Search Results (125)

Background/Objectives: Periodontitis, the sixth most prevalent disease worldwide, affects over 740 million people and disproportionately burdens transitional economies. Although artificial intelligence (AI)—including deep learning (DL) and machine learning (ML)—achieves high diagnostic accuracy in research settings, a gap persists between proof-of-concept and real-world deployment, especially where regulation is nascent, as in Kazakhstan. This scoping review maps global evidence on AI for periodontal diagnosis, risk prediction, and monitoring; evaluates governance frameworks; and proposes a contextualised implementation model for emerging health systems. Methods: Following PRISMA-ScR and PRISMA 2020 guidance, PubMed/MEDLINE, Scopus, Web of Science, Embase, and Cochrane Library were searched (January 2015–April 2025), supplemented by regulatory and grey literature; ten additional sources published after the search closure were subsequently identified through citation checking and expert peer review during revision, as a targeted amendment rather than a re-executed database search. Forty sources in total (29 peer-reviewed empirical and review studies plus 11 regulatory, grey literature, and patent documents) met the inclusion criteria and were charted thematically. Results: Two dominant paradigms emerged: image-based DL (convolutional neural networks and Vision Transformers), achieving 73–98.6% accuracy for radiographic bone loss detection, and ML-based non-clinical screening using patient-reported data and salivary biomarkers. Digital tools (smart toothbrushes, chatbots, and IoT platforms) form a third domain. Performance dropped consistently on external validation, reflecting data quality and sample size constraints. Regulatory analysis showed convergence of the EU AI Act, U.S. FDA framework, WHO guidance, and Kazakhstan’s AI Development Concept (2024–2029) around risk-based classification, transparency, and post-market surveillance. Conclusions: Safe, effective AI integration in periodontology requires a phased approach: national multimodal databases, local clinical validation, certified workflow integration, continuous monitoring, population-level surveillance, and legal governance covering liability and insurance. Kazakhstan’s evolving regulatory and digitalisation strategy may offer a context-specific case for evaluating AI adoption pathways across Central Asia and transitional economies, pending prospective validation. Full article

Digital humans are emerging as a form of mediated communication, appearing as virtual influencers, virtual agents, avatars, VTubers, AI companions, and other humanlike digital entities across social media, entertainment, commerce, and online communities. Research on how users perceive and respond to these entities remains fragmented across disciplines. This scoping review synthesizes evidence on users’ perceptions of and responses to digital humans. Following established scoping review guidance and PRISMA-ScR reporting principles, we searched Scopus, Web of Science, and EBSCO for English-language, peer-reviewed studies published between 2016 and 2025. Fifty-one studies met the inclusion criteria and were synthesized descriptively and thematically. The synthesis identified three recurring dimensions of user perception (i.e., perceived agency, perceived embodiment, and perceived social role) and three broad response domains: psychological, relational, and behavioral. These findings suggest that users’ responses to digital humans are shaped by more than human-likeness alone, with capability, embodied presence, and social role also emerging as important interpretive dimensions. The evidence base was concentrated in marketing and commerce, with limited attention to broader media contexts, long-term interaction, cultural diversity, and negative or ambivalent responses. Overall, this review provides an evidence-organizing framework for understanding how users interpret, relate to, and act toward digital humans. Full article

Artificial intelligence (AI) is changing how firms search for, interpret, and act on externally generated knowledge in digital innovation ecosystems. Existing research motivates closer examination of how resource-constrained SMEs organize the internal workflows through which AI-supported customer, market, competitor, and platform information becomes usable for innovation. This study examines how cross-border e-commerce SMEs organize AI-supported workflows and team roles to handle external knowledge for emerging inbound open innovation. The empirical design is an exploratory, theory-elaborating qualitative multiple-case study of two contrasting SMEs. The analysis draws on ten face-to-face semi-structured interviews across strategic, managerial, technical, and frontline roles, totaling 183 min 32 s (approximately 184 recorded minutes). An abductive thematic case-analysis approach combines data-near coding, within-case analysis, cross-case synthesis, and iteration with relevant theory. The analysis suggests an analytically ordered framework rather than a verified longitudinal sequence. Case A exhibits experiment-led diffusion, whereas Case B exhibits technical-partner-led workflow design. Across the cases, participants described modular human–AI–human sequences, broader task integration, changing feedback arrangements, and greater emphasis on human review. These reported arrangements are associated with three emerging capability-related practice dimensions: external knowledge sensing, knowledge recombination, and agile implementation. Disconfirming accounts show that the patterns depend on task–AI fit and that overreliance can weaken independent market judgment. The study contributes to research on AI-enabled inbound open innovation by showing that access to AI and platform data is insufficient: external inputs become actionable when SMEs reorganize workflows and roles around prompting, interpretation, recombination, and human accountability. It also elaborates how human–AI collaboration operates as an internal microfoundation of customer-centric, platform-mediated openness. For managers, the findings suggest moving from isolated tool use toward capability-oriented workflow design while retaining human review. The conclusions offer contextualized theoretical insight rather than statistical generalization. Full article

