Search Results (2,025)

Online knowledge communities serve as critical infrastructures for innovation, yet prior research relying on net-effect logic has struggled to explain why similar communities differ in knowledge diffusion quality or reveal how multiple conditions synergistically produce high-quality outcomes. Drawing on socio-technical systems theory and dimensionalizing the DeLone and McLean (D&M) model into knowledge, technical, and social subsystems, this study adopts a configurational perspective to examine the joint influence of seven conditions on knowledge diffusion quality. Using data from 307 users and integrating fuzzy-set qualitative comparative analysis (fsQCA) with necessary condition analysis (NCA), we found that no single condition is individually necessary for achieving high-quality outcomes, highlighting the nonlinear and substitutable nature of socio-technical systems. Three equifinal pathways were identified: demand–response synergy, trust-mediated integration, and emotion–utility coupling, alongside four pathways leading to the absence of high-quality outcomes, demonstrating causal asymmetry. Notably, a dual-sided configurational pattern emerged: information utility quality serves as a core condition across all high-quality pathways, while service empathy appears as a core-absent condition in the majority of non-high-quality pathways. This study advances socio-technical systems theory by operationalizing equifinality, conjunctural causality, and subsystem interdependence, and offers practical implications for platform governance. Full article

Student dropout remains a persistent challenge in higher education, with significant academic, social and institutional implications. Although machine learning (ML) models for dropout prediction have proliferated, most select input variables based on data availability rather than theory and rarely report psychometric validation of the constructs used. This study addresses both gaps by developing and validating an early warning system at a Mexican university using exclusively pre-enrollment data from more than 46,000 student records from 2014 to 2025. Following the CRISP-DM methodology, eight theoretically grounded constructs, anchored in Tinto’s integration model, Bean’s attrition model, and Cabrera et al.’s persistence model, were operationalized from institutional intake questionnaires and assessed for internal consistency using Cronbach’s alpha prior to model training. Eight supervised ML algorithms were benchmarked across distance-based (Logistic Regression, SVM, AdaBoost, ANN) and tree-based (Random Forest, XGBoost, LightGBM, CatBoost) families. A tuned and isotonically calibrated Random Forest achieved the best overall performance (recall = 0.743, F1 = 0.524, ROC-AUC = 0.734, PR-AUC = 0.478) on a strictly held-out test set that was not used at any stage of model development. The 2023–2025 cohorts, whose dropout labels were not yet observable under the institutional definition, were scored prospectively to generate operational risk profiles. SHAP analysis identified high-school GPA, parental education, and household asset indices as dominant predictors, which mapped directly onto the three theoretical frameworks. These findings demonstrate that psychometrically grounded pre-enrollment data alone can support an operationally deployable dropout detection system, enabling proactive, evidence-based retention interventions from the first day of enrollment. Full article

Citizen-sourced non-emergency reporting is an increasingly important component of smart-city governance, yet cities often lack systematic methods for evaluating alternative participation designs before deployment. This study develops a design-oriented pre-deployment evaluation framework for assessing citizen adoption of a proposed reporting application and comparing alternative incentive mechanisms. Drawing on an extended UTAUT framework, the study introduces perceived value (PV) as a mediating mechanism linking incentive framings to behavioral intention. A scenario-based survey experiment with 580 participants in the Greater Toronto Area compared the following four conditions: control, monetary incentive, recognition, and donation-based community-benefit incentive. Data were analyzed using PLS-SEM and bootstrapped mediation analysis. Results indicate that performance expectancy and attitude are the strongest predictors of behavioral intention, while trust was significantly associated with both cognitive and affective evaluations of the reporting system. Social influence becomes non-significant once these evaluations are included. Recognition and donation-based community-benefit incentives significantly increased perceived value and showed both direct and indirect effects on performance expectancy and behavioral intention. At the behavioral-intention stage, indirect effects accounted for approximately 80% of their respective total effects, indicating complementary mediation with indirect effects predominating. By contrast, the specific $2 city-credit condition showed no significant direct, indirect, or total effect. Rather than proposing a new acceptance theory, the study demonstrates how technology acceptance modeling can be repurposed as a practical decision-support framework for evaluating alternative civic-technology designs before system launch. Limitations related to the pre-deployment setting and intention-based measures are acknowledged, and future research directions involving field deployment and longitudinal participation analysis are discussed. Full article

