Search Results (85)

The rise in large language models (LLMs) has sparked renewed interest in how firms, particularly multinational aid organizations, can enhance learning related to meta-dynamic capabilities (DCs), such as agility, sensing, and adaptation, in response to disasters and humanitarian crises. A key strategic priority is developing meta-rules that combine general engagement frameworks with locally tailored action plans, grounded in cultural and institutional contexts. LLMs offer potential in supporting this need, but premature deployment risks harmful or misleading outcomes. This underscores the critical importance of collaboration between artificial and human intelligence (AI-HI). While AI brings computational power, it lacks the tacit knowledge—encompassing cultural, contextual, and intuitive understanding—that is essential in high-stakes, unpredictable environments. Our experimental study provides two core insights: (1) AI alone cannot effectively handle tasks requiring tacit knowledge, and (2) AI-HI collaboration thrives when human input guides AI using deep awareness of local social and political dynamics. We contribute to the discourse on dynamic capabilities in multinational contexts during catastrophic situations by offering practical strategies to support successful AI-HI partnerships and a framework for organizations aiming to enhance meta-DCs through responsible, human-centered use of disruptive technologies. Our findings clarify how the international dimensions of these capabilities influence their effectiveness across diverse cultural and institutional environments. Full article

Urban shrinkage has evolved from a localized phenomenon into a systemic challenge within China’s rapid urbanization, rendering traditional growth-oriented planning paradigms increasingly obsolete. However, existing research often treats shrinkage as either a passive outcome or an isolated shock, lacking a holistic perspective on how complex urban systems can adapt and reorganize under prolonged decline. This study constructs a coupling framework integrating urban shrinkage with Social–Ecological System (SES) resilience to bridge this theoretical gap. Drawing on a systematic literature review of 76 peer-reviewed articles following the PRISMA guidelines, we identify six core dimensions that drive this coupling. These dimensions consist of distinct physical and social elements. Our analysis reveals that the interactions between rigid physical environments and highly fluid social elements trigger nonlinear cascading feedback loops. While demographic contraction amplifies systemic risks, the subsequent structural release provides crucial spatial and institutional room for right-sizing. To translate these mechanisms into actionable governance strategies within the Chinese context, we propose a dual-track paradigm. Regionally, strategies emphasize collaborative risk monitoring, cross-boundary factor substitution, and industrial functional complementarity to mitigate vulnerability spillover. Locally, planning needs to pivot toward systemic downsizing and social empowerment, integrating proactive spatial reduction with agile service provision and community capacity-building. Ultimately, integrating structural reconfiguration with grassroots social learning enables shrinking cities to establish a new resilient equilibrium. While anchored in the Chinese context, this dual-track governance paradigm offers transferable insights for global shrinking cities seeking to overcome structural lock-in and foster adaptive SES resilience. Full article

Higher education institutions (HEIs) are rapidly developing to meet Industry 5.0 demands, highlighting human–machine collaboration, sustainability, and institutional resilience. Existing literature primarily explores artificial intelligence (AI), learning analytics (LA), and sustainability as discrete components within HEI. Limited studies examine how these disciplines intersect to impact institutional developments, especially from the perspective of strategic decision-making. Hence, this research explores how HEI leaders perceive the integration of artificial intelligence, learning analytics, and sustainability within strategic planning. Semi-structured interviews were conducted with 29 leaders from diverse HEIs using the Technology–Organization–Environment (TOE) and the Triple Bottom Line (TBL) theory frameworks. Thematic analysis demonstrated that AI and LA improve efficiency and decision-making but face ethical and cultural obstacles, while sustainability is often fragmented despite its reputational value. The results highlight a lack of holistic integration across domains. This research suggests theoretical and practical insights for aligning innovation and sustainable principles to build agile, ethically grounded, and future-ready universities. Full article

