Search Results (9,341)

The integration of Artificial Intelligence is transforming luxury hospitality, forcing hotels to balance advanced technology with human-centered management to sustain service excellence. Yet how Human-Centered Leadership shapes Sustained Service Performance through employees’ AI readiness and engagement remains largely unexamined. This study develops and tests an integrated framework addressing that gap. Survey data were collected from 500 employees of four- and five-star luxury hotels in the United Arab Emirates, Saudi Arabia, Qatar, and Lebanon, and analyzed using partial least squares structural equation modeling (PLS-SEM). Human-Centered Leadership was positively associated with both Sustained Service Performance and AI readiness. AI readiness predicted employee engagement, which in turn predicted Sustained Service Performance. AI readiness and employee engagement sequentially mediated the link between Human-Centered Leadership and Sustained Service Performance (β = 0.160, p < 0.001). Digital Organizational Support significantly strengthened the Human-Centered Leadership–AI readiness relationship (β = 0.137, p = 0.004). The study advances hospitality leadership research by establishing Human-Centered Leadership as a driver of Sustained Service Performance in AI-enabled settings, and by revealing the sequential psychological mechanism, AI readiness followed by engagement, through which it operates. The findings help luxury hotel managers combine Human-Centered Leadership with digital support to sustain service excellence during AI-driven transformation. Full article

Agricultural technological innovation is essential for sustainable agricultural modernization and rural development. However, agricultural technology enterprises often face persistent financing constraints because research and development (R&D) activities involve long investment cycles, high uncertainty, and delayed returns. Using a firm-level panel dataset of Chinese agricultural technology enterprises, this paper examines the effect of relational patient capital (RPC) on agricultural technological innovation by employing a two-way fixed-effects model. The results show that RPC significantly promotes agricultural innovation output. Mechanism analysis indicates that RPC enhances innovation through two channels. First, it facilitates firms’ digital transformation, thereby reducing R&D uncertainty and organizational costs. Second, it alleviates financing constraints by stabilizing cash flows to support R&D investment. The results remain robust after clustering standard errors, excluding the years affected by the COVID-19 pandemic, and employing lagged specifications. Heterogeneity analyses further reveal that the positive effect is more pronounced among small-scale enterprises and firms located in central and eastern China, where financing frictions and resource constraints are relatively more severe. By linking RPC to firm-level agricultural innovation, this study extends the literature on agricultural finance and innovation financing, highlighting the role of long-term, relationship-based capital in addressing market failures in agricultural R&D. The findings suggest that rural financial policies should encourage stable, long-term investment, strengthen financing support for small agricultural technology enterprises, and integrate patient capital with digital transformation initiatives to promote sustainable agricultural and rural development. Full article

Lifelong learning is widely recognised as a core capability for engineers working under rapid technological, professional, and societal change. This article reports a PRISMA 2020-guided systematic review and thematic synthesis of engineering education publications that explicitly position themselves in relation to lifelong learning. Searches of Web of Science, ERIC, IEEE Xplore, and the ACM Digital Library in May 2026 identified 341 records; after duplicate removal and staged screening, 68 publications were included. The synthesis was organised around four recurrent thematic layers within this explicit lifelong learning corpus: project-based and inquiry-based learning, soft skills and motivation, employability-oriented capabilities, and curriculum and co-curricular embedding. Across the corpus, the literature was stronger at naming lifelong learning and proposing educational responses than at demonstrating how development was sustained across time and context. A recurring gap separated definition, design, and evidence: conceptual breadth and pedagogical variety outpaced cumulative developmental evidence. The review contributes an explicit-corpus synthesis, a layered interpretive framework for reading these recurrent supports, and implications for curriculum design, educational practice, and future research. Full article

Digital infrastructure (DI) plays a dual role in the low-carbon transition. It supports economic operation but also consumes substantial energy. This study explores DI’s impact on carbon emission efficiency (CEE) using data on 41 cities in China’s Yangtze River Delta from 2011 to 2024. The methods used in this study include a two-way fixed effects model, mediation analysis, a panel threshold model, and a spatial Durbin model. The results show that the impact of DI on CEE is U-shaped. Industrial upgrading and technological innovation are the potential channels through which DI affects CEE. Energy efficiency has a single threshold value of 8.533. DI enhances CEE when energy efficiency exceeds this threshold. Spatial analysis indicates that both the direct and indirect effects of DI follow a U-shaped pattern. Heterogeneity analysis indicates that the environmental impact of DI varies depending on resource endowments, policy environments, and economic development levels. This study provides insights for global urban agglomerations to balance digital transformation and sustainable development. Full article

