Search Results (157)

This study aims to investigate the multifaceted effects of e-commerce-led digital chain engagement on the income of Chinese crop farmers, distinguishing between participation status and participation depth. The analysis uses data from the China Rural Revitalization Survey (CRRS) conducted in 2020, with 1815 crop-farming households as the sample. To estimate causal effects, treatment effect models and instrumental variable strategies are employed. Results show that e-commerce-led digital chain participation significantly enhances household income, and deeper digital chain engagement amplifies this effect. Mechanism analyses reveal that deep engagement promotes income through multiple channels, including improved digital preparedness, enhanced product sales performance, and increased participation in digital financial services. Heterogeneity analysis indicates that the income gains mainly stem from agricultural revenue, and are more pronounced among cooperative members, though marginal benefits from deeper engagement appear limited. Quantile regressions uncover a pronounced Matthew effect: higher-income households benefit more from digital chain embedding, thereby widening the income gap. Moreover, e-commerce-led digital chain participation also improves farmers’ income satisfaction and their expectations of income sustainability. These findings suggest that policymakers should not only promote basic e-commerce participation but also implement targeted support for deep digital chain embedding to foster inclusive growth while mitigating the Matthew effect. By shifting the focus from binary participation to embedded intensity, this study provides new insights into how e-commerce-led digital transformation shapes rural income structures, offering theoretical and empirical contributions to the literature on agricultural modernization and digital inclusion. Full article

Artificial Intelligence (AI) holds significant potential to enhance sustainable non-chemical agricultural methods (NCAM) by optimising resource management, automating precision farming practices, and strengthening climate resilience. However, its widespread adoption among farmers’ remains limited due to socio-economic, infrastructural, and justice-related challenges. This study investigates AI adoption among NCAM farmers using an Integrated Mechanism for Sustainable Practices (IMSP) conceptual framework which combines the Technology Acceptance Model (TAM) with a justice-centred approach. A mixed-methods design was employed, incorporating Fuzzy-Set Qualitative Comparative Analysis (fsQCA) of AI adoption pathways based on survey data, alongside critical discourse analysis of thematic farmers narrative through a justice-centred lens. The study was conducted in Tamil Nadu between 30 September and 25 October 2024. Using purposive sampling, 57 NCAM farmers were organised into three focus groups: marginal farmers, active NCAM practitioners, and farmers from 18 districts interested in agricultural technologies and AI. This enabled an in-depth exploration of practices, adoption, and perceptions. The findings indicates that while factors such as labour shortages, mobile technology use, and cost efficiencies are necessary for AI adoption, they are insufficient without supportive extension services and inclusive communication strategies. The study refines the TAM framework by embedding economic, cultural, and political justice considerations, thereby offering a more holistic understanding of technology acceptance in sustainable agriculture. By bridging discourse analysis and fsQCA, this research underscores the need for justice-centred AI solutions tailored to diverse farming contexts. The study contributes to advancing sustainable agriculture, digital inclusion, and resilience, thereby supporting the United Nations’ Sustainable Development Goals (SDGs). Full article

The conceptual design phase in architecture establishes the foundation for subsequent design decisions and influences up to 80% of a building’s lifecycle environmental impact. While Building Information Modeling (BIM) demonstrates transformative potential for sustainable design, its application during conceptual design remains constrained by perceived technical complexity and limited support for abstract thinking. This research examines how BIM tools can facilitate conceptual design through diagrammatic reasoning, thereby bridging technical capabilities with creative exploration. A mixed-methods approach was employed to develop and validate a Diagrammatic BIM (D-BIM) framework. It integrates diagrammatic reasoning, parametric modeling, and performance evaluation within BIM environments. The framework defines three core relationships—dissection, articulation, and actualization—which enable transitions from abstract concepts to detailed architectural forms in Revit’s modeling environments. Using Richard Meier’s architectural language as a structured test case, a 14-week quasi-experimental study with 19 third-year architecture students assessed the framework’s effectiveness through pre- and post-surveys, observations, and artifact analysis. Statistical analysis revealed significant improvements (p < 0.05) with moderate to large effect sizes across all measures, including systematic design thinking, diagram utilization, and academic self-efficacy. Students demonstrated enhanced design iteration, abstraction-to-realization transitions, and performance-informed decision-making through quantitative and qualitative assessments during early design stages. However, the study’s limitations include a small, single-institution sample, the absence of a control group, a focus on a single architectural language, and the exploratory integration of environmental analysis tools. Findings indicate that the framework repositions BIM as a cognitive design environment that supports creative ideation while integrating structured design logic and performance analysis. The study advances Education for Sustainable Development (ESD) by embedding critical, systems-based, and problem-solving competencies, demonstrating BIM’s role in sustainability-focused early design. This research provides preliminary evidence that conceptual design and BIM are compatible when supported with diagrammatic reasoning, offering a foundation for integrating competency-based digital pedagogy that bridges creative and technical dimensions of architectural design. Full article

