Search Results (416)

This study examines the determinants of talent retention in the hotel sector of Extremadura, a peripheral European region facing depopulation, labour scarcity and structural limitations that threaten the sustainability of its human capital base. Grounded in the Job Demands–Resources (JD-R) theory, the research analyses how a set of key labour resources, specifically professional training, organisational trust, job satisfaction and sustainability commitment, influence employees’ intention to remain in their organisations. These resources are conceptualised as organisational and motivational mechanisms that enhance employees’ capacity to cope with job demands and reinforce their attachment to the organisation. A quantitative survey was conducted with hotel-sector employees in Extremadura; 255 questionnaires were validated, and the proposed structural model was tested using SEM. The findings show that organisational trust is the strongest predictor of retention, followed by professional training and sustainability commitment, while job satisfaction also exerts a significant, though more moderate, effect. These results indicate that enhancing fairness perceptions, strengthening continuous training pathways and integrating sustainability-oriented values are essential strategies for retaining qualified personnel in territories with limited external opportunities. Rather than measuring human capital sustainability directly, the study shows that talent retention operates as a central empirical mechanism through which the sustainability of human capital can be supported in peripheral tourism economies. It concludes by highlighting the need for managerial practices that support transparent leadership, structured professional development and participatory sustainability initiatives, and encourages future research to incorporate longitudinal designs and direct measures of human capital sustainability. Full article

Background: Evidence on how physical and technical factors distinguish U-18 basketball levels is limited, yet these determinants may aid talent identification and development. This study examined differences in anthropometric, physical performance, and technical characteristics between high-level (HL; n = 38) and low-level (LL; n = 35) U-18 male basketball players and explored relationships between technical skills and key physical attributes across all participants. Methods: Participants were evaluated across anthropometry, physical performance, and basketball-specific technical skills. Statistical analyses assessed between-group differences and correlations, with significance set at p ≤ 0.05. Results: Compared to LL players, HL players exhibited significantly superior physical attributes, including greater height (Cohen’s d = 0.67) and arm-span (d = 0.65), reduced body fat (d = −0.58), and advanced performance metrics (10 m-speed running (d = −0.78), 20 m-speed running (d = −0.93), flexibility (d = 1.26), counter-movement jump height (d = 1.27), intermittent endurance (d = 1.18)). Technical proficiency in tasks such as 10 m- and 20 m-speed dribbling, maneuver dribbling and defensive sliding was also significantly faster in the HL group (d = −0.96, d = −1.05, d = −1.87, and d = −1.14, respectively). Several anthropometric and performance variables were strongly correlated with technical skills, indicating their relevance for distinguishing competitive levels. Conclusions: These findings underscore the interplay of physical, technical, and performance factors in high-level youth basketball. Coaches may use this information to guide targeted training strategies that support talent identification, player development, and competitive success. Full article

Amid the expansive evolution of the digital economy and the emergence of enhanced productivity paradigms, exploring the ways in which digital technology affordance propels corporate digital innovation via multifaceted cooperative routes is essential for reconfiguring industrial ecosystems, securing digital market advantages, and promoting superior advancement. This investigation employs the TOE model, merging fuzzy-set qualitative comparative analysis (fsQCA) with regression analysis. Using data from 2206 listed manufacturing companies from the A-share exchanges (2010–2023), it identifies multiple antecedent configuration pathways of digital technology affordance and examines their differential impacts on enterprise digital innovation. Key findings include the following: (1) no solitary factor serves as an obligatory prerequisite for high-quality digital technology affordance. (2) Four configuration pathways were identified: technology-organization-environment tripartite-propelled, technology-organization collaborative-propelled, technology-environment collaborative-propelled, and organization-environment collaborative-propelled variants. (3) The influence of digital technology affordance on digital innovation shows conditional dependence. Under the ternary-driven “technology-organization-environment” or synergy-driven “technology-organization” configurations, and absent conflicting enterprise goals, digital technology affordance promotes digital product innovation. Supported by collaborative configurations of technological investment, digital infrastructure, highly educated talent, institutional measures, and public service efficiency, it fosters digital process innovation. However, isolated technological investment, employees’ educational attainment, and institutional measures inhibit business model innovation. Other configurations lack significant impacts on digital business model innovation. This study elucidates the generation mechanism of digital technology affordance using configuration theory, offering empirical insights for managers to enhance digital innovation and drive high-quality economic development. The study enhances the theoretical depth by exploring technological foundations of digital technologies and addressing generalizability through framework adaptations for global contexts. Full article

