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Volume 14
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Systems, Volume 14, Issue 7 (July 2026) – 158 articles

Cover Story (view full-size image): Industry 4.0 offers interconnected control systems (ICS) with cloud platforms, Internet of Things devices, AI, and digital tools. These systems enable efficient automation, productivity, and informed decision‑making. However, it also expands the possibility of cyber‑attack of the critical infrastructure. Legacy equipment, insecure protocols, weak authentication, poor segmentation, unpatched software, and human error expose ICS to malware, ransomware, espionage, data manipulation, and disruption, leading to financial loss, equipment damage, environmental harm, and threats to human safety. Risk management requires asset identification, monitoring, and vulnerability assessment. The article emphasizes access control, network segmentation, incident response, staff training, and compliance with cybersecurity standards to strengthen resilience. View this paper
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56 pages, 1506 KB  
Article
Cognitive-Reflective Equilibration Model: An Ethical Decision-Making Framework for LLM-Based AI Systems
by Chulmin Kim and Seongjin Ahn
Systems 2026, 14(7), 881; https://doi.org/10.3390/systems14070881 - 22 Jul 2026
Viewed by 2250
Abstract
The rapid expansion of large language models (LLMs) has intensified ethical challenges related to bias, accountability, transparency, and consistency in AI-mediated decision-making. While existing AI ethics principles provide normative guidance, their application to non-deterministic, probabilistic reasoning systems remains problematic due to principle conflicts, [...] Read more.
The rapid expansion of large language models (LLMs) has intensified ethical challenges related to bias, accountability, transparency, and consistency in AI-mediated decision-making. While existing AI ethics principles provide normative guidance, their application to non-deterministic, probabilistic reasoning systems remains problematic due to principle conflicts, ambiguous interpretations, and inconsistent judgments. This study proposes the Cognitive-Reflective Equilibration Model (CREM), a novel ethical decision-making framework that integrates Piaget’s equilibration of cognitive structures with Rawls’s reflective equilibrium methodology, reinterpreting these humanistic theories as a structured ethical reasoning procedure. The model is developed in two stages: a conceptually grounded framework and an operational 20-step procedure (OCREM) executable within contemporary LLM architectures. As a proof of concept, CREM was tested across five LLMs—ChatGPT, Claude, Gemini, LLaMA, and DeepSeek—using 20 ethical dilemma scenarios, generating 4000 request–response pairs. LLM-based evaluation confirmed the procedural validity of the model—its technical executability, internal consistency, and cross-platform compatibility—rather than the ethical validity of its outcomes. To address this limitation, a supplementary evaluation by a small interdisciplinary panel of five human experts provided preliminary external evidence consistent with the LLM-based procedural findings, with more conservative ratings: the validity of the ethical judgments was rated significantly above the scale midpoint and was associated with the procedural indicators. These results indicate that the procedural quality of CREM executions was associated with expert-rated ethical acceptability. However, the absence of a baseline condition and the single-source ratings preclude causal interpretation. Generalizability across diverse ethical domains and cultural contexts remains to be investigated. Full article
(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)
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33 pages, 4819 KB  
Article
Evolution and Ecological Activation Mechanisms of Chinese Electric Vehicles’ International Image: A Complex Adaptive Systems Perspective
by Yueqin Wu and Zhipeng Yu
Systems 2026, 14(7), 880; https://doi.org/10.3390/systems14070880 - 22 Jul 2026
Viewed by 434
Abstract
Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory [...] Read more.
Amid the accelerated global transition toward sustainable electromobility, Chinese Electric Vehicles (EVs) have forged a complex, evolving communication ecosystem across overseas social media platforms. Conceptualizing global digital discourse as a complex adaptive system (CAS), this study integrates CAS theory with Competitive Framing theory to systematically elucidate the thematic configurations, framework dynamics, and ecological activation mechanisms underlying the international image of Chinese EVs. By integrating unsupervised BERTopic modeling, Large Language Model (LLM) semantic mapping, the Entropy Weight Method (EWM), and Social Network Analysis (SNA), this inquiry operationalizes a comprehensive computational communication framework to mine large-scale behavioral and textual data from YouTube. The empirical findings unveil that: (1) international audience perceptions have broken through the traditional “low-cost manufacturing” stereotype, spontaneously giving rise to a multidimensional, composite cognitive schema centered on smart ecosystems and design experiences; (2) driven by the interplay of rational and irrational user feedback loops, the ecological activation efficiencies across diverse discursive dimensions exhibit pronounced nonlinear variances, characterized by a “strong activation of intelligent ecosystems versus a long-tail stagnation of cost-effectiveness salience”; and (3) positive technological frameworks and negative geopolitical or regulatory risks engage in fierce, adversarial contestation and structural hybridization within a highly volatile network topology, culminating in a unique “dual-core” configuration. Theoretically, this study enriches the scholarly understanding of country-of-origin and corporate brand images through a complex systems lens; methodologically and practically, it offers a high-fidelity, actionable quantitative paradigm for global brand empowerment and targeted cross-border public opinion governance. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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20 pages, 319 KB  
Article
Untangling the Algorithmic Leviathan: Palantir, the Post-Factual Polity, and the Infrastructural Crisis of AI Governance in Public Administration
by Haris Alibašić
Systems 2026, 14(7), 879; https://doi.org/10.3390/systems14070879 - 22 Jul 2026
Viewed by 598
Abstract
This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using [...] Read more.
This paper extends the post-factual polity framework into AI infrastructure and public administration systems theory. It asks how proprietary analytical platforms alter the state’s capacity to produce, audit, and contest the categories through which risk, threat, eligibility, fraud, and deviance become actionable. Using a structured documentary case analysis of Palantir Technologies across United States agencies and allied jurisdictions, the study applies three diagnostic markers—categorical opacity, contestation displacement, and substitutive dependency—to examine the migration of sovereign classification into vendor-controlled infrastructure. The research gap was identified through an integrative review of public administration, AI governance, algorithmic accountability, systems theory, surveillance studies, and Palantir scholarship. The analysis distinguishes AI epistemic capture from ordinary IT vendor lock-in: the former concerns not merely technical dependence or high exit costs but the loss of public capacity to define and contest consequential administrative categories. The paper argues that administrative law, procurement reform, and algorithmic impact assessment remain necessary but insufficient when agencies lack substitutive capacity. It specifies untangling as a systems-level task involving capacity reconstruction, categorical repatriation, contractual restructuring, and procurement reorientation. Hybrid intelligence is advanced as a post-untangling architecture that embeds machine processing within contestable, accountable, and legally governed human judgment. The contribution is diagnostic, methodological, and design-oriented for AI systems governance. Full article
(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)
41 pages, 2043 KB  
Article
Climate Risk and Real Estate Bond Pricing in China
by Wenwen Zhang, Ruixin Liang and Xuepeng Qian
Systems 2026, 14(7), 878; https://doi.org/10.3390/systems14070878 - 22 Jul 2026
Viewed by 362
Abstract
Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing [...] Read more.