In higher education, institutional quality is traditionally assessed through metrics such as academic programs, research output, educational resources, and community services. However, it is important that their activities align with student expectations, particularly in relation to interactive learning environments, learning management system interaction, curricular and co-curricular activities, accessibility, support services and other learning resources that ensure academic success and, jointly, career readiness. The growing popularity of student engagement metrics as one of the key measures to evaluate institutional efficacy is now a feature across higher education. By monitoring student engagement, institutions assess the impact of existing resources and make necessary improvements or interventions to ensure student success. This study presents a comprehensive analysis of student feedback from the StudentSurvey.ie dataset (2016–2022), which consists of approximately 275,000 student responses, focusing on student self-perception of engagement in the learning process. By using classical topic modelling techniques such as Latent Dirichlet Allocation (LDA) and Bi-term Topic Modelling (BTM), along with the advanced transformer-based BERTopic model, we identify key themes in student responses that can impact institutional strength performance metrics. BTM proved more effective than LDA for short text analysis, whereas BERTopic offered greater semantic coherence and uncovered hidden themes using deep learning embeddings. Moreover, a custom Named Entity Recognition (NER) model successfully extracted entities such as university personnel, digital tools, and educational resources, with improved performance as the training data size increased. To enable students to offer actionable feedback, suggesting areas of improvement, an n-gram and bigram network analysis was used to focus on common modifiers such as “more” and “better” and trends across student groups. This study introduces a fully automated, scalable pipeline that integrates topic modelling, NER, and n-gram analysis to interpret student feedback, offering reportable insights and supporting structured enhancements to the student learning experience. Full article

As self-service technologies (SSTs) such as kiosks become embedded in service infrastructure, understanding the socio-cognitive drivers of adoption has grown in importance. This study extends the Technology Acceptance Model (TAM) by integrating Digital Readiness (DR), Trust in Technology (TT), Perceived Usefulness (PU), and Perceived Barriers (PB) into a single framework, and tests it using structural equation modeling (SEM) with survey data from 750 kiosk users in China. TT emerges as the strongest direct predictor of intention to use (IU) and also increases PU while reducing PB. The deterrent effect of PB exceeds the positive effect of PU. DR promotes adoption indirectly by raising TT and PU and lowering PB, whereas its direct path to IU is negative, suggesting a tension between readiness and heightened expectations. Multi-group analyses show that non-digital natives and low-frequency users are more sensitive to trust-related factors, whereas digital natives and high-frequency users respond more to barrier reduction. These findings integrate trust and barrier perspectives into TAM and reconceptualize DR as an ambivalent antecedent. Practically, a segment- and journey-oriented design frame centered on trust and friction provides a common reference for aligning kiosk design, KPIs, and investment decisions across industries. Full article

The rapid expansion of fashion e-commerce has raised concerns over the environmental cost of last-mile deliveries, especially in pure-player models. This preliminary study examines the estimated carbon footprint of TENDAM’s omnichannel model—based on in-store pickup and returns—compared to pure-player home delivery, using a customer-level approach across 11 Spanish cities of varying sizes. A total of 3106 face-to-face surveys were conducted in TENDAM stores, capturing data on mobility behavior, transport modes, trip chaining, and service types. Emission factors were applied using a Python-based analytical model, and results were contrasted with Monte Carlo simulations from existing literature on pure players. Our findings indicate that the average per-service footprint of the omnichannel model is around 400 g ${\mathrm{C}\mathrm{O}}_2-\mathrm{e}\mathrm{q}$, significantly lower than the 1500–3000 g ${\mathrm{C}\mathrm{O}}_2-\mathrm{e}\mathrm{q}$ range for pure players. Emissions were especially low in large cities and in street-level stores, largely due to the high rate of walking and multipurpose trips among customers. The study also includes geospatial analysis through interactive influence maps. These results suggest that dense store networks embedded in walkable urban areas can substantially reduce last-mile GHG emissions. While preliminary, the study highlights the potential for omnichannel retail to support urban decarbonization goals and sustainability when integrated with sustainable mobility patterns. Full article