Technological advancements have transformed the ways in which individuals interact with digital platforms, giving rise to data-driven ecosystems in which interface design plays a central role. Within these environments, dark patterns and manipulative and deceptive design techniques shape user behaviour in ways that undermine privacy, autonomy, and informed consent. This paper develops a harm-based analytical framework for privacy dark patterns by deriving a typology of privacy-relevant design mechanisms through a structured consolidation of four independently produced taxonomies. It then constructs a harm typology by combining an injury-based framework with the harm categories developed by regulatory authorities and maps each design mechanism to the harms it is structurally apt to produce. Finally, it identifies the provisions of EU law that each mechanism engages, across the GDPR, consumer protection law, the Digital Services Act, the Digital Markets Act, the Data Act and the AI Act. The analysis finds that the GDPR engages every mechanism identified, that coverage by post-GDPR instruments is uneven, and that one mechanism—the exploitation of relational and third-party data—engages no post-GDPR instrument squarely. The deficiency in the EU framework therefore lies not in the substance of its prohibitions but in the coordination of enforcement and in the evidentiary architecture through which non-material harm must be established. Full article

This paper examines how the body of research and innovation on electric cooking for low- and middle-income countries has evolved to the extent that electric cooking can now be argued to have the potential to influence energy system performance. Methods: The paper synthesises recent evidence on electric cooking from pilots, market developments, and system-level analysis across Africa and Asia, focusing on demand patterns, utility economics, carbon finance mechanisms, and emerging digital and financing models. Results: Electric cooking is increasingly argued to be acting as a system-strengthening source of demand, rather than a system stressor. Two reinforcing mechanisms are identified: (i) an electricity revenue loop, in which increased consumption can improve utility and mini-grid viability and support further investment, and (ii) a carbon finance loop, enabled by metered methodologies and measurable emissions reductions, which can improve household affordability and accelerate adoption. The analysis also highlights the importance of diversified demand (household, commercial, and institutional), which has great potential to improve load factors and align demand with generation. However, a persistent planning blind spot remains, with growth in electric cooking demand largely excluded from energy models. Conclusions: Electric cooking is moving from proof of concept toward tangible system integration, but scale is constrained by affordability, reliability, tariff design, fuel stacking, institutional fragmentation, and carbon market uncertainty. The findings suggest that electric cooking should increasingly be treated as a core component of energy system design, requiring coordinated policy, planning, and financing to realise its full potential. Full article

This study contributes to the literature on sustainable international investment by examining how public data openness—treated as a non-rivalrous production factor—enables firms to overcome information asymmetries, reduce financing constraints, and catalyze innovation-driven overseas expansion. Grounded in China’s dual strategic imperatives of digital factor marketization and high-quality outbound investment, we exploit the staggered rollout of municipal public data platforms (2008–2023) as a quasi-natural experiment and apply a firm- and city-level staggered difference-in-differences (DID) design to a sample of A-share listed firms. Our findings demonstrate that public data openness significantly enhances the sustainability of outward investment, measured through improved capital allocation efficiency and long-term resilience. Mechanism tests reveal that innovation capacity and eased financing constraints serve as key transmission channels, with stronger effects observed in firms with higher human capital endowments, robust internal controls, and exposure to technologically dynamic sectors. Heterogeneity analyses further indicate that the sustainability impact is amplified under environmental uncertainty, suggesting that data openness acts as an institutional buffer. The results offer actionable policy implications for aligning digital governance with corporate sustainability goals, and provide empirical grounding for integrating open data infrastructure into national strategies for responsible internationalization. Full article

