Search Results (9,894)

Artificial intelligence (AI) may influence economic performance through productivity, innovation, and the diffusion of advanced technologies, but direct country-level measures of AI adoption remain limited for small economies. This study examines the association between AI-related technological capability and economic growth in Cyprus. The descriptive analysis covers 1993–2024, while the econometric analysis uses the common annual sample for 2008–2024 after introducing one-period lags. High-technology exports as a percentage of manufactured exports are treated as an indirect indicator of technological sophistication and absorptive capacity, rather than as a direct measure of AI adoption. A sequential distributed lag ordinary least squares framework introduces current and lagged high-technology exports, merchandise trade, and inflation, with heteroskedasticity-consistent HC3 standard errors. A COVID-19 indicator for 2020–2021 and an observation-exclusion sensitivity analysis are used to assess the influence of exceptional pandemic-era movements. The preferred specification explains a substantial share of annual GDP growth variation, but the small sample requires cautious interpretation. The estimates indicate a positive contemporaneous and a negative lagged association for high-technology exports, a stronger lagged than contemporaneous trade association, and opposite-signed current and lagged inflation coefficients. The COVID-19 indicator is statistically insignificant and does not materially alter the principal coefficient pattern. Complementary DESI, Eurostat enterprise AI use, and ICT employment indicators show that Cyprus has broadly adequate digital inputs while enterprise AI adoption has not yet reached the European Union average, indicating considerable scope for further diffusion. The findings are exploratory statistical associations and do not identify causal effects of AI. The study contributes a country-specific macroeconomic assessment of technological capability, digital readiness, and growth in a small, highly open economy. Full article

This study investigates the relationships among military expenditure, economic growth, energy consumption, and carbon dioxide (CO₂) emissions using panel data for eight Central and Eastern European countries over the period 2000–2023. The analysis employs the Pooled Mean Group Autoregressive Distributed Lag (PMG-ARDL) estimator to examine both the short-run and long-run dynamics. To ensure the robustness of the findings, the CS-ARDL, Common Correlated Effects Mean Group (CCEMG), and panel FMOLS estimators are additionally applied. The results of the Pedroni, Kao, and Johansen–Fisher panel cointegration tests provide robust evidence of cointegration, confirming the existence of a stable long-run equilibrium relationship among the variables. The long-run estimates indicate that military expenditure and economic growth contribute to lower CO₂ emissions, whereas higher energy consumption increases environmental degradation. In the short run, military expenditure has no statistically significant effect on CO₂ emissions, suggesting that its environmental implications emerge only over a longer time horizon. These findings imply that strategically directed investments in the defense sector, particularly those promoting environmentally friendly technologies, energy efficiency, and technological innovation, can support the alignment of national security objectives with environmental sustainability. The results provide important policy implications for Central and Eastern European countries, highlighting that coordinated policies promoting cleaner energy use, sustainable economic growth, and environmentally conscious defense modernisation can contribute to long-term environmental and economic sustainability. Full article

Smart cities have emerged as a strategic approach to enhancing urban sustainability through the integration of digital technologies, intelligent infrastructure, and data-driven governance. However, limited research has comprehensively examined how technological, institutional, governance, and contextual factors interact to influence sustainability outcomes in industrial-city settings, particularly in Saudi Arabia. This study develops an integrated conceptual framework for sustainable smart industrial cities through a qualitative research design based on a systematic screening and synthesis of the literature, complemented by a contextual analysis of the Dammam Metropolitan Area (DMA) as an illustrative urban-industrial case under Saudi Vision 2030. A total of 86 articles, reports, and website materials were identified and screened, of which 49 sources met the inclusion criteria and were synthesized to develop the proposed framework. The analysis examined five interrelated components: smart-city applications, institutional governance, implementation conditions, contextual barriers and opportunities, and sustainability outcomes across three dimensions: environmental, social, and economic. The findings suggest that the effectiveness of smart-city applications depends not only on technological innovation but also on institutional governance, digital readiness, stakeholder participation, integrated planning, and supportive regulatory environments. The study further identifies fragmented governance, limited institutional coordination, cybersecurity risks, and high infrastructure costs as key implementation barriers, while highlighting opportunities associated with Saudi Vision 2030, digital-transformation initiatives, and circular-economy principles. The principal contribution of the study is the development of an integrated conceptual framework that explains the interactions among these five components and provides a theoretical foundation for future empirical validation, as well as conceptually informed guidance for policy development and planning in Saudi Arabia and comparable industrial-city contexts. Full article

