Search Results (3,445)

Background/Objectives: Clinical pharmacists contribute pharmacotherapy expertise to antimicrobial stewardship (AMS), but antimicrobial prescribing remains a multidisciplinary process led by treating clinicians. In physician-centered hospital systems, pharmacists’ contributions may be integrated into prescribing decisions to varying degrees depending on the organizational structures, interprofessional relationships, workflow integration, and prescribing cultures. This study aimed to explore how contextual and interprofessional factors shape the integration of clinical pharmacists’ antimicrobial stewardship contributions within multidisciplinary prescribing decisions in Jordanian hospitals. Methods: A multi-site qualitative study was conducted across nine Jordanian hospitals. Semi-structured interviews were conducted with 26 clinical pharmacists involved in antimicrobial reviews in intensive care and general medical units. Interviews incorporated the critical incident technique to elicit examples of accepted and rejected stewardship recommendations. Data were analyzed using a realist-informed approach to develop context–mechanism–outcome configurations explaining variations in pharmacists’ reported AMS contribution. Results: Pharmacotherapy expertise was necessary but not sufficient for pharmacists’ recommendations to shape antimicrobial prescribing. Leadership endorsement, structured multidisciplinary rounds, and formal documentation pathways activated mechanisms of legitimacy, credibility, and workflow visibility, supporting reported uptake of dose optimization, therapeutic drug monitoring, and selected de-escalation recommendations. In contrast, prescriber-led hierarchies, limited documentation pathways, workload pressures, and defensive prescribing cultures activated mechanisms of self-limitation, risk aversion, and limited recommendation uptake, particularly for discontinuation, duration control, and narrowing of broad-spectrum therapy. Conclusions: Strengthening AMS requires not only formal committees and guidelines but also team-based structures that integrate pharmacists’ pharmacotherapy expertise into antimicrobial review while preserving clinicians’ ultimate prescribing responsibility. Full article

Inter-organizational construction projects depend on intensive information exchange, interface coordination, and joint action among multiple organizations. However, their temporary, fragmented, and multi-actor nature often creates information asymmetry, unclear responsibility attribution, and weak trust foundations, thereby increasing collaboration costs and reducing governance efficiency. Although blockchain technology is often considered capable of addressing these problems through tamper-resistant records, transparent sharing, and smart contract execution, existing studies remain largely focused on technical functions or application scenarios. Less attention has been paid to how blockchain influences trust formation and inter-organizational collaboration through informational governance mechanisms. To address this gap, this study adopts a conceptual theory-building research design and develops a governance-oriented framework explaining how blockchain-enabled informational attributes affect trust and collaboration in construction projects. The study conceptualizes the blockchain’s governance role as an informational governance mechanism rather than a mere technical deployment, focusing on four informational attributes embedded in project transaction records: security, fidelity/authenticity, transparency, and procedural consistency. It further distinguishes interpersonal trust from inter-organizational trust, arguing that blockchain informational attributes may shape both interaction-based trust among boundary spanners and organization-level collaboration expectations. Interpersonal trust may also inform broader trust judgments toward partner organizations. At the collaboration level, the study conceptualizes inter-organizational collaboration as a process structure consisting of coordination and cooperation, with coordination creating conditions for deeper cooperation. The resulting framework links blockchain informational attributes, dual-level trust, coordination, and cooperation, and develops hypotheses for future empirical testing. This study contributes by shifting construction blockchain research from technology adoption and functional description toward informational governance explanation, extending dual-level trust logic into blockchain-enabled construction project governance, and applying the coordination–cooperation process logic to unpack how blockchain-enabled informational governance may influence inter-organizational collaboration. Full article

Community capacity building in the Citarum River Basin is critical for sustainable environmental management through the principles of the Circular Economy (CE) and the Blue Economy (BE). This study developed an integrated modeling approach combining Structural Equation Modeling (SEM) and Machine Learning (ML) to analyze the effectiveness of CE- and BE-based training programs designed to strengthen community capacity in the Citarum River Basin. The factors examined include individual characteristics, teaching quality, and organizational and environmental support, with participant commitment serving as a mediating variable in influencing training effectiveness and sustainable environmental management outcomes. In this framework, SEM was employed to validate theoretical constructs and test causal relationships among latent variables, while ML techniques, specifically Random Forests and Artificial Neural Networks (ANNs), were incorporated to enhance predictive capabilities beyond what theory-driven models alone can achieve. The results demonstrate that all exogenous variables significantly influence training performance, both directly and indirectly, with organizational and environmental support as the most dominant factor, and that the integrated SEM-ML model outperforms the standalone SEM model. The integrated SEM-ML model yielded lower prediction error rates and higher explanatory power, with SEM-ANN delivering the best overall performance. These findings underscore the value of integrating theory-based and data-driven approaches in capacity-building research, effectively addressing the trade-off between model interpretability and predictive accuracy, and providing actionable insights for designing more impactful community training programs to support sustainable management of the Citarum River Basin. Full article