This study scrutinizes the effects of generative artificial intelligence (GenAI) on sustainable performance (SP) in Chinese manufacturing firms through the mediating role of novelty-centered and efficiency-centered business model innovations (BMIs). It also explores the moderating effect of AI regulation on the GenAI–BMIs and GenAI–SP relationships. Data were collected from 1192 middle-level managers across 500 Chinese manufacturing firms using a two-wave survey design. Partial least squares structural equation modeling (PLS-SEM) was employed to test direct, mediating, and moderating relationships. The findings show that GenAI adoption has a significant positive effect on novelty-centered BMI, efficiency-centered BMI and sustainability performance. The GenAI–SP relationship is mediated by both BMIs, indicating that GenAI contributes to sustainability both directly and through innovative business practices. Moreover, AI regulation significantly strengthens the effects of GenAI on both BMI and SP, emphasizing the importance of regulatory alignment in maximizing technological benefits. This research shows that firms should emphasis AI tools and strategies to innovate their business model for better sustainable outcomes. Firms need to follow regulations and rules embedded into digitalization to ensure a sustainable competitive position in the market. Full article

This study explores the role of ethical leadership in fostering employee green behaviors (EGBs) within higher education institutions (HEIs) in the UAE. While environmental initiatives are increasingly being integrated into university operations, there has been limited empirical research examining how leadership styles influence pro-environmental behaviors among academic staff. Using a mixed-methods sequential explanatory design, our study surveyed 105 HEI employees and conducted in-depth interviews with 6 of the participants. The quantitative findings reveal a moderate but significant positive correlation between ethical leadership (EL) and EGB (ρ = 0.314, p < 0.001). The reliability scores for both EL (α = 0.888) and EGB (α = 0.754) confirmed the internal consistency of the measurement items used. The qualitative insights support the theoretical foundation drawn from Social Learning, Value–Belief–Norm, and Environmental Stewardship Theories. Employees reported modeling their green behaviors on observable leadership actions aligning with their shared moral values. A key distinction emerged between authentic and performative green behaviors, with employees responding more positively to leaders who modeled consistency and sincerity. This study concludes that ethical leadership significantly influences the environmental culture in HEIs by embedding sustainability into daily practices and institutional values. This research addresses a regional and theoretical gap, contextualizing ethical leadership in the Middle Eastern academic setting and offering practical implications for leadership development, policy alignment, and sustainable cultural transformation. Full article

Urbanization poses growing threats to archaeological heritage sites embedded within cities, necessitating innovative monitoring and documentation strategies. This study investigates the use of Unmanned Aerial Vehicle (UAV) photogrammetry for mapping and 3D modelling of urban archaeological landscapes, focusing on the Byzantine-era Didymoteicho Fortress in northern Greece. High-resolution aerial imagery was captured and processed into an orthophoto mosaic and a detailed 3D model of the site’s monuments and their urban surroundings. The UAV-based survey provided comprehensive, up-to-date spatial data that traditional ground methods could not easily achieve in dense urban settings. The results illustrate how UAV mapping can document complex heritage structures, detect risks (such as structural deterioration or encroachment by development), and inform preservation efforts. The discussion situates these findings within global heritage management practices, highlighting UAV technology as a cost-effective, accurate, and non-invasive tool for safeguarding cultural heritage in urban areas. The suggested methodology enhances heritage documentation and risk assessment, demonstrating strong potential for policy integration and proactive conservation planning in historic cities. Full article

