Search Results (3,914)

This study examines the evolving use of social software to combat online disinformation by investigating Weibo users’ attitudes toward IP location disclosure as a measure of transparency and trustworthiness. We analyzed 49,579 posts (April 2022 to May 2023) from Weibo users about IP location disclosure, categorized the topics using LDA topic modeling within the frameworks of communication privacy management, the networked public sphere, and digital democracy, and conducted sentiment analysis. We constructed separate semantic networks for positive and negative terms to examine co-occurrence patterns. The results show that Weibo users are generally negative about this policy, as IP location may reveal personally identifiable information about individuals involved in discussions of online social/political events. Mandatory transparency, while intended to enhance accountability, functions as a mandatory visibility regime that reshapes privacy boundaries and undermines inclusive deliberation. The findings contribute to the exploration of the impact of government-mandatory information privacy disclosure policies on the implementation of platform functionality, as well as changes in user sentiment, information behavior, and components of social media discourse. Full article

Environmental concerns and the increasing scarcity of resources force decision makers in the supply chain to consider reuse and re-production. Closed loop supply chain is a fundamental concept that has attracted the attention of many researchers due to its profitability for businesses as well as its positive environmental and social effects. Closed-loop supply chains and sustainability dimensions are complementary because of their mutual effects. This paper develops a mathematical model to design a sustainable closed-loop supply chain network in the wood–plastic composite industry. Due to the nature of the problem considered, a mixed-integer linear programming method is utilized. The proposed model is a multi-objective model, and the Lp-metric method is used to solve it. The proposed model is applied in a real case in Iran. The proposed model identified 17 optimal provinces for manufacturing centers, 15 for reuse centers, and 9 for reproduction centers. Verification and validation of the proposed model illustrate its capability in real world implications. Full article

This study examines how mindful consumption contributes to sustainable marketing and consumer engagement by influencing green purchase intention and life satisfaction among Generation Z, while also assessing the moderating role of social influence. Grounded in Self-Determination Theory, a survey of 1541 Thai consumers aged 18–24 was analyzed using a structural equation model and path analysis to test the mediation framework. The results show that mindful consumption significantly enhances sustainability values and purchase intentions, with sustainability values mediating the relationship between mindful consumption and both behavioral and psychological outcomes. Moreover, social influence strengthens the impact of sustainable consumption on purchase intentions, highlighting the role of peers, networks, and societal norms in promoting ethical and environmentally responsible consumer behavior. The findings extend sustainable marketing theory by highlighting mindful consumption as a driver of both behavioral (green purchase intention) and psychological (life satisfaction) outcomes. Beyond its theoretical contribution, the study offers practical insights for businesses, educators, and policymakers on fostering value-driven relationships with young consumers through mindful and socially reinforced sustainability initiatives. Promoting mindful consumption and leveraging social influence provides a pathway to engage Generation Z in sustainability-oriented lifestyles, supporting long-term consumer loyalty and achieving the United Nations’ Sustainable Development Goals. Full article

As tourism increasingly drives the revitalization of traditional villages, rural spaces are undergoing a transformation from functional living areas to spaces for cultural display and leisure. This shift has amplified the spatial usage discrepancies between multiple stakeholders, such as tourists and villagers, highlighting conflicts in spatial resource allocation and behavior path organization. Using Wulin Village, a typical example of a Minnan overseas Chinese village, as a case study, this paper introduces social network analysis to construct a “spatial–behavioral” dual network model. The model integrates both architectural and public spaces, alongside behavior path data from villagers and tourists, to analyze the spatial structure at three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The study integrates two dimensions—architectural space and public space—along with behavioral path data from both villagers and tourists. It reveals the characteristics of spatial structure under the intervention of multiple behavioral agents from three scales: village-level network completeness, district-level structural balance, and point-level node vulnerability. The core research focus of the spatial network includes the network structure of architectural and public spaces, while the behavioral network concerns the activity paths and behavior patterns of tourists and villagers. The study finds that, at the village scale, Wulin Village’s spatial network demonstrates good connectivity and structural integrity, but the behavior paths of both tourists and villagers are highly concentrated in core areas, leading to underutilization of peripheral spaces. This creates an asymmetry characterized by “structural integrity—concentrated behavioral usage.” At the district scale, the spatial node distribution appears balanced, but tourist behavior paths are concentrated around cultural nodes, such as the ancestral hall, visitor center, and theater, while other areas remain inactive. At the point scale, both tourist and villager activities are highly dependent on a few high-degree, high-cluster nodes, improving local efficiency but exacerbating systemic vulnerability. Comparison with domestic and international studies on cultural settlements shows that tourism often leads to over-concentration of spatial paths and node overload, revealing significant discrepancies between spatial integration and behavioral usage. In response, this study proposes multi-scale spatial optimization strategies: enhancing accessibility and path redundancy in non-core areas at the village scale; guiding behavior distribution towards multifunctional nodes at the district scale; and strengthening the capacity and resilience of core nodes at the point scale. The results not only extend the application of behavioral network methods in spatial structure research but also provide theoretical insights and practical strategies for spatial governance and cultural continuity in tourism-driven cultural villages. Full article