Understanding the pricing of climate risks in bond markets is relevant to financial stability. The real estate sector, characterized by geographically fixed and long-duration assets, exhibits high exposure to environmental shocks; yet, empirical matching between specific climate channels and real estate bond pricing remains sparse. This analysis examines the impact of climate risks on corporate bond credit spreads within the real estate sector by constructing three thematic indicators: transition risk (CTRI), chronic physical risk (ChroCPRI), and acute physical risk (AcuCPRI). Initial feature selection via machine learning suggests all three risk categories as predictive covariates for bond pricing. Subsequent regression estimations indicate that climate transition risk and acute physical risk expand credit spreads, whereas chronic physical risk compresses them—with these statistical patterns being more pronounced among state-owned enterprises (SOEs). Mechanism analyses yield threefold insights: first, transition risk elevates spreads by tightening financing constraints and restricting corporate asset growth, a channel concentrated in short-term tranches and low-liquidity firms; second, the counterintuitive spread-compressing effect of chronic risk is localized among firms with lower credit ratings and lower profitability, consistent with institutional climate support frameworks and strategic green adaptations; third, acute physical risk widens spreads by compressing operational cash flows and exacerbating financing friction, particularly for smaller enterprises. These channels align with the structural attributes of SOEs, which are characterized by larger asset scales, superior capital liquidity, and a higher propensity to secure state guarantees. Full article
(This article belongs to the Section Systems Practice in Social Science)
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39 pages, 2683 KB  
Article
Optimal Coordination of Bail-In and Bailout for Troubled Banks in China: An Interbank Network Contagion Approach
by Xueying Wang, Ruowei Ma and Yuang Duan
Systems 2026, 14(7), 877; https://doi.org/10.3390/systems14070877 - 22 Jul 2026
Viewed by 467
Abstract
This study examines the optimal coordination of internal and external rescue for troubled banks under systemic contagion. Using annual data for 210 Chinese commercial banks from 2013 to 2024, it constructs a region-constrained minimum-density interbank network and embeds it in an EN-GLT dual-channel [...] Read more.
This study examines the optimal coordination of internal and external rescue for troubled banks under systemic contagion. Using annual data for 210 Chinese commercial banks from 2013 to 2024, it constructs a region-constrained minimum-density interbank network and embeds it in an EN-GLT dual-channel contagion framework that captures both direct default losses and asset fire-sale losses. Each bank is sequentially treated as the initially shocked institution, and pure internal rescue, pure external rescue, and mixed rescue strategies are compared under risk-tolerance, rescue-capacity, cost, and moral-hazard constraints. The results show that capital-loss contagion and fire-sale amplification are economically meaningful under the no-rescue scenario and become stronger as the fire-sale markdown rate rises. Mixed rescue outperforms pure internal or pure external rescue in most years, with the optimal internal rescue share mainly concentrated between 30% and 55%. The findings indicate that problem-bank resolution should combine internal loss absorption with external stabilization and should be differentiated according to contagion channels, bank type, and the nature of the crisis. Full article
(This article belongs to the Special Issue Risk Engineering in an Era of Global Uncertainty)
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28 pages, 894 KB  
Article
The Social Software of Corporate Governance: A Systems Analysis of Generalized Trust and Cultural Ecosystems
by Vincent O’Connell and Fabian Jintae Froese
Systems 2026, 14(7), 876; https://doi.org/10.3390/systems14070876 - 22 Jul 2026
Viewed by 1078
Abstract
Corporate governance is a complex, adaptive open system; yet it is usually modeled, in the classical agency tradition, as a closed matrix of legal and financial contracts. Adopting an open systems perspective, we decode the “social software”—the informal institutions—behind the wide variation in [...] Read more.
Corporate governance is a complex, adaptive open system; yet it is usually modeled, in the classical agency tradition, as a closed matrix of legal and financial contracts. Adopting an open systems perspective, we decode the “social software”—the informal institutions—behind the wide variation in corporate governance across countries. At the heart of this system lies an element that comparative corporate governance research has largely overlooked: generalized trust. To our knowledge, ours is the first framework to unite open systems theory and generalized trust in explaining cross-country corporate governance. Drawing on the classic systems theory works of Ashby and Luhmann, we theorize trust as a systemic connector that absorbs complexity that formal rules would otherwise have to carry. Where trust carries that load, firms can govern through relationship-based collaboration rather than rule-based control. Using 4837 firm-year observations from 1293 firms in 23 countries (2003–2008)—a pre-crisis structural baseline—we document a robust negative association between trust and shareholder-oriented corporate governance: where trust is high, informal social regulation substitutes for formal control. The two cultural moderators—individualism and uncertainty avoidance—act on this connector in opposing directions. Individualism amplifies the substitution because monitoring conflicts with the desire for autonomy; uncertainty avoidance attenuates it because trust cannot supply the structural predictability that these cultures demand. Where individualism is high and uncertainty avoidance is low, formal control falls away steeply as trust rises; where that configuration is reversed, formal structures persist even when trust is abundant. Corporate governance architecture, these results suggest, is regulated by its surrounding cultural ecosystem—with trust as its central, and long-neglected, connector. Full article
(This article belongs to the Section Systems Practice in Social Science)
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19 pages, 8412 KB  
Article
A Compute-Efficient Human-Following Robotic Mobility System Integrating Multimodal Recognition and Hierarchical Local Planning
by Jeonghoon Kwak, Hyewon Yoon and Heeseok Shin
Systems 2026, 14(7), 875; https://doi.org/10.3390/systems14070875 - 22 Jul 2026
Viewed by 417
Abstract
This paper presents a compute-efficient human-following robotic mobility system that integrates multimodal target recognition, LiDAR-based obstacle perception, hierarchical local planning, and low-level motion control into a unified perception–planning–control architecture. The system enables real-time target tracking and obstacle avoidance on resource-constrained embedded hardware. RGB-D [...] Read more.
This paper presents a compute-efficient human-following robotic mobility system that integrates multimodal target recognition, LiDAR-based obstacle perception, hierarchical local planning, and low-level motion control into a unified perception–planning–control architecture. The system enables real-time target tracking and obstacle avoidance on resource-constrained embedded hardware. RGB-D vision and Tether Follow Sensors (TFS) are used for target recognition, while LiDAR provides local obstacle information. To reduce the computational burden of local planning, a Hierarchical Dynamic Window Approach (HDWA) is proposed. Unlike conventional DWA, which uniformly evaluates sampled motion candidates, HDWA applies Movement, Direction, and Detail dynamic windows according to obstacle conditions and target direction. This hierarchical structure reduces redundant trajectory evaluations by activating additional computation only when avoidance is required. Experimental validation on a low-cost embedded platform demonstrates that HDWA reduces the candidate-evaluation workload by 30.8–92.3% while maintaining stable target tracking and safe obstacle avoidance. In the left- and right-side avoidance scenarios, HDWA also reduced the path length by 6.5–7.8%, increased the average driving speed by 22.0–30.8%, and reduced the elapsed motion time by 24.4–28.5%. These results demonstrate the feasibility of the proposed compute-efficient system for human-following robots operating under embedded hardware constraints. Full article
(This article belongs to the Special Issue Modeling and Optimization of Transportation and Logistics System)
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19 pages, 591 KB  
Article
How Do Stakeholder Relationships Affect Construction Project Resilience? Unveiling the Complementary Role of Digitalization
by Ping Sang, Ronggui Ding, Tao Sun and Chongzheng Li
Systems 2026, 14(7), 874; https://doi.org/10.3390/systems14070874 - 21 Jul 2026
Viewed by 278
Abstract
Unexpected disruptions make it essential to develop construction project resilience. Stakeholder relationships can shape disruption handling and thus influence project resilience, yet their roles remain debated. Drawing on organizational information processing theory, this study revisits the controversy over stakeholder relationships by examining ambidextrous [...] Read more.
