Search Results (196)

The rapid expansion of corporate sustainability reporting has increased transparency requirements while raising concerns about greenwashing driven by selective, narrative-based disclosure. This study assesses the credibility of Environmental, Social, and Governance (ESG) communication by comparing corporate sustainability reports with external media coverage for a sample of 204 large firms operating in Central and Eastern Europe in 2023. Using natural language processing techniques, the analysis constructs a Greenwashing Severity Index (GSI) that captures discrepancies between firms’ ESG self-representation and external public narratives. The index combines ESG-specific focus measures, sentiment analysis, TF–IDF-based term weighting, and topic modeling to quantify imbalances in ESG communication. Results indicate moderate but widespread greenwashing across countries, industries, and firm sizes, with substantial heterogeneity linked to differences in regulatory maturity and stakeholder scrutiny. Higher alignment between corporate disclosures and external narratives is observed among larger firms and in sectors subject to stronger public accountability, while finance, aviation, and online commerce exhibit higher greenwashing severity. A propensity score matching analysis further shows that firms with imbalanced emphasis across ESG dimensions display significantly higher GSI values, consistent with strategic disclosure behavior rather than substantive sustainability engagement. Overall, the findings demonstrate that transparency alone is insufficient to ensure credible ESG communication, highlighting the need for EU sustainability governance to move beyond disclosure-based compliance toward digitalized, data-driven monitoring frameworks that systematically integrate external information sources to curb strategic ESG misrepresentation and enhance corporate accountability under evolving regulatory regimes. Full article

Power-system planning is being reshaped by rapid decarbonisation, electrification, and digitalisation, which collectively amplify uncertainty in demand, generation, technology adoption, and policy pathways. This review critically synthesises three principal optimisation paradigms used to plan under uncertainty in power systems: scenario-based stochastic optimisation, set-based robust optimisation (including adaptive and distributionally robust variants), and minimax-regret decision models. The review is positioned to address a recurrent limitation of many uncertainty-planning surveys, namely the separation between “method reviews” and “technology reviews”, and the consequent lack of decision-operational guidance for planners and system operators. The central contribution is a decision-centric framework that operationalises method selection through two explicit dimensions. The first is an information posture, which formalises what uncertainty information is credible and usable in practice (probabilistic, set-based, or probability-free scenario representations). The second is a flexibility posture, which formalises the availability, controllability, and timing of operational recourse enabled by smart-grid technologies. These postures are connected to modelling templates, data requirements, tractability implications, and validation/stress-testing needs. Smart-grid technologies are integrated not as an appended catalogue but as explicit sources of recourse that change the economics of uncertainty and, in turn, shift the relative attractiveness of stochastic, robust, and regret-based planning. Soft Open Points, Coordinated Voltage Control, and Vehicle-to-Grid/Vehicle-to-Building are treated uniformly under this recourse lens, highlighting how device capabilities, control timescales, and implementation constraints map into each paradigm. The paper also increases methodological transparency by describing literature-search, screening, and inclusion principles consistent with a structured narrative review. Practical guidance is provided on modelling choices, uncertainty governance, computational scalability, and institutional adoption constraints, alongside revised comparative tables that embed data credibility, regulatory interpretability, and implementation maturity. Full article

This study explores how young people in Finland and Portugal perceive media trust and vulnerability to information operations in the digital era. While both groups rely heavily on digital platforms for news, they view online sources as less reliable due to disinformation and fake news, especially on TikTok and Instagram. Trust and truth appear emotionally driven, with influencers and entertainment content often considered credible, increasing susceptibility to manipulation. Despite identifying as ‘digital natives’, participants rarely question source credibility or algorithmic influence, leaving them exposed to adversarial actors, such as Russia. Full article

