Search Results (302)

The transformative potential of Artificial Intelligence (AI) in urban management is severely constrained by pervasive systemic fragmentation. While AI applications demonstrate high efficacy within isolated domains, they rarely achieve the cross-domain integration necessary for realizing systemic benefits. Our prior research identified this fragmentation paradox, revealing that 91.5% of urban AI implementations operate at the lowest levels of integration. While the Urban Systems Artificial Intelligence Framework (UAIF) offers a technical blueprint for integration, realizing this vision is contingent upon organizational readiness. This paper addresses this critical gap by introducing the Urban AI Governance Maturity Model (UAIG), developed using a Design Science Research methodology. Distinguished from generic maturity models, the UAIG operationalizes Socio-Technical Systems theory by establishing a direct Governance-Technology Interlock that specifically links organizational maturity levels to the engineering requirements of cross-domain AI. The model defines five maturity levels across five critical dimensions: Strategy and Investment; Organizational Structure and Culture; Data Governance and Policy; Technical Capacity and Interoperability; and Trust, Ethics, and Security. Through illustrative applications, we demonstrate how the UAIG serves as a diagnostic tool and a strategic roadmap, enabling policymakers to bridge the gap between technical possibility and organizational reality. Full article

The transition to renewable energy technologies is one of the most important ways to achieve the sustainable development goals (SDGs) of affordable and clean energy (SDG7); industry, innovation and infrastructure (SDG9); responsible production and consumption (SDG12); and climate action (SDG13). The widespread use of renewable energy technologies in developing countries will reduce dependence on imported fossil resources, increase industrial competitiveness, and support low-carbon development. Despite all their advantages, the integration of renewable energy technologies into industrial and domestic systems in developing countries remains slow due to a number of barriers. Financial constraints, technical and technological deficiencies, political restrictions and uncertainties, and organizational and managerial inadequacies are some of the barriers to the widespread adoption of renewable energy technologies. This study aims to identify, classify, and prioritize the barriers to the implementation of renewable energy technologies by applying multi-criteria decision-making methods in a fuzzy environment, with Türkiye considered as a case study. The relative importance of the barriers identified using the Single-Valued Spherical Fuzzy SWARA method was assessed, and their interconnections and significance were systematically demonstrated. The findings will contribute to the development of policy and management strategies aligned with global sustainability goals, thereby facilitating a more effective and equitable transition to clean and resilient energy systems. Full article

The advancement of automated driving technologies offers potential safety and efficiency gains, yet safety remains the primary barrier to higher-level deployment. Failures in automated driving systems rarely result from a single technical malfunction. Instead, they emerge from coupled organizational, technical, human, and environmental factors, particularly in partial and conditional automation where human supervision and intervention remain critical. This study systematically identifies safety failures in automated driving systems and analyzes how they propagate across system layers and human–machine interactions. A qualitative case-based analytical approach is adopted by integrating the Swiss Cheese model and the SHELL model. The Swiss Cheese model is used to represent multilayer defensive structures, including governance and policy, perception, planning and decision-making, control and actuation, and human–machine interfaces. The SHELL model structures interaction failures between liveware and software, hardware, environment, and other liveware. The results reveal recurrent cross-layer failure pathways in which interface-level mismatches, such as low-salience alerts, sensor miscalibration, adverse environmental conditions, and inadequate handover communication, align with latent system weaknesses to produce unsafe outcomes. These findings demonstrate that autonomous driving safety failures are predominantly socio-technical in nature rather than purely technological. The proposed hybrid framework provides actionable insights for system designers, operators, and regulators by identifying critical intervention points for improving interface design, operational procedures, and policy-level safeguards in autonomous driving systems. Full article

