Search Results (531)

Small and medium-sized enterprises (SMEs) often face severe resource constraints and operational fragility during crises. However, little is known about how managerial resilience (MR) translates into performance through time-related psychological resources and innovation—two capabilities that are both scarce and critical under such conditions. Drawing on Temporal Motivation Theory (TMT), this study develops and tests a dual-mediation model in which employee temporal psychological capital (TPC) and employee innovative behavior (EIB) transmit the effects of MR on performance. As a core methodological innovation, we adopt a multi-method analytical strategy to provide robust and complementary evidence rather than a hierarchy of results: Partial Least Squares Structural Equation Modeling (PLS-SEM) is used to examine sufficiency-based causal pathways and quantify the mediating mechanisms; Support Vector Machine (SVM) classification offers a non-parametric predictive validation of how MR and its mediators distinguish high- and low-performance cases; and Necessary Condition Analysis (NCA) identifies non-compensatory conditions that must be present for high performance to occur. These three methods address different research questions—sufficiency, classification robustness, and necessity—therefore serving as parallel, equally important components of the analysis. A total of 455 SME managers and employees were surveyed, and results show that MR significantly enhances all three dimensions of TPC (temporal control, temporal fit, time pressure resilience) and EIB (idea generation, idea promotion, idea realization), which in turn improve employee performance. SVM classification confirms that high MR, strong TPC, and active innovation align with high performance, while NCA reveals temporal control, idea generation, and idea realization as necessary bottleneck conditions. By integrating sufficiency–necessity logic with predictive classification, our findings suggest that SMEs should prioritize leadership resilience training to strengthen managers’ adaptive capacity, while simultaneously implementing time management interventions—such as temporal control workshops, workload balancing, and innovation pipeline support—to enhance employees’ ability to align tasks with organizational timelines, execute ideas effectively, and sustain performance during crises. Full article

This paper presents a novel ontology-driven digital twin framework specifically designed for aviation maintenance and operations that addresses these challenges through semantic reasoning and explainable decision support. The proposed framework integrates seven interconnected ontologies—structural, functional, behavioral, monitoring, maintenance, lifecycle, and environmental. It collectively provides a comprehensive semantic representation of aircraft systems and their operational context. Each ontology is mathematically formalized using description logics and graph theory, creating a unified knowledge graph that enables transparent, traceable reasoning from sensor observations to maintenance decisions. The digital twin is formally defined as a 6-tuple that incorporates semantic transformation engines, cross-ontology mappings, and dynamic reasoning mechanisms. Unlike traditional data-driven approaches that operate as black boxes, the ontology-driven framework provides explainable inference capabilities essential for regulatory compliance and safety certification in aviation. The semantic foundation enables causal reasoning, rule-based validation, and context-aware maintenance recommendations while supporting standardization and interoperability across manufacturers, airlines, and regulatory bodies. The research contributes a mathematically grounded, semantically transparent framework that bridges the gap between domain knowledge and operational data in aviation maintenance. This work establishes the foundation for next-generation cognitive maintenance systems that can support intelligent, adaptive, and trustworthy operations in modern aviation ecosystems. Full article

Digital supply chain management (DSCM) has emerged as a critical driver of enterprise performance in the modern economy, yet empirical evidence on its causal impact on productivity remains limited. This study examines how DSCM adoption affects total factor productivity (TFP) by leveraging China’s supply chain innovation pilot program as a quasi-natural experiment. Using a difference-in-differences approach with propensity score matching, the analysis employs a comprehensive dataset of 2843 Chinese A-share listed companies from 2013 to 2022; the analysis reveals that DSCM adoption leads to an average TFP increase of 14.1%. This positive effect strengthens over time, demonstrating a clear dynamic of organizational learning. Mediation analysis indicates that this productivity enhancement operates through two primary channels: innovation capability enhancement (accounting for approximately 35% of the total effect) and cost efficiency improvement (21%). Crucially, heterogeneity analysis reveals that the positive effects of DSCM are significantly more pronounced in supply-chain-intensive industries, such as manufacturing, and for firms with higher R&D intensity. The findings provide robust causal evidence on the productivity effects of DSCM, offering valuable insights into its underlying mechanisms and key boundary conditions for both enterprise strategy and digital transformation policy. Full article