The massive amount of publicly available data has necessitated an increase in public and organizational awareness of the potential risks of leaking private data, whether intentionally or unintentionally. The damage caused by leaking these data depends on their degree of sensitivity. Disclosing a person’s or an organization’s private data via different social media platforms might threaten people’s lives or the organization’s reputation or finances. Handling big data, especially unstructured data, is challenging. Consequentially, many solutions have been proposed to detect sensitive data in structured containers. However, detecting sensitive data in unstructured containers is still challenging, especially with context-dependent and high-performance measurement results. In this study, experiments on certain machine learning models and two transformers—DistilRoberta and ALBERT—were conducted to detect unstructured, textual, context-dependent sensitive data. The results show that DistilRoberta demonstrated higher accuracy and recall, and was faster and lighter than ALBERT. Full article

Background: Effective nurse–patient communication is fundamental to quality care delivery, yet language barriers pose significant challenges in multicultural healthcare environments. In Saudi Arabia’s diverse healthcare landscape, nurses frequently encounter patients who do not speak Arabic, potentially compromising care quality and patient safety. Objective: To explore multilevel factors influencing communication between Saudi nurses and non-Arabic-speaking patients, using Bronfenbrenner’s ecological systems theory as a conceptual framework. Design: A qualitative descriptive study employing semi-structured interviews analyzed through reflexive thematic analysis. Setting: Four healthcare facilities (two governmental and two private hospitals) across Saudi Arabia. Participants: Eighteen Saudi registered nurses with experience caring for non-Arabic-speaking patients, recruited through purposive sampling. Methods: Semi-structured interviews (n = 18) were conducted in Arabic or English between November 2025 and February 2026. Data were analyzed using Braun and Clarke’s reflexive thematic analysis, organized within Bronfenbrenner’s ecological levels. Collaborative reflexive coding and member-checking with six participants supported analytical rigor. Results: Five main themes emerged: (1) Individual-level competencies and preparedness (microsystem), (2) Interpersonal dynamics and cultural sensitivity (microsystem), (3) Unit-level resources and organizational support (mesosystem), (4) Institutional policies and language services (exosystem), and (5) Healthcare system and societal influences (macrosystem). Participants identified language proficiency gaps, cultural misunderstandings, inadequate interpreter services, and systemic barriers as primary challenges affecting communication quality. Conclusions: Communication between Saudi nurses and non-Arabic-speaking patients is influenced by complex, interconnected factors across multiple ecological levels. Interventions should address individual competency development, organizational support systems, and policy-level changes to ensure equitable, safe, and effective communication for all patients. Full article

Objective: This paper develops a conceptual theory-building framework for understanding and managing AI-enabled workplace conflict. It reframes AI-mediated conflict as a socio-technical justice problem and proposes the Hybrid Conflict Governance Model (HCGM), which links HR policy design, ethical governance mechanisms, and workforce capability development to conflict outcomes through procedural justice, trust, contestability, and human oversight pathways. Methods: This study adopts a conceptual theory-building design based on interdisciplinary literature synthesis. Literature from human resource management, artificial intelligence governance, organizational justice, socio-technical systems theory, digital work, and workplace conflict was synthesized using a problem-driven conceptual approach. The study integrates these literature streams to develop a theoretical framework explaining how AI-enabled workplace conflict emerges and how governance mechanisms may influence conflict outcomes. Conceptual Findings: The analysis identifies four major AI-enabled conflict dynamics: algorithmic bias, algorithmic surveillance, human–AI decision misalignment, and opacity in AI-supported decision making. The proposed HCGM explains how AI system characteristics influence employee justice perceptions, trust, and conflict escalation or reduction. The model identifies four interdependent governance layers: foundational rights, structural governance, operational integration, and capability development. These layers interact dynamically and are shaped by boundary conditions including organizational culture, AI autonomy, workforce digital literacy, and regulatory context. Conclusion: AI-enabled workplace conflict cannot be managed through technical controls alone. Effective governance requires integrated HR policies, ethical oversight, transparent decision processes, human review mechanisms, and workforce capability development. The HCGM contributes to AI governance, HRM, and workplace conflict literature by explaining how socio-technical governance mechanisms shape fairness perceptions, trust, and conflict outcomes in AI-mediated workplaces. Full article

