Search Results (902)

Industry 4.0 has produced one of the fastest-growing bodies of engineering and management research; much of this output remains siloed by technology domain. This study addresses that fragmentation through a structured critical review (a review-of-reviews), synthesising 70 peer-reviewed review articles and foundational sources (2003–2026) spanning 14 technology domains. The review introduces the I4.0-STS framework, an original four-layer structure organising evidence across physical, cyber, cognitive, and socio-organisational dimensions. Five principal findings emerge. The physical and cyber layers show consistent evidence of maturity. Industry-reported lighthouse IIoT deployments show 20–30% energy and up to 39% lead-time reductions. AI-driven predictive maintenance shows 30–50% unplanned-downtime reductions. The cognitive layer (LLM-augmented digital twins and generative AI interfaces) is technically feasible but outpaces its governance frameworks. Cybersecurity remains insufficiently governed, with documented ransomware incidents in manufacturing OT environments underscoring the risks of OT–IT convergence. SME adoption and developing-economy manufacturing transformation remain comparatively under-addressed. Finally, 12 research gaps are assessed as of June 2026, five rated Open, with future research directions proposed for each, framed against the emerging Industry 5.0 agenda. All findings are second-order interpretations from the source reviews, and their limitations are stated explicitly. Full article

Land consolidation (LC) is increasingly expected to support food security, ecological restoration, rural development, and climate mitigation, yet evidence on its carbon effects remains fragmented across engineering, ecological, spatial, and governance research. This review combines bibliometric mapping with structured evidence synthesis of 355 records from WoS and CNKI to examine knowledge evolution, analytical paradigms, and their integration. The field has expanded from component-based assessments of construction emissions, soil carbon, and land-cover change toward life-cycle accounting, carbon fractions, ecosystem-service interactions, spatial optimization, and policy evaluation. WoS-indexed and Chinese-language literature show distinct but increasingly convergent orientations shaped by differences in intervention contexts, disciplinary traditions, analytical scales, and available evidence. Four complementary paradigms are identified: carbon accounting, biogeochemical processes, spatial land systems, and decision support and governance. Together, these paradigms reveal interconnected carbon pathways but remain constrained by inconsistent accounting boundaries, weak process–scale–time linkages, and limited integration with land-governance decisions. Future research should therefore advance standardized life-cycle and multi-scale accounting, mechanism-based assessment of long-term carbon and ecosystem-service dynamics, and digitally and institutionally enabled low-carbon governance. Full article

Atmospheric icing is one of the most critical meteorological hazards for unmanned aerial vehicles (UAV), whose operation under adverse conditions—high latitudes, elevated altitudes, long-endurance missions without pilot intervention—particularly exposes them to ice accumulation on aerodynamic surfaces and propellers. Unlike manned aviation, where this phenomenon has been extensively studied and regulated, a significant knowledge gap exists in the UAV domain that limits the development of effective protection systems adapted to energy constraints. This article provides an integrative review—conducted with a systematic search strategy following PRISMA reporting guidelines—of atmospheric ice formation mechanisms, their specific effects on UAV propellers, and the two most promising mitigation approaches: electrothermal modelling for the optimisation of electric heating systems and the development of functional surface materials including superhydrophobic coatings (SHC); composites with conductive nanofillers (graphene, carbon nanotubes); and piezoelectric actuators. The analysis demonstrates that hybrid systems combining passive and active strategies managed by intelligent control represent the most viable solution for extending UAV operational envelopes under known icing conditions, with a projected reduction in anti-icing system energy consumption of at least 40% relative to conventional continuous heating. This estimate is based on the most conservative published evidence: pulsed electrothermal de-icing achieves 40–60% savings versus continuous anti-icingSHC-assisted hybrid heating reduces IPS power by more than 80% on static aerofoils; and rotary-wing pulsed systems reduce mean consumption by 60–75% relative to continuous operation. Key research gaps are identified, and a prioritised future research agenda is proposed to support the development of certifiable anti-icing systems for rotary-wing UAV platforms. Full article