Based on micro-level job seeker data from 41 cities in China’s Yangtze River Delta, this study employs threshold regression to examine how inter-city distance influences talent mobility. The results reveal that distance exerts a negative impact on mobility intention and moderates the relationship between a destination’s economic level and mobility. Notably, significant threshold effects are identified at 164.1 km and 271.5 km, delineating three spatial regimes. Short-distance flows (<164.1 km) show the highest intensity, driven by strong economic incentives and high mobility. In contrast, medium-distance flows (164.1–271.5 km) prove least attractive due to offsetting effects, while long-distance flows (>271.5 km) rebound slightly as talent selectively targets major economic hubs, with distance exhibiting only weak inhibition. Crucially, these nonlinear patterns remain robust after addressing endogeneity concerns via the 2SLS method, substituting spatial distance with temporal distance, and controlling for housing prices and cultural factors. Heterogeneity analysis further indicates that individuals with bachelor’s degrees, those above age 30, and talent in labor-intensive industries exhibit greater sensitivity to distance. Conversely, knowledge-intensive sectors and top-tier economic cities demonstrate broader spatial tolerance and stronger cross-regional attraction capabilities. These findings provide a quantitative basis for developing differentiated regional talent policies. Full article

This study investigated the influence of biological maturity status on anthropometric, body composition, and physical performance characteristics in elite youth male basketball players. A total of 140 players (15.12 ± 0.78 years) competing in national elite programs were categorized as early, on-time, or late maturers according to years from peak height velocity (PHV). Each participant completed a standardized testing battery including anthropometric assessments, body composition analysis (InBody 720), countermovement jump (CMJ) with and without arm swing, drop jump from 40 cm (DJ40), linear sprints over 5–20 m, and agility tests (t-test and Lane Agility). Between-group differences were analyzed using one-way ANOVA and Bonferroni post hoc tests, while partial eta squared (ηp²) and magnitude-based inference (MBI) were applied to assess effect size and practical significance. Significant differences were observed across maturity groups (p < 0.05), with early maturers being taller, heavier, and more muscular than their on-time and late-maturing peers. Large effects were found for height (ηp² = 0.667) and body mass (ηp² = 0.455), and moderate-to-large effects for jump, sprint, and agility performance (ηp² = 0.051–0.166). MBI results indicated that most differences between early and late maturers were “very likely” or “almost certain,” highlighting their practical relevance. These findings confirm that biological maturity substantially affects physical and performance profiles in adolescent basketball players and underscore the importance of maturity-informed approaches such as bio-banding and individualized training to ensure fair evaluation and equitable talent development in youth sport. Full article

The pursuit of low-carbon economic development represents an inherent requirement for implementing the Sustainable Development Goals (SDGs) and serves as a vital support for advancing SDG 7, SDG 9, and SDG 13. Drawing on provincial data from China (2006–2023), this research investigates how digital-real convergence influences low-carbon economic development. The results demonstrate a positive contribution of this convergence to growth in the low-carbon economy, and it proves to be superior to models reliant solely on either digital-digital or real-real convergence. A notable finding is the considerable regional variation in the effect. It is strong in both eastern and western parts of the country, which stands in sharp contrast to central China, where the effect is statistically insignificant or negative. Identified as underlying mechanisms are the agglomeration of innovative talent and the accumulation of innovative capital. Additionally, a single-threshold effect of urbanization level is identified, indicating that the positive impact strengthens only after urbanization surpasses a critical value. Furthermore, digital-real convergence not only enhances local low-carbon development but also generates positive spillover effects on neighboring regions. Thus, to fully advance the SDGs, policy formulation and implementation must account for regional heterogeneity, prioritize the elevation of urbanization levels, enhance cross-regional collaboration, and amplify the enabling role of digital-real integration. Full article

