Systems doi: 10.3390/systems14080933

Authors: Jiayi Xin Zhen Zhang

Despite substantial AI technology investment, many firms fail to translate isolated AI applications into integrated capabilities that deliver strategic returns and drive business model changes. Grounded in service-dominant logic (SDL), this study proposes and empirically tests a theoretical framework that positions AI capability (AIC) as a key antecedent in the nomological network of business model innovation (BMI). Drawing on a three-wave, two-week-interval longitudinal survey of 193 Chinese digital-intensive firms across IT, technical services, and digital leasing industries, and employing PLS-SEM, we examine associations among focal constructs, specifically, the mediating role of customer responsiveness (CR) and the moderating effect of digital organizational culture (DOC). This design mitigates common method bias and establishes temporal causal ordering. Empirical results indicate that AIC positively relates to BMI both directly and indirectly through CR, and that DOC significantly enhances the indirect effect of AIC on BMI via CR, particularly under high levels of AI-enabled sensing and interpretation. However, causal inference is limited by the cross-sectional nature of the data and self-reported measures. This study makes three key theoretical contributions. First, we identify CR as a market-oriented mechanism linking AIC to BMI, shifting focus from prior internal efficiency-focused mechanisms to customer-centric value co-creation. Second, we extend SDL to the AI context by clarifying how DOC shapes the strategic transformation of ambiguous probabilistic AI outputs into market-oriented actions. Third, we introduce DOC as an internal boundary condition for AIC, complementing prior research on external environmental moderators. These findings provide actionable guidance for managers seeking to unlock the strategic value of AI investments. Findings reflect statistical associations rather than confirmed causal effects, and results are based on perceptual survey data from Chinese digital firms.

Systems doi: 10.3390/systems14080932

Authors: Qiqi Gu Ying Liu Junda Wu Xuan Zhu

This paper examines return connectedness among four channels for obtaining U.S.-dollar exposure in Argentina: the official USD/ARS rate, the informal Blue Dollar rate, and USDT/ARS prices on Binance and Bitso. We use 669 matched-date daily returns and a time-varying parameter VAR connectedness model with Barunik–Krehlik frequency decomposition. The analysis is descriptive: generalized forecast-error variance shares measure directional dependence within the estimated system but do not identify structural causality, price discovery, or systemic risk. Baseline estimates indicate that total connectedness rises from 43.57% under exchange controls and 45.26% during the crawling-peg period to 64.53% after liberalization. The two stablecoin venues have positive net-connectedness positions in the baseline estimates, while the official rate becomes a net receiver in Periods 2 and 3. Connectedness is concentrated within five- and ten-day horizons, although the share attributed to a strict three-day band is materially lower. Period 3 transmitter signs remain stable in contiguous subsamples, single-venue and aggregate-stablecoin specifications, and a six-variable system augmented with Bitcoin and a broad U.S.-dollar factor. By contrast, Period 2 directional rankings are more sensitive to alternative period boundaries and stablecoin representations and should therefore be interpreted cautiously. Rankings are also less stable in Period 1 and under deliberate one-day shifts in stablecoin closing dates, highlighting the importance of outliers and market synchronization. The results therefore provide conditional evidence on changing return interactions across segmented dollar-access venues rather than causal evidence that one venue drives another.

Systems doi: 10.3390/systems14080931

Authors: Georgiana Burlacu Ioan-Bogdan Robu Adriana Florina Popa Ionuț Viorel Herghiligiu

As financial markets are characterized by the increasing incidence and complexity of fraudulent activities, generating substantial losses for companies while posing serious risks to potential investors, financial fraud is a constantly debated issue, particularly with regard to its prevention and detection. As classical methods for detecting financial fraud have proven inefficient and time-consuming, many researchers have turned to artificial-intelligence-based methods. The use of AI-based methods for fraud detection is currently a widely debated topic, particularly regarding fraudulent financial statements. This study aims to determine the extent to which fuzzy logic contributes to financial statement fraud risk assessment. The target population comprises Romanian companies listed on the Bucharest Stock Exchange. Following analysis, a sample of 62 listed companies was selected. The analysis covers the last seven completed financial years (2018–2024). The dependent variable is represented by financial statement fraud risk (FSF), measured using the modified F-score model, under the influence of some independent variables that are defined by a series of financial ratios: return on assets, return on equity, net profit margin, leverage and working capital. Additionally, taking into account the audit opinion type and advanced statistical methods for data analysis, the research results demonstrated the importance of using fuzzy logic in fraud risk assessment by improving the process of detecting fraud in financial statements, based on specific financial ratios and statistical associations between these ratios, as fuzzy rules.

Systems doi: 10.3390/systems14080929

Authors: Lei Lyu Yantong Zhong Ziyi Zhang Guitao Zhang Hao Sun

Against the backdrop of the “Rural Revitalization” strategy, contract farming has emerged as a new mechanism and a growing research focus. This study employs differential game theory to systematically analyze the dynamic decision-making and operational performance differences among supply chain members by comparing scenarios with and without research institution participation, as well as different government subsidy policies. The innovative contribution lies in introducing a dual-effect index to capture consumers’ quality utility from agricultural products and psychological utility derived from supporting farmers. Furthermore, we explore the synergistic effects of research institutions’ technological empowerment and government subsidy strategies on supply chain efficiency. Through the above research and analysis, the study draws the following conclusions: (1) The collaborative agricultural supporting model significantly enhances both farmer yields and overall supply chain profits through technology diffusion and brand premium effects. However, revenue distribution conflicts may lead enterprises to limit their cooperation depth. (2) Government subsidies can alleviate benefit allocation conflicts. Subsidizing a research institution proves more effective at stimulating long-term technological dividends, while subsidizing an enterprise primarily ensures short-term market stability. (3) Brand advantage, technological leadership, and consumer preferences exhibit nonlinear driving effects on supply chain efficiency. Specifically, the positive impact progressively strengthens with wider brand advantages, greater technological leadership, and more intense consumer preferences. The study provides a theoretical foundation for tripartite collaboration, revealing the pivotal role of policy precision and dynamic benefit allocation equilibrium in agricultural supply chain upgrading.

Systems doi: 10.3390/systems14080930

Authors: Dan Wang Nuojia Pan Chenchen Sun Xixia Zheng Weiyou Guo

Airport Economic Zones (AEZs) in China are largely guided by central and local government planning, and official planning documents provide important textual signals of industrial priorities. To identify these priorities, this study examines 17 national-level Airport Economic Demonstration Zones (AEDZs) and collects official documents on industrial development issued by central authorities and relevant local governments. Using dictionary-based named entity recognition, word-frequency analysis, clustering, and association rule mining, we quantitatively analyze stated governmental preferences in AEDZ industrial planning, focusing on emphasized industries and their combinations. The results show that local governments frequently emphasize industries prioritized by the central government, including aviation equipment manufacturing and maintenance, electronic information, aviation logistics, professional exhibitions, and e-commerce. They also tend to combine high-end intelligent manufacturing, electronic information technology services, and air cargo transportation in planning narratives. These patterns indicate policy alignment across AEDZs, while high similarity in stated industrial priorities may signal potential risks of redundant construction and resource misallocation if not matched with differentiated implementation. The findings are interpreted as textual policy signals rather than evidence of actual implementation or industrial performance.

Systems doi: 10.3390/systems14080928

Authors: Qiang Zheng Dechun Huang

Accelerating green collaborative innovation is crucial to achieving environmental sustainability. Using panel data from Chinese A-share listed firms, this study theoretically and empirically investigates the causal association between digital and real technological integration and corporate green collaborative innovation. We reveal that digital and real technological integration significantly promotes corporate green collaborative innovation. Mechanism analysis indicates that digital and real technological integration promotes corporate green collaborative innovation by mitigating asset specificity, reducing green agency costs and broadening green knowledge breadth. Furthermore, heterogeneity analysis shows that this positive effect of digital and real technological integration on corporate green collaborative innovation is more pronounced among firms with higher internal control quality, operating in high-tech industries, and located in eastern regions. This study not only deepens our understanding of the economic consequences of digital and real technological integration, but also provides crucial insights and evidence for exploring the antecedents of corporate green collaborative innovation.

Systems doi: 10.3390/systems14080927

Authors: Yimin Feng Yuting Li Pengwei Zhang Jingxia Chen Guanhui Zhao Yusheng Liu Hongyu Li Yaguang Huang

Traditional MBSE faces challenges in verifying the dynamic performance of autonomous systems due to a semantic gap between static architecture and executable algorithms. To address this limitation, this study proposes a system-of-systems (SoS) modeling methodology that connects the Unified Architecture Framework (UAF) with multi-agent reinforcement learning. A Drivers–Challenges–Opportunities–Goals (DCOG) framework maps strategic intent to operational capabilities, which are formalized in the UAF. Driven by the UAF Domain Metamodel (DMM), a pipeline transforms behavioral and resource specifications into a Markov Decision Process (MDP). An Automatic Curriculum Learning Multi-Agent Proximal Policy Optimization (AutoCL-MAPPO) algorithm then resolves the MDP. The autonomous fleet achieves a 73% mission success rate, outperforming the standard MAPPO baseline (65%). These performance metrics are averaged over 1000 independent test episodes to ensure statistical significance, with baseline algorithms evaluated under identical environmental conditions. Using Systems Modeling Language (SysML) as a verification carrier, activity simulations confirm that the generated decision sequences conform to UAF structural logic. Metric constraint deviation analysis provides empirical feedback for iterative design refinement. This methodology establishes a verifiable digital thread, closing the loop between architecture modeling and learned behavior for autonomous SoS and Human–AI Teaming.

Systems doi: 10.3390/systems14080926

Authors: A. Yassin Ibrahim ElGabroni Paulo Peças

Smart Factory programs increasingly connect shop-floor, quality, asset and planning data, but integrated data infrastructures do not necessarily align operational decisions. This paper reviews how smart manufacturing literature explains the transition from industrial data integration to closed-loop operations synchronization and value capture in high-throughput manufacturing contexts. Using the Systematic Search Flow method, 1949 records were screened and reduced to a final portfolio of 73 studies. The papers were coded by thematic cluster, dominant technology, research method, primary theme, value-stream coverage and operations-synchronization relevance. The coding shows that roadmaps, interoperability architectures, analytics applications and digital-twin models dominate the portfolio. Explicit operations-synchronization mechanisms are addressed in 16 of the 73 studies, mainly through planning-execution coupling and digital-twin-based decision support. Coverage across value streams is uneven, with stronger evidence for Strategy, Make and Plan than for Quality and Assets. Based on this evidence map, the paper proposes a Data-Driven Operations Synchronization Stack that links operational data capture, semantic and IT/OT interoperability, analytics-supported decision-making, closed-loop synchronization and operational or financial value capture.

Systems doi: 10.3390/systems14080924

Authors: Yi-Chen Wu Chao-Che Hsu Yi-Chun Chou James J. H. Liou

Disaster preparedness and the equitable distribution of emergency relief supplies remain pressing priorities for governments worldwide. Situated along the Pacific Ring of Fire, Taiwan is particularly vulnerable to natural disasters that frequently cause severe property damage and endanger public safety, making the efficient allocation of limited relief resources in the immediate aftermath a persistent challenge for emergency management. Although prior research has addressed risk assessment and resource allocation as separate problems, few studies have incorporated region-specific social vulnerability values directly into an optimization framework for relief distribution. This raises the question of how region-specific social vulnerability can be quantitatively embedded into a relief-supply allocation model to achieve a more equitable, risk-sensitive distribution than conventional population-based approaches. To address this gap, the present study develops an integrated decision-making model combining a fuzzy inference system (FIS) with fuzzy multiple objective linear programming (FMOLP). The FIS first derives social risk values from four vulnerability dimensions, namely exposure, disaster mitigation and preparedness, readiness, and recovery, for Taiwan’s 17 counties and municipalities, drawing on multidimensional indicators from the National Science and Technology Center for Disaster Reduction (NCDR) Disaster Mitigation Database. These values are subsequently incorporated as weighting coefficients within the FMOLP model, which jointly maximizes distributional utility and minimizes procurement cost under population-based supply constraints. The results identify Hsinchu, Taichung, Chiayi, Yunlin, and Hualien as the five highest-risk regions. The model further yields a compromise allocation plan spanning all 17 administrative units, with sensitivity analysis confirming its robustness across alternative performance metrics. This study offers a replicable, data-driven decision-support tool to assist disaster preparedness planners and government agencies in relief resource allocation.

