Search Results (2,232)

Value co-creation among diverse actors in digital innovation ecosystems (DIEs) exhibits characteristics of high complexity and dynamic evolution. Grounded in the Quadruple Helix Theory, this study develops a conceptual model that interlinks “supervisory guides, knowledge providers, technology transformers, and user demand parties.” This model is defined by organizational oversight as its nexus, knowledge and technology as its foundation, outcome transformation as its core, and user needs as its orientation. Building upon this conceptual foundation, we establish a four-party evolutionary game model involving “innovation regulators (government), innovation producers (academic/research institutions), innovation decomposers (enterprises), and innovation consumers (users).” This analytical framework is then applied to systematically investigate the dynamic evolutionary mechanisms and collaborative pathways for value co-creation in DIEs. We construct the payoff matrix and replicator dynamics to derive the system’s Evolutionarily Stable Strategies (ESSs). Numerical simulations via MATLAB R2023b identify the stability conditions for each party’s strategic choices and unravel the influence mechanisms of key parameters. The results demonstrate nine distinct ESSs, categorized into three types: low-level stability, regulation-dominated transitional stability, and high-level cooperative stability. While the agents’ initial strategies do not alter the system’s final equilibrium state, they significantly impact the speed of evolutionary convergence. Critical factors—including regulators’ intervention costs, subsidy and penalty mechanisms, producers’ and decomposers’ cooperation and default costs, and consumer feedback behaviors—collectively drive the system toward the ideal (1, 1, 1, 1) equilibrium. Theoretically, this study enriches the perspective on multi-agent collaboration in value co-creation by introducing a dynamic quantitative analytical framework, thereby addressing a notable gap in the literature. Practically, it provides actionable insights for mechanism design and a solid foundation for policy optimization, aiming to foster a synergistic governance system that integrates “regulatory guidance, market incentives, and social feedback.” Full article

Cultivated strawberry (Fragaria × ananassa) is an allo-octoploid for which the genetic basis of fruit appearance traits has not been comprehensively elucidated. This study investigated the genetic architecture of fruit color and morphological traits using integrated digital phenotyping and genome-wide association analysis of a core collection of diverse strawberry germplasm maintained for Korean breeding programs. A 108-accession core collection was assembled, genotyped, and phenotyped for 12 fruit quality traits. Population structure analysis identified K = 10 genetic clusters, and a Mantel test confirmed significant genotype–phenotype correspondence (r = 0.38, p < 0.001). Genome-wide association studies (GWAS) using BLINK and MLMM identified 15 significant marker–trait associations across six traits. Pleiotropic loci on chromosomes 15 (4C) and 22 (6B) were consistently associated with fruit lightness (L*) and red channel intensity (R) in both models, and the 6B locus explained approximately 18% of the phenotypic variance for each trait. Gene Ontology enrichment implicated transcriptional regulation, SUMOylation, and plastid-to-chromoplast transition, suggesting that the identified loci influenced fruit coloration through cellular regulatory mechanisms rather than direct pigment biosynthesis. These findings provide a genomic foundation for dual-trait marker-assisted selection targeting light and vividly red fruits for strawberry breeding. Full article

In recent years, the generation and animation of avatars in virtual reality (VR) have undergone a definitive paradigm shift, transitioning from pre-rendered, manually rigged meshes to autonomous, AI-driven digital entities. While individual algorithms have been extensively studied, there is a critical lack of comprehensive synthesis regarding how these generative models impact the broader sociotechnical ecosystem of Spatial Computing. To address this gap, this systematic literature review, conducted in accordance with PRISMA guidelines, analyzed 48 primary studies to evaluate the intersection of Generative AI, hardware architecture, human psychology, and digital ethics. The synthesis reveals a deeply interdependent ecosystem. While advanced neural rendering and diffusion models (RQ1) successfully bypass traditional 3D authoring bottlenecks, their pursuit of absolute visual fidelity severely antagonizes the thermal and latency constraints of standalone mobile hardware (RQ2). The literature demonstrates that failing to mitigate these bottlenecks through hardware–software co-design (e.g., specialized ASICs, gaze-contingent foveation) inevitably shatters the user’s sensorimotor loop, collapsing the sense of agency and triggering the Kinematic Uncanny Valley (RQ3). Furthermore, as these hyper-realistic avatars achieve kinematic autonomy, they introduce unprecedented sociotechnical vulnerabilities regarding spatial privacy, dataset bias, and post-mortem digital identity (RQ4). Ultimately, this review concludes that realizing a compelling and inclusive AI-driven Metaverse is no longer an isolated computer graphics challenge; it demands a rigorous, interdisciplinary paradigm shift where algorithms, silicon architectures, and cognitive psychology are inextricably co-designed under a foundational framework of digital ethics. Full article

