Search Results (57)

As a new engine of economic development, the digital economy is profoundly reshaping the governance structure and production models of modern agriculture and has increasingly become a significant force driving the high-quality development of agricultural export trade. Based on provincial panel data from 30 Chinese provinces spanning from 2014 to 2023, this paper employs a two-way fixed effects model to systematically examine the statistical association between the digital economy and agricultural export competitiveness. Furthermore, a mediation effects model is adopted to explore the underlying transmission pathways, and a threshold effects model is applied to reveal the nonlinear characteristics of this association, thereby providing a multidimensional analysis of the underlying mechanisms. The empirical results indicate that: (1) there is a significant positive statistical association between the digital economy and agricultural export competitiveness, and this finding remains robust across a series of robustness checks; (2) upgrading of agricultural industrial structure, agricultural technological innovation, and agricultural labor transfer serve as three important transmission pathways through which the digital economy is associated with enhanced export competitiveness; (3) this positive association exhibits regional heterogeneity, with stronger effects observed in central regions and major grain-producing areas; and (4) there is a nonlinear relationship between the digital economy and agricultural export competitiveness. Finally, based on these empirical findings, several policy implications are proposed, including strengthening digital infrastructure construction, enhancing trade openness, cultivating agricultural digital talents, optimizing factor allocation structures, and improving the digital economy governance system, so as to fully unlock the potential space of the positive association between the digital economy and agricultural export competitiveness. Full article

The creation of innovative learning environments in courses to provide sustained talent support has long remained a central research concern for high-quality social development. Neglected foundational skills in professional course clusters are often a hidden barrier to higher education for sustainable development (HESD). To close five persistent foundational skill gaps across improper citation (J1), ineffective figure use (J2), poor analysis (J3), irresponsible AI use (J4), and comprehensive application (J5) within the hydraulic engineering course cluster, a four-stage deliberate practice module (5Di-40Pr-5Tr-3Cm) has been embedded into a two-week hydraulic model experiment course, and its learning outcomes are systematically evaluated. A systematic analysis of its achievement levels across neglected foundational skill indicators of J1~J5 at each stage was conducted, stratified by the overall cohort and subgroups (P: objective demand, T: behavior type, G: optimization methods). The key findings include: ① deliberate practice demonstrates better teaching outcomes than lecture-based instruction, which can be evidenced in 2026, when J5’s achievement levels at the 3Cm stage yielded a moderate effect size relative to the 2025 lecture-based condition (d = 0.42); compared with the 2024 no-intervention baseline, the cumulative effect is a obvious increasing trend (d = 1.43); ② In far-transfer subgroup diagnosis, P2 (medium objective demand) shows a rank-order reversal, low at 40Pr but higher at 3Cm, and is identified as the “partial understanding” group and providing a diagnostic anchor for tiered intervention; ③ In near-transfer pathway diagnosis, J5’s low performance in 5Tr (65.35%, below overall mean of 83.09%; CV = 7%) stems from two distinct pathways: a “knowledge-deficit pathway” (max-decay subgroups) and a “processing-load pathway” (subgroups where J1, J2 do not exhibit max decay). In addition, stage-specific thresholds (40Pr: 90%, range 60~99%; 5Tr and 3Cm: 83% ± 3%, range of for 40Pr, mean = 90%, recommended range = 60~99%; for 5Tr, mean = 83% ± 3%, range = 65~96% and 75~90%) provide quantitative benchmarks for targeted intervention. These cumulative findings are intended to advance the evaluation paradigm of engineering practice courses from “total score attainment” toward “structural diagnosis” and align with the competency-oriented philosophy of higher education for sustainable development (HESD). Full article