Unexpected disruptions make it essential to develop construction project resilience. Stakeholder relationships can shape disruption handling and thus influence project resilience, yet their roles remain debated. Drawing on organizational information processing theory, this study revisits the controversy over stakeholder relationships by examining ambidextrous learning as an underlying mechanism and digitalization as a complement. Survey data were collected from 238 construction projects. The results show that stakeholder relationships are positively related to project resilience. Exploratory learning serves as a crucial underlying mechanism, while exploitative learning exerts no significant mediating effect. Digitalization complements stakeholder relationships in facilitating exploratory learning, thereby supporting project resilience. Theoretically, this study clarifies the role of stakeholder relationships as a critical network-level enabler of project resilience, reveals the distinct roles of exploratory and exploitative learning in temporary, disruption-prone collaborations, and uncovers the complementary role of emerging digitalization in project management. Practically, this study provides managerial insights into enhancing construction project resilience against potential disruptions. Full article
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32 pages, 4706 KB  
Article
The Logic Structure Behind Mathematical Models: An Axiomatic Framework for System Dynamics
by Fan Xia and Tangdai Xia
Systems 2026, 14(7), 873; https://doi.org/10.3390/systems14070873 - 21 Jul 2026
Viewed by 371
Abstract
Modeling complex systems typically involves multiscale analysis, multiphysics coupling, and cross-domain integration. However, existing methodologies predominantly focus on constructing and solving specific equations, lacking a unified, explicit expression for underlying logical structures, which limits model comparison and combination. To address this issue, this [...] Read more.
Modeling complex systems typically involves multiscale analysis, multiphysics coupling, and cross-domain integration. However, existing methodologies predominantly focus on constructing and solving specific equations, lacking a unified, explicit expression for underlying logical structures, which limits model comparison and combination. To address this issue, this paper proposes Axiomatic System Dynamics (ASD), a formal modeling language that decouples a model’s logical structure from its mathematical form by formalizing a mathematical model as a combination of a conceptual model and its mathematical realization. Grounded in primitive concepts—state, action, and parameter—ASD introduces generalized constitutive relationships to formulate a unified representation of system evolution. On this basis, a modular “conceptual model first, mathematical model second” paradigm is established to explicitly characterize causal relationships and identify logical isomorphisms across varied mathematical models. Through case studies spanning Newtonian mechanics, ideal gases, material constitutive relationships, and soil consolidation theories, we demonstrate that classical cross-domain theories can be formalized as specific implementations of their respective underlying system dynamics “mother structure.” Ultimately, ASD provides a meta-modeling framework independent of concrete mathematical forms, establishing a methodological foundation for logical expression, model comparison, and the meso-level deductive synthesis of cross-domain theories. Full article
(This article belongs to the Section Systems Theory and Methodology)
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30 pages, 8721 KB  
Article
A Combined intPLUS and Emission-Linkage Framework for Provincial Carbon-Balance Projection
by Ge Shi, Yutong Wang, Jiantao Shi, Chuang Chen, Lin Sun and Wei Wang
Systems 2026, 14(7), 872; https://doi.org/10.3390/systems14070872 - 21 Jul 2026
Cited by 1 | Viewed by 418
Abstract
Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon [...] Read more.
Regional carbon balance emerges from the complex interplay between land-use dynamics, spatial economic activities, and ecological processes as a socio-ecological system cannot be captured by any single analytical lens. This study develops an integrated assessment workflow that links three components: (i) coefficient-based carbon emission and sequestration accounting by land-use type; (ii) intra-provincial spatial-interaction analysis operationalized through two complementary tools—the Ecological Support Coefficient (ESC) and Economic Contribution Coefficient (ECC), which characterize the local economy–ecology relationship within each city, and a gravity-based emission-linkage model that uses GDP, population, emissions, and inter-city distance to characterize the network structure of inter-city emission attraction; and (iii) the intPLUS model, which combines random-forest-derived transition probabilities with patch-generation rules to simulate multi-scenario land-use trajectories. The framework is applied to Jiangsu Province, China, across 13 prefecture-level cities, using 1995–2020 historical data and three 2030 scenarios. Model performance is validated against observed 2020 land use, with an overall Kappa coefficient of 0.82. The results reveal a stable “high-south–low-north” gradient in emissions, a contrasting “high-ECC/low-ESC” versus “low-ECC/high-ESC” combining pattern across southern and northern Jiangsu, and a hierarchical core–periphery emission-linkage network anchored on the southern metropolitan cluster. Scenario projections for 2030 show clear divergence in provincial carbon budgets, with emissions of 12,326.59×104 t, 11,745.26×104 t, and 13,243.42×104 t under natural development, ecological protection, and economic development, respectively, and corresponding sequestration of −87.10×104 t, −100.29×104 t, and −85.61×104 t. Rather than treating carbon accounting and land-use simulation in isolation, this workflow bridges the analytical gap between physical land-use transitions and socioeconomic spatial emission spillovers, translating structural interactions into actionable spatial planning strategies. Relying on widely available data, the framework demonstrates strong methodological transferability for comparable subnational systems, provided that local parameters and sink coefficients are properly recalibrated. Full article
(This article belongs to the Topic Geospatial AI: Systems, Model, Methods, and Applications)
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20 pages, 2358 KB  
Article
Computer Science Competition Awards in Chinese Higher Education: A Systems-Thinking Analysis of Expansion, Stratification, and Portfolio Configurations
by Haiyang Hou and Chunyu Zhao
Systems 2026, 14(7), 871; https://doi.org/10.3390/systems14070871 - 21 Jul 2026
Viewed by 310
Abstract
Academic competitions have become increasingly visible in higher education. This study examines the recorded expansion and distribution of computer science competition awards across Chinese universities. It treats these records as evidence of an award-visible university competition system with potential co-curricular functions, not as [...] Read more.
Academic competitions have become increasingly visible in higher education. This study examines the recorded expansion and distribution of computer science competition awards across Chinese universities. It treats these records as evidence of an award-visible university competition system with potential co-curricular functions, not as direct measures of curricular integration, student learning, institutional strategy, or internal resource flows. The conceptual framework distinguishes direct observations, derived descriptive indicators, and untested feedback propositions. The dataset contains national award records from 2012 to 2025. The main analysis uses complete annual data from 2012 to 2024; the 2025 records are used only for a provisional continuity check. Award-visible universities increased from 226 in 2012 to 1110 in 2024, and annual award records rose from fewer than 2000 to more than 40,000. Recorded awards also became less concentrated across competition categories. Inter-university inequality remained high within the award-visible sample, and the selected Theil decomposition attributed 91.47% of measured inequality to the within-province component. Adjacent-year rank correlations indicated positional stability among universities active in both years. K-means clustering identified four descriptive portfolio configurations. The results describe expansion, diversification, inequality, persistence, and portfolio heterogeneity within the recorded award system but do not establish the organizational mechanisms represented in the causal-loop framework. Full article
(This article belongs to the Section Systems Practice in Social Science)
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23 pages, 12993 KB  
Article
Shock Propagation and Emergent Resilience in a Coupled Aviation–Tourism–Macroeconomic System: Evidence from the 2026 Strait of Hormuz Disruption
by Seung-Jun Lee, Ji-Sung Kim, In-Seok Heo and Hong-Sik Yun
Systems 2026, 14(7), 870; https://doi.org/10.3390/systems14070870 - 21 Jul 2026
Viewed by 390
Abstract
Geopolitical disruptions at maritime chokepoints cascade through interconnected economic systems, yet the pathways along which such shocks travel and the nodes at which they are absorbed remain poorly understood. The aim of this study is to trace, within a single coupled framework, how [...] Read more.
