Search Results (6,236)

Smart cities and smart real estate are increasingly used to enhance urban efficiency, sustainability, and quality of life. Nevertheless, their implementation is hindered by interconnected challenges across concepts, technology, governance, economics, society, ethics, and the environment. Thus, this paper examines how and why interconnected limitations constrain the smartness of smart city and smart real estate initiatives and identifies the governance and implementation responses revealed by cross-case comparison. The analysis of secondary academic sources, policy papers, standards, and industry reports compares Toronto, Songdo, NEOM, and Barcelona. The results identify six categories of limitations: conceptual and strategic; technological and infrastructural; governance and institutional; economic and financial; social and ethical; and environmental. The analysis shows that the challenges facing these initiatives go beyond technology alone and include ambiguous definitions of smartness, poor governance, limited public involvement, privacy and surveillance risks, high implementation and maintenance costs, interoperability issues, and unverified assumptions that technological advancement will deliver sustainability. The comparison of cases highlights that the success of these initiatives depends on effective governance, responsible data management, public trust, financial feasibility, institutional coordination, and a life cycle approach. The study concludes that smartness should be understood as an integrated institutional, economic, social, technological, and environmental capacity rather than as the mere adoption of digital technologies. Full article

Background: The global healthcare landscape is being increasingly shaped by two pivotal trends: the adoption of international quality frameworks, such as Joint Commission International (JCI) accreditation, and the strategic shift towards resource-efficient models like outpatient surgery. While both areas have been extensively addressed in the healthcare literature, empirical evidence concerning their intersection remains limited. This scoping review aimed to systematically map the available literature, identify critical evidence gaps at the intersection of JCI accreditation and outpatient surgery, and examine implications for healthcare risk management and policy development. Methods: The scoping review was performed in accordance with the PRISMA-ScR guidelines. A dual-track evidence-mapping strategy was implemented across seven electronic databases and platforms (PubMed/MEDLINE, Scopus, Web of Science, Cochrane Library, eLIBRARY.ru, CyberLeninka, and Google Scholar) to search for publications published between January 2000 and May 2026. The final database searches were conducted on 15 May 2026. Data extraction was centered on study design, interventions, and key findings related to quality and risk management. Results: A total of 80 publications were included. The evidence comprised two complementary streams: literature concerning JCI accreditation and healthcare quality and safety, predominantly in hospital and general healthcare settings, and literature concerning outpatient, ambulatory, and day-surgery quality, safety, and hospital-substituting technologies. No empirical study directly evaluating the implementation or impact of JCI accreditation within outpatient surgery centers was identified. Conclusions: This scoping review identified a critical empirical evidence gap concerning the integration of accreditation and quality and risk-management frameworks in outpatient surgical settings. A hypothesis-generating conceptual framework is proposed to guide future empirical research evaluating whether and how JCI accreditation influences quality, safety, organizational, and economic outcomes in outpatient surgical settings. Full article

Hydrogen innovation requires coordination across scientific research, engineering development, infrastructure deployment, market application, and public governance. However, heterogeneous incentives can destabilize collaborative participation even under strong policy support. This study examines how science-push and market-pull shape collaborative innovation in hydrogen technology. We develop a tripartite evolutionary game involving enterprises, universities, research institutions, and government under bounded rationality. The model incorporates collaborative returns and proportional downside-risk allocation, participation costs, spillover-based outside payoffs, market demand sensitivity, technological-foresight capability, and government governance strategies. The analytical results and numerical scenario illustrations identify three equilibrium configurations: independent R&D, one-sided participation, and full bilateral participation. Their occurrence depends on collaborative returns, participation costs, downside-risk penalties, external driving intensity, and governance conditions. Under matched driving intensity, the relative effectiveness of science-push and market-pull is actor-specific and determined by the balance among collaborative returns, costs, and outside opportunities. These findings clarify the strategic conditions shaping participation in hydrogen innovation systems and provide scenario-contingent implications for benefit allocation, risk management, and collaborative governance. Full article

Artificial intelligence (AI) is developing rapidly and is gradually transforming traditional management control practices into intelligent management control, characterized by automation, analysis and decision-making. This research aims to explore this transition from traditional management control, focused on retrospective monitoring, to intelligent management control, by highlighting two further paradigms: predictive and ethical management control. The methodology is based on semi-structured interviews with Moroccan management controllers working in various sectors. These interviews provided in-depth insights into their experiences of using AI tools, the perceived benefits and the limitations encountered in their use. The results highlight that artificial intelligence makes several significant contributions to the work of management controllers, improving the efficiency of analyses, the quality of information and the speed of decision-making processes, thereby allowing them to focus on activities with higher added value. However, the study also highlights the need to develop new capabilities in prompt engineering, strategic communication and the management of human–machine relationships, in order to adapt to these transformations. Nevertheless, this evolution is facilitating a gradual repositioning of the management controller towards a more strategic role. Furthermore, it documents the specific characteristics of technology adoption in the African context, which is characterized by the phenomenon of shadow AI. Full article