Sit-to-stand (STS) transitions are clinically informative indicators of functional independence and are sensitive to compensatory strategies associated with physical decline and pain. This study presents a non-contact, non-visual framework for quantitative STS assessment using a 60 GHz frequency-modulated continuous-wave (FMCW) radar in a residential setting. We developed a signal-processing pipeline that converts intermediate-frequency radar data into range–time intensity (RTI) maps, tracks dominant torso motion, and extracts temporal, kinematic, and spectral features. Experiments were conducted across two sensing orientations (subject-facing and side-facing), five mounting heights (45–153 cm), and three execution speeds, with approximately 30 repeated cycles per condition. For normal non-compensated STS transitions, radar-derived metrics reflected expected biomechanical scaling: mean full-cycle duration decreased from $23.90$ s (slow) to $13.95$ s (medium) and $7.98$ s (fast), while peak ascent velocity increased from $0.311$ m/s to $0.358$ m/s and dominant cadence increased from 0.0416 Hz to 0.125 Hz. Simulated abnormal transitions produced distinct and quantifiable deviations. Preparatory rocking introduced an additional oscillatory phase (mean rocking duration 2.36 s), prolonging the standing transition to 4.80 s and altering trajectory regularity. Across configurations, subject-facing mid-torso mounting provided the most continuous and separable STS signatures, whereas side-facing placement and extreme heights reduced effective radial motion or introduced clutter artifacts. These findings establish practical deployment guidelines and demonstrate that radar-derived STS metrics can serve as candidate digital biomarkers for unobtrusive, privacy-preserving detection of mobility decline, compensatory pain behaviors, and functional impairment in real-world home environments. Full article

Digital Twins (DTs) are increasingly explored in tourism and hospitality as enabling technologies for smart destinations, service optimization, and data-driven decision-making. Yet these environments are inherently human-centered. Existing DT implementations, however, are largely technology-driven and focus mostly on infrastructures and operational processes. This study presents a systematic literature review of DT applications in tourism and hospitality. It combines a comparative taxonomy with a technological and data-oriented analysis to examine how these systems are currently conceptualized, implemented, and integrated. The review analyzes 42 studies, classifying them by application level, twin focus, architectural approach, and human integration. The results show a strong dominance of destination- and facility-level DTs, limited human-centered models, and a prevalent use of varied sensing technologies. There is limited attention to interoperability and semantic integration. Governance, socio-technical aspects, and real-time synchronization mechanisms are also mostly underexplored. Based on these findings, this study identifies key research gaps and calls for a shift towards Social Digital Twins (SDTs). SDTs integrate human actors, social interactions, and governance within unified modelling frameworks. This transition will require advances in semantic and ontology-driven architectures. Greater attention to privacy, trust, and user acceptance in data-intensive service environments is also needed. Full article

Understanding how policy mixes influence systemic digital transformation is a key concern in socio-technical transition research. This study explores the configurational effects of policy tools in construction digitalization to address this issue. Based on an analysis of policies issued across 31 Chinese provinces (2020–2025), key policy tools were identified using a Multi-Level Perspective (MLP), followed by fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine their configurational effects. The results show that: (1) policies can be grouped into three categories—technology application, management norms, and industrial ecology—comprising six thematic tools; (2) no single condition is essential for high or low digital development; (3) high-level outcomes are associated with three distinct configurations: “technology-talent driven”, “foundation-governance linkage”, and “system-ecology leading”, while “factor-deficient” and “system-mismatched” types are frequently linked to low-level outcomes. By identifying equifinal pathways and cross-level coordination mechanisms, the study offers configurational insights for designing digital policy mixes. While the theoretical implications are broadly applicable, the specific configurations require validation across different contexts because they depend on China’s provincial policy data. Full article

