Search Results (502)

This review provides an integrative synthesis of the R&D performance measurement literature and introduces the Beyond Traditional Metrics (BTM) framework as a multi-criteria reference model for R&D performance evaluation in the petroleum sector. Research and Development (R&D) constitutes a strategic pillar of the petroleum industry, where technological innovation supports competitiveness, operational efficiency, and the transition toward more sustainable energy systems. However, evaluating the performance of R&D projects remains a major challenge because their outcomes are often uncertain, intangible, long-term, and multidimensional. Commonly used Key Performance Indicators (KPIs)—such as cost, time, and number of deliverables—therefore provide only a partial representation of R&D effectiveness. R&D performance assessment must therefore consider the intrinsic diversity of innovation activities. Reverse engineering emphasizes replication and adaptation of existing technologies, while innovation-driven R&D seeks to create novel knowledge, technological capabilities, and strategic learning. Accordingly, the selection of performance indicators should be adapted according to project type, technological maturity, and strategic objectives. To avoid biased evaluation, the approach integrates principles derived from the Multi-Criteria Decision Analysis (MCDA) approach, enabling prioritization of criteria aligned with each project’s objectives, complexity, and organizational priorities. To move beyond simple cost and time metrics, this study revisits the meaning of “performance” in R&D and explores a multidimensional evaluation perspective capable of capturing both tangible and intangible forms of value creation by integrating five complementary dimensions: Knowledge Creation and Diffusion, Innovation Velocity, Dynamic Strategic Alignment, Team and Organizational Health, and Resilience and Robustness under technological, regulatory, operational, and market uncertainty. The framework is illustrated through an exploratory application to a hypothetical portfolio of petroleum-sector R&D projects, demonstrating its potential usefulness for benchmarking, portfolio prioritization, and multidimensional innovation assessment under conditions of uncertainty. Full article

The standardization of post-quantum cryptography has shifted the central question of migration from which algorithms to adopt toward whether organizations can change cryptography at all. Prevailing treatments describe crypto-agility as a technical property of protocols and software, an abstraction layer that lets algorithms be swapped; that framing explains part of the problem and understates the rest. Drawing on organizational-capability theory, information-technology governance scholarship, and sociotechnical systems thinking, this paper reframes crypto-agility as a sociotechnical organizational capability: the coordinated capacity of people, process, architecture, and governance to detect cryptographic change, decide on a response, and reconfigure cryptographic mechanisms across the estate while preserving security and operations. The paper specifies the capability construct and its four dimensions and proposes a five-level maturity model that folds the standardization, agility, and hybridization pillars into its indicators. A preliminary application to three organizations that document their migration publicly shows the model can be applied and discriminates among them, while revealing that public evidence under-observes the people and governance dimensions. The model is presented as a theoretically grounded artifact for future validation, not as observed data; implications for governance, procurement, and assurance are developed. Full article

Ride-sourcing can expand flexible access within urban transport systems, with its contribution to sustainable mobility shaped by the modes, trip purposes, and mobility conditions it serves. Existing research often examines service selection and post-use evaluation separately, leaving limited evidence on how these stages differ in mature, regulated markets with broad mobility choice. This study examines this distinction in the United Arab Emirates using an exploratory, sequential mixed-methods design. Fifteen semi-structured interviews supported contextual and construct refinement, followed by an online survey of 191 adults, including 143 prior ride-sourcing users. Descriptive analyses examined service-selection patterns, while a 28-indicator, nine-path exploratory covariance-structure model was estimated for 136 complete prior-user cases using maximum-likelihood estimation in SAS PROC CALIS. Cronbach’s α ranged from 0.799 to 0.961. Overall satisfaction was most strongly associated with perceived post-ride support (β = 0.58507), followed by driver behavior (β = 0.29470), application ease (β = 0.28788), and perceived safety (β = 0.15609). Cost and fare reliability and time-related service reliability showed no significant direct associations with satisfaction. A 5000-resample nonparametric bootstrap reproduced the same hypothesis-support pattern. The findings distinguish the contextual conditions associated with ride-sourcing selection from the service-experience perceptions associated with post-use evaluation. They highlight user-facing capabilities that can support dependable multimodal participation, service quality, and mobility governance within sustainable urban transport systems. Full article

