Search Results (11,215)

The global energy sector is undergoing rapid and, in many respects, irreversible transformation driven by the convergence of digital disruption, sustainability mandates, and shifting investor expectations. Technologies such as artificial intelligence (AI), blockchain, and digital twin systems are fundamentally reshaping energy operations and strategic decision-making, while ESG governance quality and renewable energy adoption have emerged as two of the most consequential determinants of corporate financial competitiveness and equity valuation. Despite growing practitioner and regulatory interest in these dynamics, limited empirical evidence exists on how ESG governance, renewable adoption, and digital disruption jointly influence financial performance and environmental outcomes across multiple sectors simultaneously. This study addresses that gap using panel data from 26 large-cap US-listed firms across five sectors over 2015–2022 (N = 208 firm-year observations for Revenue/Market Cap/ROA models; N = 91 for the CO₂ model). A multi-method econometric framework is employed, comprising Fixed Effects and Random Effects panel regression with Hausman specification testing, Difference in Differences quasi-experimental analysis, and sequential OLS path analysis with HC3 robust standard errors. Three of four hypotheses are supported. ESG governance quality generates a significant market capitalisation premium of approximately 10–14% per unit Bloomberg ESG Score improvement, after controlling for firm size and R&D intensity; no significant revenue channel effect is found once firm size is properly accounted for. Renewable energy adoption shows a marginal association with market capitalisation at the 10% significance level (FE β = 0.019, p = 0.086; RE β = 0.016, p = 0.077), suggesting capital markets may price clean energy adoption as a forward-looking signal. ESG governance quality drives within-firm CO₂ emission reduction substantially more powerfully than renewable energy quantity alone, with the Fixed Effects estimator identifying a governance-led eco-efficiency mechanism. Firm profitability functions as a cross-model financial capacity moderator, enabling simultaneous ESG investment and environmental improvement. The findings carry direct implications for corporate managers, institutional investors, and policymakers aligned with SDG 7, SDG 9, and SDG 13. Full article

The emergence of artificial intelligence (AI) in education has not only challenged conventional learning environments but has also encouraged the adoption of innovative and active pedagogical approaches, such as flipped learning, particularly when these technologies are effectively integrated. In this context, the present study examined students’ perceptions of the effectiveness of AI-supported flipped learning in fostering cognitive, affective, and 21st-century competencies among English Education students at Sultan Qaboos University (SQU). A convergent mixed-methods design was used. Quantitative data were obtained using a five-point Likert-scale questionnaire administered to 60 undergraduate English Education students, while qualitative data were collected from students’ reflective journals. The findings revealed that students generally perceived AI-supported flipped learning as positively supporting cognitive outcomes, affective outcomes, and twenty-first-century competencies. Motivation and engagement emerged as the strongest affective outcomes, while students also reported positive perceptions of cognitive development, collaboration, digital competence, and self-regulated learning. Correlation analysis further demonstrated significant positive relationships among all six constructs, with the strongest associations observed between motivation and digital competence and between cognitive outcomes and both motivation and collaboration. Qualitative findings emphasized increased engagement, deeper learning experiences, and positive attitudes toward flipped learning. Despite minor challenges related to workload and time management, overall student perceptions were highly positive. The study concluded that students perceived AI-supported flipped learning as an effective pedagogical approach for fostering academic achievement and essential 21st-century skills in higher education. Full article

The modern agricultural sector faces a multidimensional crisis, mainly consisting of an aging workforce, labor shortages, exhausting working conditions, and high rates of work-related accidents. In response, the authors carried out a systematic literature review (SLR), to explore the latest research on smart agricultural technologies, their effects on occupational safety, ergonomics, and worker health, and pinpoint obstacles to sustainable adoption. A thorough search was performed solely in the Scopus database, covering peer-reviewed publications from 2020 to 2026, strictly following the PRISMA 2020 guidelines. Based solely on Scopus, this study provides a focused synthesis, with the results suggesting that hazards such as chemical exposure and musculoskeletal strain are significantly reduced with the use of innovations such as unmanned vehicles, exoskeletons, and collaborative robots. These technologies also show great promise in cutting down resource waste, helping farmers practice sustainable agriculture. However, a recurring gap between research and real-life deployment exists, as adoption is hindered by cost considerations, reliability issues, and ergonomic problems. To achieve a sustainable technological transition in agriculture, it is necessary to simultaneously bridge three critical gaps: technological (ensuring robust field performance), ergonomic (design and testing processes based on real end-users and their needs), and socio-economic (addressing adoption barriers). Full article