Institutional theory assumes that coercive, normative, and mimetic pressures operate as parallel forces driving organizational isomorphism. This study tests this assumption by exploring the impact of pressures on eco-innovation and environmental performance in manufacturing SMEs. A two-wave, time-lagged survey of 271 manufacturing SMEs in China was analyzed using structural equation modeling (SEM) using IBM SPSS Statistics 27 and AMOS 21.0. The findings demonstrate a significant impact of institutional forces. Specifically, mimetic pressure (competitive pressure) most strongly influences eco-innovation, while normative pressure (stakeholder pressure) most strongly influences environmental performance. Coercive pressure (environmental regulations) is relatively weak. This research advances institutional theory by measuring the mediation magnitudes, revealing that these pressures affect both direct and innovation-mediated pathways. Mimetic pressure is most dependent on eco-innovation for performance (32.5% mediated), while normative pressure mostly uses direct channels (10% mediated). The fsQCA results complement these findings by providing interchangeable pathways to achieve Environmental Performance. By considering all pressures together, this study establishes a hierarchy of influence where competition stimulates eco-innovation, and stakeholders stimulate performance. This study provides evidence that these pressures are not substitutes because they affect different degrees of reliance on eco-innovation. This study shows that, under fragmented enforcement, non-regulatory pressures may be more significant than regulatory ones. Full article

Artificial intelligence (AI) is reshaping tourism service operations, smart tourism ecosystems, and digital customer engagement, yet many tourism SMEs still struggle to convert AI readiness into operational capability and firm performance. This study develops a staged capability-conversion model that integrates the Technology-Organization-Environment (TOE) framework, Social Cognitive Theory, the Resource-Based View, and Dynamic Capabilities Theory. Using a quantitative cross-sectional design, data were collected from employees and managers of tourism SMEs, particularly small and medium-sized travel agencies in mainland China. After data screening, 433 valid responses were analyzed using exploratory factor analysis, confirmatory factor analysis, covariance-based structural equation modeling, and 5000-resample bootstrap mediation testing. The results suggest that organizational readiness is the most consistent readiness-related antecedent, digital self-efficacy is positively associated with AI adoption and operational capability, and operational capability shows the strongest direct association with perceived firm performance. These findings are consistent with the view that AI-enabled value in tourism SMEs is associated less with technology adoption alone than with the conversion of readiness into behavioral and operational capabilities. Full article

Background/Objectives: Over the past decade, an academic hospital in Montréal has progressively integrated patient partnership into quality improvement committees and peer support. In January 2024, this approach was extended by appointing two patient partners to the Steering Committee, a strategic governance body. This study aimed to describe their integration, examine perceived effects and limitations from patient partners’ and executives’ perspectives, and formulate recommendations for similar initiatives. Methods: An in-depth qualitative case study was conducted between August 2024 and April 2025. Semi-structured interviews were carried out with Steering Committee members, including the two patient partners, and with the former Chief Executive Officer. Data were analyzed using thematic content analysis to identify themes related to implementation, participation, perceived contributions, and organizational conditions. Results: Integrating patient partners into the Steering Committee was unanimously perceived as relevant and value-adding. Their presence reintroduced the patient perspective, grounded deliberations in lived experience, reinforced the hospital’s mission, supported shared understanding, and encouraged simplification of complex issues. Challenges constrained more active participation, including insufficient clarity regarding roles and objectives; variable access to information due to confidentiality; technical language and acronyms; meeting formats that did not systematically create space for patient partners’ input; and incomplete institutional recognition. Variation across departments also emerged. Conclusions: Integrating patient partners into a Steering Committee is a promising governance innovation, but deliberate organizational adjustments are required. Co-constructed expectations and roles, strengthened onboarding and ongoing support, formalized information-access modalities, improved facilitation and plain-language practices, and stronger symbolic and practical recognition are needed to sustain meaningful participation. Full article

