Search Results (1,806)

With the acceleration of digital transformation in education, this paper examines how blockchain is being framed as a governance solution for trust, transparency, and decentralization. Using a hybrid bibliometric and thematic analysis of 93 Web of Science and Scopus publications, the study maps publication trends, leading outlets, author networks, and conceptual clusters. We analyze co-authorship networks, keyword co-occurrence patterns, and conceptual structures using VOSviewer version 1.6.19 and the R-based Bibliometrix package. Then, we apply qualitative coding to offer a more profound interpretation of governance stories. Findings show that blockchain in educational governance is predominantly positioned through techno-managerial lenses—focusing on secure credentials, tamper-proof records, and efficiency—while critical perspectives on power, equity, and participation remain limited. Global North institutions and computer science–oriented venues dominate the field, with little engagement from Global South contexts or educational leadership scholarship. The paper concludes by proposing a research agenda that reimagines blockchain not as a neutral tool, but as a socio-technical assemblage that must be interrogated through equity-, ethics-, and community-centered frameworks. Full article

In Taiwan, some cases charge high diagnostic fees based merely on manual visual inspection or other simple checks, which has severely undermined public trust and delayed judicial resolutions, forcing courts to repeatedly appoint alternative evaluators and prolonging dispute timelines. Based on convenient sampling under a 95% confidence level with a 10% margin of error and a 10–90% category proportion, this study analyzes 83 leakage identification cases collected through convenience sampling, covering diverse building types, leakage causes, and detection techniques such as infrared imaging, borescopes, and moisture meters. A clustering-based pricing framework was applied to classify cases by inspection methods and leakage causes and to link them with cost intervals. After rigorous filtering, cost categorization, one-hot encoding, and normalization, the model revealed three distinct cost groups and achieved an overall classification accuracy of 86.75%, with particularly high precision in the medium-cost range. The findings confirm that advanced methods (e.g., borescopes, high-pressure cleaning) correspond to higher fees, while simpler approaches (e.g., infrared imaging) remain in lower cost brackets. This framework supports transparent and standardized fee estimation, addresses long-standing pricing controversies, and enhances consumer trust in leakage diagnostics. Full article

Objectives: Despite vaccination being the most effective way of preventing infections and vaccination rates recovering worldwide after the COVID-19 pandemic, vaccine hesitancy persists. Some factors, such as psychological and social barriers, can negatively impact views on vaccines and can contribute to vaccine hesitancy. The primary objective of this structured literature review is to investigate the available evidence relating to factors affecting vaccine hesitancy within several countries in Southeastern Europe. Methods: An electronic database search was conducted to identify studies assessing the public and healthcare professionals’ (HCPs) attitudes towards vaccination in Southeastern Europe. These searches were supplemented with grey literature searches. Included studies were conducted in Bulgaria, Croatia, Romania, Serbia, and Slovenia between 1 January 2012 and 31 December 2022. Results: Of the 35 studies identified from the database searches, the most prominent theme observed across Romania, Croatia, and Bulgaria was low confidence in COVID-19 vaccines. Across all age groups, COVID-19 vaccine confidence in these regions was highly dependent on whether individuals thought vaccines were safe and effective, as well as their general trust in vaccines. Confidence in COVID-19 vaccines was seen as relatively high, with attitudes towards routine and elective vaccines being generally positive amongst the general public and HCPs, in Romania, Croatia, Serbia and Slovenia. However, uncertainty around the effectiveness of the vaccine still exists. In Bulgaria, trust in routine and elective vaccines remained low in the general public. Complacency and financial constraints were also identified as underlying causes of vaccine hesitancy. Conclusions: The main cause behind vaccine hesitancy in several countries in Southeastern Europe is distrust in vaccine effectiveness and safety. These key findings can be utilised to support evidence-based decisions regarding where to focus resources to improve public and HCP perception of vaccines in Southeastern Europe. Full article

Abusive news refers to digital content designed to maximize clicks and advertising revenue through sensational headlines, repetitive postings, or emotionally charged language, rather than upholding journalistic integrity. Despite growing concerns about its impact on media credibility and public trust, existing detection approaches lack systematic categorization and type-specific methodologies. This study addresses this gap by proposing a six-type typology of abusive news—content recycling, keyword insertion, title–body inconsistency, commercial promotion, emotionally stimulating headline, and automatically generated types—based on five analytical dimensions: content structure, authenticity, algorithmic manipulability, sensationalism, and information-ecosystem impact. We developed type-specific detection pipelines combining BERT-based embeddings, TF-IDF features, and rule-based indicators and evaluated them using a large-scale Korean clickbait corpus. Results demonstrate that BERT achieves higher F1-scores (0.89) for automatically generated content, while TF-IDF with SVM provides more stable precision (0.60) for emotionally charged articles under class imbalance. Cross-domain experiments confirm that models trained on diverse, balanced topic sets generalize better than volume-focused models, with diversity improving F1-scores by up to 0.07. BERT models show higher false positive rates on repetitive legitimate content compared to TF-IDF approaches, highlighting the importance of type-adaptive architectures and diversity-aware data design in abusive news detection systems. Full article