Systems doi: 10.3390/systems14080925

Authors: Alfredo-Israel Ramírez-Mejía Joselito Medina-Marín Norberto Hernández-Romero Eduardo-Antonio Cendejas-Castro Grettel Barceló-Alonso

The efficiency of epidemiological models is based on two characteristics: speed and accuracy. While Equation-Based Modeling (EBM) enables agile calculations, its approximations lack precision because they exclude population heterogeneity. On the other hand, Agent-Based Modeling (ABM) integrates individuality into the model, but it requires considerable processing time to obtain results. In this work, a sequential strategy is developed to generate ABM that preserves population heterogeneity while reducing processing time. This model is obtained through an optimization process that identifies the set of parameters whose approximation to the pandemic’s real data minimizes error. The process begins with Particle Swarm Optimization (PSO), whose cost function is implemented via EBM, enabling comparison of the population projections against the real data recorded by health institutions in three major Mexican cities. The resulting parameters are transferred to an ABM modeled in NetLogo and validated using Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and the Coefficient of Determination (R2). The results show that calibrated parameters during the optimization stage, when applied in ABM simulations, generate epidemiological scenarios that reflect the pandemic’s actual behavior. These findings indicate that EBM-based calibration supports ABM experimentation while reducing the computational cost of agent-level optimization.

Systems doi: 10.3390/systems14080923

Authors: Chenxi Yang Guangfan Sun

Corporate bankruptcy early warning has often been treated as a binary classification task, yet financial distress is better understood as the outcome of interacting financial conditions that must be interpreted within practical risk-management contexts. To address this issue, this study proposes a systems-oriented explainable artificial intelligence framework for corporate bankruptcy early warning. The framework implements a Hierarchical LFRM-MACI architecture in which the Local Feature Refinement Module (LFRM) refines representations within profitability, solvency, liquidity, efficiency, and growth/shareholder performance subsystems, and cross-subsystem attention models their interactions. The framework is evaluated on the public UCI Taiwanese Bankruptcy Prediction dataset under balanced and moderately imbalanced training settings. For reporting clarity, the benchmark methods are classified into traditional machine learning methods and deep learning methods; traditional tabular learners are included in the formal single-split empirical comparison, while the proposed method’s contribution is positioned as a structured deep representation with an attribution workflow rather than as an overall superiority claim over traditional machine learning models. In the single 70%/30% validation split, the proposed model obtains ROC-AUC values of 0.9286 and 0.9269 under the 1:1.0 and 1:2.5 settings, respectively. In repeated 5-fold cross-validation with five repetitions, its ROC-AUC is 0.8940 [0.8777, 0.9103] under 1:1.0 and 0.9135 [0.9001, 0.9269] under 1:2.5. To examine the interpretability of the predictions, Permutation Feature Importance (PFI) and SHAP are applied to identify subsystem-level attribution patterns across major financial subsystems. The explanation results highlight influential predictors associated with leverage pressure, profitability and asset structure, liquidity, operating efficiency, and growth/shareholder performance. These findings indicate that explainable AI can support corporate bankruptcy early warning when predictive benchmarking is combined with transparent and auditable attribution analysis for financial decision-making.

Systems doi: 10.3390/systems14080915

Authors: Galina Ilieva Tania Yankova Margarita Ruseva Delian Angelov Stanislava Klisarova-Belcheva Marin Bratkov Penyo Georgiev Angel Dimitrov

Short-form video (SFV) marketing generates structured consumer ratings and unstructured feedback that can be analyzed for e-commerce decision support. This study examines associations between five content- and creator-related perceptions, Creating Shared Value (CSV), and purchase intention among 409 respondents in Bulgaria. From a business intelligence (BI) perspective, this study combines descriptive profiling, clustering, sentiment analysis, formative partial least squares (PLS) path modeling, group-difference testing, and machine-learning prediction to produce descriptive profiles, explanatory associations, consumer segments, text-analytic findings, and predictive results. CSV is specified as a formative composite of eight noninterchangeable product/economic, community/social, and relational indicators associated with short-form video creators. Clarity (β = 0.260), willingness to use (β = 0.226), similarity (β = 0.205), likability (β = 0.177), and empathy (β = 0.130) are positively associated with CSV; CSV is positively associated with purchase intention (β = 0.567). The model explains 69.1% of CSV variance and 32.2% of purchase-intention variance. Six formative weights are significant, all absolute loadings exceed 0.72, and indicator VIFs remain below 5. The group-difference hypothesis is partially supported by 18 omnibus ANOVAs with Holm correction for multiple testing. Significant differences in both CSV and purchase-intention means are found across age groups and categories based on the number of influencers followed. Significant differences in purchase-intention means are also found across social-media use-frequency and daily SFV-viewing-time categories. Clustering, sentiment-analysis, and machine-learning results are interpreted as complementary BI outputs rather than evidence of causal effects or validated campaign effectiveness.

Systems doi: 10.3390/systems14080922

Authors: Bochen Wang Yuhan Guo Changping He

This study investigates configuration resilience in emergency medical rescue networks under disaster-induced compound disruptions. Major disasters can simultaneously intensify casualty severity, disrupt road networks, restrict response access, and increase hospital surge pressure, turning emergency preparedness into a location–capacity–transfer planning problem under uncertainty. We formulate a two-stage scenario-based mixed-integer programming model that determines the locations and capacity levels of temporary rescue sites and emergency medical facilities and then optimizes scenario-adaptive casualty transfers among affected areas, rescue sites, emergency facilities, and hospitals. The model considers 81 compound disruption scenarios defined by casualty severity, road-network disruption, response-access constraints, and hospital surge pressure. To solve large-scale instances, we develop a small-sample active-learning-assisted variable neighborhood search algorithm (AL-VNS), which combines a committee random forest surrogate with a progressive active-verification strategy to prioritize limited exact CPLEX evaluations, while keeping all accepted and reported solutions exactly evaluated. Numerical experiments show that AL-VNS achieves near-optimal performance in small- and medium-scale instances. Based on three independent runs for each large-scale instance, AL-VNS reduces average runtime by 34.71–63.73% relative to baseline variable neighborhood search (BVNS), while maintaining average objective-value differences of only 0.01–0.12% and requiring approximately 217 exact evaluations per instance. In the Ya’an case and the tested sensitivity settings, temporary rescue sites provide a relatively stable spatial triage-and-transfer backbone, whereas emergency medical facilities offer flexible surge capacity for relieving hospital pressure. The findings support resilience-oriented location and capacity planning for emergency medical rescue networks under uncertain compound disruptions.

Systems doi: 10.3390/systems14080921

Authors: John Alexander Taborda Giraldo Antonio José Lengua Martinez Ricardo Javier Diaz Pupo

Environmental governance in the Global South increasingly unfolds within complex socio-technical systems characterized by fragmented institutional capacities, distributed sensing infrastructures, and high socio-ecological uncertainty. In this context, Digital Twins have emerged as a promising paradigm for integrating cyber–physical systems with decision intelligence, yet most implementations remain limited to industrial optimization or urban infrastructure management. This article proposes an architectural framework for Digital Twins oriented toward environmental governance by integrating Artificial Intelligence of Things (AIoT), monitoring networks with complex systems modeling and cybernetic governance principles. Methodologically, the study combines conceptual development grounded in systems science and the Viable System Model (VSM) with an illustrative empirical case study from the Caribbean Mining Corridor in Colombia, where a distributed AIoT environmental monitoring network operates within the Hub Ambiental del Caribe initiative. The proposed architecture links real-time environmental sensing, predictive analytics, and recursive governance structures to enable anticipatory environmental decision-making. The results demonstrate how Digital Twin infrastructures can function as cybernetic platforms that transform fragmented environmental data into actionable decision intelligence across institutional levels. By embedding feedback mechanisms, open environmental data governance, and participatory monitoring capacities, the framework supports adaptive management in highly dynamic socio-ecological contexts. The study concludes that Digital Twins designed as governance infrastructures—rather than purely technical replicas—can significantly enhance environmental decision support systems and foster climate justice-oriented transitions in vulnerable territories of the Global South.

Systems doi: 10.3390/systems14080920

Authors: Agnieszka A. Tubis Sylwia Werbińska-Wojciechowska

Contemporary logistics systems operate in increasingly volatile and uncertain environments, where disruptions and digital transformation jointly shape system performance and continuity. In this context, resilience has emerged as a critical capability; however, existing approaches often remain fragmented, representing resilience either as isolated capabilities or aggregated indicators, while lacking an integrated conceptual architecture for resilience assessment. This study introduces the House of Resilience (HoR) as a hierarchical conceptual architecture that represents resilience as an emergent multi-layer system property and operationalizes this concept through the Fuzzy House of Resilience Assessment Model (FHOR-RAM). The model applies fuzzy logic with linguistic variables, triangular fuzzy numbers, and a hierarchical Mamdani-type fuzzy inference system to capture uncertainty in expert assessments and aggregate resilience measures at both layer and system levels. The proposed framework is illustrated through a real-world procurement system case study conducted in a manufacturing company operating within a global supply network. The results indicate a moderate overall resilience level (RHoR = 0.402), with the weakest performance observed in the structural (R3 = 0.31), strategic (R4 = 0.33), and technological and risk assessment (R1 = 0.34) layers, while the operational and adaptive layer achieved a moderate resilience level (R2 = 0.51). The diagnostic analysis identified supplier dependency, lead-time instability, limited resilience governance, and insufficient digital capabilities as the principal resilience bottlenecks. Scenario analysis demonstrated that improvements targeting individual resilience dimensions provide only limited system-level gains, while coordinated development across multiple resilience layers enables a transition toward higher resilience levels (RHoR = 0.647). The findings confirm that resilience should be addressed as a multidimensional and multi-layer system property rather than as an isolated technological capability. The study demonstrates that fuzzy inference provides an effective operational mechanism for translating the House of Resilience conceptual architecture into an interpretable and decision-oriented resilience assessment framework under uncertainty. The study demonstrates that fuzzy inference provides an effective operational mechanism for translating the House of Resilience conceptual architecture into an interpretable and decision-oriented resilience assessment framework under uncertainty. The case study illustrates how the framework can support structured resilience diagnosis and identification of potential improvement priorities within a procurement system. However, broader empirical validation, including multi-company studies and integration with organizational data sources, remains necessary to further evaluate its transferability and practical deployment potential.

Systems doi: 10.3390/systems14080918

Authors: Zhihao Li Baozhen Jiang Ruofei Wang Ye Li

Accurate traffic state estimation (TSE) is critical for the deployment of intelligent transportation systems. Physics-informed neural networks represent a significant approach for achieving TSE; however, their applicability is constrained by the availability and accuracy of physical information. To address this limitation, this study proposes a multi-task TSE method guided by operator learning (BiONet). The core architecture of BiONet is a multi-task network. Prior to network training, a bidirectional DeepONet is employed to pre-learn the mapping operators among various physical variables, thereby enabling a generalized representation of physical laws. Building upon this foundation, BiONet incorporates a reliability-gated mechanism to modulate the influence of the operator guidance, effectively preventing performance degradation caused by operator failure. Experimental results on real-world data demonstrate that BiONet can accurately reconstruct traffic states and exhibits enhanced robustness across varying levels of data penetration.

Systems doi: 10.3390/systems14080919

Authors: Peng Zhang Siyue Xu Binghao Ji Chao Sun Junhui Zhang Wenquan Li Jianying Ma

Relying solely on NEMA phases for urban green-wave control often restricts the feasible regions of network optimization, resulting in narrow bandwidths or unsolvable models. To address this limitation, this paper proposes a mixed-integer linear programming model for regional signal coordination based on a mixed-phase release strategy that integrates NEMA dual-ring phase and split phase. By utilizing shared lanes under split phasing, the model maximizes lane resource efficiency and extends coordination benefits to left-turn traffic. Introducing 0–1 decision variables establishes a unified formulation for internal phase offsets, enabling flexible, intersection-specific release selection. To balance network efficiency and fairness, the optimization objective minimizes the weighted sum of the red-wave bandwidth-to-cycle ratio, subject to spatiotemporal and clockwise closed-loop constraints. A real-world case study in Suzhou, solved via the branch-and-bound method, demonstrates that the optimal design deploys split phase at seven intersections and NEMA phases at two. VISSIM simulations confirm that compared to the NEMA-only approach, the proposed mixed model reduces red-wave bandwidth by 49.52%, average delays by 26.36%, and stops by 17.5%. The proposed model provides a system-level signal coordination framework for improving the adaptability and reliability of urban traffic control systems.

Systems doi: 10.3390/systems14080917

Authors: Yi Li Quanxi Li Kailing Liu

Supply chains increasingly face complex disruptions and rapid technological change, making the relationship between digital transformation (DT) and supply chain resilience (SCR) an important issue in supply chain management. Yet existing evidence remains fragmented across forms of DT, dimensions of SCR, contextual conditions, and performance outcomes. This study conducts a systematic review and meta-analysis of 200 studies, including 124 examining the DT-SCR relationship and 104 examining the SCR–performance relationship. The results show that DT is positively associated with SCR, with stronger associations for digital capabilities than digital technologies and for reactive than proactive SCR. The DT-SCR association is also stronger among SMEs and mature firms, in manufacturing sectors and developing countries, and in contexts characterized by high power distance, low individualism, and stronger long-term orientation, while uncertainty avoidance shows no significant moderating effect. SCR is positively associated with firm performance, with stronger associations for operational and market performance than financial performance. By integrating evidence on the antecedents, boundary conditions, and performance consequences of SCR, this study provides a more differentiated and context-sensitive understanding of the role of DT in supply chain resilience.