This hypothesis-generating integrative comparative review examines how ISO 45001:2018 is interpreted and implemented in construction safety management across the European Union (EU), the United States, and Canada. Drawing on 35 sources—including peer-reviewed studies, regulatory documents, institutional guidance, and selected practitioner literature—it compares implementation drivers, regulatory alignment, reported benefits, persistent barriers, and supply-chain governance using an analytical framework organized around six thematic dimensions. The synthesis suggests a convergence–divergence pattern: firms across the three jurisdictions pursue certification for broadly similar reported benefits and encounter similar barriers rooted in worksite dynamism, subcontractor complexity, and workforce transience. Institutional drivers, however, appear to diverge along a spectrum of regulatory emphasis: in the EU, the standard often functions synergistically with directive-based prevention duties as an integrated compliance mechanism, whereas in the United States and Canada it more commonly operates as a voluntary strategic overlay upon detailed regulatory baselines, driven mainly by market differentiation and liability management. These interpretations are propositions inferred from regulatory structures, certification statistics, and practitioner literature rather than conclusions demonstrated by primary causal studies. A mapping of ISO 45001 clauses to construction-specific challenges and a comparative propositions matrix are developed. The evidence base remains uneven—particularly for Canada, which contributes primarily regulatory and institutional evidence—and construction-specific empirical outcome evidence for ISO 45001 is limited. The paper proposes a research agenda for comparative, evidence-based evaluation of occupational health and safety management systems (OHSMS) in global construction. Full article

Small and medium-sized enterprises (SMEs) play a pivotal role in advancing sustainability, yet many struggle to translate voluntary sustainability practices into structured, strategic business models. This study examines how this gap can be bridged between voluntary certification frameworks and emerging regulatory requirements, with a specific focus on workforce-related sustainability reporting under the European Sustainability Reporting Standards (ESRS). Using a qualitative case study of a Green Globe-certified, family-owned hotel, the research conducts a comparative gap analysis between existing organisational practices and corporate disclosure requirements, applying a Policy–Action–Target–Metric (PAT&M) framework. The findings reveal strong alignment in Policies and Actions (89%) but limited alignment in Targets and Metrics (14%), indicating a gap between sustainability activities and strategic performance management. The results highlight the need to transition from compliance-oriented initiatives to integrated, data-driven management systems. The findings also suggest that voluntary certification schemes, such as Green Globe, can be a useful first step in order to achieve better corporate reporting capabilities related to environmental, social and governance (ESG) issues. As practical implications, a roadmap solution is proposed to support system-level integration, transparency, and measurable outcomes for better business resilience. Full article

This study examines the perceived competitiveness of information technology companies in Kazakhstan from a governance and innovation-ecosystem perspective. In emerging digital economies, firm competitiveness depends not only on technological capabilities, but also on the state’s ability to coordinate fragmented actors, support human capital development, improve innovation infrastructure, and create functional mechanisms for cooperation between business, government, universities, and research institutions. Using a mixed-methods design, the study combines survey data from 200 respondents across all 20 regions of Kazakhstan with 26 semi-structured expert interviews. All constructs are perception-based: the study assesses how sectoral stakeholders evaluate the determinants of competitiveness and does not measure firm performance. The results show that stakeholders perceive IT-company competitiveness as a systemic outcome shaped by human capital, technological competencies, innovation infrastructure, government support, business–science interaction, and clusterization. Human capital, innovation infrastructure, technological competencies, and clusterization-related mechanisms received the strongest evaluations. Correlation analysis further indicates that competitiveness factors are positively interconnected, supporting an ecosystem-based interpretation rather than a firm-centered explanation. The findings also reveal persistent constraints, including talent shortages, weak investment depth, regulatory instability, technological dependence, and insufficient coordination among ecosystem actors. The study contributes to administrative and strategic management research by showing that stakeholders regard clusterization as a governance mechanism for strengthening state capacity, public–private–academic coordination, and digital-sector development in an institutionally uneven emerging economy. Full article

