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.

Systems doi: 10.3390/systems14070833

Authors: Sercan Demir Gencay Sarıışık Nagehan İlhan

This study investigates the performance implications of Industry 4.0 (I4.0) adoption in manufacturing small and medium-sized enterprises (SMEs) through an integrated analytical framework combining transformation modeling, composite performance measurement, and explainable machine learning. A structured dataset of 120 SMEs across five industrial sectors was analyzed to quantify changes in operational, economic, and sustainability-related performance before and after digital transformation. A novel Manufacturing Performance Index (MPI) was developed to capture multidimensional performance outcomes. The results reveal statistically significant improvements across all performance dimensions following I4.0 adoption (p < 0.001). Notable gains include increases in production output, overall equipment effectiveness, and labor productivity, accompanied by reductions in defect rates, machine downtime, production costs, and energy consumption. The MPI increased from 43.86 to 53.22, indicating a substantial overall performance enhancement. High pre–post correlations (r > 0.80) suggest that digital transformation reinforces existing operational capabilities rather than fundamentally altering firm structures. Explainable machine learning analysis identify overall equipment effectiveness, defect rate, production cost, and machine downtime as the most influential drivers of performance. SHAP-based results further reveal nonlinear and heterogeneous effects, indicating that performance gains vary depending on firm-specific conditions and implementation strategies. Additional analyses demonstrate the presence of distinct SME archetypes and confirm that I4.0 acts as a performance amplifier, with benefits distributed unevenly across firms. The proposed framework contributes to the literature by providing a transparent, data-driven approach for quantifying and interpreting digital transformation outcomes. It also provides practical decision support for SMEs seeking to prioritize investments and benchmark performance improvements within Industry 4.0 environments.

Systems doi: 10.3390/systems14070832

Authors: Cristina-Elena Ungureanu Bogdan Fleacă Răzvan Mihai Dobrescu Elena Fleacă

At present, the organisational landscape, which aims to provide value through its product and service offerings, must have the necessary infrastructure to deliver at the expected service levels. This requires the appropriate adaptation of existing frameworks, methods, and models to their business models, thereby generating consistent deliverables across time and industries. This is also applicable to Romanian Information Technology (IT) organisations, which face increasing pressure to deliver within the expected quality, time, and budget parameters. This study aims to assess the quality of stakeholder relationship management (SRM) as a higher-order organisational capability in Romanian IT organisations through a stakeholder-oriented subset of the Malcolm Baldrige National Quality Award (MBNQA) excellence framework and to explore its contribution to organisational quality, business continuity, and organisational resilience. A pilot study was conducted, using a sample of 52 Romanian IT professionals, to examine the applicability of four stakeholder-oriented MBNQA dimensions—Leadership and Governance, Workforce, Customers and Markets, and Community Engagement—as components of SRM quality. The findings provide preliminary evidence that these dimensions represent relevant elements for assessing SRM quality within the Romanian IT sector. Among them, the Workforce dimension exhibited the strongest association with SRM quality, highlighting the potential importance of employee engagement, Workforce development, and organisational learning in stakeholder-oriented excellence. The results also support the conceptualisation of SRM quality as a multidimensional construct that emerges from the interaction of complementary organisational subsystems. Although limited by its exploratory nature, sample size, and geographical focus, this study provides an initial foundation for applying the stakeholder-oriented subset of the MBNQA framework to stakeholder relationship management in the Romanian IT sector. Future research should validate the proposed model using larger, more diverse samples and examine its applicability across organisational, industrial, and geographical contexts.

Systems doi: 10.3390/systems14070831

Authors: Tongyue Feng Yixuan Zhang Xiaomin Xie Xiaoxia Zhu

Recommendation systems reorganize culture by linking classification, visibility, and value. This article analyzes Douyin’s #tuwei ecology as a socio-technical system in which platform architecture, creator formatting, audience evaluation, and urban-rural symbolic hierarchies interact through recursive feedback. We formalize this system as four coupled operators—commensuration, sign packaging, metricized valuation, and reception feedback—and derive two observable indices: an item-level transfer-efficiency ratio and a class-level diffusion–appreciation asymmetry index. Using 123,300 public #tuwei videos collected between May and November 2024, multimodal coding of 500 videos, and sentiment analysis of 61,284 comments, we identify a stable diffusion-appreciation asymmetry. Practice-oriented rural videos receive relatively strong approval after encounter, but they diffuse less widely than spectacle-oriented performances optimized for rapid recognition, ironic forwarding, and interaction density. Corpus-level tests (χ2, N = 123,300; Cramér’s V≈0.32) and an outlier-sensitivity analysis confirm the pattern independently of the coded-sample size. To explain this pattern, we propose hyperreal differential order (HDO), a systems concept describing how platform infrastructures repackage embedded cultural practices into feed-compatible sign forms and then differentially amplify them across segmented attention circuits. We position HDO against algorithmic visibility, platformization, and metric power, showing what each leaves underspecified. The study offers a mechanism-based account of how short-video systems reorganize vernacular culture, symbolic power, and interpretive authority without relying on overt exclusion.

Systems doi: 10.3390/systems14070830

Authors: Min Zhang Xin Jin Yinan Yu

Despite extensive research on knowledge spillovers, existing literature has paid limited attention to the formation mechanism of innovation chains. To address this gap, this paper constructs a multi-party evolutionary game model including two innovators and one supporter, incorporating knowledge spillover effects, and empirically tests the theoretical predictions using data from Chinese listed companies (2016–2025). The results show that the final equilibrium is (cooperation, cooperation, support). Empirically, we find a nonlinear relationship between knowledge spillover and the formation mechanism of an innovation chain with a double threshold effect of absorptive capacity: the positive impact of spillover increases as capacity moves from low to moderate, but diminishes-while remaining positive-when capacity becomes excessively high, revealing an S-shaped pattern. The originality lies in two aspects. Theoretically, our multi-player model extends the conventional bilateral framework to better reflect real-world parallel collaboration. Empirically, our firm-level analysis of Chinese listed companies is among the first to identify a double threshold effect, moving beyond regional-level or single-threshold studies. Practically, our findings suggest that governments should tailor subsidy intensities to firms’ absorptive capacity levels—allocating more to moderate-capacity firms where spillover gains are maximized—offering a more precise strategy for promoting sustainable innovation chain development.

Systems doi: 10.3390/systems14070829

Authors: Xiaolin Wang Hangben Rong Wan Yang Junliang Liu Pei Song

Economic systems worldwide are facing an urgent need to build resilience, and digital inclusive finance (DIF), as a key component, can effectively enhance urban economic resilience (UER) through technological convergence and resource optimization. Existing research has largely examined the relationship between DIF and UER within a linear causal framework, lacking an in-depth analysis of interactions within complex systems. Drawing on complex adaptive systems (CAS) theory, this study re-examines the relationship between the two using panel data from 283 Chinese cities covering the period 2012–2024. The findings reveal that: (1) DIF significantly enhances UER, with the strongest promotional effect observed on resistance and recovery capacity, followed by innovation and transformation capacity, whilst the impact on adaptive capacity is relatively minor. (2) There are structural differences in the contributions across dimensions of DIF, and an asymmetric matching relationship exists with the subsystems of UER. (3) Mechanism tests indicate that the process by which DIF enhances UER aligns with three pathways: the reduction of transaction costs, the stimulation of consumer vitality, and the driving force of technological innovation, and that such mediation effects exhibit structural differences at the dimensional level. Furthermore, the enhancement effect of DIF is more pronounced in cities with weaker resilience, demonstrating a convergence effect. This study enriches the application of CAS theory in the field of urban economic governance and provides a reference for developing economies to enhance UER through DIF.

Systems doi: 10.3390/systems14070828

Authors: Orlando Durán Enzo Martellanz Christian Salas

Resilience assessment in production systems requires metrics that capture not only the magnitude of performance loss but also the dynamic characteristics of recovery. This paper proposes a quantitative resilience indicator, ρ, based on the vector representation of availability time series. This paper tests the hypothesis that geometric vector encoding of availability data provides a more discriminating resilience assessment than scalar aggregate metrics by capturing recovery trajectory characteristics not reflected in mean availability alone. The methodology transforms discrete availability data into geometric vectors, from which disturbance events are encoded as triangles whose properties quantify resilience loss. The indicator combines a normalized area ratio—capturing the depth and duration of the availability drop—with an Angle Factor that penalizes incomplete or slow recoveries through the inclination of a closure vector. The resilience indicator, ρ, is a damage-based metric in which lower values denote better resilience and higher values indicate greater resilience loss. The global resilience of an asset is derived as the arithmetic mean of individual event coefficients, enabling longitudinal benchmarking. The proposal is validated through a case study applied to the ball milling circuit of a sulfide copper concentrator, comparing alternative topological configurations. Results demonstrate that configurations with similar mean availability exhibit distinct resilience profiles. The proposed indicator supports data-driven decision-making in asset management and maintenance strategy design.

Systems doi: 10.3390/systems14070827

Authors: Fernando Lázaro Contreras-Bravo Mario Aguilar-Fernández

This research addresses the low productivity of manufacturing SMEs in Mexico City. Digital Transformation (DT) is described as a solution to combat this issue. However, many SMEs implement technology in isolation. A qualitative methodology was used to conduct the study. Through a systematic literature review, the proposed DT-TOC methodology was developed. It integrates technology adoption with the Theory of Constraints as a problem-identification tool. It was found that technological investment alone is not sufficient. Furthermore, a strong commitment to staff training is necessary for the technological transition to be effective. The main contribution of this research is the creation of a diagnostic framework focused on the Mexican context. The DT-TOC methodology offers SMEs a logical and sequential guide to technology adoption, with the goal of process optimization. This contributes to technological sovereignty and the strengthening of the manufacturing industry, one of the country’s largest contributors to the economy.

