Search Results (741)

In the current era, Indian agriculture faces a significant demand for increased food production, which has led to the integration of advanced technologies to enhance efficiency and productivity. Drones have emerged as transformative tools for enhancing precision agriculture, reducing costs, and improving sustainability. This study provides a comprehensive review of drone adoption in Indian agriculture by examining its effects on precision farming, crop monitoring, and pesticide application. This research evaluates technological advancements, regulatory frameworks, infrastructure, farmers’ perceptions, and the financial accessibility of drone technology in the Indian agricultural context. Key findings indicate that, while drone adoption enhances efficiency and sustainability, challenges such as high costs, lack of training, and regulatory barriers hinder widespread implementation. This paper also explores the growing market for agricultural drones in India, highlighting key industry players and projected market growth. Furthermore, it addresses regional differences in adoption rates and emphasizes the increasing social acceptance of drones among Indian farmers. To bridge the gap between potential and practice, the study proposes several policy and institutional recommendations, including government-led financial incentives, training programs, and public–private partnerships to facilitate drone integration. Moreover, this review article also highlights technological advancements, such as AI and IoT, in agriculture. Finally, open issues and future research directions for drones are discussed. Full article

The rise in illicit financial activities across the South Africa–Zimbabwe corridor, with an estimated annual loss of $3.1 billion demands advanced AI solutions to augment traditional detection methods. This study introduces FALCON, a groundbreaking hybrid transformer–GNN model that integrates temporal transaction analysis (TimeGAN) and graph-based entity mapping (GraphSAGE) to detect illicit financial flows with unprecedented precision. By leveraging data from South Africa’s FIC, Zimbabwe’s RBZ, and SWIFT, FALCON achieved 98.7%, surpassing Random Forest (72.1%) and human auditors (64.5%), while reducing false positives to 1.2% (AUC-ROC: 0.992). Tested on 1.8 million transactions, including falsified CTRs, STRs, and Ethereum blockchain data, FALCON uncovered $450 million laundered by 23 shell companies with a cross-border detection precision of 94%, directly mitigating illicit financial flows in Southern Africa. For regulators, FALCON met FAFT standards, yielding 92% court admissibility, and its GDPR-compliant design (ε = 1.2 differential privacy) met stringent legal standards. Deployed on AWS Graviton3, FALCON processed 2 million transactions/second at $0.002 per 1000 transactions, demonstrating real-time scalability, making it cost-effective for financial institutions in emerging markets. As the first AI framework tailored for Southern Africa’s financial ecosystems, FALCON sets a new benchmark for ethical AML solutions in emerging economies with immediate applicability to CBDC supervision. The transparent validation of publicly available data underscores its potential to transform global financial crime detection. Full article

This study investigates the role of Big Data Analytics Capabilities (BDACs) in ambidexterity explorative innovation (EXPLRI) and exploitative (EXPLOI) innovation for achieving a sustainable performance (SP) in the manufacturing sector of a resource-constrained developing economy. While a BDAC has been widely linked to innovation in developed economies, its effectiveness in developing contexts shaped by indigenous innovation practices like Jugaad remains underexplored. Anchored in the Resource-Based View (RBV) and Dynamic Capabilities (DC) theory, we propose a model where the BDAC enhances both EXPLRI and EXPLOI, which subsequently leads to an improved sustainable performance. We further examine the Jugaad capability as a cultural moderator. Using survey data from 418 manufacturing firms and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM), results confirm that BDA capabilities significantly boost both types of innovations, which positively impact sustainable performance dimensions. Notably, Jugaad positively moderates the relationship between EXPLOI and financial, innovation, and operational performance but negatively moderates the link between EXPLRI and innovation performance. These findings highlight the nuanced influence of culturally embedded innovation practices in BDAC-driven ecosystems. This study contributes by extending the RBV–DC framework to include cultural innovation capabilities and empirically validating the contingent role of Jugaad in enhancing or constraining innovation outcomes. This study also validated the Jugaad capability measurement instrument for the first time in the context of Pakistan. For practitioners, aligning data analytics strategies with local innovative cultures is vital for sustainable growth in emerging markets. Full article

This theoretical essay aims to analyze some of the socio-economic innovations introduced by Platform Capitalism Specifically, it focuses on two main aspects: first, the digital platform as a radical organizational innovation. Digital platforms represent a structural novelty in the market economy, signaling a new organization of production and labor. Second, the essay examines the role of platforms in directly generating value through the concept of “network value”. To this end, it explores the function of “business intelligence” as a strategic and competitive tool. Finally, the paper discusses the key issues associated with platform capitalism, which could threaten its social sustainability and contribute to economic and financial instability. These issues include the increasing commodification of everyday activities, the devaluation of paid labor in favor of free production driven by platform users (the so-called prosumers), and the emergence of proprietary and financial monopolies. Hence, digital platforms do not inherently ensure comprehensive social and environmental sustainability unless supported by targeted economic policy interventions. Conclusively, it is emphasized that defining robust social welfare frameworks—which account for emerging value creation processes—is imperative. Simultaneously, policymakers must incentivize the proliferation of cooperative platforms capable of fostering experimental circular economy models aligned with ecological sustainability. Full article

