Search Results (150)

Background/Objectives: Healthcare professionals operate in complex and demanding environments characterized by high workloads, emotional strain, and organizational pressures that can undermine well-being. According to Self-Determination Theory, the fulfillment of core psychological needs (autonomy, competence, and relatedness) leads to increased job satisfaction, a key indicator of occupational well-being. Additionally, leadership plays a central role in shaping needs-fulfilling environments. Drawing on Leader–Member Exchange Theory (LMX), which emphasizes that high-quality leader-follower relationships foster greater discretion, provide learning opportunities, and build constructive team interactions, this study aimed to examine whether supportive leadership is associated with job satisfaction through the mediation of autonomy, team task cohesion, and perceived training opportunities. Methods: Data were collected from a local health authority in Northern Italy through an anonymous online survey, completed by 697 healthcare professionals, including 546 non-medical healthcare staff (primarily nurses) and 151 physicians. Structural equation modeling with a robust maximum likelihood estimator was employed to test the mediation model, including professional role as a covariate. Results: Higher LMX was positively and directly associated with job satisfaction, through the partial mediation of autonomy, team cohesion, and training opportunities, all positively associated with satisfaction. Team task cohesion showed the strongest associations with both LMX and satisfaction. Physicians reported slightly higher levels of autonomy, training opportunities, and job satisfaction than non-medical professionals. Conclusions: The findings suggest that supportive leadership contributes to healthcare professionals’ job satisfaction both directly and indirectly by contributing to core needs fulfillment. Interventions that strengthen relational quality, promote team cohesion, and enhance professional development may help sustain well-being and adaptive functioning in high-demand healthcare environments. Full article

Intra-urban commuting plays a fundamental role in shaping urban spatial structure and daily mobility patterns. Existing studies have largely explained commuting flows using attribute-based or distance-centred approaches. Such approaches overlook the interdependent and relational nature of commuting within complex urban systems. This study constructs a subdistrict-level commuting network using anonymised mobile phone signalling data from Hangzhou, China, and a valued exponential random graph model (valued ERGM) to examine how commuting flows are generated through the interaction of network self-organization, local job-housing conditions, and multi-dimensional proximity. The results reveal strong endogenous dependence exemplified by reciprocal commuting ties. Employment agglomeration and public rental housing provision are associated with stronger integration of subdistricts within the commuting network, while high housing prices and certain residential amenities are associated with reduced inter-subdistrict commuting. Beyond geographic distance, metro connectivity, administrative affiliation, and social interaction are significantly associated with commuting flows. This study advances a relational explanation of intra-urban commuting and demonstrates the methodological value of valued ERGMs for analysing weighted urban flow networks. The findings have implications for integrated transport, housing, and governance strategies, particularly transit-oriented development, cross-jurisdictional coordination, and the strategic siting of affordable housing, aimed at promoting more locally embedded and sustainable urban mobility. Full article

Green innovation is a crucial aspect of an enterprise’s core competitiveness and long-term sustainable development, garnering significant attention from both academic scholars and industry practitioners. However, while existing research has primarily focused on green innovation at the organizational level, the mechanisms driving green innovation behaviors at the individual level have not been thoroughly explored in the literature. This study is grounded in the classic Job Demands–Resources (JD-R) theoretical framework and highlights the interplay between job demands (such as environmental ethics and corporate environmental strategies) and job resources (such as green human resource management practices and green transformational leadership). It also integrates individual-level characteristics, specifically green mindfulness and connectedness to nature, to construct a multidimensional interactive model aimed at uncovering the complex mechanisms driving employees’ green innovation. To achieve this, the study employs fuzzy-set qualitative comparative analysis (fsQCA). The findings suggest that no single condition is necessary for employee green innovation. However, connectedness to nature consistently appears across all core configurations, indicating a prominent “enabling” effect. This suggests that employee green innovation is an active and proactive form of environmentally responsible behavior, largely driven by individuals’ emotional affinity with nature. Additionally, connectedness to nature serves as a foundational source of intrinsic motivation for environmental awareness and acts as a catalyst across multiple pathways. Configurational analysis reveals an equifinal pattern, identifying three distinct motivational pathways: (1) Self-motivation Combined with Resource Support; (2) Self-motivation Combined with Job Demands; and (3) Triple Interaction of Demand, Resources, and Individuals. This study possesses both theoretical and practical significance in systematically examining green innovation behaviors at the individual level. Full article