Driven by the Sustainable Development Goals (SDGs) and digital transformation, digital governance of megaprojects has become a key means of advancing sustainability objectives. However, the existing scholarship lacks a holistic understanding of digital governance in megaprojects, and the sustainability-oriented research landscape has yet to be systematically mapped. This study takes the Web of Science Core Collection as the primary data source, complemented by citation tracking, to identify and screen 127 publications published between 2016 and 5 March 2026. Bibliometric analysis is employed to delineate the developmental characteristics and research hotspots of the field. Subsequently, a theoretical analytical framework—Megaproject Sustainable Digital Governance (MSDG)—is constructed, integrating three core dimensions: the project lifecycle, sustainability, and digital governance. Guided by this framework, a coding scheme is developed for the content analysis of the 127 selected papers. The findings reveal that digital technology governance permeates the entire project lifecycle and garners the most scholarly attention, whereas digital derivatives governance is conspicuously understudied across all lifecycle stages. In terms of sustainability, the economic dimension dominates the discourse, while environmental issues are insufficiently represented. This paper aims to map the full landscape of lifecycle-oriented research on sustainable digital governance of megaprojects and proposes two future research agendas to facilitate the transition toward sustainable digital governance. Full article

The Sauvé–Kapandji (SK) procedure is a salvage operation that fuses the distal radioulnar joint (DRUJ) and creates an intentional pseudarthrosis in the proximal ulna, thereby relieving pain while preserving forearm rotation and ulnar-sided bony support. This narrative review (1) traces the historical evolution of the original and modified SK procedures, (2) organizes the modifications within a two-axis framework—distal bone handling and proximal soft-tissue stabilization—to clarify their indications and outcomes, and (3) proposes an indication-based decision framework keyed to bone quality and etiology, together with a research agenda to validate it. Along the bony axis, the technique differentiates stepwise according to bone quality—traditional SK, the fragment-interposition (Nakamura) type, and the ulnar-head rotation (Fujita) type; along the soft-tissue axis, the amount and position of ulnar resection appear to be the more fundamental determinants of stump stability. SK and the Darrach procedure are broadly equivalent in major outcomes, but SK may be advantageous in patients at risk of ulnar carpal translation or with high functional demand. Given that the evidence is predominantly Level IV, this two-axis framework is offered not as an established guideline but as a hypothesis-generating conceptual framework requiring validation by future comparative research. As a narrative review, this work was not designed as a systematic review or meta-analysis and does not include a registered protocol, formal quality appraisal, or quantitative data pooling. Full article

The integration of Artificial Intelligence (AI) into human resources management is driving a profound transformation in the evolution of management, and even more so in the management of human talent, which is the primary resource of any organization. This research provides an in-depth analysis of the impact of AI on core human resource management processes, covering the automation of operations that enables the exploration of dimensions such as talent acquisition, training, potential development, mental well-being, strategic workforce planning, job design, diversity, compensation, equity and inclusion, change management, culture and sustainability. The purpose of this study is to systematically synthesize the existing evidence on the impact of artificial intelligence on human management processes, identifying the scientific consensus, emerging contradictions, research gaps, and implications for sustainable organizational development. A systematic review was conducted of various sources published between 2020 and 2025 from databases such as ScienceDirect and Scopus, among others, using predefined Boolean search strategies, explicit inclusion and exclusion criteria and a structured thematic synthesis narrowing down the main studies based on search criteria. It was determined how algorithms are changing the employer-employee relationship within organizations. The findings indicate that the effectiveness of AI depends on the development of a hybrid intelligence that preserves the human factor consideration. It is concluded that AI enables the optimization of cultural change management, analytical precision, and ethical oversight—which are irreplaceable and critical human competencies in today’s digital age. This review contributes to the literature by providing a comprehensive synthesis of recent evidence, identifying unresolved research gaps, and proposing a future research agenda that will lead to the development of sustainable, responsible, and people-centered AI in human resource management. Full article