This study investigates the influence of corporate governance on reducing financial statement fraud (FSF) in Jordanian service and industrial companies listed on the Amman Stock Exchange from 2018 to 2022. To achieve this, the study employed the Beneish M-score model to assess the likelihood of FSF and logistic regression to examine the influence of corporate governance structure on fraud mitigation. The study identified 13 independent variables, including board size, board director’s independence, board director’s compensation, non-duality of CEO and chairman positions, board diversity, audit committee size, audit committee accounting background, number of annual audit committee meetings, external audit fees, board family business, the presence of women on the board of directors, firm size, and market listing on FSF. The study included 74 companies from both sectors—33 from the industrial sector and 41 from the service sector. Primary data was collected from financial statements and other information published in annual reports between 2018 and 2022. The results of the study revealed a total of 295 cases of fraud during the examined period. Out of the 59 companies analyzed, 21.4% demonstrated a low probability of fraud, while the remaining 78.6% (232 observations) showed a high probability of fraud. The results indicate that the following corporate governance factors significantly impact the mitigation of financial statement fraud (FSF): independent board directors, board diversity, audit committee accounting backgrounds, the number of audit committee meetings, family business involvement on the board, and firm characteristics. The study provides several recommendations, highlighting the importance for companies to diversify their boards of directors by incorporating different perspectives and experiences. Full article

The accurate estimation of urban property values is a key challenge for appraisers, market participants, financial institutions, and urban planners. In recent years, machine learning (ML) techniques have emerged as promising tools for price forecasting due to their ability to model complex relationships among variables. However, their application raises two main critical issues: (i) the risk of overfitting, especially with small datasets or with noisy data; (ii) the interpretive issues associated with the “black box” nature of many models. Within this framework, this paper proposes a methodological approach that addresses both these issues, comparing the predictive performance of three ML algorithms—k-Nearest Neighbors (kNN), Random Forest (RF), and the Artificial Neural Network (ANN)—applied to the housing market in the city of Salerno, Italy. For each model, overfitting is preliminarily assessed to ensure predictive robustness. Subsequently, the results are interpreted using explainability techniques, such as SHapley Additive exPlanations (SHAPs) and Permutation Feature Importance (PFI). This analysis reveals that the Random Forest offers the best balance between predictive accuracy and transparency, with features such as area and proximity to the train station identified as the main drivers of property prices. kNN and the ANN are viable alternatives that are particularly robust in terms of generalization. The results demonstrate how the defined methodological framework successfully balances predictive effectiveness and interpretability, supporting the informed and transparent use of ML in real estate valuation. Full article

Amid intensifying global ESG regulations and the expanding influence of green finance, China’s digital economy policies have emerged as key institutional instruments for promoting corporate sustainability. Leveraging the implementation of the National Big Data Comprehensive Pilot Zone as a quasi-natural experiment, this study utilizes panel data of Chinese listed firms from 2009 to 2023 and applies multi-period Difference-in-Differences (DID) and Spatial DID models to rigorously identify the policy’s effects on corporate ESG performance. Empirical results indicate that the impact of digital economy policy is not exerted through a direct linear pathway but operates via three institutional mechanisms, enhanced information transparency, eased financing constraints, and expanded fiscal support, collectively constructing a logic of “institutional embedding–governance restructuring.” Moreover, disruptive technological innovation significantly amplifies the effects of the transparency and fiscal mechanisms, but exhibits no statistically significant moderating effect on the financing constraint pathway, suggesting a misalignment between innovation heterogeneity and financial responsiveness. Further heterogeneity analysis confirms that the policy effect is concentrated among firms characterized by robust governance structures, high levels of property rights marketization, and greater digital maturity. This study contributes to the literature by developing an integrated moderated mediation framework rooted in institutional theory, agency theory, and dynamic capabilities theory. The findings advance the theoretical understanding of ESG policy transmission by unpacking the micro-foundations of institutional response under digital policy regimes, while offering actionable insights into the strategic alignment of digital transformation and sustainability-oriented governance. Full article