Against the background of climate uncertainty, market volatility, and evolving regulatory environments, firms embedded in agricultural value chains face increasing pressure to maintain sustainable growth. This study examines China’s A-share-listed Traditional Chinese Medicine (TCM) firms to explore how internal organizational factors and external institutional conditions jointly shape firm-level sustainable growth capability. This setting is characterized by strong ecological dependence, strict quality regulation, deep policy embeddedness, and supply-chain sensitivity. Drawing on the resource-based view, dynamic capability theory, contingency theory, and the institutional environment perspective, this study applies fuzzy-set qualitative comparative analysis (fsQCA) to 2023 cross-sectional data from 59 A-share-listed TCM firms. The results show that no single condition constitutes a necessary condition for high sustainable growth capability. Instead, high sustainable growth capability is mainly achieved through three configurational pathways: innovation-driven growth, policy-supported development, and market-responsive strategy. Low sustainable growth capability follows asymmetric pathways, mainly reflected in the mismatch between innovation capability and the institutional environment, and the coexistence of high financing constraints and low agility response. The findings indicate that sustainable growth capability is not the result of isolated factors, but a context-specific configurational outcome shaped by innovation, agility response, internationalization, equity governance, ESG performance, government support, marketization level, and financing conditions. This study provides a configurational explanation for growth research on agriculture-related firms and offers differentiated pathway implications for managers and policymakers. Full article

Providing regulation-compliant, high-fidelity training in aircraft maintenance remains challenging for institutions of education, where access to real aircraft, specialist tools, and operational environments is limited by cost, safety, and resource factors. This paper presents the design, in-house development, and pilot deployment of a virtual reality (VR) training system for an operationally critical maintenance procedure—Airbus A320 nose landing gear (NLG) wheel removal, strictly following the official Airbus Aircraft Maintenance Manual (AMM). Managed by an Agile-based methodology, the application, programmed with the Unity engine, uses full-size 3D CAD models and domain-expert input iteratively for quality-assured and rapid deployment. The system was piloted with aeronautical engineering students at the Vietnam Aviation Academy (VAA), achieving significant engagement and perceived gains for procedure knowledge and skill development. Positive comments emphasized the realistic, interactive, and repeatable quality of the simulation. Usability issues related to controller handling, cybersickness, and the absence of haptic feedback, however, suggest opportunities for refinement. This paper reports an early published case study of VR use in commercial aircraft maintenance training that is practically replicable and scalable, and developed in alignment with applicable civil aviation procedural requirements. It suggests that such a high-fidelity VR training platform can provide an accessible solution for aviation stakeholders to help bridge classroom training and real-world application in safety-critical training contexts. Full article

The Fifth Industrial Revolution (Industry 5.0) foregrounds human–machine collaboration, sustainability, and resilience as organizing principles for next-generation cyber-physical systems. Yet the identity and access management (IAM) architectures inherited from Industry 4.0 remain perimeter-centric, policy-static, and blind to the behavioral dynamics of human–AI teaming. This paper introduces the Human-Centric Zero Trust Identity Architecture (HC-ZTIA), a novel framework that repositions identity as the adaptive control plane for Industry 5.0 environments. HC-ZTIA integrates three mutually reinforcing innovations: (1) a Joint Embedding Predictive Architecture (JEPA)-driven Behavioral Identity Assurance Engine (BIAE) that learns abstract world models of operator and machine-agent behavior to perform continuous, context-aware identity verification without relying on raw biometric surveillance; (2) a Privacy-Preserving Adaptive Authorization Protocol (PP-AAP) employing zero-knowledge proofs and federated policy evaluation to enforce least-privilege access across human, non-human, and hybrid identity classes while satisfying data-minimization mandates; and (3) a Resilience-Oriented Trust Degradation Model (RO-TDM) that provides formally verified fail-safe identity governance under adversarial, degraded, or disconnected operating conditions characteristic of operational technology (OT) and critical infrastructure. The framework is grounded in the Agile-Infused Design Science Research Methodology (A-DSRM) and formally extends National Institute of Standards and Technology (NIST) SP 800-207 and the Cybersecurity and Infrastructure Security Agency (CISA) Zero Trust Maturity Model by addressing five identified gaps in human-centric identity governance. Simulation results, validated through Monte Carlo trials with 95% confidence intervals, provide preliminary evidence that HC-ZTIA reduces identity-related breach exposure by 73.2% (±4.1%) while maintaining sub-200 ms authorization latency under the simulated conditions, offering a principled bridge between Zero Trust rigor and Industry 5.0 human-centricity. Full article