Geopolitical disruptions at maritime chokepoints cascade through interconnected economic systems, yet the pathways along which such shocks travel and the nodes at which they are absorbed remain poorly understood. The aim of this study is to trace, within a single coupled framework, how the 2026 Strait of Hormuz oil-price shock propagated through Korean-origin aviation demand, Southeast Asian destination tourism, and the macroeconomies of four oil-importing economies, and to identify where and how the shock was absorbed. Treating the disruption as an exogenous perturbation, we follow its diffusion across four interacting nodes: oil prices, Korean-origin aviation demand to Southeast Asian destinations, destination-level tourism flows, and national macroeconomic states. Using a triple-difference design with event-study and placebo-year tests on monthly route-level data, and supported by explicit parallel-trend, control-stability, and route-classification robustness checks, we find that aviation demand to leisure routes contracted by roughly 27%, accompanied by an almost identical fall in flight frequency and an unchanged load factor—a pattern consistent with a market-clearing capacity adjustment. The effect concentrated after the March blockade and stabilized thereafter, suggesting a self-limiting rather than self-amplifying dynamic. Downstream, destination-level arrivals did not contract proportionally, plausibly buffered by source-market substitution that scales with a destination’s market diversification. Along the macroeconomic branch, the same shock co-moved with rising inflation and depreciating currencies, but the magnitude of these responses varied with fuel-pricing and exchange-rate regimes. We interpret these findings as an exploratory, systems-level account in which chokepoint oil shocks act as multi-node propagation-and-absorption processes, where resilience appears to emerge endogenously from market substitution and policy–regime heterogeneity rather than being externally imposed. Full article
(This article belongs to the Section Systems Practice in Social Science)
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26 pages, 860 KB  
Article
Game-Based Learning Strengthens Attitudinal and Normative Pathways for Sustainable Tourism Practice
by Rafael Robina-Ramírez, Ana Leal-Solís, Aloysius Roets and Manuel Jesús Sánchez-González
Systems 2026, 14(7), 869; https://doi.org/10.3390/systems14070869 - 21 Jul 2026
Viewed by 325
Abstract
This study investigates how serious games enhance sustainability learning in tourism through cognitive–emotional activation and agency-driven behavioural pathways. The sample comprised 354 students from hospitality, culinary arts, and tourism programmes and 255 employees from 15 four- and five-star hotels in Extremadura (Spain). Across [...] Read more.
This study investigates how serious games enhance sustainability learning in tourism through cognitive–emotional activation and agency-driven behavioural pathways. The sample comprised 354 students from hospitality, culinary arts, and tourism programmes and 255 employees from 15 four- and five-star hotels in Extremadura (Spain). Across six workshops (2023–2025) using Hotel Giant 2 and Typsy, participants simulated environmental, operational, and managerial challenges. A mixed-methods design combined longitudinal quantitative modelling with qualitative reflections. Results show two main theoretical contributions: sustainability learning develops through recursive emotional-normative cycles, and behavioural consolidation differs sharply between students and employees. Practically, MGA indicates distinct learning trajectories, while full-partial mediation highlights layered reinforcement mechanisms. Future research should examine identity-based meaning construction in sustainability adoption. Full article
(This article belongs to the Section Systems Practice in Social Science)
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17 pages, 491 KB  
Article
How Evaluation Frameworks Shape the Economic Value of Vessel Traffic Service: Evidence from Full-Sample Vessel Trajectories
by Luling Zeng and Xingfeng Duan
Systems 2026, 14(7), 868; https://doi.org/10.3390/systems14070868 - 21 Jul 2026
Viewed by 307
Abstract
Economic evaluation of Vessel Traffic Service (VTS) has long relied on sampling-based observations and average-cost assumptions, yet limited attention has been paid to whether evaluation frameworks themselves shape how the value of digital maritime governance is identified and represented. Drawing on full-sample vessel [...] Read more.
Economic evaluation of Vessel Traffic Service (VTS) has long relied on sampling-based observations and average-cost assumptions, yet limited attention has been paid to whether evaluation frameworks themselves shape how the value of digital maritime governance is identified and represented. Drawing on full-sample vessel trajectories and dispatch records from a major Chinese hub port during 2018–2020, this study develops a parallel comparative design to compare a traditional sampling-based framework with a full-sample evaluation approach under identical traffic conditions. The results show that differences in evaluation frameworks affect not only the overall magnitude of estimated benefits, but also their composition. Compared with the traditional approach, the full-sample framework generates substantially higher estimates of economic value and assigns greater importance to routine coordination, where repeated small operational adjustments accumulate over time. By contrast, weather disruptions and channel-constrained coordination become relatively less prominent. These differences appear to be associated with variation in temporal benchmarks, sample coverage, and cost assignment strategies. Rather than identifying a single “correct” valuation, the study highlights the sensitivity of VTS economic assessment to methodological assumptions and suggests that, in digitally coordinated public infrastructure systems, evaluation frameworks may shape which forms of governance value become visible and incorporated into policy and investment decisions. Full article
(This article belongs to the Section Systems Practice in Social Science)
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36 pages, 3407 KB  
Article
Rumor Propagation in Multilingual Environments and Stochastic Dynamics: A Modeling Study Based on Age Structure and Isolation Intervention
by Tingting Zhou, Shuzhen Yu, Zhiyong Yu and Haijun Jiang
Systems 2026, 14(7), 867; https://doi.org/10.3390/systems14070867 - 20 Jul 2026
Viewed by 458
Abstract
This paper investigates rumor dissemination dynamics with generalized nonlinear incidence in multilingual environments. We propose a novel stochastic modeling framework that integrates age structure, mandatory isolation mechanisms, and media intervention. Firstly, a deterministic model is constructed by coupling ordinary differential equations (ODEs) and [...] Read more.
This paper investigates rumor dissemination dynamics with generalized nonlinear incidence in multilingual environments. We propose a novel stochastic modeling framework that integrates age structure, mandatory isolation mechanisms, and media intervention. Firstly, a deterministic model is constructed by coupling ordinary differential equations (ODEs) and partial differential equations (PDEs), followed by a thorough analysis of the positive invariant set of the model solutions. Secondly, we further establish a relevant stochastic differential system to account for environmental randomness in networks, and prove the existence and uniqueness of its global positive solution. Subsequently, using Itô’s formula and the strong law of large numbers, several sufficient conditions for the disappearance of rumors are derived. Meanwhile, based on the Khasminskii method, the existence of a unique stationary distribution is analyzed under the continuous spread of rumors. Finally, the theoretical findings are verified through numerical simulations, which demonstrate that strengthening isolation intervention can markedly reduce the peak size of rumor spread and accelerate rumor extinction. Meanwhile, real case data from the Weibo platform further verify the favorable practical adaptability of the proposed model. Full article
(This article belongs to the Section Complex Systems and Cybernetics)
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26 pages, 4308 KB  
Article
Operational Hedging Against Supply Disruptions: The Strategic Value of Partial Vertical Ownership
by Baichuan Gong, Xiaobing Liu and Yanlei Guo
Systems 2026, 14(7), 866; https://doi.org/10.3390/systems14070866 - 20 Jul 2026
Viewed by 280
Abstract
In an era characterized by frequent global supply chain disruptions, manufacturers increasingly explore strategies to safeguard operational continuity, including equity participation in upstream suppliers. This paper investigates partial vertical ownership (PVO), defined as a manufacturer’s partial equity stake in an upstream supplier that [...] Read more.