Systems doi: 10.3390/systems14080914

Authors: Ming Xia

Online knowledge communities serve as critical infrastructures for innovation, yet prior research relying on net-effect logic has struggled to explain why similar communities differ in knowledge diffusion quality or reveal how multiple conditions synergistically produce high-quality outcomes. Drawing on socio-technical systems theory and dimensionalizing the DeLone and McLean (D&M) model into knowledge, technical, and social subsystems, this study adopts a configurational perspective to examine the joint influence of seven conditions on knowledge diffusion quality. Using data from 307 users and integrating fuzzy-set qualitative comparative analysis (fsQCA) with necessary condition analysis (NCA), we found that no single condition is individually necessary for achieving high-quality outcomes, highlighting the nonlinear and substitutable nature of socio-technical systems. Three equifinal pathways were identified: demand–response synergy, trust-mediated integration, and emotion–utility coupling, alongside four pathways leading to the absence of high-quality outcomes, demonstrating causal asymmetry. Notably, a dual-sided configurational pattern emerged: information utility quality serves as a core condition across all high-quality pathways, while service empathy appears as a core-absent condition in the majority of non-high-quality pathways. This study advances socio-technical systems theory by operationalizing equifinality, conjunctural causality, and subsystem interdependence, and offers practical implications for platform governance.

Systems doi: 10.3390/systems14080908

Authors: Weiwei Song Zongping Yu Yuxiang An Jiayuan Wang

Green collaborative innovation is a complex socio-technical system involving interactions among governments, universities, enterprises, and financial institutions. However, existing studies often examine these actors separately or focus on bilateral relationships, limiting the understanding of how multi-agent strategies jointly evolve toward sustainable innovation. This study develops a four-player evolutionary game model incorporating regional governments, universities, local enterprises, and green financial institutions to investigate the dynamic evolution of green collaborative innovation under bounded rationality. The model integrates fiscal incentives, technology commercialization returns, financial constraints, reputation effects, and social advocacy mechanisms to explore the conditions under which collaborative strategies emerge and stabilize. Theoretical analysis identifies the evolutionary conditions of different strategic combinations, while numerical simulations examine how key factors influence the evolution process. The results reveal that enterprise deep green transformation plays a central role in shaping the evolution of the entire collaborative innovation system. Government subsidies and financial constraints jointly influence enterprises’ transformation decisions, while technology commercialization returns and research support determine universities’ incentives for green R&D. Green financial institutions contribute not only through capital provision, but also through environmental risk assessment and market-based governance. In addition, social advocacy indirectly affects the evolutionary process by influencing reputation-related incentives and strengthening institutional accountability. This study contributes to green innovation and systems science research by providing a multi-agent evolutionary framework that explains the interactions among policy, technology, finance, and institutional factors. The findings suggest that effective green collaborative innovation requires coordinated governance mechanisms that integrate fiscal support, financial regulation, technology transfer, and social supervision.

Systems doi: 10.3390/systems14080916

Authors: Kanietova Aiymbubu Kang Chen Xiaoya An Xu Xin Jichun Li Haiyan Dai Chunliang Liu

A floating storage and regasification unit (FSRU) provides flexible infrastructure for liquefied natural gas (LNG) import, storage, regasification, and regional distribution. Its operation requires coordinated decisions on vessel deployment, sailing speed, loading and discharge timing, terminal inventory, berth use, and backup pipeline supply, while carbon taxation changes the relative cost of alternative vessel and operating choices. This paper formulates this operational planning problem as a maritime inventory routing problem (MIRP) for an LNG distribution network centered on an FSRU. The MIRP formulation integrates FSRU and terminal inventories, heterogeneous vessel capacities, berth constraints, discrete speed choices, pipeline backup, and direct operational carbon costs. To solve the resulting large model, we use a column generation framework with a labeling algorithm for the pricing subproblem, dual stabilization, and a diving heuristic for integer recovery. Each column represents a complete vessel schedule, and the restricted master problem coordinates schedule selection, task coverage, vessel use, backup supply, and aggregate berth capacity. Computational experiments are conducted on calibrated synthetic instances. A complete enumeration benchmark for a small instance provides a diagnostic check, while larger experiments examine computation time, feasible incumbent values, time discretization, instance size, and sensitivity to carbon taxes. The results show nonlinear responses in cost and emissions: low tax rates produce limited operational changes, intermediate rates induce clearer shifts toward vessels with lower emissions at moderate cost increases, and very high stress test rates yield diminishing marginal emission reductions. These findings show how integrated scheduling can support coordinated FSRU planning under carbon costs, while the observed transition points remain specific to the tested instances.

Systems doi: 10.3390/systems14080910

Authors: Jing Liu Yi Feng Kang Jiang

Novice drivers often exhibit poor lane-changing competence due to ineffective risk identification and control instability. Existing driver training primarily focuses on mechanical skills, lacking systematic psychological interventions. To address this gap, this study constructs a four-stage intervention program (attention, retention, reproduction, and motivation) utilizing Bandura’s observational learning theory. A within-subject pre–post design driving simulator experiment was conducted with 46 drivers (16 experienced and 30 novices). Multimodal data—including eye movements, physiological responses, and vehicle kinematics—were collected and analyzed. The results demonstrate that the proposed intervention effectively bridged the initial significant capability gap between the two groups. Post-intervention, novice drivers exhibited significantly optimized visual search strategies, reduced mental workload, and enhanced vehicle control stability during lane-changing maneuvers. Ultimately, no statistical differences remained between novices and experienced drivers across all core indicators, fully validating the research hypotheses. These findings offer robust theoretical support and a practical paradigm for reforming novice driver training, contributing to reduced lane-changing accidents and enhanced active road safety.

Systems doi: 10.3390/systems14080913

Authors: Ruifen Sun Han Wang Yang Shao Pengfei Li Alireza Khani

Fragmentation in road networks and hierarchical imbalances among nodes increase circulation impedance in China’s county-level delivery networks, hindering efficient county–township–village circulation. This study integrates spatial syntax, complex network analysis, hierarchical connection-cost analysis, and a coupling–coordination degree model to develop a network evaluation framework for identifying structural bottlenecks and analyzing circulation efficiency. Using eight counties in Shaanxi Province as empirical cases, we quantify road-network accessibility using a spatial syntax axial model and examine hierarchical node organization across county, township, and village tiers using complex network indicators. Network-level circulation efficiency is analyzed using road accessibility, hierarchical connection costs, county–village connection impedance, and average path length. Correlation and regression analyses are further used to examine the relationship between morphology–structure coordination and network impedance. The results show that higher road-network integration is generally associated with lower hierarchical connection costs, and higher morphology–structure coordination is significantly associated with lower county–village connection impedance and shorter average path length. The proposed network-level evaluation framework provides a structural basis for identifying accessibility deficiencies, hierarchical connectivity constraints, and morphology–structure mismatches, thereby informing efforts to reduce circulation impedance in county–township–village delivery networks.

Systems doi: 10.3390/systems14080907

Authors: Roxana-Mariana Nechita Cătălina-Monica Alexe Cătălin-George Alexe Dana-Corina Deselnicu

This study represents the third stage of a structured research program focused on human-related factors in the success of research and development (R&D) projects. Building on prior work that identified 49 success factors and examined relationships among 28 human-centric variables using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, the present paper addresses the lack of formal validation of factor inclusion within the system. In this study, validation refers to assessing whether the identified factors are sufficiently integrated into the network of relationships to justify their inclusion within the human resource system associated with R&D project success. The research introduces a multi-layer analytical approach to refine the initial framework into a validated system. First, Matrix Impact Cross-Reference Multiplication Applied to a Classification (MICMAC) is applied to the influence matrix derived from DEMATEL to classify factors based on influence and dependence. Autonomous variables with low influence and low dependence are removed, resulting in a reduced set of structurally integrated factors. Second, Multidimensional Scaling (MDS) is used to examine similarity patterns among the remaining factors and to provide an additional perspective on the consistency of the system structure. Third, the ϕ coefficient is applied to analyse co-occurrence patterns and identify groups of factors that tend to appear together. The results support the delimitation of a core human resource system composed of factors that occupy central positions within the interaction structure and exhibit stable association patterns. This integrated approach reduces ambiguity in factor selection and provides a clearer representation of how human-related factors are organised within R&D projects. The findings contribute to the literature on project success by offering a structured procedure for system delimitation and by identifying priority areas that can support managerial and organisational decision-making in R&D environments.

Systems doi: 10.3390/systems14080912

Authors: Kwan Soo Shin In Seok Kang Munho Lee

Generative artificial intelligence (AI) has diffused rapidly, yet adoption has not reliably progressed to AI transformation (AX). Firms grant tool access but fail to redesign workflows, clarify accountability, govern risks, or measure value. The gap is a socio-technical systems problem, not a productivity problem: AI tools are inserted into existing routines without redesigning task interdependencies, decision rights, oversight loops, or performance feedback. This paper develops AX-5R, a socio-technical systems architecture that converts fragmented AI use into accountable, governable, and measurable work systems. Synthesizing seven literature streams, it maps failure modes to five interdependent design functions: readiness, redesign, role, risk, and return. AX-5R treats transformation as joint optimization of technical and social subsystems requiring configurational alignment. A supplementary ablation probe generated 252 artifacts across three workflows and seven prompt arms from two language models, scored by blinded cross-provider judges; the full frame outscored a sham five-part control and its four artifact-relevant ablations, significant under two-sided Holm-corrected testing, with an independent human-expert-rating check. With a failure-mode derivation matrix, implementation artifacts, and six testable propositions, the framework specifies a minimum architecture in which readiness sets boundaries, redesign restructures tasks, role assigns accountability, risk establishes control, and return supplies learning feedback.

Systems doi: 10.3390/systems14080911

Authors: Hongbo Li Wei Chen Zehui Wei Qingkang Zhu Xianchao Zhang

Knowledge-intensive projects in research and development (R&D), software, and high-technology sectors are delivered by knowledge workers who each command several skills. When many such projects compete for a shared pool of multi-skilled workers, and neither project arrival times nor durations are known in advance, deciding who works on what at each moment becomes a sequential decision problem. Therefore, we propose the multi-skilled, multi-project dynamic scheduling problem with random project arrivals and uncertain durations and formulate it as a Markov decision process (MDP) that minimizes the total human resource cost. Since the cost-to-go function is computationally intractable, we develop a rollout-based approximate dynamic programming (ADP) algorithm that approximates it via Monte Carlo simulation embedded with a randomized base policy and restricts the action space to ten representative allocation policies. On benchmarks extended from the Project Scheduling Problem Library (PSPLIB), the proposed policy lowers the average total cost by 3.1% to 16.7% relative to Q-learning on medium- and large-scale instances while completing more projects with shorter delays; on small-scale instances Q-learning attains a lower nominal cost, revealing a trade-off among cost, completion rate, and delay.

Systems doi: 10.3390/systems14080909

Authors: Rui Cheng Yuxin Wang Mengdan Liu

Humanitarian supply chains often face sudden demand surges, disrupted transportation, limited inventory visibility, and fragmented coordination under disaster uncertainty. Digital twins can improve real-time visibility and adaptive resource allocation, but their effectiveness depends on stable collaboration among public, private, and platform actors. This study develops a digital twin-oriented tripartite evolutionary game and simulation framework involving the public sector, private logistics/material enterprises, and a digital twin platform provider. The model examines how digital twin-oriented governance, active resource and data sharing, and high-quality platform operation co-evolve under bounded rationality. Stability analysis identifies the conditions for convergence to the desired collaborative equilibrium. Numerical simulations, including global sensitivity analysis, stability-region analysis, scenario comparison, stochastic evolutionary dynamics, and Monte Carlo tests, show that enterprise sharing costs and platform operation costs are key barriers, whereas subsidies, platform payments, credible constraints, digital twin visibility, collaboration synergy, and disaster uncertainty promote collaboration. Scenario comparison further indicates that technology-only digital twin construction is insufficient, while adaptive incentive-compatible governance generates shorter convergence time and lower collaboration loss. The findings highlight digital twins as socio-technical coordination infrastructure requiring incentive-compatible governance in humanitarian supply chains.