AI-enabled simulation platforms are increasingly used for talent development, offering scalable, low-risk practice and algorithmic feedback for interpersonal HR capabilities such as difficult conversations, negotiation, and consultation. Yet HRD evaluation cautions that within-platform improvement may not transfer to authentic interactions once dashboards and metrics disappear. We conceptualize AI-enabled simulation training as metricized scaffolding and integrate scaffolding theory with exploration–exploitation learning dynamics to explain when algorithmic supports fade into self-regulation rather than produce metric adaptation (score chasing). We introduce Algorithmic Feedback Literacy (AFL)—learners’ capability to interpret algorithmic feedback, calibrate its authority, and translate cues into portable self-scaffolds—as a mechanism linking metricized practice to transfer. We test this model in an AI-enabled virtual internship and career development program that combines repeated simulation attempts with an authentic consultation assessment. Using platform traces (592 attempts nested within 90 learners), instructor-rated consultation performance as a transfer outcome, and 102 learner reflections, we examine how distributed practice span and practice dose related to simulation gains and transfer, and how consultation complexity changes, which interactional behaviors predicted success. Distributed practice span predicted transfer beyond within-platform gains, while practice dose operated primarily through measurable simulation improvement. Complexity shifted performance relevance toward governance and boundary-setting behaviors. Qualitative evidence identified two developmental pathways: productive internalization, where learners converted metric cues into routines and mode-switching strategies, and metric adaptation, where learners optimized scores without internalizing broader professional judgment. We discuss design implications for AI-enabled HRD and retention-oriented capability building: cultivate AFL through transparency and calibration supports, design for deliberate fading, and pair metricized refinement with authentic assessments to strengthen transfer. Full article

The cultivation of future civil engineering talents cannot be separated from experimental and practical teaching. The course “Structural Experiment: Theory and Practice” is designed to cultivate students’ good practical abilities. This study addresses the shortcomings in the course, where the traditional model—marked by a lack of preparatory training, an overreliance on verification experiments, and outdated equipment—led to a theory–practice disconnect and low student engagement. To overcome these issues, a teaching reform was implemented focusing on three initiatives: enhancing hands-on training, adding exploratory experiments, and opening research laboratories. Corresponding measures included revising the syllabus, upgrading equipment, and establishing an open-lab system. Evaluation of the outcomes indicates that these measures effectively improved teaching. By integrating practical, diverse, and open research experiences, the reformed framework better promotes students’ skill transfer and innovative capacity. This demonstrates that a shift from a verification-focused to a student-centered, competency-oriented model is essential for achieving an organic integration of theory and practice. Full article

The association between digital economy (DE) and manufacturing transformation (MT) is conditioned by regional structural characteristics, yet little is known about how this association varies within provinces that are peripheral at the national scale. This study examines Yunnan Province, China, as a dual-peripheral context, where regions are simultaneously distant from national economic cores and internally structured along a pronounced core–intermediate–periphery gradient. Using prefecture-level panel data from 16 cities over 2011–2023, the analysis shows that the positive association between DE and MT is spatially attenuated along this gradient. Furthermore, three key regional factors—transportation infrastructure, industrial agglomeration, and technological talent—correspond to distinct spatial conditioning patterns. Transportation infrastructure exhibits an extensible but spatially bounded pattern, industrial agglomeration is most strongly associated with intermediate prefectures, and technological talent displays a highly concentrated pattern within the provincial core. These differentiated patterns indicate that internally differentiated peripheral structures are associated with different forms of spatial conditioning in the observed DE–MT association, rather than producing a uniform spatial pattern. Based on these findings, region-specific strategies targeting connectivity, industrial coordination, and talent development are recommended to support inclusive and context-sensitive manufacturing transformation. The study provides an analytically transferable perspective by highlighting how different regional conditions may correspond to different spatial reaches of digital–manufacturing transformation within peripheral systems. Full article

Academic satisfaction is positioned as a fundamental psychological construct with regard to academic success, linked to well-being and cognitive engagement. The aim of the study was to evaluate the satisfaction of secondary school students with high intellectual abilities (HIA) participating in the university mentoring program in the Basque Country Autonomous Community. A quantitative methodology was used with a sample of 90 students. A purpose-developed 17-item questionnaire with 17 items used to measure the different areas that make up the multidimensionality of satisfaction. The results indicate very high levels of overall satisfaction, with particular emphasis on the quality of the bond and the mentor’s expertise. Likewise, through confirmatory factor analysis (CFA), the validity of the instrument was tested under the ‘Big Four’ model (improvement-oriented learning, individualization, feedback and practical tasks with minimal transfer). It is concluded that the mentoring program used constitutes a suitable educational response that harmonizes intellectual challenge with the psychosocial support necessary to transform potential into talent. Full article