Background: Chemokines play a pivotal role in the progression of osteoarthritis (OA), but their exact mechanisms remain unclear. This study aimed to identify potential chemokine-associated biomarkers and investigate their causal relationships with OA. Methods: Transcriptome and genome-wide association study (GWAS) data were obtained from public databases, while chemokine-related genes (CRGs) were sourced from the literature. Initially, CRGs were expanded, followed by Mendelian randomization (MR) analysis, differential expression analysis, machine learning, and receiver operating characteristic (ROC) curve plotting to identify potential biomarkers. The causal relationships between these biomarkers and OA, as well as their biological functions, were further explored. Results: Fourteen candidate genes were identified for machine learning analysis, with DDIT3, CEBPB, CX3CR1, and ARHGAP25 emerging as feature genes. CEBPB and CX3CR1, which exhibited AUCs > 0.7 in the GSE55235 and GSE55457 datasets, were selected as potential biomarkers. Notably, CEBPB expression was lower, while CX3CR1 expression was elevated in the case group. Furthermore, both genes were co-enriched in spliceosome, lysosome, and cell adhesion molecule pathways. MR analysis confirmed that CEBPB and CX3CR1 were causally linked to OA and acted as protective factors (IVW model for CEBPB: OR = 0.9051, p = 0.0001; IVW model for CX3CR1: OR = 0.8141, p = 0.0282). Conclusions: CEBPB and CX3CR1 were identified as potential chemokine-related biomarkers, offering insights into OA and suggesting new avenues for further investigation. Full article

This study provides the first empirical evidence on the impact of innovation and firm growth on performance across G7 economies, using a unique panel dataset of 252 firms from 2020 to 2024. This study examines two core dimensions of firm performance—labor productivity and asset turnover—and employs multiple innovation proxies, including R&D Intensity, R&D-to-Assets, and R&D Growth Rate. To address potential endogeneity arising from reverse causality and omitted variable bias, the author implements the heteroskedasticity-based instrumental variable estimator, which constructs internal instruments from the model’s error structure. The study’s results reveal a consistent and significant positive causal effect of innovation on labor productivity, confirming its role as a driver of firm-level efficiency. However, innovation exhibits a negative and significant association with asset turnover, highlighting short-term trade-offs in operational efficiency, particularly in firms with aggressive R&D strategies. This study further finds that these effects are moderated by firm profitability and industry conditions, suggesting the importance of strategic and contextual alignment in innovation outcomes. Taken together, the findings offer new insights into the dual nature of innovation, enhancing productivity while imposing transitional efficiency costs and carrying significant implications for corporate innovation strategy and public policy in advanced economies. Full article

As rural populations age at an accelerating pace, the role of public spaces in enhancing the quality of life and promoting social engagement among older adults has become increasingly important. However, a significant cognitive gap persists between the actual needs of elderly users and the intentions of designers, often resulting in suboptimal design outcomes and underutilized spaces. This study centers on the concept of user–designer cognitive synergy, aiming to establish a systematic framework to bridge this cognitive divide and improve the design quality of age-friendly rural public spaces. Employing Grounded Theory, the FKANO model, and the DEMATEL method, this study systematically elicited user needs, classified their attributes, and mapped causal relations to determine priority drivers. Applied in a representative rural case, the framework identified Environmental and Operations Management and Spatial Accessibility as the most critical needs, while Smart-Friendliness and Safety Organization were also shown to play significant roles. These findings directly informed targeted spatial strategies such as barrier-free circulation, robust nighttime safety systems, intergenerational hubs, and an operations backbone. Post-occupancy evaluation confirmed high satisfaction across safety, accessibility, functionality, social participation, and environmental comfort. The results demonstrate the framework’s effectiveness in translating complex needs into actionable design strategies, offering both theoretical insights and practical guidance for age-friendly rural public space development. Full article

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion evolution focus on a single, fixed scale, which risks overlooking clearer causal patterns at other scales and thus limiting predictive power and practical applicability. To address this, we develop a multiscale congestion evolution modeling framework grounded in causal emergence theory. Within this framework we build dynamical models at multiple spatiotemporal scales using dynamic Bayesian networks (DBNs) and quantify the causal strength of these models using effective information (EI) and singular value decomposition (SVD)-based diagnostics. Using road networks from three central Kunshan regions, we validate the proposed framework across 24 spatiotemporal scales and five demand scenarios. Across all three regions and the tested scales, we observe evidence of causal emergence in congestion evolution dynamics. When results are pooled across regions and scenarios, models built at the 10 min/150 m scale exhibit stronger and more coherent causal structure than models at other scales. These findings demonstrate that the proposed framework can identify and help build dynamical models of congestion evolution at appropriate spatiotemporal scales, thereby supporting the development of proactive traffic management and effective resilience enhancement strategies for urban transport systems. Full article