Technological advances such as Vehicle-to-Grid (V2G) have the potential to support renewable energy integration and grid stability, but large-scale deployment depends on users’ willingness to participate, particularly in public charging environments. While prior research has examined V2G from technical feasibility and system-level perspectives, everyday public settings remain unexplored. This study investigates electric vehicle (EV) users’ willingness to engage in V2G services in public spaces, with a focus on incentives, expectations, and how participation aligns with existing routines and parking conditions. A mixed-method approach was applied, combining a survey of 544 car users with two waves of user-centered interviews. The survey data were analyzed using factor analysis and linear regression models, while the interview data were thematically analyzed. The results show that users’ evaluations of V2G are shaped by sustainability expectations, perceived efficiency, and uncertainties, and preferences for public V2G participation are strongly influenced by convenience, clarity of the offer, and perceived control. Home charging practices emerged as a key reference point shaping expectations of public V2G services. Across both methods, simple and transparent incentives, such as reduced charging or parking costs, were consistently preferred over more complex reward models, including point-based systems or dynamic energy trading. Concerns related to control over trips, battery degradation, trust in service providers, and added complexity remain important barriers to participation. The findings highlight the need for user-centered and socio-technical design of public V2G services that align with users’ everyday routines, parking conditions, and expectations to support broader adoption beyond the home context. Full article

This systematic review examines how digital technologies enable circular economy (CE) transitions across sectors and value chains. Analysing 266 peer-reviewed publications (2016–2025), we develop a comprehensive taxonomy of digital enablers—including IoT, AI, blockchain, cloud computing, additive manufacturing, and digital platforms—and map their applications to circular strategies such as reuse, remanufacturing, and recycling. Our findings reveal that data-driven technologies dominate CE implementation, with 89% of studies involving data collection, storage, analysis, or sharing functions. IoT emerges as the foundational technology for real-time tracking and monitoring, while AI and big data analytics optimise circular processes and predict maintenance needs. Blockchain ensures traceability and trust in circular supply chains, and cloud computing provides scalable infrastructure for collaboration. Manufacturing (41%) and construction (15.5%) are the most studied sectors, with strong European research leadership reflecting policy drivers such as Digital Product Passports. We identify three impact types: enabling (process optimisation), disruptive (business model innovation), and facilitating (ecosystem collaboration). Key barriers include technical complexity, organisational resistance, high implementation costs, and regulatory gaps. The review concludes with recommendations for integrated, multi-stakeholder approaches to realise a digitally enabled circular economy. Full article

The histopathological classification of ovarian carcinoma is fundamental for patient management. While microscopic evaluation by pathologists is the current diagnostic standard, it is known to be subject to interobserver variability, which can affect consistency in treatment decisions. This study addresses this clinical need by developing and validating a deep learning-based diagnostic support tool designed to enhance the objectivity and reproducibility of this classification. In this work, we address a key challenge in computational pathology—the tendency of attention mechanisms to overfit by concentrating on limited features—by systematically evaluating a direct regularization method within multiple instance learning (MIL) models. The models were trained and validated using 10-fold cross-validation on a public training set of 538 whole-slide images and further tested on an independent public dataset for the more challenging task of molecular subtype classification. We utilized features from a foundational model pre-trained on histopathology data to represent tissue morphology. Our findings demonstrate that directly regularizing the attention mechanism with a stochastic approach provides a statistically significant improvement in accuracy and generalization, highlighting its power as a robust technique to mitigate overfitting for this clinical task. In direct contrast to the reported variability in manual assessment, our final model achieved high consistency and accuracy, with a balanced accuracy of 0.854 and a Cohen’s Kappa of 0.791. The model also demonstrated strong generalization on the molecular classification task. Its attention mechanism provides visual heatmaps for pathologist review, fostering interpretability and trust. We have developed a highly accurate and generalizable artificial intelligence tool that directly addresses the challenge of interobserver variability in ovarian cancer classification. Its performance highlights the potential for artificial intelligence to serve as a decision support system, standardizing histopathological assessment. Full article