Objective: This study aimed to explore the direct relationships between social determinants and behavioral clusters, as well as their potential indirect associations mediated by edentulism. Methods: Information on social variables (collected in Wave 3, 2006/07), edentulism (Wave 5, 2010/11), and health-related behaviors (Wave 7, 2014/15) was drawn from the English Longitudinal Study of Ageing (ELSA). Baseline sociodemographic characteristics, including age, gender, ethnicity, education, and wealth, were accounted for. Latent class analysis (LCA) was applied to four behavioral indicators—smoking status, alcohol consumption, fruit and vegetable intake, and physical activity—to identify behavioral clusters. A confirmatory factor analysis (CFA) was then used to construct a latent variable representing social support and social networks. Two structural equation models (SEM) were developed to examine both the direct associations between social support/network and behavioral clusters, and the indirect associations mediated by edentulism. Results: In LCA, the two-class model was the best fit for the data. Class 1 (risky behaviors) had 7%, while Class 2 (healthy behaviors) had 93%. In SEM Model 1, higher social support/network levels predicted being in the healthy cluster directly (SC = 0.147) and indirectly (SC = 0.009). In Model 2, accounting for wealth and education, higher levels of social support/network maintained the direct association with the healthy cluster (SC = 0.132), but the indirect path lost significance. Conclusions: This study found that greater social support was associated with healthier behaviors, and this relationship may be mediated by edentulism. Health policies that encourage social interaction could therefore improve both general and oral health. Full article

The sustainable development of agrifood systems is a pressing global challenge, highlighting the need for frameworks that guide responsible investment and community engagement. The Principles for Responsible Investment in Agriculture and Food Systems (CSA-IRA), approved by the Food Security Council in 2014, provide such a framework. Recognizing this opportunity, the FAO selected the Gesplan Research Group of the Polytechnic University of Madrid in 2016 to promote these principles in Latin America, the Caribbean, and Spain, leveraging the expertise of PhD graduates in Projects and Planning for Sustainable Rural Development. The main objective of this research was to explore how teaching, research, and civil society engagement can be integrated to operationalize CSA-IRA principles and foster sustainable development. To achieve this, the study applied the “Working with People” model across multiple countries and contexts, using university–business collaborations to implement practical, socially responsible initiatives. Over nine years, the approach generated a network of 46 universities and 52 agrifood companies across 12 countries, demonstrating effective multi-stakeholder collaboration. The accumulated experience led to the proposal of the Metauniversity—a “university of universities”—as an innovative instrument to scale knowledge transfer, research, and community engagement. These findings highlight that structured, collaborative networks can translate CSA-IRA principles into tangible actions, offering a replicable model for sustainable agrifood development globally Full article

Geolocating Twitter users from social media data holds significant value in applications such as targeted advertising, disaster response, and social network analysis. However, existing social network-based geolocation methods tend to focus primarily on mention relations while neglecting other critical interactions like retweet relationships. Moreover, effectively integrating diverse social features remains a key challenge, which limits the overall performance of geolocation models. To address these issues, this paper proposes a novel Twitter user geolocation method based on multi-graph feature fusion with a gating mechanism, termed MGFGCN, which fully leverages heterogeneous social network information. Specifically, MGFGCN first constructs separate mention and retweet graphs to capture multi-dimensional user relationships. It then incorporates the Information Gain Ratio (IGR) to select discriminative keywords and generates Term Frequency–Inverse Document Frequency (TF-IDF) features, thereby enhancing the semantic representation of user nodes. Furthermore, to exploit complementary information across different graph structures, we propose a Structure-aware Gated Fusion Mechanism (SGFM) that dynamically captures differences and interactions between nodes from each graph, enabling the effective fusion of node representations into a unified representation for subsequent location inference. Experimental results demonstrate that the proposed method outperforms existing state-of-the-art baselines in the Twitter user geolocation task across two public datasets. Full article