In the cybersecurity attack and defense space, the “attacker” and the “defender” form a dynamic and symmetrical adversarial pair. Their strategy iterations and capability evolutions have long been in a symmetrical game of mutual restraint. We will introduce modern Intrusion Detection Systems (IDSs) from the defender’s side to counter the techniques designed by the attacker (APT attack). One major challenge faced by IDS is to identify complex attack paths from a vast provenance graph. By constructing an attack behavior tracking graph, the interactions between system entities can be recorded, but the malicious activities of attackers are often hidden among a large number of normal system operations. Although traditional methods can identify attack behaviors, they only focus on the surface association relationships between entities and ignore the deep causal relationships, which limits the accuracy and interpretability of detection. Existing graph anomaly detection methods usually assign the same weight to all interactions, while we propose a Causal Autoencoder for Graph Explanation (CAGE) based on reinforcement learning. This method extracts feature representations from the traceability graph through a graph attention network(GAT), uses Q-learning to dynamically evaluate the causal importance of edges, and highlights key causal paths through a weight layering strategy. In the DARPA TC project, the experimental results conducted on the selected three datasets indicate that the precision of this method in the anomaly detection task remains above 97% on average, demonstrating excellent accuracy. Moreover, the recall values all exceed 99.5%, which fully proves its extremely low rate of missed detections. Full article

To enhance the understanding and management of fire risks in subway stations, this study aims to identify critical fire risk transmission paths using an integrated PSR–DEMATEL–ISM approach. A comprehensive evaluation framework is first constructed based on the Pressure–State–Response (PSR) model, systematically categorizing 22 influencing factors into three dimensions: pressure, state, and response. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is then employed to analyze the causal relationships and centrality among these factors, distinguishing between cause and effect groups. Subsequently, Interpretive Structural Modeling (ISM) is applied to organize the factors into a multi-level hierarchical structure, enabling the identification of risk propagation pathways. The analysis reveals five high-centrality and high-causality factors: fire safety education and training, completeness of fire management rules and regulations, fire smoke detection and firefighting capability, operational status of monitoring equipment, and effectiveness of emergency response plans. Based on these key drivers, six major transmission paths are derived, reflecting the internal logic of fire risk evolution in subway environments. Among them, chains originating from Fire Safety Education and Training (S6), Architectural Fire Protection Design (S7), and Completeness of Fire Management Rules and Regulations (S16) exhibit the most significant influence on system-wide safety performance. This study provides theoretical support and practical guidance for proactive fire prevention and emergency planning in urban rail transit systems, offering a structured and data-driven approach to identifying vulnerabilities and improving system resilience. Full article

Despite widespread adoption, traditional Agile project management practices often fail to ensure successful delivery of enterprise-scale software projects. One key limitation lies in the absence of a conceptually defined structure for the various types of Agile activities and their interactions. As a result, Agile methodologies typically lack formal indicators for evaluating the semantic content and progress status of project activities. Although widely used tools for Agile project management, such as Atlassian Jira, capture operational data, project status assessment interpretation remains largely subjective—relying on the experience and judgment of managers and team members rather than on a formal knowledge model or well-defined semantic attributes. As Agile project activities continue to grow in complexity, there is a pressing need for a modeling approach that captures their causal structure in order to describe the essential characteristics of the processes and ensure systematic monitoring and evaluation of the project. The complexity of the corresponding model must correlate with the causality of processes to avoid losing essential properties and to reveal the content of causal interactions. To address these gaps, this paper introduces a causal Agile process model that formalizes the internal structure and transformation pathways of Agile activity types. To our knowledge, it is the first framework to integrate a recursive, causally grounded structure into Agile management, enabling both semantic clarity and quantitative evaluation of project complexity and progress. The aim of the article is, first, to describe conceptually different Agile activity types from a causal modeling perspective, its internal structure and information transformations, and, second, to formally define the causal Agile management model and its characteristics. Each Agile activity type (e.g., theme, initiative, epic, user story) is modeled using the management transaction (MT) framework—an internal model of activity that comprises a closed-loop causal relationship among management function (F), process (P), state attribute (A), and control (V) informational flows. Using this framework, the internal structure of Agile activity types is normalized and the different roles of activities in internal MT interactions are defined. An important feature of this model is its recursive structure, formed through a hierarchy of MTs. Additionally, the paper presents classifications of vertical and horizontal causal interactions, uncovering theoretically grounded patterns of information exchange among Agile activities. These classifications support the derivation of quantitative indicators for assessing project complexity and progress at a given point in time, offering insights into activity specification completeness at hierarchical levels and overall project content completeness. Examples of complexity indicator calculations applied to real-world enterprise application system (EAS) projects are included. Finally, the paper describes enhancements to the Jira tool, including a causal Agile management repository and a prototype user interface. An experimental case study involving four Nordic EAS projects (using Scrum at the team level and SAFe at the program level) demonstrates that the Jira tool, when supplemented with causal analysis, can reveal missing links between themes and initiatives and align interdependencies between teams in real time. The causal Agile approach reduced the total number of requirements by an average of 13% and the number of change requests by 14%, indicating a significant improvement in project coordination and quality. Full article