As China transitions to a higher development stage, the environmental dividends of smart-digital upgrades are growing more prominent. Boosting green total factor productivity (GTFP) in manufacturing is critical for fostering emerging quality-driven productive forces. Employing provincial data from 30 regions (2013–2023), this research utilizes the super-efficiency SBM, fixed-effects, mediation, and moderation models to investigate both the impact and operative pathways of digital-intelligent transformation on manufacturing GTFP. The empirical evidence suggests that: (1) The positive effect of digital-intelligent transformation on manufacturing greening proves robust across endogeneity corrections and various sensitivity tests. (2) Heterogeneity tests indicate that the promotional effect is more pronounced in eastern provinces and areas with weaker pollution loads. (3) Mechanism analysis identifies a dual-edged pathway: while technological innovation serves as a positive conduit, labor structure optimization unexpectedly acts as a suppression channel, weakening the overall positive impact. (4) Moderating effect analysis demonstrates that factor market development strengthens the positive relationship between digital-intelligent transformation and manufacturing GTFP. (5) Threshold analysis based on environmental regulation intensity indicates that the marginal effect of digital-intelligent transformation gradually declines as environmental regulation becomes more stringent. Overall, the findings suggest that the green effects of digital-intelligent transformation are not automatic but depend on regional industrial structures, factor market conditions, and environmental regulatory intensity. Crucially, this study reveals a potential “green paradox” in China’s manufacturing digitalization process: although digital-intelligent technologies stimulate innovation and labor upgrading, their contribution to emission reduction is partially offset by a misalignment in labor allocation and technological orientation, resulting in a net weakening of the green transformation effect. Therefore, policymakers should promote the coordinated development of digital infrastructure, factor market reform, and environmental regulation, while actively guiding skilled labor toward green innovation activities to accelerate manufacturing green transformation and foster new quality productive forces. Full article

Deepfakes, synthetic audiovisual content produced by deep generative models, have escalated into a critical threat across civilian and military domains, enabling identity fraud, disinformation campaigns, and evidence fabrication. In high-stakes environments, ranging from journalism and finance to healthcare and legal contexts, the consequences extend to severe misinformation, market manipulation, identity fraud, and the erosion of institutional trust. This entry explores how modern visual intelligence and computer-vision techniques are used to detect deepfakes. It outlines key deepfake generation models, such as GANs, autoencoders, neural rendering, and diffusion systems, while also explaining how adversarial methods enhance realism and challenge existing detectors. The overview highlights visual artifacts, digital patterns, and physiological cues commonly leveraged in detection and reviews major CNN, transformer, and frequency-based approaches. It also summarizes evaluation practices and the difficulty of achieving strong generalization. Finally, it identifies emerging directions, including modern intelligence techniques for civilian and military content verification. This survey covers generation architectures (GANs, latent diffusion, neural rendering, video synthesis), the spatial, temporal, frequency-domain, and physiological artifacts they produce, and the detector families that exploit them. We examine evaluation benchmarks and protocols, highlighting cross-generator generalization as the field’s central open challenge. Beyond detection, we discuss cryptographic provenance standards, watermarking, and regulatory frameworks (EU AI Act, DSA, GDPR). We conclude that effective deepfake governance requires defense in depth integrating forensic detection, verifiable provenance, and institutional accountability. Full article

The voluntary carbon market (VCM) has faced sustained legitimacy stress since 2023, when peer-reviewed work found that fewer than one in six issued credits represented a real emission reduction. In parallel, tokenisation through Web3 protocols, decentralised autonomous organisations (DAOs), and regenerative finance (ReFi) infrastructures introduced new participants interacting with incumbent registries without a shared coordination framework. Existing scholarship examines commons governance, complex system governance (CSG), and tokenised carbon markets largely in isolation; the gap addressed here is the absence of an integrated system-of-systems (SoS) governance treatment of the tokenised VCM. This study develops and empirically applies a polycentric SoS governance framework for the tokenised VCM, structured around four research questions and five foundational contributions. We treat the tokenised VCM as an SoS that is polycentric in configuration but not by design, and develop a system-of-systems engineering (SoSE) governance reading of it. We reformulate Ostrom’s eight design principles as SoS governance criteria for digital–physical hybrid commons, map each to CSG metasystem functions, and apply the framework to four cases: KlimaDAO, Toucan Protocol, Regen Network, and the post-2023 Verra reforms. Qualitative coding is complemented by on-chain and Base Carbon Tonne spot-price evidence from October 2021 to December 2025. Disclosure by an analytical intermediary acted on the SoS roughly seven weeks before formal regulatory action and was associated with about 90% of the observed bridging slowdown, interpreted descriptively rather than causally. We derive an eight-item reform agenda, six DAO–registry interface specifications, and a five-level governance maturity rubric. Full article