Privacy-by-design (PbD) considers privacy in the entire lifecycle of information systems and personal data. Although a wide variety of techniques for PbD exist, a framework that considers privacy in the full context of both systems design and organizational development remains absent. This research aims to design, validate, implement, and evaluate a PbD Focus Area Maturity Model as a guiding artifact for the application of PbD (PbD-MM). The PbD-MM was created using a design science approach. A set of previously coded PbD activities were used to formulate capabilities for the maturity matrix. The PbD-MM describes 14 focus areas and 60 capabilities, with their dependencies creating 10 maturity levels. The PbD-MM was validated through a focus group with PbD practitioners and implemented in a web application offering self-assessment and reporting. A total of 46 completed assessments were collected through online distribution. We find a broad basis of capabilities in PbD practice, with further developed governance and compliance, and a positive correlation between the highest-scoring focus area and higher overall maturity. The PbD-MM offers a structuring of PbD activities and an assessment instrument to support the development of organizational PbD capabilities. Full article

Artificial intelligence (AI) is reshaping the hospitality industry at an unprecedented pace. However, existing hospitality research has overwhelmingly concentrated on customer-facing applications, including service robots, chatbots, personalization, and revenue management, while largely overlooking the internal operational systems that sustain hotel performance. As a result, the concept of AI maturity within back-of-house hotel operations remains theoretically undefined, fragmented across functional domains, and lacks an integrated framework for assessment. This study addresses this critical gap by asking a fundamental research question: What does AI maturity actually mean for hotel back-of-house operations? Drawing upon a systematic literature review following the PRISMA protocol, this study synthesizes evidence from 18 studies spanning the interdisciplinary fields of hospitality management, operations management, information systems, and artificial intelligence to examine how AI is transforming core internal hotel functions. The review identifies current applications, implementation patterns, organizational enablers, barriers to adoption, and emerging trends across human resource management, procurement, finance and accounting, inventory management, housekeeping planning, maintenance, energy management, and managerial decision support. Building on these findings, the study develops the Artificial Intelligence Maturity in Back-of-House Operations (AIM-BoH) Framework, a domain-specific conceptual framework designed to conceptualize AI maturity across hotel back-of-house functions. The framework conceptualizes AI maturity as a multidimensional organizational capability encompassing technological adoption, process automation, decision intelligence, data readiness, human–AI collaboration, governance and ethical preparedness, and measurable operational outcomes. By moving beyond technology-centric perspectives, the framework provides a comprehensive model for understanding how AI creates organizational value through the integration of internal hotel processes. The proposed framework advances hospitality literature by establishing a common theoretical foundation for understanding AI maturity in internal hotel operations while offering hotel executives a structured conceptual lens for considering organizational capability development in the planning of digital transformation initiatives. The article concludes by proposing a research agenda for the empirical validation, refinement, and cross-cultural application of the AIM-BoH Framework, positioning it as a reference model for future hospitality AI research and practice. Full article

This study presents an integrated geochemical approach to distinguish between genetic and reservoir-related factors controlling oil compositional variability, evaluate reservoir compartmentalization, and reconstruct hydrocarbon migration pathways within the Nuraly field and the Akshabulak group of fields in the South Turgay Basin, Kazakhstan. The study aims to develop and validate an integrated approach combining biomarker analysis and oil fingerprinting to improve the reliability of oil genetic interpretation, assess reservoir fluid communication, and reconstruct secondary hydrocarbon migration pathways. This study analyzed 164 unique crude oil samples from the Akshabulak and Nuraly fields. Oil fingerprinting was performed on all 164 samples, including 128 samples from the Akshabulak group and 36 samples from the Nuraly field. A representative subset of 75 samples, comprising 39 Akshabulak oils and 36 Nuraly oils, was additionally analyzed for biomarkers. Oil fingerprinting was conducted using low thermal mass multidimensional gas chromatography (LTM-MD-GC), whereas biomarker analysis was performed using gas chromatography–mass spectrometry (GC–MS). Principal component analysis (PCA) and hierarchical cluster analysis were applied separately to the oil-fingerprinting and biomarker datasets. The resulting classifications were subsequently compared and integrated to distinguish source-related genetic variability from reservoir-related compositional effects, including hydrocarbon migration, oil mixing, and reservoir compartmentalization. The proposed approach is based on the complementary diagnostic capabilities of the applied geochemical methods. Biomarkers provide information on the origin of organic matter, depositional environment, and thermal maturity of the source rocks, whereas oil fingerprinting is sensitive to hydrocarbon migration processes and the degree of hydrodynamic connectivity between reservoirs. The results indicate that the investigated oils are predominantly derived from terrigenous organic matter of lacustrine origin. The Akshabulak group is characterized by genetic homogeneity of oils despite pronounced reservoir compartmentalization, whereas the Nuraly field contains at least two genetically distinct oil populations and hydrocarbon mixing zones. Regional hydrocarbon migration was reconstructed from southeast to northwest. Paleochannel sandstones were identified as high-permeability migration conduits, while tectonic faults and facies heterogeneity were recognized as the principal controls on reservoir hydrodynamic isolation. The results demonstrate that integrating biomarker analysis with oil fingerprinting provides an effective tool for distinguishing between genetic and reservoir-related controls on oil compositional variability, evaluating reservoir compartmentalization, and improving the reliability of geological and reservoir models in structurally complex petroleum systems. Full article