The growing complexity of school leadership, intensified by increasing accountability and administrative demands, has heightened the need to understand principals’ wellbeing. This study examines the wellbeing of 161 public primary school principals in Greece, focusing on the factors that shape their professional experience and overall functioning. Survey findings indicate generally high levels of emotional, cognitive, social, psychological, and spiritual wellbeing, although physical wellbeing was noticeably lower. A significant gender difference emerged, with male principals reporting higher overall wellbeing than female principals, highlighting the relevance of gendered experiences in leadership roles. Job satisfaction also proved central, showing a strong positive association with all six dimensions of wellbeing and underscoring its importance for sustaining principals’ resilience and effectiveness. Beyond individual characteristics, several organizational factors—including relationships with staff, working conditions, school climate, and administrative workload—were identified as key contributors to principals’ wellbeing and daily practice. These conditions illustrate how organizational environments can enhance or strain leaders’ capacity to navigate evolving role expectations. Qualitative insights further clarified how personal attributes and school-level circumstances interact with these broader dynamics. Overall, the study illuminates the interplay between gender, job satisfaction, and contextual factors in shaping principals’ wellbeing, offering evidence to inform targeted support within contemporary educational settings. Full article

In this paper, a Multiple-Objective Memetic Algorithm (MOMA) is proposed to address the Energy-Efficient Distributed Assembly Permutation Flow-Shop Scheduling Problem (EEDAPFSP) by explicitly exploiting the structural and objective symmetries inherent in the scheduling process, with the dual objectives of minimizing the maximum completion time (makespan) and total energy consumption (TEC). The EEDAPFSP is a complex NP-hard optimization problem in modern sustainable manufacturing that balances production efficiency and environmental sustainability. During the global search phase, a symmetry-preserving dual-search framework is constructed, in which diverse and potential regions in the solution space are explored by symmetrically generating time-dominant product sub-sequences (TDPSs) and energy-dominant product sub-sequences (EDPSs) in the individuals of each iteration, enabling complementary exploration from time- and energy-oriented perspectives. This is accomplished through the incorporation of a variable-weight metric technique and a first product fixed strategy into an estimation distributed algorithm-based hyper-heuristic (EDAHH), so as to maintain a balanced and symmetric probabilistic modeling of decision patterns with respect to the makespan and energy consumption. In the local search phase, two problem-specific designed neighborhood structures are proposed to refine the job sequences corresponding to the TDPS and EDPS in the superior sub-population, effectively reducing both the makespan and TEC. A box-level $\epsilon$ dominance technique based on the crowding distance is proposed for Pareto archive updating. Additionally, an energy-saving strategy is embedded throughout the algorithm, incorporating three mechanisms—job processing delay, machine shutdown and restart control, and speed regulation—to further optimize TEC during both the global and local search phases. Finally, extensive computational experiments are carried out, and the results demonstrate that the MOMA achieves significantly better performance in terms of the inverted generational distance ($IGD$) and the quality metric $\rho$ compared with state-of-the-art algorithms. The resulting Pareto front of non-dominated solutions provides a comprehensive set of trade-offs between energy consumption and the makespan, offering decision makers flexible and efficient scheduling options. Full article