Systems doi: 10.3390/systems14080906

Authors: Qing Yang Qiuying Wang Gang Liu

From a systems-thinking perspective, firms can be understood as adaptive socio-technical systems in which strategic resource allocation, innovation activities, organizational capabilities, and environmental conditions interact through dynamic feedback processes. This study examines whether strategic focus alternation, defined as the recurrent reallocation of resources between technology focus and market focus, is associated with firm resilience. Using 30,930 firm-year observations of Chinese A-share listed firms from 2010 to 2024, we construct a firm-level measure of strategic focus alternation and examine its association with resilience using industry and year fixed-effects models, complemented by multiple robustness analyses, propensity score matching, instrumental-variable estimation, Heckman two-stage estimation, and within-firm analyses. The results indicate that strategic focus alternation is positively associated with firm resilience, although the magnitude of this association is economically modest, suggesting that strategic focus alternation represents one contributing element within a broader organizational resilience system rather than a dominant managerial determinant. Strategic focus alternation is also positively associated with persistent innovation and ambidextrous innovation synergy; however, mediation analyses do not support these innovation outcomes as statistically significant transmission mechanisms linking strategic focus alternation to resilience. Furthermore, while the capability to undertake strategic focus alternation is positively associated with resilience across firms, greater within-firm alternation intensity, measured by switching frequency and magnitude, does not generate additional resilience benefits and is marginally negatively associated with resilience, indicating potential coordination and adjustment costs. Entrepreneurial orientation and knowledge integration capability further strengthen the positive association between strategic focus alternation and resilience. Finally, the findings suggest that organizational resilience depends less on frequent strategic switching than on disciplined strategic reconfiguration that maintains dynamic stability while enabling organizational adaptation under uncertainty. By integrating systems thinking with research on strategic resource allocation, organizational resilience, and innovation, this study provides a more comprehensive understanding of how firms balance strategic flexibility and organizational stability in dynamic environments.

Systems doi: 10.3390/systems14080905

Authors: Guhao Zhang Hao Yang Chenkai Wang Zhipeng Zhou Shilei Hu

Existing research lacks consensus on how digital transformation affects financial distress, and traditional linear models struggle to capture complex variable dynamics. This study examines the link from prediction and association perspectives by constructing a high-precision financial risk prediction model and using explainable methods to explore feature contributions. Using 2010–2024 data from Chinese A-share listed companies, we apply Logit regression to assess the association between digital transformation and financial distress, and introduce an XGBoost model with SHAP for prediction and interpretability. Results show that XGBoost outperforms Logit (AUC, PR_AUC). Logit regression reveals a negative correlation between digital transformation and distress probability, which SHAP further supports. Higher digitalization corresponds to lower predicted risk, with non-linear characteristics: at low levels, marginal contributions fluctuate; beyond a threshold, the mitigation effect stabilizes. Feature importance identifies the asset-liability ratio as the top risk factor, while digital transformation and operating profit margin serve as key protective features. This paper contributes by constructing a digital transformation index and integrating it into the financial distress prediction model, comparing traditional econometric models with machine learning models, and using SHAP to provide an interpretable framework for understanding the digital transformation–financial distress association.

Systems doi: 10.3390/systems14080904

Authors: Olufunke Oladimeji

Despite the growing importance of Systems Engineering (SE) in addressing complex engineered systems, a persistent gap remains between undergraduate SE education and industry workforce expectations. Existing studies have primarily examined workforce readiness, competency development, curriculum design, and industry requirements as independent issues. However, limited research has investigated how these factors interact dynamically over time to influence the SE skills gap. The aim of this study is to examine how educational preparation, SE perception, and workforce transitions interact over time to influence the undergraduate SE workforce pipeline and resulting skills gap. A system dynamics stock-and-flow model was developed to represent the undergraduate SE workforce pipeline based on findings from a systematic literature review and a causal loop diagram. The model was used to evaluate the effects of intervention strategies through scenario analysis, and the results suggest that under baseline assumptions, the model produced a widening skills gap, while isolated interventions produced partial improvements. The combined intervention produced the strongest improvement in skilled workforce growth and skills gap reduction. This study contributes a dynamic perspective on undergraduate SE workforce development and provides an exploratory simulation framework for evaluating policies intended to improve SE workforce readiness.

Systems doi: 10.3390/systems14080902

Authors: Yuanbo Li Peixuan Li

Many decision systems require search over continuous parameters or discrete route structures. This study examines whether a rank-weighted population architecture can support both representations while retaining an interpretable decision mechanism. We propose the Chinese Bureaucratic System Algorithm (CBSA), which transforms objective-based population ranks into either continuous movement or edge-based routing probabilities. We expect rank aggregation to be most useful when higher-ranked solutions contain stable information that can be reused in later searches. The framework is instantiated as a continuous variant (CBSA-C) and a discrete routing variant (CBSA-D), and its computational complexity and limiting search properties are analyzed. On the 2014 Congress on Evolutionary Computation (CEC2014) benchmark suite, CBSA-C obtains the best average rank among five algorithms and is strongest on multimodal and hybrid functions. On 50 capacitated vehicle routing instances, CBSA-D ranks second behind iterated local search while outperforming four population- or edge-based baselines. Across all 56 Solomon time-window routing instances, it is most competitive on clustered classes and weaker on random and mixed classes. These results support a conditional interpretation: rank-weighted aggregation is useful when objective ranks are informative and the representation contains reusable structure, while stronger local-search methods remain preferable when such structure is absent.

Systems doi: 10.3390/systems14080903

Authors: Xuan Liu Youxin Qian Guohui Zhou Guangyu Xu Junmin Lyu Legend Zhang Ergang Wen

Urban–rural public facility disparity is a systemic challenge that reflects dysfunctional feedback loops within and across social and technical subsystems. Traditional administrative-unit-based analyses cannot capture such systemic alignments. This study applies the marker-controlled watershed segmentation (MWS) algorithm, the Two-Step Floating Catchment Area (2SFCA) method, and spatial clustering methods sequentially to dynamically separate urban from rural areas and assess the urban–rural disparity in public facility provisions within the Chengdu–Chongqing economic circle (CCEC). The results indicate: (1) The distribution of public facilities in the CCEC is characterized by significant spatial inequity, predominantly manifested as a substantial disparity between urban and rural areas. (2) A significant misalignment exists between the supply of public facilities and the demands of vulnerable groups. Rural residents, low-income groups, and elderly people are at a disadvantage in accessing public facilities. (3) Rural residents are concentrated in low-income areas and have the most limited ability to obtain public facility services and resources, especially in terms of healthcare facilities. By replacing predefined administrative boundaries with image-derived functional urban–rural patches, the proposed framework enables a more spatially realistic assessment of urban–rural public facility disparities. This approach provides a transferable tool for system-level decision-making in regional planning, particularly for targeting resource allocation in underserved areas.

Systems doi: 10.3390/systems14080900

Authors: Hyejin Park Minseo Lee Jinil Han

This study considers a 1-m-1 hybrid flow shop scheduling problem that simultaneously incorporates four practical constraints: lot streaming, no-wait, blocking, and sequence-dependent setup times. Although each of these characteristics has been studied individually in the literature, their joint consideration in a single HFS model has received little attention. The problem is motivated by a real-world order sequencing problem in insulation board manufacturing, where all four constraints arise simultaneously from the production process. To formally characterize the problem, we develop a mixed-integer programming formulation that captures all operational constraints. For practical-scale problems, we propose several dispatching heuristics that can obtain sufficiently good solutions within a short computation time. We further develop a genetic algorithm as an independent solution approach to obtain high-quality solutions close to the optimum within a reasonable computation time. Computational experiments on instances generated based on real insulation board production characteristics demonstrate that the proposed algorithms outperform a benchmark greedy rule, and sensitivity analyses reveal the effects of setup time magnitude and the number of parallel machines on scheduling performance.

Systems doi: 10.3390/systems14080901

Authors: Rui Neves-Silva Paulo Pina

Product–Service Systems (PSS) require feedback loops that connect stakeholder experience with design decisions, but distributed feedback is often difficult to transform into actionable design knowledge. This paper synthesises previously distributed results from the DIVERSITY project into an integrated decision-support framework for feedback-driven PSS design. The framework combines social-sentiment representation, reach/influence weighting, opinion modelling, monitoring, extraction and prediction modules, and a hierarchical similarity mechanism for exploring likely sentiment toward new PSS configurations. It was implemented in the DIVERSITY engineering environment and assessed through a preliminary polarity plausibility check, controlled testing with synthetic data generated by OpinionSim, and three industrial business cases. The results indicate that the framework can support segmentation-aware interpretation of stakeholder feedback, shorten the path from market signals to design action, and provide exploratory evidence for early PSS concept generation and redesign. The predictive component is presented as an exploratory design-support mechanism, with forecasting accuracy remaining a topic for future empirical validation. The paper contributes an integrated feedback-to-design framework for PSS and clarifies the conditions under which sentiment-based indicators are most useful in B2C and B2B settings.

Systems doi: 10.3390/systems14080899

Authors: Rui Cheng Mengdan Liu Jida Liu

Urban fire emergency response depends on the coordinated functioning of road access, firefighting water supply, and command systems. This study develops an 18-node Bayesian network to assess Response Resilience in interdependent road–water–command systems using 200 coded urban fire emergency cases. The model links 12 observable conditions to Road Capacity, Water Capacity, and Command Capacity, which jointly influence Response Effectiveness and Response Disruption Severity; these outcomes subsequently determine Response Resilience. Baseline inference indicates a 34% probability of effective response, a 42% probability of high Disruption Severity, and a 17% probability of high resilience. Scenario analysis shows that weak Command Capacity produces the most adverse single-subsystem outcome, while strong Command Capacity partially offsets simultaneous road and water constraints. Sensitivity analysis identifies Fire Lane Availability, Water Pressure, Water Coordination, Unified Command, Resource Mobilization, and Alarm Dispatch as influential nodes. The integrated intervention increases the effective response rate to 50%, reduces high Disruption Severity to 32%, and raises high resilience to 25%. A 1000-replication bootstrap supports the sampling stability of the principal estimates and intervention effects. The findings provide probabilistic decision support for coordinated urban fire–Response Resilience improvement.

Systems doi: 10.3390/systems14080898

Authors: Fabian Goihl Yannick Lindebauer Richard von Esebeck Jivka Ovtcharova Thomas Vietor

Automotive systems are experiencing a rapid increase in complexity driven by the transition towards software-defined vehicles, autonomous functionalities and increasingly interconnected E/E architectures. This transformation intensifies variability across hardware and software domains and challenges established engineering approaches. Traditional modular product development (MPD) provides structural mechanisms to manage hardware complexity, while systems and software product line engineering (SPLE) offers methods for managing software variability. However, these paradigms are typically applied in isolation and lack an integrated methodology capable of addressing cross-domain variability and architectural synchronization in automotive systems. This paper investigates how SPLE and MPD can be systematically integrated to manage variability and architectural complexity in automotive systems. Following a design-oriented research approach, industry requirements are derived from an automotive case study at an OEM. Existing SPLE and modularization approaches are analyzed against these requirements, revealing gaps in cross-domain traceability, synchronization mechanisms, and lifecycle coordination. Based on this analysis, we propose an integrated methodology that combines variability modeling principles from SPLE with architectural modularization concepts. The approach enables management of module structures, supporting system-level consistency in automotive environments. The main contribution is a model-based-integration framework that bridges variability management and modular architecture design to address increasing system complexity in the automotive industry.

Systems doi: 10.3390/systems14080897

Authors: Shijie Zhang Renmiao Wu Zheng Xu

The stability and collaborative development of digital platform ecosystems depend on the establishment and maintenance of harmonious interfirm relationships. However, extant research has paid limited attention to the processes through which trust is cultivated among platform participants or to the potential negative behaviours of core firms that may erode such trust. Adopting the perspective of ecosystem actors, this study specifically examines how trust in core firms is formed and how possible negative behaviours by core firms may influence this trust relationship. In doing so, it addresses a significant gap in the literature on the dynamics of core firm–subordinate firm (hereinafter referred to as “actors”) relationships in digital platform ecosystems. Drawing on signalling theory, this study examines whether and how the perceived ecological niche creativity and key resource sharing intention of core firms influence actors’ trust intention through the reputation of core firms. It also incorporates the opportunistic behaviour and manipulation power of core firms into the model as important contextual moderators in order to examine whether these factors erode core firm–actor trust relationships within the ecosystem. Based on survey data from 347 actors operating in digital platform ecosystems, the results show that: (1) the ecological niche creativity and key resource sharing intention both significantly enhance actors’ trust intention; (2) the reputation of core firms mediates both of these relationships; and (3) the opportunistic behaviour negatively moderates the relationship between the reputation of core firms and actors’ trust intention, whereas the manipulation power of core firms positively moderates this relationship. Adopting the perspective of ecosystem actors, this study provides an in-depth analysis of the mechanisms through which trust relationships with core firms are established. This examination differs from the prevailing view in the literature that trust within digital platform ecosystems is primarily algorithmic in nature. In doing so, it extends the theoretical perspective of trust research in digital platform ecosystem relationships and offers useful implications for core firms in guiding and maintaining relationships among ecosystem participants and supporting the development of platform ecosystems.