While integrating intelligent machines enhances workplace efficiency, it paradoxically correlates with increased employee turnover. To explain this efficiency–turnover paradox, this study draws primarily on conservation of resources theory to investigate how employees’ dependence on intelligent machines triggers turnover intention. Using data from a two-wave longitudinal survey with a two-month lag among 241 manufacturing employees in China, we tested a serial mediation model. Results indicate that dependence on intelligent machines does not directly predict turnover intention. Instead, it exerts an indirect effect through a resource-depleting serial mechanism. By transferring human–machine interaction patterns to interpersonal relations, machine dependence triggers workplace objectification, which in turn exacerbates workplace loneliness. Faced with this severe relational and emotional depletion, employees develop turnover intention as a self-protective strategy. By integrating technological, socio-cognitive, emotional and behavioral dimensions, the findings demonstrate that digital reliance undermines employee retention primarily through a cascade of interpersonal objectification and emotional loneliness, underscoring the necessity of techno-humanistic management practices. Full article

In contemporary Information Technology (IT) enterprises, workforce role configuration decisions sit at the intersection of leadership strategy, corporate Artificial Intelligence (AI) transformation and sustainable operational governance. This study investigates how heterogeneous workforce role configurations affect Service Level Agreement (SLA) compliance in SLA-aware Business Process Model and Notation (BPMN) IT business processes, framing this as a people management and leadership decision problem with explicit Environmental, Social and Governance (ESG)-oriented trade-offs. A validated MATLAB Simulink simulation tool is employed to conduct structured scenario testing across combinations of human role configurations and AI maturity levels, measuring their impact on key Service Level Objectives (SLOs) and Key Performance Indicators (KPIs). To support leadership decision making, a Workforce Sustainability Index (WSI) is introduced that integrates SLA compliance with workforce cost and AI dependency risk, where AI dependency risk captures the social dimension of ESG by emphasizing workforce skill development, upskilling and reskilling pathways, human oversight and responsible AI adoption. The simulation results reveal that no universally optimal configuration exists; the best-performing workforce design depends on organizational context and leadership priorities, as captured through scenario-based weight configurations representing performance-driven, cost-driven, human-centric and balanced governance orientations. These findings provide IT leaders and organizational decision-makers with actionable, evidence-based guidance for sustainable workforce design in the context of corporate AI transformation, contributing to the broader discourse on people management, merit-based organizational performance, responsible AI governance and sustainable digital operations management. Full article

Artificial intelligence (AI)-based digital twins (DTs) are emerging as a groundbreaking paradigm in rehabilitation and physiotherapy, enabling the creation of dynamic virtual representations of patients for continuous monitoring, prognostic assessment and personalised therapeutic interventions. This article presents a global, interdisciplinary review of AI-based DT technologies in rehabilitation settings utilising the Internet of Medical Things (IoMT), with particular emphasis on the integration of wearable and implantable sensor systems in next-generation wireless healthcare applications. The article analyses how multimodal wearable sensors, implantable devices and smart wireless communication networks can support the acquisition of real-time biomechanical and physiological data for adaptive rehabilitation. By combining perspectives from biomedical engineering, physiotherapy, computational intelligence and wireless healthcare systems, this article highlights the emerging opportunities and challenges associated with the creation of scalable digital twin ecosystems for precision rehabilitation. The proposed vision contributes to the development of smart, connected and personalized rehabilitation infrastructures, in line with future paradigms of healthcare and wireless communication. Full article

To deepen our understanding of human–ecosystem interactions, researchers need to be able to retrieve and analyze historical dark data such as plant and animal phenology, but there are often barriers to doing so. Despite the development of online digitization and other tools, including library search engines, digital collections, machine translation, OCR (optical character recognition), HTR (handwritten text recognition), and generative AI technologies, and the establishment of standards and frameworks (e.g., FAIR Principles and the International Image Interoperability Framework), barriers to converting analog records to digital records (“digital barriers”) and to translating local languages to an international common language (“language barriers”) still remain. We present a case study example of the use of historical dark data in phenology in Japan and the digital and language barriers encountered. We then briefly summarize factors and challenges hindering use of this data and describe the benefits of further removal of these barriers. Full article

