Search Results (10,422)

With the development of smart classrooms and digital learning platforms, multimodal learning behavior data provide a new sensing basis for understanding students’ cognitive states and psychological risk warnings. However, existing educational data mining methods mainly focus on performance prediction, dropout warning, or surface-level emotion recognition, while continuous and interpretable modeling of deeper cognitive biases and related psychological risks remains insufficient. To address this issue, we propose MLBS-Net, a multimodal learning behavior sensing network for teaching feedback that jointly models students’ textual expressions, behavioral sequences, classroom interactions, and psychological auxiliary signals. MLBS-Net integrates theory-guided textual cognitive bias encoding, temporal behavioral state modeling, and reliability-aware cross-modal fusion to capture psychologically interpretable cognitive patterns, characterize dynamic learning-state changes, and adaptively integrate multimodal information according to data quality and task contribution while providing interpretable feedback for teachers. Experimental results show that MLBS-Net achieves a Macro-F1 of 0.855 for cognitive bias recognition and an AUC of 0.891 for psychological risk warning, outperforming traditional machine learning, unimodal deep learning, and standard multimodal methods. Ablation results further support the effectiveness of theory-guided semantic encoding, temporal behavioral modeling, reliability estimation, and multitask learning. These findings demonstrate that MLBS-Net can jointly characterize cognitive biases and potential psychological risks from multisource learning behaviors, providing a feasible approach for learning-state sensing, risk warning, and interpretable teaching support in smart education. Full article

Existing studies often reduce private firms’ environmental behaviors to mere greenwashing, rarely unpacking the underlying mechanisms that drive polluting enterprises to develop and sustain substantive compliance. This paper constructs a “Relational Self-Governing” analytical framework grounded in Self-Governance Theory’s core premise of stakeholder interdependence, identifying local social relationality as a critical mechanism that addresses the theory’s inherent limitations of narrow actor scope and interest-bound action logic. This mechanism drives polluting firms to maintain stable, substantive environmental compliance that exceeds minimum legal requirements. This finding expands Self-Governance Theory’s interest-centered practical strategy spectrum and enriches the analytical connotation of corporate agency embedded in localized environmental governance practices. Hierarchical environmental supervision pressures push firms to align operational targets with governmental environmental objectives, while embedded local social relations reinforce compliance incentives to sustain long-term substantive environmental practices. Three core relational paths underpin this process: goal alignment with local governments, peer recognition among firm owners, and shared environmental responsibility with local residents. Leveraging these paths, firms build stable interactive systems spanning government information sharing, inter-firm mutual supervision, and resident collaborative action, collectively constructing organizational legitimacy. This process gradually institutionalizes substantive environmental compliance and fosters public interest-aligned compliance behaviors. This study counters the one-sided greenwashing narrative in prior literature, extending Self-Governance Theory’s application by unpacking the unique operational logic of corporate substantive compliance in environmental governance. Full article

The contact angle of a liquid drop on a solid surface is a simple yet powerful experimental technique for characterizing the surface properties of materials. This method uses a liquid drop as a probe to investigate the chemical and physical properties of a solid surface, but it can also be used in reverse—a well-characterized surface can be employed to study the properties of a liquid. The versatility of the technique can also be extended to submerged systems, where a gas bubble serves as the probe. The contact angle is the angle measured at the solid–liquid and liquid–gas interfaces at the triple-phase point where a solid, liquid, and gas meet. This angle reflects the balance of forces at the three interfaces, and because this balance is highly sensitive to surface properties, contact angle is one of the most surface-sensitive techniques available. The interface can be analyzed from multiple perspectives: empirically through the measured angle (hydrophobic/hydrophilic), thermodynamically using principles such as the Young–Dupré equation, from a physical point of view (the Cassie–Baxter and Wenzel models) and from a chemical standpoint by considering intermolecular forces and the specific chemical nature of the substances involved. Despite its experimental simplicity, contact angle analysis can provide deep insights, ranging from a surface’s wettability (hydrophilicity or oleophobicity) to estimation of van der Waals interaction on a surface. Thanks to its ease of use, its flexibility, and a century of established physical and chemical theory, contact angle measurement has a wide range of applications. These include determining the surface tension of materials, determining the presence of a hierarchical nanostructure at the surface (superhydrophobicity), measuring the percentage of alcohol in a hydroalcoholic solution, assessing surface stability, and many other applications. This article presents a comprehensive review of the diverse applications of contact angle measurements, aiming to provide researchers with a detailed overview of its potential uses and the valuable information that can be obtained. Full article

