Search Results (45)

Digital isolation represents a contemporary paradox in which increased connectivity through social media and digital technologies does not necessarily translate into improved social integration or psychological well-being. This review synthesizes current evidence on the relationship between digital environments and mental health, with a focus on mechanisms underlying loneliness, anxiety, depression, and related outcomes. The findings indicate that problematic and passive use of social media—particularly when associated with social comparison processes and Fear of Missing Out (FoMO)—is consistently linked to increased levels of depressive symptoms, anxiety, sleep disturbances, and reduced well-being. At the same time, the evidence highlights substantial heterogeneity, suggesting that the impact of digital technologies is moderated by user characteristics, age, patterns of engagement, and psychosocial context. Importantly, digital technologies may also serve compensatory and protective functions by facilitating social support, especially in conditions of objective isolation. Key mediating mechanisms include cyberbullying, social exclusion, emotional contagion, and internalization of body image standards. The concept of “digital loneliness” emerges as a useful framework for understanding the discrepancy between constant connectivity and perceived relational insufficiency. Practical implications emphasize the need for targeted interventions focusing on digital literacy, healthy usage patterns, and psychosocial support rather than simplistic reduction in screen time. Overall, digital isolation should be conceptualized as a qualitative dysfunction of mediated social interaction rather than a purely quantitative effect of technology exposure. Full article

Crypto-asset markets have been rapidly evolving during the past years, being under the spotlight of a diverse set of actors in the financial ecosystem, including investors, financial institutions, regulators and academics. Their potential interconnections with the traditional financial markets are important, and identifying them can provide useful insight in a diversity of areas such as risk contagion and mitigation, price formation, portfolio management and regulatory framework design. In order to identify such interconnections, various lines of research are followed. Specifically, the correlation between prominent stock market indices and crypto-assets from 2018 to 2025 is examined, while their volatility is also evaluated. Furthermore, the relevant effect of news, events and announcements is explored. The results are based on both daily and high-frequency datasets, with the use of the latter focusing on intra-day variation. The analysis of the results identifies existing interconnections between 2020 and 2025, as well as the important respective impact of news and announcements. An additional generic outcome is the usefulness of high-frequency datasets in the crypto-asset context. The conclusions are useful for all actors in the financial ecosystem. Future work can focus on the extension of the research to additional markets or crypto-assets. Full article

This study develops an integrated computational model to illuminate the micro-dynamics through which transient emotional contagion evolves into stable social identity within organizations, with a specific focus on hybrid work environments. Drawing on organizational psychology and employing an agent-based modeling approach, we formalize a four-stage process—Emotional Cycle, Emotional Memory Accumulation, Cognitive Formation, and Enhancement Effect—that captures how fleeting affective experiences coalesce into enduring group identification. Our simulations reveal that cognitive heterogeneity moderates this pathway, leading to slower but more robust identity formation. Gender differences emerge as significant, with females demonstrating higher susceptibility to emotional contagion, while males’ identification is more strongly influenced by issue relevance. Crucially, exploratory simulations contrasting high- and low-hybridity configurations demonstrate that dispersed, digitally mediated work attenuates the emotional feedback loop, slows consensus formation, and heightens the risk of sub-group silos, thereby fundamentally reshaping the identity formation pathway. This research provides a mechanistic explanation of the emotional foundations of organizational culture and offers managers an evidence-based, dynamic framework for strategically cultivating collective identity in an increasingly hybrid world. Full article

The dynamic evolution of collective emotions across the news dissemination life-cycle is a powerful yet underexplored signal in affective computing. While phenomena like the spread of fake news depend on eliciting specific emotional trajectories, existing methods often fail to capture these crucial dynamic affective cues. Many approaches focus on static text or propagation topology, limiting their robustness and failing to model the complete emotional life-cycle for applications such as assessing veracity. This paper introduces C-STEER (Cycle-aware Sentiment-Temporal Emotion Evolution), a novel framework grounded in communication theory, designed to model the characteristic initiation, burst, and decay stages of these emotional arcs. Guided by Diffusion of Innovations Theory, C-STEER first segments an information cascade into its life-cycle phases. It then operationalizes insights from Uses and Gratifications Theory and Emotional Contagion Theory to extract stage-specific emotional features and model their temporal dependencies using a Bidirectional Long Short-Term Memory (BiLSTM). To validate the framework’s descriptive and predictive power, we apply it to the challenging domain of fake news detection. Experiments on the Weibo21 and Twitter16 datasets demonstrate that modeling life-cycle emotion dynamics significantly improves detection performance, achieving F1-macro scores of 91.6% and 90.1%, respectively, outperforming state-of-the-art baselines by margins of 1.6% to 2.4%. This work validates the C-STEER framework as an effective approach for the computational modeling of collective emotion life-cycles. Full article