Background: Nursing education has evolved in response to changing social, cultural, political, and healthcare contexts. Although Cultural–Historical Activity Theory (CHAT) has increasingly been applied in medical education research, its application to the historical development of nursing education remains limited. This study examined the historical evolution of Mongolian nursing education through the lens of CHAT to identify patterns of activity system transformation and their implications for contemporary nursing education. Methods: A historical documentary review was conducted using CHAT as the primary analytical framework. Historical evidence was interpreted through Engeström’s activity system model, and transformations across successive historical periods were analyzed by examining changes in the object, mediating tools, institutional rules, community, division of labour, and outcomes of nursing education. Results: The findings demonstrate that the development of Mongolian nursing education was shaped by four complementary analytical dimensions: the Social Determinants of Health (SDOH), Diversity, Equity and Inclusion (DEI), political ideology, and paradigm shifts. These factors collectively influenced the transformation of nursing education activity systems from traditional caregiving practices to a modern, research-oriented, and internationally connected educational system. The analysis further revealed how social reforms, ideological transitions, and educational restructuring generated successive transformations in the organization, objectives, and professional identity of nursing education. Conclusions: This study provides a theoretically grounded historical interpretation of Mongolian nursing education through the lens of CHAT. The findings demonstrate that nursing education evolves through dynamic interactions among social, political, cultural, and educational factors, offering valuable insights for curriculum development, educational reform, and the advancement of nursing education in rapidly changing societies. Full article

This article systematically analyzes the problems of various social groups at many levels, studying the importance of culture in education and the social environment, and identifying research trends among world scientists over the past 5 years (2022–2026). A systematic literature review was conducted in January 2026 using the PRISMA 2020 methodology, as a result of which 109 peer-reviewed, indexed publications from Scopus, Web of Science, and ERIH+ were selected and analyzed. As a result, the most current research trend was identified to use cultural intelligence theory, measurement tools, scales, and concepts in the analysis of multi-level problems of various social groups. The main conclusion: culture in education and its social environment in the context of social groups has not been studied systematically enough; therefore, cultural intelligence measurements can contribute to the understanding of the causes of problems of various social groups involved in the educational process in four dimensions: (1) metacognitive, (2) cognitive, (3) motivational, and (4) behavioral. The development of cultural intelligence in an individual person promotes a dialogue between different social groups that is constructive, open, and based on mutual empathy. Originality of the study: a systemic analysis of multi-level problems of social groups in the context of education, culture, and social environment was carried out, determining theoretical and empirical aspects and their contradictions. Full article

Foster care placements involve various role players from the child’s entry into the system through the child’s exit. Positive role fulfilment implies acting in accordance with the respective role expectations while also being dynamic in nature. Key participants in the foster care system include social workers, foster parents, and foster children. These role players all have roles necessary for the foster care placement to be positive. Using role and identity theory, with an underlying Africentric philosophy, as a theoretical framework, this qualitative study sought to describe social workers’ perspectives on positive role fulfilment among role players within the foster child’s care circle in supporting well-being. Data were collected from twelve participants, comprising ten foster care social workers and two social work supervisors, through a focus group discussion and one-on-one interviews. In addition, two foster care policy developers were consulted as key stakeholders to provide contextual insight into the legislative and policy environment. Data were analysed using reflexive thematic analysis. Findings showed that social workers face structural challenges, including high caseloads, a shortage of social workers and uncertainty regarding roles, which sometimes make it difficult to fulfil their roles as expected. With that said, they believed that positive role fulfilment influenced the positive nature of foster care placements. When roles are known and met, the care circle enhances the well-being of the foster child. The study concludes that social workers know what they are required to do for the child’s well-being. The recommendation is that an Africentric foster care supervision and monitoring framework be developed to sensitise the role players of their roles and responsibilities in the foster care system. Full article