To address the pervasive challenges of resource inefficiency and static management in the hospitality sector, this study proposes a novel management framework that synergistically integrates Model Predictive Control (MPC) with Green Human Resource Management (GHRM). Methodologically, the framework establishes a dynamic closed-loop architecture that cyclically links environmental sensing, predictive optimization, plan execution and organizational learning. The MPC component generates data-driven forecasts and optimal control signals for resource allocation. Crucially, these technical outputs are operationally translated into specific, actionable directives for employees through integrated GHRM practices, including real-time task allocation via management systems, incentives-aligned performance metrics, and structured environmental training. This practical integration ensures that predictive optimization is directly coupled with human behavior. Theoretically, this study redefines hospitality operations as adaptive sociotechnical systems, and advances the hospitality energy-saving management framework by formally incorporating human execution feedback, predictive control theory, and dynamic optimization theory. Empirical validation across a sample of 40 hotels confirms the framework’s effectiveness, demonstrating significant reductions in daily average water consumption by 15.5% and electricity usage by 13.6%. These findings provide a robust, data-driven paradigm for achieving sustainable operational transformations in the hospitality industry. Full article

Predictive maintenance (PdM) has emerged as a critical enabler for improving reliability, operational efficiency, and regulatory compliance in pharmaceutical manufacturing. Despite its proven effectiveness in several industrial sectors, PdM adoption within the pharmaceutical industry remains comparatively limited due to sector-specific technical, regulatory, and organizational constraints. This paper presents a structured technical analysis of recent academic and industrial works addressing PdM implementation in pharmaceutical manufacturing systems. The analysis examines applied AI and machine learning techniques, sensor and data acquisition strategies, implementation maturity, and regulatory considerations relevant to highly regulated environments. The findings indicate that, while PdM solutions can significantly improve equipment availability and reduce maintenance-related costs, major barriers persist, including limited failure data, validation and re-validation requirements, and organizational resistance to data-driven maintenance practices. Based on this analysis, the paper argues that hybrid approaches combining physics-based models (PBMs) with data-driven methods and explainable artificial intelligence (XAI), supported by digital twins and robust data governance frameworks, represent a practical and regulation-aware pathway for the broader adoption of predictive maintenance in pharmaceutical manufacturing Full article

A strong safety culture is essential for managing human factors in complex systems and constitutes a strategic resource for supporting the sustainable operation of organizations. However, conventional approaches remain limited by unclear conceptual boundaries and a lack of mechanisms linking safety culture with other organizational safety elements. To address these gaps, this study develops a sustainable safety culture construction method grounded in accident causation theory. Using the 24Model, we establish a concise “culture–system–ability–acts” framework that operationalizes the pathways through which safety culture shapes organizational safety performance. The method integrates four components: conceptual clarification of safety culture, quantitative assessment, factor identification based on the 24Model, and Bayesian network analysis to quantify interdependencies among culture, systems, ability, and acts. Empirical evidence from coal mining enterprises shows that safety culture influences safety performance indirectly by shaping system implementation quality, workers’ safety ability, and safety-related actions. Enhancing “demand of safety training” substantially mitigated system deficiencies related to ineffective implementation of procedures, failure in enforcing procedures, lack of qualifications, and insufficient supervision. Improved training also strengthened workers’ knowledge of accident cases, consequences of violations, and technical standards, thereby reducing competence-related gaps and promoting more consistent safety supervision behaviors. Sensitivity analysis highlights the importance of reinforcing “safety responsibilities of line departments” and improving the dissemination of safety knowledge, particularly accident case knowledge. Overall, the findings empirically validate the dynamic “culture–system–ability–acts” transmission mechanism of the 24Model and provide a structured, quantitative pathway for advancing sustainable safety culture development. Full article

