Search Results (757)

Scholars have long shown that post-separation abuse continues through legal channels and that legal institutions often reinforce existing social relations. Nevertheless, little is known about how abused mothers’ legal experiences shape their understanding of legality and how this dynamic may function to perpetuate coercive control. Drawing on in-depth interviews with 32 Israeli mothers co-parenting with abusive ex-partners, this study offers a phenomenological account of how post-separation abused mothers experience family law proceedings, based on a feminist imperative to bring their voices to center stage. The analysis reveals a dialectical legal consciousness comprising three interconnected orientations—characterized by internal contradictions and tensions that paradoxically serve to maintain rather than disrupt existing power relations: Institutional Trust and Disillusionment in the law’s protective promise, Institutional Asymmetry as experienced from the abused mothers’ perspective, and Recognizing Entrapment—the realization that legal processes reproduce the very dynamics they sought to escape. Abused mothers thus describe a paradoxical relationship with the law of both needing and distrusting a system that mandates continued contact with their abusers. Caught in a second-order abusive relationship, they feel compelled to comply with processes they perceive as harmful. We term this Entrapped Legal Consciousness—a form of legal subjectivity shaped by institutional norms that reconfigure resistance and reinscribe coercive control. This study offers empirical and theoretical insight into how legality may become a mechanism for cultivating continued control. Full article

The successful implementation of digital transformation initiatives depends critically on public trust in experts guiding these processes. In today’s digital media environment, expert trust faces significant challenges, potentially hindering sustainable innovation adoption. This study investigates how expert credibility dimensions and information characteristics shape trust in digital transformation experts among Chinese social media users. We employed a mixed-methods approach combining a survey of 850 Chinese social media users, a quasi-experiment testing a digital expert verification feature, and secondary data analysis. The study measured multiple dimensions of expert trust while examining relationships with expert cognition factors and media usage variables through regression, mediation, and structural equation modeling. Expert trust in digital transformation exists at moderate levels (M = 6.82/10), with higher trust in digital innovation research (M = 7.12) than specific sustainability recommendations (M = 6.59). Expert authenticity emerged as the strongest predictor of trust (β = 0.27), followed by professional competence (β = 0.21). A “digital exposure paradox” emerged whereby higher volumes of expert information negatively predicted trust (β = −0.18), while information quality positively predicted trust (β = 0.25). The digital verification feature causally enhanced trust (DID = 0.57), with institutional sources strengthening trust while user-generated content diminished it. The findings reveal that digital transformation expert trust involves multi-dimensional evaluations beyond traditional credibility assessments. The “digital exposure paradox” suggests that prioritizing information quality over quantity, demonstrating expert authenticity, and implementing verification mechanisms can enhance trust and accelerate sustainable digital transformation adoption. Full article

Sustainable economic development requires transparent and consistent institutional communication from monetary authorities to maintain long-term financial stability and public trust. This study investigates the latent authorial structure and stylistic heterogeneity of central bank communications by applying stylometric analysis and unsupervised machine learning to official announcements of the Central Bank of the Republic of Turkey (CBRT). Using a dataset of 557 press releases from 2006 to 2017, we extract a range of linguistic features at both sentence and document levels—including sentence length, punctuation density, word length, and type–token ratios. These features are reduced using Principal Component Analysis (PCA) and clustered via Hierarchical Clustering on Principal Components (HCPC), revealing three distinct authorial groups within the CBRT’s communications. The robustness of these clusters is validated using multidimensional scaling (MDS) on character-level and word-level n-gram distances. The analysis finds consistent stylistic differences between clusters, with implications for authorship attribution, tone variation, and communication strategy. Notably, sentiment analysis indicates that one authorial cluster tends to exhibit more negative tonal features, suggesting potential bias or divergence in internal communication style. These findings challenge the conventional assumption of institutional homogeneity and highlight the presence of distinct communicative voices within the central bank. Furthermore, the results suggest that stylistic variation—though often subtle—may convey unintended policy signals to markets, especially in contexts where linguistic shifts are closely scrutinized. This research contributes to the emerging intersection of natural language processing, monetary economics, and institutional transparency. It demonstrates the efficacy of stylometric techniques in revealing the hidden structure of policy discourse and suggests that linguistic analytics can offer valuable insights into the internal dynamics, credibility, and effectiveness of monetary authorities. These findings contribute to sustainable financial governance by demonstrating how AI-driven analysis can enhance institutional transparency, promote consistent policy communication, and support long-term economic stability—key pillars of sustainable development. Full article