Experimental Governance (EG) has emerged as a strategic framework for managing complexity in high-uncertainty environments. However, its application in the private sector remains fragmented, often conflated with purely operational tools. This study addresses this gap by performing a conceptual transfer of EG principles into the domain of business processes. Through an expanded Systematic Literature Review (SLR) of 41 peer-reviewed articles (covering the period 2004–2026), we identify what we term the ‘Internalization Paradox’: while firms rapidly adopt experimental methodologies like Agile or Lean, they often fail to embed them into formal governance structures that ensure long-term accountability and institutional learning. This updated review incorporates cutting-edge discussions on Artificial Intelligence (AI) governance, experimentalist metagovernance, and the strategic regulation of uncertainty. Our findings suggest that organizational resilience is not merely a byproduct of technological readiness, but an emergence of ‘Institutionalized Experimentalism’. We propose a Conceptual Framework that operationalizes EG through iterative feedback loops, corporate sandboxes, and adaptive decision rights, providing a robust roadmap for future empirical research in management and organizational theory. Full article

This study conducts a systematic literature review (SLR) of 406 peer-reviewed studies on sustainability in higher education published between 2014 and 2025. Guided by the PRISMA 2020 framework and the PICo criteria, this review identifies thematic patterns, institutional enablers, and barriers shaping sustainability integration. Data were manually screened and thematically coded using a structured extraction template. The findings reveal a conceptually active yet uneven field, with curriculum and pedagogy dominating discourse, while leadership, policy coherence, transformative learning, and global citizenship are less examined. Barriers such as institutional inertia and fragmented policies persist, but enabling factors, including digital agility, collaborative governance, and community partnerships, are attracting attention. Resilience and climate change education remain underexplored, indicating a gap between institutional strategies and sustainability goals. This review contributes by (i) identifying critical under-researched areas, (ii) refining a keyword framework to guide future inquiry, and (iii) introducing the Sustainability in Higher Education (SHE) Institutional Maturity Matrix (SHE-IMM), a conceptual model categorising institutions into foundational, transitional, and transformative stages of sustainability integration. The review received no external funding, and the authors declare there are no competing interests. Full article

Background: This study examines the influence of AI-driven supply chains on the adoption of automation and robotics within Jordanian manufacturing firms, emphasizing the role of supply chain adaptability and agility as mediators and market turbulence as a moderator. Methods: Drawing on dynamic capabilities theory and institutional theory, the study develops a conceptual model and tests it using data collected from 337 managers through an online survey. The analysis was carried out through partial least squares structural equation modeling (PLS-SEM). Results: The results show that AI-driven supply chains significantly enhance both adaptability and agility. However, only agility has a direct and significant effect on the adoption of automation and robotics, while market turbulence significantly moderates the connection between supply chain adaptability and the adoption of automation and robotics, but not the relationship between agility and adoption. Conclusions: Theoretically, the study provides insight into the interplay among internal dynamic capabilities in shaping technology adoption under external uncertainty. These results provide actionable implications for managers operating in developing economies like Jordan, highlighting the significance of building agile capabilities and adopting AI technologies to support innovation. The study is limited by its focus on a single country and sector; future research should explore other industries and incorporate additional moderating or mediating variables. Full article