Systems doi: 10.3390/systems14070826

Authors: Yifei Gao Shaowen Zhan Dan Yuan

Digital technologies are increasingly embedded in tourism performing arts, yet technology-intensive projects do not automatically generate profound cultural experiences. To explain this high-investment-but-low-experience dilemma, this study conceptualizes digital technology-enabled cultural experience as a configurational outcome of technological conditions and visitor-side psychological processing. Drawing primarily on the S-O-R framework and interpreted from a socio-technical systems perspective, digital technology interactivity and innovativeness are treated as stimulus conditions, while cognitive evaluation, scenario construction, and flow experience are treated as organism-level processing conditions. Psychological ownership theory is employed as a supplementary interpretive lens—rather than as a directly measured or tested mechanism—to explain why divergent configurational pathways may converge on high cultural experience through control, intimate understanding, and self-investment. Using fuzzy-set qualitative comparative analysis (fsQCA) with 540 valid questionnaires from visitors to tourism performing arts, the study finds that no single condition is necessary for high cultural experience. Instead, five sufficient configurations are identified: interaction–cognitive compensation, interaction–cognitive–flow synergy, dual-technology traction, innovation–scenario–immersion drive, and innovation–cognitive–flow compensation. The pathway to non-high cultural experience is asymmetric and is mainly characterized by the concurrent absence of technological, cognitive, scenario, and flow conditions. The findings re-specify the application of S-O-R theory in a configurational analytical context and provide a cautious socio-technical explanation of how technological and psychological conditions jointly shape cultural experience.

Systems doi: 10.3390/systems14070825

Authors: Amrutha Baburaj Saniyat Islam Caroline Swee Lin Tan

Artificial intelligence (AI) is reshaping the global fashion industry, yet its adoption within the Australian market (a geographically isolated, SME-dominated sector) remains under-researched. This study explores the current state of AI implementation, the principal barriers, and the perceived opportunities in Australian fashion businesses, with particular attention to sustainability. Using a qualitative, exploratory design, we conducted semi-structured interviews with nine professionals spanning fashion-technology start-ups, sustainable manufacturing, textile recycling, ethical certification, and global retail planning, and analyzed them thematically through the Technology–Organization–Environment (TOE) framework. The findings indicate that AI adoption is nascent and fragmented, typically entering firms through vendor-embedded features rather than deliberate strategy. Contrary to international studies that foreground cost and data infrastructure as primary barriers, participants in this sample identified a risk-averse organizational culture and limited perceived need as the dominant constraints. Sustainability emerged as the most significant yet largely unrealized opportunity, spanning reduced overproduction, fewer size-related returns, and improved textile recycling. These exploratory insights offer a context-specific reading of the TOE framework, suggesting that organizational culture may weigh more heavily than technological capability in isolated, SME-dominated markets, and that cultural change, supported by tiered education and industry-specific value propositions, is needed to realize AI’s sustainability potential.

Systems doi: 10.3390/systems14070824

Authors: Weiwei Kong Haiqing Hu Yirong Liu Jianqi Qiao Jianjun Liu

Against the backdrop of deep digital transformation and the increasing integration of AI into new product development (NPD), understanding the associations between different forms of human–AI interaction and team innovation has become an important organizational challenge. Grounded in the knowledge-based view and the Stimulus–Organism–Response (SOR) framework, this study investigates the associations between different forms of human–AI interaction and team innovative behavior, and the mediating role of knowledge domain coupling. Drawing on survey data collected from 268 NPD team managers serving as key informants for their respective teams in Chinese manufacturing firms, we examine two distinct forms of human–AI interaction: enhanced human–AI interaction (EHI) and impeded human–AI interaction (IHI). The results show that EHI is positively and significantly associated with both knowledge domain coupling and team innovative behavior, whereas IHI exhibits an inverted U-shaped relationship with knowledge domain coupling and team innovative behavior. Furthermore, complementary knowledge coupling and iterative knowledge coupling jointly serve as the knowledge-processing mechanisms linking human–AI interaction and team innovative behavior. This study contributes to the human–AI interaction and team innovation literature by explaining how different forms of human–AI interaction are associated with team innovation through distinct knowledge-processing mechanisms. The findings also offer practical guidance for managers seeking to dynamically balance different forms of human–AI interaction to foster innovation in AI-enabled NPD teams.

Systems doi: 10.3390/systems14070823

Authors: Xueyu Zhang Zezhong Ma Yuancheng Ma Illisriyani Ismail

Large language models (LLMs) are increasingly discussed in digital government research, but existing studies remain fragmented across application opportunities, technical performance, and governance risks, with limited synthesis of how they support service provision within digital government service processes. Using the concept of interpretive support, this study examines: (1) what forms of interpretive support LLMs provide; (2) what task- and service-level effects are reported; and (3) what governance conditions shape responsible integration. Drawing on a socio-technical systems perspective, the study conducts a PRISMA-informed systematic review and thematic synthesis of 60 studies from the Web of Science Core Collection and Scopus. The findings show a shift from stand-alone question answering to broader forms of interpretive support, including rule explanation, service navigation, complaint interpretation, issue routing, and back-office knowledge structuring. The strongest evidence concerns efficiency, responsiveness, and accessibility, whereas claims about trust, accountability, and wider public value remain less well supported. The review concludes that public value is conditional rather than automatic, depending on reliability, legal boundaries, responsibility allocation, data security and privacy, and organizational capacity. Future research should examine whether these service-level gains persist in routine service environments and how governance mechanisms affect service quality, equity, and accountability.

Systems doi: 10.3390/systems14070822

Authors: Jiaming Zhou Sitian Yang Qiuyi Jiang Zhongguo Xu

The revitalization of idle rural homesteads constitutes a key policy instrument for promoting rural revitalization and enhancing rural household income. However, existing studies predominantly adopt a single-actor or policy-instrument perspective and lack a systematic examination of the complex coupling relationships among multiple actors, resource endowments, and institutional environments. Adopting a rural operational perspective, this study explores how internal and external elements interact to reshape rural systems and identifies the configurational paths through which multiple actors’ behavioral logics drive efficient idle homestead revitalization. Drawing on symbiosis theory, we constructed a systemic analytical framework. A mixed-methods design combining fuzzy-set Qualitative Comparative Analysis and case studies of 35 typical villages in Zhejiang Province was employed. The findings reveal that (1) there exist four equifinal high-performance paths—market-driven, government-led, multi-actor co-governance, and endogenous development; (2) efficient revitalization does not rely on any single actor but depends on the dynamic alignment between actor behavioral logics, resource endowments, and the institutional environment; and (3) a robust policy environment can compensate for resource endowment deficiencies, whereas superior resource conditions allow for optimal efficiency, even with streamlined actor participation. Theoretically, this study extends symbiosis theory to the field of rural governance. Practically, it offers transferable governance solutions for Global South countries confronting rural decline and addressing rural poverty.

Systems doi: 10.3390/systems14070821

Authors: Xianyun Wu Xihao Chang Shihui Yu

Understanding when coordinated cooperation emerges in multi-agent systems, and when it fails, is a central question in the study of complex socio-economic dynamics. This paper develops a tripartite evolutionary game among three boundedly rational populations—local governments, agribusinesses, and smallholder farmers—and applies it to the diffusion of green agricultural technology, using conservation tillage in Northeast China’s black soil region as a concrete setting. The model incorporates a performance-based subsidy that is disbursed only upon realized adoption, thereby foreclosing subsidy capture. We derive the replicator dynamics, analyze the asymptotic stability of all eight pure-strategy equilibria, and, for the regime in which the system fails to converge, construct a first integral analytically and verify its conservation numerically to machine precision. Four findings emerge. First, the intuitively ideal fully coordinated state is dynamically unstable; the stable desirable outcome is one in which the government withdraws while the market sustains adoption. Second, under low initial willingness the system does not converge to any evolutionarily stable strategy but enters a sustained, conservative (center-type) oscillation—a policy-reversal trap that transient interventions cannot extinguish, placing the dynamics within the class of Hamiltonian-like evolutionary systems. Third, a sharp critical threshold in initial willingness partitions the state space into distinct basins of attraction, rendering policy effectiveness state-dependent. Fourth, within the modeled trap, credible penalties and enhanced agribusiness service capacity can engineer escape, whereas untargeted farmer subsidies are markedly less effective and may even deepen the oscillation. These results recommend treating public intervention as a transitional, regime-aware catalyst with a built-in exit. Beyond the agricultural setting, the analysis illustrates how conservative oscillations and multiple basins shape the controllability of multi-agent cooperation systems.

Systems doi: 10.3390/systems14070820

Authors: Ali Ahsan Leela Waheed Claire Davison Ritu Sharma

Evaluating Management Information System (MIS) effectiveness in tertiary education remains conceptually fragmented, with existing models primarily emphasizing technical performance rather than organizational interdependencies. This study develops a structural framework for assessing institutional MIS effectiveness using a sequential mixed-methods design integrating inductive construct development, expert validation, and quantitative dependency modeling. Fifteen themes and fifty-eight subthemes were identified as core dimensions of MIS capability within tertiary education institutions. Dependency and reliance analyses reveal strong systemic interconnections, indicating that institutional MIS functions operate as an integrated organizational system rather than independent domains. The framework is represented as a directed weighted graph to preserve enabling relationships, and a maximum spanning arborescence is derived to provide an interpretable implementation backbone. By modeling structural interdependencies explicitly, the proposed framework supports more informed prioritization of MIS development, strategic alignment across institutional functions, and evidence-based governance decision-making. The framework is intended primarily for evaluating organizational, governance, administrative, and institutional dimensions of MIS effectiveness within tertiary education institutions and does not explicitly assess user-level constructs such as usability, interface design, accessibility, or individual user experience. The study establishes a foundation for future empirical validation and cross-institutional benchmarking.

Systems doi: 10.3390/systems14070819

Authors: Jianfeng Liu Yanying Wei Saihong Wang Zuoji Dong

Multilevel public finance is a social-administrative system in which authority, fiscal resources, information, and implementation responsibilities circulate across government tiers. China’s Province-Managing-County (PMC) reform provides a case for evaluating how governance redesign affects county-recorded fiscal expenditure. We define the system boundary as the province–prefecture–county fiscal governance chain and decompose the reform into administrative power delegation (D1), which changes decision rights, and fiscal direct reporting (D2), which changes fiscal-flow paths. Using a county-level panel of 2219 counties in 31 provinces from 2000 to 2019, we combine generalized synthetic control, Matrix Completion, panel unconditional quantile regression, and spatial diagnostics. The average effect is positive in the preferred gsynth specification and the Matrix Completion benchmark, but the magnitude is model-dependent: 16.7% under gsynth and 8.1% under Matrix Completion, with further sensitivity to latent-factor choices. Reform-type estimates and a common-model CATE equality test suggest stronger estimated effects for D1 than D2, interpreted as institutional heterogeneity rather than causal dominance. Distributional and spatial diagnostics indicate weaker lower-tail effects and geographically uneven absorption. The findings suggest that changing decision rights and fiscal-flow paths can reshape county fiscal system outputs.