Historical geographic information systems (HGIS) have evolved from auxiliary tools for digitizing historical materials and displaying maps into infrastructural research environments that connect multidisciplinary forms of historical spatial knowledge production. Yet infrastructure alone does not fully capture the field’s value-oriented epistemic goals. Building on existing research on HGIS and historical spatial data infrastructures (HSDIs), this integrative literature review proposes Historical Geomatics as an agenda-setting heuristic framework organized around the core question of how HGIS can restructure knowledge production in historical geography. It synthesizes the field’s knowledge traditions, workflows, analytical paradigms, and future directions. First, it clarifies that HGIS function across research traditions as a tool, a method, and an environment that can use HSDIs to organize heterogeneous historical materials. Second, it conceptualizes spatialization as a continuous workflow of spatial element recognition, geographic attribute assignment, standardized modeling, and validation and revision. Standardization and explicit uncertainty representation are treated as prerequisites for research quality, while platform-based and public HGISs extend the lifecycle of historical spatial data. Third, the review groups existing scholarship into four analytical paradigms: spatiotemporal reconstruction; urban morphology and spatial structure; networks, mobility, and social space; and place, landscape, and memory. Finally, it examines how geospatial artificial intelligence (GeoAI) is reshaping HGIS knowledge production and argues that Historical Geomatics may serve as an agenda-setting heuristic for the next stage of HGIS. The principal opportunities lie not in accumulating additional cases, but in strengthening HSDIs, improving multimodal automation, representing uncertainty explicitly, and rebalancing space and place, models and narratives, and technical efficiency and historical context. Full article

Tourism scholarship has long treated technological innovation as an accelerant bolted onto a stable practice: people move through physical space to consume experiences, then return home. This conceptual paper asks what happens to the field’s theory once that assumption is relaxed. Rather than forecasting a single future, the paper develops a structured conceptual foresight framework to examine how emerging technological trajectories may challenge the constitutive assumptions of tourism theory. Drawing on tourism scholarship alongside developments in computation, the biosciences, and cognitive science, the framework integrates established concepts from futures studies including Voros’s futures cone, Inayatullah’s causal layered analysis, and Dator’s four generic futures to explore alternative trajectories for tourism. This analysis identifies four constitutive assumptions on which tourism theory quietly rests, namely geographic displacement, embodied co-presence, the destination as passive stage, and the tourist–host dyad, together with a typology of de-anchoring that traces what becomes of the field as each assumption loosens. Four conceptual scenarios follow: the dematerialization of place, biologically mediated travel, autonomous destination ecosystems, and the erosion of the tourist–host distinction. Some sit in the cone’s plausible band; others are placed at its preposterous edge on purpose to stress-test the field’s categories rather than to predict events. Throughout, the analysis stays tethered to tourism and hospitality concerns: destination economics, the work of destination management organizations, labor displacement, experiential inequality, the measurement infrastructure behind tourism statistics, and the shift from sustainability toward regeneration. We close with a research agenda for an anticipatory, theory-building stream within the field. Full article

Higher education institutions face a dual imperative: expanding access while reducing their environmental and operational footprint. Grounded in Triple Bottom Line theory and the Sustainable Development Goal (SDG) interlinkages framework, this study examines faculty and postgraduate students’ perceived associations between online learning—across environmental, economic, educational, and health dimensions—and five SDGs (SDG3, SDG4, SDG8, SDG12, and SDG13), and how these perceived dimensions interrelate to form a coherent structural model. Data were collected from 306 faculty members and postgraduate students via a five-point Likert-scale questionnaire and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). All nine direct and four specific indirect path relationships were statistically significant at p < 0.001. Perceived economic rationalization exerted the strongest direct path coefficient on perceived responsible consumption (β = 0.648) and the strongest specific indirect effect on perceived climate action (β = 0.371), while perceived health and well-being served as the central pathway statistically linking perceived online learning benefits to perceived quality education and decent work outcomes (β = 0.557 and 0.608, respectively). The structural model statistically accounted for 42–54% of the variance in these perceived SDG-related outcomes. These findings suggest that online learning may be perceived by faculty and postgraduate students as an integrated structural lever—rather than a source of isolated benefits—for advancing interconnected dimensions of the 2030 Agenda in higher education; objective verification of these perceived associations (e.g., using institutional records or environmental metrics) remains an important direction for future research. Theoretical and practical implications for university leaders and policymakers are discussed. Full article