Economic and technical barriers to agricultural technology adoption are widely documented, but less is known about how social and psychological factors shape farmers’ intention to adopt smart irrigation systems in post-communist transition economies. This study examines smart irrigation adoption intentions among 368 farmers in Albania, an EU-candidate country characterised by smallholder farming, land fragmentation and uneven technological diffusion. Drawing on the Technology Acceptance Model (TAM), the Unified Theory of Acceptance and Use of Technology (UTAUT), and motivation perspectives, we analyse how social influence, self-efficacy and task-technology fit shape adoption intentions. Partial Least Squares Structural Equation Modelling (PLS-SEM) shows that social influence is the strongest direct predictor of intention to use smart irrigation systems, while self-efficacy and task-technology fit also contribute to farmers’ perceptions and intentions. Perceived usefulness has a stronger role than perceived ease of use, suggesting that farmers prioritise practical benefits such as water efficiency, productivity and farm-level utility over usability alone. The findings show how social embeddedness, farmer confidence and task compatibility shape smart irrigation intentions in a post-communist agricultural context, and suggest that policy should strengthen peer learning, farmer champions and community-based diffusion mechanisms. Full article

The current trajectory of Artificial Intelligence (AI) development represents a critical phase transition from a tenable academic pursuit to an untenable industrial behemoth, and ultimately toward an unsustainable environmental burden. This conceptual review and perspective article synthesizes evidence across sustainability science, information theory, AI governance, and regulatory studies to argue that Brute Force AI constitutes a systemic sustainability threat whose resolution requires a return to Algorithmic Parsimony. We formally redefine sustainability in sensu lato through a contrapositive logical criterion applicable to any system. We introduce an operationalized Intelligence-per-Joule ($\mathcal{I}/J$) index and Sustainability Index S, defined in terms of mutual information gain relative to thermodynamic and computational resource expenditure, and demonstrate their interpretive application across landmark AI systems. A quantitative synthesis of published empirical data across six landmark models (BERT through GPT-4) documents the Intelligence–Cost Divergence: the growing chasm between logarithmic capability gain and exponential resource cost. We further distinguish warranted from unwarranted scale—acknowledging that some large-scale capabilities are qualitatively irreplaceable—while arguing that the current default toward scale without efficiency justification is ecologically and socially indefensible. We introduce the Symbiotic Policy Covenant—a three-pillar governance framework encompassing Algorithmic Parsimony Mandates, Expanded Waste Taxonomy, and an AI Equity Safeguard addressing both access and benefit inequity—operationalized through a proposed ISO/IEC 42001-Plus standard addendum. We conclude that genuine intelligence and genuine sustainability are not in tension but are, at their mathematical foundations, the same aspiration. Full article

Anthropomorphism has shown inconsistent effects on human judgment, yet the underlying cognitive mechanisms remain underspecified. This research develops and tests a Dual-Pathway Calibration Model (DPCM) integrating dual-process theory, construal level theory, metacognition theory, and regulatory focus theory to explain how anthropomorphic cues influence cognitive processing in human–artificial intelligence (AI) interaction. Three experiments (N = 832) manipulated anthropomorphism and cue inconsistency, examining metacognitive calibration, regulatory focus, and perceived autonomy as boundary conditions. Results revealed that anthropomorphism activates two parallel pathways: (1) a Social Closeness Pathway engaging System 1 processing through reduced psychological distance and enhanced affective trust (indirect effect = 0.18, 95% CI [0.13, 0.24]) and (2) a Cognitive Evaluation Pathway triggering System 2 processing through perceived uncertainty when cue inconsistency is present (ηp² = 0.06). Metacognitive calibration moderated the AI-reliance–decision-quality relationship (β = 0.17, p = 0.007). Regulatory focus moderated pathway activation, with promotion focus strengthening Pathway A (ηp² = 0.15) and prevention focus strengthening Pathway B (ηp² = 0.05). Anthropomorphism exhibited an inverted U-shaped relationship with decision outcomes (quadratic b = −0.05, p = 0.001). These findings extend dual-process theory by specifying conditions triggering intuitive versus analytical processing of anthropomorphic agents, contribute to metacognition theory by demonstrating calibration as a critical determinant of AI-assisted judgment quality, and advance regulatory focus theory by showing motivational orientation shapes social cue processing from artificial agents. Full article