I show here that if we construct D-branes not in the form of infinite superpositions of string modes, in order to satisfy the technical condition of coherence by means of eigenstates of annihilation operators, but instead insist on an approximate but much more physical and practical definition based on phase coherence, we obtain finite (and hence realistic) superpositions of string modes that would form realistic D-branes that would encode (at least as a semiclassical approximation) various quantum properties. Re-deriving the AdS/CFT duality by starting in the pre-Maldacena limit from such realistic D-branes would lead to quantum properties on the AdS side of the duality. Causal structures can be modified in various many-particle systems, including strings, D-branes, photons, or spins; however, there is a distinction between the emergence of an effective causal structure in the inner degrees of freedom of a material, in the form of a correlation-generated effective metric, for example, in a spin liquid system, and the emergence of a causal structure in an open propagating system by using classical light. I will show how an Uhlmann gauge construction would add stability to a modified causal structure that would retain the shape of a closed causal loop. Various other ideas related to the quantum origin of the string length are also discussed and an analogy of the emergence of string length from quantum correlations with the emergence of wavelength of an electromagnetic wave from coherence conditions of photon modes is presented. Full article

Background: Mobility plays a fundamental role in causal reasoning (causal inference or cause–effect learning), which is essential for brain development at early ages. Children naturally develop causal reasoning through interaction with their environment. Therefore, children with severe motor disabilities (GMFCS levels IV–V), who face limited opportunities for interaction, often show delays in causal reasoning. Objective: This study investigates how a wheelchair-mounted, semi-autonomous mobility platform operated via a simple switch may enhance causal learning in children with severe disabilities, compared with traditional therapies. However, due to the scarcity of participants who meet the inclusion criteria and the need for long-term evaluation, recruitment poses a significant challenge. This study aims to provide an initial assessment of the platform and collect preliminary data to estimate the required sample size and number of sessions for future studies. Methods: We conducted a pilot randomized controlled trial (RCT) to assess platform usability and its effect on reaction time and keystroke accuracy. Four children, aged 8.5 ± 2.38, participated in seven 30 min sessions. They were randomly assigned in equal numbers, with two participants in the intervention group (using the platform) and two in the control group (receiving standard therapy). Usability was evaluated through a questionnaire completed by two therapists. Key outcome measures included the System Usability Scale (SUS), reaction time (RT), and keystroke accuracy (NIS). Results: Despite the small sample size and recruitment challenges, the data allowed for preliminary estimates of the sample size and number of sessions required for future studies. Therapists reported positive usability scores. Children using the platform showed promising trends in RT and NIS, suggesting improved engagement with cause–effect tasks. Conclusions: The findings support the feasibility and usability of the mobility platform by therapists, although some improvements should be implemented in the future. No conclusive evidence was found regarding the platform’s effectiveness on causal learning, despite a positive trend over time. This pilot study also provides valuable insights for designing larger, statistically powered trials, particularly focused on NIS. Full article

