existing healthcare management systems are ineffective in handling a number of patient data, which leads to single points of failure. To address these issues, elliptic curve cryptography (ECC) with Curve25519 is utilized to enhance security in cloud storage, particularly within healthcare management systems. The ECC with Curve25519 is optimized for efficient and fast scalar multiplication, which reduces computational overhead and enhances performance. The curve parameters are selected to prevent vulnerabilities and ensure security against known attacks. Moreover, it is efficient in maintaining the integrity of patient records, which reduces storage and bandwidth requirements. The ECC with Curve25519 achieves lower Key-Gen, prove, verify, proving key size, and verification key size of 13.7 s, 48 s, 0.608 s, 13.27 Mb, and 123.70 Kb, respectively, in comparison with proxy re-encryption algorithm with zero-knowledge proof (ZKP).
Chi Zhang, Fenhua Bai, Xiaohui Zhang, Jinhua Wan · 6 authors
As a middleware technology in distributed computer systems, blockchain systems represent a paradigm for achieving node interconnectivity. Despite this, technical differences between various blockchain networks have led to the emergence of a phenomenon known as multi-chain, where inter-chain communication has become a trust barrier. Cross-chain technology is a powerful tool that allows data to flow between different blockchain networks, breaking down data barriers and enabling seamless data transfer. However, cross-chain identification may lead to potential risks such as the exposure of private information and data loss or tampering. In this brief, we propose Universal Cross-Chain Permissioned Blockchain (UCCPB) architecture, which connects single permissioned chains into a multi-chain system. Based on this, the Cross-Chain Anonymous Identity Authentication (CCAIA) model is proposed, which implements privacy-preserving chain identity registration and verification through zero-knowledge proof without a trusted setup. Furthermore, we propose the Proof of Cross-Chain Invocation (PoCI) mechanism of UCCPB, which consists of a node election and consensus on the invocation result. This mechanism ensures the correctness of the cross-chain invocation results and incentivizes nodes to participate in UCCPB. Our experiments show that the proposed UCCPB achieves a balance between performance and privacy while improving the security of cross-chain invocations.
Dobrotă Gabriela, DAN NICOLETA, BUTĂNESCU-VOLANIN REMUS-CONSTANTIN
Public finance sustainability represents a fundamental pillar of macroeconomic stability and a key determinant of the ability of states and local communities to cope with major economic shocks. Against the backdrop of successive crises over the past two decades—financial, health-related, and geopolitical—the relationship between fiscal sustainability and community resilience has gained increasing attention in both economic scholarship and European institutional debates. The aim of this article is to examine the linkage between fiscal sustainability and the resilience of local communities through an integrated approach that combines cross-country analysis at the European Union level with an in-depth assessment of Romania’s experience. The study relies on Eurostat data covering the period 2015 2023 and focuses on fiscal indicators, the degree of fiscal decentralization, and the capacity of local communities to translate public resources into economic and institutional resilience. The methodological framework includes descriptive and comparative analysis, alongside the construction of a composite Community Resilience Index. The empirical findings reveal substantial disparities across EU Member States and indicate that fiscal sustainability, when accompanied by functional fiscal decentralization and strategically oriented public investment, is associated with higher levels of community resilience. In the case of Romania, the gap between a relatively moderate level of public debt and comparatively low community resilience is largely explained by limited local fiscal autonomy and persistent institutional constraints.
Crowdfunding for social goods has become a transformative force in India's development ecosystem, emerging as a crucial citizen-driven financing model for healthcare assistance, educational support, social welfare, environmental conservation, and community development projects.As India progresses toward achieving the United Nations Sustainable Development Goals (SDGs), the importance of innovative, decentralized, and participatory funding mechanisms has grown significantly.Traditional sources of funding-government schemes, philanthropic donations, CSR initiatives, and institutional grants-are often insufficient to meet the enormous financial needs of low-income and marginalized communities.In this context, digital crowdfunding platforms such as Ketto, Milaap, ImpactGuru, Donatekart, and GiveIndia offer flexible, inclusive, and accessible channels for mobilizing public contributions.Unlike commercial crowdfunding, donation-based crowdfunding provides no financial returns to donors.Therefore, donors' decisions are fundamentally shaped by behavioural finance factors rather than economic incentives.This research adopts a behavioural finance perspective to examine the psychological, emotional, cognitive, and social determinants that influence campaign success for SDG-aligned social crowdfunding projects in India.The study investigates how donor motivations-including altruism, empathy, moral obligation, warm-glow effect, identity-driven giving, and social influence-interact with campaign design elements, platform architecture, and trust signals to determine fundraising outcomes.Findings from prior research and platform-level data indicate that trust remains the strongest driver of donation intention.Indian donors tend to be risk-averse due to concerns about fraud, misrepresentation, and misuse of funds.As a result, trust-building mechanisms-such as verified fundraisers, authentic documentation, medical proof, transparent financial breakdowns, institutional endorsements, and frequent campaign updates-significantly increase credibility and donor confidence.Emotional storytelling is another powerful determinant; campaigns featuring identifiable beneficiaries, vivid visuals, personal narratives, and urgent medical needs evoke stronger empathy and are more likely to attract support.Social proof and herding behaviour also play a critical role.Donors frequently look to the actions of others to validate campaign legitimacy, especially when information is limited.High engagement metrics-number of donors, comments, shares, early contributions-signal popularity and urgency, triggering positive herding effects that accelerate the fundraising process.Campaigns that achieve early momentum typically experience higher visibility, stronger network effects, and higher conversion rates.In India, where community networks, family ties, religious identity, and regional affiliations are strong, such social cues significantly enhance campaign reach:
The global Smart Contract Market was valued at USD 1.83 billion in 2023 and is projected to reach USD 7.78 billion by 2030, growing at a CAGR of 23.0% from 2024 to 2030. Smart contracts are self-executing agreements encoded on blockchain platforms that automatically enforce contractual terms when predefined conditions are met, reducing intermediaries and transaction costs. The market spans multiple sectors, including BFSI, healthcare, real estate, transportation, and government services. Growth is driven by the increasing adoption of blockchain in financial institutions, rising cybersecurity threats, and initiatives for digital asset management. Market restraints include high development and auditing costs, alongside limited flexibility. Innovations in scalability solutions and AI integration are expected to expand market potential, making smart contracts more robust, efficient, and widely deployable. This manuscript provides a comprehensive analysis of market dynamics, segmentation, regional trends, and competitive landscape of the smart contract industry.