A novel theoretical study of Boltzmann-Shannon entropy arising in information-statistic theory applied to black-hole physics is proposed. The invariant setting implemented is represented by the manifestly-covariant quantum-gravity theory expressed in canonical Hamiltonian form. In such a framework the appropriate statistical interpretation relies on the configuration-space quantum expectation value of physical observables over the $4-$ scalar quantum-gravity probability density function (PDF). A representation for the black-hole Boltzmann-Shannon entropy is obtained for a Gaussian PDF profile and by establishing simultaneously a relationship between the black-hole invariant energy-content and the mean value of the quantum-gravity nonlinear Bohm potential. This yields a non-trivial functional dependence of the Boltzmann-Shannon entropy on the black-hole surface area, to be interpreted as a quantum statistical entropy counting black-hole bulk quantum-gravity states. The mathematical setting is shown to preserve manifest covariance and be self-contained within quantum-gravity realm. Comparisons with literature treatments dealing with thermodynamic or kinetic-statistical entropies that lead to the Bekenstein-Hawking black-hole surface entropy linear relation or its proposed quantum modifications are discussed. Full article

This study examines consumer acceptance of genetically modified foods (GMFs) by integrating the Theory of Planned Behavior (TPB), brand equity dimensions, and sociodemographic factors within a configurational framework. Drawing on a sample of 405 Spanish consumers, the research combines correlational analysis with fuzzy-set qualitative comparative analysis (fsQCA) to identify the causal paths leading to both purchase intention and purchase non-intention. The findings show that the traditional TPB constructs and brand equity dimensions are positively associated with purchase intention, whereas belonging to older generations is negatively associated with it. However, the configurational analysis reveals a more nuanced picture. Purchase intention is explained by four configurations, grouped into two main profiles: one based on favorable subjective norms among younger consumers, and another centered on the joint presence of brand trust and brand familiarity. In contrast, purchase non-intention is explained by seven configurations, mainly characterized by the absence of favorable TPB conditions and weak brand equity, especially lack of trust and familiarity. The results confirm the causal asymmetry between acceptance and rejection of GMFs, as the configurations leading to purchase intention are not the mirror image of those leading to purchase non-intention. These findings challenge the assumption that GMF acceptance and rejection are symmetrical phenomena, and demonstrate that brand trust and familiarity complement attitudinal factors and constitute relevant conditions in several configurations associated with purchase intention. From a managerial and policy perspective, the findings suggest that promoting GMFs’ acceptance requires not only improving attitudes and perceived social approval, but also fostering trustworthy and familiar brand environments that may contribute to reducing uncertainty, alongside credible scientific information and effective institutional oversight. Full article

Against the dual background of restructuring global green trade rules and advancing China’s “dual-carbon” strategy, environmental information disclosure has increasingly become a crucial strategic tool for firms seeking to enhance international competitiveness. Existing studies have mainly examined the quantity, content, and quality of environmental information disclosure, while paying insufficient attention to its relative disclosure position, a strategic feature of information presentation that may affect signal salience and attention allocation. Drawing on signaling theory and the attention-allocation perspective, this study examines Chinese A-share listed exporters by matching environmental information disclosure positions observed during 2021–2023 with firms’ export product quality during 2022–2024. The final baseline sample contains 1472 firm-year observations. The findings show that earlier environmental information placement is significantly associated with higher subsequent export product quality. This relationship is mainly concentrated in the difference between Position_1 and Position_3, whereas Position_2 does not differ significantly from Position_1. The main result remains robust to alternative coding of disclosure position, additional controls for environmental disclosure volume, adjustment of the sample period, a panel-preserving placebo test, and an external peer-based instrumental-variable analysis. The three-step mechanism tests provide channel-consistent evidence for two potential transmission channels. More prominent environmental information placement is associated with a higher likelihood of ISO 14001 certification, reflecting stronger externally verifiable green credibility, and with greater realized green innovation output, reflected in granted green patents. Both mechanism variables are positively associated with subsequent export product quality. This study extends the literature on environmental information disclosure by shifting attention from static disclosure characteristics to the strategic prominence of information presentation, enriches the micro-level application of signaling theory in international trade, and provides practical insights for firms and regulators to improve the prominence, credibility, and substantive consistency of environmental disclosure while promoting export-quality upgrading. Full article