Ellipsometric mapping measurements and Bayesian evaluation were performed with a non-collimated, imaging ellipsometer using an LCD monitor as a light source. In such a configuration, the polarization state of the illumination and the local angle of incidence vary spatially and spectrally, rendering conventional spectroscopic ellipsometry inversion methods hardly applicable. To address these limitations, a multilayer optical forward model is augmented with instrument-specific correction parameters describing the polarization state of the monitor and the angle-of-incidence map. These parameters are determined through a Bayesian calibration procedure using well-characterized Si-SiO₂ reference wafers. The resulting posterior distribution is explored by global optimization based on simulated annealing, yielding a maximum a posteriori estimate, followed by marginalization to quantify uncertainties and parameter correlations. The calibrated correction parameters are subsequently incorporated as informative priors in the Bayesian analysis of unknown samples, including polycrystalline–silicon layers deposited on Si-SiO₂ substrates and additional Si-SiO₂ wafers outside the calibration set. The approach allows consistent propagation of calibration uncertainties into the inferred layer parameters and provides credible intervals and correlation information that cannot be obtained from conventional least-squares methods. The results demonstrate that, despite the broadband nature of the RGB measurement and the limited number of analyzer orientations, reliable layer thicknesses can be obtained with quantified uncertainties for a wide range of technologically relevant samples. The proposed Bayesian framework enables a transparent interpretation of the measurement accuracy and limitations, providing a robust basis for large-area ellipsometric mapping of multilayer structures. Full article

Research has shown that preferences exist in following information from coherent sources and that incoherent material can diminish overall trust in sources from readers. In line with relational frame theory, coherence, or “sense-making”, has emerged as an important factor in the process of rule-following, but some research has been confounded by the degree of extremity used to establish coherence. The present study investigated the role of speaker coherence in rule-following preferences between newspaper outlets. Participants (N = 64) each viewed four news articles that had been published across two anonymized Irish newspaper outlets. Each article was categorized by its level of coherence and level of controversy. Rule-following was measured through the likelihood of participants adopting new habits and following general advice from the news outlet after reading each story. Participants also selected their preferred outlet from which to follow general advice at the end of the study. Results indicated that participants had greater rule-following preferences for coherent outlets, regardless of how controversial the material was perceived to be. Speaker coherence was discussed in terms of its impact on the perceived credibility of media outlets and avenues for reducing polarization. Full article

This study investigates how consumer brand trust is shaped by the interplay of sustainability disclosure valence (positive/negative), domain (social/environmental), and information source credibility (internet influencer/scientific report). Using a mixed-methods approach, combining a series of focus groups and a 2 × 2 × 2 between-subjects scenario experiment with a sample of 354 university students, we analyzed both the main and interactive effects of these factors on brand trust via hierarchical regression. The findings confirm that positive disclosures in both social and environmental domains significantly enhance brand trust. We observed a significant synergistic interaction, where consistent positive disclosures across both sustainability domains yield the greatest increase in trust. The study uncovers a domain-specific boundary condition for source credibility. While the source of information significantly moderates the impact of social sustainability disclosures—with influencers failing to generate the same punitive impact as scientific reports regarding social transgressions—source credibility exerts no significant influence on environmental disclosure processing. These findings suggest that consumers process environmental data as technical information (source-neutral) but social data as moral signals (source-dependent). Practically, the results suggest that brands require a holistic sustainability communication strategy and rely on highly credible sources for sensitive social messaging, especially when managing reputational risk or responding to negative disclosures. Full article