Background: Interoperability is fundamental for advancing digital health and digital therapeutics, particularly with the integration of technologies such as artificial intelligence (AI), blockchain, and federated learning. Low- and middle-income countries (LMICs), where digital infrastructure remains fragmented, face specific challenges in implementing standardized and scalable systems. Methods: This scoping review was conducted using the Arksey and O’Malley framework, refined by Levac et al., and the Joanna Briggs Institute guidelines. Five databases (PubMed, Scopus, IEEE Xplore, ACM Digital Library, and Google Scholar) were searched for peer-reviewed English language studies published between 2015 and 2025. We identified 255 potentially eligible articles and selected a 10% random sample (n = 26) using Stata 18 by StataCorp LLC, College Station, TX, USA, for in-depth data charting and thematic synthesis. Results: The selected studies spanned over 15 countries and addressed priority technologies, including mobile health (mHealth), the use of Health Level Seven (HL7)’s Fast Healthcare Interoperability Resources (FHIR) for data exchange, and blockchain. Interoperability enablers include standards (e.g., HL7 FHIR), data governance frameworks, and policy interventions. Low- and Middle-Income Countries (LMICs) face common issues related to digital capacity shortages, legacy systems, and governance fragmentation. Five thematic areas were identified: (1) policy and governance; (2) standards-based integration; (3) infrastructure and platforms; (4) emerging technologies; and (5) LMIC implementation issues. Conclusions: Emerging digital health technologies increasingly rely on interoperability standards to scale their operation. Although global standards such as FHIR and the Trusted Exchange Framework and Common Agreement (TEFCA) are gaining momentum, LMICs require dedicated governance, infrastructure, and capacity investments to make equitable use feasible. Future initiatives can benefit from using science- and equity-informed frameworks. Full article

This article analyses journalistic decisions in the face of disinformation, focusing on the case of Vaza Jato in Brazil. Drawing on a mixed-methods approach—combining critical discourse analysis of online articles with semi-structured interviews with two editors—the study explores how two ideologically contrasting newspapers (Folha de S.Paulo and Gazeta do Povo) framed and justified their editorial positions regarding the publication of hacked content. The findings reveal distinct narrative strategies, degrees of epistemological openness, and levels of institutional trust in the judiciary and political actors. The results also show how editorial decisions are shaped by broader concerns about professional legitimacy, audience trust, and the ambiguous boundary between journalism and disinformation. This article contributes to research on disinformation, editorial ethics, and media trust, proposing an analytical framework applicable to other high-risk communication contexts. Full article

The value of personal data can only be unlocked through efficient circulation. This study explores a multi-party collaborative mechanism for personal-data trading, aiming to improve data quality and market vitality via incentive-compatible institutional design, thereby supporting the high-quality development of the digital economy. Symmetry enhancement refers to the use of strategies and mechanisms to narrow the information gap among data controllers, operators, and demanders, enabling all parties to facilitate personal-data transactions on relatively equal footing. Drawing on evolutionary-game theory, we construct a tripartite dynamic-game model that incorporates data controllers, data operators, and data demanders. We analyze how initial willingness, payoff structures, breach costs, and risk factors (e.g., data leakage) shape each party’s strategic choices (cooperate vs. defect) and their evolutionary trajectories, in search of stable equilibrium conditions and core incentive mechanisms for a healthy market. We find that (1) the initial willingness to cooperate among participants is the foundation of a virtuous cycle; (2) the net revenue of data products significantly influences operators’ and demanders’ propensity to cooperate; and (3) the severity of breach penalties and the potential losses from data leakage jointly affect the strategies of all three parties, serving as key levers for maintaining market trust and compliance. Accordingly, we recommend strengthening contract enforcement and trust-building; refining the legal and regulatory framework for data rights confirmation, circulation, trading, and security; and promoting stable supply–demand cooperation and market education to enhance awareness of data value and compliance, thereby stimulating individuals’ willingness to authorize the use of their data and maximizing its value. Full article