This study examines how Green Human Resource Management (GHRM) is linked to sustainable organizational performance, encompassing environmental, economic, and social outcomes through the capability-building mechanisms of green innovation (GI) and green organizational culture (GOCL) in emerging manufacturing systems. Drawing on the Resource-Based View and capability-based sustainability perspectives, GHRM is conceptualized as a strategic organizational capability that enables firms in developing economies to beyond short-term regulatory compliance toward measurable and integrated sustainability performance outcomes. Survey data were collected from 446 managerial and technical respondents in Ethiopia’s garment and textile industrial parks, one of Africa’s fastest-growing industrial sectors facing significant sustainability challenges. Using Partial Least Squares Structural Equation Modeling (PLS-SEM) with bootstrapping-based mediation analysis, the results show that GHRM is positively associated with sustainable organizational performance, with GI and GOCL operating as key mediating mechanisms that translate HR-related practices into measurable sustainability outcomes. The findings highlight the role of GHRM in strengthening firms’ adaptive and developmental sustainability capabilities by fostering pro-sustainability mindsets and innovation-oriented behaviors, which are particularly critical in resource-constrained and weak-institutional contexts. The study contributes to sustainability and management literature by explicitly linking Green HRM to triple-bottom-line performance through a capability-building framework and by providing rare firm-level empirical evidence from a low-income emerging economy. Practically, the results provide guidance for managers and policy makers to design, monitor, and evaluate HRM systems that intentionally cultivate human, cultural, and innovative capabilities to support long-term organizational sustainability transitions. Full article

Explainability in deep learning (XDL) for healthcare is increasingly portrayed as essential for addressing the “black box” problem in clinical artificial intelligence. However, this universal transparency mandate may create unintended consequences, including cognitive overload, spurious confidence, and workflow disruption. This paper examines a fundamental question: Is explainability a panacea that resolves AI’s trust deficit, or a Pandora’s box that introduces new risks? Drawing on general systems theory we demonstrate that the answer is profoundly context dependent. Through systemic analysis of current XDL methods, Saliency Maps, LIME, SHAP, and attention mechanisms, we reveal systematic disconnects between technical transparency and clinical utility. This paper argues that XDL is a context-dependent systemic property rather than a universal requirement. It functions as a panacea when proportionately applied to high-stakes reasoning tasks (cancer treatment planning, complex diagnosis) within integrated socio-technical architectures. Conversely, it becomes a Pandora’s box when superficially imposed on routine operational functions (scheduling, preprocessing) or time-critical emergencies (e.g., cardiac arrest) where comprehensive explanation delays lifesaving intervention. The paper proposes a risk-stratified framework recognizing that a specific subset of healthcare AI applications—those involving high-stakes clinical reasoning—require comprehensive explainability, while other applications benefit from calibrated transparency appropriate to their clinical context. We conclude that explainability is neither a cure-all nor an inevitable harm, but rather a dynamic equilibrium requiring continuous rebalancing across technical, cognitive, and organizational dimensions. Full article

Large language models (LLMs) and other foundation models are rapidly being woven into enterprise analytics workflows, where they assist with data exploration, forecasting, decision support, and automation. These systems can feel like powerful new teammates: creative, scalable, and tireless. Yet they also introduce distinctive risks related to opacity, brittleness, bias, and misalignment with organizational goals. Existing work on AI ethics, alignment, and governance provides valuable principles and technical safeguards, but enterprises still lack practical frameworks that connect these ideas to the specific metrics, controls, and workflows by which analytics teams design, deploy, and monitor LLM-powered systems. This paper proposes a conceptual governance framework for enterprise AI and analytics that is explicitly centered on LLMs embedded in analytics pipelines. The framework adopts a three-layered perspective—model and data alignment, system and workflow alignment, and ecosystem and governance alignment—that links technical properties of models to enterprise analytics practices, performance indicators, and oversight mechanisms. In practical terms, the framework shows how model and workflow choices translate into concrete metrics and inform real deployment, monitoring, and scaling decisions for LLM-powered analytics. We also illustrate how this framework can guide the design of controls for metrics, monitoring, human-in-the-loop structures, and incident response in LLM-driven analytics. The paper concludes with implications for analytics leaders and governance teams seeking to operationalize responsible, scalable use of LLMs in enterprise settings. Full article

