Search Results (5,553)

Artificial intelligence (AI) has become a highly relevant tool in higher education. However, its acceptance by university students depends not only on technical or functional characteristics, but also on cognitive, contextual, and emotional factors. This study proposes and validates a model of acceptance of the use of AI devices (MIDA) in the university context. The model considers contextual variables such as anthropomorphism (AN), perceived value (PV) and perceived risk (PR). It also considers cognitive variables such as performance expectancy (PEX) and perceived effort expectancy (PEE). In addition, it considers emotional variables such as anxiety (ANX), stress (ST) and trust (TR). For its validation, data were collected from 517 university students and analysed using structural equations (CB-SEM). The results indicate that perceived value, anthropomorphism and perceived risk influence the willingness to accept the use of AI devices indirectly through performance expectancy and perceived effort. Likewise, performance expectancy significantly reduces anxiety and stress and increases trust, while effort expectancy increases both anxiety and stress. Trust is the main predictor of willingness to accept the use of AI devices, while stress has a significant negative effect on this willingness. These findings contribute to the literature on the acceptance of AI devices by highlighting the mediating role of emotions and offer practical implications for the design of AI devices aimed at improving their acceptance in educational contexts. Full article

In the context of the rapid growth of the Internet of Things and mobile health services, sensors and smart wearable devices are continuously collecting and uploading dynamic health data. Together with the long-term accumulated electronic medical records and multi-source heterogeneous clinical data from healthcare institutions, these data form the cornerstone of intelligent healthcare. In the context of medical data sharing, previous studies have mainly focused on privacy protection and secure data transmission, while relatively few have addressed the issue of incentive mechanisms. However, relying solely on technical means is insufficient to solve the problem of individuals’ willingness to share their data. To address this challenge, this paper proposes a three-party Stackelberg-game-based incentive mechanism for medical data sharing. The mechanism captures the hierarchical interactions among the intermediator, electronic device users, and data consumers. In this framework, the intermediator acts as the leader, setting the transaction fee; electronic device users serve as the first-level followers, determining the data price; and data consumers function as the second-level followers, deciding on the purchase volume. A social network externality is incorporated into the model to reflect the diffusion effect of data demand, and the optimal strategies and system equilibrium are derived through backward induction. Theoretical analysis and numerical experiments demonstrate that the proposed mechanism effectively enhances users’ willingness to share data and improves the overall system utility, achieving a balanced benefit among the cloud platform, electronic device users, and data consumers. This study not only enriches the game-theoretic modeling approaches to medical data sharing but also provides practical insights for designing incentive mechanisms in IoT-based healthcare systems. Full article

The TEAPOTS project aims to meet farmers’ waste management needs by converting agricultural waste into renewable energy and, in parallel, plant biostimulants. Surveys conducted in Germany, Greece, and Italy identified farmers’ waste management practices and their willingness to participate in the TEAPOTS Integrated Solution (TIS). Results show general interest in providing waste to TIS owners. Financial returns and soil improvement are key motivators, with the logistics of waste collection and transfer emerging as major challenges. The study highlights the potential of TIS while emphasizing the need for logistics solutions and enhanced pro-environmental attitudes. Full article

Coastal flooding can result from multiple interacting drivers and can be a complex, challenging topic for learners to grasp. Interactive learning with apps offers new opportunities for improving comprehension and engagement. We present the Floodport app, an educational interactive tool that puts students in the role of coastal risk analysts exploring how natural hazards threaten port safety. Users have to adjust key parameters, including high tides, storm surges, terrestrial rainfall contribution, sea-level rise, and engineered features such as dock height. These forces, individually or jointly, result in water-level rises that may flood the app’s port. The app supports exploration of mitigation designs for the port. Developed in Excel and Python 3.11.4 and deployed as an R/Shiny application, Floodport was used as a classroom game by 153 students with no prior knowledge on coastal flooding concepts. Pre–post survey statistical analysis showed significant learning gains and positively correlation with willingness to engage further. Floodport was found to be a useful tool for basic introduction to flooding concepts. The results indicate strong pedagogical promise and potential for using the app beyond the classroom, in contexts such as stakeholder engagement and training. Full article