Digital-twin-enabled human–machine collaboration (HMC) has increasingly been proposed as a system-level approach for connecting human operators, robots, sensors, artificial intelligence modules, and manufacturing resources. However, the literature varies substantially in what is called a digital twin, how physical and virtual models are coupled, whether models are updated from physical data, and how far systems have progressed beyond simulation or controlled laboratory demonstrations. This structured integrative review examines the conditions under which a digital twin can function as an integration layer for HMC in sustainable smart manufacturing, rather than assuming that such integration is already established industrial practice. The literature corpus was assembled through searches of the Web of Science Core Collection, Scopus, and IEEE Xplore, complemented by Google Scholar-based citation tracking and backward and forward citation tracing. The core search focused on studies published from 1 January 2020 to 5 August 2026, while earlier seminal studies were retained to support definitions and historical context. Studies were screened using explicit criteria for manufacturing relevance, physical–virtual coupling, state synchronization or model updating, feedback capability, and validation setting, and were critically coded by model type, integration mechanism, deployment maturity, and sustainability evidence. The review compares multimodal perception and human-state modeling, intention understanding and augmented interaction, task allocation and shared planning, digital-twin architectures, adaptive control and safety verification, and human–AI decision-making. The evidence indicates that digital twins are promising as coordination and verification layers, but many reported systems remain conceptual, simulation-based, or limited to controlled physical prototypes. Key barriers include model fidelity, online model updating, real-time synchronization, cross-platform interoperability, safety assurance, human-data governance, and the limited availability of directly measured sustainability outcomes. Future work should prioritize validated hybrid models, traceable model-update mechanisms, staged virtual-to-physical deployment, interoperable data contracts, and longitudinal evaluation of technical, human, economic, and environmental performance. Full article

Autonomous vehicles increasingly require capabilities that extend beyond perception towards contextual understanding, semantic reasoning, and explainable decision-making. Knowledge graphs (KGs) have emerged as a promising solution by integrating heterogeneous sensor data, traffic regulations, domain knowledge, and contextual information into unified semantic frameworks. This review systematically examines knowledge graph-based intelligent reasoning in autonomous driving through a PRISMA 2020-guided analysis of 47 peer-reviewed studies identified from the literature published from 1 January 2018 to 31 January 2026. The findings reveal that semantic scene understanding and ontology-based representations currently dominate the field, with 66.0% of studies integrating knowledge graphs with deep learning approaches. Neuro-symbolic methods and explainable AI components were identified in 38.3% and 34.0% of publications, respectively, indicating increasing research interest in hybrid and transparent AI architectures. The analysis further demonstrates that 80.9% of studies remain limited to benchmark datasets and simulation environments, whereas only 19.1% provide real-world validation, suggesting relatively low technological maturity and limited industrial readiness. Although KG-enabled approaches substantially improve contextual awareness, hidden hazard anticipation, and explainability compared with conventional perception-centric architectures, major challenges remain regarding scalability, ontology interoperability, semantic error propagation, real-time reasoning, and certification requirements. The review identifies the convergence of knowledge graphs, large language models, and neuro-symbolic AI as a promising direction for next-generation autonomous driving systems. Future research should therefore focus on uncertainty-aware reasoning, adaptive explainability, standardised evaluation methodologies, and certification-oriented real-world deployment strategies. Full article

Integrating Corporate Social Responsibility (CSR) into business strategy requires organizational practices that convert sustainability- and stakeholder-related intentions into measurable and managerially usable information. This study examines whether distinct Business Performance Measurement (BPM) maturity profiles can be identified among hospitality SMEs and how these profiles are associated with sustainability- and stakeholder-oriented measurement practices. BPM maturity is defined as a multidimensional organizational capability reflected in the breadth, integration, monitoring, interpretation, and use of performance information, rather than merely in the possession of a formal measurement system. Using cross-sectional survey data from 600 decision-makers in Hungarian and Romanian accommodation and food service SMEs, the study applies Principal Component Analysis and Two-Step Cluster Analysis. Three profiles emerge: Integrated, Transitional, and Low BPM Maturity. Firms in the Integrated profile report broader use of financial, customer-related, employee-related, sector-specific, and sustainability-oriented performance information, whereas firms in the Low profile report more limited measurement practices. Sustainability- and development-related measurement practices provide the strongest relative contribution to profile differentiation in both national models. The findings demonstrate systematic associations between BPM maturity profiles and sustainability- and stakeholder-oriented measurement practices. They are consistent with the theoretical interpretation that BPM maturity may provide organizational conditions supporting the operationalization of responsible management intentions. However, the cross-sectional design does not establish causal direction, and the study does not directly measure formal CSR implementation or sustainability outcomes. Full article