In an increasingly technologized and automated world, workplace safety and health remain a major global challenge. After decades of regulatory frameworks and substantial technical and organizational advances, the expanding interaction between humans and machines and the growing complexity of work systems are gaining importance. In parallel, the digitalization of Industry 4.0/5.0 is generating unprecedented volumes of safety-relevant data and new opportunities to move from reactive analysis to proactive, data-driven prevention. This review maps how artificial intelligence (AI), with a specific focus on natural language processing (NLP) and large language models (LLMs), is being applied to occupational risk prevention across sectors. A structured search of the Web of Science Core Collection (2013–October 2025), combined OSH-related terms with AI, NLP and LLM terms. After screening and full-text assessment, 123 studies were discussed. Early work relied on text mining and traditional machine learning to classify accident types and causes, extract risk factors and support incident analysis from free-text narratives. More recent contributions use deep learning to predict injury severity, potential serious injuries and fatalities (PSIF) and field risk control program (FRCP) levels and to fuse textual data with process, environmental and sensor information in multi-source risk models. The latest wave of studies deploys LLMs, retrieval-augmented generation and vision–language architectures to generate task-specific safety guidance, support accident investigation, map occupations and job tasks and monitor personal protective equipment (PPE) compliance. Together, these developments show that AI-, NLP- and LLM-based systems can exploit unstructured OSH information to provide more granular, timely and predictive safety insights. However, the field is still constrained by data quality and bias, limited external validation, opacity, hallucinations and emerging regulatory and ethical requirements. In conclusion, this review positions AI and LLMs as tools to support human decision-making in OSH and outlines a research agenda centered on high-quality datasets and rigorous evaluation of fairness, robustness, explainability and governance. Full article

The rapid advancement of Artificial Intelligence (AI) has profoundly transformed organizational systems, reshaping how employees interact with technology and adapt to changing work environments. However, the systemic mechanisms through which AI-induced technostress influences employee career growth remain insufficiently understood. Grounded in a socio-technical systems perspective, this study conceptualizes organizations as adaptive systems where technological, organizational, and human subsystems dynamically interact. We propose a dual-path framework that distinguishes between challenge-related technostressors (a resource-gain process) and hindrance-related technostressors (a resource-loss process), elucidating how AI-related pressures can simultaneously foster and hinder career development. Furthermore, employee resilience and organizational AI support are incorporated as systemic moderators that modulate the intensity of these effects within the human–AI–organization system. Using two-stage survey data from 326 matched pairs of employees and supervisors, results largely support the proposed model, with some pathways showing marginal significance. The findings reveal that AI challenge-related technostressors stimulate proactive adaptation and skill development, whereas hindrance-related technostressors generate anxiety and insecurity, thereby impeding growth. This research extends systems theory by demonstrating how technostressors function as an emergent property of human–technology interactions and provides actionable insights for designing more adaptive and resilient socio-technical work systems. Full article

Background/Objectives: Home care plays a crucial role in contemporary healthcare systems, particularly in the long-term care of people with chronic and progressive illnesses. Family caregivers often experience substantial physical, emotional, and organizational burden. Telemedicine and digital health applications have the potential to support home care by improving health monitoring, communication, and care coordination. However, their use among family caregivers remains inconsistent, and little is known about how organizational support structures such as telemedicine centers influence acceptance and everyday use. This study aims to examine the benefits of telemedicine in home care and to evaluate the role of telemedicine centers as supportive infrastructures for family caregivers. Methods: A mixed-methods design was applied. Quantitative data were collected through an online survey of 58 family caregivers to assess the use of telemedicine and digital health applications, perceived benefits, barriers, and support needs. This was complemented by an in-depth qualitative case study exploring everyday caregiving experiences with telemedicine technologies and telemedicine center support. A systematic literature review informed the theoretical framework and the development of the empirical instruments. Results: Most respondents reported not using telemedicine or digital health applications in home care. Among users, telemedicine was associated with perceived improvements in quality of care, particularly through enhanced health monitoring, improved communication with healthcare professionals, and increased feelings of safety and control. Key barriers to adoption included technical complexity, data protection concerns, and limited digital literacy. Both quantitative findings and the qualitative case study highlighted the importance of structured support. Telemedicine centers were perceived as highly beneficial, providing technical assistance, training, coordination, and ongoing guidance that facilitated technology acceptance and sustained use. Conclusions: Telemedicine and digital health applications can meaningfully support home care and reduce caregiver burden when they are embedded in supportive socio-technical structures. Telemedicine centers can function as central points of contact that enhance usability, trust, and continuity of care. The findings suggest that successful implementation of telemedicine in home care requires not only technological solutions but also accessible organizational support and targeted training for family caregivers. Full article