In response to recent policies on sustainable finance, nature restoration, soil protection, and biodiversity conservation, it is increasingly important for projects to assess their impacts on natural capital to safeguard Ecosystem Services (ES). Nature-Based Solutions (NBSs) are recognized as strategic tools for fostering cost-effective, nature- and people-centered development. Yet, standard economic and financial assessment methods often fall short, as many ES lack market prices. Indirect, ecosystem-based approaches—such as ES monetization and environmental cost accounting—are therefore critical. This study evaluates the feasibility of investing in NBSs by estimating their economic and financial value through indirect ES valuation. An empirical methodology is applied to quantify environmental costs relative to ES delivery, using Willingness to Pay (WTP) as a proxy for the economic relevance of NBSs. The proposed ES-Cost Accounting (ES-CA) framework was implemented across major NBS categories in Europe. Results reveal that the scale of NBS implementation significantly influences both unit environmental costs and ES provision: larger interventions tend to be more cost-efficient and generate broader benefits, whereas smaller solutions are more expensive per unit but provide more localized or specialized services. The findings offer practical guidance for robust cost–benefit analyses and support investment planning in sustainable climate adaptation and mitigation from an ES perspective. Full article

The accelerating integration of artificial intelligence (AI) into cybersecurity operations has introduced new challenges and opportunities for modernizing incident response (IR) practices. This study explores how cybersecurity practitioners perceive the adoption of intelligent automation and the readiness of legacy frameworks to address AI-driven threats. A structured, two-part quantitative survey was conducted among 194 U.S.-based professionals, capturing perceptions on operational effectiveness, trust in autonomous systems, and the adequacy of frameworks such as NIST and SANS. Using binary response formats and psychometric validation items, the study quantified views on AI’s role in reducing mean time to detect and respond, willingness to delegate actions to autonomous agents, and the perceived obsolescence of static playbooks. Findings indicate broad support for the modernization of incident response frameworks to better align with emerging AI capabilities and evolving operational demands. The results reveal a clear demand for modular, adaptive frameworks that integrate AI-specific risk models and decision auditability. These insights provide empirical grounding for the design of next-generation IR models and contribute to the strategic discourse on aligning automation capabilities with ethical, scalable, and operationally effective cybersecurity response. Full article

Individuals with developmental disabilities often experience physical and mental chronic conditions from early childhood, which can negatively affect their education, employment, and social participation without appropriate interventions. These impairments frequently limit the acquisition of essential daily living skills, including personal hygiene skills. This study examined a multicomponent intervention package—comprising video prompting, step-by-step instruction based on task analysis, systematic error correction, and reinforcement—to support the acquisition of handwashing skills for two adolescents with developmental disabilities (Autism Spectrum Disorder and Intellectual Disability) and toothbrushing skills for one adolescent. A nonconcurrent multiple-baseline design across participants and skills was employed, including baseline, intervention, maintenance, and generalization phases. The intervention was conducted over eight weeks. The results indicated low and stable baseline performance for both participants, followed by a systematic increase in performance after the introduction of the intervention, reaching accuracy levels between 80% and 91%. Participants demonstrated meaningful improvements in hygiene skill performance following intervention. These gains were maintained over time and generalized to new settings, although a few task steps continued to require prompting. Furthermore, teachers and parents rated the intervention as highly feasible, practical, and useful for supporting hygiene skills, while students reported enjoyment, perceived improvement, and willingness to participate again. Overall, the findings suggest that structured, evidence-based instructional approaches may support increased functional participation in essential daily living skills, particularly when complete independence may not be attainable for all individuals. Full article

Accurately identifying electric vehicle (EV) users with high demand response (DR) potential is critical for grid stability but remains challenging due to behavioral heterogeneity, data sparsity, and the subjectivity of expert-dependent methods. In particular, the absence of behavior labels and the low temporal frequency of EV charging events limit the effectiveness of conventional rule-based and clustering approaches. To address these issues, we propose a novel unsupervised framework that integrates deep behavioral modeling with multi-source indicator fusion. Our approach begins by developing a behavior recognition model robust to sparse data, effectively characterizing user charging patterns. Subsequently, a multi-dimensional potential feature system is established. A key innovation lies in our unsupervised weighting mechanism, which automatically learns the importance of each indicator by assessing inter-indicator correlations, thereby eliminating subjective bias. Finally, the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) is employed to rank users comprehensively based on their fused potential scores. Case studies on a large-scale real-world EV charging dataset demonstrate that the proposed method can effectively distinguish high-potential users from low-potential ones. The results indicate clear separability across multiple behavioral and willingness-related dimensions. This provides a practical and data-driven basis for targeted DR incentive design and user-side flexible resource planning. Full article