Considering rising global uncertainties and intensifying resource and environmental pressures, it has become an inevitable trend to add more ecologically green factors to the traditional industrial chain resilience system and build a system of green industrial chain resilience (GICR). To address the inherent tension between security and green goals, this study develops a novel two-dimensional analytical framework encompassing fracture repair capacity and development regeneration capacity. This framework provides the theoretical foundation for constructing a pioneering city-level evaluation system for GICR. Employing this system and a suite of spatial econometric methods, we empirically analyze the spatiotemporal evolution, convergence, and driving mechanisms of GICR across 245 Chinese cities. The main findings are threefold. First, the proposed framework effectively captures the complexity of GICR, revealing an overall upward trend but significantly widening regional disparities, with a persistent core-periphery spatial pattern. Second, convergence analysis uncovers a club convergence dynamic nationwide, characterized by a notable “high-level equilibrium lock-in” in the advanced eastern region, in contrast to the catch-up convergence observed in central, western, and northeastern China. Third, geographical detector analysis identifies talent agglomeration as the paramount driver, with its interaction with other factors producing nonlinear enhancement effects. These findings underscore that enhancing GICR requires regionally differentiated strategies: policies must break the innovation lock-in in the east, embed resilience standards into industrial transfer in the central and western regions, and prioritize talent as the core lever for synergistic capacity building. Full article

Against the backdrop of deep integration of technological innovation and industrial innovation, this study constructs a theoretical model incorporating market and government behavior. Utilizing panel data from 284 prefecture-level cities across China from 2013 to 2023, it empirically analyzes the roles and mechanisms of efficient market and proactive government in facilitating the deep integration. Findings indicate that the market’s “push–pull mechanism” promotes the deep integration of technological and industrial innovation, enhancing output performance. However, market mechanism exhibits diminishing marginal output performance: beyond a certain threshold, the force to drive further performance improvements weakens. The government’s role can positively influence the market mechanism’s ability to drive deep integration of technological innovation and industrial innovation, and growth in output performance. In coordinating government and market efforts, the following principles should be observed: as market mechanism matures, gradually enhance technological innovation by allocating human capital to research activities, particularly basic research; formulate industrial policies that progressively prioritize science and technology innovation activities; and advance the development of public goods, such as technology transfer and commercialization platforms. To advance the deep integration of technological and industrial innovation, policy implications include consistently leveraging market mechanisms, coordinating government and market efforts to design policies for capital and talent mobility, and promoting the construction of conversion platforms like concept-validation centers and science incubators. Full article

Under the dual carbon strategy and the sweeping tide of digital transformation in education, higher education confronts an urgent imperative: cultivating talent equipped with interdisciplinary skills and sustainable decision-making capabilities. To meet this critical challenge, this study pioneers the PAMD (Patient Capital–Accounting–Matrix–Development) interdisciplinary teaching framework. Rooted firmly in Education for Sustainable Development (ESD) principles, PAMD uniquely weaves together patient capital, carbon asset accounting, and linear algebra matrix modeling. Utilizing a quasi-experimental design with undergraduate business students, we implemented “Carbon Asset Accounting and Low-Carbon Transition Investment Analysis” as a case study. We rigorously evaluated teaching effectiveness across academic performance, competency, and cognitive attitude dimensions using Welch’s t-test, Hedges’ g, and ANCOVA. After controlling for baseline scores, the experimental group significantly surpassed the control group in comprehensive decision-making (81.22 vs. 72.41, g = 0.71) and matrix modeling competency (3.74 vs. 3.22, g = 0.77). The experimental cohort also demonstrated consistent gains in carbon accounting reporting precision and data representation clarity. Cognitive assessments revealed moderate effect sizes for both low-carbon investment literacy and interdisciplinary learning interest. These compelling results demonstrate that embedding a long-term value orientation into accounting representation and matrix modeling powerfully cultivates students’ ability to transfer interdisciplinary knowledge and make sound sustainable decisions within complex contexts. This study offers a robust, evidence-based, and replicable pathway for driving sustainability-oriented interdisciplinary reform within business education. Full article