In an era characterized by frequent global supply chain disruptions, manufacturers increasingly explore strategies to safeguard operational continuity, including equity participation in upstream suppliers. This paper investigates partial vertical ownership (PVO), defined as a manufacturer’s partial equity stake in an upstream supplier that supports strategic cooperation and risk information access without full vertical integration. We model a single manufacturer, an unreliable primary supplier with private disruption risk information, and a reliable backup supplier. Relative to a non-ownership (NO) baseline, PVO is modeled as a benchmark governance mechanism that can improve the manufacturer’s information set before backup capacity is reserved. The analytical results show that expected profit is convex in the unit stockout penalty under the specified convex capacity cost structure and deterministic demand assumptions. They also show that the information dividend from PVO is governed by the variance of the disruption probability, while its economic magnitude depends on the scale of stockout losses, backup procurement costs, and the equity return condition. By benchmarking PVO against a clarified option contract strategy, we identify parameter regions in which equity-based information access can outperform contractual hedging. The study reframes PVO as a conditional operational hedging instrument and clarifies the assumptions under which it can strengthen supply chain resilience. Full article
(This article belongs to the Section Supply Chain Management)
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24 pages, 1240 KB  
Article
Digital Trust Risk in AI-Enabled Platform Government: A Comparative Systems Analysis of Privacy and Cybersecurity Policy Models in South Korea, Estonia, and Taiwan
by Sohyun Park and Seunghwan Myeong
Systems 2026, 14(7), 865; https://doi.org/10.3390/systems14070865 - 20 Jul 2026
Viewed by 432
Abstract
South Korea has achieved internationally recognized digital government capacity and platform-based service integration, yet recent privacy and cybersecurity incidents reveal growing risks to digital trust. This article examines how national governance models shape digital trust risk under conditions of platform dependency, large-scale data [...] Read more.
South Korea has achieved internationally recognized digital government capacity and platform-based service integration, yet recent privacy and cybersecurity incidents reveal growing risks to digital trust. This article examines how national governance models shape digital trust risk under conditions of platform dependency, large-scale data breaches, and artificial intelligence (AI)-enabled cybersecurity threats. Drawing on a comparative socio-technical systems perspective, the study analyzes three digitally advanced but institutionally distinct cases: South Korea, Estonia, and Taiwan. South Korea is examined as a reactive platform-accountability model, Estonia as an architecture-based data-auditability model, and Taiwan as a civic-resilience and joint-defense model. Rather than applying a formal quantum-probability model, the article uses the QP-Gov framework as a bounded analytical lens for interpreting context sensitivity, latent trust, accountability visibility, and abrupt trust-risk shifts. Methodologically, the study adopts a most-different systems design and combines structured profile analysis, case tracing, and an evidence matrix. The comparison focuses on five dimensions: digital density, data concentration, accountability visibility, incident responsiveness, and AI-era readiness. The findings show that digital trust depends not simply on technological sophistication, but on whether citizens can observe how data are accessed, how breaches are handled, how responsibility is allocated, and whether institutions learn from incidents before trust damage becomes systemic. The analysis suggests that Korea’s reactive model demonstrates strong post-incident investigative and regulatory capacity but remains vulnerable when accountability becomes visible only after major breaches. By contrast, Estonia and Taiwan illustrate alternative mechanisms of preventive auditability and resilience-based preparedness. The article concludes by proposing a sequenced policy pathway for South Korea, including citizen-facing data-access logs, systemic platform duties, breach-consequence dashboards, AI-agent audit trails, zero-trust infrastructure, and independent digital trust oversight. Full article
(This article belongs to the Special Issue Beyond One-Size-Fits-All Approaches: Digital Maturity Models Across Industries, Countries, and Organizational Systems)
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29 pages, 5937 KB  
Article
From Quantity-Based to Capacity-Aware Planning: Building Workload Control Readiness in a High-Variety Engineer-to-Order Manufacturer
by Alireza Ahmadi, Alessandra Cantini, Stefano Frecassetti, Federica Costa and Alberto Portioli-Staudacher
Systems 2026, 14(7), 864; https://doi.org/10.3390/systems14070864 - 20 Jul 2026
Viewed by 339
Abstract
High-variety engineer-to-order (ETO) manufacturers often rely on quantity-based planning logic, in which planned output is weakly connected to finite machine and labor capacity. This disconnect can create workload peaks, congested queues, and delivery unreliability. Workload Control (WLC) offers a capacity-aware planning logic for [...] Read more.
High-variety engineer-to-order (ETO) manufacturers often rely on quantity-based planning logic, in which planned output is weakly connected to finite machine and labor capacity. This disconnect can create workload peaks, congested queues, and delivery unreliability. Workload Control (WLC) offers a capacity-aware planning logic for ETO environments, but its implementation depends on informational conditions that many firms do not initially possess, including order traceability, production time data, capacity visibility, and data-quality control. Although WLC research has demonstrated its potential through analytical and simulation-based studies, empirical and longitudinal evidence on how firms build these preconditions remains limited. This paper investigates how WLC informational readiness is progressively developed in practice. Based on an action-learning-informed longitudinal case study in the shaft department of a European manufacturer of customized complex electrical machines, the study identifies four cumulative readiness stages: diagnosing the planning problem, establishing order traceability and performance visibility, building capacity visibility, and addressing the data-quality layer. The findings show how technical data infrastructure and organizational routines jointly support the transition from quantity-based planning toward capacity-aware planning. The paper contributes a practice-grounded process model of WLC informational readiness by shifting attention from the design of WLC mechanisms to the informational and organizational conditions required before such mechanisms can operate reliably. For practice, it offers a case-derived staged roadmap for manufacturers that cannot move directly from quantity-based planning to full WLC implementation. Full article
(This article belongs to the Special Issue Human-Centric and Inclusive Emerging Approaches for Sustainable and Resilient Industry 5.0 Systems)
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27 pages, 3419 KB  
Article
Prediction of Financial Distress Risk for Green Enterprises from the Perspective of Climate Resilience
by Haoying Niu, Qinzi Xiao and Mingyun Gao
Systems 2026, 14(7), 863; https://doi.org/10.3390/systems14070863 - 20 Jul 2026
Viewed by 378
Abstract
Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers [...] Read more.
Traditional financial distress early-warning models mostly rely on lagged structured financial indicators, which fail to capture the potential credit risks associated with the climate transition of green enterprises. Taking A-share listed green companies from 2015 to 2024 as research samples, this paper centers on the core research question of whether mandatory climate narratives in annual reports can deliver incremental risk warning information beyond accounting indicators. Based on textual data from annual reports, this study constructs a corporate climate resilience indicator by integrating word frequency statistics and sentiment analysis. Two data-partitioning schemes (random sampling and time-series extrapolation) are adopted to compare the predictive performance of four ensemble learning models. Extended tests are further conducted via SHAP values, partial dependence plots, polynomial Logit regression, interaction effect regression and grouped regression. The results indicate that the climate resilience indicator carries incremental information supplementary to financial indicators and possesses predictive power for financial distress. XGBoost demonstrates optimal adaptability to the hybrid feature framework, combining financial data and climate textual features. The climate resilience indicator exerts synergistic effects with financial variables and presents a non-linear statistical correlation with default probability. This study verifies that climate narratives disclosed in annual reports can serve as valid early-warning signals for credit risks. The conclusions provide empirical evidence for financial risk control, corporate disclosure management and the formulation of climate regulatory policies. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Development)
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28 pages, 2156 KB  
Systematic Review
X-AI Techniques for Human–AI Teams: The Implementation-Design Framework
by John Turner, Hoda Parvaneh Shirazi, Heesun Kim, Jiajia Du, Yeonji Jung and Xiaoyan Xu
Systems 2026, 14(7), 862; https://doi.org/10.3390/systems14070862 - 20 Jul 2026
Viewed by 592
Abstract
Explainable artificial intelligence (X-AI) techniques aim to make the actions and decisions of autonomous systems understandable to humans interacting with these systems. In human–AI teams, explainability supports individual understanding and coordination, shared mental models, and collective decision-making among humans and AI agents. Research [...] Read more.
