Search Results (324)

Background: The outbreak of the COVID-19 pandemic has highlighted the importance of online health consultations, as they can help reduce the risk of contagion and infection. However, due to limited trust, these services have not yet gained widespread adoption and usage among patients. Objective: This research aims to examine the impact of perceived quality on patients’ adoption and usage of online health consultations from three perspectives: emotional support, responsiveness, and service continuity. Additionally, this research further explores the moderating effects of online service prices on these relationships. Methods: Based on trust theory, this research constructs theoretical models and empirically tests them by using a panel dataset that comprises 1255 physicians and 65,314 physician–patient communication records. Results: The empirical results confirm that emotional support, responsiveness, and service continuity positively influence patients’ adoption and usage behaviors. Additionally, higher online service prices negatively moderate the impact of emotional support and responsiveness on adoption behavior. Moreover, increased online service prices weaken the positive relationship between emotional support and usage behavior while strengthening the positive relationship between service continuity and usage behavior. Conclusions: This research extends the existing literature on online health services and provides practical guidance for platform managers, physicians, and policymakers to improve overall service acceptance. Full article

Homelessness is characterised by a wide range of risk factors of a multidimensional and unstable nature. The COVID-19 pandemic intensified these risk factors associated with homelessness but also prompted the development of prevention and care actions. This study identified and mapped the intervention programmes carried out for people experiencing homelessness in the wake of the COVID-19 pandemic. To achieve the study purpose, a thematic analysis of the scientific literature was conducted following the search strategy and analysis methodology characteristic of informetrics and scientometrics. The sources of information used were WoS, Scopus, PubMed, PsycINFO and ERIC. The paucity of planned actions, most of which have a local impact, reinforces the need to strengthen research that presents robust evidence on this issue. China and Europe are under-represented compared to other types of studies linked to COVID-19 and the prevalence of homelessness. Several clusters are distinguished among the plans: they are carried out in buildings or in geographical areas and according to the impact on the group (preventive, substance-related disorder support, health care and diagnostic). Among the emerging themes, health and social variables are represented, including communication and trust between health, community and homeless groups. The reduction in the thematic dimensionality shows equal planning between health care actions (81.8%) and psychosocial and prevention support (72.8%), an aspect that confirms the importance of joint actions. In this line, among the various clusters of the network analysis, the relationship between hotel, mental health support, substance-related disorder, social intervention and access to permanent housing was found. The studies analysed also highlight social exclusion, stigma, victimisation, living conditions and the risk of contagion among this group. This situation has not gone unnoticed among the studies analysed, which present proposals for the continuation of the projects. Full article

Under the background of climate change, the risk spillover within the energy system is constantly intensifying. Clarifying the coupling relationship between entities within the energy system can help policymakers propose more reasonable policy measures and strengthen risk prevention. To estimate the risk spillover of energy-related systems, this paper constructs five subsystems: the fossil fuel subsystem, the electricity subsystem, the green bond subsystem, the renewable energy subsystem, and the carbon subsystem. Then, a quantitative risk analysis is conducted on two major energy consumption/carbon emission entities, China and Europe, based on the DCC-GARCH-CoVaR method. The result shows that (1) Markets of the same type often have more significant dynamic correlations. Of these, the average dynamic correlation coefficient of GBI-CABI (the Chinese green bond subsystem) and FR-DE (the European electricity subsystem) are the largest, by 0.8552 and 0.7347. (2) The high correlation between energy markets results in serious risk contagion, and the overall risk spillover effect within the European energy system is about 2.6 times that within the Chinese energy system. Of these, EUA and CABI are the main risk connectors of each energy system. Full article