This study investigates how Corporate Social Responsibility (CSR) influences financial performance in the Middle Eastern banking sector through the mediating roles of corporate reputation, employee engagement, and innovation orientation. Drawing on stakeholder theory and the resource-based view, a survey of 297 senior banking executives was analyzed using structural equation modeling. The results show that CSR has both a direct positive impact on financial performance and an indirect effect by strengthening intangible resources. Among the mediators, innovation orientation emerged as the strongest pathway, followed by employee engagement and reputation. Collectively, the model accounted for more than 60% of the variance in financial performance, confirming that socially responsible strategies are not symbolic but yield tangible economic value. In the Middle Eastern banking sector—characterized by regulatory turbulence, cultural expectations, and digital transformation—CSR initiatives such as financial inclusion programs, green financing, and Sharia-compliant services provide both legitimacy and resilience. These findings highlight the strategic importance of embedding CSR into banking practices, showing that socially responsible institutions not only secure reputational gains but also cultivate motivated employees, foster innovation, and achieve sustainable profitability. By situating CSR within the unique context of Middle Eastern banking, this study extends the literature on CSR—performance linkages in emerging markets and demonstrates how intangible capabilities can be mobilized to secure long-term financial sustainability. Full article

The transition toward human-centered innovation models, as reflected in Industry 5.0 frameworks, calls for the integration of sustainability and digital ethics into higher education. Despite the growing international discourse, little is known about how systematically these dimensions are embedded in curricula in Central and Eastern Europe. This study addresses this gap by analyzing the extent to which Polish higher education institutions (HEIs) incorporate elements of sustainable development and digital ethics into their educational programs. Drawing on survey data from 187 Polish HEIs, we employed Cramér’s V and chi-square tests to explore bivariate associations, multiple correspondence analysis (MCA) to examine patterns among categorical variables, and ordinal logistic regression to identify key predictors of curricular integration. The results reveal that institutions offering Industry 5.0-oriented specializations and maintaining regular cooperation with enterprises are significantly more likely to achieve full integration of sustainability and ethics, whereas many others remain at a stage of only partial adoption. These findings underscore the uneven progress of curricular reforms and highlight the importance of institutional capacity and external partnerships. This study contributes to theory by extending institutional and resource-based perspectives to curriculum innovation, and it contributes to practice by recommending targeted accreditation standards, cross-sector partnerships, and interdisciplinary modules (e.g., “Artificial Intelligence and Society,” “Sustainable Technology Futures”) as concrete mechanisms for embedding ethical and sustainable innovation competencies in higher education. Implications for policy, institutional practice, and future research are discussed. Full article

Background: The intersection of aging and disability is an important social issue. The rehabilitation system of older adults with disabilities is a complex social system including various social units. This study aims to investigate the impact of the “inclusion of rehabilitation services in basic medical insurance” (IRSMI) policy on the utilization of rehabilitation services and annual household healthcare expenditure among older adults with disabilities. Methods: Using the data of China Disabled Persons’ Condition Monitoring Survey (2009–2012), this study employed the difference-in-differences method to analyze the impact of IRSMI on rehabilitation services utilization and household healthcare expenditure, and further examined the differential effects of the policy on service utilization across subpopulations with different demographic characteristics, including gender, age, and disability severity. The Heckman two-stage model corrects for sample selection bias caused by the share of households with zero health expenditures. Event-study specification was applied to assess the validity of the parallel trends assumption in the DID framework. Baron & Kenny’s three-step method was used to explore the potential mediating mechanism. Results: (1) IRSMI significantly increased the likelihood of utilizing rehabilitation services among older adults with disabilities (OR = 1.349), but this kind of promotive effect mainly focus on males (OR = 1.530), middle-aged and older disabled individuals (OR = 1.423), and those with mild disabilities (OR = 1.444). (2) The implementation of IRSMI contributed to an approximately 20.3% increase in annual healthcare expenditures for households with older adults with disabilities ($\beta$ = 0.185). (3) IRSMI significantly promoted the increase in household healthcare expenditures for high-income older adults with disabilities ($\beta$ = 0.181), but had limited impact on low- and middle-income groups. (4) Rehabilitation services utilization played a mediating role in the relationship between IRSMI and household healthcare expenditure, with about 19.0% of the increase in annual household healthcare expenditures attributable to the enhanced utilization of rehabilitation services. Conclusions: In the complex social system of rehabilitation for older adults with disabilities, the IRSMI policy significantly increases the likelihood of rehabilitation services utilization and substantially raises annual household healthcare expenditures. However, the heterogeneous effects across gender, age, disability severity, and income levels reflect structural inequities embedded in the rehabilitation system, underscoring the need for adaptive and equity-oriented interventions. Full article