Explainable artificial intelligence (X-AI) techniques aim to make the actions and decisions of autonomous systems understandable to humans interacting with these systems. In human–AI teams, explainability supports individual understanding and coordination, shared mental models, and collective decision-making among humans and AI agents. Research has shown that X-AI enhances trust in autonomous systems, improves human–AI team performance, and supports collaboration across domains including aviation, finance, healthcare, hospitality, and sports. However, X-AI technologies face difficult challenges, including a lack of transparency and interpretability due to complex underlying models, also known as the “black-box” nature of AI systems. These technologies also lack any universally accepted evaluation metrics and have limited generalizability across applications. One deficit in the X-AI literature is that most frameworks focus on individual-level outcomes, with limited attention to team-level processes. The current study conducted a systematic literature review adhering to PRISMA guidelines and the SALSA framework. This study introduces the Implementation-Design (I-D) framework that organizes X-AI approaches along two dimensions: implementation, ranging from visual to interactive approaches, and design, ranging from isolated explanations to workflow-integrated systems. This framework captures lower-level engagement, involving individual users, to higher-level understanding that is necessary for teams and collectives. Findings indicate that visual explanation approaches support user engagement, while interactive workflow approaches promote deeper understanding, appropriate reliance, and distributed cognition within human–AI teams. Implications highlight the need for team-oriented explainability grounded in shared mental models, transactive memory systems, and collaborative X-AI artifacts. Practical guidelines are included to support researchers and practitioners in selecting appropriate X-AI techniques based on their context and level of analysis. The I-D framework is offered as a conceptual organizing model to guide research and practice, and empirical validation is identified as a priority for future work. Full article
(This article belongs to the Special Issue Human-AI (H-AI) Teams: Designing for Human-AI Interactions)
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25 pages, 1787 KB  
Article
Fiscal Shocks and Strategic Resilience Traps in Metro PPP Project Ecosystems: Scenario-Based Evidence from Post-Land-Finance China
by Yuqing Wu, Rui Wang, Yongjian He, Yun Zhou and He Zhang
Systems 2026, 14(7), 861; https://doi.org/10.3390/systems14070861 - 19 Jul 2026
Viewed by 338
Abstract
Fiscal shocks in post-land-finance China are weakening the funding basis of capital-intensive metro public-private partnership (PPP) projects, but the system-level mechanism through which public fiscal stress becomes subcontractor-level financial viability pressure remains underexplained. This study examines a section-level metro PPP project ecosystem in [...] Read more.
Fiscal shocks in post-land-finance China are weakening the funding basis of capital-intensive metro public-private partnership (PPP) projects, but the system-level mechanism through which public fiscal stress becomes subcontractor-level financial viability pressure remains underexplained. This study examines a section-level metro PPP project ecosystem in a sub-provincial Chinese city to trace this transmission mechanism and its financial implications. The analysis combines de-identified audit evidence and interviews with a scenario-based structural NPV model and 800,000 model-generated Monte Carlo realizations under calibrated institutional scenarios. The evidence indicates that quasi-bureaucratic SPV internal capital-market arrangements convert fiscal shortfalls into vertical and horizontal cross-subsidization practices, preserving short-term project continuity while shifting cash-flow pressure downstream. This condition is defined as a strategic resilience trap: practices that preserve short-term project continuity while potentially eroding the project ecosystem’s long-term adaptive capacity. Under calibrated assumptions, improving the contract-payment channel reduces model-generated losses by approximately 4%, suggesting that payment punctuality addresses only one part of the wider internal capital-market mechanism. Full article
(This article belongs to the Special Issue Systems Thinking for Business Strategic Management: Innovation and Resilience)
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22 pages, 749 KB  
Article
The Innovation Spillover Effects of Forward-Looking Information Disclosure by Supply Chain Hub Firms: Based on the Moderating Role of Node Firms’ Information Absorptive Capacity
by Yimeng He, Lirong Chen, Chunguang Sheng and Kerui Niu
Systems 2026, 14(7), 860; https://doi.org/10.3390/systems14070860 - 19 Jul 2026
Viewed by 371
Abstract
The impact of chain-leading (hub) firms’ forward-looking information disclosure on supply chain collaborative innovation remains underexplored. Based on panel data of Chinese A-share manufacturing listed firms from 2014 to 2024, this study adopts large-scale textual analysis to construct a time-varying indicator measuring hub [...] Read more.
The impact of chain-leading (hub) firms’ forward-looking information disclosure on supply chain collaborative innovation remains underexplored. Based on panel data of Chinese A-share manufacturing listed firms from 2014 to 2024, this study adopts large-scale textual analysis to construct a time-varying indicator measuring hub firms’ forward-looking disclosure, and systematically examines its innovation spillover effects and internal mechanisms. The results show that hub firms’ forward-looking disclosure is positively associated with a significant increase in the R&D investment intensity of supply-chain node firms. This spillover effect is negatively moderated by node firms’ information absorptive capacity, reflecting a typical information substitution effect. Heterogeneity tests further reveal that the spillover effect is more pronounced among node firms with larger scale, higher supply-chain network centrality, and stronger supply-chain relationship specificity (proxied by higher customer concentration). In addition, such innovation spillovers are positively associated with improved corporate financial performance, and this profit-conversion effect is more pronounced among high-leverage firms, which is consistent with an implicit endorsement mechanism that helps alleviate financing constraints. Combining empirical evidence with industrial governance practice, this paper expands the theoretical boundary of supply chain collaborative innovation and provides actionable recommendations for optimizing information disclosure rules and formulating differentiated industrial innovation policies. Full article
(This article belongs to the Section Supply Chain Management)
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31 pages, 5322 KB  
Article
Network Dynamics and Key Transmission Pathways of a Provincial Innovation System: A County-Level Analysis of Jiangsu Province, China
by Jia Shao and Xingping Wang
Systems 2026, 14(7), 859; https://doi.org/10.3390/systems14070859 - 18 Jul 2026
Viewed by 305
Abstract
Provincial innovation systems can be understood as complex adaptive networks in which connectivity and resilience-enabling conditions emerge from heterogeneous local capabilities, spatial constraints, and relational configurations. Taking 95 county-level units in Jiangsu Province, China, as the study area, this study constructs a county-level [...] Read more.
Provincial innovation systems can be understood as complex adaptive networks in which connectivity and resilience-enabling conditions emerge from heterogeneous local capabilities, spatial constraints, and relational configurations. Taking 95 county-level units in Jiangsu Province, China, as the study area, this study constructs a county-level innovation capability index for 2020–2023 across four dimensions: innovation input, innovation output, innovation environment, and innovation performance. Using this index as the mass variable, a gravity-based potential innovation linkage network is developed, and network analysis indicators are applied to examine its stage-specific structural changes, functional differentiation, and key transmission pathways within the model-derived network. The results show that county-level innovation capability increased across the four observations, while the intra-provincial south–north gradient remained evident. The potential network became increasingly connected, but this increase in connectivity did not lead to structural equalization. Instead, linkages were selectively reinforced around high-capability nodes and spatially proximate areas, indicating local clustering, potentially path-dependent organization, and selective connectivity. County-level units performed differentiated systemic roles as core-organizing, system-supporting, and connector nodes, shaped jointly by innovation capability, spatial location, and network embeddedness. The identified key transmission pathways exhibited a multi-level structural configuration involving intra-cluster reinforcement, intercity corridor continuity along the Yangtze River, and short-chain embedding of peripheral nodes. These findings suggest that provincial innovation systems may exhibit selective structural organization rather than uniform relational development, shaped by capability asymmetry, spatial proximity, and relational configuration. Because the network is derived from innovation capability and geographical distance, the identified linkages and pathways represent model-estimated relational opportunities rather than directly observed knowledge, technology, or innovation flows. Within this interpretive boundary, county-level nodes and key transmission pathways provide insights into system connectivity and resilience-oriented governance. Full article
(This article belongs to the Special Issue The Dynamics and Resilience of Innovation Networks in a Disruptive World)
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23 pages, 3415 KB  
Article
Enriching Idealized Design: A Multimethod Framework Integrating Systems Thinking Tools
by Ali Hamidi, Fisnik Dalipi and Sadaf Salavati
Systems 2026, 14(7), 858; https://doi.org/10.3390/systems14070858 - 18 Jul 2026
Viewed by 406
Abstract
Complex and wicked problems characterized by high levels of uncertainty motivate the use of multimethodology (or methodological pluralism) in which different methodologies and methods are combined to better approach problems and develop solutions. This methodological pluralism has been applied across disciplines, including the [...] Read more.