As the relationship between cryptocurrency mining activities and electricity consumption becomes increasingly close, the risk spillover effect is steadily drawing a lot of attention to the energy and cryptocurrency markets. For the purpose of studying the risk contagion between the cryptocurrency and energy market, this paper constructs a risk contagion network between cryptocurrency and China’s energy market using complex network methods. The tail risk spillover effects under various time and frequency domains were captured by the spillover index, which was assessed by the leptokurtic quantile vector autoregression (QVAR) model. Considering the spatial heterogeneity of energy companies, the spatial Durbin model was used to explore the impact mechanism of risk spillovers. The research showed that the framework of this paper more accurately reflects the tail risk spillover effect between China’s energy market and cryptocurrency market under various shock scales, with the extreme state experiencing a much higher spillover effect than the normal state. Furthermore, this study found that the tail risk contagion between cryptocurrency and China’s energy market exhibits notable dynamic variation and cyclical features, and the long-term risk spillover effect is primarily responsible for the total spillover. At the same time, the study found that the company with the most significant spillover effect does not necessarily have the largest company size, and other factors, such as geographical location and business composition, need to be considered. Moreover, there are spatial spillover effects among listed energy companies, and the connectedness between cryptocurrency and the energy market network generates an obvious impact on risk spillover effects. The research conclusions have an important role in preventing cross-contagion of risks between cryptocurrency and the energy market. Full article

This study investigates the mechanisms underlying the diffusion of risk information about genetically modified organisms (GMOs) on the Chinese social media platform Weibo. Drawing upon social contagion theory, we examine how endogenous and exogenous mechanisms shape users’ information-sharing behaviors. An analysis of 388,722 reposts from 2444 original GMO risk-related texts enabled the construction of a comprehensive sharing network, with computational text-mining techniques employed to detect users’ attitudes toward GMOs. To bridge the gap between descriptive and inferential network analysis, we employ a Shannon entropy-based approach to quantify the uncertainty and concentration of attitudinal differences and similarities among sharing and non-sharing dyads, providing an information-theoretic foundation for understanding positional and differential homophily. The entropy-based analysis reveals that information-sharing ties are characterized by lower entropy in attitude differences, indicating greater attitudinal alignment among sharing users, especially among GMO opponents. Building on these findings, the Exponential Random Graph Model (ERGM) further demonstrates that both endogenous network mechanisms (reciprocity, preferential attachment, and triadic closure) and positional homophily influence GMO risk information sharing and dissemination. A key finding is the presence of a differential homophily effect, where GMO opponents exhibit stronger homophilic tendencies than non-opponents. Despite the prevalence of homophily, this paper uncovers substantial cross-attitude interactions, challenging simplistic notions of echo chambers in GMO risk communication. By integrating entropy and ERGM analyses, this study advances a more nuanced, information-theoretic understanding of how digital platforms mediate public perceptions and debates surrounding controversial socio-scientific issues, offering valuable implications for developing effective risk communication strategies in increasingly polarized online spaces. Full article

Over the past decade, the rapid growth of supply chain finance (SCF) in developing countries has made it a key profit driver for commercial banks and financial firms. In parallel, financial risk control in SCF has attracted more and more attention from financial service providers and has gained research momentum in recent years. This study analyzes the contagion mechanism of SCF-related risks faced by commercial banks through examining SCF network topology. First, this study uses complex network theory to integrate an SEIR epidemic model (Susceptible–Exposed–Infectious–Recovered) into financial risk management. The model simulates how financial risks spread in supply chain finance (SCF) under banks’ strategic, tactical, or operational interventions. Then, some key points for financial risk control from the perspective of commercial banks are obtained by investigating the risk stability threshold of the financial network of SCF and its stability. Numerical simulations show that effective interventions—such as strengthening loan guarantees to reduce the number of exposed firms—significantly curb risk transmission by restricting its scope and shortening its duration. This research provides commercial banks with a quantitative framework to analyze risk propagation and actionable strategies to optimize SCF risk control, enhancing financial system stability and offering practical guidance for preventing systemic risks. Full article

The COVID-19 pandemic has heightened awareness of airborne disease susceptibility, leading to the development and adoption of various preventive technologies. Among these, microwave sanitization, which inactivates virions through non-thermal mechanical resonance, has gained significant scientific credibility. Laboratory tests have demonstrated its high efficacy, prompting further investigation into its effectiveness in real-world settings. This study employs multi-physical, fluid-dynamic and electromagnetic simulations of office environments to evaluate the reduction of contagion risk. By integrating these simulations with virus inactivation experimental laboratory results, we observed that the introduction of a microwave sanitization device significantly reduces the risk of contamination among individuals in the same environment. These findings suggest potential applications and further studies in other everyday scenarios. Full article