This paper presents the design, implementation, and evaluation of a low-code document management and approval system developed on the Microsoft Power Platform. The solution integrates Power Apps, Power Automate, SharePoint Online, and Azure Active Directory to enable secure, traceable, and device-independent workflows for managing organizational documents. By combining graphical interfaces, automated approval logic, and enterprise-grade identity management, the system supports real-time collaboration and compliance with records’ governance standards. A comparative analysis with traditional enterprise content management and open-source web architectures demonstrates substantial advantages in deployment speed, scalability, and auditability. Empirical results from a six-week pilot involving multiple users indicate a reduction in approval cycle time, high user satisfaction, and strong cost-efficiency relative to conventional development models. The findings highlight how low-code ecosystems operationalize digital transformation by empowering non-technical users to automate complex workflows while maintaining security and governance integrity. This work contributes to the understanding of information system democratization, showing that low-code platforms can extend digital participation, improve organizational agility, and support sustainable operational efficiency across distributed environments. Full article

Background/Objectives: In the context of evolving healthcare systems, effective communication represents a fundamental skill for ensuring quality care and addressing the psychosocial needs of oncology patients. In line with the new challenges of nursing education, this study explores communication between nurses and oncology patients, analyzing facilitating and hindering factors from the nurses’ perspective within the hospital setting. Methods: A descriptive qualitative study was conducted using one-on-one semi-structured interviews. The interviews lasted from 15 to 30 min. The study population consisted of nurses working in the Medical Oncology units of the Regina Elena Institute in Rome (IRE). Data were analyzed using the Framework Analysis method by Ritchie and Spencer. Results: The sample consisted of 20 nurses with an average age of 33.5 years. Six main themes emerged: communication as the pillar of the care relationship between technical and human aspects, the need for a balance between closeness and personal protection, the influence of language and personalized approaches on communication, the stimulation of specific training needs, and barriers to nursing communication. Conclusions: Nurses recognize communication as an integral part of the care process and as a key competency for addressing the complex needs of oncology patients. However, inadequate training, time constraints, and staff shortages represent significant barriers, highlighting the need to invest in specific training programs and organizational strategies to improve the quality of care. Full article

Digital Transformation (DT) is a strategic challenge that reshapes the way companies operate. Nevertheless, its adoption in the construction industry remains slow. This paper analyzes the DT process in Brazilian construction companies through two phases. Initially, an exploratory study was conducted with 17 firms using semi-structured interviews with their Technical Directors. Second, three companies were selected for case studies involving 14 in-depth interviews, observation, and document analysis. Data underwent content analysis. In the exploratory phase, DT was found to be mainly pursued to improve construction efficiency. Barriers were strongly associated with individual aspects, especially limited knowledge about technologies and resistance to change, reinforced by difficulties in implementing organizational changes. Most problems that DT seeks to address are concentrated in the technical department and construction site. Companies adopted approaches such as technology investments, open innovation, organizational restructuring, and training, but the success of these strategies depends on top management engagement and employee acceptance. Besides cultural barriers, technological obstacles, system integration and digital delay were identified, along with process difficulties such as the complexity and costs of the DT journey. Indirect sustainability objectives also emerged, indicating that DT is perceived as both technological advancement and a means to transform the sector. Finally, based on the empirical findings, a multi-level framework comprising 12 strategies for DT in the construction industry was proposed. Overall, the empirical field investigated remains in the early stages of DT, with experimentation with technologies and a focus on efficiency, characteristics of digitization, a step prior to total transformation. The study provides a valuable diagnosis of DT to support the digital transition and informs policymakers in designing initiatives that foster DT, contributing to sector sustainability and SDG9. Full article