Public trust in artificial intelligence (AI) is often assumed to promote acceptance by reducing perceived risks. Using a nationally representative survey of 10,294 Chinese adults, this study challenges that assumption and introduces the concept of vigilant trust. We argue that trust in AI does not necessarily diminish risk awareness but can coexist with, and even intensify, attention to potential harms. By examining four dimensions of trust—trusting stance, competence, benevolence, and integrity—we find that all of them consistently enhance perceived benefits, which emerge as the strongest predictor of AI acceptance. However, trust shows differentiated relationships with perceived risks: benevolence reduces risk perception, whereas trusting stance is associated with higher perceptions of both benefits and risks. Perceived risks do not uniformly deter acceptance and, in some contexts, are positively associated with willingness to adopt AI. By moving beyond the conventional view of trust as a risk-reduction mechanism, this study conceptualizes vigilant trust as a mode of engagement in which openness to AI is accompanied by sustained awareness of uncertainty. The findings offer a more nuanced understanding of public acceptance of AI and its implications for governance and communication. Full article

The global demand for argan oil has grown considerably in recent years, creating economic opportunities while raising concerns about ecosystem degradation and the sustainability of production systems. To support long-term viability, several initiatives have promoted environmentally friendly practices and fair value-chain models. However, the effective market integration of these initiatives depends on understanding consumer behavior and preferences toward sustainable products. This study aims to identify the determinants influencing consumers’ purchase intention for sustainable argan oil using an extended framework of the Theory of Planned Behavior (TPB). A structural equation modeling approach was applied to analyze responses from adult consumers with a minimum education level of secondary education. The results show that consumer attitude, perceived behavioral control, and willingness to pay have significant positive effects on purchase intention, while ecological literacy exerts an indirect influence through attitude, social norms, perceived behavioral control, and willingness to pay. In contrast, ecological literacy has no significant direct impact. These findings improve the understanding of behavioral mechanisms underlying green product consumption and offer insights into designing marketing strategies that align with sustainability values and promote responsible consumer choices. Full article

Violence against women remains prevalent, yet many survivors do not engage with services even where health infrastructure exists. This study investigated the role of institution-facing resources, Institutional Trust (ITR) and Procedural Justice (PJ), and the role of interpersonal resources, Social Support Provided (SSP), in women’s formal care-seeking intentions, as mediated by Psychological Distress (PSS) and General Self-Efficacy (GSE). An online survey was administered to women in Greece (n = 392) and the United Kingdom (n = 328), yielding a sample of 718. To compare the structural paths in the model across the two countries, measurement invariance was first explored, while the model was estimated through multi-group structural equation modeling. Across the pooled sample, PJ and GSE predicted HSB firmly, while ITR had no direct link to the construct. SSP did not directly predict HSB, but was linked to GSE in all models. The results of the interaction and group-difference models showed PJ and SSP had a slight indirect effect through GSE, while distress-based pathways were weaker and context-dependent. Multi-group models revealed significant cross-national differences: the direct effect of ITR and PSS on GSE was stronger in the United Kingdom than in Greece. The direct effect of PJ/GSE and SSP/GSE also had a stronger impact in Greece than in the United Kingdom. Overall, the results indicate that the willingness of women to seek help is less driven by their trust in institutions and more driven by their expectations of fairness in provider interaction and their perceived personal capability, where social support plays a role as the antecedent increasing women’s Perceived Self-Efficacy. The implications include prioritizing procedurally just practices, designing interventions that enhance self-efficacy for system navigation, and mobilizing informal networks as partners in the help-seeking process. Full article

This research explores the roles of perceived teacher support, L2 grit, and Foreign Language Enjoyment (FLE) in willingness to communicate (WTC) in English among Pakistani university students, thereby filling a contextual gap in Pakistani multilingual society. It utilized a quantitative cross-sectional design based on the WTC pyramid model by MacIntyre et al. and positive psychology. Adapted scales were used to gather data on 1050 multidisciplinary Pakistani English learners, who were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The main findings can be summarized as follows: (1) perceived teacher support had a small but significant direct effect on L2 WTC; (2) L2 grit had a strong and significant direct effect on L2 WTC; and (3) more importantly, FLE had a significant mediating effect. Indirectly, teacher support was the key factor in improving the L2 WTC, as evidenced by a significant increase in FLE. Though the impact of L2 grit was mostly direct, it was also indirect through FLE. This model explained 45.9 percent of the variation in L2 WTC. These findings highlight FLE, a favorable emotion, as the key channel through which environmental support (teacher support) and personal resilience (L2 grit) are translated into communicative willingness. The results confirm the inclusion of positive psychology into the multi-layered L2 WTC model, which emphasizes the importance of FLE in connecting cognition and emotion. This has important pedagogical implications for EFL/ESL contexts in Pakistan, where teachers should create engaging learning experiences, provide multidimensional support, and foster learners’ perseverance to enhance communicative interaction. Full article