This paper investigates systemic tail dependence within the crypto-asset ecosystem by examining interconnectedness across eight major tokens spanning Layer 1 cryptocurrencies, DeFi tokens, stablecoins, and infrastructure/governance assets. We employ a novel partial correlation-based network framework and quantile-specific connectedness measures to examine how co-movement patterns evolve under normal and extreme market conditions from September 2021 to March 2025. Unlike conventional correlation or variance decomposition approaches, our methodology isolates direct, tail-specific transmission channels while filtering out standard shocks. The results indicate strong asymmetries in dependence structures. Systemic risk intensifies during adverse tail events, particularly around episodes such as the Terra/Luna crash, the USDC depeg, and Bitcoin’s 2024 halving cycle. Our analysis shows that ETH, LINK, and UNI are key assets in spreading losses when the market falls. In contrast, the stablecoin DAI tends to absorb some of the stress, helping reduce risk during downturns. These results indicate critical contagion pathways and suggest that regulation targeting protocol-level transparency, liquidity provisioning, and interoperability standards may reduce amplification mechanisms without eliminating interdependence. Our findings contribute to the emerging literature on crypto-systemic risk and offer actionable insights for regulators, DeFi protocol architects, and institutional investors. In particular, we advocate for the incorporation of tail-sensitive network diagnostics into real-time monitoring frameworks to better manage asymmetric spillover risks in decentralized financial systems. Full article

This study examines the daily top 100 cryptocurrencies on Uniswap V3. It denoises the correlation coefficient matrix of cryptocurrencies by using sliding window techniques and random matrix theory. Further, this study constructs a time-varying correlation network of cryptocurrencies under different thresholds based on complex network methods and analyzes the Uniswap V3 network’s time-varying topological properties and risk contagion intensity of Uniswap V3. The study findings suggest the presence of random noise on the Uniswap V3 cryptocurrency market. The strength of connection relationships in cryptocurrency networks varies at different thresholds. With a low threshold, the cryptocurrency network shows high average degree and average clustering coefficient, indicating a small-world effect. Conversely, at a high threshold, the cryptocurrency network appears relatively sparse. Moreover, the Uniswap V3 cryptocurrency network demonstrates heterogeneity. Additionally, cryptocurrency networks exhibit diverse local time-varying characteristics depending on the thresholds. Notably, with a low threshold, the local time-varying characteristics of the network become more stable. Furthermore, risk contagion analysis reveals that WETH (Wrapped Ether) exhibits the highest contagion intensity, indicating its predominant role in propagating risks across the Uniswap V3 network. The novelty of this study lies in its capture of time-varying characteristics in decentralized exchange network topologies, unveiling dynamic evolution patterns in cryptocurrency correlation structures. Full article

This study employs the TVP-VAR-DY model to examine the risk spillover effects and dynamic interactions between traditional energy markets and green financial markets across both time and frequency domains. Furthermore, it evaluates the influence of climate policy uncertainty on these risk spillovers. The findings reveal substantial risk spillover effects between traditional energy markets and green financial markets. In the time domain, the total spillover effects exhibit distinct time-varying characteristics, with particularly pronounced changes under the influence of policy shocks. In the frequency domain, risk spillovers are significantly higher in the short term compared to the medium and long term. Additionally, climate policy uncertainty emerges as key driver of intensified risk spillovers between markets, with its influence initially increasing and then gradually diminishing over time. This study not only provides theoretical support for optimizing climate policies but also offers empirical evidence for prevention and mitigation of risk contagion between energy and green financial markets. Full article

This paper examines the extent of stock-returns’ co-movements among firms in different countries and explores how various measures of closeness affect those co-movements by estimating a spatial autoregressive (SAR) convex combination model that merges four weight matrices—geographical distance, bilateral trade, sector similarity, and company size—into one global matrix. Our results reveal strong spatial stock-market dependence, show that spatial proximity is better captured by financial-distance measures than by pure geographical distance, and indicate that the weight matrix based on sector similarities outperforms the other linkages in explaining firms’ co-movements. Extending the traditional SAR model, the study simultaneously evaluated cross-country and within-country dependencies, providing insights valuable to investors building optimal portfolios and to policymakers monitoring contagion and systemic risk. Full article