The rapid spread of misinformation on social media has emerged as a major societal issue. Over 40% of British social media news-sharers admitted they had shared inaccurate or fake news. The extensive distribution of false information causes public trust deterioration while modifying public opinions and potentially destabilizing social and political systems. There are profound challenges due to this hard-to-detect, hard-to-stop reality and the financials and societal implications are remarkable. As an attempt to limit the challenges created from misinformation this paper introduces some preliminary work on detection of fake news and verification of their reliability based on online content. Large language models (LLMs) are being used along with natural language processing (NLP) techniques to evaluate news articles through their linguistic and contextual characteristics. Several models are compared on how they can typically identify typical indicators of misinformation through the analysis of extensive verified datasets to develop an ability to classify content as authentic or fabricated. This work has been through thorough testing to determine its operational effectiveness and dependability after completion. We present a relatively easy-to-use tool which enables a wide range of people also for those without a background in computer science to easily verify news accuracy before sharing or trusting it. This work could help to stop false information from spreading while promoting fact-based discussions and improving digital literacy skills. The research demonstrates how technology fights the fake news crisis to create an informed digital environment which supports public conversation protection and information integrity in the modern digital age. Full article

Chatbots have spurred keen academic interest in the last decade, with researchers focusing primarily on the factors impacting chatbot adoption by consumers. Based on a systematic literature review (SLR) of 202 selected peer-reviewed papers published from 2015 to 2025, this article aims to achieve the following objectives: (1) describe publication trends, including relevant journals and highly-cited papers; (2) analyze the methodological approaches and theoretical models employed in the field; (3) investigate the drivers and barriers to consumers’ adoption of chatbots; and (4) outline directions for future research. Among the findings, the review reveals that perceived usefulness, perceived ease of use, anthropomorphic characteristics, consumer trust in chatbot applications, and the ability of chatbots to emulate a human-like personality are key drivers of chatbot adoption, whereas perceived risks and anxiety are the most reported barriers. This study offers several future research avenues and highlights the importance of considering the role of emotions and personality traits. Full article

In the context of rural sustainability and poverty alleviation within the developing world, a key dilemma facing the international community is to identify suitable strategies and mechanisms to bring multiple stakeholders together to work in efficient and sustainable ways. This paper focuses on the Public–Private–Producer Partnership (4P), a model that involves cooperation between government agencies, business firms, and small-scale producers to foster mutual trust and enhance collaboration through infrastructure development and capacity building in the agricultural value chain. Drawing on evidence from China, Indonesia, Rwanda, Ghana, and Nigeria, this study examines the impact of 4P on crop productivity, agricultural infrastructure, market access, stakeholder empowerment, employment, the land tenure system, and household income. This paper combines value chain analysis, Theory of Change mapping, and both qualitative and quantitative evaluation techniques to assess how the 4P model functions in different institutional and ecological contexts. While the model promotes inclusive growth, it also faces challenges such as price volatility, insufficient long-term sustainability, and limited integration of smallholder farmers into formal value chains. The paper discusses policy implications for improving the 4P model’s effectiveness in poverty alleviation and local economic development, highlighting the importance of better governance structures, financial mechanisms, and market stability. This paper sheds new light on inclusive, justified, and sustainable collaboration mechanisms for participatory agencies and individuals. Full article

Experiential learning through simulations offers a unique but often underutilized opportunity to bridge the gap between pedagogy and empirical research. This study addresses this gap by transforming the Pemberton’s Dilemma simulation from a classroom exercise into a quantitative, empirical research project to investigate the dynamics of trust, cooperation, and opportunistic behavior. To address questions related to such trust interactions, the simulation was modified to include variable payout stakes, restricted and permitted communication phases, and an additional surprise round to measure long-term trust reputation effects. From 2017 through 2025, data was gathered from a convenience sample of 611 students from a large public university in the Northwestern United States. Results indicate that non-trusting behavior has a significantly greater mirroring effect than trusting behavior and that higher financial stakes frequently prime groups toward opportunistic hedging. While opportunistic strategies yielded greater short-term gains, longitudinal analysis revealed a significant positive correlation between consistent trust and monetary outcomes. Furthermore, the surprise round data confirmed that prior trust violations severely diminished cooperation and earnings in the future unknown round. The study supports the benefits of integrating quantitative research into pedagogical experiential tools to advance scholarly understanding (in this case of trust dynamics and the vital role of transparent communication and sustainability-compatible strategies), enhance student learning, and to provide data-driven recommendations for organizations. Full article

Building on Gutman’s (1987) argument that architectural practice should reflect the nature of the problem, this article explores four eras of architectural practice: the Patronage Model, the Clientage Model, the Transitional Models, and Future Models. Each era is examined in relation to six “Questions of Praxis”: (1) What is the nature of the problem?, (2) What is the nature of the intervention?, (3) What knowledge is valued?, (4) What is the stance toward the problem?, (5) What is the continuity in the relationship?, and (6) What is the prioritization of professional obligations? Through a comparative analysis of questions 2–5—the analytic core of action-taking—alongside four drivers of change in today’s volatile, uncertain, complex, ambiguous world, yields 16 possible futures for architects. Further synthesis identifies five primary roles for architects of the future: systems-thinking designer (embracing complexity), steward (building trust amid volatility), facilitator (reducing ambiguity through shared meaning), curator (making sense of uncertainty), and strategic forecaster (transforming volatility into preparedness). These roles embody a care-based approach—prioritizing ongoing relationships over episodic interventions, collective capacity-building over expert prescriptions, and adaptive readiness over static solutions. This reflects the positioning of architecture as a public good, focused on strengthening social, ecological, and systemic foundations so communities not only withstand disruption but also adapt, learn, and thrive through it. Full article