The creation of unified, open, secure, reliable, and agile data spaces is essential for collecting, storing, and sharing data in a standardized and accessible manner, promoting data reuse and addressing current interoperability limitations. In this context, this research presents a proof of concept for a unified agronomic data space based on the structured integration of heterogeneous open data sources. The central hypothesis is that the automated acquisition, preprocessing, and harmonization of publicly available agronomic data can significantly improve accessibility, usability, and interoperability for agricultural decision support applications. To this end, a comprehensive analysis of relevant open data sources was conducted, followed by the design and implementation of configurable algorithms for automated data downloading, cleaning, validation, and integration. The proposed approach explicitly addresses key challenges such as heterogeneous data formats, inconsistent spatial and temporal resolutions, missing values, and outlier detection. As a result, a unified access point was developed, providing reliable agronomic information, including (i) preprocessed climatological time series, (ii) crop and phytosanitary data, (iii) high-resolution aerial orthophotography, (iv) remote-sensing imagery, (v) pest-related information, and (vi) time series of major vegetation indices. The proof of concept was implemented for olive groves in the Andalusian region of Spain; however, the methodology is fully transferable to other crops, regions, and institutional contexts where comparable open data sources are available. The results demonstrate the potential of shared agronomic data spaces to enhance data reuse, support scalable analytics, and facilitate interoperable, data-driven agricultural management beyond the specific regional case study. Full article

Quantum computing threatens the security foundations of global financial systems, exposing long-lived data and signed digital assets to “harvest-now, decrypt-later” attacks. While the timeline for cryptographically relevant quantum computers remains uncertain, regulatory signals from the USA, UK, EU, Canada, and Australia converge: financial institutions and payment infrastructures must begin migrating to post-quantum cryptography (PQC) now to preserve confidentiality, integrity, and systemic stability. This paper maps emerging standards and roadmaps, contrasting binding requirements like the EU’s DORA crypto-agility provisions with non-binding guidance from NIST, ENISA, and ETSI. Despite a shared intent to secure high-risk use cases by 2030–2031 and complete migration by 2035, divergences in enforcement and milestones create uncertainty for cross-border banks and financial market infrastructures. In parallel, technical adoption is advancing: major browsers, cryptographic libraries (OpenSSL/BoringSSL), and CDNs (e.g., AWS CloudFront) have deployed hybrid PQC key exchange in TLS 1.3, proving confidentiality defenses are viable at internet scale. The paper synthesizes historical transition lessons, sector-specific regulatory drivers, and operational constraints in payment infrastructures to derive a new, principle-based migration: crypto-agility, risk-prioritized scoping, hybrid deployment, vendor and supply-chain alignment, independent testing, and proactive supervisory engagement. Acting now reduces long-tail exposure and ensures readiness for imminent compliance and interoperability deadlines. Full article

Timely identification of infectious disease threats is essential for public health readiness. Conventional indicator-based surveillance systems, while dependable for tracking established pathogens, frequently lack the agility and sensitivity to detect new infections promptly. Syndromic surveillance, which examines pre-diagnostic and non-specific health indicators from many data sources in near real time, serves as a significant complementary method that improves early warning and situational awareness. This narrative study analysed selected experiences with syndromic surveillance, utilising peer-reviewed literature and institutional records. Four primary data streams were examined: emergency department and hospital records, pharmacy and over the counter (OTC) sales, participative citizen-generated data, and hybrid multi-source systems. Syndromic indicators were reported to identify outbreaks two to fourteen days before standard laboratory reporting across many trials. Data from the emergency department exhibited the highest sensitivity and specificity (47.34% and 91.95%, respectively), whereas pharmacy and participative data offered early indicators at the community level. Integrated systems like ESSENCE (Johns Hopkins University Applied Physics Laboratory, Laurel, MD, USA) and SurSaUD^® (Saint-Maurice cedex, Paris, France) attained enhanced accuracy yet necessitated significant data integration and governance capabilities. Syndromic surveillance enhances epidemic preparation by detecting atypical health-seeking behaviours and variations from baseline patterns prior to formal diagnosis. Nonetheless, its efficacy is contingent upon data quality, interoperability, and contextual adaptation. Countries like Bulgaria could improve national early-warning capabilities and overall health security through the gradual adoption of pilot projects and integration with existing surveillance networks. Full article