Museums serve as experimental spaces for smart cities. The hypothesis suggests that museums can offer insights into transitioning smart cities from a mainly data-centric model to one that emphasises sensory experiences. This qualitative study investigates how digitalisation shapes the sensory experiences of museum engagement among older adults in Istanbul, with a particular focus on how digital tools mediate embodied, sensory experiences in museum environments. A case study was conducted at Istanbul Modern, employing sensewalking as a mobile, sensory method to capture embodied perceptions during movement through space. Data analysis focused on four sensory modalities—visual, auditory, haptic and olfactory—and their interaction with museum digital interfaces. The findings reveal a predominantly visual museum experience, where lighting design and the legibility of digital labels enhance orientation and interpretive clarity. Auditory engagement is shaped by layered ambient sounds combined with digital audio content. Haptic experiences emerge through both material textures and interactive digital interfaces, while olfactory perceptions remain subtle. Importantly, digital tools extend engagement beyond vision by introducing complementary auditory and haptic layers, thereby enhancing accessibility for older users. The study situates its insights within broader discussions of age-friendly, accessible museum design and practices of the smart city. Full article

[Problem] Converting viral tourism popularity into long-term destination sustainability is a central governance challenge in the digital era. [Aim] This study aims to explicitly measure how emotional value mediates the transition from ephemeral online traffic to durable offline place attachment. [Methodology] Adopting a descriptive mixed-methods approach, data were collected through semi-structured interviews with 16 purposively selected participants (including tourists and locals) recruited via on-site intercepts and online snowball sampling. The inclusion criterion required active engagement with Harbin’s digital tourism discourse. Qualitative transcripts were coded using the NVivo 12 software and subsequently converted into panel data. Grey Panel Relational Clustering was then utilized to geometrically track tourist emotional trajectories. [Results] The analysis identified three structural tourist typologies—the Full-Link Empathy Type, Pragmatic Verification Type, and Traffic-Driven Co-conspirator Type—and revealed three corresponding synergistic paths driving online–offline integration: Virtual–Real Isomorphism, Complementarity, and Symbiosis. [Conclusions] The findings demonstrate that sustainable destination resilience depends fundamentally on the qualitative composition of emotional engagement across different tourist types, rather than sheer visitor volume. [Implications] This study contributes an empirically grounded, emotional value-driven framework to sustainable tourism theory, offering differentiated governance strategies for destinations navigating the volatility of platform-driven attention economies. Full article

Sustainable logistics for perishable products has received heightened attention, as evidenced by an expanding corpus of academic research. Aligning with this international perspective, the current study provides a comprehensive assessment of the research landscape, identifying cutting-edge developments and emerging trends in sustainable logistics for perishable products in the context of the transition from Industry 4.0 toward Industry 5.0. Utilizing the Systematic Reviews and Meta-Analyses (PRISMA) framework, a systematic review is conducted on 104 peer-reviewed articles sourced from the Scopus and Web of Science databases, published between 2021 and 2025. Based on an in-depth examination of seven key research themes, various research directions are suggested to guide future investigations. The observations and conclusions drawn from this analysis aim to establish a solid foundation for advancing knowledge on digitalized and sustainable logistics for perishable goods. Full article