Background/Objectives: Missed Nursing Care is a global concern that affects nurses’ well-being and patients’ safety. Despite global recognition of Missed Nursing Care, there is limited synthesized evidence that determines its characteristics in a Middle Eastern context. The purpose of the study is to synthesize the existing evidence about the prevalence of Missed Nursing Care among nurses in hospitals, the types of care missed, and reasons for Missed Nursing Care in the Middle East. Methods: A systematic literature review is conducted by using a comprehensive search in CINAHL, Scopus, and ScienceDirect databases for studies published between 2020 and 2025 and utilizing the MISSCARE Survey. Results: 25 studies met the inclusion criteria. The reported prevalence of Missed Nursing Care ranged between 1.06 and 2.9 out of five, indicating a low to moderate level. Frequent missed care activities included ambulation, hygiene, mouth care, and patient teaching. Contributing factors were staffing shortages, heavy workload, resource limitations, and communication issues. Missed Nursing Care critically affected patients’ outcomes, reduced job satisfaction, and caused moral distress and a higher intent to leave the profession. Conclusions: Missed Nursing Care remains a significant, complex challenge in the Middle East. Therefore, understanding this phenomenon in the region is needed. Collaborative efforts among policymakers, administrators, and nursing leaders are essential to implement targeted interventions, supportive policies, and ongoing research to minimize Missed Nursing Care across the Middle East. Full article

Artificial intelligence (AI), a transformative force, has revolutionised various aspects of human life and business operations. This has led to a drastic mutation of the career landscape, embedded with vast opportunities as well as challenges, particularly concerning career sustainability (CS). Despite myriad studies on CS, the paradoxical interplay of AI and CS remains underexplored, particularly for expatriates (expats). To address the aforementioned gap, our study incorporates an affordance perspective (AFP), positioning AI as an object and CS as a user context. Specifically, this study investigates whether AI facilitates the orchestration of an enhanced sustainable career within the boundary conditions of knowledge sharing (KS), encompassing both tacit and explicit knowledge pertinent to AI, cultivated through managerial initiatives and employee-driven activities. The study conducted a quantitative survey among 490 expats working in AI-integrated environments in China. The results reveal a curvilinear (U-shaped) relationship between AI and CS, where AI affordance at a moderate level enhances career adaptability and skill development. However, digital affordances become complex beyond a certain threshold, creating several career concerns, such as job insecurity and role ambiguity. Furthermore, the moderating effect of tacit and explicit KS mitigates numerous career disruptions while fostering long-term career growth. The study framed AI as both a tool and a collaborator that illuminates the importance of AI–human intelligence (AI–HI) synergy and knowledge augmentation in navigating digital transitions. Moreover, implications for international career development and human-oriented digital transformation are also discussed. Full article

The opto-mechanical automated manufacturing process, characterized by stringent process constraints, dynamic disturbances, and conflicting optimization objectives, presents significant control challenges for traditional scheduling and control approaches. We formulate the scheduling problem within a closed-loop control paradigm and propose a novel bi-level intelligent control framework integrating Deep Reinforcement Learning (DRL) and Bayesian Optimization (BO). The core of our approach is a bi-level intelligent control framework. An inner DRL agent acts as an adaptive controller, generating control actions (scheduling decisions) by perceiving the system state and learning a near-optimal policy through a carefully designed reward function, while an outer BO loop automatically tunes the DRL’s hyperparameters and reward weights for superior performance. This synergistic BO-DRL mechanism facilitates intelligent and adaptive decision-making. The proposed method is extensively evaluated against standard meta-heuristics, including Genetic Algorithm (GA) and Particle Swarm Optimization (PSO), on a complex 20-jobs × 20-machines flexible job shop scheduling benchmark specific to opto-mechanical automated manufacturing. The experimental results demonstrate that our BO-DRL algorithm significantly outperforms these benchmarks, achieving reductions in makespan of 13.37% and 25.51% compared to GA and PSO, respectively, alongside higher machine utilization and better on-time delivery. Furthermore, the algorithm exhibits enhanced convergence speed, superior robustness under dynamic disruptions (e.g., machine failures, urgent orders), and excellent scalability to larger problem instances. This study confirms that integrating DRL’s perceptual decision-making capability with BO’s efficient parameter optimization yields a powerful and effective solution for intelligent scheduling in high-precision manufacturing environments. Full article