This study analyses the evolution of the financial literature on climate risk, examining the integration of artificial intelligence techniques into its measurement and management. To this end, a bibliometric approach is employed based on 221 articles indexed in the Web of Science Core Collection, using the Bibliometrix package. Moving beyond existing descriptive bibliometric reviews on ESG and green finance, the novelty of this paper lies in its analytical focus on how financial science operationalises quantitative AI mechanisms to price and integrate climate transition risk into asset and portfolio valuation. The structural analysis reveals that natural language processing (NLP) and digital transformation acting as driving motor themes, suggesting that the reviewed literature associates AI innovation policies with the mitigation of corporate greenwashing and enhance information transparency. Furthermore, while machine learning algorithms establish the cross-cutting predictive foundation for risk assessment, empirical evidence unveils a critical academic shift of traditional ‘financial performance’ towards a declining quadrant, indicating that empirical studies frequently find that that multi-phase investments in risk technologies do not yield immediate financial returns. Finally, the study maps a persistent geographical gap where emerging markets lack the data infrastructure of advanced economies, alongside isolated high-dimensional causal econometric niches like double machine learning. This analytical mapping provides key implications for global risk management and future quantitative research avenues. Full article

The rapid integration of generative artificial intelligence into digital advertising, combined with growing consumer concern about greenwashing, has created a dual credibility burden in which sustainability messages must be evaluated for both environmental truthfulness and authenticity of authorship—a challenge intensified by emerging AI disclosure regulations such as Article 52 of the EU AI Act. This study investigates how ad source (AI-generated vs. human-created), source label (labelled as AI vs. labelled as human), and environmental claim strength (vague vs. strong) jointly influence perceived greenwashing and green trust among Generation Z consumers. A 2 × 2 × 2 mixed factorial experiment was conducted with 154 undergraduate participants randomly assigned to one of four source–label conditions and exposed to both vague and strong environmental claims; data were analysed through one-way ANOVAs with Tukey HSD post hoc tests and paired-samples t-tests. All eight hypotheses were supported: AI attribution consistently elevated greenwashing perceptions and depressed green trust, while strong claims significantly reduced greenwashing perceptions and elevated trust within both correctly disclosed conditions. Most notably, the disclosed label—rather than the actual generative source—emerged as the dominant psychological cue, with human content mislabelled as AI suffering the same credibility penalty as genuine AI content. The findings reveal a transparency paradox with significant implications for AI disclosure regulation and sustainable marketing practice. Full article

The article critically examines the emergence of technofeudalism within the European Union’s legal framework, drawing on the theoretical contributions of Yanis Varoufakis, Alfred C. Yen, and Katrina Geddes. We argue that the EU’s historically market-oriented regulatory architecture contributed to conditions that facilitated the rise of dominant technology companies exercising quasi-governance functions over digital environments, extracting value from users while evading meaningful democratic accountability. Our analysis distinguishes between two categories of enabling legislation: structural rules, which govern corporate status, taxation, and market consolidation; and action-oriented rules, which regulate platform behavior, algorithmic governance, consumer relations, and data protection. We demonstrate how fragmented national tax regimes, ineffective merger control, under-regulated algorithms, asymmetric consumer protections, unclear liability frameworks for online content, exploitable private international law mechanisms, and inadequately enforced data protection standards collectively reinforce Big Tech’s dominance. While recent regulatory interventions such as the Digital Services Act and Digital Markets Act represent important steps, they remain embedded in a market-oriented paradigm that insufficiently addresses the broader social, cultural, and democratic implications of platform power. The article concludes by calling for a more coherent, democratically grounded approach to digital regulation—one that moves beyond fragmented, reactive policymaking toward a comprehensive framework capable of strengthening democratic accountability and public oversight within the digital sphere. Full article