Education is experiencing a paradigm shift, evolving from traditional learning methods to computer-tool-based education, and now toward the integration of Generative Artificial Intelligence. While classical methods offer structured and standardized learning, they often do not fully address individual learner needs and accessibility. The rise of digital technologies introduced adaptive learning platforms, online classrooms, and interactive educational tools, expanding the reach and flexibility of educational systems. Today, Generative Artificial Intelligence tools are redefining the education landscape by personalized learning experiences, automating content generation, and providing real-time feedback. Intelligent tutoring systems and personalized assessments empower students with customized learning pathways that enhance engagement and academic performance. This paper presents a meta-survey that systematically examines the role of Generative Artificial Intelligence in education, following PRISMA guidelines to analyze trends, frameworks, and research outcomes across a curated body of academic literature. Special attention is given to the emergence of commercial Generative Artificial Intelligence tools, which are increasingly embedded in learning environments. A structured comparison framework and research questions guide the review, offering insights into how Generative Artificial Intelligence technologies are shaping pedagogical practices, influencing assessment, and raising new ethical and technical challenges. The paper also explores future directions, highlighting how Generative Artificial Intelligence is driving the emergence of new learning models. Full article

Accurate monitoring of the coverage and distribution of photosynthetic (PV) and non-photosynthetic vegetation (NPV) in the grasslands of semi-arid regions is crucial for understanding the environment and addressing climate change. However, the extraction of PV and NPV information from Unmanned Aerial Vehicle (UAV) remote sensing imagery is often hindered by challenges such as low extraction accuracy and blurred boundaries. To overcome these limitations, this study proposed an improved semantic segmentation model, designated SegFormer-CPED. The model was developed based on the SegFormer architecture, incorporating several synergistic optimizations. Specifically, a Convolutional Block Attention Module (CBAM) was integrated into the encoder to enhance early-stage feature perception, while a Polarized Self-Attention (PSA) module was embedded to strengthen contextual understanding and mitigate semantic loss. An Edge Contour Extraction Module (ECEM) was introduced to refine boundary details. Concurrently, the Dice Loss function was employed to replace the Cross-Entropy Loss, thereby more effectively addressing the class imbalance issue and significantly improving both the segmentation accuracy and boundary clarity of PV and NPV. To support model development, a high-quality PV and NPV segmentation dataset for Hengshan grassland was also constructed. Comprehensive experimental results demonstrated that the proposed SegFormer-CPED model achieved state-of-the-art performance, with a mIoU of 93.26% and an F1-score of 96.44%. It significantly outperformed classic architectures and surpassed all leading frameworks benchmarked here. Its high-fidelity maps can bridge field surveys and satellite remote sensing. Ablation studies verified the effectiveness of each improved module and its synergistic interplay. Moreover, this study successfully utilized SegFormer-CPED to perform fine-grained monitoring of the spatiotemporal dynamics of PV and NPV in the Hengshan grassland, confirming that the model-estimated f_PV and f_NPV were highly correlated with ground survey data. The proposed SegFormer-CPED model provides a robust and effective solution for the precise, semi-automated extraction of PV and NPV from high-resolution UAV imagery. Full article

Artificial intelligence (AI) is rapidly redefining both computer science and cybersecurity by enabling more intelligent, scalable, and privacy-conscious systems. While most prior surveys treat these fields in isolation, this paper provides a unified review of 256 peer-reviewed publications to bridge that gap. We examine how emerging AI paradigms, such as explainable AI (XAI), AI-augmented software development, and federated learning, are shaping technological progress across both domains. In computer science, AI is increasingly embedded throughout the software development lifecycle to boost productivity, improve testing reliability, and automate decision making. In cybersecurity, AI drives advances in real-time threat detection and adaptive defense. Our synthesis highlights powerful cross-cutting findings, including shared challenges such as algorithmic bias, interpretability gaps, and high computational costs, as well as empirical evidence that AI-enabled defenses can reduce successful breaches by up to 30%. Explainability is identified as a cornerstone for trust and bias mitigation, while privacy-preserving techniques, including federated learning and local differential privacy, emerge as essential safeguards in decentralized environments such as the Internet of Things (IoT) and healthcare. Despite transformative progress, we emphasize persistent limitations in fairness, adversarial robustness, and the sustainability of large-scale model training. By integrating perspectives from two traditionally siloed disciplines, this review delivers a unified framework that not only maps current advances and limitations but also provides a foundation for building more resilient, ethical, and trustworthy AI systems. Full article