The rapid paradigm shift from traditional keyword-matching algorithms to AI-driven answer engines has fundamentally disrupted Search Engine Optimization (SEO). As Large Language Models (LLMs) power modern Search Generative Experiences (SGEs), organizations must transition from legacy web analytics to sophisticated Business Intelligence (BI) frameworks to capture visibility. Despite the immense strategic implications of this shift, academic literature remains fragmented across computer science, information systems, and digital marketing management. To bridge this gap, this paper adopts an integrative literature review methodology, synthesizing 70 high-value studies selected from an initial corpus of 11,382 papers filtered to 2962 on-topic studies. Rather than utilizing restrictive systematic protocols (e.g., PRISMA) that isolate empirical data within narrow boundaries, the integrative approach enables a holistic synthesis of emerging, multi-disciplinary concepts necessary to decode a rapidly evolving phenomenon. Through this methodological lens, this study introduces the Signal–Structure–Surface–Score (4S) lifecycle framework, illustrating how AI-BI systems capture conversational search intents (Signal), architect machine-readable, entity-based data (Structure), optimize content for LLM retrieval and Generative Engine Optimization (Surface), and define novel attribution metrics for zero-click environments (Score). Furthermore, the paper maps the critical technical and strategic landscape, systematically evaluating prevailing trends (e.g., zero-click searches, AI-generated content velocity), core organizational challenges (e.g., search data attribution loss, algorithmic opacity), and emerging strategic opportunities (e.g., real-time intent mapping, competitor LLM audit trails). Ultimately, this paper bridges the gap between AI search mechanics and strategic BI measurement, providing a robust future research agenda designed to guide scholars and practitioners in navigating data-driven visibility in the age of generative search. Full article

This review surveys recent deep learning approaches for solving integral equations, categorizing them into three methodological families: physics-informed embedding, spectral/topological acceleration, and hybrid symbolic–numeric frameworks. The main findings are threefold. First, these methods achieve promising empirical accuracy in oscillatory, high-dimensional, and singular-kernel settings, yet their theoretical foundations remain largely incomplete. Second, from an axiomatic perspective, most approaches lack rigorous guarantees of convergence, stability, and spectral consistency; we formulate five testable propositions that a complete theory should satisfy. Third, we identify five specific unresolved theoretical questions and outline a focused research agenda toward a mathematically rigorous theory of neural operator approximation for integral equations. The novelty of this review lies in its dual computational–axiomatic evaluation and its provision of a structured, problem-oriented framework for future investigations. Full article

Artificial intelligence (AI) is transforming healthcare and the built environment, yet its application to healthcare real estate (HRE) remains fragmented and poorly understood. This study systematically reviews AI applications across the HRE asset lifecycle to identify evidence gaps and evaluate their potential to improve decision-making, operational performance, and sustainable healthcare infrastructure. Following the Joanna Briggs Institute methodology and PRISMA-ScR guidelines, the search identified 2881 records, of which 87 studies met the inclusion criteria. Building on the evidence gaps identified through this mapping, this study develops conceptual contributions, including a lifecycle maturity index, the Algorithm-to-Asset-Value Translation Chain, and the AI-HREDF, that serve as theoretically grounded, testable proposals for future empirical investigation. Each study was classified by lifecycle stage, evidence directness, and evidence strength. Only 14 studies (16%) provided direct evidence linking AI to HRE decisions, while most focused on operations and facility management, leaving major gaps in site selection, planning, construction, and investment. This review identifies three evidence translation gaps that prevent AI advances from becoming measurable improvements in asset performance and financial value. To address these challenges, we propose the AI-Integrated Healthcare Real Estate Decision Framework (AI-HREDF), the Algorithm-to-Asset-Value Translation Chain, and a research agenda for future work. The findings provide a foundation for integrating AI into healthcare infrastructure planning, management, and investment while supporting more resilient, resource-efficient, and sustainable healthcare facilities. Full article