Positive Leadership in Higher Education is a key process; however, its connection to the mental health and quality of life of women in leadership roles at universities has been explored only to a limited extent. This study aimed to analyze the structural relationships between positive leadership, positive mental health, and quality of life in women university leaders, and to explore the lived experiences and coping strategies that explain these associations. A mixed-methods explanatory sequential design (QUAN → qual) was employed. 45 women holding senior administrative positions in Peruvian universities completed three standardized scales. The quantitative results reveal that positive leadership significantly predicted positive mental health, which in turn strongly explained quality of life, confirming a partial mediation model. The qualitative results highlighted persistent psychosocial and structural challenges—gender bias, the glass ceiling, role overload, institutional pressure, impostor syndrome, and isolation—alongside coping strategies focused on self-care, emotional regulation, purpose-driven leadership, empowerment, sorority, and organizational transformation with a gender perspective. Positive leadership emerges as a key organizational resource that enhances women’s mental health and quality of life. Institutional cultures grounded in inclusion, care, and relational leadership are essential for sustainable and transformative women’s leadership in higher education. The WISE: Women Integrated for Sustainable Empowerment is presented as a practical action guide for positive female leadership. Full article

Background/Objectives: Migraine is a common, disabling neurological disorder disproportionately affecting women during hormonally sensitive phases like menarche, pregnancy, and menopause. Despite awareness of sex-specific risk factors, management remains fragmented and predominantly neurologist-led, with limited coordination. To address these gaps, this project aimed to develop a national expert consensus, endorsed by scientific societies, on multidisciplinary migraine management in women across the lifespan, integrating perspectives from neurology, gynecology, endocrinology, cardiology, and general medicine. Methods: A two-round Delphi survey was conducted among 145 Italian clinicians representing the five specialties. The Scientific Board formulated 50 questions, each consisting of a variable number of statements, covering diagnostic approaches, hormonal therapies, comorbidities, and organizational care pathways. Statements were rated on a nine-point Likert scale, and consensus was defined using pre-specified criteria based on median scores and agreement. Results: Overall, 79 of 145 statements (54%) achieved consensus. High-level agreement emerged on sex-informed diagnostic strategies, including systematic gynecological and endocrinological evaluation and hormonal profiling in women with migraine. Round 2 facilitated consensus on contentious issues, such as avoiding estrogen-containing contraceptives in migraine with aura, individualized thrombotic risk assessment during menopause, and structured interdisciplinary coordination—particularly among neurologists, gynecologists, and general practitioners—during fertility planning and assisted reproduction. Qualitative feedback emphasized the need to update clinical pathways, implement standardized referral models, and strengthen interprofessional communication. However, persistent divergence remained on selected topics, particularly the role of hormonal contraceptives as a first-line approach to migraine management in women of reproductive age, reflecting different priorities between gynecologists and neurologists. Conclusions: This Delphi initiative provides the first national multidisciplinary consensus on migraine management in women in Italy. The findings support the development of sex- and life-stage-specific clinical guidance and integrated care models tailored to the complex needs of women with migraine. Full article

Construction companies, petrochemical enterprises, and airports are examples of large-scale organizational–technical systems (OTSs) and are characterized by a distributed structure, numerous parallel technological and business processes, and substantial energy consumption. The control of such systems is implemented through hierarchical distributed systems that require the regular collection, synchronization, and analysis of large volumes of heterogeneous data. This paper proposes a methodology for performance analysis and energy consumption optimization in OTSs based on the combined use of hierarchical control, business process modeling in BPMN and DRAKON notations, and the use of snapshots—consistent global states of a distributed system captured at specified time instants. The specifics of snapshot generation algorithms are discussed, including copy-on-write, the Chandy–Lamport algorithm, cloud orchestration, and log-based point-in-time recovery. A snapshot acquisition optimization problem is formulated, which minimizes the deviation of the captured state from the actual state under constraints on frequency, synchronization delay, and cost. The feasibility of the approach is illustrated by a numerical example of energy redistribution between the levels of a hierarchical control system using distributed model predictive control (DMPC). The advantages of the method include obtaining an objective “as is” picture, the applicability of control-theoretic methods for distributed systems based on big data processing, the ability to localize faulty subsystems, and its utility in assessing a company’s condition for stakeholders. Full article