Background/Objectives: Sleep disturbances are increasingly implicated in age-related cognitive decline and may contribute to heterogeneity in cognitive and social–cognitive functioning in Mild Cognitive Impairment (MCI). Nevertheless, it remains unclear whether longitudinal actigraphy-derived sleep parameters predict social cognition, including theory of mind (ToM), across amnestic (aMCI) and non-amnestic (naMCI) MCI subtypes. This study examined longitudinal trajectories of cognitive control and ToM in individuals with aMCI and naMCI and healthy older adults, and investigated whether actigraphy-derived sleep indices prospectively predict subsequent cognitive control and higher-order social cognition across these groups. Methods: A total of 179 participants (46 healthy controls, 75 aMCI, 58 naMCI) were assessed across three waves over approximately 16–20 months. Actigraphy-derived sleep indices were obtained using wrist actigraphy. Cognitive control was assessed with Delis–Kaplan Executive Function System subtests, whereas ToM was evaluated through tasks involving non-literal language comprehension, metaphor and proverb interpretation, higher-order mentalizing, and emotion recognition. Longitudinal trajectories were examined using mixed-design ANOVAs, and prospective sleep effects were tested using longitudinal path models. Results: Both cognitive control and ToM declined over time in the MCI groups, whereas healthy controls showed relative stability. Subtype differences in cognitive control emerged primarily in higher-order planning and rule-monitoring processes, with naMCI showing greater impairment. ToM revealed a clearer and more consistent gradient (healthy controls > aMCI > naMCI), particularly in higher-order inferential tasks. Among the actigraphy-derived sleep indices, only TST prospectively predicted later ToM performance, whereas prospective associations with cognitive control were limited and inconsistent. Conclusions: Reduced sleep duration was prospectively associated with poorer subsequent performance in higher-order social cognition, independently of the executive-control measures examined. These findings highlight phenotypic heterogeneity across MCI subtypes and suggest that objectively measured sleep duration may serve as a clinically relevant longitudinal marker of social–cognitive vulnerability. Future studies are needed to determine whether interventions targeting sleep duration are associated with improved social–cognitive outcomes. Full article

This study presents a clear and reliable framework for classifying the intent behind scientific citations. It combines multi-model reasoning with concepts from social choice theory. Instead of using a single model, this framework employs three open Large Language Models Gemma, LLaMA, and Mistral. Additionally, we combine their ranked outputs using an exponentially weighted Borda method. By doing so, this approach increases agreement among high-confidence predictions, maintains ranking information, and produces stable, high-quality supervision signals. Consequently, it boosts reliability while remaining transparent. To create a strong experimental basis, we built a large, balanced dataset from the UnarXive corpus, which contains structured full-text scientific articles and citation networks. First, we automatically pulled citation contexts and organized them within a DuckDB-based analytical setup. Then, we rebalanced the dataset across rhetorical categories to enhance representativeness and minimize bias. Finally, we categorized each citation context into one of five roles: background, methodology, comparison, extension, or critique. As a result, the resulting dataset provides a robust foundation for training and evaluation. We trained a SciBERT classifier using these ensemble-generated annotations and tested it on a five-category citation intent classification task. The model achieved a macro F1-score of 0.83, an outstanding result for this type of classification. Indeed, this level of performance shows strong reliability given how challenging it is to differentiate closely related citation functions. Moreover, it demonstrates that combining multiple models produces valuable and distinct supervision signals, capturing subtle rhetorical and semantic patterns that single models often overlook. Furthermore, the framework enhances interpretability. Specifically, the explicit weighting system clarifies how each model contributes to the final outcome. In addition, the deterministic tie-breaking method ensures the outputs are consistent and reproducible. Taken together, these design choices maintain explainability without sacrificing effectiveness. Full article