Systems doi: 10.3390/systems14070818

Authors: Claudiu George Bocean Adriana Scrioșteanu Sorina Gîrboveanu Marius Mitrache Mirela Sîrbu Ionuț-Cosmin Băloi Adrian Florin Budică-Iacob Maria Magdalena Criveanu

The rapid expansion of artificial intelligence (AI) is revolutionizing economic systems by reshaping production, labor organization, and productivity patterns. In the construction industry, which remains highly labor-intensive, project-based, and structurally heterogeneous across European countries, AI can support productivity, planning, safety monitoring, cost estimation, and decision-making. However, its implementation also poses specific challenges, including fragmented workflows, heterogeneous construction sites, limited digital skills, poor data interoperability, high adoption costs, resistance to organizational change, and temporary adjustment costs that may delay the visibility of productivity gains. In this paper, we evaluate the effect of Artificial Intelligence (AI) on labor productivity in the construction sector of European Union countries, taking into account labor input and sector output, and search for structural trends from a systems perspective. AI adoption is defined as the share of construction businesses that use at least one AI technology. Productivity is estimated per worker and per hour using a dataset of EU countries from 2023 to 2024. The effects of AI adoption, labor input, and output on productivity are evaluated using multivariate log-linear regressions in SPSS. Hierarchical and K-means clustering reveal groups of countries with comparable digital and labor performance. The results demonstrate that construction output is the key driver of labor productivity. In contrast, AI adoption shows a small, negative association with productivity, consistent with short-term adjustment costs in the early stages of digital transformation. Cluster analysis reveals diverse country profiles in AI use, productivity, and labor intensity. Overall, the findings underscore the need for a gradual approach to managing digital change in construction, supported by complementary organizational and skills-based measures.

Systems doi: 10.3390/systems14070817

Authors: Sijia Tao Yitong Zhao Tao Hong

As Artificial Intelligence (AI) evolves from passive tools into proactive actors within socio-technical systems, traditional social network theories face fundamental limitations in explaining AI’s structural power. Drawing on the Network Capabilities framework, this study investigates the mechanism of AI power generation within homogeneous communities from a structural hole perspective. This study analyzes a COVID-19 vaccine interaction network (N = 9314) on X via social network analysis, Propensity Score Matching (PSM), counterfactual simulations, and weighted Independent Cascade Model (ICM) dynamics. The results reveal that bot-like agents do not rely on traditional brokerage to acquire power; instead, they execute a Tight Integration strategy by filling micro-structural holes. After isolating the confounding effects of connection scale via rigorous Propensity Score Matching, it creates an anomalous high-density, high-constraint configuration, with these algorithmic agents exhibiting significantly higher network constraint (0.514) than comparable human users (0.453). Counterfactual removal experiments demonstrate a profound structural dependence of the social system on AI: their removal triggers a systemic cascade collapse, decreasing the largest connected component (LCC) size by a factor of 82.9 and topologically isolating 79.7% of human users. Furthermore, transitioning from static structural analysis to dynamic simulations, ICM simulations confirm AI’s topological redundancy translates into substantial information diffusion dominance (Cohen’s d = 1.081). Revealing AI’s power generation mechanism provides essential governance insights and strategic approaches for mitigating AI-driven information cocoons and group polarization.

Systems doi: 10.3390/systems14070816

Authors: Miao Yu Zhongsheng Hua Jianguang Zhang

This paper investigates the dynamic coopetition and capacity-sharing strategies between an Integrated Manufacturer and a Developer under R&D uncertainty, focusing on the governance of strategically scarce capacity. By constructing a two-stage game model, we analyze how government intervention and risk-hedging mechanisms influence the allocation of idle strategically scarce capacity in innovation-driven industries. The findings reveal a two-sided paradoxical behavioral pattern: in the low-probability R&D interval, rather than relying on safe contract manufacturing, the Integrated Manufacturer counter-intuitively reduces collaborative duration to aggressively gamble on its immature product. Conversely, in the high-probability R&D interval, where conventional wisdom predicts an aggressive pivot to self-production, the manufacturer paradoxically extends or maintains the contract manufacturing duration, driven by the partner’s full cost-sharing incentive mechanism. Furthermore, To maximize the total supply output of strategically scarce resources during collaboration, we uncover a non-linear ‘counterproductive subsidy trap’ and propose a binary ‘critical mass’ policy rule: governments should either withhold subsidies entirely or commit sufficient funding to bypass the supply deficit zone. This framework provides a theoretical foundation for managing scarcity in capital-intensive sectors such as biopharmaceuticals and semiconductors.

Systems doi: 10.3390/systems14070815

Authors: Claudiu George Bocean Luminița Popescu Dalia Simion Natalița Maria Sperdea Carmen Puiu Roxana Cristina Marinescu Enescu Maria

Generative artificial intelligence is gradually infiltrating contemporary socio-technical systems, affecting work practices, labor market participation, and the dynamics of labor-related costs. In this paper, we analyze cross-sectional relationships between generative AI use among active individuals and two macro-level labor-market indicators: the labor force participation rate and the evolution of nominal unit labor costs. The research is based on comparative European data. It analyses the use of AI in personal, professional, educational, and recent contexts as an expression of the interaction of human agency, technological infrastructure, and the institutional environment. The methodology combines multivariate modelling and regression with exploratory factor analysis and clustering to examine the statistical associations between AI use and labour-market indicators, as well as the emergent typologies of countries with diverse profiles. Results indicate stable links between the extent of generative AI use and employment, and unit labour cost disparities, suggesting that generative AI use is associated with broader socio-economic development patterns, without implying a causal relationship. The results highlight the need for an integrative view of AI adoption, linking human behaviors to organizational procedures and governance structures to ensure a responsible and fair transition.

Systems doi: 10.3390/systems14070814

Authors: Enrique Díaz de León López Roberto Palacios Rodríguez

Output-based indicators in entrepreneurial ecosystem governance systematically misclassify pre-threshold structural progress as policy failure, because feedback dynamics produce no immediate output signal. This study examines how institutional coordination shapes those dynamics. Using system dynamics modelling, we construct a three-stock model (active startups, entrepreneurial capabilities, and institutional support). Calibration is performed via structured expert elicitation using the Repertory Grid Technique (RGT), enabling institutionally grounded parameter estimation where comparable time-series data are unavailable. Three policy scenarios—fragmented support, financial intensification without coordination, and coordinated early intervention—are simulated for Mexico and the United Kingdom. Resource intensification alone yields only temporary gains when feedback structures remain fragmented. Coordinated intervention activates reinforcing feedback among all three stocks, enabling self-sustaining growth beyond a critical coordination threshold. The United Kingdom crosses this threshold earlier due to stronger baseline conditions; Mexico responds later but with larger proportional gains. The model provides a feedback-structural diagnostic that distinguishes pre-threshold structural assembly from genuine stagnation, with direct implications for the design of evaluation frameworks in fragile institutional contexts. RGT demonstrates potential as a calibration strategy for feedback models in data-sparse settings.

Systems doi: 10.3390/systems14070812

Authors: Zilin Yang Xiaoxuan Liu Le Hao Xinping Wang

Shared energy storage is emerging as a pivotal institutional and technological arrangement for increasing power-system flexibility, integrating renewable electricity, and improving the allocation of storage resources. Focusing on power generation enterprises, this study develops a Technology–Organization–Environment (TOE) model that incorporates perceived risk and perceived benefit to explain how participation intentions toward shared energy storage are formed. Structural equation modeling and fuzzy-set qualitative comparative analysis (fsQCA) show that technological compatibility, technological maturity, top-management support, organizational slack, subjective norms, policy support, and market competition shape participation intentions by reducing perceived risk and strengthening perceived benefit. Perceived risk significantly suppresses participation intention, whereas perceived benefit significantly promotes it. The fsQCA results identify three configurational pathways to high participation intention: benefit–risk co-activation, market competition and benefit-driven participation, and market–policy dual activation. These findings show that participation in shared energy storage is generated by interdependent technological, organizational, and environmental conditions rather than by any single determinant. The study offers evidence for refining a shared-energy-storage policy and improving business models in the transition to a new power system.

Systems doi: 10.3390/systems14070813

Authors: Bingtao Qin Xiaoqian Yang Yongwei Yu Junhua Guo Huaiyu Zheng

Digital and green technological innovations are increasingly viewed as interdependent components of firm transformation, yet their combined effects are still insufficiently understood from a systems perspective. Rather than treating digitalization and greening as separate innovation trajectories, this study conceptualizes their integration as digital–green collaborative technological innovation and examines whether this socio-technical synergy improves firm-level total factor productivity (TFP). Using panel data for Chinese A-share listed companies from 2008 to 2022, we construct firm-level measures of digital innovation, green innovation, and their interaction based on patent information, and employ multi-dimensional fixed-effects models to assess their productivity effects. The results show that digital–green collaborative technological innovation significantly improves corporate TFP, and this finding remains robust after addressing potential endogeneity, replacing the TFP measure, excluding information-technology-intensive industries, expanding the sample period, and employing invention patents as an alternative proxy for innovation quality. Heterogeneity analysis indicates that the productivity-enhancing effect is more pronounced among non-state-owned enterprises, firms located in central China, and firms operating in regions with stronger intellectual property protection. Further decomposition of TFP reveals that digital–green synergy promotes firm transformation through three channels: improving micro-level production efficiency, optimizing labor allocation efficiency, and enhancing capital allocation efficiency. These findings contribute to systems-oriented research on technological innovation by showing how complementary digital and green capabilities jointly reshape production systems and factor allocation. They also provide practical implications for firms and policymakers seeking to promote sustainable transformation through integrated innovation strategies.