Emerging markets face growing pressures to integrate sustainable English business practices while maintaining economic growth, particularly in addressing environmental challenges and achieving carbon neutrality goals. English Financial information extraction becomes crucial for supporting green finance initiatives, Environmental, Social, and Governance (ESG) compliance, and sustainable investment decisions in these markets. This paper presents FinATG, an AI-driven autoregressive framework for extracting sustainability-related English financial information from English texts, specifically designed to support emerging markets in their transition toward sustainable development. The framework addresses the complex challenges of processing ESG reports, green bond disclosures, carbon footprint assessments, and sustainable investment documentation prevalent in emerging economies. FinATG introduces a domain-adaptive span representation method fine-tuned on sustainability-focused English financial corpora, implements constrained decoding mechanisms based on green finance regulations, and integrates FinBERT with autoregressive generation for end-to-end extraction of environmental and governance information. While achieving competitive performance on standard benchmarks, FinATG’s primary contribution lies in its architecture, which prioritizes correctness and compliance for the high-stakes financial domain. Experimental validation demonstrates FinATG’s effectiveness with entity F1 scores of 88.5 and REL F1 scores of 80.2 on standard English datasets, while achieving superior performance (85.7–86.0 entity F1, 73.1–74.0 REL+ F1) on sustainability-focused financial datasets. The framework particularly excels in extracting carbon emission data, green investment relationships, and ESG compliance indicators, achieving average AUC and RGR scores of 0.93 and 0.89 respectively. By automating the extraction of sustainability metrics from complex English financial documents, FinATG supports emerging markets in meeting international ESG standards, facilitating green finance flows, and enhancing transparency in sustainable business practices, ultimately contributing to their sustainable development goals and climate action commitments. Full article

Managing finances in a supply chain today is not as straightforward as it once was. The world is constantly shifting—markets fluctuate, risks emerge unexpectedly—and companies are continually trying to stay one step ahead. In all this, financial resilience has become more than just a strategy. It is a survival skill. In our research, we examined how newer technologies (such as blockchain and the Internet of Things) can make a difference. The idea was not to reinvent the wheel but to see if these tools could actually make financing more transparent, reduce some of the friction, and maybe even help companies breathe a little easier when it comes to liquidity. We employed two optimization methods (Non-dominated Sorting Genetic Algorithm II (NSGA-II) and Multi-Objective Particle Swarm Optimization (MOPSO)) to achieve a balanced outcome. The goal was lower financing costs, better liquidity, and stronger resilience. Blockchain did not just record transactions—it seemed to build trust. Meanwhile, the Internet of Things (IoT) provided companies with a clearer picture of what is happening in real-time, making financial outcomes a bit less of a guessing game. However, it gives financial managers a better chance at planning and not getting caught off guard when the economy takes a turn. Full article

The integration of renewable energy into power grids is imperative for reducing carbon emissions and mitigating reliance on depleting fossil fuels. In this paper, we develop symmetric and asymmetric evolutionary game-theoretic models to analyze how user–grid cooperation in peak shaving can be enhanced by incorporating whole-process democracy (deliberative governance) into decision-making. Our framework captures excess returns, cooperation-driven profits, energy pricing, participation costs, and benefit-sharing coefficients to identify equilibrium conditions under varied subsidy, cost, and market scenarios. Furthermore, this study integrates the theory, path, and mechanism of deliberative procedures under the perspective of whole-process democracy, exploring how inclusive and participatory decision-making processes can enhance cooperation in renewable energy systems. We simulate seven scenarios that systematically adjust subsidy rates, cost–benefit structures, dynamic pricing, and renewable-versus-conventional competitiveness, revealing that robust cooperation emerges only under well-aligned incentives, equitable profit sharing, and targeted financial policies. These scenarios systematically vary these key parameters to assess the robustness of cooperative equilibria under diverse economic and policy conditions. Our findings indicate that policy efficacy hinges on deliberative stakeholder engagement, fair profit allocation, and adaptive subsidy mechanisms. These results furnish actionable guidelines for regulators and grid operators to foster sustainable, low-carbon energy systems and inform future research on demand response and multi-source integration. Full article

As sustainability becomes a strategic priority across global financial services, its implementation in emerging insurance markets remains insufficiently understood. This study explores the integration of environmental, social, and governance (ESG) principles within Saudi Arabia’s insurance sector, combining content analysis of corporate disclosures with qualitative insights from industry stakeholders. The research investigates how insurers embed ESG principles into their operations, the development of sustainable insurance products, and their perceived financial and regulatory implications. The findings reveal gradual progress in ESG integration, primarily driven by governance reforms aligned with national development agendas, while social and environmental dimensions remain comparatively underdeveloped. Stakeholders identify regulatory ambiguity, data limitations, and technical capacity as persistent barriers, but also point to increasing investor and consumer interest in sustainability-aligned offerings. This study offers policy and managerial recommendations to advance ESG principle adoption, emphasizing standardized disclosures, capacity-building, and product innovation. It contributes to the limited empirical literature on ESG principles in Middle Eastern insurance markets and highlights the sector’s potential role in promoting inclusive and sustainable finance. Full article