Cross-phase collaborative management in complex product systems (CoPS) development is inherently challenged by heterogeneous organizational coupling and stochastic disturbances. Although digital twin (DT) and model-based systems engineering (MBSE) technologies provide foundations for physical–virtual synchronization and model traceability, existing approaches remain fragmented in three respects: insufficient requirements-traceable architectural integration, limited cross-phase semantic interoperability and runtime evolution, and weak operational links between semantic reasoning and adaptive decision models. To address these gaps, this paper proposes an MBSE-driven digital twin framework with semantic enhancement. First, a four-layer architecture is derived using the MagicGrid methodology, encompassing physical–virtual mapping, semantic reasoning, decision support, and service interaction. Second, a collaboration-oriented SysML profile is developed to standardize the representation of tasks, resources, materials, disturbances, and management constraints across engineering phases. Third, a knowledge-driven adaptive collaboration mechanism maps runtime disturbance inputs into semantic states, propagates their cross-phase impacts, supports process-topology reconfiguration, and generates decision-ready constraints for adaptive management. A case-based prototype for aero-engine turbofan blade development demonstrates the feasibility of the mapping–reasoning–decision chain and provides case-level evidence of improved cross-phase coordination under controlled disturbance scenarios. The results indicate a feasible engineering pathway from perceptive DT functions toward reasoning-enabled collaborative decision support. Full article

In the context of the global green development wave and the rapid iteration of digital technology, digital empowerment has become the core driving force for high-end manufacturing enterprises to achieve green transformation. At present, China’s manufacturing industry is facing the dual pressures of tightening resource and environmental constraints and industrial upgrading. How to break the bottleneck of green development through digital technology innovation has become a key issue to be solved urgently. Based on the techno-economic paradigm, green development theory and value creation theory, this study constructs a theoretical analysis framework for the green development of a digital-enabling manufacturing industry and deeply analyzes the mechanisms of digital technology (such as big data, Internet of Things, artificial intelligence, etc.) in optimizing energy allocation, improving production efficiency and reducing environmental emissions. By selecting 303 manufacturing enterprises of different scales in China as samples, the structural equation model is used for empirical tests. The results show that (1) digital empowerment has a significant positive impact on the green value performance of manufacturing enterprises, and (2) green development plays an intermediary role between digital empowerment and the green value performance of enterprises; that is, digital technology indirectly promotes green development by improving energy conservation and emission reduction, green innovation and green upgrading of enterprises. The research reveals the internal logic of digitally enabling the green development of Chinese manufacturing enterprises and provides a theoretical basis and implementation path for enterprises to formulate the innovation mechanism of digital–green development. Full article

Background/Objectives: Digital transformation is a strategic priority for healthcare systems, requiring nurses to develop competencies that enable the safe, effective, and critical use of digital technologies in clinical practice. Understanding nurses’ digital competence profile is essential to inform leadership, education, and workforce development strategies. This study aimed to assess digital competence among Portuguese nurses, examine its relationship with soft skills, and identify priority areas for professional development. Methods: A quantitative, descriptive-correlational, cross-sectional study was conducted with a nationally recruited convenience sample of Portuguese nurses. Data were collected using a Digital Competence Assessment Questionnaire based on the European Digital Competence Framework for Citizens (DigComp) and Soft Skills Inventory. Associations between digital competence and soft skills were analysed using descriptive, inferential, and multivariable statistical methods with IBM SPSS Statistics version 30.0. Results: Based on the exploratory classification of the knowledge/performance score, 52.0% of participants fell within the Intermediate and 27.2% within the Advanced proficiency intervals. Adapting and Coping, Analyzing and Interpreting, and Interacting and Presenting were positively associated with self-reflected digital competence. Multivariable analysis showed that soft skills accounted for a larger proportion of variance in self-reflected digital competence than in the knowledge/performance test score; however, the explanatory value of the latter model was limited. Conclusions: Participants were predominantly classified within the Intermediate and Advanced proficiency intervals, although comparatively lower descriptive performance was observed in Safety. The findings highlight the potential complementary role of technical competencies and soft skills in digital capability and suggest the value of further investigating targeted educational approaches. These findings may inform nursing leadership, education, and workforce-development strategies aimed at supporting digital competence and sustainable digital transformation. Full article