Workplace health and safety issues have long plagued the construction industry. While safety efforts have traditionally focused on physical risks, increasing attention is being paid to mental health and work-related stressors, which can negatively affect both productivity and safety. In Saudi Arabia, the construction sector presents a unique context because of its highly diverse, multinational workforce. Workers of different nationalities often operate on the same job site, leading to potential communication barriers, cultural misunderstandings, and inconsistent safety practices, all of which may amplify stress and safety risks. This research aims to investigate the influence of work-related stressors on construction workers’ safety in Saudi Arabia and identify which stressors most significantly contribute to the risk of injury. A structured questionnaire was distributed to 349 construction workers across 16 job sites in Saudi Arabia. The survey measures ten key stressors identified in the literature, including job site demand, job control, job certainty, skill demand, social support, harassment and discrimination, conflict with supervisors, interpersonal conflict, and job satisfaction. Data were analyzed using logistic regression and Pearson correlation to examine relationships between stressors and self-reported injuries. The findings indicated that work-related stressors significantly predict workplace injury. While the first regression model showed a modest effect size, it was statistically significant. The second model identified job site demand and job satisfaction as the most influential predictors of injury risk. Work-related stressors, particularly high job demands and low job satisfaction, substantially increase the likelihood of injury among construction workers. These findings emphasize the importance of incorporating psychosocial risk management into construction safety practices in Saudi Arabia. Future studies should adopt longitudinal designs to explore causal relationships over time and include qualitative methods such as interviews to gain a deeper understanding. Additionally, factors such as nationality, organizational policies, and management style should be investigated to better understand their moderating effects on the stress–injury relationship. Full article

We investigate whether China’s Value-Added Tax (VAT) Credit Refund policy influences firms’ sustainable development capability (SDC), which reflects innovation-driven growth and green development. Exploiting the 2018 implementation of the VAT Credit Refund policy as a quasi-natural experiment, we employ a difference-in-differences (DID) approach and find causal evidence that the policy significantly enhances firms’ SDC. This suggests that fiscal instruments like VAT refunds are valued by firms as drivers of long-term sustainable and high-quality development. Our mediating analyses further reveal that the policy promotes firms’ SDC by strengthening artificial intelligence (AI) capabilities and facilitating intelligent transformation. This mechanism “AI Capability Building—Intelligent Transformation” aligns with the socio-technical systems theory (STST), highlighting the interactive evolution of technological and social subsystems in shaping firm capabilities. The heterogeneity analyses indicate that the positive effect of VAT Credit Refund policy on SDC is more pronounced among small-scale and non-high-tech firms, firms with lower perceived economic policy uncertainty, higher operational diversification, lower reputational capital, and those located in regions with a higher level of marketization. We also find that the policy has persistent long-term effects, with improved SDC associated with enhanced ESG performance and green innovation outcomes. Our findings have important implications for understanding the SDC through the lens of STST and offer policy insights for deepening VAT reform and promoting intelligent and green transformation in China’s enterprises. Full article

In today’s dynamic and highly uncertain environment, organizations, particularly in the aviation sector, face increasing challenges that demand resilient, flexible, and data-driven change management decisions. Responding to the growing need for structured approaches to managing complex uncertainties—geopolitical tensions, economic volatility, social shifts, rapid technological advancements, environmental pressures and regulatory changes—this research proposes a theoretical change management model for aviation service providers, such as airports. Integrating three analytical approaches, the model offers a robust, multi-method approach for supporting sustainable transformation under uncertainty. Normative analysis using Bayesian decision theory identifies influential external environmental factors, capturing probabilistic relationships, and revealing causal links under uncertainty. Prescriptive planning through scenario theory explores alternative future pathways and helps to identify possible predictions, offer descriptive evaluation employing fuzzy comprehensive evaluation, and assess decision quality under vagueness and complexity. The proposed four-stage model—observation, analysis, evaluation, and response—offers a methodology for continuous external environment monitoring, scenario development, and data-driven, proactive change management decision-making, including the impact assessment of change and development. The proposed model contributes to the theoretical advancement of the change management research area under uncertainty and offers practical guidance for aviation organizations (airports) facing a volatile external environment. This framework strengthens aviation organizations’ ability to anticipate, evaluate, and adapt to multifaceted external changes, supporting operational flexibility and adaptability and contributing to the sustainable development of aviation services. Supporting aviation organizations with tools to proactively manage systemic uncertainty, this research directly supports the integration of sustainability principles, such as resilience and adaptability, for long-term value creation through change management decision-making. Full article