This study uses the Contingent Valuation Method (CVM), a quantitative approach, with interval regression and Ordinary Least Squares (OLS) models to assess the non-use values of the Zhongjieshan and Liuheng Marine Ranches. The aim of the study is to quantify the monetary value of non-market benefits, examine socioeconomic influences on stakeholders’ Willingness to Pay (WTP), and provide a basis for ecological compensation mechanisms. Zhongjieshan’s annual non-use value is estimated at 28.99–30.81 million CNY (Chinese Yuan) (median WTP 74.33–78.99 CNY per person), while Liuheng’s value is higher at 108–111 million CNY (median WTP 150.20–153.89 CNY per person), suggesting greater ecological and recreational potential at Liuheng. The results show robust model performance, with minimal WTP differences. WTP for Liuheng is primarily influenced by income and environmental awareness, while Zhongjieshan shows a distance-decay effect. Visitor profiles reveal that Zhongjieshan attracts younger, moderately educated visitors, while Liuheng draws more highly educated, economically diverse groups. These findings suggest that Zhongjieshan should prioritize community-based co-management, while Liuheng should focus on high-quality, technology-driven ecological leisure development. The study also emphasizes the need for targeted awareness campaigns and supports the creation of diversified ecological compensation mechanisms beyond government funding. Full article

Agrivoltaic systems (AVs) combine agricultural production with photovoltaic energy generation, enabling the dual use of land resources. This approach has gained increasing attention as a promising strategy to address pressing social, environmental, and energy challenges. Although the global expansion of AVs is accelerating, empirical research remains limited—particularly regarding farmers’ perspectives on adopting such systems. This study investigates Greek farmers’ perceptions and attitudes toward the adoption of photovoltaic technologies in agricultural practices. For this purpose, a questionnaire-based survey was conducted on a sample of 287 participants selected using purposive convenience sampling, based on predefined inclusion criteria relevant to the objectives of the study. The data were analyzed using a binary logistic regression model to identify factors positively associated with farmers’ willingness to adopt AVs. The findings reveal that 46.3% of farmers expressed willingness to adopt AVs, indicating a moderate level of acceptance. The logistic regression results indicated that higher education levels (OR = 3.53, p = 0.007), membership in farmers’ organizations (OR = 2.00, p = 0.001), and familiarity with agro-energy concepts (OR = 3.49, p = 0.016) significantly increased farmers’ motivation to engage as renewable energy producers. The model demonstrates a moderate level of explanatory power (Nagelkerke R² = 0.37). The study’s findings provide valuable insights into the key factors influencing farmers’ willingness to adopt AVs, contributing to a deeper understanding of the decision-making processes involved. Based on these findings, it is recommended that agricultural policies and community-based renewable energy initiatives focus on targeted education and extension services, the strengthening of farmers’ organizations to facilitate collective decision-making, and the implementation of focused agro-energy information campaigns. Full article

This study develops an immersive marine Information and Communication Technology (ICT) convergence framework designed to enhance coastal climate resilience by improving accessibility, visualization, and communication of scientific research on Dokdo (Dok Island) in the East Sea. High-resolution spatial datasets, multi-source marine observations, underwater imagery, and validated research outputs were integrated into an interactive virtual-reality (VR) and web-based three-dimensional (3D) platform that translates complex geophysical and ecological information into intuitive experiential formats. A geospatially accurate 3D virtual model of Dokdo was constructed from maritime and underwater spatial data and coupled with immersive VR scenarios depicting sea-level variability, coastal morphology, wave exposure, and ecological characteristics. To evaluate practical usability and pro environmental public engagement, a three-phase field survey (n = 174) and a System Usability Scale (SUS) assessment (n = 42) were conducted. The results indicate high satisfaction (88.5%), strong willingness to re-engage (97.1%), and excellent usability (mean SUS score = 80.18), demonstrating the effectiveness of immersive content for environmental education and science communication crucial for achieving Sustainable Development Goal 14 targets. The proposed platform supports stakeholder engagement, affective learning, early climate risk perception, conservation planning, and multidisciplinary science–policy dialogue. In addition, it establishes a foundation for a digital twin system capable of integrating real-time ecological sensor data for environmental monitoring and scenario-based simulation. Overall, this integrated ICT-driven framework provides a transferable model for visualizing marine research outputs, enhancing public understanding of coastal change, and supporting sustainable and adaptive decision-making in small island and coastal regions. Full article