Studies examining emoji applications in digital marketing and advertising are characterized by considerable heterogeneity in their theoretical orientation, methodologies, and contextual factors. A domain-based systematic literature review with the Theory-Context-Characteristics-Methodology (T-C-C-M) framework following PRISMA guidelines was conducted to answer how emojis are researched in marketing, and a bibliometric review was constructed to shed light on important aspects. We found a field growing in volume yet immature, with a diversity of theories and methodologies used to explore the multiple roles of emojis. An analysis of explicit and implicit theories identified that almost a quarter of studies are atheoretical, and the mostly used theories are the Emotions as Social Information Theory (EASI) and the emotional contagion theory. Emojis are mainly researched in social media and in the travel and food industry. The most common methodological categories are experimental designs, with emojis used as independent variables in simple designs. Despite the focus on short-term outcomes (engagement, purchase intention), little attention was given to advertising and to field experiments, constraining ecological validity. Our study reveals the need for a robust theoretical framework that can explain the multiple functions of emojis, and EASI emerged as the leading theory to be tested more extensively. Full article

This study investigates the asymmetric responses of AI and ESG Exchange Traded Funds (ETFs) to geopolitical and financial uncertainty, with a focus on resilience across market regimes. The NASDAQ-100 and MSCI ESG Leaders indices are used as proxies for thematic ETFs, and their dynamic interlinkages are examined in relation to volatility indicators (VIX, GPR), alternative assets (Bitcoin, Ethereum, gold, oil, natural gas), and safe-haven currencies (CHF, JPY). A daily dataset spanning the 2016–2025 period is analyzed using Quantile-on-Quantile Regression (QQR) and Wavelet Coherence (WCO), enabling a granular assessment of nonlinear, regime-dependent behaviors across quantiles. Results reveal that ESG ETFs demonstrate stronger downside resilience under extreme uncertainty, maintaining stability even during periods of elevated geopolitical and financial risk. In contrast, AI-themed ETFs tend to outperform under moderate-risk conditions but exhibit greater vulnerability during systemic stress, reflecting differences in asset composition and investor risk perception. The findings contribute to the literature on ETF resilience and cross-asset contagion by highlighting differential behavior patterns under varying uncertainty regimes. Practical implications emerge for investors and policymakers seeking to enhance portfolio robustness through thematic diversification during market turbulence. Full article

Objectives: This study aimed to enhance the understanding of factors influencing changes in binge drinking (BD) behavior during the COVID-19 pandemic, with a particular focus on its impact on the health of individuals aged 12 to 25 years. Methods: A systematic review was conducted, encompassing studies published between January 2020 and September 2024. Articles were retrieved from PubMed, Web of Science, and Scopus, following PRISMA guidelines and the Joanna Briggs Institute (JBI) review protocols. Inclusion criteria targeted studies focusing on BD during the COVID-19 pandemic in adolescents or school-aged individuals without specific medical conditions. Exclusions included studies limited to a single gender, ethnicity, or profession, as well as doctoral theses and editorials. JBI tools were used to assess the quality of the selected studies. Results: From 33 studies (19 cross-sectional and 14 longitudinal), trends in BD during the pandemic varied: 2 studies reported an increase, while 21 indicated a decrease. Key factors linked to increased BD included pandemic stressors (e.g., isolation, social disconnection and non-compliance with restrictions), psychosocial issues (e.g., depression, anxiety, boredom, and low resilience), prior substance use, and sociodemographic variables (e.g., low education, economic extremes, living arrangements, and limited family support). Female gender and academic disengagement were also risk factors. Conversely, factors like stay-at-home orders, fear of contagion, family support, studying health sciences, and resilient coping strategies contributed to reduced BD. Other variables, such as pandemic stress and self-efficacy, had inconsistent effects. Conclusions: Factors contributing to increased BD included pandemic-related stress, mental health conditions, and unhealthy habits, while protective factors included stay-at-home orders, social support, and resilient coping. The study highlights the need for effective prevention and intervention strategies, emphasizing a holistic approach in healthcare, early detection, and tailored interventions, particularly for vulnerable groups such as adolescents. Full article