Complex and wicked problems characterized by high levels of uncertainty motivate the use of multimethodology (or methodological pluralism) in which different methodologies and methods are combined to better approach problems and develop solutions. This methodological pluralism has been applied across disciplines, including the systems thinking field, where scholars employed complementary methodologies and methods. Idealized design and interactive planning represent one such systems methodology that offers a structured action-oriented approach to redesigning a system toward an idealized state rather than attempting to predict it. Although conceptually well-established, idealized design has been noted for its limited practical guidance, particularly in identifying where and how to intervene in a system. The present paper addresses this gap by proposing an enriched idealized design framework that integrates rich pictures from soft systems methodology and causal loop diagrams, system archetypes, and the leverage points framework from system dynamics. Each tool is integrated at a specific phase of the idealized design process, forming a progressive cascade while preserving the original sequence of the idealized design approach. A retrospective empirical demonstration drawing on a longitudinal study on computational thinking (CT) education is presented to illustrate the conceptual coherence of the framework, and potential directions for future work, including prospective validation, are discussed. Full article
(This article belongs to the Special Issue Systems Thinking and Design for Transformative Innovation)
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27 pages, 2625 KB  
Article
Integrated Mixed-Integer Programming Models to Minimize the Number of Operators in Manufacturing Cells
by Takayuki Kataoka, Katsumi Morikawa and Katsuhiko Takahashi
Systems 2026, 14(7), 857; https://doi.org/10.3390/systems14070857 - 17 Jul 2026
Viewed by 236
Abstract
Cellular manufacturing (CM) has been extensively studied and is widely recognized as a resilient and effective production system. With respect to labor-intensive cells, recent studies have primarily concentrated on mixed-integer programming (MIP) models, often embedded within multi-phase solution frameworks. However, the computational complexity [...] Read more.
Cellular manufacturing (CM) has been extensively studied and is widely recognized as a resilient and effective production system. With respect to labor-intensive cells, recent studies have primarily concentrated on mixed-integer programming (MIP) models, often embedded within multi-phase solution frameworks. However, the computational complexity of such models remains a significant challenge. To address this issue, several studies have adopted hierarchical, multi-phase approaches that decompose the problem into more tractable subproblems, thereby significantly reducing computation time and enhancing their applicability in real-world environments, albeit at the cost of potential optimality. Considering the improvement in computer processing power in recent years, a new integrated mixed-integer programming model without phases is proposed and compared with the two-phase model via numerical experiments in this paper. In addition, considering unique multi-objective optimization models using integer and fractional parts without Pareto solutions, the newly proposed model is subjected to a comprehensive comparison with the two-phase model. As a result, it is demonstrated that, in the reported experimental setting and under the tested configuration of the two-phase procedure, the proposed model can lead to more feasible solutions that require fewer operators than the two-phase model under variable demand across 100 weeks. These findings pertain to the tested setting rather than representing a general property of the method. In addition, in the same setting, the proposed model can also lead to a smaller cumulative value of the secondary assignment-count metric than the two-phase model. Full article
(This article belongs to the Special Issue Scheduling and Optimization in Production and Transportation Systems (2nd Edition))
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24 pages, 1513 KB  
Article
AI Agents for Supply Chain: Path Following Control Using Adaptive Kalman Filter Reinforcement Learning
by Petru Stefan Vasian, Ionut Dancau, Catalin Dumitrescu, Eduard-Cristian Popovici and Petrica Ciotirnae
Systems 2026, 14(7), 856; https://doi.org/10.3390/systems14070856 - 17 Jul 2026
Viewed by 305
Abstract
Path following control is a fundamental problem in robotics and autonomous systems, essential for the navigation of vehicles, drones, and mobile robots. The goal is to maintain a system on a predefined path despite external perturbations and complex system dynamics. Reinforcement learning (RL) [...] Read more.
Path following control is a fundamental problem in robotics and autonomous systems, essential for the navigation of vehicles, drones, and mobile robots. The goal is to maintain a system on a predefined path despite external perturbations and complex system dynamics. Reinforcement learning (RL) offers a promising approach to overcome these limitations, allowing agents to learn control rules for following the optimal road route correlated with environmental conditions. Among RL algorithms, the Soft Actor-Critic (SAC) method stands out by combining the advantages of calculations made based on variables and rules. They will use an “actor” to select actions and a “critic” to evaluate the quality of these actions, facilitating efficient and stable learning. This paper proposes the integration of a Parallel Adaptive Kalman Filters (KF) with a Soft Actor-Critic (SAC) agent to create a highly robust system for path following control (PFC). This architecture combines optimal state estimation with maximum-entropy reinforcement learning to handle sensor noise, environmental disturbances, and continuous actuator controls. Full article
(This article belongs to the Special Issue AI Applications in Transportation and Logistics)
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25 pages, 1204 KB  
Article
Digital Transformation and Green Innovation Performance in New Energy Enterprises: A Configurational Analysis of Complex Resource Systems Using fsQCA
by Xiangyu Chen, Xiaofeng Xu and Da Tong
Systems 2026, 14(7), 855; https://doi.org/10.3390/systems14070855 - 17 Jul 2026
Viewed by 288
Abstract
Green innovation performance (GIP) in new energy enterprises emerges from complex interactions among technological, organizational, and institutional resource subsystems, yet existing research predominantly applies linear, single-factor approaches that fail to capture this systemic complexity. Drawing on the Resource-Based View (RBV) and systems thinking, [...] Read more.