Artificial intelligence (AI) has significantly influenced higher education, accelerating the arrival of College 4.0. Given its core mission of cultivating talent through teaching, understanding how AI can empower teaching in higher education is crucial. Utilizing second-hand survey data from the Zhejiang Provincial Department of Education, this study empirically diagnoses the status of AI-empowered teaching in higher education across 81 universities, 4085 faculty members, and 24,095 students, by descriptive statistical analysis. The results reveal critical structural misalignments. At the institutional level, while 94% of universities have formulated AI plans, a severe disciplinary imbalance exists, with science and engineering accounting for 60.1% of specialized courses compared to only 4.5% in agriculture and medicine. At the faculty level, a “high cognition, low practice” gap is evident; although willingness is high, 96% of instructors lack significant industry practice experience. At the student level, a substantial misalignment appears between the demand for AI skills and educational supply. Based on these findings, we propose targeted strategies for optimizing resource allocation and establishing cross-boundary teacher training systems to promote AI-empowered teaching to achieve sustainable higher education. Full article

This study explores how the unique characteristics of Generation Y/Millennials and Generation Z influence talent retention challenges in the hospitality sector and identifies Human Resource Management (HRM) strategies that effectively address these generational dynamics. The multigenerational workforce in this industry spans multiple cohorts, each with distinct work values and expectations, and these generational differences in work attitudes have compelled Human Resource (HR) departments to adopt a range of tailored recruitment and retention strategies. This qualitative research examines how hotel directors and HR managers in the city of Porto, Portugal perceive the recruitment challenges associated with younger generations and what strategies they use to overcome them. The findings highlight specific traits of Millennial and Generation Z employees, confirming that these cohorts present unique recruitment and retention challenges, and validate targeted response strategies to address those challenges. Overall, the study sheds light on sector-specific hurdles, multigenerational management issues, and effective HRM practices for attracting and retaining young employees (Generation Y/Millennials and Generation Z). Full article

The influence of the digital economy on green technological innovation is essential for the attainment of Sustainable Development Goals (SDGs). Based on panel data from 30 Chinese provinces between 2011 and 2023, this study establishes a dual fixed-effects model to investigate how the digital economy affects green technological innovation, considering both quantity and quality. It innovatively explores the roles of high-tech industry agglomeration, high-tech talent agglomeration, and their synergistic agglomeration. This study reveals the following: (1) The digital economy has a significant promotional effect on both the quantity and quality of green technological innovation, and this finding has been consistently verified through an array of robustness tests. (2) Mechanism results show that high-tech industry agglomeration, high-tech talent agglomeration, and their synergistic agglomeration all have a “multiplier effect”, but the impact intensity of synergistic agglomeration is less than that of single agglomeration. (3) Further exploration of the threshold effect of synergistic agglomeration shows that, concerning the quantity of green technological innovation, a higher level of synergistic agglomeration corresponds to a stronger promotional effect. In terms of quality, the promotional effect reaches its peak after the degree of synergistic agglomeration crosses the first threshold and weakens after crossing the second threshold. (4) Heterogeneity analysis reveals that the positive impacts of the digital economy on green innovation are more pronounced in Eastern and Central China than in its western regions. Moreover, a lower environmental regulation intensity favors innovation quantity, while a higher intensity promotes quality. Additionally, the facilitative effect is the strongest in regions where greater attention is given by the government to green development. This study offers practical insights for sustainable global development, particularly in the context of developing nations. Full article