Fog computing is one of the rapidly growing platforms with an exponentially increasing demand for real-time data processing. The fog computing market is expected to reach USD 8358 million by the year 2030 with a compound annual growth of 50%. The wide adaptation of fog computing by the industries worldwide is due to the advantages like reduced latency, high operational efficiency, and high-level data privacy. The highly distributed and heterogeneous nature of fog computing leads to significant challenges related to resource management, data security, task scheduling, data privacy, and interoperability. The task typically represents a job generated by the IoT device. The action indicates the way of executing the tasks whose decision is taken by the scheduler. Task scheduling is one of the prominent issues in fog computing which includes the process of effectively scheduling the tasks among fog devices to effectively utilize the resources and meet the Quality of Service (QoS) requirements of the applications. Improper task scheduling leads to increased execution time, overutilization of resources, data loss, and poor scalability. Hence there is a need to do proper task scheduling to make optimal task distribution decisions in a highly dynamic resource-constrained heterogeneous fog computing environment. Flow Q learning (FQL) is a potential form of reinforcement learning algorithm which uses the flow matching policy for action distribution. It can handle complex forms of data and multimodal action distribution which make it suitable for the highly volatile fog computing environment. However, flow Q learning struggles to achieve a proper trade-off between the expressive flow model and a reduction in the Q function, as it relies on a one-step optimization policy that introduces bias into the estimated Q function value. The Tiny Language Model (TLM) is a significantly smaller form of a Large Language Model (LLM) which is designed to operate over the device-constrained environment. It can provide fair and systematic guidance to disproportionally biased deep learning models. In this paper a novel TLM guided flow Q learning framework is designed to address the task scheduling problem in fog computing. The neutrality and fine-tuning capability of the TLM is combined with the quick generable ability of the FQL algorithm. The framework is simulated using the Simcan2Fog simulator considering the dynamic nature of fog environment under finite and infinite resources. The performance is found to be good with respect to parameters like execution time, accuracy, response time, and latency. Further the results obtained are validated using the expected value analysis method which is found to be satisfactory. 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

Employee retention remains a critical challenge in human resource management (HRM). Embeddedness theory offers a vital framework to understand retention dynamics, yet its development within HRM lacks systematic mapping. This study systematically examines the intellectual evolution, thematic clusters, and emerging trends of embeddedness theory in HRM from 1995 to 2025, addressing three research questions: (1) How has the theory developed over time? (2) What are the key research themes and conceptual structures? (3) Which emerging trends can inform future HRM practice? A bibliometric and science mapping analysis was conducted on 562 peer-reviewed articles from Web of Science using co-citation, co-word clustering, and keyword evolution techniques. Three distinct phases were identified: formative (1995–2005), expansion (2006–2015), and maturation (2016–2025). Findings reveal a dual focus on micro-level constructs, including job satisfaction and turnover intention, and macro-level themes, such as organizational commitment and performance. Recent trends highlight organizational and institutional contexts, cross-cultural perspectives, and post-pandemic dynamics. The study provides the first comprehensive longitudinal mapping of embeddedness theory in HRM, clarifying its intellectual structure, key contributors, and evolving research frontiers. These insights offer actionable guidance for scholars and practitioners, emphasizing the integration of multi-level and contextual factors to enhance employee retention in increasingly complex and globalized work environments. Full article

This study explores the complex psychological mechanisms linking career adaptability to work engagement under AI-driven workplaces. We examine the mediating role of job satisfaction and investigate a key hypothesis: that the adaptive benefits of AI self-efficacy are dampened by the emotional costs associated with AI anxiety. A dual-analytical approach was employed on a sample of 311 young Chinese workers. First, we conducted conditional process analysis using PROCESS Model 11 with 5000 bootstrapped samples to test for conditional indirect effects. Second, we utilized latent variable structural equation modeling for robust validation at the structural level. Analyses were adjusted for demographic and occupational covariates. As a result, the initial PROCESS analysis revealed that the key triple interaction (career adaptability × AI self-efficacy × AI anxiety) was statistically significant in all three test models (e.g., Model 1: b = −0.3509, p = 0.0075). Further analysis showed that the positive moderating effect of AI self-efficacy was contingent on AI anxiety; it was strongest at low AI anxiety and weakest (but still significant) at high AI anxiety. However, the more robust latent variable SEM (CMIN/DF = 1.569, CFI = 0.939, RMSEA = 0.043) revealed a critical separation of effects. The indirect effect operates exclusively through intrinsic job satisfaction, which was significantly predicted by the unified second-order career adaptability factor (b = 1.361, BCa 95% CI [1.023, 1.967]). The path from extrinsic satisfaction to WE was non-significant (b = 0.107, BCa 95% CI [−0.030, 0.250]). Furthermore, the SEM isolated a significant direct positive effect from the unified career adaptability factor to work engagement (b = 0.715, BCa 95% CI [0.385, 1.396]). This study highlights that the adaptability–engagement link operates via two distinct mechanisms: an indirect pathway from a unified career adaptability construct through intrinsic job satisfaction, and a direct pathway from career adaptability to work engagement. While PROCESS analysis suggests that anxiety dampens confidence, our SEM results clarify that this should be interpreted cautiously, as the mediation pathway via extrinsic satisfaction is not robust to measurement error. These findings underscore a multi-faceted mandate for organizations: leaders must not only manage AI anxiety but also foster holistic career adaptability to enhance intrinsic job quality and build direct engagement. Full article