This article investigates how secondary teachers in two Australian independent schools engaged with generative AI (GenAI) tools to support teaching and learning. Using a qualitative case study methodology and the Technology Acceptance Model (TAM) as a sensitising framework, the study examines teachers’ perceptions of usefulness, ease of use, and the challenges they encountered. Over three months, teachers participated in professional learning groups supported by university-led workshops and peer collaboration. The findings show that teachers’ adoption of GenAI was not simply determined by tool availability or perceived efficiency. Rather, teachers’ perceptions of usefulness and ease of use developed through cycles of experimentation, prompt refinement, peer sharing, and ethical reflection. The study contributes a contextually grounded interpretation of TAM by showing how collaborative professional learning shaped teachers’ movement from initial experimentation towards more purposeful pedagogical integration. The findings suggest the value of considering how perceptions of usefulness and ease of use may develop over time through sustained engagement with GenAI. Full article

Background/Objectives: Adherence to the Mediterranean diet has declined in recent decades, highlighting the need for practical, technology-enabled tools to support its adoption. This study developed and evaluated MAI-DIET, a clinician-supervised, data-driven, AI-assisted programmatic framework designed to generate Greek–Mediterranean recipe-based meal plans for apparently healthy community-dwelling adults. Methods: MAI-DIET is combined with standardized food-composition and recipe libraries and a rule-based module that performs meal-plan generation and nutrient calculations. Claude Opus 4.7 supported predefined operator-supervised transformation tasks, and provided the interface for launching the rule-based module. Six 28-day recipe-based dietary plans were generated for hypothetical adults with energy goals ranging from 1600 to 2600 kcal/day. The generated plans were not tested in the intended population. For each plan, detailed nutritional analysis was performed according to predefined criteria. The quality of the dietary plans was assessed using validated scores, i.e., MedDietScore, dietary phytochemical index (DPI), and GR-UPFAST. Early-stage acceptability was evaluated by 103 healthcare professionals using a five-point Likert questionnaire. Results: All plans met the predefined energy criteria, while most nutrient targets were achieved. Deviations were observed for sodium, particularly in the higher-energy plans (reaching +36.3%), and for calcium, which was 17.5% below the EFSA population reference intake in the 1600 kcal/day plan. MedDietScore ranged between 35 and 36/55, while DPI was 47.0–50.9% and GR-UPFAST was 2.0–4.5/70. Overall acceptability was favorable (3.84 ± 0.59), with Cronbach’s alpha values of 0.838–0.952. Conclusions: The findings support the technical feasibility of the MAI-DIET framework and its preliminary acceptability among healthcare professionals. Further evaluation is required before conclusions can be drawn regarding its practical effectiveness. Full article

Generative artificial intelligence (GenAI) has been rapidly integrated into higher education, yet current studies largely rely on quantitative frameworks that capture initial adoption, leaving university EFL teachers’ motivational evolution for continued use of GenAI underexplored. To address this gap, this longitudinal qualitative study explores the motivational evolution and psychological need experience of 15 university EFL teachers’continued use of GenAI grounded in Self-Determination Theory (SDT). Data were collected through two rounds of semi-structured interviews over one year and analyzed in NVivo 11 PLUS. Our findings reveal a dynamic evolution of motivation from external regulation to value identification and professional identity integration. Crucially, need satisfaction and need frustration provide important psychological foundations for teachers’ motivation internalization. Notably, while need satisfaction directly facilitates university EFL teachers’ continued use of GenAI, need frustration can actually act as a potential catalyst that facilitates EFL teachers to form long-term continued use of GenAI through iterative reflection and instructional adjustment. This study extends the application of SDT within the GenAI education context, indicating that university EFL teachers’ continued use of GenAI is not merely a process of technology adoption, but also a process of professional growth and professional identity integration. The results provided preliminary implications for teacher professional development, GenAI design and education policy. Full article

Weeds create a significant challenge in agricultural production by competing with crops for essential resources such as nutrients, sunlight, and water. This competition leads to reduced crop yields and quality, resulting in substantial economic losses. Consequently, there is a critical need for effective weed control strategies to mitigate the impact of unwanted plant growth and ensure sustainable agricultural practices. In precision agriculture, enabling autonomous navigation between crop rows during tasks such as weeding and harvesting presents a significant research challenge, particularly when leveraging cost-effective technological solutions. Effective robot navigation requires adaptive traffic management strategies and robust object recognition capabilities to distinguish between cultivated and uncultivated areas. In fact, the integration of computer vision techniques into these systems is essential for optimizing trafficability in cropping fields and enhancing robots’ dynamics for better working efficiency in agricultural environments. This review addresses the challenge of enhancing inter-row navigation in field crops and delivering reliable guidance for autonomous agricultural robots. A PRISMA-based systematic review methodology was adopted to identify, screen, and analyze 38 relevant studies selected from the Scopus and Web of Science databases. The selected studies are classified according to their target platform (Unmanned Ground Vehicles and Unmanned Aerial Vehicles) and grouped into three methodological categories: conventional computer vision, deep learning architectures, and hybrid approaches. The findings provide practical guidance for selecting appropriate vision-based crop row detection technologies according to the application requirements and highlight key research directions toward more robust, cost-effective, and adaptable autonomous navigation systems. This article presents an outline of artificial-intelligence-based row detection methods used in agricultural fields and a classification of related semantic segmentation approaches. Unlike previous surveys, it provides an overview of the technological progress in agricultural robots and navigation based on systems vision for crop row detection, with a focus on comparisons balancing technical performance with economic and practical constraints. Full article