Urban ecosystem services (ESs) are increasingly recognized as critical determinants of residents’ quality of life and well-being. This study develops a data-driven demand–supply matching framework to integrate ES concepts into community-level planning and service performance evaluation. Based on 312 resident surveys across 10 communities in Wuhan, China, we identify the key environmental attributes shaping perceived service quality. A random forest (RF) algorithm is employed to assess the relative importance of environmental features, while a multinomial logit (Mlogit) model quantifies their specific effects. The results highlight that community autonomy, neighborhood relations, environmental awareness, and infrastructure—such as broadband networks and security systems—play pivotal roles in improving service quality. Although provisioning and regulating ESs, such as safety and infrastructure, are relatively well established, cultural services that promote social cohesion and civic participation remain under-supported. These findings uncover the heterogeneity of residents’ environmental expectations and provide actionable insights for incorporating ES-oriented thinking into community planning and fiscal decision-making. By bridging ecological theory with operational urban governance, this study contributes a replicable approach for advancing more inclusive and sustainable community development. Full article

Objectives: A retrospective ecological study was conducted to analyze the relationship between the incidence of myocardial infarction with ST-segment elevation (STEMI) and various sociodemographic factors in municipalities within the Community of Madrid, a high-income and low-cardiovascular-risk European region. Methods: This study analyzed a database of patients registered in the regional network for STEMI care from January 2014 to December 2018. Thirty-four municipalities with populations greater than 10,000 inhabitants were included. The mean annual incidence of STEMI (iSTEMI) was estimated for each locality, and several variables of wealth, employment and social investment were obtained from public databases. Results: During the period of the study, 2561 confirmed STEMI cases were recorded in the selected localities, with an average incidence of 23 events per 100,000 inhabitants and year. The mean age was 62, with 83% of patients being male. Among municipalities included in the study, a significant direct correlation was found between iSTEMI and unemployment rate (r = 0.354, p = 0.04). A significant inverse correlation was found with all wealth-related variables, mainly with a composed deprivation (poverty) index (r = −0.624, p < 0.001) and the percentage of employees in the financial sector (r = −0.497, p = 0.003). No correlation was found between iSTEMI and the sociodemographic or public investment variables retrieved. Multiple regression analysis showed that the model best fitted when energy billed per inhabitant and mean income tax per taxpayer were introduced. Conclusions: Residents of areas with lower incomes and higher unemployment rates may be at a greater risk of STEMI. This should be taken into account when planning cardiovascular prevention and community health management. Full article

Purpose—This study explores the determinants of entrepreneurial intention (EI) among university students, analyzing entrepreneurial motivation (EM) as a mediator and perceived institutional support (PIS) as a moderator within the Theory of Planned Behavior (TPB) framework. Design/Methodology/Approach—Using Partial Least Squares Structural Equation Modeling (PLS-SEM), data from 128 students at the Polytechnic Institute of Cávado and Ave, Portugal, were analyzed to assess direct, indirect, and moderating effects of entrepreneurial attitudes, education, and social norms. Findings—EM significantly mediates the relationship between attitude concerning entrepreneurship (ACE), perceived social norms (PSN), entrepreneurial education (EE), and EI, reinforcing its role in bridging individual and educational influences with entrepreneurial behavior. However, PIS does not significantly moderate the EM-EI relationship, suggesting institutional support alone is insufficient to enhance motivation’s impact on EI. This challenges assumptions about institutional effectiveness and highlights the importance of entrepreneurial ecosystems, social capital, and mentorship networks as alternative enablers. Implications—The study extends TPB by incorporating mediation and moderation effects, offering a deeper understanding of personal, social, and institutional influences on EI. This study contributes by simultaneously modeling entrepreneurial motivation as mediator and perceived institutional support as moderator within a TPB framework. Such integration remains rare, particularly in Southern European higher education contexts, and our findings nuance current assumptions by revealing when institutional supports may fail to strengthen motivational pathways. The findings emphasize the need for education policies that integrate experiential learning, entrepreneurial ecosystems, and mentorship to foster entrepreneurial mindsets. Originality/Value—This research challenges the assumed role of institutional support, highlighting motivation as a key driver of EI and providing new insights into policy-driven entrepreneurship promotion in higher education. Full article

In the digital age, generative artificial intelligence (GenAI) development has brought about structural transformations in higher education. This study examines how students’ regular use of artificial intelligence tools brings a new active player into the educational process. This is an “uninvited assistant” that changes traditional models of teaching and learning. This study was conducted using the following standard methods: bibliometric analysis, student survey using an electronic questionnaire, primary processing and graphical visualization of empirical data, calculation of statistical indicators, t-statistics, and z-statistics. As the results of the bibliometric analysis show, the evolution in the perception and integration of artificial intelligence within higher education discussions, as evidenced by the comparison of network visualizations from 2020 to the present, reveals a significant transformation. Based on a quantitative survey of 1197 undergraduate students in five Eastern European countries, this paper proposes a conceptual shift from the classic two-dimensional (2D) model of higher education services based on university teacher–student interactions to a three-dimensional (3D) model that includes artificial intelligence as a functional third player (an uninvited assistant). Statistical hypothesis testing confirms that students need AI and regularly use it in the learning process, facilitating the emergence of this new player. Based on empirical data, this study presents a hypothetical 3D model (X:Y:Z), where the Z-axis reflects the intensity of AI use. This model challenges traditional didactic frameworks and calls for updating educational policies, ethical standards, and higher education governance systems. By merging digital technologies and social change, the results provide a theoretical and practical basis for rethinking pedagogical relationships and institutional roles in the digital age. Full article