Smart home technologies are increasingly powered by artificial intelligence (AI), offering convenience, energy efficiency, and security, but also raising serious concerns around privacy and cybersecurity. This study seeks to explore the factors that affect the adoption of AI-powered smart home devices by extending the Trust in Technology Model (TTM) to incorporate cybersecurity awareness. The objective is to better understand how users’ trust in technology, institutions, and specific devices, combined with their cybersecurity awareness, influences adoption behavior. A quantitative research design was used, and Structural Equation Modeling (SEM) was employed to examine the assumed relationships among the variables. The results confirm that propensity to trust, in general, technology significantly enhances institution-based trust, which in turn positively influences trust in specific technologies. Trust in specific technologies and cybersecurity awareness were both found to strongly increase users’ intention to adopt AI-powered smart home devices. Moreover, users’ intentions showed the strongest effect on deep structure use, highlighting that positive behavioral intention is a key driver of actual, advanced utilization of these technologies. These results highlight the importance of trust-building and awareness initiatives for fostering wider adoption. This research extends the current literature on technology adoption and provides a framework that can help explain the user’s adoption of AI-powered smart home devices. Its originality lies in integrating cybersecurity awareness into the TTM, offering both theoretical contributions and practical implications for policymakers, developers, and marketers. Full article

Antimicrobial resistance (AMR) poses a significant global threat to human and animal health, necessitating robust surveillance and stewardship tools. While existing systems address aspects of veterinary AMR, a comprehensive, user-centric dashboard for U.S. veterinarians remains a critical unmet need. This study aimed to identify U.S. veterinarians’ preferences and perceived needs for such a dashboard, to help guide its design and development. A cross-sectional survey was conducted between January and March 2024, targeting U.S. veterinarians through professional channels. The survey instrument captured demographics, experiences with existing tools, preferences for data types and visualizations, desired technical specifications, and open-ended feedback. Of the 677 respondents, a near-unanimous consensus (over 75%) emerged on the importance of functionalities like antimicrobial stewardship education, off-label use guidance, surveillance data, and empirical treatment support. Over 70% expressed comfort sharing aggregated geographic and de-identified animal data. A strong preference was observed for making the dashboard accessible by veterinary colleges (78.87%), diagnostic laboratories (72.61%), and federal agencies (USDA: 71.47%, CDC: 66.67%, FDA: 62.11%), indicating a desire for a collaborative, authoritative system. The findings provide a robust foundation for developing a U.S. veterinary AMR dashboard. Future phases should adopt an iterative, user-centered design, incorporating qualitative research with diverse stakeholders and piloting a prototype with preferred institutional partners. This approach will ensure a trusted, sustainable tool that effectively translates surveillance data into actionable insights for improved animal and public health. Full article

Citizen science is increasingly recognized as a potential catalyst for sustainability transitions, climate action, and behavioral change by fostering collaboration between scientists and the public. While it offers benefits such as mutual learning, awareness raising, and improved outcomes, sustaining long-term diverse engagement remains a challenge. Research to date has largely emphasized data outcomes and initial participation, often overlooking the relational, social, and practical dimensions crucial for continued involvement. A disconnect persists between researchers’ data-driven goals and participants’ personal motivations, compounded by insufficient training and institutional support for engagement. This paper presents a novel framework for enhancing citizen engagement, drawing on a state-of-the-art literature review and focus group insights from the H2020 I-CHANGE project. It identifies enablers for and barriers to participation, reframing the latter as opportunities for support. The findings are organized into four themes: (1) call for participation, focusing on intrinsic motivation and local relevance; (2) project design, highlighting inclusive tools and communication; (3) a collaborative process, emphasizing trust, clarity, and support; and (4) participation benefits, including meaning, recognition, and social connection. This study underscores the need to build trust, foster relationality, and align expectations. It proposes practical engagement criteria and calls for deeper exploration of the relational foundations of citizen science. Full article