Digital technologies are increasingly adopted in corporate liquidity management, yet whether financial digitalization enables firms to achieve more effective working capital adjustment remains insufficiently understood. Financial shared services (FSS) may strengthen information integration, process standardization, and operational coordination, but existing research provides limited evidence on how external supply chain conditions shape the relationship between FSS and working capital adjustment effectiveness. Prior studies have focused primarily on adjustment speed rather than adjustment effectiveness, namely the extent to which firms maintain working capital close to target levels. Using panel data from Chinese A-share listed firms from 2014 to 2023, this study examines whether FSS is associated with the effect of working capital adjustment (DEV) and whether supply chain concentration moderates this relationship. Drawing on dynamic trade-off theory and information asymmetry theory, this study employs high-dimensional fixed-effects models to examine how internal information capabilities and external supply chain conditions jointly shape working capital adjustment. The findings show that firms adopting FSS tend to exhibit smaller deviations from target working capital levels, which is consistent with more effective adjustment. However, this association becomes weaker as supply chain concentration increases, suggesting that external dependence may constrain firms’ ability to translate enhanced internal information capabilities into improved working capital outcomes. Further analysis suggests that customer concentration plays a more prominent moderating role. This study extends the understanding of digital-enabled financial management beyond internal process improvement and identifies supply chain structure as an important boundary condition relevant to the value of FSS. Full article

The rapid growth of e-commerce has intensified last-mile delivery challenges in Thailand, where rising parcel volumes contribute to failed deliveries and increased carbon emissions. Smart parcel lockers offered a promising solution, though their adoption depends on consumer acceptance. However, existing research has not sufficiently examined how trialability, performance expectancy, and perceived risk operate alongside core TAM beliefs within an integrated framework, particularly in emerging markets. This study extends the Technology Acceptance Model (TAM) with constructs from Diffusion of Innovation (DOI) theory and the Unified Theory of Acceptance and Use of Technology (UTAUT) to examine the determinants of smart parcel locker adoption, incorporating trialability, performance expectancy, and perceived risk. A quantitative survey was conducted with 397 Thai consumers with prior online shopping and parcel delivery experience. Data were analyzed using covariance-based structural equation modeling (CB-SEM). The model explained 79.3%, 94.3%, and 82.7% of the variance in perceived ease of use, attitude, and intention. Trialability is the strongest predictor, working through perceived ease of use, while attitude and performance expectancy together drove intention. Contrary to traditional TAM, perceived usefulness did not significantly affect attitude, and perceived risk had no significant effect. These findings suggest that, for simple self-service delivery technologies, first-hand experience is more influential than emphasizing usefulness or safety concerns. This contributes to technology acceptance theory and offers practical guidance to increase smart parcel locker usage. Full article

Promoting the adoption of climate-smart agricultural technologies (CSATs) is essential for food security and achieving the Sustainable Development Goals (SDGs). However, adoption rates remain low. The objective of this study is to reveal the associations between financial instruments and farmers’ adoption of CSATs. Using survey data from 1219 farmers in climatic transition zone of China, the Probit model, and mediation model were used to empirically test the associations of credit and insurance on farmers’ adoption of CSATs. The results show that both credit and insurance are positively associated with CSATs adoption, with insurance exhibiting a stronger marginal effect than credit. Mediation analysis reveals that credit and insurance are positively associated with farmers’ adoption behavior through attending technical training, strengthening subjective norms, and improving risk attitudes. Heterogeneity analysis indicates that these associations vary significantly across different climatic sub-regions. The findings provide evidence-based policy insights for leveraging targeted financial instruments to accelerate CSATs adoption among smallholders in China and other countries. Full article