Systems doi: 10.3390/systems14070811

Authors: Yexin Chen Xinyu Liu Zhi Wang Yuxin Xie Xinyi Shen Jiayi Zhao Ziqin Wang Chen Su

Distributed photovoltaics (DPV) is rapidly expanding under the “Dual Carbon” strategy, yet suffers from a systemic imbalance of prioritizing construction over service provision. Existing research focuses predominantly on techno-economic analyses, neglecting holistic service lifecycle examination, systematic urban-rural comparison, and actionable design tools. This study employs qualitative methods, including literature review, case analysis, service blueprinting, and stakeholder analysis, to examine DPV full-lifecycle services across rural household and urban commercial and industrial contexts. Findings reveal multi-stage service breakpoints, including early-stage information asymmetry, mid-stage unclear responsibility allocation, and late-stage recycling absence alongside significant urban-rural variations. To address these gaps, the study proposes a sustainable service design strategy balancing efficiency, equity, and resilience, comprising visualized rights-responsibility checklists, tiered revenue distribution schemes, and standardized full-lifecycle service blueprints. This work contributes a lifecycle service architecture, a systematic urban-rural comparative perspective, and practical governance tools for DPV sustainability. This work makes the key contribution that includes a full lifecycle service architecture as the core framework, systematic urban-rural comparison as the analytical perspective, and actionable governance and service tools as the practical outputs.

Systems doi: 10.3390/systems14070810

Authors: Ana Afonso Rui Borges Lopes

Process reengineering, crucial for boosting productivity and competitiveness, often faces challenges in accurately capturing current processes and predicting outcomes. For modelling and analysing systems, business process management (BPM) and simulation are among the most popular methodologies and tools. This work showcases the study and suggested improvement of a bottling line using BPM and simulation. BPM provided the overall methodology, while simulation modelled the current state, analysed resource utilisation, and explored improvement scenarios. This study revealed that the filling activity was the main bottleneck, thus becoming the primary focus for improvement. Simulating different scenarios allowed for a comprehensive understanding of process dynamics and the effects of the proposed changes. The chosen scenario is expected to achieve a 10% increase in the throughput of large gas bottles, a 9% reduction of cycle time, and an 11% reduction in the bottleneck’s waiting time.

Systems doi: 10.3390/systems14070809

Authors: Rasmus Gahrn-Andersen

The purpose of this Special Issue has been to thematize the systemic aspects of human cognition [...]

Systems doi: 10.3390/systems14070808

Authors: Juan Ding Suxia Liu Daojian Yang Jingjing Zhang

From the perspective of symbiosis theory, collaborative work safety governance in industrial parks can be understood as a dynamic multi-actor symbiotic system jointly composed of the park management committee, leading enterprises, and small and medium-sized enterprises. To examine how reciprocal symbiosis among multiple actors affects system-level safety performance and sustainable operation, this study develops a differential game model in which the work safety level of the industrial park evolves dynamically over time. The model comparatively analyzes the work safety efforts, discounted payoffs of symbiotic units, and the overall work safety level of the park under different decision-making mechanisms and reciprocal symbiosis modes. The results show that: (1) under the leading-enterprise-dominated mode, as the coefficient of work safety resource sharing increases, the overall work safety level of the park correspondingly improves; however, once this coefficient exceeds a certain threshold, it becomes unfavorable to the further growth of the leading enterprise’s own benefit; (2) under the park-management-committee-led mode, as the cost-sharing coefficient increases, the overall work safety level of the park continues to improve, but an excessively high cost-sharing coefficient weakens the incentive for the park management committee to increase its benefit; and (3) under certain conditions, the leading-enterprise-dominated mode, the park-management-committee-led mode, and the deep collaboration mode can all improve both the park’s work safety level and the discounted payoffs of symbiotic units, but the deep collaboration mode performs best in enhancing the overall work safety level and the total system payoff. Compared with the park-management-committee-led mode, the leading-enterprise-dominated mode is more sensitive in its impact on the total payoff of the industrial park. These findings indicate that sustainable work safety improvement in industrial parks cannot rely solely on increasing governance inputs or on the actions of a single actor. Instead, it requires a system-wide balance among safety performance, economic efficiency, resource-sharing sustainability, and the long-term stability of collaborative relationships. The study provides a system-oriented theoretical basis for optimizing reciprocal symbiosis mechanisms and promoting the sustainable development of industrial parks.

Systems doi: 10.3390/systems14070807

Authors: Azucena Marques João Vieira Elisabete Teixeira Nuno Marques de Almeida

This study examines the importance of integrating collaborative ecosystem theory into the broadband transdisciplinary approach of Engineering Asset Management (EAM). The authors seek to contribute to moving beyond traditional organization-centric EAM approaches toward systemic, multi-actor value co-creation models. This systematic review aims to investigate existing work on value co-creation across different areas and assess whether these works can be transposed to EAM. To achieve this, a structured, systematic literature review was conducted in accordance with PRISMA guidelines, using Scopus and Web of Science as information sources. The search combined the keywords “collaborative,” “ecosystem,” “value co-creation,” and “model,” resulting in 72 peer-reviewed articles. Bibliometric analysis was performed using EndNote (version 2025.2), Excel (version 16.110.2), and VOSviewer (version JAVA 1.0), complemented by thematic analysis structured around the three research questions. The results reveal a significant increase in publications after 2021, the predominance of ecosystem and value co-creation concepts, and the existence of fragmented conceptual, governance, and analytical models. The findings confirm a persistent gap that needs to be addressed to properly integrate collaborative ecosystems and enable value co-creation in EAM, providing a structured foundation for developing an integrated ecosystem-based asset management framework in this space. By conducting a structured, systematic literature review and synthesizing conceptual, governance, and analytical models, this research consolidates fragmented knowledge and proposes an integrative perspective that positions value co-creation, multi stakeholder governance, and digital enablement as central mechanisms for enhancing ecosystem performance and sustainability in asset-intensive environments.

Systems doi: 10.3390/systems14070806

Authors: Jackey Yu-You Wang

Original Equipment Manufacturers (OEMs) frequently face a “digitalization paradox” in which injecting startup innovations directly into zero-tolerance production environments may disrupt core operations. Using a 15-year longitudinal dataset, executive survey evidence, CEO interviews, organizational observations, and micro-level AI execution logs from a leading hardware OEM, this study examines the structural relationships through which asset-heavy firms may internalize open innovation while protecting operational stability. We contribute to the Dynamic Capabilities theory by conceptualizing the Innovation Translation Interface, operationalized as a corporate accelerator, as a structural buffer that helps isolate and absorb open-innovation friction. Furthermore, we introduce the concept of a domain-grounded Digital Knowledge Infrastructure. By training internal AI on accumulated operational failures and iterative engineering records, firms’ practices are associated with enhanced knowledge externalization and reduced reliance on seniority-based knowledge transfer. The findings suggest that these structural configurations are positively associated with the capability development of Systematized Innovation Capability (SIC), offering an exploratory framework for legacy manufacturers seeking to balance open innovation, AI-enabled learning, and production reliability.

Systems doi: 10.3390/systems14070805

Authors: Ruixin Mao Xihong Wang

This study explores the nonlinear impact of digital-intelligent transformation on enterprise value co-creation. It examines the mediating role of organizational agility and investigates the influence of network embeddedness on the effectiveness of digital-intelligent transformation. This article draws on a sample of 418 manufacturing enterprises and employs a questionnaire survey and hierarchical regression analysis to test the hypotheses. It divides digital-intelligent transformation into two dimensions: digital-intelligent technology level and digital-intelligent application scope. Organizational agility is used as the mediating variable, and network embeddedness is used as the moderating variable. This study reveals an inverted-U relationship between digital-intelligent transformation and value co-creation. The impact of digital-intelligent technology level and digital-intelligent application scope on value co-creation is mediated through organizational agility. Network embeddedness is associated with a wider range over which digital-intelligent transformation relates positively to value co-creation before returns diminish. The findings indicate that firms reporting excessive digital-intelligent transformation tend to report lower value co-creation. This research enriches the literature on digital-intelligent transformation. It points out that digital-intelligent transformation has a nonlinear impact on value co-creation. The study uses organizational agility as the mediating mechanism and network embeddedness as the boundary condition. These findings provide a reference for enterprises seeking to implement digital-intelligent transformation in a balanced and sustainable way.

Systems doi: 10.3390/systems14070804

Authors: Meiqi Wang Zhuo Zhang

With the continuous embedding of small- and medium-sized manufacturing enterprises (SMMEs) into manufacturing platform ecosystems, selecting the optimal resource reconstruction model has become a critical decision for both platforms and SMMEs. However, the existing literature provides limited insight into how different resource types compare in terms of their efficiency in enabling capability reconstruction. Addressing this gap, this paper constructs asymmetric Stackelberg game-theoretic models for the platform–SMME collaborative system and systematically investigates efficiency differentiation and strategy selection under three resource models: the fund resource model, the knowledge resource model, and the fund and knowledge hybrid model. Drawing on dynamic capability theory, the models formalize the transmission paths through which heterogeneous resources affect SMMEs’ capability reconstruction, and derive the equilibrium conditions governing resource input, pricing, revenue-sharing, and payoff outcomes. The findings reveal three key insights. (1) As dynamic capability matures and price sensitivity rises, the platform’s optimal strategy shifts systematically from fund resource model through hybrid model to knowledge resource model, requiring dynamic model switching aligned with the SMME’s capability path and market conditions. (2) Fund-driven improvement operates through two paths, while knowledge-driven improvement operates through four paths and the upstream paths, creating a win-win zone and downstream paths generating diverging interests. (3) Dynamic capability, particularly conversion efficiency of “information-management,” reduces resource input while increasing both parties’ payoffs, shifting the strategy from a fund resource model to a knowledge resource model. The findings point to a shift from resource provision to capability-driven collaboration, requiring dynamic model switching by platforms, precision matching by SMMEs, and shared investment in dynamic capabilities for sustainable mutual benefit.