The growing complexity and dynamism of industrial and organizational projects require management approaches that can effectively adapt to uncertainty and rapidly changing operational environments. In this context, this study proposes a methodology to identify the most suitable management approach—predictive, agile, or hybrid—in complex projects. Building on the “Approach suitability tool” of the Project Management Institute’s (PMI), the methodology integrates quantitative assessments of complexity and systemic risk. This is achieved through the analysis of stakeholder and risk networks, using metrics such as cyclomatic complexity and the coevolution parameter g, which allow for a deeper understanding of interactions and the evolution of project elements. The methodology was validated in three peacekeeping missions of the United Nations: UNMISS in South Sudan, MONUSCO in the Democratic Republic of Congo, and MINUSTAH in Haiti. The results confirm that the methodology accurately identifies the most appropriate management approach, emphasizing the effectiveness of hybrid approaches in complex and volatile environments. The proposed methodology serves as a valuable tool for optimizing project management in diverse contexts, enabling a quantitative and systematic evaluation of complexity and risk. It is adaptable and applicable to a wide range of complex projects, improving decision-making and planning in uncertain settings. Furthermore, by incorporating resilience as a cross-cutting principle, the methodology strengthens the ability of projects and their teams to maintain functionality and sustain learning even in highly volatile environments, where continuous adaptation becomes a critical success factor. In this sense, resilience emerges as the property that allows projects to absorb disruptions, reorganize, and preserve their core purpose without losing cohesion or direction. Full article

The capacity of the United Kingdom (UK) to prosecute technology-enabled financial and economic crime (FEC) is increasingly shaped by the Collingridge dilemma. Even though the dilemma was broadly conceptualized in technology governance, its application to prosecutorial and enforcement practice, evidentiary standards, and criminal liability attribution represents uncharted scholarly territory. Through socio-legal mixed methods combining doctrinal analysis, case studies, and comparative analysis, the paper shows how the dilemma’s two horns or pillars (i.e., early epistemic uncertainty and late institutional inertia) manifest in criminal law and regulatory contexts. The paper finds that just like the European Union and United States, the UK criminal enforcement ecosystem exhibits both horns across cryptocurrency, algorithmic trading, artificial intelligence (AI), and fintech domains. By integrating supplementary theories such as responsive regulation, precautionary principles and technological momentum, the study advances a socio-legal framework that explains enforcement inertia and doctrinal gaps in liability attribution for emerging technologies. The paper demonstrates how epistemic uncertainty and institutional entrenchment shape enforcement outcomes and proposes adaptive strategies for anticipatory governance including technology-literate capacity building, anticipatory legal reform, and data-driven public-private coordination. These recommendations balance ex-ante legal clarity (reducing uncertainty) with ex-post enforcement agility (overcoming entrenchment) to provide a normative framework for navigating the Collingridge dilemma in FEC prosecution. Full article

Research laboratories in universities face a complex challenge: they must manage multiple projects, diverse teams, and tight deadlines, often with limited resources. While the business world has long used agile and quality management tools to navigate such complexity, these methods are surprisingly rare in academic research. In this study, we set out to bridge this gap. We implemented a combined management model, blending agile Scrum practices with proven quality tools like the Ishikawa diagram and PDCA cycle, within a pharmaceutical sciences research lab. Over a six-month period, we diagnosed key issues, created a structured action plan, and introduced an online platform to monitor progress continuously. Our approach led to a significant increase in productivity, with 65% of targeted articles being published or submitted and 75% of general lab activities completed. Perhaps just as importantly, communication improved dramatically, and the lab successfully met all its institutional deadlines. We conclude that this hybrid framework is not just a theoretical idea but a practical and powerful innovation. It provides a tangible blueprint for other research groups looking to enhance their productivity, streamline communication, and build a more adaptive and effective research culture in the face of academic complexity. Full article