Global angel investment and venture capital are key financial drivers supporting the long-term growth of technology companies, and they play a vital role in improving the global science and technology innovation financial system and advancing green and sustainable transformation. This paper uses data on technology-sector companies listed on the A-share market from 2017 to 2025 to construct a multi-period DID model. It empirically examines the impact of angel investment and venture capital on the sustainable development of technology companies and investigates the moderating effect of ESG performance. The study finds that angel investment can significantly enhance the level of sustainable development in technology firms. Mechanism tests indicate that angel investment indirectly empowers sustainable development by attracting and introducing venture capital. The moderating effect shows that strong ESG performance positively reinforces the promotional role of angel investment and venture capital in the sustainable development of technology firms. Heterogeneity analysis reveals that these enhancement and moderating effects are more pronounced in high-tech industries, private enterprises, and asset-light technology firms. These findings provide empirical evidence and policy guidance for governments worldwide to direct venture capital toward supporting science and technology enterprises, help technology firms improve their ESG governance systems, and achieve long-term sustainable operations. Full article

The building envelope is a key lever for reducing energy demand and carbon emissions in the built environment. However, conventional envelope systems remain largely static and are unable to respond effectively to changing climatic conditions. Biomimetics has emerged as a promising approach for the development of climate-adaptive envelope solutions. Nevertheless, research in this field remains fragmented across disciplines, and its evolution and technological maturity have not yet been systematically assessed. This study proposes an integrated analytical framework combining a bibliometric analysis of 2.007 Scopus-indexed documents, based on a VOSviewer keyword co-occurrence network, with a cluster-guided state of the art review, and a Technology Readiness Level (TRL) assessment of selected biomimetic envelope solutions. The TRL assessment is conducted using explicit operational criteria. The analysis identifies three main research clusters: (C1) environmental-performative, focusing on energy efficiency and envelope optimisation; (C2) material-experimental, addressing biomimetic composites and innovative materials; and (C3) technological fabrication, centred on digital fabrication, smart materials, and 4D printing. Temporal trends reveal a shift after 2018 from materials science-oriented studies towards computational design and adaptive manufacturing, providing quantitative evidence of a transition previously described mainly in qualitative terms. The review highlights a strong focus on solar-shading applications, while energy harvesting and passive thermoregulation remain comparatively underexplored. The TRL assessment shows that more than 80% of the analysed solutions are concentrated at TRL 3, indicating an early stage of technological development. The main barriers include limited material durability, non-standardised production costs, and regulatory constraints. The findings suggest that future progress will depend less on the identification of new biological inspirations and more on advancing the technological maturity and industrial scalability of existing concepts. This will require integrated developments in materials, parametric design, life-cycle assessment, and regulatory frameworks. Full article

Construction projects are increasingly complex, fragmented, and data-intensive, making cross-organizational collaboration routine while leaving teams “information-rich but knowledge-poor”; that is, large volumes of drawings, BIM models, meeting records, site reports, and sensor data are produced, but they are not always transformed into reusable, context-sensitive knowledge for future decisions. To consolidate dispersed evidence and capture the shift from digital to intelligent knowledge sharing (KS), this study conducts a PRISMA-guided systematic literature review of KS in construction project management from 2015 to 2025. Through quantitative and qualitative analyses of 163 core articles, this paper identifies five central themes: organizational and behavioral mechanisms, performance–innovation–sustainable value, digital technology empowerment, knowledge mechanisms and organizational learning, and risk–supply chain–governance and security. The study also synthesizes key challenges in knowledge sharing and outlines future research directions. These findings provide guidance for researchers, practitioners, and policymakers seeking efficient, credible, and sustainable systems to enhance project management effectiveness. Full article