Systems doi: 10.3390/systems14080896

Authors: Xun Liu Jiapu Zang Shenghao Zhang Lingna Lin

Construction projects are increasingly complex, fragmented, and data-intensive, making cross-organizational collaboration routine while leaving teams “information-rich but knowledge-poor”; that is, large volumes of drawings, BIM models, meeting records, site reports, and sensor data are produced, but they are not always transformed into reusable, context-sensitive knowledge for future decisions. To consolidate dispersed evidence and capture the shift from digital to intelligent knowledge sharing (KS), this study conducts a PRISMA-guided systematic literature review of KS in construction project management from 2015 to 2025. Through quantitative and qualitative analyses of 163 core articles, this paper identifies five central themes: organizational and behavioral mechanisms, performance–innovation–sustainable value, digital technology empowerment, knowledge mechanisms and organizational learning, and risk–supply chain–governance and security. The study also synthesizes key challenges in knowledge sharing and outlines future research directions. These findings provide guidance for researchers, practitioners, and policymakers seeking efficient, credible, and sustainable systems to enhance project management effectiveness.

Systems doi: 10.3390/systems14080895

Authors: Gabriela Mariutac Claudiu Brandas Otniel Didraga Mihai Plesa

The voluntary carbon market (VCM) has faced sustained legitimacy stress since 2023, when peer-reviewed work found that fewer than one in six issued credits represented a real emission reduction. In parallel, tokenisation through Web3 protocols, decentralised autonomous organisations (DAOs), and regenerative finance (ReFi) infrastructures introduced new participants interacting with incumbent registries without a shared coordination framework. Existing scholarship examines commons governance, complex system governance (CSG), and tokenised carbon markets largely in isolation; the gap addressed here is the absence of an integrated system-of-systems (SoS) governance treatment of the tokenised VCM. This study develops and empirically applies a polycentric SoS governance framework for the tokenised VCM, structured around four research questions and five foundational contributions. We treat the tokenised VCM as an SoS that is polycentric in configuration but not by design, and develop a system-of-systems engineering (SoSE) governance reading of it. We reformulate Ostrom’s eight design principles as SoS governance criteria for digital–physical hybrid commons, map each to CSG metasystem functions, and apply the framework to four cases: KlimaDAO, Toucan Protocol, Regen Network, and the post-2023 Verra reforms. Qualitative coding is complemented by on-chain and Base Carbon Tonne spot-price evidence from October 2021 to December 2025. Disclosure by an analytical intermediary acted on the SoS roughly seven weeks before formal regulatory action and was associated with about 90% of the observed bridging slowdown, interpreted descriptively rather than causally. We derive an eight-item reform agenda, six DAO–registry interface specifications, and a five-level governance maturity rubric.

Systems doi: 10.3390/systems14080894

Authors: Bo Ren Xinying Fan Lili Xu

China is a major producer, consumer, and exporter of automobiles. As an important component of the national industrial system, the automotive industry is associated with strong economic support functions, notable industrial spillover effects, and significant technological externalities, and its core values constitute a powerful driving force in achieving the global Sustainable Development Goals. Accordingly, this paper establishes a complex network evolutionary game model that involves two types of automobile manufacturers (established and latecomer automakers) in a strategic interaction within an exogenous environment jointly shaped by the government and the consumer community. We conduct a numerical simulation analysis to explore the organic relationships between the core elements within the system and the long-term performance of the automotive industry. The main findings are as follows. First, in adopting sustainable business models (SBMs), latecomer automakers exhibit a “high-start, low-end” evolutionary trajectory, whereas established automakers follow a “low-start, high-end” convergence path. Second, regarding the characteristics of game rules, the proportion of automakers that adopt SBMs is positively correlated with a larger proportion of ESG consumer groups, stronger comprehensive production and consumption subsidy standards, a more favorable expected payoff, and stronger market advantages on the part of established automakers. Finally, regarding network-structure characteristics, the proportion of automakers that adopt SBMs is positively correlated with a moderate total number of automakers, a reasonable proportion of established automakers, and a higher edge-addition probability. Moreover, this proportion is nearly independent of the noise interference coefficient, thus indicating that the mathematical model constructed as part of this study exhibits strong anti-interference capability.

Systems doi: 10.3390/systems14080893

Authors: Lina Yang Yin Se

Geographical indication (GI) products are often understood through terroir, certification, and branding, yet less attention has been paid to the feedback dynamics through which their market value is amplified and destabilized. This paper applies systems thinking to the post-harvest value–trust subsystem of Xinhui Chenpi, a traditional Chinese aged citrus pericarp product with GI protection, to examine how value amplification and systemic vulnerability emerge through interacting feedback mechanisms. Drawing on 28 semi-structured interviews conducted between September 2023 and December 2024, supplemented by participant observation, policy and standard documents, field-based market observations, and contextual media materials, the study develops a preliminary causal loop diagram (CLD) with 15 endogenous feedback variables, 3 boundary value-input variables, 4 exogenous contextual inputs, and 21 causal links (18 endogenous and 3 value-input). The model identifies two reinforcing loops and two balancing loops through which price expectations, holding incentives, credible circulation supply, perceived scarcity, misrepresentation, and trust erosion interact. The trust-erosion loop shows how premium-driven misrepresentation increases claim uncertainty, weakens open-market consumer trust, reduces credible open-market liquidity, and further contracts credible circulation supply. Buyer exit and delayed supply response operate as limited balancing mechanisms because their effects are segment-dependent and constrained by aging and verification delays. The proposed CLD suggests that high-value mechanisms in aging-dependent GI products may also generate structural vulnerabilities, with implications for managing consumer trust, claim verification, and credible circulation in premium markets.

Systems doi: 10.3390/systems14080892

Authors: Miguel Fuentes

This article reconsiders escalation in international politics by shifting the analytical focus from discrete events to transformations in political order. While existing approaches have generated important insights into the dynamics of conflict, particularly through the analysis of decision-making, interaction, and violence intensity, they tend to conceptualize escalation as unfolding against a relatively stable political background. This article builds on these contributions while advancing a complementary perspective in which escalation is understood as a regime-level transformation driven by the progressive loss of adaptive political capacity, the ability of political systems to sustain non-coercive coordination under stress. Drawing on debates on temporality, resilience, and anticipation, it shows how escalation reshapes institutional mediation, informational environments, and relational structures, progressively reorganizing political order around coercion. The analysis clarifies the distinction between escalation as variation within an existing order and escalation as transformation of that order, and reflects on the implications of this perspective for the study of war and security. The framework is empirically applied to the destruction of informational coordination infrastructure during the Gaza War (2023–2025), where documented patterns of journalistic infrastructure elimination offer a contemporary case through which to examine the mechanisms by which coercive coordination becomes entrenched.

Systems doi: 10.3390/systems14080891

Authors: Jingyi Wang Wenyuan Lv Yanyan Ma

Against the backdrop of the dual convergence of the digital economy and sustainable development strategies, digital innovation has emerged as a pivotal driver for reshaping firms’ competitive advantages and fulfilling social responsibilities. In this study, the analysis draws on a sample of Chinese listed firms from 2014 to 2023. ESG performance is measured with Hua Zheng ESG scores, digital innovation is captured via digital patent identification, and a dual-mediation framework is employed to examine internal organizational and external supply chain mechanisms, along with heterogeneity analysis. The findings indicate that digital innovation exerts a significant positive effect on ESG performance and reveal a unique dual mediating mechanism: within the internal boundary of the firm, digital innovation releases available organizational slack significantly through internal resource orchestration, and this abundant slack provides a necessary resource buffer for ESG investment; within the external network of the firm, digital innovation reduces supplier concentration significantly, thereby enhancing bargaining and supply chain discourse power, which in turn improves corporate ESG performance. Heterogeneity analysis further demonstrates that the promoting effect of digital innovation on ESG performance is more pronounced in subsamples characterized by CEO duality, low technological intensity, and high levels of marketization. By drawing on the resource-based view and resource dependence theory respectively to explain internal and external mediating pathways, this study advances understanding of the complementary mechanisms through which digital innovation enhances ESG performance. It demonstrates how digital innovation comprehensively reshapes firm capabilities, providing valuable insights for formulating strategic ESG initiatives in the digital era.

Systems doi: 10.3390/systems14080890

Authors: Ismail Sheik Jobo Dubihlela Bibi Zaheenah Chummun

Artificial intelligence, digital payments and platform-based services are reshaping the operating conditions of Africa’s informal economy and micro-enterprise sector. While mobile money, digital marketplaces, app-mediated logistics and algorithmic scoring systems are commonly presented as instruments of financial inclusion and enterprise modernisation, their effects on informal traders remain uneven, conditional and under-governed. This systematic review synthesises recent peer-reviewed evidence on AI-mediated digitalisation pathways for informal and micro-enterprises in Africa, with particular attention to mobile money, platform payments, app-based logistics, digital credit, algorithmic management and platform governance. Following PRISMA-informed systematic review procedures, this review analyses 60 peer-reviewed, DOI-bearing articles published between April 2022 and June 2026 through a mechanism–outcome synthesis approach. The final corpus was selected through database searching, duplicate removal, title-and-abstract screening, full-text eligibility assessment, quality appraisal and mechanism–outcome coding. The findings show that digitalisation can expand market access, reduce cash-handling risks, create transaction histories, strengthen customer reach, improve operational continuity and support household resilience. However, the same digital infrastructures may also intensify livelihood vulnerability through opaque scoring, unexplained account freezes, fee shocks, exclusionary verification procedures, algorithmic ranking losses, data extraction and weak dispute-resolution mechanisms. The review therefore argues that informal enterprise digitalisation should not be understood only as a technology adoption issue, but as a socio-technical systems governance challenge. The article contributes a governance-and-risk framework linking local infrastructure, platform and payment design, AI (artificial intelligence) mediation, adoption conditions and livelihood outcomes. It further identifies minimum policy and design protections required for inclusive and resilient participation, including transparent fees, proportionate verification, human appeal channels, explainable restrictions, data-use consent, timely settlement and contingency mechanisms during digital outages or erroneous flags. The review concludes that sustainable digital inclusion for African informal micro-enterprises depends not merely on access to digital tools, but on the fairness, transparency, recoverability and accountability of the systems through which traders participate.

Systems doi: 10.3390/systems14080888

Authors: Chao Zeng Changhao Zhao Xiaoting Huang Changxi Ma

Parking space availability forecasting is challenging due to noise contamination, non-stationary dynamics, and complex multi-scale temporal dependencies. This study proposes a wavelet-enhanced Autoformer with dynamic multi-kernel decomposition for parking space availability forecasting. Discrete wavelet transform is used to enhance the input sequence by suppressing high-frequency disturbances while preserving the dominant temporal structure. Within the Autoformer framework, a dynamic multi-kernel decomposition module is developed by introducing multiple moving-average kernels with different scales and an input-dependent dynamic weighting mechanism, enabling the adaptive decomposition and fusion of trend and fluctuation components and thereby improving the representation of complex multi-scale temporal patterns. The proposed framework is evaluated using real parking operation data collected from the Liangshan Jingyuan parking lot in Chongqing, China, including 1248 observations with a 2 h sampling interval. Multi-step forecasting experiments are conducted under short-, medium-, and long-term prediction settings with different label-length configurations. Across different forecasting horizons and label-length settings, the proposed model consistently outperforms the original Autoformer, PatchTST, and a range of conventional and recent forecasting baselines. In particular, under the 24-step forecasting setting, the proposed method reduces MAE, RMSE, and WMAPE by approximately 10% relative to the original Autoformer. Moreover, the results suggest that the dynamic multi-kernel mechanism improves the model’s ability to adaptively capture scale-dependent temporal patterns across different forecasting tasks.

Systems doi: 10.3390/systems14080889

Authors: Roksolana Kanzamanova Seunghwan Myeong

Free generative AI (GenAI) services appear to democratize access, but freemium platform models can create post-access stratification after basic connectivity and initial use have been achieved. This article conceptualizes paid commitment to GenAI as an emergent outcome of a socio-technical system composed of platform tiers, user capabilities, task environments, economic constraints, affective feedback, and institutional access. Using a cross-sectional 2025 survey of 2000 South Korean adults aged 18–69, the study models the observed states of non-use, free use, and paid commitment through sequential logit models. Descriptive results show that 70.6% of respondents used GenAI in the previous year, yet only 20.6% of users, or 14.6% of the total sample, paid for access. Practical AI literacy is positively associated with both initial use and paid commitment, whereas critical AI awareness is associated with initial use but not payment. Negative emotions are negatively associated with initial use but positively associated with paid commitment among users, a pattern consistent with vigilant and intensive engagement. The premium digital divide is defined not as a fourth level of the digital divide, but as a tier-mediated extension of second-level inequality that may condition access to later educational, occupational, or civic benefits. Because the data are cross-sectional and combine different GenAI platforms, the estimates are ecosystem-level associations and do not establish causal direction or platform-specific effects.

