Blockchain Papers

Abhay Kumar Yadav, Virendra P. Vishwakarma

No abstract is available for this record.

Malla Jogarao, Ch. Kameswari

Abstract The quick implementation of technology in Industry 4.0 which includes the Internet of Things (IoT), artificial intelligence (AI), blockchain, and robotics leads to supply chain management (SCM) transformation as it alters workforce profiles. This chapter examines the fundamental links between human factors, like employee transition, and sustainable SCM in Industry 4.0 specifically for the United Nations Sustainable Development Goals (UNSDG) 2030 targets as well as the post-COVID-19 environment. It explores how technology transforms talents and promotes robotic–human teamwork as well as impacts workforce well-being. The analysis of job displacement challenges, ethical problems, and labour shortages, together with identifying opportunities for new skills development and resilient supply chain implementations. Special emphasis is placed on sustainable workforce solutions through actual implementations and practical frameworks for workforce protection through ongoing learning and efficient management of organizational change along with policy collaboration. It explores both ethical and social consequences through an examination of employee data security risks, social inequality problems, and employee mental health effects related to fast technology acceptance. The future research areas include AI autonomous driving vehicles in different fields, smart manufacturing, and health care sustainability. The extensive evaluation provides essential information that benefits scholars, policymakers, together with industrial leaders who need to understand human capital management in sustainable supply chains using Industry 4.0 technologies.

Nguyễn Thị Thanh Bình

This review synthesizes theoretical and empirical insights from 1055 peer-reviewed articles on artificial intelligence (AI), corporate governance, and ethics. Situated in the corporate governance and accounting literature, it develops a computational framework to identify thematic patterns and conceptual links among AI, transparency, accounting, governance, and ESG. Using latent Dirichlet allocation, co-occurrence network analysis, sentence-level semantic similarity, and exploratory regression, the study identifies three recurring configurations of conceptual association: (1) Ethics, Governance, and Transparency; (2) Machine Learning, Finance, Blockchain, and Accounting; and (3) Corporate, ESG, and Accounting. The findings indicate that these themes are repeatedly connected within the scholarly literature.

Iryna Kravchuk, Nataliia Valinkevych, Oksana Prysiazhniuk

This paper substantiates the theoretical and applied foundations of investment management for forming and developing resilient distribution networks in agribusiness. Under global food market transformations, systemic macroeconomic instability, and geopolitical shocks, conventional linear investment models prove ineffective for long-term planning. To bridge this gap, this research adapts advanced economic frameworks directly to agricultural supply chains, shifting the focus from discrete physical asset valuation to ecosystem-wide synergy. This is achieved by combining classic capital planning with portfolio diversification, real options valuation (ROV), behavioral finance, stakeholder-driven ESG metrics, and decentralized financial tools (DeFi). The study proposes a hierarchical digitization model of the investment process powered by artificial intelligence (AI) and Big Data. This system operates at three spatial levels: national (for comprehensive stress-testing against geopolitical shocks), regional (deploying predictive digital twins of logistics clusters to optimize infrastructure placement), and local (facilitating agile capital allocation and behavioral consumer analysis). This structure ensures capital flows efficiently into highperforming channels while minimizing bottlenecks. To address the trade-off between environmental requirements and financial risks, the study introduces the "Two-Factor Balanced Development Matrix." This model links financial credit scoring with multidimensional ESG profiling. Counterparties are categorized into operational quadrants (e.g., Green Leaders, Traditional Pragmatists, Eco-Startups) to determine customized trade credit lines and commercial terms. Finally, the research outlines integrated risk mitigation instruments, including green trade finance (IFC, EBRD), eco-premium forward contracts, and parametric climate insurance. These measures reduce non-performing loans, lower the cost of capital, and improve the Scope 3 emission rating for distributors.