Mechanized harvesting of subsurface crops has long been confronted with the critical engineering dilemmas of high damage rates and high impurity rates. Traditional taxonomic classification methods based on botanical families and genera fail to provide effective guidance for the engineering research and development of harvesting machinery. From an engineering perspective, this paper proposes a novel classification logic that categorizes subsurface crops into three major types based on their soil burial depth and physical distribution characteristics: shallow-soil clustered growth type (0–20 cm), mid-soil scattered growth type (20–40 cm), and deep-soil vertically rooted type (>40 cm). The harvesting bottlenecks of representative crops within these strata, including potato, onion, peanut, sweet potato, cassava, and yam, are systematically elucidated. Furthermore, this review provides an in-depth analysis of the current state of frontier core technologies, such as bionic drag reduction excavation, flexible multi-stage separation, microscopic discrete element method (DEM) simulation, kinematic optimization, and AI-based visual perception. This paper aims to reveal the common bottlenecks in subsurface crop harvesting and prospect future developmental trends centered on the deep integration of machinery and agronomy as well as intelligent perception and adaptation, thereby providing a solid theoretical foundation and engineering reference for the innovation of global agricultural machinery. Full article

The digital transformation of “Specialized, Refined, Distinctive, and Innovative” (SRDI) firms is a vital indicator of high-quality development within the digital economy and has attracted considerable attention. This study empirically examines the effect of digital transformation on enterprise value, employing big data text recognition techniques on panel data from Chinese A-share-listed SRDI firms from 2009 to 2023 to measure the intensity of corporate digital transformation. The empirical results reveal a significantly positive correlation between digital transformation and firm value for SRDI firms, a conclusion that remains robust after endogeneity and sensitivity analyses. Mechanism tests further indicate that digital transformation is significantly and positively associated with four dimensions of technological innovation, all of which are closely linked to firm value: innovation input, innovation density, innovation capability, and innovation performance. Additionally, we recognize supply chain concentration, the extent of informatization, and government subsidies as critical moderators that enhance the association between digital transformation and firm value. This study offers detailed empirical evidence about the transformation and innovation behaviors of SRDI firms, clarifying the underlying mechanisms and contingent elements that influence value creation in the digital age. Full article

In the era of digital transformation and artificial intelligence, strengthening STEM education is indispensable to technological innovation and social progress. However, the scarcity of effective strategies and empirical evidence for the early identification of STEM-gifted learners constitutes a critical research gap. This study employed a systematic review approach to synthesize existing identification strategies for K-12 STEM-talent learners. Following the PRISMA framework, 26 peer-reviewed empirical and review articles were retrieved and analyzed from nine databases. Findings revealed that the majority of studies originated in the United States, peaked in 2018, and predominantly comprised empirical designs targeting high school students. Identification methods demonstrated an observed shift from holistic to more individualized approaches, encompassing intelligence and spatial ability testing, academic achievement measures, creativity and problem-solving assessments, portfolio evaluations, subject-specific competitions, and teacher or parent recommendations. Cognitive reasoning, creativity, spatial ability, and problem-solving emerged as core indicators, whereas socioeconomic status, cultural background, teacher and parental support, and gender expectations significantly influenced identification processes and talent development. Future research should empirically validate existing identification framework, account for cross-cultural variability, and examine the longitudinal efficacy of emerging artificial intelligence applications. This review synthesizes existing scholarship on K-12 STEM talent identification and delivers evidence-based recommendations to refine identification and assessment frameworks across diverse educational contexts. Full article