In developing countries, technology-based startups (TBSs) play a vital role in driving innovation, and they significantly contribute to the generation of jobs and economic development. However, despite their importance, startups have a high failure rate worldwide, and a major contributing factor is a lack of funding. The objective of this study is to compare the existing financing mechanisms in Ethiopia, Uganda, and Kenya and determine the relative position of Ethiopia in the financing landscape. This study was based on resource-based theory and signaling theory. A desk research methodology was employed, and a total of 70 sources were reviewed. The data sources include academic literature, publications from the World Bank, local reports, government policies of the three nations, articles published in reputable journals, and global database indexes. Articles were also selected based on their relevance to the research question and the credibility of the publication. The comparison was carried out based on identifying similarities and differences in economic indicators, the innovation performance of the countries, the innovation eco-system, the types of existing financing mechanisms in each country, and various government policies and initiatives. We also validated our findings by cross-checking information from multiple sources to avoid bias. The results reveal that Ethiopia is lagging behind in most of the parameters set for comparison, while its neighbors, Uganda and Kenya, have a relatively better status in general. Finally, this study has theoretical and practical implications. Full article

Smart contracts rely on blockchain oracles to access off-chain data, yet existing oracle designs often face challenges such as untrustworthy data sources, weak temporal guarantees, and limited verifiability. This work presents Ivy Oracle, a robust and time-trustworthy data feed framework that enhances the reliability and auditability of off-chain information for smart contracts. Ivy Oracle integrates trusted execution environments (TEEs) for secure data acquisition, an external time server for authenticated timestamps, and a PageRank-based trust model to evaluate source credibility. We implement and evaluate Ivy Oracle on the Ethereum Sepolia testnet, demonstrating that it achieves up to 63.6% lower on-chain gas consumption than Chainlink for signature verification while maintaining only a slight increase in communication overhead due to its dual-attestation mechanism. These results confirm that Ivy Oracle provides strong time trustworthiness and data reliability with minimal performance cost, making it suitable for latency-sensitive blockchain applications. Full article

This study investigates the relationship between Environmental, Social, and Governance (ESG) performance and the financial performance of startups in emerging economies. We posit that strong ESG practices serve as a critical signal of quality and legitimacy, helping to alleviate the liability of newness by mitigating information asymmetries for external stakeholders. Analyzing a longitudinal dataset of startups, we find a positive main effect of ESG on financial performance. Further, we demonstrate that this relationship is contingent on factors at multiple levels. The positive effect of ESG is weakened in contexts of high firm digitalization, greater analyst coverage, and developed regional institutions, as these factors act as substitutes by providing alternative sources of credible information, thereby reducing the unique signaling value of ESG. Conversely, intense industry competition amplifies the ESG advantage, as the signal becomes more critical for differentiation. Our findings contribute by shifting the ESG discourse to the entrepreneurial context and offering a nuanced, multi-level understanding of when a firm’s sustainable investment is most pivotal as a strategic asset for startups navigating institutional voids. Full article

Strong communication is central to the translation of breast cancer screening availability into uptake. This experiment tests the role of design features of screening advertisements in directing visual attention in screening-eligible women (≥40 years). To this end, a within-subjects eye-tracking experiment (N = 30) was conducted in which women viewed six static public service advertisements. Predefined Areas of Interest (AOIs), Text, Image/Visual, Symbol, Logo, Website/CTA, and Source/Authority—were annotated, and three standard measures were calculated: Time to First Fixation (TTFF), Fixation Count (FC), and Fixation Duration (FD). Analyses combined descriptive summaries with subgroup analyses using nonparametric methods and generalized linear mixed models (GLMMs) employing participant-level random intercepts. Within each category of stimuli, detected differences were small in magnitude yet trended towards few revisits in each category for the FC mode; TTFF and FD showed no significant differences across categories. Viewing data from the perspective of Areas of Interest (AOIs) highlighted pronounced individual differences. Narratives/efficacy text and dense icon/text callouts prolonged processing times, although institutional logos and abstract/anatomical symbols generally received brief treatment except when coupled with action-oriented communication triggers. TTFF timing also tended toward individual areas of interest aligned with the Scan-Then-Read strategy, in which smaller labels/sources/CTAs are exploited first in comparison with larger headlines/statistical text. Practically, screening messages should co-locate access and credibility information in early-attention areas and employ brief, fluent efficacy text to hold gaze. The study adds PSA-specific eye-tracking evidence for breast cancer screening and provides immediately testable design recommendations for programs in Greece and the EU. Full article