Systems doi: 10.3390/systems14080887

Authors: Gianfranco Minati

We elaborate on computational emergence (CE), understood as the emergent acquisition of specific abilities from specific forms of computation, such as artificial neural networks and cascades of rule iterations found in cellular automata. CE leads to the acquisition of properties such as learning abilities, morphological pattern formation, and coherence, and arises from computational mechanisms. We also elaborate on emergent computation (EC), understood as the emergent acquisition of computational abilities by communities of phenomenologically interacting agents, potentially through appropriate interlinkages among them, as in emerging networks. Processes of interaction are understood generically as forms of mutually active interdependence, which can be modeled as self-generated networks. EC arises from phenomenological mechanisms of interaction among agents and leads to the acquisition of properties such as coherent behaviors, resilience, robustness, and collective intelligence. The reason for distinguishing between these two types of emergence is that doing so may open new approaches to modeling collective behavior, especially in artificial ones, such as swarms of unmanned aerial vehicles (UAVs), where introducing parametric and structural changes is more feasible. Combining the two approaches—(a) phenomenological, networked EC arising from populations of interacting (b) in turn computationally emergent agents—allows the consideration of research directions such as identifying relationships between combinations of CE and emergently acquired computational properties within the conceptual frameworks of networked neural networks and intersected neural networks, i.e., networks that share neurons. Such research directions are expected to enable approaches for influencing collective behaviors and complex systems in a non-invasive way, including swarms of UAVs (or drones), autonomous cyborg swarms, and coherent communities of artificial devices equipped with sensors, edge artificial intelligence, and secure communications. We consider the mesoscopic nature of complexity in collective behaviors as a continuous negotiation between these two forms of emergence, with EC playing a macroscopic role and CE a microscopic role. We conclude that this general framework relates to the concept of “The Middle Way” in physics by focusing on what occurs “in between” systems (such as between intersecting neural networks and their dynamic networking) and within transient spaces where non-invasive intervention may be possible and appropriate for guiding, modifying, and inducing changes in complex emergent systems.

Systems doi: 10.3390/systems14080886

Authors: Marina Proença Carla Alessandra Dos Santos Ana Maria Machado Toaldo Artur Morgan Tomás Sparano Martins

Although digital readiness is widely recognized as a prerequisite for digital transformation, existing research predominantly adopts a determinant-based perspective, offering limited insight into how readiness actually emerges within organizations. To address this gap, this study develops a systems-based and process-oriented framework explaining how organizational factors translate into digital readiness in small firms. Drawing on Resource-Advantage Theory and a socio-technical systems perspective, a qualitative cross-cultural multiple-case study was conducted in six small service firms located in Brazil and the United Kingdom. The cross-cultural design enabled the identification of recurring organizational mechanisms across different institutional contexts. The findings reveal that organizational factors such as corporate culture, knowledge management, organizational learning, and leadership do not directly generate digital readiness. Instead, their influence depends on Project Management Capability (PMC), which emerged as the central mechanism coordinating and integrating these resources into coherent digital initiatives. Firms with stronger PMC exhibited more structured and strategically aligned digital transformation trajectories, regardless of resource availability. This study contributes to the literature by conceptualizing digital readiness as an emergent organizational outcome rather than a static condition and by positioning PMC as the critical link between organizational factors and digitally enabled competitive advantage in small firms.

Systems doi: 10.3390/systems14080885

Authors: Lorant Bucs Anna Bucs Viorica-Mirela Ştefan-Duicu Cristina Nicolau

Artificial intelligence (AI) is increasingly reshaping production management by enabling data-driven optimization, predictive decision-making, and more adaptive operational systems across manufacturing. With research activity in this area growing rapidly, the literature is fragmented; thus, understanding the current reconfigurations of production management driven by AI has become difficult, though necessary. This study offers a comprehensive analysis of scientific publications (n = 439) on AI in production management published between 2016 and 2026. Research was conducted through an adapted selection process guided by Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) to ensure transparency, while data were analyzed using VOSviewer and complementary statistics, combining keyword co-occurrence mapping, subject area classification, and country and author collaboration networks. Findings indicate an increase in research activity, with computer science, engineering and telecommunications emerging as the primary foundations of the field. The analysis identifies three interconnected thematic directions: AI-enabled smart manufacturing and operational optimization, including scheduling, production planning, predictive maintenance, and quality control; sustainability- and resilience-oriented production and supply chain transformation; and digital twin-based, human-centered, and knowledge-driven production systems. Moreover, international collaboration patterns show a highly globalized research landscape led by Germany, France, Italy, Sweden, Norway and South Korea. Overall, bibliometric patterns suggest an emerging framing of AI in production management research which needs more attention and further development.

Systems doi: 10.3390/systems14080884

Authors: Gila Kurtz Meital Amzalag

The growing presence of generative artificial intelligence (GenAI) in learning environments highlights a need to understand self-regulated learning (SRL) as these technologies enable new pathways for independent learning. This study examined the cognitive and personal factors associated with the self-reported metacognitive use of GenAI within the SRL framework, focusing on perceived self-regulation skills, perceived usefulness, and learning motivation. An online survey was conducted among 1265 participants recruited through snowball sampling. The findings indicate that perceived personal skills and perceived usefulness are the strongest predictors of self-reported metacognitive use of GenAI. In contrast, learning motivation emerged as a negative predictor, suggesting that motivation does not necessarily translate into effective engagement with GenAI-based learning. Additionally, perceived usefulness partially mediated the relationship between motivation and metacognitive use. The findings extend SRL theory to GenAI-driven learning environments and highlight the importance of fostering learners’ self-regulation skills and awareness of the value of GenAI for effective adoption.

Systems doi: 10.3390/systems14080883

Authors: Vincent O’Connell Philip Onderwaater

Does asymmetric cost behavior require an uncertain environment to emerge? We study cost “stickiness” and “anti-stickiness”—the asymmetric response of costs to sales increases and decreases—as the output of a complex adaptive socio-technical system in which managerial cognition (the social subsystem) couples demand signals to committed resources and adjustment frictions (the technical subsystem). Because optimism and pessimism can arise only under uncertainty, expectation-driven asymmetry should be observed only once the environment is sufficiently unpredictable. Using 163,415 U.S. firm-year observations (1970–2012), with environmental uncertainty measured as firm-specific sales volatility relative to industry, we find that cost stickiness is statistically absent under low uncertainty and strengthens as uncertainty rises, whereas anti-stickiness is present across uncertainty levels and, on average, weakens as uncertainty increases. Both relations are non-linear, peaking at intermediate-to-high uncertainty, and an order-backlog test detects no statistically discernible expectation-driven asymmetry under low uncertainty. The evidence is consistent with environmental uncertainty acting as an enabling condition of expectation-driven adaptation, and shows that anti-stickiness is not the mirror image of stickiness—with implications for modeling coupled adaptive responses in organizational systems.

Systems doi: 10.3390/systems14080882

Authors: Xinquan Liu Zhaolong Ren

Cross-border transportation networks show considerable complexity and vulnerability under the influence of geopolitical risks. To identify critical nodes and simulate the propagation paths of failure risk within cross-border transportation networks, this study develops a multilayer cross-border transportation network model that integrates four transport modes: waterway, road, rail, and air transport. First, composite edge weights are calculated using the Criteria Importance Through Intercriteria Correlation (CRITIC) method based on inter-node transport distance, transport time, and geopolitical risk (GPR) exposure score. These weights capture the joint effects of transport efficiency and risk exposure across different transport corridors. Second, an Improved Weighted K-shell (IWKS) model is proposed for critical-node identification by integrating three network topological indicators: degree centrality, weighted degree, and closeness centrality. Third, an edge-risk-based Susceptible–Infected–Recovered (SIR) propagation model is introduced. Monte Carlo simulations are then used to estimate the simulation-based propagation influence of each node. Finally, the identification performance of the IWKS model is compared with that of conventional centrality indicators. The IWKS scores are also significantly correlated with the simulation-based propagation influence under three types of correlation tests. The findings support the applicability of the proposed IWKS model in critical-node identification and system vulnerability research of multilayer cross-border integrated transportation networks.

Systems doi: 10.3390/systems14070881

Authors: Chulmin Kim Seongjin Ahn

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.

Systems doi: 10.3390/systems14070880

Authors: Yueqin Wu Zhipeng Yu

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.

Systems doi: 10.3390/systems14070879

Authors: Haris Alibašić

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.

Systems doi: 10.3390/systems14070878

Authors: Wenwen Zhang Ruixin Liang Xuepeng Qian

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.

Systems doi: 10.3390/systems14070877

Authors: Xueying Wang Ruowei Ma Yuang Duan

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.

Systems doi: 10.3390/systems14070876

Authors: Vincent O’Connell Fabian Jintae Froese

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.

Systems doi: 10.3390/systems14070875

Authors: Jeonghoon Kwak Hyewon Yoon Heeseok Shin

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.

Systems doi: 10.3390/systems14070874

Authors: Ping Sang Ronggui Ding Tao Sun Chongzheng Li

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.

Systems doi: 10.3390/systems14070873

Authors: Fan Xia Tangdai Xia

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.

Systems doi: 10.3390/systems14070872

Authors: Ge Shi Yutong Wang Jiantao Shi Chuang Chen Lin Sun Wei Wang

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.

Systems doi: 10.3390/systems14070871

Authors: Haiyang Hou Chunyu Zhao

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.

Systems doi: 10.3390/systems14070870

Authors: Seung-Jun Lee Ji-Sung Kim In-Seok Heo Hong-Sik Yun

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.

Systems doi: 10.3390/systems14070869

Authors: Rafael Robina-Ramírez Ana Leal-Solís Aloysius Roets Manuel Jesús Sánchez-González

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.

Systems doi: 10.3390/systems14070868

Authors: Luling Zeng Xingfeng Duan

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.

Systems doi: 10.3390/systems14070867

Authors: Tingting Zhou Shuzhen Yu Zhiyong Yu Haijun Jiang

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.

Systems doi: 10.3390/systems14070866

Authors: Baichuan Gong Xiaobing Liu Yanlei Guo

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.

Systems doi: 10.3390/systems14070865

Authors: Sohyun Park Seunghwan Myeong

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.

Systems doi: 10.3390/systems14070864

Authors: Alireza Ahmadi Alessandra Cantini Stefano Frecassetti Federica Costa Alberto Portioli-Staudacher

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.

Systems doi: 10.3390/systems14070863

Authors: Haoying Niu Qinzi Xiao Mingyun Gao

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.

Systems doi: 10.3390/systems14070862

Authors: John Turner Hoda Parvaneh Shirazi Heesun Kim Jiajia Du Yeonji Jung Xiaoyan Xu

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.

Systems doi: 10.3390/systems14070861

Authors: Yuqing Wu Rui Wang Yongjian He Yun Zhou He Zhang

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.

Systems doi: 10.3390/systems14070860

Authors: Yimeng He Lirong Chen Chunguang Sheng Kerui Niu

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.

Systems doi: 10.3390/systems14070859

Authors: Jia Shao Xingping Wang

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.

Systems doi: 10.3390/systems14070858

Authors: Ali Hamidi Fisnik Dalipi Sadaf Salavati

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.

Systems doi: 10.3390/systems14070857

Authors: Takayuki Kataoka Katsumi Morikawa Katsuhiko Takahashi

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.

Systems doi: 10.3390/systems14070856

Authors: Petru Stefan Vasian Ionut Dancau Catalin Dumitrescu Eduard-Cristian Popovici Petrica Ciotirnae

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.

Systems doi: 10.3390/systems14070855

Authors: Xiangyu Chen Xiaofeng Xu Da Tong

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.

Systems doi: 10.3390/systems14070854

Authors: Qing Wang Jihai Zhang

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.

Systems doi: 10.3390/systems14070853

Authors: Yufei Xia Jikang Sun Jiarun Liu Kun Fang Huiyi Shi Na Li

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.

Systems doi: 10.3390/systems14070852

Authors: Xiangyu Feng Tao Li Peng Liao Yingxu Rui

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.