Although smart-city benchmarking has produced many indices and rankings, cities still lack a practical way to assess whether successful initiatives can be transferred across institutional contexts and converted into implementable urban roadmaps. In this study, we aimed to develop and empirically test a paired donor–recipient “problem–solution” methodology that bridges comparative city analysis with implementation readiness gap assessment, addressing the persistent disconnect between smart-city benchmarking and actionable transfer guidance. The smart-city ecosystem was decomposed into eight functional dimensions covering digital foundations, service platforms, finance and procurement, innovation capacity, governance, legal adaptability, and citizen participation. The method was applied to the Ottawa-Astana pair using a systematic desk-based analysis of publicly available strategic documents, legislation and policy frameworks, and implementation materials (e.g., roadmaps, program guidelines, departmental plans, and monitoring outputs). Data were analyzed using a structured gap analysis algorithm employing a three-level qualitative compliance scale (Full Compliance, Partial Compliance, and Non-compliance) to assess recipient city status against donor benchmarks across all eight functional dimensions. The results reveal Astana’s partial compliance with the Ottawa benchmark, with moderate readiness and pronounced “hard-soft” asymmetry; that is, greater progress in regard to infrastructure and platforms, but persistent gaps in adaptive regulation, experimentation-friendly legal instruments, and participatory governance. These findings suggest that progressing toward a Smart City 2.0 model requires prioritizing regulatory sandboxes, adaptive procurement pathways for pilots, and scalable civic-tech mechanisms alongside continued investment in talent and innovation ecosystems—understood here as interconnected networks of universities, technology parks, civic-tech communities, and incubation infrastructure that collectively sustain capacity for technology absorption and local adaptation. The proposed paired framework is replicable and supports phased, actionable transfer roadmaps for policymakers. Full article

In response to problems inherent in conventional hydraulic engineering education including compartmentalized courses, fragmented knowledge delivery, overlapping and omitted content, and insufficient development of students’ integrated practical competencies this study develops an instructional model for a coordinated curriculum group based on the OBE-BOPPPS teaching theory. The curriculum cluster model aims to integrate interdisciplinary course content, restructure curriculum structure hierarchy, eliminate disciplinary barriers, and establish clear stratified and interrelated knowledge relationships. The model centers on competency development, constructing a three-dimensional “agent–objective” system that connects “teacher–student–curriculum” with “knowledge–competency–literacy.” It further establishes a multi-indicator evaluation system encompassing teachers, students, and courses. The comprehensive evaluation employing Principal Component Analysis, Entropy Weight Method, and CRITIC method demonstrates that the curriculum group teaching model significantly outperforms traditional course-based instruction in transcending disciplinary boundaries, enhancing knowledge systematicity, improving teaching precision, and strengthening knowledge acquisition as well as students’ comprehensive competencies. This approach achieves dynamic optimization and precision feedback in the teaching process, effectively facilitating the systematic transfer of knowledge and the holistic development of students’ innovative practical abilities. It thereby provides a scientific pathway and empirical support for the reform of hydraulic engineering education and the cultivation of high-quality talent. Full article

This study investigates how strategic foresight can enhance FinTech governance and policy resilience in emerging economies, using Kuwait as an illustrative case. It aims to identify which foresight interventions should be prioritized across alternative futures to strengthen innovation, security, and institutional adaptability within the digital finance ecosystem. A scenario-based Multi-Criteria Decision Analysis (MCDA) framework is applied, combining the Analytic Hierarchy Process (AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). Expert evaluations were conducted to assess five foresight interventions against eight policy and performance criteria across three plausible scenarios: Optimistic Growth, Status Quo, and Crisis and Contraction. Sensitivity analyses were performed to validate the stability of intervention rankings. The results reveal distinct priorities under each scenario: SME-oriented digital finance platforms and talent development dominate under growth and stability, while cybersecurity investment becomes paramount during crisis conditions. Regulatory fast-tracking maintains a consistent, moderate influence across all contexts. These outcomes underscore the need for adaptive, context-sensitive policy design that accommodates uncertainty. The framework provides policymakers with a structured approach to align FinTech strategies with long-term national visions such as Kuwait’s Vision 2035, while offering transferable insights for other emerging economies. The study’s originality lies in integrating strategic foresight and MCDA for FinTech governance—a methodological and practical contribution to foresight-informed policymaking. Full article