This study addresses the complexity and high cost associated with traditional temporary formwork in post-cast strips. An innovative use of fish-scale mesh as a permanent interface material is proposed. This material serves as both construction framework and a connector during the service phase. The study investigates the enhancement mechanism of fish-scale mesh on the interfacial bond between new and old concrete. A multi-parameter experimental framework combined with full-field strain measurement was employed. Three specimen types with varying hole heights and numbers were designed and fabricated. Macro-mechanical properties were evaluated through double-shear and splitting-tensile tests. The interfacial strain field was quantified with high precision and visual clarity using Digital Image-Correlation (DIC) technology. Results show that introducing fish-scale mesh alters the interface failure mode. In mesh-free specimens, failure occurs through brittle interfacial delamination. With fish-scale mesh, failure transitions to ductile rupture within the concrete body. The number of holes has the greatest effect on shear strength, followed by hole height. Among all configurations, the fish-scale mesh with a hole height of 6 mm and 30 holes demonstrated the best performance. The shear strength and splitting strength increase by about 32% and 13%, respectively, compared to mesh-free specimens. Based on experimental results and theoretical derivation, a shear-bearing capacity formula for the fish-scale mesh interface is proposed. This model innovatively incorporates the shear key area of the fish-scale mesh, along with weakening and disturbance coefficients derived from experimental data. The theoretical model was validated against experimental results, showing a good agreement and supporting the use of fish-scale mesh as a permanent interface material. Full article

Regional digital–real integration (RDRI) refers to the deep integration of digital technologies into the real economy, through which digital technologies reshape industrial production, organizational processes, and value creation. Amid global industrial restructuring and increasing economic uncertainty, RDRI has become a key driver of high-quality economic development in China. Using A-share listed manufacturing firms, this study constructs a multidimensional index of enterprise resilience (ER) covering resistance, recovery, development, and innovation. It then applies the entropy weight method and the coupling coordination model to examine the impact of RDRI at the city level on ER. The results show that RDRI significantly improves ER, and the conclusion remains robust after endogeneity checks and robustness tests. Moreover, the results show that the positive effects of RDRI are stronger for firms in eastern China, capital-intensive firms, State-Owned Enterprises (SOEs), and high-tech firms. RDRI also enhances ER by optimizing resource allocation, improving firm productivity, and easing financing constraints. The paper provides theoretical insight and empirical evidence for deepening RDRI and strengthening ER. Full article

Green and resilient agriculture can strike a balance between agricultural production and ecological conservation, ensuring a continuous and stable supply of food and agricultural products. Based on provincial panel data from 31 provinces in China during 2014–2023, this study constructs an evaluation system for agricultural green resilience and employs the fixed-effects model, mediation effect model, and spatial Durbin model to empirically examine the impact mechanism and spatial effects of digital inclusive finance on agricultural green resilience. The main findings are as follows: (1) Digital inclusive finance significantly enhances agricultural green resilience. (2) Agricultural technological innovation plays a mediating role in the relationship between digital inclusive finance and agricultural green resilience. (3) Digital inclusive finance generates significant positive spatial spillover effects on agricultural green resilience, not only improving local agricultural green resilience but also promoting coordinated development in neighboring regions. (4) Regional heterogeneity analysis indicates that the promoting effect is strongest in the central region, followed by the western region, while no significant effect is observed in the eastern region. From the perspective of different dimensions, the effect intensity follows the order of coverage breadth, depth of use, and degree of digitalization. These findings suggest that digital inclusive finance serves as an important driver of agricultural green resilience and sustainable agricultural transformation in China. Therefore, policymakers should further expand digital financial inclusion, strengthen support for agricultural technological innovation, and promote coordinated regional development to enhance agricultural green resilience. Full article