This systematic review examines AI literacy in English language education through a social–ecological perspective. Guided by Bronfenbrenner’s ecological systems theory, the review synthesizes 51 empirical studies (published since 2024) to examine how AI literacy is conceptualized, operationalized, and related to English language learning and teaching outcomes. The findings indicate predominantly positive relationships between AI literacy and language outcomes, including improvements in L2 writing proficiency, motivation, willingness to communicate, and teacher efficacy. However, the literature is dominated by microsystem-level studies, while meso-, exo-, and macro-level influences remain underexplored. This review contributes to the field by identifying critical gaps in multi-level research and advancing AI literacy as an ecologically embedded construct. Full article

Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article

Corporate governance is a complex, adaptive open system; yet it is usually modeled, in the classical agency tradition, as a closed matrix of legal and financial contracts. Adopting an open systems perspective, we decode the “social software”—the informal institutions—behind the wide variation in corporate governance across countries. At the heart of this system lies an element that comparative corporate governance research has largely overlooked: generalized trust. To our knowledge, ours is the first framework to unite open systems theory and generalized trust in explaining cross-country corporate governance. Drawing on the classic systems theory works of Ashby and Luhmann, we theorize trust as a systemic connector that absorbs complexity that formal rules would otherwise have to carry. Where trust carries that load, firms can govern through relationship-based collaboration rather than rule-based control. Using 4837 firm-year observations from 1293 firms in 23 countries (2003–2008)—a pre-crisis structural baseline—we document a robust negative association between trust and shareholder-oriented corporate governance: where trust is high, informal social regulation substitutes for formal control. The two cultural moderators—individualism and uncertainty avoidance—act on this connector in opposing directions. Individualism amplifies the substitution because monitoring conflicts with the desire for autonomy; uncertainty avoidance attenuates it because trust cannot supply the structural predictability that these cultures demand. Where individualism is high and uncertainty avoidance is low, formal control falls away steeply as trust rises; where that configuration is reversed, formal structures persist even when trust is abundant. Corporate governance architecture, these results suggest, is regulated by its surrounding cultural ecosystem—with trust as its central, and long-neglected, connector. Full article

Modern energy internets and large-scale industrial systems are becoming increasingly complex. Consequently, the rapid response capability of energy infrastructure during sudden failures has become a core element to ensure the stable operation of the social economy. Emergency repair path planning (ERPP) is a complex nonlinear combinatorial optimization problem. It is characterized by dynamic uncertainties, such as fluctuating task durations and variable traffic accessibility. This paper proposes a cross-regional emergency repair path planning (CR-ERPP) optimization model considering dynamic path conditions. The model takes into account jurisdiction ownership, cross-regional dispatch costs, path weights (congestion coefficient, grade coefficient, quality coefficient) and accident risk levels. The primary objective of this model is to minimize the total repair cost. Furthermore, an Adaptive Bitterling Fish Optimization with Evolutionary Game Theory (ABFO-EGT) is developed. It introduces adaptive mechanisms, evolutionary game theory, and a symmetric mutation strategy. These enhancements are designed to overcome the inherent limitations of traditional swarm intelligence algorithms, namely unbalanced search behavior and premature convergence to local optima. Performance analysis demonstrates that the ABFO-EGT algorithm exhibits superior convergence stability and global search capability. Case study results show that the proposed method significantly reduces the total repair cost. Specifically, the cost is reduced by 33.2% compared to manual decision-making, 27.6% compared to the GWO algorithm, and 9.1% compared to both the ACO and PSO algorithms. This study provides an efficient and reliable decision support tool for emergency management of large-scale energy systems. Full article