Large language model–based agentic AI systems can independently interpret user goals, develop plans, and interact with external tools. These capabilities introduce security concerns that extend beyond traditional access control. However, existing Zero Trust Maturity Models, such as the CISA ZTMM, mainly focus on how resources are accessed and provide limited guidance on how to evaluate actions taken after access has been granted. This paper proposes AI-ZTMM, which extends CISA’s five-pillar structure to action-level trust evaluation. The model defines forty-one security Functions based on ten threat categories and thirty-one security requirements and introduces Action Space and seven Action Risk Factors for organizational self-assessment. Its scope includes software agents and the software action layer of agents in IoT, robotic, and OT/ICS environments. The model was refined through reviews by eleven domain experts and evaluated using thirty-eight MITRE ATLAS case studies. The CISA ZTMM lacked directly relevant controls for 59.7% of the analyzed attack stages, whereas AI-ZTMM addressed 75.4% of this gap, achieving a combined direct coverage of 84.4%. These results show that AI-ZTMM complements the CISA ZTMM by providing action-level security controls. Full article

Background/Objectives: Nanoparticle carriers can enhance cancer immunotherapy by improving tumor delivery, activating innate immune responses, and remodeling tumor microenvironments. This review evaluates four categories of formulations: poly(lactic-co-glycolic acid) (PLGA) nanoparticles, mesoporous silica nanoparticles (MSNs), magnetic or iron oxide nanoparticles (MNPs), and covalent organic frameworks (COFs). Methods: A reproducible PubMed audit identified 568 records. A rule-assisted title-and-abstract screen, followed by verification, removed 320 reviews, non-primary publications, and reports lacking qualifying in vivo formulation evidence. Of 248 potentially relevant reports, 15 primary studies were selected as representative examples; the remaining 233 were not classified as ineligible but were not selected as representative examples. These reports yielded 16 formulation-level records. One author conducted screening and extraction without protocol registration, duplicate review, or formal risk-of-bias scoring. Results: PLGA demonstrates the most robust polymer-level regulatory and manufacturing precedent; however, it remains limited by cargo instability, burst release, and challenges associated with process transfer. Biodegradable mesoporous silica nanoparticles (MSNs) facilitate pore-based protection and cytosolic delivery of cyclic dinucleotides, although their degradation and clearance are dependent on formulation specifics. Magnetic nanoparticles (MNPs) integrate magnetic targeting, imaging, and hyperthermia capabilities but necessitate formulation-specific magnetic characterization, field dosimetry, and repeated-dose safety assessments. Covalent organic frameworks (COFs) provide extensive stimulus-responsive and catalytic functionalities but exhibit the least mature evidence concerning biodegradation, scalable manufacturing, and independent reproducibility. Efficacy data across different studies were not pooled due to heterogeneity in models, schedules, comparators, and tumor-growth-inhibition formulas. Conclusions: The evidence does not endorse a universal platform ranking. Translation depends on standardized immune endpoints, explicit efficacy formulas, quantitative biodistribution assessments, mechanism-confirming experiments, repeat-dose toxicology studies, scalable manufacturing processes, and independent replication. The resulting evidence map serves as a descriptive and hypothesis-generating tool rather than a meta-analysis or clinical-priority scoring system. Full article

Small and medium-sized enterprises (SMEs) face increasing cyber threats, while existing cyber resilience approaches remain largely conceptual or provide static assessments with limited predictive capability. This study develops a dynamic mathematical framework for analysing and forecasting cyber resilience in SMEs. Building upon a previously developed conceptual model, the framework formalises the interactions among company security, cyber risk, cybersecurity capability, incident response and recovery, and digital maturity using normalised state variables, bounded nonlinear difference equations, and autoregressive forecasting. The theoretical analysis establishes boundedness of the state variables, equilibrium existence, and local stability of the proposed dynamic system. The framework further integrates computational resilience metrics, a Dynamic Resilience Index (DRI), scenario analysis, and sensitivity analysis within a unified analytical structure. An illustrative simulation demonstrates the computational implementation of the framework by generating resilience trajectories, supporting conditional forecasting, and comparing alternative cybersecurity scenarios. The study concludes that cyber resilience can be represented as a dynamic and measurable organisational capability. The proposed framework provides a transparent and extensible mathematical basis for continuous resilience monitoring, predictive analysis, and evidence-based cybersecurity decision-making in resource-constrained SMEs. Full article