Systems doi: 10.3390/systems14070803

Authors: Zhongguo Xu Qiuyi Jiang Sitian Yang Mengxiang Luo

As a crucial nature-based climate change solution, the realization of blue carbon’s value is primarily impeded by three interrelated obstacles in developing nations: a weak scientific foundation, fragmented market structures across different levels, and the absence of dedicated institutional frameworks. This study focuses on the institutional bottlenecks hindering the marketization of blue carbon in developing countries. By integrating Complex Adaptive Systems (CASs), Coase’s Theory of Property Rights, and the Institutional Analysis and Development (IAD) framework, we constructed a theoretical framework encompassing five dimensions: transaction objects, transaction participants, transaction markets, transaction rules, and benefit distributions. This framework was validated through an empirical analysis of the blue carbon practice in Ningbo, China. The research identified three critical stages in Ningbo’s governance trajectory: initial founding, synergistic stagnation, and iterative upgrading. The core determinant of governance success or failure lies in the capacity to continuously optimize this five-dimensional rule system through a combination of “top-down regulation” and “bottom-up innovation”, thereby reducing transaction costs and generating effective incentives. The findings indicate that successful blue carbon governance depends on establishing a rule system that is both locally adaptive and dynamically adjustable. The analytical framework proposed in this study offers a systematic new perspective for developing countries to remedy institutional gaps in blue carbon governance. By demonstrating China’s locally adapted innovations in resource rights delineation and value realization, it also promotes greater inclusiveness and diversity within the global blue carbon standard system, contributing a governance approach from the Global South that is applicable to countries at varying stages of development.

Systems doi: 10.3390/systems14070802

Authors: Qifan Fu

Workplace heat exposure under climate change is not only a physical hazard but also a recurrent governance problem involving regulators, employers, and workers. This paper develops a tripartite evolutionary game to examine how workplace heat-risk governance evolves through the interaction of strict regulation, employer prevention investment, and worker symptom reporting. Normal-form Nash and Stackelberg benchmarks are used to clarify the static and sequential counterparts of the evolutionary stability conditions. Heat-risk intensity is introduced as an environmental state that affects accident probability, expected losses, governance costs, and institutional benefits. The model identifies a heat-dependent cooperative stability region: full cooperation is locally stable only when digital monitoring, enforcement incentives, employer investment support, and worker reporting protection jointly satisfy threshold conditions. Extended payoff specifications allow monitoring and regulatory strictness to directly affect worker reporting incentives and link government payoffs to accident-risk reduction. The results show that monitoring and reporting protection may be partially substitutable in the worker-reporting channel but remain non-equivalent at the system level. Long-horizon, shock-recovery, and industry-specific diagnostics further show that isolated instruments often generate partial-governance regimes rather than system-wide cooperation. The findings frame workplace heat adaptation as a complex adaptive governance problem requiring coordinated technological visibility, credible enforcement, employer incentives, and protected worker reporting.

Systems doi: 10.3390/systems14070801

Authors: Huijin Cheng Yuxi Song

The rise of e-commerce live-streaming has reshaped the communication of product quality and corporate social responsibility (CSR) information, making consumer reference quality an important factor in supply chain operational decisions. This paper investigates how the reference quality effect influences CSR efforts, the manufacturer’s quality effort, and profits among supply chain members, and compares dynamic decisions and equilibrium outcomes across three sale modes: traditional e-commerce mode (mode T), brand-led self-streaming mode (mode B), and influencer live-streaming mode (mode I). We develop a differential game model for a two-echelon supply chain comprising a manufacturer and a brand owner to derive optimal decisions and equilibrium outcomes and compare them across these modes. We then introduce a reward–penalty contract supported by blockchain technology to examine its role in improving supply chain coordination under decentralized decision making. The results show that a stronger reference quality effect encourages the manufacturer to increase quality effort but reduces the CSR efforts of both the manufacturer and the brand owner, as well as live-streaming effort. Among the three sale modes, mode I induces the highest levels of the manufacturer’s quality effort and CSR effort by both members, and leads to the greatest accumulation of brand reputation. However, the manufacturer’s profit ranking across sale modes is conditional. When the demand enhancing effect of influencer effort is limited and the reference quality effect is strong, the manufacturer may earn a higher profit in mode B than in mode I. Although the reward–penalty contract enhances the manufacturer’s CSR and quality effort, excessive reward–penalty intensity increases the manufacturer’s cost burden and may reduce its profit below the level achieved under decentralized decision making. Blockchain technology enhances the effectiveness of the reward–penalty contract in coordinating the supply chain, thereby further increasing total supply chain profit under contract coordination. When blockchain plays a sufficiently strong role, total supply chain profit under the contract may even exceed that under centralized decision making without blockchain.

Systems doi: 10.3390/systems14070800

Authors: Zihao Wu Ruibing Ji Yuxin Liang Shengling Zhang

The smart cities policy (SCP) is one of the key initiatives driving digital transformation in China, delivering a significant digital impact on enterprises within the region. This paper employs a multiple-period difference-in-differences (DID) model to systematically analyze the impact of the SCP on total factor productivity (TFP) of A-share listed companies in China. The results show that the SCP significantly enhances enterprise TFP. Heterogeneity analysis reveals that the policy has a more pronounced positive effect in regions with stronger intellectual property protection, as well as in non-manufacturing sectors and state-owned enterprises (SOEs). Furthermore, the SCP improves enterprise TFP through various dimensions, such as boosting innovation capacity, optimizing human capital structure, reducing operational costs, and improving investment efficiency. This study provides a solid theoretical foundation and policy recommendations for the further optimization and promotion of the SCP.

Systems doi: 10.3390/systems14070799

Authors: Dario J. Quiroga-Parra Beatriz E. Hernández Arias Lainiver Mendoza Munar Harry Valencia Ayala Luis Omar Alpala

Industry 4.0 is widely recognized as a strategic enabler of productivity, flexibility, and competitiveness in manufacturing systems. However, in emerging economies, declared preparedness for digital transformation often exceeds reported implementation. This study examines the readiness–adoption gap of Industry 4.0 in surveyed large manufacturing companies in Valle del Cauca, Colombia. An empirical cross-sectional study was conducted through a structured survey administered to large manufacturing companies in the region. The instrument was grounded in the Technology Acceptance Model and informed by a sociotechnical understanding of Industry 4.0 adoption. It captured perceptions of ICT infrastructure, workforce capabilities, expected benefits, implementation challenges, ease of use, and the reported implementation status of 20 Industry 4.0 technologies. Data from 59 valid responses were analyzed using descriptive statistical techniques, cross-technology comparisons, and an aggregate descriptive synthesis of readiness and adoption indicators. The findings suggest that adoption remains concentrated in foundational technologies such as cybersecurity, cloud computing, and data analytics, while advanced technologies, including artificial intelligence, cyber–physical systems, digital twins, and immersive technologies, show limited implementation despite favorable readiness perceptions. The aggregate synthesis points to a medium-to-high readiness proxy and a substantially lower average adoption level, suggesting the presence of a readiness–adoption gap. The study contributes empirical evidence from large manufacturing companies and derives two practical outputs: a readiness–adoption matrix and a staged roadmap for capability building and technology prioritization. These outputs are presented as context-specific exploratory tools rather than universally validated models.

Systems doi: 10.3390/systems14070798

Authors: Michail Toanoglou Thomas Krabokoukis Leonard Jackson Evangelos Papadimitriou

Coastal and island destinations increasingly rely on data-driven governance to manage seasonality, resource scarcity, overtourism, and climate risk. This article analyzes 30 official documents issued between 2017 and 2025 by nine Spanish and Portuguese Sustainable Tourism Observatories in the UN Tourism INSTO network. Rather than treating observatory language as verified policy implementation, the study uses computational content analysis to measure institutional discourse about monitoring and action. The Actionability Index (ActI), action-share, and log-ratio metrics are interpreted as exploratory, compositional text indicators, not direct evidence of governance effectiveness. Results show a negative document-level association between monitoring intensity and the ActI (r = −0.3951, p = 0.0307, alpha = 0.05), but denominator coupling and report-genre heterogeneity require a restrained interpretation: the pattern signals a monitoring-action imbalance that warrants follow-up validation rather than causal proof of a governance failure. Quartile-based profile labels are treated as sample-internal descriptors, with Barcelona interpreted as a boundary case whose action discourse is concentrated around tourism carrying capacity. A pre/post-2020 comparison is reported only as an unbalanced descriptive contrast. The contribution is a transparent, reproducible framework for auditing whether monitoring intelligence is connected to actionable discourse, together with explicit limits and validation priorities for future work.

Systems doi: 10.3390/systems14070797

Authors: Junping Qiu Yanhong Wang

The market-based allocation of data elements is critical for unlocking economic value in the digital economy. Using policy tools theory, this study examines China’s institutional framework for data marketization through a three-dimensional framework: policy issuers × policy tools × configuration paths. Based on a quantitative analysis of 569 policy units from 113 central and local documents (2020–2025), the study reveals key insights. First, central and local governments exhibit a clear division of labor: the central government prioritizes governance and ownership using information and regulatory tools, while local governments focus on circulation via service and environmental creation tools. However, distribution mechanisms remain underdeveloped, hindering operational effectiveness. Second, policy paths emphasize circulation and governance, with ownership and distribution systems lagging, leading to continuous infrastructure expansion but difficulties in market transactions. Third, service and environmental creation tools dominate, while regulatory and incentive tools are weaker. It reflects that the government prefers low-cost, low-conflict tools rather than transformative ones. These findings illuminate institutional complexities in data marketization and provide actionable recommendations for optimizing policy frameworks to balance ownership, circulation, distribution, and governance.

Systems doi: 10.3390/systems14070796

Authors: Fang Shao Jianing Guo

Information systems (IS) innovation by employees is critical for digital transformation, yet prior research often examines external social resources and internal cognitive antecedents separately. From a systems perspective, we integrate social capital theory and social cognitive theory to construct a dual-path model. Using survey data from 350 employees in Chinese enterprises, we investigate how social networks (external path) and individual expectations (internal path) predict employees’ innovative IS usage behavior, and how external versus internal innovation incentives moderate these relationships. Results show that both social networks and individual expectations have significant positive effects, with network heterogeneity and performance expectations being the strongest predictors; achievement expectations show no significant effect. External innovation incentives positively moderate the social-network path, whereas internal innovation incentives positively moderate the individual-expectations path, revealing a differentiated “internal–external fit” pattern. Based on these findings, we propose a systemic “factors–incentives–behavior” framework. This study advances the dual-path understanding of IS innovation and provides guidance for designing targeted incentive systems.