Background. T1DM is a significant chronic condition that necessitates regular medical monitoring, dietary and physical activity supervision, and daily blood glucose monitoring and insulin therapy. The management of this disease and the transition to adolescence often have a significant psychosocial impact on the individual and their family. Objective. The objective of this correlational study was to examine the reciprocal influence between adolescents and their caregivers, with a particular focus on the beneficial effect of receiving psychological support sessions from family members and adolescents with T1DM in a pilot study. Methods. An indicator variable was developed to facilitate an analysis of changes occurring prior to, as well as following, the administration of the treatment in question. Family caregivers received two therapy sessions, and we analyzed their perceived caregiver stress. Adolescents had five sessions, and the reduction in emotional distress was studied in them. Results. The sample comprised 15 adolescent–family caregiver dyads. All parents were mothers of adolescents, with a mean age of 47.67 and 13.47 years, respectively. Descriptive statistics and Spearman correlations were conducted. Following the completion of the psychological counseling sessions, the data revealed a significant positive correlation between the perceived reduction in global stress experienced by the caregiver and the adolescent’s emotional distress, with correlation coefficients of 0.74 and 0.61, respectively. Furthermore, a positive relationship was observed between the reduction in existing difficulties in family role adjustment and the reduction in emotional distress among diabetic youth, with correlation coefficients of 0.72 and 0.57. The frequency of emotional distress of the caregiver also correlated with adolescent emotional distress, with a coefficient of 0.60. Conclusions. The findings of this study provide evidence for the circularity of family systems change. A positive emotional contagion effect is observed in the improvements in stress and emotional distress experienced during adolescence and in the family’s adjustment to T1DM, as reported by caregivers and their children who received psychological support sessions. Full article

This review aims to systematically evaluate existing literature on reducing suicides along railroads, with specific focus on effectiveness, limitations, and research gaps in the current evidence base. Database searches were conducted in PubMed, PsycInfo, Scopus, Embase, and CINAHL covering studies published until 30 November 2024. After screening 623 studies and their references, 51 studies were included; 26 empirically assessed rail-related prevention interventions and 25 provided relevant qualitative insights. Physical barriers like removal of grade crossings, addition of fencing, and platform screen doors (PSDs) showed significant promise. Full-height PSDs eliminated all suicides and half-height PSDs significantly reduced suicide incidence. Fencing was found to be effective but raised concerns about feasibility and must be part of a comprehensive approach to mitigate potential displacement. Safe media reporting was linked to decreased suicides and a reduced risk of contagion, and CCTV monitoring and suicide pits also showed potential but had limited research. Other strategies showed mixed evidence and required additional evaluation. Some studies, particularly on physical barriers, showed possible displacement effects to other stations, highlighting the need for studies larger in geographic and temporal scope. Our findings support certain prevention interventions, but generalizability is limited by scope of research and methodological concerns. Overall, our findings highlight the need for broader, long-term studies to confirm efficacy and establish comprehensive, scalable approaches for policy implementation. Full article

The collapse of Silicon Valley Bank (SVB) on 11 March 2023, and the subsequent depegging of the USDC stablecoin highlighted vulnerabilities in the interconnected financial ecosystem. While prior research has explored the systemic risks of stablecoins and their reliance on traditional banking, there has been limited focus on how banking sector shocks affect digital asset markets. This study addresses this gap by analyzing the impact of SVB’s collapse on the stability of major stablecoins—USDC, DAI, FRAX, and USDD—and their relationships with Bitcoin and Tether. Using daily data from October 2022 to November 2023, we found that the SVB incident triggered a series of depegging events, with varying effects across stablecoins. Our results indicate that USDC, often viewed as one of the safer stablecoins, was particularly vulnerable due to its reliance on SVB reserves. Other stablecoins experienced different impacts based on their collateral structures. These findings challenge the notion of stablecoins as inherently safe assets and underscore the need for improved risk management and regulatory oversight. Additionally, this study illustrates how machine learning models, including gradient boosting and random forests, can enhance our understanding of financial contagion and market stability. Full article

This study investigates the integration of non-economic policies into the framework for assessing macroeconomic coherence as applied by the Chinese government, with a particular focus on green policies. We examine the impact of non-economic factors on social disagreement and investor disagreement (expectations), and how these influences interact with macroeconomic regulation, employing both empirical evidence and dynamic stochastic general equilibrium (DSGE) theoretical models. In the basic analysis section, we merge statistical data on social divergence with policy implementation, utilizing multiple regression and deep neural network models. Our findings provide direct evidence that non-economic policies significantly regulate social sentiment. Additionally, theoretical analyses using contagion models, grounded in real textual data on social and investor divergence, demonstrate that expectations of social sentiment can ultimately affect economic variables. In the extended analysis, we enhance the classic DSGE model to delineate the pathways through which non-economic policies impact the macroeconomy. Drawing from our analyses, we propose specific optimization measures for non-economic policies. The results indicate that targeted policy optimization can effectively manage social disagreement, thereby shaping expectations and harmonizing non-economic with economic policy initiatives. This alignment enhances the coherence of macroeconomic policy interventions. The innovative contribution of this study lies in its provision of both theoretical and empirical evidence supporting the formulation of non-economic policies for the first time, alongside specific recommendations for improving the consistency of macroeconomic policies. Full article