In today’s volatile business environment, securing a sustainable competitive advantage hinges on retaining and effectively managing talent. While talent turnover is inevitable, strategic internal human resource (HR) transfers offer a solution to prevent talent outflow and supplement skill gaps. However, previous models for identifying internal substitutes often focus solely on individual work capabilities, neglecting the critical role of group interactions and collaborative structure. Drawing on social network theory, transactive memory systems, and person–group fit, this study proposes a graph-based analytical approach that models the organization as a complex system. Our methodology provides a holistic framework that integrates both (1) individual capabilities and (2) group-level characteristics (e.g., work-relationship networks and cluster-level similarity) to identify the most suitable substitutes. At the macroscopic level, we use an inductive graph neural network (GraphSAGE) to learn node embeddings from a work relationship network constructed from process event logs and to quantify group-level similarity. At the microscopic level, we compute dynamic collaboration intensity, frequency, and task similarity between employees over time. To validate the approach, we develop four simulation scenarios using an enriched incident management process event log and implement them in a SimPy-based simulator, benchmarking against an existing method that considers only individual factors. Across all scenarios, the proposed dual-factor model significantly outperforms the baseline in terms of efficiency, accuracy, and suitability. This research provides a practical, validated algorithm that supports evidence-based workforce management and more effective internal talent allocation. Full article

This scoping review examines how workplace language proficiency and corporate language policies function as dimensions of human capital, shaping employee mobility and organisational outcomes in multilingual contexts. Drawing on 12 empirical studies (2010–2025), supplemented by one influential review work used for context, the review integrates two analytical lenses: (1) language ceilings and walls, which capture invisible barriers to vertical and horizontal mobility, and (2) the Language Needs Analysis (LANA) framework, which categorises language demands at the individual, organisational, and operational levels. Findings indicate that language proficiency and inclusive language policies act as strategic resources that enhance employability, cross-border collaboration, and knowledge transfer. Conversely, rigid monolingual policies often reproduce inequalities and limit career progression. The review highlights the role of language-sensitive HRM in developing sustainable talent pipelines, advancing diversity and inclusion, and strengthening workforce resilience. Methodologically, this study applies PRISMA-ScR guidelines to ensure transparency and rigour, while offering a framework for future research at the intersection of human capital theory, language policy, and global HRM. By reframing communicative competence as career capital, the review underscores the need to integrate language training and policy design into broader human capital development strategies. Full article

Food legumes play a pivotal role in China’s food security, nutritional health, and green development strategies due to their unique advantages. This paper presents an empirical study on the economic evaluation of scientific research on pest and disease control for food legumes. Using panel data from 31 Chinese provinces from 2008 to 2023, we employ a Double Machine Learning (DML) approach to identify the impact of investment in plant protection research on food legume outputs. The results indicate a steady increase in China’s investment in this field, with an average annual growth rate of 5.19% from 2008 to 2023, and the total investment in 2023 was 2.14 times that of 2008. Investment in plant protection research effectively mitigates output losses and leads to significant production increases. Specifically, a 1% increase in research investment corresponds to a 0.2% increase in food legume output. This effect remains robust across various algorithms, time windows, and control variable settings. Based on these findings, we recommend: (1) increasing financial support and talent acquisition for research on food legume pests and diseases to enhance the stability and sustainability of research investment; (2) strengthening cooperation mechanisms between research institutions and enterprises to leverage their respective strengths and promote the commercialization of research outcomes and regional variety extension; (3) establishing a diversified research investment system that explores a co-construction model guided by the government, involving enterprises, and utilizing public–private partnerships to reconcile the conflict between long research cycles and market demands; (4) fostering a dual-track linkage between regional technological innovation and enterprise product commercialization to improve the efficiency of technology transfer and application; and (5) strengthening R&D in cutting-edge fields like Artificial Intelligence to improve the efficiency and precision of pest and disease control. Full article