Background: This study is particularly relevant to the Romanian context, where relatively few empirical investigations have examined post-pandemic health perceptions and levels of trust in public institutions. The purpose of this study is to investigate the long-term impact of the COVID-19 pandemic on health perceptions and trust in the healthcare system by examining key socioeconomic and epidemiological factors. Methods: A cross-sectional online survey was conducted among Romanian adults (N = 423), between March and April 2025. Demographic data, lifestyle habits, mental health, and access to healthcare were assessed. Statistical analyses included both bivariate (chi-square test) and multivariable logistic regression models to identify independent associations. Results: 31.9% of participants reported increased stress and anxiety during the pandemic. Decreased trust in the healthcare system (75.6%) and a perceived reduction in life expectancy (74.3%) were also noted as a consequence of the COVID-19 pandemic. Perceived life expectancy decline was linked to lower education and inconsistent healthcare behavior. Conclusion: In our sample, the perception of decreased life expectancy reflects not only epidemiological realities but also emotional and social responses to crises. Individuals’ trust, behavior, and shared vision of the future have also been challenged during the COVID-19 pandemic. Full article

The societal effects of cybersecurity are widely discussed, but it remains less clear how software security regulations specifically contribute to building a resilient society, particularly in relation to Sustainable Development Goals 5 (Gender Equality), 10 (Reduced Inequalities), and 16 (Peace, Justice and Strong Institutions). This study investigates this connection by examining key EU and U.S. strategies through comparative legal analysis, software development (SDLC) case studies, and a normative–sociological lens. Our findings reveal that major regulations—such as the EU’s Cyber Resilience Act and the U.S. SBOM rules—are not merely reactive, but proactively embed resilience as a fundamental mode of operation. This approach structurally reallocates digital risks from users to manufacturers, reframing software security from a matter of compliance to one of social fairness and institutional trust. We conclude that integrating ‘resilience by design’ into technology rules is more than a technical fix; it is a mechanism that makes digital access fairer and better protects vulnerable populations, enabling technology and society to advance cohesively. Full article

As HEIs face increasing hybrid environment and communication demands, digital tools and AI can offer timely solutions (for more inclusive and more effective mediation. By applying Media Richness and Social Presence theories, we study how platforms like Zoom and AI-powered chatbots can replicate attributes similar to trust and empathy in virtual contexts. Case studies from pioneering institutions show that hybrid mediation models that blend traditional and digital approaches can enhance engagement as well as fairness. Despite possible disadvantages such as fatigue and ethics, emotionally intelligent AI and immersive technology are pointing toward a more adaptable, empathetic leadership paradigm. Full article

Background/Objectives: Multiple sclerosis (MS) is a chronic demyelinating disease where early identification of patients at risk of conversion from clinically isolated syndrome (CIS) to clinically definite MS remains a critical unmet clinical need. Existing machine learning approaches often lack interpretability, limiting clinical trust and adoption. The objective of this research was to develop a novel two-stage machine learning framework with comprehensive explainability to predict CIS-to-MS conversion while addressing demographic bias and interpretability limitations. Methods: A cohort of 177 CIS patients from the National Institute of Neurology and Neurosurgery in Mexico City was analyzed using SeruNet-MS, a two-stage framework that separates demographic baseline risk from clinical risk modification. Stage 1 applied logistic regression to demographic features, while Stage 2 incorporated 25 clinical and symptom features, including MRI lesions, cerebrospinal fluid biomarkers, electrophysiological tests, and symptom characteristics. Patient-level interpretability was achieved through SHAP (SHapley Additive exPlanations) analysis, providing transparent attribution of each factor’s contribution to risk assessment. Results: The two-stage model achieved a ROC-AUC of 0.909, accuracy of 0.806, precision of 0.842, and recall of 0.800, outperforming baseline machine learning methods. Cross-validation confirmed stable performance (0.838 ± 0.095 AUC) with appropriate generalization. SHAP analysis identified periventricular lesions, oligoclonal bands, and symptom complexity as the strongest predictors, with clinical examples illustrating transparent patient-specific risk communication. Conclusions: The two-stage approach effectively mitigates demographic bias by separating non-modifiable factors from actionable clinical findings. SHAP explanations provide clinicians with clear, individualized insights into prediction drivers, enhancing trust and supporting decision making. This framework demonstrates that high predictive performance can be achieved without sacrificing interpretability, representing a significant step forward for explainable AI in MS risk stratification and real-world clinical adoption. Full article