Background/Objectives: Healthcare workforce shortages and the challenges facing healthcare professionals are among the most pressing issues confronting contemporary health systems. This study examines the social representations of public, private, and public–private partnership (PPP) healthcare organisational models and professional practice elicited from public-sector physicians and nurses in Portugal. Methods: A cross-sectional survey was conducted between October 2022 and May 2023 using a non-probability sample of 308 physicians and nurses classified as public-sector professionals. Participants completed a free-association task concerning the public, private, and PPP healthcare models and their professional practice. The evoked terms were examined using prototypical analysis within the structural approach to Social Representations Theory. The representation of the public healthcare sector included elements such as accessibility, universality, equity, slowness, disorganisation, and resource limitations. The private healthcare sector was characterised by elements including cost, profit, speed, organisation, inaccessibility, and service quality. Results: The representation of PPPs included better management, efficiency, profit, interests, and quality, alongside a relatively frequent “No opinion” response. Professional practice was characterised by elements including caring, dedication, empathy, resilience, exhaustion, perceived undervaluation, and remuneration. Conclusions: These findings describe the social representations elicited from the present sample of public-sector physicians and nurses and should not be interpreted as evidence of objective differences between healthcare organisational models or as direct measures of motivation, resilience, burnout, sector preference, or working conditions. The findings are exploratory and hypothesis-generating and may inform future comparative research involving professionals working across different healthcare settings, as well as studies using dedicated measures of professional and occupational outcomes. Full article

The measurement accuracy issue of zero-sequence current transformers (CTs) has long been a critical factor restricting the accuracy of fault line selection in distribution networks. Although existing research methods are relatively mature in theory, their on-site application is limited by the measurement precision of zero-sequence CTs. To address this problem, this paper proposes a fault line selection strategy for distribution networks based on dynamic and accurate measurement of zero-sequence current. Firstly, from the data perspective, this paper analyzes the fault characteristics of various electrical quantities in different operation stages of distribution networks. Combined with system characteristics, an accurate measurement method for zero-sequence current amplitude is subsequently put forward. Afterwards, a distribution network fault line selection algorithm optimized by an attention mechanism-based multi-scale convolutional neural network is constructed. Finally, verification results based on the IEEE standard test system demonstrate that the proposed method enhances the capabilities of feature extraction and side information aggregation, realizes efficient and accurate localization of faulty lines, and exhibits strong robustness under noisy conditions. Full article

Global supply chains face increasingly frequent disruptions, which require organizations to strengthen supply chain resilience (SCR). Drawing on Organizational Information Processing Theory (OIPT), this study examines how digital transformation and organizational learning combine to enhance SCR. Using data from 61 Chinese high-technology firms, fuzzy-set qualitative comparative analysis (fsQCA) identifies two pathways to high SCR. The first is an ambidextrous learning-driven pathway. Exploitative and exploratory learning jointly help firms refine existing routines and develop adaptive responses to disruption-induced uncertainty. The second is a digitally driven pathway. Digital strategic planning and digital ecosystem coordination help firms structure disruption-related information and coordinate responses across supply chain partners. The configurations for non-high SCR further show that exploratory learning is important for interpreting unfamiliar signals and generating adaptive responses. These findings extend OIPT by showing that SCR emerges from distinct forms of fit between information processing requirements and organizational capabilities. They therefore provide a configurational explanation of resilience formation under uncertainty. Full article

Innovation is an everyday imperative in the information and communication technology (ICT) sector, yet the psychological conditions through which well-being is associated with innovative action remain insufficiently understood. This study investigates whether subjective vitality and creative self-efficacy constitute distinct psychological pathways—an energy-based and a belief-based mechanism, respectively—through which psychological well-being becomes associated with innovative work behavior, thereby providing a more differentiated examination of the assumption that psychologically healthy employees are more likely to behave innovatively. An online survey was administered to 463 ICT-sector employees in Türkiye, and partial least squares structural equation modeling (PLS-SEM) was used to test direct and indirect relationships with comprehensive assessment of measurement quality, predictive relevance, and effect sizes. Creative self-efficacy emerged as a meaningful indirect pathway linking psychological well-being to innovative work behavior, whereas subjective vitality, though strongly predicted by well-being, showed no significant relationship with innovative outcomes. These findings suggest that not all positive psychological states are equally innovation-relevant: among ICT employees, the indirect association between well-being and innovation was evident through employees’ confidence in their own creative capability rather than through feelings of psychological energy—a distinction with meaningful implications for both theory and practice in knowledge-intensive, digitally dynamic organizations. Full article

This study develops a game-theoretic model of a platform-led supply chain in which a manufacturer invests in green innovation under a cap-and-trade regulation. We examine how three information-sharing strategies—no sharing (NI), free sharing (FI), and paid sharing (TI)—affect the manufacturer’s green innovation level, both firms’ profits, and total supply chain carbon emissions. Our analysis reveals that the effect of information sharing is state-dependent: when the realized market potential exceeds its prior expectation, sharing stimulates green innovation, raises platform profit, and reduces emissions; when demand falls below expectations, the opposite holds. For the platform, paid sharing delivers the highest expected profit by extracting the full value of demand information. Furthermore, the emission-reducing effect of information sharing outweighs the demand expansion effect under favorable demand conditions, leading to a net decrease in total carbon emissions. These results underscore the governance role of demand information in aligning economic and environmental goals in platform supply chains, and they offer actionable guidance for managers and policymakers. Full article