Against the backdrop of the dual convergence of the digital economy and sustainable development strategies, digital innovation has emerged as a pivotal driver for reshaping firms’ competitive advantages and fulfilling social responsibilities. In this study, the analysis draws on a sample of Chinese listed firms from 2014 to 2023. ESG performance is measured with Hua Zheng ESG scores, digital innovation is captured via digital patent identification, and a dual-mediation framework is employed to examine internal organizational and external supply chain mechanisms, along with heterogeneity analysis. The findings indicate that digital innovation exerts a significant positive effect on ESG performance and reveal a unique dual mediating mechanism: within the internal boundary of the firm, digital innovation releases available organizational slack significantly through internal resource orchestration, and this abundant slack provides a necessary resource buffer for ESG investment; within the external network of the firm, digital innovation reduces supplier concentration significantly, thereby enhancing bargaining and supply chain discourse power, which in turn improves corporate ESG performance. Heterogeneity analysis further demonstrates that the promoting effect of digital innovation on ESG performance is more pronounced in subsamples characterized by CEO duality, low technological intensity, and high levels of marketization. By drawing on the resource-based view and resource dependence theory respectively to explain internal and external mediating pathways, this study advances understanding of the complementary mechanisms through which digital innovation enhances ESG performance. It demonstrates how digital innovation comprehensively reshapes firm capabilities, providing valuable insights for formulating strategic ESG initiatives in the digital era. Full article

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether managers’ perceptions of logistics-oriented AI utilization are associated with perceived firm performance through innovation capability and logistics efficiency, with managerial support treated as a secondary boundary condition. Cross-sectional survey data from 254 middle- and senior-level managers in Chinese logistics firms were analyzed using IBM SPSS Statistics 27 and IBM SPSS Amos 29 (IBM Corp., Armonk, NY, USA), and the PROCESS macro version 4.2 (Andrew F. Hayes, Calgary, AB, Canada), with Model 83 and 5000 bootstrap samples. Perceived AI utilization was positively associated with innovation capability, logistics efficiency, and perceived firm performance. Both mediators showed significant indirect effects, and their sequential indirect effect was also significant. The two individual indirect effects did not differ significantly, but both exceeded the sequential indirect effect. The proposed sequential, reverse-sequence, and parallel-mediation models produced identical fit indices, whereas the restricted direct-effects model showed weaker fit. Neither the AI utilization–managerial support interaction nor the moderated mediation indices was significant. Exploratory item-level analyses showed differentiated associations for demand forecasting and order allocation and for AI infrastructure; the pattern remained stable among 194 respondents involved in AI- or digital transformation-related activities. Innovation capability and logistics efficiency appear to function as complementary mechanisms, with a smaller capability-to-process pathway. Their relative ordering cannot be determined from the cross-sectional data. As the data are self-reported, the findings represent associations among managerial perceptions rather than objective causal effects. Sustainability implications are limited to operational efficiency because environmental outcomes were not directly measured. Full article

Electric vehicle manufacturing firms face increasing supply chain vulnerability due to component shortages, technological interdependence, raw material volatility, and demand uncertainty. This study aims to examine whether digital transformation can be translated into a resilience-building capability and to identify the transmission channels. Using panel data from Chinese A-share listed electric vehicle manufacturing firms from 2014 to 2023, we employ a double machine learning framework to estimate the relationship between digital transformation and supply chain resilience while accounting for high-dimensional firm-level controls. The results show that digital transformation is positively associated with supply chain resilience. This finding remains robust across alternative sample-splitting ratios, different machine learning algorithms, alternative winsorization thresholds, and sample restrictions. It also holds after addressing potential endogeneity using instrumental variable estimation. Mechanism tests indicate that digital transformation contributes to resilience by promoting technological innovation and reducing managerial transaction costs. Heterogeneity analysis further shows that the effect is more pronounced among firms with stronger market positions, larger firms, and vehicle manufacturers. These findings suggest that digital transformation is not merely a tool for operational upgrading but also an important organizational capability for strengthening supply chain resilience in electric vehicle manufacturing. Full article