Systems doi: 10.3390/systems14070795

Authors: Hanna Li Yu Chen

Artificial intelligence applications are rapidly integrated into supply chain management systems, and play a vital role in coping with supply chain disruption risks from external shocks. From the perspective of a production network, based on the data of Chinese listed companies from 2013 to 2023, this paper systematically examines the direct effect and network spillover effect of artificial intelligence application on supply chain disruption risk. The results show that: First, artificial intelligence applications can significantly reduce supply chain disruption risks. The adoption of AI by upstream enterprises produces spillover effects through production networks, and indirectly cuts down the disruption risks of downstream enterprises. Second, AI applications mainly function through three paths: supply chain concentration, corporate agency cost and physical flow efficiency of enterprises. Third, the risk reduction effect of AI applications is more prominent in eastern regions, low-tech industries and state-owned enterprises. The research conclusions have important theoretical and practical implications for enterprises to build risk management systems and enhance system resilience.

Systems doi: 10.3390/systems14070794

Authors: Jaewoo Lee

Complex cyber-physical systems in the automotive and avionics domains increasingly consolidate safety-critical and non-critical functions onto shared multicore platforms. A central design challenge in these environments is ensuring strict timing guarantees while allowing graceful degradation under resource contention. In partitioned multiprocessor mixed-criticality systems, a mode switch on one processor forces all low-criticality (LC) tasks on that processor to degrade to their mandatory execution budgets, even though neighboring processors may have spare capacity. Existing approaches either focus on uniprocessor systems or address only the offline partitioning problem without considering the runtime survivability of LC tasks. This paper proposes IMC-PALM (Imprecise Mixed-Criticality via Partitioning and Adaptive Lightweight Migration), a two-phase framework for imprecise mixed-criticality (IMC) multiprocessor systems. The offline phase combines a tightened EDF-VD-IMC schedulability test with Mode-Balanced Partitioning (MBP). The tightened test identifies high-criticality tasks whose HI-mode utilization yields a tighter bound, while MBP allocates tasks based on mode-specific residual capacity. The runtime phase migrates LC tasks from a mode-switched processor to other processors that remain in LO mode, exploiting the per-processor isolation property of partitioned scheduling. Simulation results with 2, 4, and 8 processors show that MBP with the tightened test improves the acceptance ratio by 12.3 %p over existing algorithms under standard conditions. Furthermore, runtime migration reduces the degraded job ratio by 28.1 %p compared to the no-migration baseline under these standard settings, of which 4.6 %p is attributable to home-processor recovery. The benefit grows with the number of processors and remains robust under realistic migration overheads.

Systems doi: 10.3390/systems14070793

Authors: A. S. M. Bakibillah Md Abdus Samad Kamal Jun-ichi Imura

While plug-in hybrid electric vehicles (PHEVs) can significantly reduce fuel consumption and emissions, their real-world benefits strongly depend on heterogeneous driver usage patterns. Understanding these usage patterns is therefore essential for optimizing energy management and electrification policies. This study presents a data-driven framework for identifying and characterizing PHEV driving behavior using two primary indicators: the Utility Factor (UF), which quantifies the proportion of electric-mode driving, and the annual vehicle kilometers traveled (VKT), which measures driving intensity. A Gaussian Mixture Model (GMM) is employed in a transformed feature space characterized by the logit of UF and the logarithm of VKT to capture nonlinear relationships and diverse usage patterns. The Bayesian Information Criterion (BIC) is used to find the optimal number of behavioral clusters. To assess the robustness of the clustering, we perform a bootstrap stability analysis and compare it with k-means and a density-based clustering method (DBSCAN). Based on an analysis of real-world PHEVs, three distinct usage patterns are identified. The dominant cluster (96.6%) exhibits moderate electric usage (UF = 0.38) with an annual mileage of 18,617 km and fuel consumption of 3.89 L/100 km, whilst two smaller clusters represent near-full electric operation (2.3%, UF about 1.0, negligible fuel) and low-mileage users (1.1%, approximately 2444 km/year). The clustering demonstrates high assignment confidence (posterior entropy <10−4) and moderate stability, with a mean Adjusted Rand Index (ARI) of 0.448. The findings indicate that the proposed probabilistic clustering framework offers a comprehensible and statistically robust method for identifying diverse PHEV usage patterns. These insights can support adaptive energy management strategies, effective planning of charging infrastructure, and evidence-based policies to maximize the real-world electrification benefits of PHEVs.

Systems doi: 10.3390/systems14070792

Authors: Yanbing Li Munan Li Yuan Wang Sing Lui So

As digital technologies reshape competitive dynamics, firms face increasing pressure to keep pace with the innovation activities of their peers. While digital transformation is often viewed as a driver of sustainable development, less attention has been paid to how peer firms’ digital innovation relates to corporate environmental, social, and governance (ESG) performance within the broader innovation system. Using panel data from Chinese A-share listed firms from 2012 to 2023, this study constructs an industry-level measure of peer digital technology innovation based on the digital patenting activities of other firms in the same narrowly defined industry. The results show that peer digital technology innovation is significantly associated with a decline in ESG performance, revealing trade-offs between digital competitive adaptation and sustainability outcomes. Mechanism analysis further provides suggestive evidence consistent with two potential channels: heightened digital risk perception and stronger managerial short-term orientation. Heterogeneity analysis shows that this negative association is pronounced in small and medium-sized enterprises, tech-intensive industries, and highly competitive markets. In addition, external governance conditions shape the magnitude of this relationship: stronger intellectual property protection amplifies the negative association, whereas greater investor attention attenuates it. Supplemental analysis of ESG rankings further indicates that digital competition may allow specific firms to enhance their relative standing. These findings provide new evidence on the unintended consequences of digital competition and contribute to the literature on corporate digitalization and sustainability.

Systems doi: 10.3390/systems14070791

Authors: Ali Ahsan Hayden McDonald Ratna Saha Claire Davison

Artificial intelligence (AI) is rapidly transforming higher education. Notably, institutions are increasingly deploying AI-enabled chatbots across administrative, teaching and learning, and research functions. Despite the proliferation of these tools, most conversational systems remain generic and disconnected from subject-level context, curriculum design, and academic integrity requirements. A design-oriented framework is proposed in this paper to improve the effectiveness of AI-enabled chatbots used in teaching and learning, embedded within curriculum. The framework is grounded in systems thinking for context-aware AI-enabled learning systems that operate within bounded pedagogical and disciplinary environments. Adopting a design science approach, the study synthesises expert-informed insights from pedagogical, programmatic, and subject-level perspectives to develop a framework that integrates context alignment, instructional control, and integrity-preserving guardrails. The resulting artefact was verified through artefact-focused testing against the proposed design objectives using a structured non-human evaluation protocol, including requirement-based testing, baseline comparison with generic AI systems, academic integrity stress testing, and robustness analysis. The study proposes and verifies a framework intended to support curriculum alignment, instructional control, and academic integrity preservation within AI-enabled learning systems. The paper contributes a systems-oriented framework for embedding AI within educational systems while preserving pedagogical intent and governance requirements. Implications for scalable deployment of AI in higher education and future human-centred evaluation are discussed.

Systems doi: 10.3390/systems14070790

Authors: Yuanhan Weng Nan Li

Achieving accurate early warning of technological paradigm shift risks is crucial for high-tech enterprises to proactively manage strategic risks and seize opportunities for technological change. Based on a systematic identification of early warning factors for technological paradigm shift risks, this study constructs a chain-structured early warning model that integrates structural analysis, weight calculation, and intelligent algorithms. First, the Interpretive Structural Model (ISM) is used to analyze the hierarchical structure and dependencies among early warning factors, revealing the transmission path of risks from deep-rooted sources to surface-level phenomena. Second, the Analytic Network Process (ANP) and entropy method are integrated to synthesize subjective and objective information, calculating comprehensive weights for each factor and indicator while considering their mutual influences, thereby clarifying the priorities for risk management. Finally, addressing the nonlinear and small-sample characteristics of risk early warning, an intelligent early warning model based on a Deep Autoencoder Network (DAN) is constructed. Empirical testing on 75 high-tech enterprises shows that: ISM divides nine early warning factors into three levels with clear transmission relationships; ANP-entropy weights indicate that “technology assessment,” “enterprise competition,” and “innovation effort” are the core driving factors with the highest weights; and the DAN model, after training, achieves 93.33% accuracy in classifying technological paradigm risk levels on the test set, significantly outperforming traditional benchmarks such as One-Class SVM and Random Forest, demonstrating powerful nonlinear pattern recognition and adaptive assessment capabilities. This study provides methodological innovation and practical tools for achieving dynamic and intelligent early warning of technological paradigm risks.

Systems doi: 10.3390/systems14070789

Authors: Hojun Lee Kyu-Cheol Choi Gi-Chan Kim Jaeho Jung Seok-Ho Rhi

Autonomous driving sensor fusion housings (SFHs) integrate LiDAR, radar, camera, and computing modules within a shared mechanical and thermal enclosure. This review examines how coefficient of thermal expansion (CTE) mismatch among housing polymers, aluminum heat spreaders, substrates, and solder joints can contribute to interfacial delamination, solder joint fatigue, optical misalignment, and Thermomechanical Coupling Interference (TMCI). Using a structured narrative review of 99 publications and authoritative standards from primarily 2009 to 2026, the article organizes the evidence into a 4 × 4 taxonomy linking four failure mechanisms with experimental, computational, AI/ML, and qualification-oriented approaches. The review explicitly distinguishes direct literature evidence, transferred package-level evidence, model-based extrapolation, and author-derived conceptual estimates. Accordingly, TMCI temperature increments, sensor spacing values, optical drift estimates, and lifetime projections are discussed only as case-specific screening-level hypotheses unless directly validated in the cited literature. Five research gaps are identified: standardized multi-sensor TMCI validation, aging-corrected material and solder fatigue databases, long-term qualification of thermally conductive nanocomposites, SFH-specific validation of physics-informed digital twins, and integrated multi-failure testing. The contribution of this article is therefore primarily structural and agenda setting: it clarifies what is supported by direct evidence, what is transferred from adjacent domains, and what remains to be validated before robust SFH-level reliability guidance can be established.