Amid growing global economic uncertainty, strengthening tourism economic resilience is critical to the economic sustainability of tourism destinations. Using panel data for 288 Chinese prefecture-level cities from 2010 to 2024, this study uses the staggered designation of the National New-Generation Artificial Intelligence Innovation and Development Pilot Zones as a quasi-natural experiment and applies a staggered difference-in-differences (DID) model to examine whether artificial intelligence (AI) development promoted by the pilot-zone initiative enhances tourism economic resilience. Results show that the pilot-zone initiative significantly enhances city-level tourism economic resilience, and this finding remains robust across a series of endogeneity and robustness checks. Mechanism analysis identifies data factor utilization, technological innovation, and industrial structure upgrading as three parallel channels. Moderation analysis shows that the resilience-enhancing effect of the pilot-zone initiative is stronger in cities with more developed digital infrastructure, higher levels of marketization, and greater human resources. Heterogeneity analysis reveals overall regional heterogeneity and a stronger effect in resource-based cities. These findings clarify the mechanisms and boundary conditions linking AI development promoted by the pilot-zone initiative to tourism economic resilience and provide implications for technology-enabled sustainable tourism development. Full article

In science, technology, engineering, arts, and mathematics (STEAM)-based music education, sound serves as a shared object of listening, inquiry, design, representation, and artistic expression. Sound is approached as musical material, a physical phenomenon, a technological object, a design problem, and a mathematical structure. Learners investigate vibration, pitch, loudness, timbre, duration, rhythm, melody, and acoustic experience; use digital tools to record, replay, visualize, arrange, and create sound; and apply design processes to plan, test, revise, and present sound-based products or performances. Musical understanding is developed through singing, movement, instrumental performance, composition, improvisation, and reflection, while mathematical reasoning is supported through beat, duration, sequence, proportion, pattern, and timing. In this approach, music provides an integrated context in which learners connect sensory experience with conceptual understanding. It is relevant across general music education contexts because it supports listening, discrimination, comparison, organization, creation, performance, and reflection, with activities adapted to learners’ ages, prior musical experience, and educational levels. Full article

Fractures are weak surfaces of rock; they are common in underground engineering and are prone to causing engineering disasters. The fracture parameters of rock are the crucial foundation for stability evaluation in engineering. The accurate identification of rock fractures is important for engineering support design, as it is helpful in preventing and reducing engineering accidents caused by fractures. At present, there are few technical methods for fracture identification. Digital drilling test technology provides a new approach to rock fracture identification. In this study, a multi-functional rock mass drilling test system is employed to conduct testing in fractured rock. The response laws of drilling parameters to different fracture positions and angles are analyzed, and an identification model for rock mass fracture parameters while drilling is developed. The results from tests show that the average error in identifying rock mass fracture positions using the fracture parameter identification model is 5.34 mm, and the average error in identifying fracture angles is 2.01°. This study provides a theoretical basis for on-site testing and assessment of rock mass fractures in underground engineering. Full article

Artificial intelligence (AI) can be appraised simultaneously as useful, capable, threatening, and demanding. Yet research typically treats attitudes toward AI as a single favorable-unfavorable continuum, obscuring how these evaluations coexist within individuals. Using a three-wave, time-lagged survey of 379 first-year university students in China, we examined configurations of six AI-related appraisal indicators: perceived humanlikeness, adaptability, and quality of AI; AI use anxiety; awareness of smart technology, artificial intelligence, robotics, and algorithms (STARA) as a career threat; and AI creative self-efficacy. Latent profile analysis supported four profiles: Positive Empowerment (19.2%), Anxious Acceptance (37.5%), Low-Perception Detached (37.2%), and High-Perception High-Vigilance (6.1%). R3STEP models showed that learning agility, digital literacy, future work self salience, and perceived university digital support were prospectively associated with profile membership, particularly in distinguishing the Low-Perception Detached profile from the more engaged profiles. BCH comparisons further showed that the Positive Empowerment and High-Perception High-Vigilance profiles reported relatively high levels of both career crafting and self-perceived employability, whereas the Low-Perception Detached profile reported the lowest levels of both outcomes. Notably, the High-Perception High-Vigilance profile combined elevated AI use anxiety and STARA awareness with strong career-development engagement, while the Low-Perception Detached profile combined comparatively low threat perceptions with weak career crafting and employability. These findings demonstrate that higher AI-related threat was not necessarily associated with weaker career preparation and, conversely, that low perceived threat was not necessarily associated with greater adaptive readiness. Overall, the results position students’ responses to AI as configurations of opportunity appraisal, threat, and AI creative self-efficacy rather than as uniformly positive or negative attitudes, thereby highlighting the need for differentiated university career education and AI-readiness interventions. Full article