Artificial intelligence development offers new solutions for enhancing corporate carbon performance and is crucial for promoting sustainable business practices. This study investigates the dynamic impact of artificial intelligence (AI) policy on corporate carbon performance using time series panel data of Chinese A-share listed companies from 2010 to 2024. Leveraging the staggered establishment of the National New Generation Artificial Intelligence Innovation Development Pilot Zones as a quasi-natural experiment, we develop a multi-period difference-in-differences framework with time-varying treatment. Our time series-based identification strategy addresses serial correlation and time-varying confounding factors through robust clustering and event study specifications. The findings reveal that AI policy significantly improves corporate carbon performance, a conclusion that remains robust after rigorous endogeneity tests, placebo checks, and counterfactual analyses. Using dynamic panel models, this study traces the temporal evolution of policy effects and demonstrates that AI exerts indirect effects through three time-lagged pathways: micro-level technological diffusion, future industry development, and the progressive accumulation of digital infrastructure and computing resources. Heterogeneity analysis reveals differentiated impacts across micro- and macro-levels, providing granular insights for forecasting heterogeneous treatment effects. By integrating panel time series econometrics with causal inference, this study contributes to the literature on corporate carbon performance while expanding analytical frameworks for understanding AI’s enabling effects. The findings offer policy insights and empirical benchmarks for forecasting green transition trajectories, with direct implications for green finance and sustainable economic development. Full article

The impact of oncological diseases extends far beyond the clinical patient, profoundly affecting the mental health of caregivers, family members, and volunteers who navigate complex emotional landscapes of grief, anxiety, and trauma. While the domain of digital health has seen a proliferation of serious games aimed at pediatric patient education and treatment adherence, the specific perspective of the “second-order patient”, the caregiver or survivor, remains significantly under-explored. The primary objective of this study is to systematically review the current state of interactive narratives in oncology, palliative care, and grief support, identifying research gaps to inform the broader design space of empathy-driven serious games. Following the PRISMA guidelines, 31 articles were selected from an initial query of 116 records. Interventions were categorized into Serious Games, Games, and Gamification. The analysis reveals a critical thematic transition: early interventions relied heavily on biological “battle” metaphors to empower patients, whereas the current literature advocates for “thanatosensitive” designs that foster empathy. However, a distinct research gap persists regarding narratives that explore post-loss meaning reconstruction and the hospital volunteer experience. Synthesizing these findings, this paper establishes an evidence-based theoretical framework demonstrating a significant opportunity for games that prioritize dialogue and emotional processing over traditional winning conditions. As a practical application of these findings, we also briefly outline the conceptualization of a prototype simulating a widower’s experience volunteering in a palliative ward, shifting the ludic focus from defeating a disease to navigating loss. Full article

Currently, Large Language Models (LLMs), exemplified by ChatGPT, are accelerating technological development across various domains, including the environmental domain, owing to their powerful text-generation and information-processing capabilities. With changes in global climate and environmental conditions, environmental sustainability has emerged as a major global challenge. Leveraging LLMs to advance environmental sustainability and mitigate current environmental problems is considered a valuable and effective approach. This study aims to systematically synthesize research progress and core challenges in current LLMs for promoting sustainability-related fields, and to comprehensively analyze the application contexts, impacts, and development potential of various LLMs within the environmental sector. Following the PRISMA-ScR guidelines, a comprehensive search was conducted across six databases: Web of Science (WOS), Scopus, ACM Digital Library, IEEE Xplore, ScienceDirect, and Google Scholar. A total of 20 articles were ultimately included for analysis. The findings indicate that LLMs play a positive role in maintaining environmental sustainability and promoting the low-carbon energy transition. The applications of LLMs span six core domains: the green transition, carbon emission management, air quality assessment, smart city operations, map analysis, and human cognition and behavioral observation. However, the training and operation of current LLMs consume considerable resources, which creates an inherent conflict with the goals of sustainable development. Future efforts must focus on developing a secure, equitable, and scalable LLM support system to advance environmental sustainability. This requires optimizing model energy efficiency and ensuring a balance between performance, reliability, and environmental impact. These endeavors are crucial for addressing environmental problems and guaranteeing the sustainable progression of LLMs across diverse environmental contexts. Full article