Systems doi: 10.3390/systems14070788

Authors: Ionuț Jianu Maria-Daniela Tudorache Constantin-Ștefan Simion Ana-Maria Iulia Santa Eliza Nicoleta Negoi Andrei Hrebenciuc Dumitru Alexandru Bodislav

This paper aims to assess the relationship between foreign direct investment (FDI) and economic development/employment rate over the period 2013–2023 for Romania, as well as for other European Union country groups (Central and Eastern Europe, Northern and Western Europe and Peripheral Europe). In this respect, we used the Panel FEGLS method adjusted with cross-section SUR and found a positive relationship between FDI and Gross Domestic Product (GDP) per capita for all panels, the strongest estimated relationship being identified for Romania (followed by the one specific to Central and Eastern European states), considering the important role of the level of economic development in shaping these differences. Regarding the relationship between FDI and employment rate, we also found positive coefficients, the highest ones being identified for Central and Eastern Europe and Romania. However, the weakest estimated relationship between FDI and GDP per capita/employment was identified for the Peripheral Europe countries.

Systems doi: 10.3390/systems14070787

Authors: Jinpeng Fu Xiangan Ding

Improving regional economic resilience is a point addressed in the sustainable development goals (SDGs; i.e., SDG 8 and SDG 11). The Yangtze River Delta (YRD) has demonstrated excellent economic resilience during the COVID-19 pandemic, largely due to the persistent green transition of the YRD in the past two decades. This paper uses a single-case method combined with the perspective of evolutionary economic geography to systematically investigate the process of green transition in the YRD (2000–2023) at both vertical and horizontal levels and proposes an integrated multi-dimensional system framework to reveal the collaborative logic of the overall green transition action and the internal mechanism of enhancing economic resilience in the YRD. The findings indicate that the combination of external factors such as contradiction change, magnifying crises, economic stabilization, and policy steering has driven the historical inevitability of green transition in China. Under such conditions, the YRD not only completed development in terms of primitive accumulation of space (coordinated development, i.e., chassis), industry (orderly upgrade, i.e., engine), and governance (equal supply, i.e., lubricant) earlier but also ensured the stability of this triangle, injecting sustained strong momentum into the rapid recovery of the economy under the impact. The solidification of green concepts further enhances the sustainability and strength of the YRD’s economic resilience. These findings provide beneficial experience on how to resume production after the pandemic or lay out cities in developing countries that are still in rapid urbanization in advance.

Systems doi: 10.3390/systems14070786

Authors: Yannan Li Xiaoxiao Geng

In the context of accelerating artificial intelligence (AI) development, this study explores how AI Usage contributes to employee job performance and innovation performance by activating cognitive and HR system–level mechanisms. Adopting an integrative individual–organizational perspective, this study examines the mediating roles of AI self-efficacy and digital human resource management (HRM) practices in translating AI adoption into employee performance outcomes. Survey data were collected from firms located in major Chinese cities (Beijing, Shenzhen, Xi’an, and Zhengzhou), resulting in 750 valid responses for analysis. The results indicate that AI self-efficacy and digital HRM practices function as significant positive mediators, facilitating the conversion of AI adoption into enhanced work performance and innovation outcomes. Theoretically, this study advances knowledge management studies by highlighting the complementary roles of individual cognitive beliefs and HR systems in enabling AI-driven learning and capability development. Practically, the findings suggest that organizations should embed AI technologies in HR systems that foster learning, knowledge utilization, and continuous innovation.

Systems doi: 10.3390/systems14070785

Authors: Xinqi Li Pengcheng Liu

We re-conceptualize corporate debt default risk (EDF) as an emergent state variable of a coupled financial system and ask how capital-market opening reshapes its equilibrium. Extending the structural credit-risk framework with three interacting subsystem channels—external financing, investment efficiency, and information disclosure—we derive a closed-form result showing that an exogenous increase in liberalization strictly reduces the system-level corporate debt default probability through three complementary channels. We then exploit the staggered roll-out of China’s Shanghai–Hong Kong and Shenzhen–Hong Kong Stock Connect (HSGT) programs as a quasi-natural experiment on a panel of 21,351 firm-year observations over 2011–2023. A difference-in-differences (DID) estimator confirms a significant stabilizing effect on the firm’s market-implied default probability that is robust to an extensive battery of identification and specification checks; mechanism regressions confirm all three model-implied channels. The stabilizing effect is further amplified in firms facing greater environmental uncertainty and greater customer concentration—precisely the regimes in which our model predicts the underlying subsystem coupling to be most fragile. Our findings recast capital-market opening as a system-level intervention that simultaneously re-balances financing, investment, and information subsystems of the financial system, with implications for financial-stability policy in emerging economies.

Systems doi: 10.3390/systems14070784

Authors: Xiaoyu Ju Junru Jiang Huicong Yu Xinpei Qiao

Against the background of China’s “dual carbon” goals and the continued advancement of the green and low-carbon transformation of resource-based industries, resource-based enterprises urgently need to rely on green innovation to overcome development constraints characterized by high resource dependence, strong environmental pressures, and mounting transformation challenges. Patient capital, with its long-term orientation, stable support, and risk-sharing characteristics, can provide sustained financial backing and governance support for green innovation in resource-based enterprises; however, its underlying mechanism remains to be further explored. Drawing on patient capital theory, this study constructs a “capital–ESG–innovation” analytical framework to examine the impact of patient capital on green innovation in resource-based enterprises and its mechanism of action. Using Chinese A-share listed resource-based enterprises from 2014 to 2023 as the research sample, this study measures patient capital from two dimensions, namely stable equity and relational debt, and conducts empirical analysis through panel regression and multiple robustness tests. The results show that patient capital significantly promotes green innovation in resource-based enterprises, with both relational debt and stable equity playing positive roles. Mechanism tests reveal that ESG performance serves as an important mediating channel through which patient capital promotes green innovation. Further analysis indicates that the level of regional marketization strengthens the green innovation effect of patient capital, and this effect is more pronounced in large enterprises, enterprises subject to stronger media supervision, and enterprises whose executives have higher green cognition. This study enriches the literature on the relationship between patient capital and green innovation and provides empirical evidence for cultivating long-term capital and promoting the green and low-carbon transformation of resource-based enterprises.

Systems doi: 10.3390/systems14070783

Authors: Zhengyuan Zhou Lei Wang Yujie Zhang

Against the backdrop of geopolitical restructuring and rapid digital transformation, foreign investment security reviews in host countries have become an increasingly important institutional constraint on cross-border business activities. From a broader organizational perspective, such regulatory mechanisms shape the external environment in which emerging market multinational enterprises (EMNEs) make international investment decisions and conduct cross-border acquisitions. This paper explores the moderating effects of EMNE network and internal agency problems on the duration of reverse cross-border M&A (CBMA) under host country security reviews. Utilizing a Negative Binomial regression model, we empirically analyze 503 reverse CBMAs undertaken by Chinese firms in developed economies from 2003 to 2022. The findings reveal that host country security reviews significantly prolong M&A duration. Notably, business group affiliation weakens this positive relationship, whereas political networking strengthens it. Regarding internal governance factors, Type I agency problems reinforce the delaying effect of foreign investment security reviews, while Type II agency problems weaken it. This study provides practical implications for managers and policymakers seeking to improve firms’ responses to increasingly complex regulatory environments.

Systems doi: 10.3390/systems14070782

Authors: Tao Ma Jie Yang Xiaolei Wang

Against the backdrop of the accelerated advancement of regional economic integration, industrial synergy in urban agglomerations has become a core pathway to break homogeneous competition, enhance urban productivity and achieve sustainable economic growth, yet existing studies have mostly focused on the local effects of industrial agglomeration, and complementary linkages from the perspective of industrial chain supply and demand, as well as their cross-city spatial spillover mechanisms, remain insufficiently explored. Taking the nine cities in the Pearl River Delta (PRD) as the research object, this paper constructs an Industrial Complementarity index (ICI) based on urban panel data from 2012 to 2017 and multi-regional input–output tables. The findings reveal the following: (1) Industrial complementarity in the PRD exhibits significant uneven distribution characteristics, with the network structure gradually evolving from a single-core concentrated pattern centered on Shenzhen in 2012 to a multi-polar dispersed pattern centered on Zhaoqing, Zhongshan, and Dongguan in 2017. Resource-based cities play a key fundamental connecting role in the intermediate input supply network. (2) Industrial complementarity significantly promotes urban productivity growth, and its impact is mainly realized through spatial spillover channels. Moreover, productivity spillovers show an obvious distance decay characteristic, and marginal cities obtain significantly higher marginal benefits from spillovers than core cities. (3) Mechanism tests indicate that financial deepening and human capital accumulation are important channels through which industrial complementarity affects urban productivity.

Systems doi: 10.3390/systems14070781

Authors: Enrique Fernández-Vilas Marcos Iglesias Carrera Juan R. Coca

This paper develops an exploratory agent-based model of mediated vulnerability in deafblindness-related social exclusion. The revised model treats exclusion as a relational process shaped by multidimensional mediation, education, income and local interaction. Access to social-health mediation is defined as a composite construct that can be decomposed into communication support, mobility and orientation support, health-care continuity, educational and assistive support, and administrative recognition. The reported NetLogo implementation analyses the aggregate access variable generated in BehaviourSpace, while the revision makes explicit the subdimensional update rules required for future empirical calibration. The simulation begins with 1000 agents and runs for 500 ticks. The analysed export contains 1593 runs across six values of risk threshold, with 300 runs for each threshold from 0.1 to 0.5 and 93 runs for threshold 0.6. The results distinguish mediated vulnerability, continuous exclusion, binary exclusion, episodic exclusion and consolidated exclusion. Peak exclusion remains substantial across threshold conditions, whereas final binary exclusion is almost absent under the implemented averaging rules. One-way and Welch ANOVA show that risk-threshold does not significantly affect peak exclusion, mean exclusion or final exclusion, but it has a very strong effect on education and derived social power. Regression models indicate that mean access and mean income are the strongest predictors of exclusion severity, while risk-threshold has no significant direct effect after these variables are included. External validation is treated as a pattern-oriented comparison rather than a prevalence prediction: the model is consistent with the empirical claim that exclusion among persons with deafblindness is mediated by communication, mobility, education, health-care and economic barriers, but it should not be read as an estimate of real-world prevalence.