This article discusses the relevant problem of ensuring transport connectivity under the conditions of temporal restrictions of the road network, which arise during repair, communal and emergency operations. It is established that the existing organizational and intellectual methods of traffic management do not eliminate physical decrease in road capacity, while construction of stationary structures with different levels is limited by high costs and long terms of implementation. The above substantiates the need for the development of mobile overpasses as adaptive engineering solutions ensuring continuity of the traffic flows. The purpose of the research is to develop a scientifically substantiated theoretical and experimental methodology for designing a mobile overpass as an integrated system “structure-moving load”, taking into account its dynamic behavior. The paper proposes an integrated approach based on the use of physical similarity theory and dimensionless analysis. A differential equation of dynamic bending of a beam on an elastic foundation is formulated taking into account inertia, damping, base reaction and the effect of a moving mass, and then its nondimensionalization is performed to obtain a similarity criteria system. The scientific novelty of the research consists in developing a system of dimensionless criteria to describe the relationship between the structural, dynamic and operational parameters of a mobile overpass, as well as in the formation of a criterion base for large-scale modeling and transfer of the results to full-scale structures. The proposed methodology describes the mobile overpass as an integrated transport-engineering system accounting for the coupled interaction between the deformable structure, moving traffic load, elastic foundation, and damping effects. Experimental verification was performed on a specially designed stand in the scale 1:4. The results obtained showed the quasi-static nature of the structure performance with moderate damping and rigid base. It is established that the distribution of engineering stresses along the span length has a regular character and retains its shape when the load level changes, which confirms fulfillment of similarity conditions. Regression analysis revealed a close to linear dependence of stresses on the load mass with a high degree of confidence $(R^2\approx 0.995)$. The practical significance of the research consists in creating an engineering method for express design of mobile overpasses, which allows for assessing their stress–strain state, stability and serviceability without expensive full-scale tests. The proposed approach can be used in designing temporary transportation structures under the conditions of urban area, and in operation in areas of road operations and emergency situations. Full article

Autonomous AI agents capable of holding digital assets, signing transactions, and executing smart contracts on public blockchain networks have moved from research prototypes to active deployment over the past two years. Despite this pace of adoption, no systematic treatment of their architecture, coordination protocols, and governance structures exists that spans the full design space. This survey addresses that gap through a systematic review of the literature from 2019 to 2026, covering 177 peer-reviewed publications and 14 system documentation sources, identified through a structured search of IEEE Xplore, the ACM Digital Library, Scopus, and arXiv. We classify deployed and proposed systems along four architectural dimensions: on-chain execution, off-chain agents with on-chain settlement, verifiable off-chain computation, and multi-agent on-chain interaction. Then, we examine the coordination mechanisms through which agents reach collective decisions, covering auction-based protocols, cooperative multi-agent reinforcement learning, token-incentive structures, and gossip-based peer-to-peer coordination. Governance is treated as a distinct dimension, analysed through a technical lens, covering on-chain parameter control, dispute resolution, and DAO structures, and an organizational one, covering accountability, incentive alignment, principal–agent dynamics, and regulatory compatibility. We survey applications across decentralized finance, supply chain, IoT, and agent marketplace domains, and identify six open research problems whose resolution is a prerequisite for broader deployment. The convergence of mechanism design and multi-agent reinforcement learning in asynchronous blockchain environments is identified as the direction of greatest near-term research value. Full article

Supply chain digitalization has increased the need for reliable systems that support transparency, traceability, trust, and structured interorganizational information exchange. Electronic Data Interchange (EDI) remains widely used for standardized business transactions, while blockchain offers decentralized verification, data immutability, and stronger data governance. However, blockchain–EDI integration depends not only on technical compatibility but also on organizational capacity, partner alignment, financial resources, and regulatory preparedness. Existing studies mainly examine blockchain benefits and adoption barriers, with limited attention to readiness assessment before implementation. This study develops an analytical framework for evaluating organizational preparedness for blockchain–EDI integration in supply chains. Five readiness dimensions are identified from the literature: technological, organizational, partner, financial, and regulatory readiness. These dimensions are measured using a 0–5 scoring system, combined into a weighted readiness score, and linked to a logistic function that estimates integration success under different complexity levels. Deterministic simulation, Monte Carlo simulation, and sensitivity analysis are used to examine the model. The results show a nonlinear readiness-success relationship under assumed parameter values: low readiness is associated with limited estimated success, medium readiness forms a transition zone, and high readiness supports more stable estimated outcomes. The framework is positioned as a methodological readiness assessment model rather than an empirically validated predictive system. It provides a basis for future empirical calibration, pilot testing, and validation using organizational implementation data. Full article