Systems doi: 10.3390/systems14070851

Authors: Guy André Boy

Context is at the heart of systemic cognition, a new theoretical framework that supports human systems integration (HSI) of sociotechnical systems (STSs). An STS is a complex system that involves humans and machines. Our claim is that we cannot discuss, analyze, design, and assess an STS without considering the contexts in which it is designed, developed, validated, operated, maintained, and ultimately dismantled. More specifically, this is why we need a clearer understanding of what we mean by “context” in STS design and operations. The central assumption of the systemic cognition approach is that we gradually become familiar with the complexity of an STS through the creation and refinement of conceptual models in context. This process is incremental and integrative (i.e., anytime emerging behaviors, phenomena, and properties are discovered from experience, they are integrated into the STS at work). We then need not only to provide a situated framework for operations people but also to guide engineering people in HSI. This is the purpose of systemic cognition in context (SCC). Therefore, how can we figure out relevant contexts? How can we model them? How do we integrate them into the systemic cognition framework to support HSI? More specifically, how are systemic cognition resources (SCRs) framed and articulated in appropriate contexts? This article proposes a constructivist approach for further defining context-aware systemic cognition that encompasses the entire life cycle of an STS and is useful for tangibility testing and traceability. A review of relevant philosophical approaches to context is presented, along with several concrete implementation efforts. Examples are used to support the SCC framework being developed.

Systems doi: 10.3390/systems14070850

Authors: Roee Peretz

This article develops a systems-thinking framework to analyze and govern interconnections among the Sustainable Development Goals (SDGs). Contemporary policy challenges, including food insecurity, climate change, water and energy security, inequality, urbanization, and conflict, are treated as complex systems characterized by feedback loops, delays, cross-sector dependencies, and unintended consequences. Drawing on a structured literature review, qualitative case analysis, and framework synthesis, the article translates core systems concepts into an operational workflow for coherent policy decision-making. The framework combines causal-loop analysis, leverage-point identification, cross-sector coordination, adaptive monitoring, and iterative learning. Two illustrative policy arenas, Zero Hunger and Climate Action, demonstrate how a systems lens can reveal reinforcing and balancing feedback loops, clarify synergies and trade-offs among SDGs, and support more coherent interventions. The article contributes to systems research by connecting conceptual tools from systems thinking with practical governance mechanisms for SDG implementation. It also identifies institutional, methodological, political economy, and financing barriers that limit adoption and proposes strategies for embedding systems analysis into policy design, budgeting, evaluation, and adaptive governance.

Systems doi: 10.3390/systems14070849

Authors: Hyun-Kyung Lee Cheolhee Yoon Bong Gyou Lee

Artificial intelligence (AI) is increasingly embedded in high-stakes socio-technical systems, intensifying concerns about autonomy, accountability, data rights, and fundamental-rights protection. This article develops an exploratory, expert-informed Human-Centred AI (HCAI) pre-design governance framework that translates selected risk-based obligations of the EU Artificial Intelligence Act into early organisational decisions about human oversight, data accountability, documentation, and bounded algorithmic autonomy. Using a sequential mixed-methods design, the study combines an Analytic Hierarchy Process (AHP) survey of 28 experts with think-aloud interviews with 15 of those respondents. The AHP results show that, among the governance criteria included in the model, AI design objectives received the highest upper-level priority and human oversight and control received the highest global priority, followed by personal information protection, design ethics, intellectual property rights protection, and limits of algorithmic autonomy. The interviews explain these priorities by showing that experts framed trustworthy AI governance as a problem of controllability, responsibility allocation, traceable data use, rights protection, and verifiable human intervention rather than model performance alone. The study contributes by defining pre-design governance as a bounded initial consideration-stage decision structure, combining AHP-based priority evidence with qualitative justification logic, and proposing a preliminary governance package of decision points, minimum evidence artefacts, and illustrative operational check criteria. The package is not presented as a validated legal compliance model; instead, it provides an expert-informed translation pathway for future organisational, sector-specific, and empirical validation.

Systems doi: 10.3390/systems14070848

Authors: Xinquan Liu Yanlei Guo Yike Bi Zhaolong Ren

To address the multi-objective conflicts among cost, time, and carbon emissions in Guangxi cross-border container multimodal transport—under the influence of factors such as tariffs, fluctuating customs clearance efficiency, and carbon emission policies—this paper develops a multi-objective optimization model that integrates mixed time windows and scenario analysis. The model incorporates multiple elements, including transportation cost, transshipment cost, tariff cost, time value of cargo, carbon tax cost, in-transit and customs clearance time, and transshipment-related carbon emissions, making it more aligned with real-world cross-border operational scenarios. To effectively solve this complex model, an improved NSGA-II algorithm (I-NSGA2) is designed, which introduces an adaptive crossover and mutation operator along with an elite retention strategy to enhance convergence speed and solution diversity, while embedding a scenario parameter response mechanism to accommodate dynamic fluctuations in key parameters. Subsequently, an evaluation framework is constructed using the entropy weight–TOPSIS method to select recommended routes with favorable cost–time–carbon trade-offs from the Pareto frontier. A case study based on the Nanning–Kuala Lumpur route is conducted for validation. Experimental results demonstrate that the I-NSGA2 algorithm significantly outperforms MOPSO, MOEAD, and the standard NSGA-II in terms of IGD and HV metrics; time-sensitive cargo tends to favor rail-dominated routes, while low-cost cargo prefers combined road–water–rail routes. This study effectively addresses the route selection problem for Guangxi cross-border container multimodal transport under varying key parameters, and also provides a research foundation for optimizing cross-border multimodal transport route selection in other regions.

Systems doi: 10.3390/systems14070847

Authors: Hyungkun Hahm Sungbok Chang Jungho Suh

This study develops a computational framework for quantifying how technological disruptions are associated with shifts in media–public discourse alignment. Using the public release of ChatGPT (30 November 2022) as a temporal breakpoint, we examine whether the broad public availability of generative AI was associated with structural changes in the relationship between media agendas and online public discourse in South Korea. The framework combines PPMI-weighted semantic network construction with QAP correlation, MRQAP regression and Cohen’s q effect-size analysis, applied to 181,081 Korean-language texts encompassing media agendas (national newspapers, economic dailies, regional newspapers, and broadcast news) and public agendas (online communities) over six years (2019–2025). Results reveal that national newspapers lost their dominant agenda-setting position, with public alignment declining sharply (r: 0.678 → 0.430, Cohen’s q = 0.369, large effect), while economic papers rose from lowest to second-highest alignment (r: 0.352 → 0.567) by addressing market and industry dimensions that direct AI experience could not supply. Broadcasting emerged as the dominant structural anchor of public discourse (unique coefficient β: 0.263 → 0.569; its removal alone lowers model fit from R2 = 0.517 to 0.347), while national newspapers’ unique contribution reversed in sign. Collective media explanatory power itself remained essentially stable (R2 = 0.537 → 0.517), indicating a structural reconfiguration of which media align with public discourse rather than a wholesale weakening of media–public alignment—consistent with agenda-melding, in which publics integrate media coverage with firsthand technological experience. A residual analysis further shows that the variance media agendas leave unexplained is not noise but a structured, public-specific layer of discourse organized around the hands-on use of generative-AI tools (e.g., ChatGPT, image generation)—direct evidence of a bounded public autonomy in which public discourse is distinct from, and not reducible to, media agendas. These findings demonstrate the framework’s utility for detecting how publicly accessible AI adoption—exemplified by ChatGPT—is associated with shifts in media–public structural dynamics within discourse ecosystems and carry implications for computational social science, technology communication, and applied network analysis.

Systems doi: 10.3390/systems14070846

Authors: Yinan Lu Weinuo Li Yue Cai

Large language models (LLMs) can assist lesson planning, but simple prompts often yield incomplete and misaligned outputs. This study proposes a three-step prompt framework grounded in Bloom’s taxonomy, Adaptive Control of Thought-Rational (ACT-R) theory, Gagné’s nine events, and problem-chain theory, decomposing planning into objective, unit, and activity design. Using three DeepSeek models (R1, V3, 32B) and five prompting strategies, 150 lesson plans were generated on ten computer networking topics. Coverage of Gagné’s nine events and functional quality were evaluated via an LLM judge and human validation. All theory-based strategies significantly outperformed naive prompting, raising Gagné’s event coverage above 90% in the full corpus and from 74.1% to 89.8–93.5% in human ratings. Functional quality scores improved by up to 17.3% (LLM judge) and 53.6% (human raters). Gagné’s five-stage design outperformed ACT-R’s three-stage design under base conditions, while problem-chain guidance benefited ACT-R substantially. Model capability moderated gains: smaller models benefited most in structural completeness, stronger reasoners achieved higher absolute quality. These findings demonstrate that pedagogically grounded, multi-stage prompts are designed to reconfigure teacher-artificial intelligence (AI) interaction from passive output consumption toward structured collaborative design, offering a scalable intervention for integrating LLMs into instructional workflows.

Systems doi: 10.3390/systems14070845

Authors: Rui Cheng Yuwei Song Yuxin Wang

Smart-city governance increasingly relies on data infrastructures to connect public agencies, digital platforms, and urban services; however, complex emergencies continue to expose fragmentation in information sharing, administrative responsibilities, and cross-boundary coordination. A key unresolved question is whether data-driven policy experimentation can strengthen the institutional foundations of emergency collaboration. In this study, we examine China’s National Big Data Comprehensive Pilot Zones as a systemic governance intervention and apply a multi-period difference-in-differences model to estimate the effect of pilot-zone construction on the policy-text-based institutionalization of emergency collaboration, using provincial panel data for 30 Chinese provinces from 2010 to 2022. The results show that pilot-zone construction significantly strengthens institutionalized emergency collaboration in provincial policy systems. Mechanism tests provide evidence consistent with two complementary pathways: emergency-related technological innovation, measured by granted patents screened through IPC/CPC classifications and title–abstract keywords, supports task-specific capacities such as risk sensing, early warning, emergency communication, command support, and resource allocation; digital government strengthens administrative interoperability, data sharing, platform-based coordination, and standardized interdepartmental procedures. Heterogeneity analyses show stronger effects in eastern, middle-income, and severely aging regions, suggesting that policy effectiveness depends on implementation capacity, absorptive capacity, and emergency-service demand. This study contributes to systems governance and smart-city research by showing how data-driven policy experimentation can shape the formal institutionalization of emergency collaboration. The findings should be interpreted as evidence of institutionalized policy attention and formal collaborative arrangements rather than direct evidence of field-level emergency response performance.

Systems doi: 10.3390/systems14070844

Authors: Shaopeng Zhang Xinyu Ren Yinhao Yang

In the era of carbon neutrality, urban competition has evolved from single-dimensional economic rivalry to multidimensional competition emphasizing green and sustainable development. However, it remains unclear whether digital infrastructure, the physical backbone of the digital economy, can externally enhance urban green competitiveness. Building on the measurement of urban green competitiveness through the super-efficiency SBM model, this study employs the “Broadband China” policy as an exogenous shock and uses a Staggered difference-in-differences (SDID) model to systematically analyze the impact and mechanisms of digital infrastructure on urban green competitiveness. The SDID model is adopted in this study for the following reason: the Broadband China strategy constitutes a phased rollout policy shock. By treating this policy as a quasi-natural experiment, the SDID framework enables more accurate identification of its causal impact on urban green competitiveness. The results show that the construction of digital infrastructure significantly promotes urban green competitiveness. Further analysis reveals that digital infrastructure enhances green competitiveness primarily through green technological innovation, industrial upgrading, and economic agglomeration, with the effect of economic agglomeration being stronger than that of the other two channels. Heterogeneity analysis shows that moderate government intervention amplifies the green effects of digital infrastructure. Moreover, the direct effect of digital infrastructure on green competitiveness is more significant in western cities than in eastern and central cities. This study provides empirical evidence for optimizing digital infrastructure development and achieving a “dual drive” of digitalization and green transformation.

Systems doi: 10.3390/systems14070843

Authors: Shuchih Ernest Chang Kai-Chun Chung Chung-Hua Chu

As a booming application domain in sharing economy, the crowd logistics services (CLSs) have emerged in recent years to take advantage of under-utilized resources for generating economic value. However, unduly designed CLS system platforms may suffer substantial problems such as sensitive information exposure, excessive commission fees, and trust issues. To mitigate such problems, we propose an approach comprising four initiatives: (1) exploring the applicability of blockchain technology and its affiliated technology, smart contract, in CLSs to manifest blockchain-enabled benefits including service traceability, process transparency, system automation and disintermediation; (2) adopting blockchain and smart contract technologies to design a blockchain application system architecture (BASA) suitable for reengineering current CLSs; (3) demonstrating the blockchain-based crowd logistics services (BCLSs) system design, implementation, and deployment details; and (4) evaluating the functionality and benefit of BCLSs approach to confirm its feasibility and applicability. After presenting the system design and implementation outcomes, this study elaborates the benefits and implications of BCLS systems through four theoretical frameworks: e-Commerce Value Creation Theory (VCT), Innovation Diffusion Theory (IDT), Principal Agent Theory (PAT), and Transaction Cost Analysis (TCA), deriving important findings and implications. Such benefits and implications suggest that BCLSs may help CLSs (1) mitigate PAT frictions and reduce transaction costs; (2) redefine value creation and accelerate innovation diffusion; (3) eliminate platform monopolies and achieve real-time settlement; (4) implement data sovereignty and enhance privacy/security; and (5) reconfigure trust mechanisms and generate digital credit assets. The research results of this study may help the CLS industry clarify the BCLS system’s upgrade path, promote business model innovation, and enhance fair governance and social sharing.