E-commerce live streaming is increasingly constrained by the “anchor dilemma” of talent shortages and reputational volatility. Virtual anchors are viewed as a critical nexus for intelligent and sustainable e-commerce transformation, offering scalable and low-carbon potential. Yet, their user experience and perception remain underexplored. Methodologically, this study adopts a mixed empirical design combining literature review, expert interviews, and a structured questionnaire survey (N = 309), followed by reliability testing, paired-sample t-tests, and Importance–Performance Analysis (IPA) to assess user expectations and perceptions. The integrated analysis resulted in a framework of fourteen evaluative attributes, within which spectacle and cross-platformity emerged as distinguishable dimensions observed in participants’ assessments. The results show that expectations (M = 4.41) significantly exceed perceptions (M = 3.74), with all 14 importance–performance gaps reaching significance. Interactivity, professionalism, and technological maturity emerged as priority areas for improvement, while spectacle and novelty were confirmed as key advantages, and credibility and emotional bonding outperformed expectations. Based on these findings, a phased strategy is proposed: short-term optimization of interaction and knowledge support, mid-term development of human–AI collaboration and platform adaptability, and long-term establishment of governance and commercialization ecosystems. The study enriches virtual anchor research and highlights that enhancing core competencies is essential to transform novelty into enduring sales and brand equity, providing a practical pathway for e-commerce’s intelligent and sustainable transformation. Full article

This study examines whether an integrated human-talent system—incorporating recruitment/selection, development, appraisal/feedback, recognition, supportive leadership, and role utilization—is associated with job performance in a public technical organization. Using a quantitative, non-experimental, cross-sectional design, we surveyed 101 employees and constructed composite Likert indices for talent management and job performance. Reliability was acceptable (α = 0.850; α = 0.814). Kolmogorov–Smirnov tests indicated non-normal distributions (p < 0.001); thus, non-parametric procedures were used. Spearman’s correlation showed a moderate, positive association between overall talent management and job performance (ρ = 0.523, p < 0.001), with the “incorporate” process (competency-aligned recruitment/selection) displaying the strongest process-level link (ρ = 0.569, p < 0.001). Segment profiles (contract type, tenure, functional area, and gender) suggest unequal exposure to talent-supportive conditions within the workforce, which may help explain distributional differences in perceived performance. We discuss managerial implications for reducing intra-organizational disparities by ensuring fair access to development, feedback, and recognition systems. While the study does not directly measure well-being, the findings align with theoretical accounts that connect equitable access to talent resources with healthier work environments and better performance. Full article

Transformations in HEIs (Higher Education Institutions) in recent years have positioned education alongside research, development, and innovation, creating the necessary framework for achieving a positive impact on society and economies. A Principal Factor Analysis was employed using 19 variables from eight Eastern European countries over a three-year period (2022–2024). The six main factors are noted with F1 (innovation and collaboration in R&D), F2 (performance and investment in academic research), F3 (advanced technological production and talent influx), F4 (evolution over time/systemic progress), F5 (cluster development), and F6 (investment in education). These explain over 83% of the total variance, ensuring a robust representation of the original data. The results of the analysis show, in some countries, strengths in specific areas (e.g., EE in innovation, CZ in academic research, and SK in high-tech manufacturing). Meanwhile, a general trend of decreasing scores at the systemic progress level can be observed in most nations, suggesting a slowdown in the overall development momentum. At the same time, significant volatility was observed in cluster development (F5) and investment in education (F6) across the sample. These findings provide a condensed, multidimensional framework for comparative analysis and policy formulation, highlighting specific strengths and vulnerabilities in the regional innovation landscape. Full article