Balancing decarbonization with grid reliability requires managing renewable volatility through Renewable Portfolio Standards (RPSs) or Capacity Remuneration Mechanisms (CRMs) alongside carbon emission trading (CET). However, their comparative systemic impacts remain unclear. This study evaluates the strategic equivalence of CET-RPS and CET-CRM regulations using a Stackelberg duopoly model. We endogenize grid-side storage investment and parameterize source-storage synergy to establish a rigorous equivalence mapping anchored in a unified macroeconomic penetration target. The results reveal that the CRM exhibits dual systemic impacts contingent on technological readiness. Immature conditions necessitate excessive capacity prices, inducing speculative over-investment. Conversely, mature synergy creates a substitution effect that efficiently offsets public compensation budgets. Furthermore, the dynamic policy phase boundary demonstrates that stricter decarbonization targets require higher technological readiness for the CRM to dominate the RPS in social welfare. Additionally, imposing stringent administrative capacity derating factors on storage fails to alter its physical deployment, instead unintentionally transferring wealth to conventional high-carbon generators. Regulators must therefore synchronize capacity market deployment with storage technology maturity and adopt dynamic capacity accreditation to prevent such distributional distortions. Full article

Against the backdrop of global efforts to address climate change and China’s “dual carbon” strategic goals, transportation buildings, as a key sector for energy consumption and carbon emissions, are crucial for green and low-carbon transformation. Taking Chengdu Tianfu Station as the research object, this paper systematically analyzes its low-carbon strategies and technical paths throughout the entire process from design and construction to operation. Firstly, the paper describes how Tianfu Station takes the three-star green railway passenger station as its goal and deeply analyzes the integration of its key technologies from three dimensions: low-carbon energy systems, low-carbon resource recycling, and low-carbon environmental regulation. Finally, the paper prospectively discusses Tianfu Station’s carbon management strategies for future operations. Through performance-oriented integrated design and the systematic application of advanced technologies, large-scale transportation hubs can effectively reduce carbon emissions throughout their life cycle while ensuring efficient operation and a comfortable experience, providing a reference for the green and low-carbon construction of similar buildings. Full article

Against the backdrop of global carbon-neutrality targets, the circular utilization of end-of-life power batteries has become a critical pathway for securing resources and reducing environmental risks. Digital technologies are increasingly reshaping circular supply-chain management. High-performing circular suppliers are essential to the efficient operation of digital circular supply chains. However, existing research on circular supplier selection (CSS) still faces three gaps: a theoretical gap in which digitalization is neglected in circular supply chains, an information-representation gap causing evaluation distortion, and an aggregation-mechanism gap arising from the inadequate treatment of interdependencies among decision variables. To address these issues, this study constructs a decision model based on probabilistic uncertain linguistic T-spherical fuzzy sets (PULTSFSs) and Choquet-integral-based aggregated sum product assessment (CASPAS). First, this study develops a multilevel evaluation system that closely integrates digital management and control capabilities. By combining PULTSFSs with the Choquet integral (CI), a CASPAS group decision-making model that captures interaction effects among decision elements is proposed. The strategic procurement of nickel-cobalt-manganese 811 (NCM811) cathode-material precursors by CATL is used as a case study. The results show that this indicator system is better suited to digital circular supply-chain management scenarios than traditional frameworks. In the CATL case, the recycled-material supplier h₃ achieves a comprehensive score of 0.660, 5.60% higher than that of the second-ranked alternative. By capturing interdependencies among evaluation elements, the proposed model exhibits stronger ranking discriminability and robustness than traditional independent-attribute methods, thereby providing a practical foundation and an efficient tool for digital supply-chain management in the power-battery recycling industry. Full article

The adoption of social media by businesses continues to be a relevant topic in research and professional practice, although there is still disagreement regarding the factors that explain its adoption, particularly among small and medium-sized tourism businesses. This study analyses the adoption of social media by lodging businesses in Cantabria (Spain) using the Technology Acceptance Model as an analytical framework. Based on empirical research conducted with a sample of 102 hotel owners and managers, a structural equation model was estimated using SmartPLS to examine the role of ease of use and perceived usefulness in social media adoption. The results indicate that ease of use primarily facilitates adoption, while perceived usefulness emerges as the decisive factor in the strategic evaluation of these platforms. This pattern suggests that companies adopt social media when they identify clear benefits in terms of business, reputation, or customer relationships. The conclusions highlight the need for policies and practices that promote a more conscious and value-oriented integration of social technologies, understood as resources capable of strengthening both customer focus and business management and competitiveness in the tourism sector. Full article