Artificial Intelligence of Things (AIoT), which refers to the combination of Artificial Intelligence (AI) and the Internet of Things (IoT), has become an important research area in smart farming. Although the literature on AIoT has expanded rapidly, little is known about its overall development, intellectual structure, and emerging research directions. To bridge this research gap, this paper examines the AIoT literature in smart farming using a bibliometric approach based on publications indexed in the Web of Science Core Collection. Bibliometric techniques, including performance analysis, keyword co-occurrence analysis, and bibliographic coupling, were employed to examine publication trends, most relevant journals in the field, top scholars, countries, institutions, and research themes. The findings show a rapid growth of AIoT research, driven mainly by engineering and computer science disciplines. Five major research themes are identified, covering AI applications in agriculture, IoT-enabled smart farming, intelligent sensing systems, agricultural data management, and prediction models. Based on these findings, the study discusses the evolution of AIoT research, identifies current research gaps, and proposes a future research agenda focusing on artificial intelligence, interoperability, cybersecurity, data quality, technology adoption, sustainability, and responsible innovation. This study provides an extensive overview of AIoT research in smart farming and provides useful directions for researchers and practitioners interested in the future development of digital agriculture. Full article

Despite longstanding conceptual and measurement concerns surrounding transformational leadership, environmental applications of the paradigm have continued to proliferate through target-specific formulations and increasingly complex explanatory models. Yet whether these developments have resolved, or merely reproduced, the broader theoretical and methodological concerns that characterize transformational leadership research remains unclear. Against this backdrop, this systematic review, conducted and reported in accordance with the PRISMA 2020 guidelines, critically synthesizes the empirical literature on transformational leadership in employee green behavior research while situating it within this broader theoretical and methodological context. Peer-reviewed English-language articles published since 2000 were retrieved from Scopus, Web of Science, and ABI/INFORM and screened against predefined eligibility criteria, resulting in 64 studies. Results revealed limited empirical engagement with the theory’s linchpin of follower transformation. Rather than modeling change in follower self-concept, most studies adopted Bass’s four-pillar framework and examined associations between follower-perceived behaviors of transformational leaders and environmental outcomes within diverse mediational and conditional process models. Across almost all studies, higher perceptions of transformational leadership were positively associated with employee green behavior, irrespective of whether the construct was operationalized using general or environmentally specific formulations. Organizational factors, most notably perceived green organizational climate, emerged as the most frequently examined mediating mechanism and contextual boundary condition linking transformational leadership to employee green behavior. Both general and target-specific formulations were predominantly operationalized using measures based on the Multifactor Leadership Questionnaire (MLQ), raising concerns regarding the intelligibility of cumulative evidence. We conclude by outlining a research agenda aimed at enhancing theoretical coherence, measurement validity, and methodological rigor. Full article

Background: Blockchain adoption in supply chain management has attracted growing academic and practitioner attention; yet the impact varies significantly by implementation context, and the knowledge of the conditions driving this variation remain limited. Methods: This study conducts a systematic review of 112 papers to systematically synthesize how blockchain integration affects supply chain management practices (SCMPs), including upstream practices (e.g., supplier partnerships), downstream practices (e.g., customer relationships), and practices spanning both sides of the supply chain (e.g., information sharing and information quality). Results: The review finds that the benefit of blockchain adoption depends on a firm’s supply chain position, cost structures, and market conditions. Two theoretical perspectives are used to interpret these findings: the resource-based view, which viewed blockchain as a capability for integrating processes and transactions across organizations, and the practice-based view, which viewed blockchain as an imitable activity requiring context-specific deployment conditions. Fourteen propositions are developed to guide future research. Conclusions: This review provides practitioner guidance for evaluating when and how blockchain is likely to generate values across different SCMPs. Also, it provides researchers with a theory-grounding agenda for testing the proposed propositions empirically. Full article