The transport sector contributes nearly 20% of New Zealand’s total greenhouse gas emissions, making it crucial for interventions to meet the 2050 net-zero target. Transitioning to electric vehicles (EVs) presents a sustainable solution but poses challenges in electricity distribution due to unpredictable EV charging behaviours. This research addresses these challenges by developing three mathematical models that optimise EV charging patterns, manage power flow along distribution lines and incorporate battery storage systems. Using the Tāmaki area as a case study, the models analyse total energy demand and optimal battery storage size, revealing that a 3.49 MWh battery system could mitigate the projected 2040 peak daily grid energy demand of 541.5 MWh and avoid costly power line upgrades. The study also introduces a vehicle-to-grid (V2G) integration model, showcasing its potential to reduce grid dependence and improve energy utilisation. The findings provide critical insights for Auckland’s electricity distribution companies, supporting strategic asset upgrades and offering evidence-based guidance for government policies on EV adoption. In summary, this research provides innovative solutions for optimising EV charging infrastructure, benefiting utility companies and policymakers by informing data-driven decisions. The comprehensive approach, which includes power flow, battery storage, and V2G technology, presents a scalable framework for international cities facing similar challenges, promoting global sustainable transport solutions towards achieving international climate targets and sustainable urban development. Full article

How does consumer engagement shape a firm’s technology adoption strategy? Based on the knowledge-based perspective, this paper explores the impact mechanism of consumer engagement on corporate technology for good using survey data from medium–high R&D intensity manufacturing firms within China’s industrial clusters. The research sample covers five industries with medium-to-high R&D intensity, categorized according to the OECD classification. The findings suggest that consumer engagement positively affects knowledge co-creation and corporate technology for good, and knowledge co-creation plays a mediating role in the relationship between consumer engagement and corporate technology for good. Further analysis reveals that knowledge absorption ability positively moderates the relationship between consumer engagement and knowledge co-creation, and the mediating effect of knowledge co-creation on the relationship between consumer engagement and corporate technology for good is positively moderated by knowledge absorption ability. The study expands the research on the driving factors of corporate technology for good from the stakeholder theory perspective, providing insights for firms to facilitate consumer value co-creation. Full article

Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector. Full article

Marine fisheries play a vital role in ensuring food supply and sustaining livelihoods in coastal regions of China. However, the expansion of aquaculture has led to increasing carbon emissions and mounting pressure on resources and the environment, making the improvement of ecological efficiency a critical issue for the sustainable development of the industry. From the perspective of carbon emissions as undesirable output, this paper employs the DEA-SBM model and the GML index to measure the ecological efficiency of marine fisheries across nine coastal provinces in China from 2006 to 2023. Furthermore, using fixed-effects models, mediation-effect models, and grouped regression models, this study empirically examines the impacts of fishermen’s income and environmental regulations on the ecological efficiency of marine fisheries and their transmission mechanisms. The results indicate that ecological efficiency exhibits fluctuating trends across provinces, with significant inter-provincial disparities. Fishermen’s income has a significant positive effect on ecological efficiency, while environmental regulations show a significant negative effect. Digitalization level significantly promotes ecological efficiency, whereas fishery disaster losses significantly inhibit it. Technological adoption intention plays a partial mediating role in the pathways through which both fishermen’s income and environmental regulations affect ecological efficiency. Significant regional heterogeneity is observed, with the eastern coastal region exhibiting the strongest effects of various factors and the northern coastal region showing the weakest. Accordingly, this paper proposes differentiated enhancement pathways for ecological efficiency from four dimensions, including technological innovation-driven development, industrial structure optimization, environmental policy regulation, and regional coordinated governance, with the aim of providing theoretical foundations and policy references for the low-carbon transformation and sustainable development of marine fisheries in China’s coastal areas. Full article

Nigerian secondary schools face significant challenges integrating fourth industrial revolution (4IR) technologies. Little is known about how contextual factors influence teachers’ familiarity with these technologies and their perceptions of student engagement, learning outcomes, and instructional efficiency. Using a convergent parallel mixed-methods design, this study examined those factors in public and private secondary schools across one Local Government Area within each of four Nigerian states (Imo, Rivers, Osun, and Kogi). The quantitative component surveyed 189 teachers (92 public, 97 private). The qualitative component interviewed nine teachers and conducted two focus groups with 12 teachers (six in each group). Quantitative findings showed that teachers reported moderate familiarity with most 4IR technologies (grand mean = 3.11) and positive perceptions of their influence on student engagement (grand mean = 3.64) and learning outcomes (grand mean = 3.80). Private school teachers reported significantly higher familiarity with six of seven technologies and more positive perceptions of engagement (p < 0.001) and learning outcomes (p < 0.001) than public school teachers, except for WhatsApp (p = 0.332). Qualitative thematic analysis revealed four themes explaining these differences: (1) structural and infrastructural inequities (electricity, internet, devices) shape technology engagement; (2) teachers sustain integration through personal financial sacrifice and informal peer support; (3) professional development is perceived as contextually disconnected; and (4) technological engagement reflects interactions between generational confidence, pedagogical identity, and school culture. The joint display of quantitative and qualitative findings shows convergence on the conclusion that infrastructure, not teacher attitude, is the main barrier to 4IR adoption. Policymakers should prioritise investment in electricity, internet, and devices over blaming teachers. Full article