Jyoti Kumari, Pradeep Yadav, Chandra Prakash Bhargava

The convergence of digital technologies and sustainability assessments is changing the dynamics of environmental governance and corporate accountability. This chapter analyzes the way in which artificial intelligence, IoT, blockchain, digital twins, cloud analytics, and robotic process automation technologies contribute to sustainability assessment through their use in measuring, monitoring, and reporting on the environmental and social performance of corporations. Relying on the latest academic research, legislation, and business practice in this field, the chapter considers theoretical background, real-world applications, and governance issues related to digital sustainability assessment. A comprehensive analytical structure is provided, comprising data gathering, analysis, verification, and reporting, alongside comparative tables of relevant technologies, methods, legislation, problems, and solutions.

Y. Su

With the rapid advancement of industrial Internet technologies and intelligent wireless sensing infrastructures, efficient data acquisition and information transmission have become fundamental to modern textile supply chain management. The integration of electromagnetic-enabled Internet of Things (IoT) devices, RFID technologies, and intelligent communication networks provides essential support for real-time financial monitoring and digital taxation services. Against this background, this paper investigates the application of intelligent finance and taxation in textile industry supply chains by proposing an integrated framework based on artificial intelligence, blockchain, cloud computing, and IoT technologies. The framework enables transparent financial management, automated tax compliance, dynamic supply chain finance, and end-to-end traceability through seamless integration of operational, financial, and logistics data. Key applications, including blockchain-based material provenance verification, AI-driven credit assessment, automated customs and tax processing, and intelligent risk management, are systematically analyzed. The proposed architecture improves supply chain transparency, operational efficiency, sustainability, and resilience while facilitating data-driven decision-making across textile production and distribution processes. Furthermore, the study demonstrates that intelligent finance and taxation can establish a unified digital ecosystem for financial governance and supply chain collaboration, providing valuable technical references for wireless industrial information acquisition, smart sensing, and communication-assisted digital management in future intelligent manufacturing environments.

Shahab Azim, Lala Rukh, Shakir Ullah

Crypto currency is one of most interesting financial innovation of 21st century. Crypto currency trading not only involve financial literacy while trading but also there are psychological factors affecting the decision of traders. Keeping in view the psychological factors and investors’ decision, this research study is designed to investigate the complex interplay between psychological triggers and market dynamics in the cryptocurrency sector in Pakistan, specifically examining how these elements coalesce to drive investor behavior and market volatility. While traditional financial models often attribute asset fluctuations to technological or fundamental shifts, this study posits that cryptocurrency markets are fundamentally driven by human perception and emotional reactivity. Utilizing a quantitative methodological approach, data was collected from a sample of 175 experienced traders to analyze the impact of emotional states, market sentiment, and behavioral discipline on trading outcomes. The empirical results, derived through multiple linear regression analysis, reveal that the model possesses a high level of explanatory power, accounting for 56% of the variance in emotional trading behavior (R2=0.56R2=0.56). Market sentiment emerged as the primary determinant of impulsive trading (β=0.48β=0.48), demonstrating that external social cues often exert a stronger influence on decision-making than internal emotional states. Among specific psychological variables, Fear, Uncertainty, and Doubt (FUD) were identified as the most significant predictors of rash choices (β=0.34β=0.34), while the Fear of Missing Out (FOMO) also demonstrated a substantial, though secondary, effect (β=0.21β=0.21). Conversely, the study found that trading experience and the application of systematic strategies serve as vital moderating factors that decrease emotional reactivity and enhance behavioral stability (β=−0.19β=−0.19). The findings contribute to the fields of behavioral finance and digital economics by illustrating that the volatility inherent in digital assets is a systemic byproduct of individual psychological biases aggregated through digital narratives. The research concludes that achieving a sustainable financial ecosystem requires moving beyond purely technical regulations. Instead, it advocates for the implementation of behaviorally-informed safeguards, such as algorithmic "cooling-off" periods and sentiment-aware trading tools, to mitigate the risks associated with reactive investing. Ultimately, this work provides a blueprint for a more resilient digital financial future by prioritizing human factors in market governance.