As a crucial indicator for measuring regional social equity and equality of opportunity, intergenerational mobility exerts an important impact on the employment quality of the agricultural migrant population. However, despite extensive research on migrant employment, limited attention has been paid to how intergenerational mobility interacts with localized technological environments and fiscal resource constraints to shape the labor assimilation of rural-to-urban migrants. This study assesses this relationship by constructing an urban intergenerational educational mobility index and analyzing the China Migrants Dynamic Survey (CMDS) data. The results indicate that intergenerational mobility significantly improves the employment quality of the migrant population. Mechanism analysis was used to reveal that the digital economy exerts a positive regulatory effect, acting as a form of technological empowerment that enhances the transition of structural opportunities into tangible employment prospects. Conversely, local fiscal pressure exerts a negative regulatory effect, imposing contractive resource constraints that attenuate the promotional dividends of social mobility. Heterogeneity analysis results further demonstrate that the positive impact of intergenerational mobility is more prominent in cities with higher public education expenditure, higher levels of marketization, and fewer traditional cultural constraints. These findings suggest that geographical mobility alone does not automatically guarantee high-quality employment; rather, enhancing institutional openness, expanding digital infrastructure, and optimizing the allocation of public resources are essential to translating equity of structural opportunity into decent work. Full article

This study examines whether stablecoin and Bitcoin transaction volumes in Brazil are associated with domestic macroeconomic conditions. Using monthly data from August 2019 to December 2025, the analysis compares the association of domestic economic activity, proxied by the IBCBr, and the BRLUSD exchange rate with the transaction volumes of stablecoins and Bitcoin reported in the open data records of the Receita Federal do Brasil, the Brazilian Tax Administration. The empirical strategy distinguishes between long-run relationships in log levels and short-run dynamics in log differences, applies Johansen cointegration and ARDL bounds testing, and estimates an error correction model for stablecoins. Global crypto market controls are used as complementary measures to assess the contrast between the two assets. The results show that stablecoin transaction volume is positively and significantly associated with Brazilian economic activity in both long-run and short-run specifications and that this association is not explained by global stablecoin activity. The exchange rate is associated with stablecoin volume mainly through a structural long-run channel rather than immediate monthly effects. In contrast, Bitcoin transaction volume does not exhibit a robust association with domestic economic activity and is instead more strongly associated with global Bitcoin volume. The findings suggest that stablecoins may act as a domestically embedded macro-financial instrument in Brazil. This finding reflects transactional demand, liquidity management, or demand for dollar-linked assets, whereas Bitcoin behaves as a more globally oriented and comparatively detached digital asset. This distinction has important implications for policy, as stablecoins may have stronger implications for monetary transmission, digital dollarisation, and financial intermediation than Bitcoin-focused analyses indicate. Full article

Green fintech operates at the intersection of sustainable finance, digital innovation, and financial-sector risk governance. It promises to improve the allocation of capital toward environmentally sustainable activities by lowering information costs, scaling disclosure tools, automating environmental verification, and widening access to green investment products. Yet the same digital features that make green fintech attractive—speed, scalability, data intensity, platform intermediation, cross-border distribution, and algorithmic decision-making—can also transform apparently local regulatory weaknesses into broader financial-stability concerns. This article examines how regulatory risk associated with green fintech may evolve into systemic risk under conditions of market concentration, weak data governance, regulatory fragmentation, greenwashing amplification, and financial interconnectedness. It develops a mechanism-based conceptual framework rather than an econometric test. The framework connects three regulatory dimensions—regulatory clarity and scope, supervisory consistency, and innovation facilitation—with five systemic-risk transmission channels: market concentration, data and model risk, regulatory arbitrage, greenwashing amplification, and financial interconnectedness. The article draws on sustainable-finance regulation, the financial-stability literature, fintech scholarship, and official supervisory documents, including the EU Sustainable Finance Disclosure Regulation, the EU Taxonomy Regulation, the Digital Operational Resilience Act, and the ESG Ratings Regulation. The central argument is cautious but policy-relevant: green fintech does not automatically create systemic risk, but regulatory uncertainty and supervisory gaps may become systemic when they are embedded in digital infrastructures that scale quickly and are relied upon by multiple financial institutions. The article contributes to risk scholarship by shifting the analysis from compliance-level regulatory risk to transmission mechanisms through which green-finance innovation may affect market integrity and financial stability. Full article