Background: YouTube has become one of the most widely used platforms for medical education and patient information. However, the accuracy and reliability of such unregulated content remain highly variable and sometimes misleading. This study aimed to evaluate the quality, reliability, and educational value of YouTube videos related to poisonings, insect bites, and envenomations using validated scoring systems. Methods: A cross-sectional analysis of YouTube videos was conducted using the search terms “approach to insect bites and stings,” “approach to poisonings,” “approach to scorpion envenomation,” “approach to snake envenomation,” and “approach to mushroom poisoning.” Searches were performed in incognito mode on August 1, 2025. Only English-language videos shorter than one hour were included. Video quality and reliability were evaluated using the Global Quality Score (GQS), modified DISCERN (mDISCERN), and Journal of the American Medical Association (JAMA) benchmarks, while viewer engagement was measured using the Video Power Index (VPI). Results: A total of 279 videos were analyzed. The mean ± SD scores were as follows: GQS, 3.53 ± 1.09; mDISCERN, 3.53 ± 1.08; and JAMA, 2.63 ± 0.96. Based on the GQS, 59.5% of the videos were high quality, 20.8% moderate quality, and 19.7% low quality; thus, approximately 40% of the evaluated videos (low- and moderate-quality categories combined) did not meet optimal quality standards. Videos on snake envenomation and general poisoning had significantly higher quality and reliability scores (p < 0.001). Educational, physician-sourced, and physician-presented videos achieved higher GQS, JAMA, and mDISCERN values (p < 0.001 for all). However, no significant differences were found in the VPI, indicating that popularity metrics did not correlate with content quality. Conclusions: YouTube provides wide access to poisoning-related educational materials, but content quality varies considerably, and a substantial proportion of videos fall below acceptable quality thresholds. Videos produced or presented by physicians are more reliable, whereas popularity is not a valid indicator of scientific accuracy. Active involvement of healthcare professionals and academic institutions, together with platform-level quality verification and visibility strategies, is essential to improve the credibility and impact of online health information. Full article

This study examines how information frictions in climate policy credibility shape carbon border adjustment mechanisms when trading partners cannot fully verify each other’s commitment to green industrial policies. A dynamic signaling framework models exporters’ policy commitment capacity as private information, incorporating Bayesian belief updating subject to signal noise and reputation decay. The analysis derives a Perfect Bayesian Equilibrium characterizing optimal CBAM tariff responses conditional on importers’ evolving credibility assessments. The calibrated model achieves strong empirical validation (R² = 0.884, explaining 88% of tariff variance; rank correlation $\rho =0.950$), with Monte Carlo simulations demonstrating robust internal consistency (RMSE = 2.56 percentage points). Results identify a critical belief threshold ($p_t<0.3$) triggering hyperelastic tariff responses: below this credibility level, small perception declines generate disproportionately steep border tax increases (elasticity ≥ 2), trapping countries in high-tariff equilibria despite genuine commitment. Information frictions impose aggregate welfare losses equivalent to 30% of potential coordination gains, decomposed into four sources: information opacity (40%), cognitive biases in belief formation (27%), policy distortions induced by credibility concerns (20%), and reputation maintenance costs (13%). These quantitative patterns, while illustrative within the baseline calibration, motivate testable implications regarding elasticity asymmetries and credibility persistence. The framework identifies targeted policy interventions—third-party verification of commitment durability, rule-based tariff adjustment protocols, and institutional commitment devices—systematically prioritized by marginal welfare impact to guide beliefs away from credibility traps while maintaining environmental rigor. Full article