Green innovation performance (GIP) in new energy enterprises emerges from complex interactions among technological, organizational, and institutional resource subsystems, yet existing research predominantly applies linear, single-factor approaches that fail to capture this systemic complexity. Drawing on the Resource-Based View (RBV) and systems thinking, this study employs fuzzy-set qualitative comparative analysis (fsQCA) on a sample of 54 Chinese A-share listed new energy enterprises—spanning wind power, solar power, hydrogen energy, energy storage, and new energy equipment manufacturing—observed over the 2019–2023 period, to examine the configurational pathways through which these firms achieve high GIP. Green patent grants serve as the outcome measure, and six conditions spanning three resource subsystems are considered: digital transformation and R&D intensity (technological subsystem), firm size and ownership structure (organizational subsystem), and government subsidies and carbon emission performance (institutional subsystem). Three key findings emerge. First, none of the six conditions is individually necessary for high GIP (all consistency scores below 0.90), indicating that high GIP reflects combinations of resources rather than a single driver. Second, the six sufficient configurations identified collapse into two distinct pathway clusters: a “SOE digital-empowerment-driven” cluster, in which digital transformation combines with R&D investment, government subsidies, or organizational scale within state-owned enterprises, and a “resource–capability synergy and substitution” cluster, in which scale resources, R&D investment, and policy support combine with or substitute for digital transformation regardless of ownership. Third, digital transformation appears in five of the six pathways, indicating that it functions as a key—but not universal—enabling element whose effectiveness depends on its alignment with other system components. Beyond confirming that multiple, equally valid resource combinations lead to high GIP, this study’s principal contribution is to embed RBV within an explicit systems framework, showing how technological, organizational, and institutional resources interact as subsystems of a single socio-technical system, and to translate the resulting configurations into differentiated, pathway-specific guidance for enterprises and policymakers navigating the low-carbon energy transition. Full article
(This article belongs to the Section Systems Practice in Social Science)
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33 pages, 2828 KB  
Article
Dynamic Feedback Regulation in Multi-Agent Emergency Supply Stockpiling: An Evolutionary Game and System Stability Perspective
by Qing Wang and Jihai Zhang
Systems 2026, 14(7), 854; https://doi.org/10.3390/systems14070854 - 17 Jul 2026
Viewed by 307
Abstract
In the context of increasingly frequent and highly unpredictable unconventional emergencies and growing supply chain uncertainty, traditional static reward-and-punishment mechanisms often fail to curb enterprises’ speculative stockpiling, leading to strategic oscillations and instability in collaborative emergency supply stockpiling systems. To address the lack [...] Read more.
In the context of increasingly frequent and highly unpredictable unconventional emergencies and growing supply chain uncertainty, traditional static reward-and-punishment mechanisms often fail to curb enterprises’ speculative stockpiling, leading to strategic oscillations and instability in collaborative emergency supply stockpiling systems. To address the lack of attention to dynamic governance mechanisms, this paper develops a tripartite evolutionary game model involving the government, stockpiling enterprises, and the public under the assumption of bounded rationality. The model examines how a dynamic reward-and-punishment mechanism affects the evolution of collaborative stockpiling strategies and system stability. Results show that under a static mechanism, enterprise strategies are highly sensitive to fluctuations in speculative returns and regulatory costs, making stable equilibrium difficult to achieve. In contrast, a behavioral-state-dependent dynamic mechanism adjusts reward-and-punishment intensities in response to feedback on enterprise behavior, uses changes in the proportion of enterprises choosing responsible stockpiling as the trigger for adaptive adjustment, reshapes enterprises’ payoff structures, and thereby forms an adaptive governance mechanism based on behavioral feedback. This suppresses speculative stockpiling and promotes convergence toward stability. The analysis indicates that increasing reward-and-punishment intensity does not necessarily improve governance effectiveness: excessive penalties may increase volatility, whereas an appropriate range of reward-and-punishment intensities improves system stability and governance efficiency. Public oversight functions primarily as a phased external constraint; as responsible stockpiling behavior gradually stabilizes, the system’s dependence on sustained high-intensity oversight gradually decreases. These findings provide a decision-support framework for policymakers to translate evolutionary game analysis into adaptive administrative regulation. Full article
(This article belongs to the Special Issue Operation and Supply Chain Risk Management)
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52 pages, 1187 KB  
Article
Beyond AI Narratives: AI Washing and Organizational Resilience
by Yufei Xia, Jikang Sun, Jiarun Liu, Kun Fang, Huiyi Shi and Na Li
Systems 2026, 14(7), 853; https://doi.org/10.3390/systems14070853 - 17 Jul 2026
Viewed by 605
Abstract
Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as [...] Read more.
Artificial intelligence (AI) is widely viewed as a technological foundation for organizational resilience. Yet firms may strategically exaggerate their AI-related narratives without corresponding substantive investment. This study examines whether such AI washing is associated with lower organizational resilience. We conceptualize AI washing as a narrative–investment misalignment within organizational systems, in which symbolic AI claims move ahead of substantive AI investment and capability formation. Based on Chinese A-share listed firms during 2010–2024, we develop a firm-level AI washing index by comparing firms’ within-industry ranking in AI disclosure with their within-industry ranking in actual AI investment. AI disclosure is identified from annual reports using a large language model, while actual AI investment is measured through AI-related software and hardware investments. Using double-debiased machine learning, we estimate a significantly negative association between AI washing and organizational resilience. Economically, a one-standard-deviation increase in AI washing is associated with a decline in organizational resilience equivalent to approximately 3.276% of the average annual change in organizational resilience. This estimated pattern remains stable when we employ alternative variable constructions, replace the machine learning algorithms, adjust the cross-fitting folds, use propensity score matching, and further apply a deep instrumental variable strategy. Mechanism tests based on organizational legitimacy provide evidence consistent with legitimacy-related transmission channels, suggesting that AI washing is associated with lower resilience through weakened pragmatic, moral, and cognitive legitimacy under the maintained mediation assumptions. Further analysis reveals an asymmetric pattern: firms whose AI narratives exceed actual investment experience lower resilience, whereas firms whose actual investment exceeds external narratives exhibit higher resilience. The negative estimated association is particularly evident in high-tech industries, enterprises with established bank-firm ties, and enterprises with higher educational heterogeneity in their top management teams. This study advances research on AI disclosure and organizational resilience by showing that symbolic AI narratives can signal system-level fragility when technological claims are misaligned with substantive capability formation. Full article
(This article belongs to the Special Issue Artificial Intelligence in Socio-Technical Systems: Human, Organizational, and Ethical Dimensions of Intelligent Transformation)
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37 pages, 3661 KB  
Article
Performance Pursuit Behavior of Autonomous Vehicles: An Area-Based Driving Strategy for Autonomous Vehicles Considering Multi-Objective Optimization at Signalized Intersections
by Xiangyu Feng, Tao Li, Peng Liao and Yingxu Rui
Systems 2026, 14(7), 852; https://doi.org/10.3390/systems14070852 - 17 Jul 2026
Viewed by 296
Abstract
Autonomous driving technology enables precise motion control and creates substantial opportunities for improving the operation of signalized intersections. However, the performance trade-offs among autonomous vehicles at signalized intersections, and their impacts on the operation process, will directly affect the further application of autonomous [...] Read more.
Autonomous driving technology enables precise motion control and creates substantial opportunities for improving the operation of signalized intersections. However, the performance trade-offs among autonomous vehicles at signalized intersections, and their impacts on the operation process, will directly affect the further application of autonomous driving in the intelligent transportation system. Addressing this research focus, this paper proposes a multi-objective driving strategy for autonomous vehicles based on the scene characteristics of signalized intersections. Firstly, the intersection and its adjacent control area are treated as an integrated decision region, and an autonomous vehicle driving performance model at signalized intersections is established to evaluate economy, comfort, and efficiency performance. Secondly, a multi-stage trajectory generation method combining phase division, candidate trajectory generation, and real-time trajectory adjustment is further developed to adapt the ego vehicle to traffic conditions while maintaining safe and smooth motion. Thirdly, a multi-objective optimization problem is formulated to generate optimal trajectories for each autonomous vehicle within the region. Finally, weight sensitivity analysis, application adaptability analysis, and an analysis of system-level key factors and system-level impacts are conducted to explore the strategy optimization potential. The case studies reveal that the proposed strategy achieves improvements of 50.0%, 33.3%, and 21.6% in comfort, economy, and efficiency, respectively, compared with the common strategy. In future research, more specific and complex practical factors will be incorporated into the proposed strategy. The strategy helps to reveal the performance-oriented behavior of autonomous vehicles at signalized intersections and provides methodological support for the wider application of autonomous driving in intelligent transportation systems. Full article
(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)
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