This study develops and empirically validates a framework integrating cultural factors into sustainable supply chain management (SSCM) for emerging economies. We introduce the Cultural Affinity Index (CAI), a multi-dimensional construct quantifying cultural compatibility between supply chain partners based on language compatibility, regional trust, trade networks, and historical trade patterns. Using publicly available data from UN COMTRADE, the World Bank, and Hofstede Insights, we analyze 850 supplier–manufacturer dyads across five Southeast Asian countries (2019–2023). Through Monte Carlo simulation with empirically calibrated parameters, we demonstrate that high cultural affinity (CAI > 0.7) shows positive associations with economic performance (+18.0%), environmental compliance (+12%), and social sustainability (+32%) compared to baseline scenarios. We test both linear and interaction models, finding that language compatibility and regional trust exhibit synergistic effects ($\beta$ = 0.15, p < 0.01). Multi-objective optimization reveals Pareto-optimal solutions achieving simultaneous improvements across all triple bottom line dimensions. Sensitivity analysis confirms robustness across varying cultural weights (±20%) and institutional contexts. The framework’s effectiveness varies by institutional quality, with stronger associations in weaker institutional environments (correlation = −0.92). While focused on manufacturing, we discuss adaptations for service sectors. This research provides both theoretical contributions to the SSCM literature and practical tools for organizations managing culturally diverse supply chains in emerging markets. Full article

The intersection of Artificial Intelligence (AI) and education is revolutionizing learning and teaching in this digital era, with Generative AI and large language models (LLMs) providing even greater possibilities for the future. The digital transformation of language education demands innovative approaches that combine pedagogical rigor with explainable AI (XAI) principles, particularly for low-resource languages. This paper presents a novel methodology that integrates Business Process Model and Notation (BPMN) with Multi-Agent Systems (MAS) to create transparent, workflow-driven language tutors. Our approach uniquely embeds XAI through three mechanisms: (1) BPMN’s visual formalism that makes agent decision-making auditable, (2) Retrieval-Augmented Generation (RAG) with verifiable knowledge provenance from textbooks of the National Institute of Languages of Luxembourg, and (3) human-in-the-loop validation of both content and pedagogical sequencing. To ensure realism in learner interaction, we integrate speech-to-text and text-to-speech technologies, creating an immersive, human-like learning environment. The system simulates intelligent tutoring through agents’ collaboration and dynamic adaptation to learner progress. We demonstrate this framework through a Luxembourgish language learning platform where specialized agents (Conversational, Reading, Listening, QA, and Grammar) operate within BPMN-modeled workflows. The system achieves high response faithfulness (0.82) and relevance (0.85) according to RAGA metrics, while speech integration using Whisper STT and Coqui TTS enables immersive practice. Evaluation with learners showed 85.8% satisfaction with contextual responses and 71.4% engagement rates, confirming the effectiveness of our process-driven approach. This work advances AI-powered language education by showing how formal process modeling can create pedagogically coherent and explainable tutoring systems. The architecture’s modularity supports extension to other low-resource languages while maintaining the transparency critical for educational trust. Future work will expand curriculum coverage and develop teacher-facing dashboards to further improve explainability. Full article