Accurate imputation of missing pavement-condition data is critical for proactive infrastructure management, yet it is complicated by spatial non-stationarity—deterioration patterns and data quality vary markedly across regions. This study proposes a Spatially Gated Mixture-of-Experts (SG-MoE) imputation model that explicitly encodes spatial heterogeneity by (i) clustering road segments using geographic coordinates and (ii) supervising a gating network to route each sample to region-specialized expert regressors. Using a large-scale national pavement management database, we benchmark SG-MoE against a strong baseline under controlled missingness mechanisms (MCAR: missing completely at random; MAR: missing at random; MNAR: missing not at random) and missing rates (10–50%). Across scenarios, SG-MoE consistently matches or improves upon the baseline; the largest gains occur under MCAR and the challenging MNAR setting, where spatial specialization reduces systematic underestimation of high crack-rate sections. The results provide practical guidance on when spatially aware ensembling is most beneficial for infrastructure imputation at scale. We additionally report comparative results under three missingness mechanisms. Across five random seeds, SG-MoE is comparable to the single LightGBM baseline under MCAR/MAR and achieves its largest gains under MNAR (e.g., sMAPE improves by 0.82 points at 10% MNAR missingness).

Based on a sample of Chinese listed firms from 2010 to 2024, this study employs a complex adaptive systems (CAS) lens to investigate the spillover effects of core-firm digital transformation (CDT) on innovation within supply chain networks. It reveals that CDT fosters supplier innovation while impeding customer innovation. Heterogeneity analysis shows that the spillover effect is more pronounced when suppliers (customers) are non-state-owned, larger, more competitive, and have more able managers. Mechanism analysis suggests that the spillover effect is realized through resource and competition mechanisms. This study contributes to the literature by integrating CAS theory with established supply chain management perspectives to provide a systemic understanding of digital transformation’s ripple effects, offering valuable insights for both managers navigating digital ecosystem evolution and policymakers designing industrial innovation strategies.

Predictive analytics for financial distress plays an important role in enterprise risk management and everyday business decisions. Most past studies mainly use accounting indicators that come from standard financial reports. This study adds analyst-forecast financial indicators and places them in a data-driven business intelligence setup to improve how companies predict financial distress. We work with seven real datasets to test several predictive models and run statistical checks to see how analyst forecasts work with historical financial data. The results show that analyst-forecast indicators can clearly improve prediction accuracy and make the results easier to understand. From an enterprise systems view, this study pushes traditional financial distress prediction toward a smarter analytics setup that supports real-time, explainable, and data-based risk assessment. The findings provide useful ideas for both the theory and practice of designing business intelligence systems and financial decision-support tools for companies.

The automotive industry is undergoing a significant transition, where the development of Battery Electric Vehicles (BEV) and the increasing use of intelligent vehicle functions are transforming vehicles into advanced Cyber-Physical Systems. For heavy-duty OEMs, this transition challenges a Product Development (PD) heritage inherent in an ecosystem of established processes, IT systems, and organization structures. This study primarily comprises semi-structured interviews, conducted at a heavy-duty OEM, and a focused literature search. The study contributes by the following: (i) identifying key PD challenges in the ICE–BEV transition, (ii) outlining obstacles in adopting Model-Based Systems Engineering (MBSE) for managing architectural complexity, and (iii) synthesizing recommendations for architecture-driven collaboration. Interview findings, highlighted intertwined challenges such as fragmented architecture descriptions across physical and software domains, weak continuity between early-phase system context and detailed design, and collaboration constrained by inconsistent terminologies, strained communication channels, and manual reconciliation of architectural information through documents and disconnected tools. These factors hinder function-component traceability and concurrent development across domains. While MBSE is often recommended to address such issues, practical obstacles are noted, including trade-offs between modeling effort and fidelity, limited support for early spatial layout integration, difficulties in bridging physical and software architectures, and the limited integration of document-based practices preferred in early conceptual phases. Based on these insights, the study recommends architecture-driven collaboration anchored in a federated vehicle-architecture description, supported by a distributed systems-engineering function. A layered development approach combining document artifacts with progressively rigorous MBSE is advised for early-phase agility, later-stage traceability, and structured information flow.

With the development of the internet and the increasing maturity of social media ecosystems, online collective actions within digital social networks have become more frequent and exhibit new patterns. Research on the mechanisms and governance of online collective action has become a crucial aspect of corporate crisis management. This study constructs an online social network with scale-free (BA) characteristics, defines individual properties and interaction rules based on the SIR model, and simulates the outbreak and intervention of online collective actions. The findings indicate that netizens with similar cognitions spontaneously form virtually clustered groups under the influence of triggering events, progressing through emotional arousal, emotional expression, and emotional mobilization, ultimately leading to large-scale online collective action. Collective actions initiated by opinion leaders tend to be more widespread, sudden, and persistent. To mitigate online collective actions, companies should enhance the transparency of core event information, promptly address the demands of action groups, soothe public sentiment, respond to societal concerns, and strengthen guidance over opinion leaders in public discourse.

This study examines the role of Digital Twin technologies in advancing Environmental, Social, and Governance performance within multinational corporations. Grounded in socio-technical systems theory and stakeholder theory, the research investigates how digital twins facilitate the integration of organizational capabilities with external accountability mechanisms. A multi-method research design is employed, comprising in-depth case studies, capital market event analysis, and machine learning-assisted regression to capture both qualitative and empirical insights. Case evidence from Siemens, Unilever, Tesla, and BP reveals that DT adoption is associated with measurable ESG gains, including reduced emissions, improved safety, enhanced supplier compliance, and accelerated reporting cycles. Event study findings show statistically significant abnormal returns following ESG-oriented DT announcements, while regression analysis confirms a positive association between DT adoption and ESG performance. Governance structures are explored as potential moderators of this relationship. The findings underscore DTs as strategic enablers of ESG value creation, beyond their technical utility. By enhancing transparency, auditability, and stakeholder trust, DTs contribute to both internal transformation and external legitimacy. This research advances the discourse on ESG digitalization and offers actionable implications for corporate leaders and policymakers seeking to foster sustainable, technology-driven governance in complex global value chains. However, because the quantitative component relies on cross-sectional data, the relationships identified should be interpreted as associations rather than definitive causal effects.