The safe and stable operation of coal-fired generating units is of critical strategic importance for ensuring the reliable supply of power systems. However, the fault evolution of industrial thermal systems exhibits the characteristics of strong nonlinearity and a long incubation period, coupled with the extreme scarcity of key fault samples (Few-shot) in actual production, which severely limits the engineering application of traditional data-driven diagnostic methods. Existing deep learning models, which are highly dependent on massive and balanced labeled data, not only struggle to overcome the overfitting bottleneck in scenarios with scarce fault samples, but also frequently introduce severe label noise (Label Noise) by ignoring the physical incubation period of faults, resulting in the degradation of the model’s decision boundary. To address the above challenges, this paper proposes a novel fault diagnosis framework integrating multi-scale spatiotemporal feature engineering and the Tabular Prior-Data Fitted Network (TabPFN). Starting from the physical mechanism of the system, this paper develops a dynamic label cleaning strategy based on multivariate statistical deviation, which accurately defines the fault divergence point to eliminate the noise in the incubation period. The constructed multi-scale spatiotemporal feature engineering integrating first-order difference and sliding window statistics can effectively map the transient mutation and steady-state evolution trend of the system. The introduced pre-trained TabPFN model based on the Transformer architecture, relying on its Bayesian inference capability and in-context learning (In-Context Learning) mechanism, can realize parameter-tuning-free and efficient classification for scarce samples. Experiments based on high-fidelity dynamic simulation data from GE Steam Power show that under the strict setting of limiting the training set to only 2000 samples, the proposed method achieves a comprehensive diagnostic accuracy of up to 99.29% and an F1-score of 0.9929 for seven typical operating conditions. Multi-dimensional comparative experiments and ablation studies confirm that the proposed framework comprehensively outperforms six mainstream baseline models, including XGBoost and SVM, in terms of precision, recall, and anti-interference robustness, and also delivers outstanding performance when benchmarked against deep learning models. This provides a brand-new theoretical perspective and technical paradigm for equipment health management in the context of industrial big data. Full article

Demolition is recognised as a strategic construction process enabling the transition between an asset and a building cycle. Rather than removing a structure, it requires a design and management approach that preserves safety, material quality, traceability, and environmental performance. This paper develops a demolition-management framework linking pre-demolition assessment, method selection, hazardous-material control, waste classification, logistics, safety measures, environmental mitigation, and documentation-based traceability. The framework is applied to the partial demolition of a precast reinforced-concrete storage building within an industrial compound in Treviso, Italy. The project involved time constraints, limited working space, adjacent structures, and asbestos–cement components. The case study illustrates a phased process including asbestos abatement, perimeter slab cutting, crane-assisted dismantling, controlled top-down mechanical demolition, dust suppression, and coordinated temporary storage and waste transport documentation. Results show that safety and circular-economy objectives are mutually reinforcing when demolition is planned as an integrated and auditable end-of-life construction-management process, focused on removing, separating, classifying, and routing materials through pathways. In constrained industrial settings, sequencing, staging areas, specialised subcontracting, and documentation readiness are decisive for reducing interference, maintaining operational continuity, and supporting material recovery. The study proposes a workflow, while acknowledging the limitations of a single-case design and the absence of multi-project quantitative data. Full article

Digital transformation in higher education institutions requires approaches that recognize the diversity of organizational contexts and the varying priorities expressed by academic units. This study analyzes digital transformation objectives identified in the institutional development plans of six faculties and schools at the Universidad Nacional Autónoma de México using a document-based frequency prioritization approach. The study examines how institutional plans articulate digital transformation objectives in relation to organizational characteristics, management structures, and available resources, adopting a systems perspective to account for the interactions among these organizational conditions. A key contribution of this research is the use of institutional planning documents as an analytical basis for identifying seven core dimensions that characterize digital transformation initiatives in the university-analyzed context, providing a framework for understanding how academic units define and organize their transformation priorities. The proposed frequency-based prioritization approach operationalizes this comparison by calculating the relative weight of each dimension based on the number of technological objectives classified within it. The findings show that digital transformation priorities vary according to institutional autonomy, management arrangements, disciplinary profiles, existing capabilities, and available resources. In large and diverse universities characterized by decentralization and complex organizational structures, digital transformation does not follow a single pathway; instead, it develops through context-dependent strategies shaped by the specific conditions and needs of each academic unit. Full article

Biological invasions threaten tropical biodiversity, yet the molecular mechanisms underlying successful invaders remain poorly understood. Thunbergia alata (Black-eyed Susan) is an aggressive “genomic orphan” invader in Andean forests, causing significant ecological disruption. This study contributes as one of the few transcriptomic profiles of light vs. shade responses in T. alata, providing a foundational molecular resource for this species. We performed differential expression analysis under varying light conditions, revealing a massive transcriptomic shift involving over 4000 differentially expressed genes. Our findings suggest a robust and stable homeostatic adaptation mechanism, characterized by the up-regulation of SnRK1 subunits for energy sensing and strategic management of Reactive Oxygen Species (ROS) within the thylakoid membrane. Furthermore, the fine-tuning of PIF/Auxin modules and the condition-specific induction of NAC and WRKY transcription factors facilitate shade avoidance and rapid vertical growth. The identification of a substantial reservoir of species-specific “not classified” genes suggests that novel genetic elements contribute to T. alata’s adaptive success. By elucidating these key regulatory networks, this research provides an important genomic baseline for future studies on adaptive evolution and the development of molecularly informed strategies for managing and controlling this invasive species in new environments. Full article