Stockpiles and run-of-mine (ROM) pads are critical control points between mine production and processing plant feed. The grade, quality mix, and variability of material delivered to the crusher and mill are largely determined by how these structures are designed, built, and reclaimed. Despite the operational significance of stockpile management, the field remains fragmented across five distinct research streams—physical blending theory, stockpile state modelling, reclaim sequencing and equipment scheduling, plant-feed and stockpile blending optimization, and sensor-driven reconciliation and closed-loop control—with limited integration between them. This paper presents a systematic review of 27 sources published between 1976 and 2025, including peer-reviewed journal articles, conference papers, a preprint, a book, and one industry publication. The literature search was conducted in May 2025 using Scopus, Web of Science, and Google Scholar, with records screened by title/abstract and full text for direct relevance to stockpile reclamation or grade blending in mining operations. A structured coverage matrix identifies that studies combining high spatial fidelity with strong optimization rigor are consistently absent from the literature, and that uncertainty handling and sensor-driven or real-time capability remain substantially underdeveloped. Six research gaps are identified and prioritized by practical significance, implementation readiness, and literature maturity. The four most operationally critical gaps concern: spatially explicit reclaim scheduling under live ROM-pad constraints; tractable multi-attribute blending formulations for polymetallic operations; uncertainty propagation to plant-feed predictions; and field-scale closed-loop validation. The review provides a structured development roadmap for ROM-pad optimization frameworks and identifies the specific integration challenges that must be addressed to move the field from static stockpile monitoring toward adaptive, sensor-updated decision support. Full article

Hydrogen is not intrinsically low-carbon; its environmental value depends on how, where, and with which energy system it is produced. This PRISMA 2020 systematic review synthesizes 28 peer-reviewed life cycle assessment (LCA) studies on major hydrogen production pathways, including steam methane reforming, electrolysis, biomass-based routes, thermochemical cycles, and emerging photoelectrochemical systems. Unlike reviews focused only on carbon intensity, this study jointly evaluates environmental performance, economic feasibility, and technology readiness to identify where apparent advantages remain robust and where they disappear under real deployment conditions. The evidence shows that renewable-powered electrolysis can deliver the lowest greenhouse gas emissions when supported by additional low-carbon electricity, but the same technology can lose its climate benefit in fossil-dominated grids. Biomass and emerging routes diversify supply options but introduce water, land, material, and maturity trade-offs that are often underrepresented in policy narratives. Regional conditions, especially grid carbon intensity and resource availability, explain much of the variation observed across studies. The review also identifies persistent methodological gaps, including inconsistent system boundaries, limited dynamic grid modelling, weak treatment of indirect land use effects, and insufficient accounting for system-level benefits from flexible electrolysis. Overall, the findings support performance-based carbon intensity standards, region-specific deployment strategies, and more transparent LCA methods capable of capturing hydrogen’s role in integrated energy systems. Full article

Inefficiencies and low productivity persist in the construction industry due to limited digital integration and weak data use in decision-making. This study examines how internal data analytics, such as the systematic use of organisational data-like cost reports, safety logs, and project schedules, can enhance decision-making and organisational capability in New Zealand’s small- and medium-sized construction enterprises (SMEs). A comprehensive systematic literature review following PRISMA guidelines analysed 76 peer-reviewed empirical and theoretical studies (2015–2025). A thematic synthesis was conducted using NVivo 12 Plus and VOSviewer to identify patterns grounded in Evidence-Based Management, the Knowledge-Based View, and Bounded Rationality theories. The research highlights that analytics tools, including Building Information Modelling, Decision Support Systems, and Internet of Things platforms, enable real-time visibility, predictive forecasting, and coordination, thereby transforming operational data into strategic intelligence. However, adoption barriers persist, with technical interoperability issues, organisational resistance, low data literacy, and weak governance structures, significantly impacting resource-constrained SMEs. The study proposes a strategic framework that addresses four critical domains: robust data governance, leadership commitment and training, alignment with maturity models, and integration of emerging technologies. These domains demonstrate potential for standardisation and capacity building within SMEs, which also have implications for SMEs in New Zealand. Overall, the research provides a socio-technical framework which positions analytics as a transformative enabler of organisational learning, governance transparency, and sustainable performance and could support the development of an evidence-based construction sector. Full article