The development of stock markets is pivotal for economic growth, particularly through the mobilization of idle resources into productive investments. Despite recent reforms to enhance Uzbekistan’s capital market, public engagement remains limited. This study examines the behavioral determinants of stock market investment intention and participation among university students, employing the Theory of Planned Behavior (TPB) and Partial Least Squares Structural Equation Modeling (PLS-SEM). The model investigates the influence of digital literacy, financial literacy, social interaction, herding behavior, overconfidence bias, risk tolerance, and financial well-being on investment intention and behavior. A survey of 369 university students was conducted to assess the proposed relationships. The results reveal that risk tolerance, overconfidence bias, and herding behavior significantly and positively affect investment intention, while digital literacy demonstrates a notable negative effect, suggesting caution in assuming technology readiness automatically translates to investment readiness. Investment intention, in turn, strongly predicts actual participation and mediates several of these effects. Conversely, financial literacy, financial well-being, and social interaction showed no significant direct or mediating influence. Additionally, differences according to gender and academic background were observed in how intention translates into behavior. The findings underscore the need for integrated financial and behavioral education to enhance market participation and contribute to policy discourse on youth financial engagement in emerging economies. Full article

This study examines the impact of geopolitical risk (GPR) on the connectedness dynamics among the sovereign bonds of the emerging seven (E7) and the Group of Seven (G7) countries. Initially, a quantile-based vector-autoregressive (Q-VAR) connectedness approach is used to calculate the total connectedness index (TCI) among sovereign bonds under different market states. Then, the impact of GPR on the TCI at the median and tails is estimated to examine if GPR affects the TCI among sovereign bonds. Using daily yields from 30 January 2012, to 17 June 2024, the findings show that the GPR is one of the significant determinants of the TCI among sovereign bonds during normal and extreme market conditions. Other determinants of the TCI include yields on Treasury bills (T-bills), the exchange rate, and the financial market volatility index. The impact of GPR on the TCI varies significantly during different GPR episodes and bond market conditions. The effect of GPR on the TCI among sovereign bonds yields is higher during war times and when bond yields are average. These findings can be utilized by investors seeking to achieve international diversification and policymakers aiming to mitigate the effects of heightened geopolitical risk on financial stability. Furthermore, GPR can be used as an early signal tool for systematic tail risk spillovers among sovereign bonds. Full article

This paper considers the impact of Central Bank Digital Currencies (CBDCs) on the world’s financial systems with a special emphasis on G20 economies. Using quarterly macro-financial data for the period of 2000 to 2024, collected from the IMF, BIS, World Bank, and Atlantic Council, a Global Vector Autoregression (GVAR) model is applied to 20 G20 countries. The results reveal significant heterogeneity across economies: CBDC shocks intensify emerging market financial instability (e.g., India, Brazil), while more digitally advanced countries (e.g., UK, Japan) experience stabilization. Retail CBDCs increase disintermediation risks in more fragile banking systems, while wholesale CBDCs improve cross-border liquidity. This article contributes to the literature by providing the first GVAR-based estimation of CBDC spillovers globally. Full article

This study examined the dynamic interconnectedness and portfolio implications within the cryptocurrency ecosystem, focusing on five representative digital assets across the core functional categories: Layer 1 cryptocurrencies (Bitcoin (BTC) and Ethereum (ETH)), decentralized finance (Uniswap (UNI)), stablecoins (Dai), and crypto infrastructure tokens (Maker (MKR)). Using the Extended Joint Connectedness Approach within a Time-Varying Parameter VAR framework, the analysis captured time-varying spillovers of return shocks and revealed a heterogeneous structure of systemic roles. Stablecoins consistently acted as net absorbers of shocks, reinforcing their defensive profile, while governance tokens, such as MKR, emerged as persistent net transmitters of systemic risk. Foundational assets like BTC and ETH predominantly absorbed shocks, contrary to their perceived dominance. These systemic roles were further translated into portfolio design, where connectedness-aware strategies, particularly the Minimum Connectedness Portfolio, demonstrated superior performance relative to traditional variance-based allocations, delivering enhanced risk-adjusted returns and resilience during stress periods. By linking return-based systemic interdependencies with practical asset allocation, the study offers a unified framework for understanding and managing crypto network risk. The findings carry practical relevance for portfolio managers, algorithmic strategy developers, and policymakers concerned with financial stability in digital asset markets. Full article