The rapid evolution of cyber threats requires intelligence sharing between organizations while ensuring data privacy and contributor credibility. Existing centralized cyber threat intelligence (CTI) systems suffer from single points of failure, privacy concerns, and vulnerability to adversarial manipulation. This paper introduces TrustFed-CTI, a novel trust-aware federated learning framework designed for privacy-preserving CTI collaboration across distributed organizations. The framework integrates a dynamic reputation-based trust scoring system to evaluate member reliability, along with differential privacy and secure multi-party computation to safeguard sensitive information. A trust-weighted model aggregation mechanism further mitigates the impact of adversarial participants. A context-aware trust engine continuously monitors the consistency of threat patterns, authenticity of data sources, and contribution quality to dynamically adjust trust scores. Extensive experiments on practical datasets including APT campaign reports, MITRE ATT&CK indicators, and honeypot logs demonstrate a 22.6% improvement in detection accuracy, 28% faster convergence, and robust resistance to up to 35% malicious participants. The proposed framework effectively addresses critical vulnerabilities in decentralized CTI collaboration, offering a scalable and privacy-preserving mechanism for secure intelligence sharing without compromising organizational autonomy. Full article

Background: Renal stone disease is a common urological condition considered to be greatly affected by lifestyle, dietary practices, and hydration status. With the rapid advancement and remarkable rise in digital communication, social media has become an important source of health information. However, little is known about its effects on raising public awareness of dietary and fluid-related risk factors for kidney stone formation, particularly in Middle Eastern populations. Aim: We aimed to evaluate the impact of social media platforms on public awareness of dietary habits and fluid consumption in relation to kidney stone prevention. Methods: A cross-sectional survey was applied to 980 adults with varying demographic characteristics. Data on social media use, dietary and fluid knowledge, and attitudes toward kidney stone prevention were collected through structured questionnaires. Statistical analyses, including regression and mediation models, were employed to identify predictors of awareness and explore pathways linking social media use to knowledge and attitudes. Results: Among the 980 participants (mean age = 29.9 ± 11 years; 55.4% males), 69.9% held university degrees, and 7.2% had a history of kidney stones. The overall awareness of kidney stone prevention varied, with most of the participants recognizing the protective role of adequate hydration (67%) and the adverse impact of soft-drink consumption (73.2%), while knowledge of dietary contributors such as animal protein and tea was limited. Greater knowledge and more appropriate attitudes were associated with older age, female gender, following healthcare professionals, and engagement with medical websites, YouTube, and TikTok. Mediation analysis revealed that social media influenced awareness indirectly through improvements in knowledge and attitudes. Conclusions: This study reveals that the digital environment shapes both public knowledge of and attitudes toward kidney stone prevention, though critical knowledge gaps persist regarding complex dietary factors. Mediation analysis indicated that the digital influence is likely channeled through improvements in knowledge and attitudes. We emphasize that source credibility is paramount; relying on official medical websites and following health professionals were the most effective strategies for boosting awareness. Therefore, expert-led educational strategies must be integrated into public health protocols. Full article

This study investigates the perception-to-purchase journey by examining how consumer artificial intelligence (AI) literacy influences the effectiveness of AI-generated sponsored vlogs (AISVs), an emerging digital marketing format. Using survey data from 413 consumers and structural equation modeling, we develop and test the AI Literacy Perception–Decision Model (AILPDM). Results show that AI literacy affects information adoption through three pathways: emotional value, information usefulness, and source credibility. Separate SEM analyses further suggest that the direct effect of AI literacy on purchase intention was observed in experiential service AISVs, whereas in tangible product AISVs the effect operated mainly through information adoption. The AILPDM framework advances marketing theory by tracing a decision pathway from AI literacy, through perceived value and information adoption, to purchase intention, thereby demonstrating how technological competence evolves from a cost barrier into a cognitive resource that shifts source credibility evaluation from peripheral to central processing. For practitioners, the findings suggest differentiated strategies: Marketers of experiential services should emphasize anthropomorphic elements, whereas marketers of tangible products should prioritize technological transparency to foster consumer trust. Full article