This study describes a comprehensive bibliometric analysis of shadow banking and financial contagion dynamics from 1996 to 2022. Through a holistic approach, our study focuses on quantifying the impact and uncovering significant trends in scientific research related to these interconnected fields. Using advanced bibliometric methods, we explored the global network of publications, identifying key works, influential authors, and the evolution of research over time. The results of the bibliometric analysis have highlighted an annual growth rate of 22.05% in publications related to the topics of shadow banking and financial contagion, illustrating researchers’ interest and the dynamic nature of publications over time. Additionally, significant increases in scientific production have been recorded in recent years, reaching a total of 178 articles published in 2022. The most predominant keywords used in research include “systemic risks”, “risk assessment”, and “measuring systemic risk”. The thematic evolution has revealed that over time, the focus on fundamental concepts used in analyzing these two topics has shifted, considering technological advancements and disruptive events that have impacted the economic and financial system. Our findings provide a detailed insight into the progress, gaps, and future directions in understanding the complex interplay of shadow banking and financial contagion. Our study represents a valuable asset for researchers, practitioners, and policymakers with a keen interest in understanding the dynamics of these critical components within the global financial system. Full article

Our aim is to provide an insight into the procedures and the dynamics that lead the spread of contagious diseases through populations. Our simulation tool can increase our understanding of the spatial parameters that affect the diffusion of a virus. SIR models are based on the hypothesis that populations are “well mixed”. Our model constitutes an attempt to focus on the effects of the specific distribution of the initially infected individuals through the population and provide insights, considering the stochasticity of the transmission process. For this purpose, we represent the population using a square lattice of nodes. Each node represents an individual that may or may not carry the virus. Nodes that carry the virus can only transfer it to susceptible neighboring nodes. This important revision of the common SIR model provides a very realistic property: the same number of initially infected individuals can lead to multiple paths, depending on their initial distribution in the lattice. This property creates better predictions and probable scenarios to construct a probability function and appropriate confidence intervals. Finally, this structure permits realistic visualizations of the results to understand the procedure of contagion and spread of a disease and the effects of any measures applied, especially mobility restrictions, among countries and regions. Full article

This article analyzes the risk contagion mechanism of local government implicit debt from the perspective of multi-subject collaboration, considering interaction effects among different influencing factors. On this basis, with the help of complex network theory and mean field theory, a risk contagion model of local government implicit debt is constructed, and then the evolution characteristics and control strategies for risk contagion of local government implicit debt are analyzed theoretically and simulated. The main findings obtained from the study are: (1) A scale-free network is not conducive to the risk contagion of local government implicit debt, while the opposite is true for a random network. (2) Information openness accuracy and information disclosure strategy both exhibit a positive “U” shaped relationship with the risk contagion of local government implicit debt. Debt management level, emotional tendency, risk preference level, credit policy robustness, accountability mechanism soundness, and perfection of laws and regulations are all negatively correlated with the risk contagion of local government implicit debt. (3) In order to effectively reduce the risk contagion intensity of local government implicit debt, local governments at all levels should continuously strengthen their debt management capabilities and information openness, and the central government should continuously improve accountability mechanisms, laws, and regulations. At the same time, financial institutions and the media should actively play the role of “stabilizers”. However, the local government implicit debt risk is an inherent risk, and its control focus should be on reducing rather than eliminating the risk. Full article

To study the impact of land—use structure and landscape pattern on water quality at different spatial scales in the Dan River Basin (Qin Ling Mountains, China), water samples from 21 sites along the Dan River were collected in 2022 during the dry and wet seasons, and nine water quality indices were tested. Land—use composition and landscape pattern indices at riverine reach, riparian, and sub—basin were obtained, and correlation analysis and redundancy analysis (RDA) were used to determine the relationship with water quality. The results are as follows. (1) Water quality in the Dan River is better in the wet season than in the dry season; the main pollutants are total nitrogen (TN) and total phosphorus (TP). (2) The impact of land—use composition and landscape pattern on water quality has a scale effect; riverine reach can best explain the water quality. (3) Agricultural land and forest have the greatest impacts on water quality; agricultural land and construction land aggravate the deterioration of water quality, while forest, grassland, and water area have positive effects on water quality. The largest patch index (LPI) and contagion index (CONTAG) were positively correlated with pollutants, while Patch richness density (PRD), Patch shape (PD), Shannon’s diversity index (SHDI), and landscape shape index (LSI) were negatively correlated with pollutants, indicating that with an increase in the impact of human activities on landscapes, the degree of fragmentation decreases patch richness, landscape shape tends to be simplified, and water pollution is eventually aggravated. Land planners should focus on optimizing the land—use structure and landscape pattern to increase the diversity of the landscape. Therefore, strict environmental regulations must be established. Full article