This systematic review builds upon 51 published empirical studies out of 354 studies that were published between 2020 and 2025 to assess the effectiveness of building-scale digital twins (DTs) in providing thermal comfort and energy efficiency, and improving the indoor environment and system reliability. The results show that there is a rapidly developing field focused on five thematic clusters: system architecture, artificial intelligence and machine learning (AI/ML)-driven control, human-centric engagement, predictive maintenance, and blockchain-enabled cybersecurity. Existing DT frameworks not only achieve real-time building information modeling (BIM)–Internet of Things (IoT) integration with prediction errors under 10%, but reinforcement learning controllers are also able to achieve 25–40% heating, ventilation, and air conditioning (HVAC) energy savings, and human-centric interfaces increase thermal satisfaction from 0.64 up to 1.2 Likert points. Predictive maintenance models have diagnostic accuracies of 91–97%, and new blockchain applications enhance data integrity, but largely at the prototype level. The cross-cluster convergence signifies the transition towards adaptive, socio-technical systems with an equilibrium of efficiency, comfort, reliability, and trust. The major weaknesses identified in this paper were a lack of longitudinal validation, climatic bias and ethical governance. A framework of a modular six-layer architecture is proposed after the review of 51 studies, which facilitates scalable, interoperable, and ethically robust DT deployments. Full article

Geological hazards induced by large-scale and high-intensity mining activities worldwide are primary drivers of regional ecological degradation and pose significant threats to human safety and property. To construct efficient monitoring systems and enhance early warning capabilities, it is essential to clarify the formation mechanisms of various hazards and the suitability of corresponding technologies. Focusing on four typical geological hazards prevalent in mining areas (surface subsidence, ground fissures, landslides, collapses, and sinkholes), this paper characterizes their specific features and monitoring requirements. It systematically analyzes the physical principles, accuracy levels, and technical advantages and limitations of ground-based, aerial, and spaceborne monitoring, as well as multi-source remote sensing data fusion and emerging technologies (e.g., distributed optical fiber, light detection and range, microseismical monitoring, and deep learning). Utilizing case studies from an open-pit coal mine in Turkey and a loess gully mining area in China, the paper evaluates the effectiveness of methods like multi-temporal InSAR and UAV photogrammetry in identifying the evolution of these hazards. The findings indicate that the technological framework for mining area monitoring is transitioning from single-method approaches to integrated systems. However, given the complex mining environment, several bottleneck challenges remain, including single data dimensions, the limited environmental adaptability of aerospace remote sensing, insufficient stability of deep monitoring equipment, and weak anti-interference capabilities under extreme operating conditions. Consequently, this paper proposes that future innovations in geological hazard monitoring in mining areas will focus on multi-platform hierarchical collaboration, the development of multi-parameter fusion early warning criteria, and the construction of digital and visual platforms. Constructing a comprehensive monitoring system characterized by multi-scale collaboration and dynamic prediction capabilities is vital for improving safety standards in mining areas and achieving coordinated development between resource exploitation and environmental protection. The findings provide a theoretical foundation for the precise prevention and control of mining hazards, as well as for land ecological restoration. Full article

Amid rapid digital transformation in emerging economies, firms face increasing pressure to modernize accounting information systems (AIS) to enhance transparency and decision usefulness. Limited evidence exists on when IT investment yields differential returns across firm life-cycle stages, particularly in transition economies. Vietnam, as a transition economy with a state-led digital transformation agenda, provides a relevant context to examine whether firm age moderates the relationship between information technology (IT) investment and AIS quality. AIS quality is conceptualized through two dimensions: system quality and information quality. Using survey data from 649 Vietnamese enterprises, the study employs SPSS and the PROCESS macro (version 4.2) to test moderation effects. The results show that IT investment positively affects both system quality and information quality, while firm age also has a positive direct effect on AIS quality. However, the interaction between IT investment and firm age is negative, indicating that the marginal benefits of IT investment are weaker in mature firms than in younger firms, as structural rigidity and legacy system constraints limit the effective leverage of additional IT investment despite higher baseline AIS quality. The study contributes by linking the IS Success Model with an Organizational information processing theory-based contingency perspective, identifying firm age as a structural boundary condition that explains heterogeneous IT returns within an institutional digital governance context. The findings suggest that managers and policymakers should align digital investment and regulatory strategies with organizational maturity to support sustainable digital business ecosystems. Full article