Despite the aim of municipal boundaries re-demarcation to improve capacity, functionality, economic viability, and service delivery in South African municipalities, it often leads to community unrest, protests, and violent confrontations. This article examines socio-political factors that contribute to the ongoing disputes in Vuwani and Malamulele communities, Limpopo, South Africa, and their implications for social cohesion and local governance in a post-apartheid context. Semi-structured interviews were conducted with 52 community members and other stakeholders who are directly involved in or affected by the disputes. Guided by territoriality theory, through thematic analysis, the study findings reveal that re-demarcation disputes are intertwined with political interests, differentiated ethnic and cultural identities, inadequate community consultation, and governance power imbalances. By situating Vuwani and Malamulele communities within wider debates on municipal border disputes, this study calls for an inclusive, transparent, and culturally sensitive approach to the implementation of municipal boundary changes. This is crucial for building united, peaceful, and sustainable communities, and for local government to regain public trust in post-apartheid South Africa. Full article

There has been a recent increase in the use of social media platforms (SMPs), as well as a large increase in scientific journals and academic article publications. We need to study if and how much academics, scholars and researchers trust SMPs as sources, i.e., citations, for writing their research articles. The purpose of this research is to explore the relationship between SMPs and bibliographic article citations for ten years between 2010 and 2019, with 31 December marking the official identification of COVID-19, a milestone that affected the whole world, including academic publishing. By using a citation retrieval tool written in Java, the citations referring to the URLs of 6432 articles from 14 Q1 open access education journals ranked by the SCImago platform were extracted. The retrieved URLs were stored in a relational database, preprocessed and cleaned, and analyzed using SQL queries to identify and quantify citations originating from SMPs. The findings showed that there were 112 instances, which corresponds to 1.8% of the articles, of an SMP post being used as a citation. Out of the 17 SMPs checked, eight were used, with the most popular being YouTube, having a percentage of 68% of the aforementioned 112 citations, followed by Twitter (now X) with approximately 13.5% and then by Facebook with around 7%. Most of these in-text citations were found at the Introduction and the Design/Methodology sections of the papers. Other important findings of this study were that about 2% of the URL citations referred to blogs and wikis and that one in 100 articles used Wikipedia in the bibliography. Also, for a 26-year period from 1999 to 2024, it was observed that the number of journals increased by 82.8%, while the number of open access journals showed an impressive 552.14% increase. The findings of this study could lead to changes in the metadata design of bibliographic databases, like the way of searching them, and to a review of the life cycle duration of sustainable access to the content of the cited SMPs. Full article

This article examines how artificial intelligence and algorithmic systems are reconfiguring truth regimes in digital societies, introducing the concept of “Artificial Truth” to describe an emerging form of epistemic governance where knowledge production and validation become infrastructural functions of sociotechnical systems. The study develops an integrated theoretical framework combining Foucault’s notion of truth regimes, Bourdieu’s theory of symbolic capital and fields, and Actor-Network Theory’s constructivist approach. Through conceptual analysis, the article investigates how algorithmic recommendation systems, generative AI, and automated fact-checking operate as epistemic devices that actively shape what is recognized as credible, authoritative, and true in public discourse. The analysis reveals three fundamental transformations: (1) the restructuring of trust economies, with epistemic authority shifting from institutional expertise to platform-native capital based on engagement metrics and affective proximity; (2) the emergence of generative AI as an epistemic actor producing “synthetic truth” through linguistic fluency rather than propositional understanding; (3) the institutionalization of computational veridiction in algorithmic fact-checking systems that translate situated epistemic judgments into probabilistic classifications presented as neutral. These dynamics configure a regime where truth is evaluated less by correspondence with reality and more by computational plausibility and platform integration. The article’s primary contribution lies in providing a unified theoretical framework for understanding contemporary transformations of epistemic authority, moving beyond disinformation studies to analyze AI as an epistemic actor. By integrating classical sociological perspectives with Science and Technology Studies, it conceptualizes algorithmic systems as epistemic infrastructures that embody specific power relations, restructure symbolic capital economies, and distribute epistemic authority asymmetrically, with profound implications for democratic knowledge, citizen epistemic agency, and public sphere pluralism. Full article