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing differences in GSCM levels from a temporal perspective under the drive of AI. Based on 2019–2023 panel data of enterprises, this paper innovatively integrates the random forest algorithm with dynamic qualitative comparative analysis (QCA) to reveal the configurational effects of technological, organizational, and environmental factors in enterprises’ GSCM practices. The findings demonstrate that no single factor is a necessary condition for enterprises to implement GSCM; configurational analysis identifies two driving models: “AI technology innovation-driven (Configuration 1 and Configuration 2)” and “strategic resource-driven (Configuration 3)”; Configuration 1 combines research and development (R&D) investment and green awareness among executives with the enabling role of government subsidies; Configuration 2 couples R&D Investment with strong funding capacity, again facilitated by the presence of government subsidies; Configuration 3 combines AI technology adoption and green awareness among executives, supported by the necessary funding capacity and government subsidies. Additionally, inter-group analysis reveals no significant temporal effect among configurations but shows phased evolutionary characteristics. This paper has thoroughly explored the complex paths for enhancing GSCM of manufactory enterprises under the influence of AI. It is recommended that the government refine and strengthen targeted subsidy policies to better support the adoption and integration of AI in advancing GSCM within the manufacturing sector. Concurrently, manufacturers must align technology, organizational structure, and external factors, specifically through core AI technology improvements, enhanced executive green awareness, and the mobilization of government and external funding. These advancements have led to high-level GSCM within enterprises, allowing them to achieve high-quality and sustainable development.

This paper examines the changes in the level of informal and shadow economy, mapping their evolution within the EU and measuring their implications on economic growth. The study also addresses the issue of conceptual differences in the methodology for measuring these phenomena. We used a two-dimensional methodological approach, combining theoretical and empirical analysis. Initially, the bibliometric analysis—conducted exclusively on the Web of Science Core Collection to ensure methodological rigour, international comparability, and high-quality, standardised data—reveals the evolution of the subject and the inconsistencies in the conceptualisation and measurement of phenomena associated with the shadow economy. Subsequently, the normative analysis highlighted the most relevant norms, directives, and projects developed and applied at the European Union level to prevent and combat tax evasion activities. Finally, the empirical dimension of this study was conducted through structural equation modelling and fixed and random effects regressions, using data from the EU 27 member states for the period 2000–2022. Our results reveal a potential relationship between the level of scientific research and the prevalence of the shadow economy within EU countries and indicate a negative effect of the informal economy on economic growth, as undeclared work produces goods and services that are consumed in the informal economy and hinders economic growth. Since the level of the shadow economy has not decreased proportionally with the increase in the GDP per capita, we conclude that the efforts to combat the shadow economy are insufficient, and tax administration needs to be more drastic and efficient.

The integration of large language models (LLMs) into project-based learning (PBL) holds significant potential for addressing enduring pedagogical challenges in engineering education, such as providing scalable, personalized support during complex problem-solving. Grounded in Self-Determination Theory (SDT), this study investigates how different LLM usage strategies impact student learning within a blended engineering geology PBL context. A one-semester quasi-experiment (N = 120) employed a 2 (usage mode: individual/shared) × 2 (interaction restriction: restricted/unrestricted) factorial design. Mixed-methods data, including surveys, interaction logs, and reflective reports, were analyzed to assess learning engagement, psychological needs satisfaction, cognitive interaction levels, and project outcomes. Results demonstrate that the individual use strategy significantly outperformed shared use in enhancing engagement, needs satisfaction, higher-order cognitive interactions, and final project scores. The restricted interaction strategy effectively served as a metacognitive scaffold, optimizing the learning process by promoting deliberate planning. Notably, individual autonomy did not undermine collaboration but enhanced it by improving the quality of individual contributions to group work. Students also developed robust critical verification habits to navigate LLM “hallucinations.” This research identifies “individual autonomy” as the core mechanism and “moderate constraint” as a crucial design principle for LLM integration, providing an empirically supported framework for harnessing generative AI to foster both motivational and cognitive outcomes in engineering PBL.

In an increasingly interconnected world, the capacity of societies to withstand, adapt to, and recover from crises is a central challenge for security and sustainable development. Yet, despite extensive research on resilience, the mechanisms through which systemic vulnerabilities emerge and propagate across social domains remain poorly understood. This paper addresses this gap by proposing a network-based framework: the Complex Analysis for Socio-Environmental Adaptation (CASA), which models resilience as a graph-structured system. Each node in CASA represents a social or infrastructural component whose resistance is derived from indicators of installed capacities, while edges capture interdependencies among sectors. We formalize a damage propagation model in which the loss of capacity in one node dynamically affects connected components, revealing the topological patterns that drive systemic fragility. Comparative simulations demonstrate that CASA’s topology amplifies the impact of highly connected nodes, rendering them crucial for resilience planning. An application to a real-world case demonstrates how initial disruptions in access to drinking water cascade into governance, economic, and social instabilities. The results provide both theoretical and operational insights, highlighting that resilience depends not only on the strength of individual components but also on the network architecture that links them. CASA thus offers a replicable and data-informed approach for identifying drivers of social conflict and guiding anticipatory resilience strategies in complex territorial systems.