With the growing global emphasis on sustainable development goals, Environmental, Social, and Governance (ESG) factors have emerged as critical considerations in shaping economic policies and strategies. This study employs the ARMA-eGARCH-skewed t and Vine Copula models, combined with the CoVaR method, to investigate the dependence structure and risk spillover pathways across various industrial sectors in China within the ESG framework. By modeling the complex interdependencies among sectors, this research uncovers the relationships between individual industries and the ESG benchmark index, while also analyzing the correlations across different sectors. Furthermore, this study quantifies the risk contagion effects across distinct industries under extreme market conditions and maps the pathways of risk spillovers. The findings highlight the pivotal role of ESG considerations in shaping industrial structures. Empirical results demonstrate that industries such as agriculture, energy, and manufacturing exhibit significant systemic risk characteristics in response to ESG fluctuations. Specifically, the identified risk spillover pathway follows the sequence: agriculture → consumption → ESG → manufacturing → energy. The CoVaR values for agriculture, energy, and manufacturing indicate a significant potential for risk contagion. Moreover, sectors such as real estate, finance, and information technology exhibit significant risk spillover effects. These findings offer valuable empirical evidence and a theoretical foundation for formulating ESG-related policies. This study suggests that effective risk management, promoting green finance, encouraging technological innovation, and optimizing industrial structures can significantly mitigate systemic risks. These measures can contribute to maintaining industrial stability and fostering sustainable economic development. Full article

In the face of accelerating digitalization and growing systemic vulnerabilities, the ability to make accurate, real-time economic decisions has become a critical capability for financial and institutional stability. This study investigates how edge computing infrastructures influence decision-making accuracy, responsiveness, and risk containment in economic systems, particularly under the threat of financial contagion. A synthetic dataset simulating the interaction between economic indicators and edge performance metrics was constructed to emulate real-time decision environments. Composite indicators were developed to quantify key dynamics, and a range of machine learning models, including XGBoost, Random Forest, and Neural Networks, were applied to classify economic decision outcomes. The results indicate that low latency, efficient resource use, and balanced workload distribution are significantly associated with higher decision quality. XGBoost outperformed all other models, achieving 97% accuracy and a ROC-AUC of 0.997. The findings suggest that edge computing performance metrics can act as predictive signals for systemic fragility and may be integrated into early warning systems for financial risk management. This study contributes to the literature by offering a novel framework for modeling the economic implications of edge intelligence and provides policy insights for designing resilient, real-time financial infrastructures. Full article

In the context of an ever-evolving global economy, ASEAN companies face dynamic systemic risk that reshapes their financial interrelationships. This study examines the transmission of these risks using advanced graph theory techniques, particularly the measurement of eigenvector centrality based on Euclidean distances, combined with a hidden model that incorporates macroeconomic variables, such as GDP. The research focuses on identifying critical nodes within the corporate network, evaluating their contagion potential—both in terms of reinforcing resilience and amplifying vulnerabilities—and analyzing the influence of external factors on the network’s structure and behavior. The findings offer an innovative framework for managing systemic risk and provide strategic guidelines for the formulation of economic policies in emerging ASEAN markets. Full article

In the globalized economic system, environmental, social, and governance (ESG) factors have emerged as critical dimensions for assessing non-financial performance and ensuring the long-term sustainable development of businesses, influencing corporate behavior, investor expectations, and regulatory landscapes. This article applies the VAR-DY network analysis method to construct a large-scale financial volatility spillover network covering all Chinese stocks. It explores the risk transmission paths among different ESG-rated groups and analyzes the patterns and impacts of risk transmission during extreme market volatility. The study finds that as ESG ratings decrease from AAA to C, the network’s average shortest path length and average connectedness strength decreases, indicating that highly rated companies play a central role in the network and maintain their ESG ratings through close connections, positively affecting market stability. However, analyses of the 2015 Chinese stock market crash and the COVID-19 pandemic show a general increase in volatility spillover effects. Notably, the direction of risk spillover in relation to ESG ratings was opposite in these two events, reflecting differences in the underlying drivers of market volatility. This suggests that under extreme market conditions, traditional risk management tools need to be optimized by incorporating ESG factors to better address risk contagion. Full article