Systems doi: 10.3390/systems14070842

Authors: Huichao Wu Li Zhu Quhan Chen Haoyu Deng

The formation mechanisms of Urban Vitality have been constrained by the limitations of traditional linear driving hypotheses, and the fragmented analysis of subjective and objective factors. Integrating the explainable machine learning model XGBoost-SHAP with multi-source geospatial data, this study constructs a systematic analysis framework at the community scale, comprising 275 community units across five administrative districts in Changsha. It constructs a systematic analysis framework that unifies objective environmental conditions and subjective perceptions—a deliberate departure from the fragmented approaches dominant in previous vitality research. Through this objective-subjective integrated lens, it explores the spatial patterns, non-linear driving mechanisms, and variable interaction effects of Urban Vitality. The XGBoost model achieves a cross-validated R2 of 0.794 and an RMSE of 0.031, ensuring interpretative reliability for exploring non-linear mechanisms. The results indicate that Urban Vitality exhibits a spatial pattern characterized by “high-value aggregation in the core, gradient decay in the periphery, and local fragmentation,” with a significant siphon effect observed in the core area. Partial Dependence Plot (PDP) analysis reveals that the impacts of variables on vitality can be categorized into four patterns: continuous upward, threshold leap, inverted U-shaped, and weak or sample-concentrated. Univariate dependence plots further delineates fine-grained threshold effects, including “threshold triggering, nterval suitability, high-value suppression, and co-occurrence signals.” Furthermore, bivariate interactions reveal four synergistic mechanisms: Building Density must match road network support; Functional Aggregation should synergize with locational value; transportation nodes must integrate with activity-support capacity; and Street View quality and human demand mutually regulate each other. The conclusion asserts that urban stock renewal must transcend the mindset of single-factor maximization and shift towards a precision governance approach of “threshold activation, synergistic matching, and zoning intervention,” thereby providing a quantitative decision-making basis for human-oriented, fine-grained urban regulation.

Systems doi: 10.3390/systems14070841

Authors: Weiwei Wu Xiaoxuan Wang

In response to intensifying environmental pressures and the rapid pace of digital transformation, firms are increasingly turning to artificial intelligence (AI) as a tool to support sustainable development. Using panel data from Chinese A-share-listed manufacturing firms from 2016 to 2023, this study examines the relationship between AI adoption and two forms of green innovation: green product innovation and green process innovation. The results reveal that AI adoption is positively associated with both forms of green innovation, with a stronger association observed for green product innovation. The relationship between AI adoption and green innovation also varies across organizational and market contexts. CEO turnover weakens the association between AI adoption and green product innovation but strengthens its association with green process innovation. Market competition further strengthens the positive association between AI adoption and both types of green innovation. Further heterogeneity tests indicate that these associations tend to be more pronounced among high-tech firms and smaller firms. This study provides new evidence on the relationship between AI adoption and different forms of green innovation. It further clarifies the organizational and market conditions under which AI is more closely linked to corporate green transformation.

Systems doi: 10.3390/systems14070840

Authors: Zirui Li Guangchong Chen Shiqi Chen Guangyao Chen Fenghua Tian Qiming Li

Escalating threats from natural disasters and technical failures underscore the critical role of infrastructure resilience in sustaining operations and reducing losses. Existing studies have advanced resilience optimization, but still provide limited explanation of how resilience improvements are converted into stakeholder-specific gains or how such heterogeneous gains can guide strategy prioritization. This study aims to develop a gain-oriented infrastructure resilience optimization framework that explicitly links resilience strategies, resilience increments, stakeholder-specific gains, stakeholder groups, and aggregated system-level benefits. The framework is operationalized through a fuzzy-evidential Bayesian Network (FEBN) parameterized by triangular fuzzy numbers, Dempster–Shafer evidence theory, and Noisy-Or model, complemented by Dynamic Bayesian Network (DBN) analysis to simulate long-term optimization dynamics. A case study of Nanjing Metro Line 7 demonstrates the framework’s applicability. The results show that proactive strategies, particularly component reliability design (RS 1-1) and emergency response planning (RS 4-1), are high-leverage interventions for improving aggregated gain. Failure-scenario, parameter-perturbation, and baseline-comparison analyses indicate that the main prioritization results remain broadly stable under uncertainty and consistent with non-evidence BN baselines. The DBN results further reveal that operator-centered optimization improves aggregated gain over time but may produce asymmetric benefits. Overall, the framework supports transparent, stakeholder-oriented resilience strategy prioritization in socio-technical infrastructure systems.

Systems doi: 10.3390/systems14070839

Authors: Lei Wang Qingpeng Lu Tingqiang Chen

Against the background of the low-carbon energy transition, China’s energy system faces energy structure risks caused by high-carbon path dependence, external dependence on oil and gas, and insufficient new energy substitution capacity. This paper decomposes China’s energy structure risk into three subsystems: high-carbon lock-in risk, oil and gas external dependence risk, and new energy transition vulnerability risk. It uses system dynamics to construct a causal feedback structure and dynamic model, and links the three risk subsystems to observable indicators based on China’s historical data for parameter calibration and model validation. On this basis, the paper analyzes the formation mechanism of China’s energy structure risk. The findings are as follows: (1) China’s energy structure risk is not a static outcome caused by a single factor shock, but the result of the simultaneous presence, interaction, and continuous accumulation of three types of risks, as supported by the historical calibration results. (2) Changes in risk source strength and risk linkage intensity alter both the internal composition and overall level of energy structure risk; stronger linkages among risks make local risks more likely to evolve into structural risks. Robustness tests based on alternative weights, dynamic response parameters, and benchmark models confirm the stability of these findings. (3) The decision-making relationship between the energy supply department and the energy financial system further affects the formation of China’s energy structure risk. Compared with the Nash non-cooperative game and the finance-led Stackelberg game, the cooperative game is associated with higher entity governance input and financial support intensity, and therefore corresponds to a lower long-term level of energy structure risk under the model assumptions.

Systems doi: 10.3390/systems14070838

Authors: Yujie Wang Yingzi Zhang Guijie Qi

In online innovation communities, idea diffusion constitutes a core mechanism through which user-generated ideas are transformed into collective innovation outcomes. However, existing research remains fragmented and lacks a process-oriented explanation of how ideas become actionable and trigger diffusion. This study draws on affordance theory to explain how ideas become actionable and drive diffusion in online innovation communities. Cognitive affordance is operationalized as operability and detail richness, entry affordance as actionability signaling and linguistic hedging, and collaborative affordance as mediating structural position and interaction depth. Using text mining and social network analysis on idea-level data and employing a negative binomial regression model, we find that the effects of affordances are inherently nonlinear. While some affordances exhibit positive linear effects, others follow U-shaped relationships, suggesting threshold-dependent activation mechanisms. The results suggest that AI may influence the activation of affordances and alter certain affordance-diffusion relationships under specific conditions. Theoretically, this study advances affordance theory by introducing an activation-based perspective that captures the nonlinear transformation from perceived possibilities to realized actions. It also integrates content and structural dimensions into a unified framework for understanding idea diffusion. Practically, the findings offer insights for platform governance, particularly in designing content structures, participation mechanisms, and AI-assisted tools that effectively facilitate innovation diffusion.

Systems doi: 10.3390/systems14070837

Authors: Imo Enang Iniobong Enang Ikpe Justice Akpan

Industrial control systems (ICSs) are the operational technology that monitors and directs physical processes across critical infrastructure. They sit at the core of Industry 4.0. Once these systems are connected to digital platforms and service chains, a flaw in one component is no longer confined to that component. Conventional practice still treats disclosed vulnerabilities as isolated events to be patched, which leaves an open question: does the way vulnerabilities accumulate across ICS infrastructure amount to systemic risk, a property of the system rather than of any single flaw? We examine this using the ICS-CERT Vulnerability Dataset, analysing 60,378 vulnerability-product records that cover 2327 unique Common Vulnerabilities and Exposures (CVE) entries, 416 vendors and 14,577 affected products from 2012 to 2020. We construct a System Risk Index (SRI) that aggregates vulnerabilities to the vendor-year level, weighted by severity and exploitability. An ordinary least squares (OLS) model with heteroscedasticity-robust standard errors explains vulnerability severity (R2 = 0.99). A second model explains system-level risk (R2 = 0.91). Vulnerability-type diversity, measured through the Common Weakness Enumeration (CWE) taxonomy, is the strongest driver of SRI (β = 1.25, p < 0.001), ahead of mean exploitability (β = 0.29, p < 0.001) and product breadth (β = 0.06, p = 0.001). Annual ICS disclosures rose from 115 in 2012 to 503 in 2019, an increase of 337 per cent. Risk is concentrated: the five largest vendors account for 33.3 per cent of disclosed CVEs and more than 60 per cent of the cumulative SRI, with one vendor carrying over twice the cumulative SRI of the next. Quantile regression confirms the severity findings at the median. The pattern indicates that digital transformation redistributes risk into a connected, vendor-level property of the infrastructure beneath product–service delivery. Oversight should therefore track the diversity and exploitability of a vendor’s vulnerabilities rather than severity alone, concentrating scrutiny on the small set of vendors that carry most of the systemic exposure.

Systems doi: 10.3390/systems14070836

Authors: Weihua Qu Zupei Guo

Global climate challenges and regulatory pressures have strengthened the link between carbon risk and corporate financial distress. We examine the impact of carbon risk on corporate financial distress and its underlying mechanisms using China’s accession to the Paris Agreement as an exogenous shock, employing a combination of difference-in-differences and double machine learning approaches. We find that high-carbon firms are significantly less likely to experience financial distress compared to low-carbon firms in China. Mechanism analysis indicates that the relationship between carbon risk and corporate financial distress is positively moderated by the green innovation effect, ESG performance, and media attention. The heterogeneity analysis indicates that carbon risk mitigates financial distress more pronouncedly in high-tech industries, high-pollution industries, competitive markets, and firms with strong environmental governance practices. Furthermore, we investigate whether carbon risk is an effective predictor of financial distress. Based on XGBoost (eXtreme Gradient Boosting, version Python 3.13) and SHAP (Shapley Additive Explanations) value analysis, we find that carbon risk significantly enhances the accuracy and explanatory power of financial distress prediction models. The results provide important guidance for policymakers in creating low-carbon strategies, businesses in improving financial management, and investors in assessing carbon risk.

Systems doi: 10.3390/systems14070835

Authors: Mahnaz Asgari Sooran Venkat Allada Adewole Adegbola

Customer satisfaction offers economic benefits and competitive advantage to manufacturing enterprises. However, a gap exists in understanding how to achieve customer satisfaction due to the complexities involved in coordinating various subsystems of an enterprise in an efficient manner. In this paper, we identified four internal and external-to-the-firm customer satisfaction drivers which include: government policies, supply chain reliability, knowledge management, and manufacturing performance criteria such as on-time delivery, quality and cost. We then developed a conceptual framework to define the non-linear mathematical relationships between drivers using system dynamics modeling. We conducted “what-if “scenario analyses for a base case, optimistic case and pessimistic case using three intervention policies: “investing resources in knowledge management”, “inclusion of a safety stock”, and “decreasing cost of inventory” to study the impact of these policies on customer satisfaction. This work contributes to customer satisfaction research by presenting a decision-support tool that will enable the leadership of manufacturing enterprises to create a simplistic mental model of the complex interactions of an enterprise using easy-to-use mathematical relationships to capture system dynamics over time.

Systems doi: 10.3390/systems14070834

Authors: Meng Zhang Tongxin Wu Xin Zheng

The digitalization of tax administration (DTA) represents a transformative institutional reform; however, its influence on regional entrepreneurship remains both theoretically ambiguous and empirically understudied. Drawing on institutional theory, we examine whether and how DTA is associated with regional entrepreneurial entry by reshaping entrepreneurial resource allocation. Using China’s “Golden Tax Project III” reform as a quasi-natural experiment, we apply a staggered difference-in-differences approach to a panel of 282 cities from 2009 to 2022. The results show that the implementation of GTP III is associated with higher levels of entrepreneurial entry. Mechanism analyses provide evidence consistent with two primary channels: the agglomeration of technological talent and improved access to financial resources. Furthermore, the enabling effects of DTA are more pronounced in regions with more developed digital infrastructure, stronger administrative capacity, and higher institutional quality. These findings extend the literature by linking tax digitalization to regional entrepreneurial entry and by clarifying the mechanisms and boundary conditions under which its effects vary.