The rapid emergence of generative AI is transforming how employees engage with technology to perform tasks, make decisions, and create value. Despite its transformative potential, empirical findings on AI adoption remain inconsistent, particularly regarding how job characteristics shape employees’ confidence and readiness to use generative AI. Grounded in the Task–Technology Fit framework and self-efficacy theory, this research examines the curvilinear relationship between job complexity and AI self-efficacy and its subsequent effects on AI adoption readiness and behavior. We conducted two survey studies to test the proposed hypotheses using structural equation modeling. Results reveal that employees in both low- and high-complexity roles exhibit a low level of AI self-efficacy and a subsequent lower level of AI adoption behaviors compared to those in moderately complex roles. These findings challenge the assumption that highly skilled roles typically lead AI integration and instead highlight the importance of aligning task structure with AI capabilities. This study advances theory by introducing a non-linear boundary condition to technology adoption and offers practical guidance for organizations to design jobs and training programs that cultivate confidence and foster sustainable human–AI collaboration. Full article

This article introduces the ATHENA competency model, a systemic framework designed to conceptualize and support the development of creativity and complex skills in professional and educational contexts. Creativity, increasingly seen as essential across sectors, requires the coordination of cognitive, motivational, emotional, social, and sensorimotor resources. ATHENA conceptualizes competencies as emergent, agentic behaviors, not static possessions, arising from the coordination of five dimensions: cognition, conation, knowledge, emotion, and sensorimotion. These are subdivided into 60 facets, each described across four progressive mastery levels, enabling fine-grained diagnosis and developmental roadmaps. To operationalize this framework, ATHENA includes three modules: Skills, which models the requirements of professional tasks; Profile, which analyzes learner populations and contextual constraints; and LEARN, a repertory of pedagogical activities linked to ATHENA facets. The article illustrates the system through two case studies of creative job activities—graphic design and workshop facilitation—demonstrating how ATHENA aligns abstract competencies with practical training interventions. The model bridges theoretical research in psychology, creativity, and education with instructional design. Future work aims to refine its applicability, scalability, and cross-cultural relevance. Full article

Cold rolling is the precision finishing stage in the steel production process, and its scheduling optimization is essential for enhancing production efficiency. To address the complex process constraints and objectives, this paper proposes a multi-granularity random mutation genetic algorithm (MGRM-GA) for cold rolling scheduling optimization. First, a multi-objective collaborative optimization model is established to integrate the production cost and process constraints. Then, high-quality initial solutions are generated based on greedy heuristic rules to fulfill the cold rolling constraints. Finally, four random mutation strategies are designed at different task granularities and unit levels to search diverse candidates. The standard flexible job shop scheduling problem (FJSP) datasets and practical cold rolling production data are studied to validate the feasibility and competitiveness of the MGRM-GA. Experimental results show that the MGRM-GA achieves a 94.2% improvement in objective function optimization, a 14.8-fold increase in throughput, and a 94.8% reduction in execution time on cold rolling data. Compared with the heuristic mutation algorithm, MGRM-GA increases population heterogeneity and avoids premature convergence, which enhances global search ability and scheduling performance. Full article