While artificial intelligence (AI) is expected to drive paradigm-shifting transformations, many initiatives result in merely incremental optimization. Anchored in strategic human capital theory, this study shifts the analytical focus from the scale of elite technical talent, conceptualized as AI stars, to the configuration of their knowledge structures to unpack this paradox. Using a dataset of 1270 medical AI patents from corporate R&D teams, we employed high-dimensional fixed-effects models to examine these dynamics. The results reveal that while the knowledge diversity of AI stars acts as a potent engine for breakthrough innovation, this generative capacity is attenuated by excessive domain familiarity. Specifically, direct domain familiarity (derived from internal experience) and indirect domain familiarity (absorbed through external collaborative networks) negatively moderate this relationship, a dynamic theorized to operate through internal cognitive entrenchment and external relational conformity, respectively. Extending the efficiency-driven consensus regarding bilingual expertise, these findings demonstrate that excessive domain embeddedness transforms from an informational bridge into a restrictive constraint during paradigm-shifting innovations, particularly within highly institutionalized environments. Full article

Although peripheral ties are essential for social integration, traditional aging theories suggest that they naturally atrophy as older adults withdraw from physical social settings and prioritize intimate relationships. Digital technologies, however, offer new affordances that enable older adults to actively sustain such ties. Through six online focus groups with 32 older Chinese WeChat users (aged 55–74), this study examines how older adults manage their peripheral networks in the digital environment. Three findings emerge. First, older adults maintain a distinct typology of digital peripheral ties—Former Acquaintances, Present-day Acquaintances, and “Net Friends.” Second, they sustain these ties through strategically optimized observational engagement, tracking their networks with minimal interpersonal friction. Third, the benefits of these ties—practical information, identity maintenance, and direct emotional companionship—converge into a lightweight yet meaningful support circle that complements the strong-tie core. By uncovering these practices, this study shows that older adults are active curators of their digital social networks, providing empirical insight into the reconfiguration of late-life social convoys and design implications for age-friendly platforms. Full article

Drawing on self-determination theory (SDT) and service-dominant logic (SDL), this study examines how stress-relief-oriented escape, self-fulfillment, and digital information environments are associated with emotion value co-creation among Generation Z cultural heritage tourists. Survey data from 457 adult Generation Z tourists who had visited cultural heritage destinations in Shanxi Province, China, were analyzed using partial least squares structural equation modeling. Stress-relief-oriented escape was negatively associated with both self-fulfillment and emotion value co-creation, while self-fulfillment was positively associated with emotion value co-creation and carried a significant indirect association between escape and emotion value co-creation. The moderation results revealed contrasting digital boundary conditions: higher media promotion progressively attenuated the negative escape relationships to statistical non-significance, whereas stronger GenAI personalization was associated with increasingly negative relationships. These findings extend tourism motivation research by positioning stress-relief-oriented escape as a recovery-oriented motivational starting condition whose implications depend on the experiential outcome considered. By integrating SDT and SDL, the study identifies self-fulfillment as a measured psychological pathway connecting motivational orientation with emotion value co-creation and shows that this relationship is conditioned differently by interpretive media and preference-adaptive GenAI environments. The findings offer implications for heritage communication and GenAI-enabled tourism design. Full article

The growing use of generative artificial intelligence (GAI) in engineering education has created a need for measures that capture students’ creativity with these tools in engineering contexts. This research developed and validated the GAI-Assisted Engineering Creativity Scale (GECS) across six studies involving university engineering students. Study 1 generated and refined the initial item pool through theory-informed content validation. Study 2 identified a four-dimensional structure comprising Intentionality, Authenticity, Human–AI Collaboration, and Critical Integration. Study 3 confirmed this structure, compared alternative measurement models, and examined measurement invariance across gender. Study 4 evaluated short-term test–retest reliability and informed the final item refinement. Study 5 examined convergent and discriminant validity in relation to established measures of AI-assisted creativity and general creative attributes and behaviors. Study 6 evaluated associations with creative thinking, critical thinking, creative self-concept, and general creativity scale. The final 16-item GECS demonstrated satisfactory internal consistency, a replicable four-factor structure, moderate short-term temporal stability, and meaningful relationships with theoretically relevant constructs. The findings support the GECS as a multidimensional measure of students’ creativity assisted by GAI in engineering work. Full article