Entrepreneurial defection, which leads to the loss of core talents and key resources, poses a severe threat to the innovation capacity and sustainable development of entrepreneurial enterprises. However, the mechanism through which it influences ambidextrous innovation—a key driver of sustainability comprising exploitative (short-term optimization) and exploratory (long-term breakthrough) activities—remains empirically unclear. Grounded in the Conservation of Resources Theory, this study analyzes 553 questionnaires from entrepreneurial enterprises (≤8 years old) to investigate the impact of entrepreneurial defection on ambidextrous innovation, focusing on the chain mediating roles of entrepreneurial learning and organizational change. The findings reveal that (1) Entrepreneurial defection does not directly promote ambidextrous innovation; its positive effect is fully mediated by entrepreneurial learning, organizational change, and their sequential path. (2) Significant heterogeneity exists in this mediating effect: the path via entrepreneurial learning is stronger for exploitative innovation, whereas the path via organizational change exerts a weak negative interference on exploratory innovation, though the positive effect of learning remains dominant. (3) Entrepreneurial learning is the core mediator, accounting for over 70% of the total indirect effect, and facilitates a “learning-change-innovation-sustainability” cycle. This study fills a critical empirical gap and extends the application of Conservation of Resources Theory. It offers practical insights for entrepreneurs: by strengthening learning and guiding organizational change, the pressure of resource loss can be transformed into a driver for dual innovation, enabling a transition from crisis recovery to sustainable development capability upgrading. Full article

To address the issue of “studying agriculture but not engaging in agricultural work” in Chinese agricultural universities, this study developed a practical teaching quality framework based on the CIPP model. The framework includes four dimensions: goal identity, resource support, process experience, and ability and emotional gain. A questionnaire survey was conducted among 433 students majoring in agricultural economics and management. The study combined structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) to explore how practical teaching quality influences behavioral intention to serve “agriculture, rural areas, and farmers”. The findings are as follows: (1) All four dimensions significantly positively influence behavioral intention, but the pathways differ. Process experience has the strongest direct effect (β = 0.51). Resource support (β = 0.125) and ability-emotional gain (β = 0.155) are partially mediated by student satisfaction, while goal identity is completely indirectly driven through satisfaction. (2) Three configurations for high behavioral intention are identified: the all-dimensional driven type (coverage 52.8%), the emotion-experience driven type (coverage 7.5%), and the experience-ability compensation type (coverage 12.5%), with an overall consistency of 93.46%; (3) The study confirms the applicability of the CIPP model and customer satisfaction theory in agricultural education. It reveals a multiple transmission mechanism of “quality–satisfaction–behavioral intention”. The study provides a theoretical basis and practical paradigm for constructing a precision practical teaching system and strengthening talent support for rural revitalization. Full article

The widespread application of Generative Artificial Intelligence (Gen-AI) is transforming educational practices and driving pedagogical innovation. While cultivating higher-order thinking (HOT) represents a central educational goal, its achievement remains an ongoing challenge. Current evidence regarding the impact of Gen-AI on HOT is relatively fragmented, lacking systematic integration, particularly in the analysis of moderating variables. To address this gap, a meta-analysis approach was employed, integrating data from 29 experimental and quasi-experimental studies to quantitatively assess the overall impact of Gen-AI on learners’ HOT and to examine potential moderating factors. The analysis revealed that Gen-AI exerts a moderate positive effect on HOT, with the most significant improvement observed in problem-solving abilities, followed by critical thinking, while its effect on creativity is relatively limited. Moderation analyses further indicated that the impact of Gen-AI is significantly influenced by experimental duration and learners’ self-regulated learning (SRL) abilities: effects were strongest when interventions lasted 8–16 weeks, and learners with higher SRL capacities benefited more substantially. Based on the research findings, this study proposed that Gen-AI should be systematically integrated as a targeted instructional tool to foster HOT. Medium- to long-term interventions (8–16 weeks) are recommended to enhance learners’ problem-solving and critical thinking abilities. At the same time, effective approaches should also be explored to promote creative thinking through Gen-AI within existing pedagogical frameworks. Furthermore, individual learner differences should be accounted for by adopting dynamic and personalized scaffolding strategies to foster SRL, thereby maximizing the educational potential of Gen-AI in cultivating innovative talents. Full article