This paper proposes a hybrid deep learning model for robust and interpretable sentiment classification of Twitter data. The model integrates Bidirectional Encoder Representations from Transformers (BERT)-based contextual embeddings, a Bidirectional Long Short-Term Memory (BiLSTM) network, and a custom attention mechanism to classify tweets into four sentiment categories: Positive, Negative, Neutral, and Irrelevant. Addressing the challenges of noisy and multilingual social media content, the model incorporates a comprehensive preprocessing pipeline and data augmentation strategies including back-translation and synonym replacement. An ablation study demonstrates that combining BERT with BiLSTM improves the model’s sensitivity to sequence dependencies, while the attention mechanism enhances both classification accuracy and interpretability. Empirical results show that the proposed model outperforms BERT-only and BERT+BiLSTM baselines, achieving F1-scores (F1) above 0.94 across all sentiment classes. Attention weight visualizations further reveal the model’s ability to focus on sentiment-bearing tokens, providing transparency in decision-making. The proposed framework is well-suited for deployment in real-time sentiment monitoring systems and offers a scalable solution for multilingual and multi-class sentiment analysis in dynamic social media environments. We also include a focused characterization of the dataset via an Exploratory Data Analysis in the Methods section. Full article

India’s aviation sector, crucial for connectivity, economic growth, and national integration, faces sustainability measurement challenges focused solely on carbon emissions. This study proposes the Aviation Sustainability Performance Index (ASPI-India), spanning four pillars: Environmental Stewardship, Social Responsibility, Governance Maturity, and Economic Resilience. Measurable indicators are derived from regulatory filings, commercial flight databases, geospatial tracking, and targeted surveys. Data sources include DGCA safety audits, AAI operational statistics, ADS-B flight path data, and passenger satisfaction surveys from 2010 to 2024. Fixed-effects panel models link ASPI-India to operational and financial outcomes like load factor stability, CASK, and credit rating resilience. Quasi-experimental designs exploit policy shocks through difference-in-differences estimation. Factor analysis validates the four-pillar structure, and robustness checks compare entropy, PCA, and equal weighting. Results show that a one-standard-deviation increase in ASPI-India improves load factor stability, ancillary revenue share, and credit terms, especially for carriers with diversified route networks. The framework provides actionable insights for airlines, regulators, and investors to embed sustainability in aviation management. Full article

This paper investigates the role of symmetry and asymmetry in the learning process of modern machine learning models, with a specific focus on feature representation and optimization. We introduce a novel symmetry-aware learning framework that identifies and preserves symmetric properties within high-dimensional datasets, while allowing model asymmetries to capture essential discriminative cues. Through analytical modeling and empirical evaluations on benchmark datasets, we demonstrate how symmetrical transformations of features (e.g., rotation, mirroring, permutation invariance) impact learning efficiency, interpretability, and generalization. Furthermore, we explore asymmetric regularization techniques that prioritize informative deviations from symmetry in model parameters, thereby improving classification and clustering performance. The proposed approach is validated using a variety of classifiers including neural networks and tested across domains such as image recognition, biomedical data, and social networks. Our findings highlight the critical importance of leveraging domain-specific symmetries to enhance both the performance and explainability of machine learning systems. Full article

This study aims to develop and evaluate an AI- and big-data-based innovation system for proactively managing ESG (Environmental, Social, and Governance) risks within the tourism supply chain. Drawing on heterogeneous data sources including supply chain records, news articles, social media, and public databases, the research employs advanced methodologies such as network analysis, anomaly detection, natural language processing (including greenwashing detection), and predictive modeling. Through this comprehensive approach, the study demonstrates the feasibility and effectiveness of a dynamic AI-driven ESG risk management system that delivers reliable risk identification and quantitative performance evaluation. The theoretical contribution lies in bridging AI-driven ESG evaluation frameworks with sustainable tourism and hospitality literature, moving beyond static, indicator-based assessments toward a more systematic, replicable, and predictive methodology capable of capturing the dynamic, multiscalar, and networked nature of tourism supply chains. Ultimately, this research provides tourism and hospitality firms with a powerful tool to enhance transparency, mitigate ethical and reputational risks, and strengthen stakeholder trust, while offering actionable insights for managers and policymakers developing data-driven ESG integration strategies. Full article