Systems doi: 10.3390/systems14070780

Authors: Darli Vieira Raimundo Kennedy Vieira Alencar Bravo

Requirements management is fundamental to complex projects, especially in areas such as engineering, infrastructure, and defense. This article develops an integrative theoretical framework for requirements management in complex projects, grounded in a PRISMA-guided systematic literature review with a qualitative synthesis of the key dimensions of the field. In this review, 136 studies selected from an initial set of 519 records identified across multiple databases were reviewed. Five pillars were found to underpin the proposal: (i) the definition and traceability of requirements, (ii) the mitigation of uncertainties and risks, (iii) team maturity, (iv) digitalization and organizational transformation, and (v) the application of model-based systems engineering (MBSE). A literature review revealed that high-quality requirements reduce errors, improve predictability, and optimize resources, whereas digital approaches and collaborative practices strengthen the adaptive capacity of projects. Thus, in the proposed framework, these dimensions are organized into a hierarchical structure, with an emphasis on the integration of technical, organizational, and digital processes. One limitation is the lack of empirical validation, necessitating future studies on the practical application of the model in real projects, interviews with experts, and the development of operational metrics. This conceptual model is aimed at contributing to the literature and supporting more resilient, automated, and sustainability-oriented practices in complex environments.

Systems doi: 10.3390/systems14070779

Authors: Aarón Raya Manel Soler Sanz Javier Palanca Vicente Julián Vicente J. Botti

In this paper, we introduce SPADE, a framework engineered for building Digital Twins through Multi-Agent Systems. The architecture is inherently scalable and distributed, aligning perfectly with the demands of modern Digital Twin environments. We implement the Agents and Artifacts meta-model via the SPADE Artifacts extension, which serves as a structured interface connecting autonomous agents with their physical system counterparts. To demonstrate the framework’s efficacy, we detail a case study involving urban traffic management in Valencia, Spain. In this implementation, we model 386 street segments as individual agents responsible for managing traffic flows and coordinating redistribution efforts. The research delineates a MAS-based communication strategy spanning the entire network and introduces a consensus algorithm specifically designed to manage traffic rerouting when a street is closed. Finally, we present results from a series of experimental trials and evaluate the system’s broader potential. By synthesizing diverse data sources and providing an interactive dashboard for visualizing network conditions, this work demonstrates how SPADE can serve as a robust foundation for Digital Twin development, illustrating its potential for real-world urban applications through a conceptual implementation grounded in open sensor data.

Systems doi: 10.3390/systems14070778

Authors: Ping Yu Yuying Tan

Resource allocation exhibits systematic divergence across heterogeneous time horizons, which aggregate measures fail to capture. This paper models allocation as a multi-component dynamic system composed of three coupled mechanisms: long-term accumulation, short-cycle response, and phase stabilization. A dual-horizon framework is developed to integrate finite-horizon local adjustment with infinite-horizon path evolution. The results show that system dynamics are governed by the coupling of these mechanisms under a shared resource constraint, which endogenously generates a unified adjustment signal and redistributes marginal resources across components. This process jointly determines short-term deviation correction, structural stabilization, and long-term equilibrium formation. Equilibrium is therefore characterized as an emergent system state shaped by intertemporal feedback and dynamic interactions. Numerical simulations further illustrate the dynamic adjustment paths and confirm the theoretical mechanisms. The framework provides a system-level explanation for divergent findings by showing how structural decomposition reveals heterogeneous mechanisms obscured under aggregate representations.

Systems doi: 10.3390/systems14070776

Authors: Xiuxiu Gao Ying Zhou

Effective identification of corporate default risk is crucial for maintaining financial stability and safeguarding investors’ interests. Existing models remain limited in addressing class imbalance and the dynamic evolution of default-related features over time. To overcome these challenges, we propose an adaptive spherical neighborhood resampling and class-specific reliability evidential reasoning model (ASNR-crER). By combining feature-weighted minority sample reconstruction with reliability-guided recursive evidence fusion, the proposed model aims to improve the prediction accuracy of both default and non-default firms under class imbalance. This study uses Chinese listed small enterprises from 2000 to 2023 as the research sample, comprising 10,449 firm-year observations from 2182 firms. By matching default status in year t with firm indicators from t-0 to t-5, six rolling prediction windows are constructed. The empirical results show that: (1) Compared with mainstream benchmark methods, ASNR-crER achieves the best overall performance in terms of accuracy, AUC, and F1 across all prediction windows, indicating that it can more reliably identify high-risk default firms while maintaining strong recognition of non-default firms. (2) SHAP analysis indicates that financial, non-financial, and macroeconomic indicators exert time-varying effects on corporate default risk. Financial indicators, including “Retained earnings/total assets”, “Other receivables/current assets”, and “Annualized return on assets”, reflect internal capital accumulation and profitability, serving as key predictors of default risk. Non-financial indicators, such as “Top 10 Tradable Shares H-index” and “Top 10 shareholders H-index”, can provide supplementary signals for medium-term risk identification. Macroeconomic indicators, including “M2 YoY growth rate”, “Urban HH per capita income”, and “Benchmark short-term loan rate”, show stronger explanatory power in longer prediction windows. Therefore, this study provides an effective early-warning tool for financial institutions and relevant stakeholders to identify high-risk firms, and enriches empirical evidence on the time-varying drivers of corporate default risk.

Systems doi: 10.3390/systems14070777

Authors: Minseong Kim Joongho Chang

As artificial intelligence becomes increasingly embedded in healthcare operations, governance can no longer be treated solely as a static set of principles or regulatory requirements. This study proposes a Human-Centered Experience Engineering (HCEE)-based healthcare AI governance architecture designed as a closed-loop operational control system for stabilizing and adaptively managing AI-enabled healthcare systems. Rather than treating patient and employee experience as downstream outcomes, the framework repositions them as governance input signals, operationalized as the Patient Experience Index (PXI) and Employee Experience Index (EXI). The architecture integrates Policy, Governance, Control, and Experience through recurrent feedback, trigger-control rules, and adaptive learning mechanisms that translate experiential signals into ongoing operational adjustment. To examine how this architecture affects system behavior, agent-based simulation compares four scenarios: S1 (no governance), S2 (sense-only), S3 (full HCEE closed loop), and S4 (full HCEE under exogenous stress). Results show a consistent pattern of S1 < S2 < S3 in both PXI and EXI, indicating that sensing alone yields limited improvement, whereas feedback-coupled sensing, control, and learning produce stronger stabilization and performance gains. In S4, the same architecture demonstrates recovery, re-stabilization, and adaptive reinforcement under shock. These findings suggest that effective healthcare AI governance depends on whether feedback loops are architected to function operationally. These results are based on synthetic experience indices and are interpreted as conceptual, mechanism-level evidence.

Systems doi: 10.3390/systems14070775

Authors: Yushang Hu Yaqing Liu Zanxin Wang

Although the digital economy (DE) and sustainable urban systems have drawn growing attention, how DE drives urban energy transition (UET) remains insufficiently understood. Employing panel data from 266 Chinese cities spanning 2011–2021, this study applies fixed-effects, mediation, moderation, and spatial Durbin models to examine the impact of DE on UET. UET is measured by a composite index that captures energy system performance and transition readiness. The results show that DE is positively associated with UET, with a one-unit increase in DE corresponding to a 0.1566-unit increase in UET. This finding remains robust after employing an instrumental-variable approach and a battery of robustness checks, including an exogenous policy shock. This positive effect is partially mediated by reduced resource misallocation and industrial structure upgrading. When electricity intensity is treated as a moderating factor, cities with higher electricity intensity exhibit a more pronounced positive effect of DE on UET. Further evidence indicates that DE advances UET in the eastern, central, and western regions, but has a limited impact in the northeastern region. Additionally, DE exerts positive spillover effects, thereby advancing energy transition in neighboring cities.

Systems doi: 10.3390/systems14070774

Authors: Lizette Gericke Corné Stephanus Lodewyk Schutte

The unprecedented rate of technological advances, accelerated industry disruptions and social and environmental sustainability crises require very different business organizations from the traditional paradigm. The main research question for this paper is: What change (paradigm shift) is needed for South African business organizations to be future-fit? The paper introduces an integrated, transdisciplinary paradigmatic model of an emerging, progressive future business organization in South Africa, as mostly influenced by Western futurists, and proposes an understanding of the paradigm shift required in our socially constructed reality for such organizations to emerge. A multi-method methodology, based on complexity theory and a transdisciplinary approach, was developed and applied. The researcher’s conceptualization of a ‘paradigm’, focusing on language-based representations, is explicated as a theoretical foundation. Textual analyses, including corpus linguistics, of practitioner-focused literature were used to elicit concept maps (or domain models) of the shared, societal-level mental models of a South African business organization for two periods: (1) the Traditional Business Organization, and (2) a Progressive Future Business Organization. The outcomes were compared using a novel qualitative method, resulting in a proposed set of societal-level ontological shifts needed for a progressive organizational future. The study shows a paradigm shift to complexity and social responsibility, and the need for transdisciplinarity to reflect complex, integrated organizational realities.

Systems doi: 10.3390/systems14070773

Authors: Salem Ben Zarraa Sarvnaz Baradarani Kolawole Iyiola Ahmad Bassam Alzubi

This study acknowledges the role of high-involvement work practices (HIWPs) in promoting proactive behavior among public organization employees, addressing the need to obtain further insights into the mechanisms and identify contingencies (i.e., both conditional and individual factors) that might impede the effectiveness of such practices. This builds on emerging empirical studies in the public management literature by drawing on social exchange theory and social cognitive theory to empirically test the impact of HIWPs on employees’ proactive behavior, using data collected through a two-wave, time-lagged survey design with a one-month interval from Turkish public organizations. The mediating role of public relations values was also examined, along with the moderating roles of role breadth self-efficacy and employees’ use of normative public values. Relying on 554 data obtained from Turkish public organization employees, this study finds that HIWPs positively impact employees’ proactive behavior and public relations values. Public relations values positively impact employees’ proactive behavior and partially mediate the link between HIWPs and employees’ proactive behavior. Role breadth self-efficacy moderates the positive relationship between HIWPs and public relations values, with the relationship being stronger for employees with high role breadth self-efficacy than for those with low. Employees’ use of normative public values moderates the positive link between public relations values and employees’ proactive behavior, with the relationship being stronger for employees with high use of normative public values than for those with low. The main theoretical and practical implications of the study’s outcomes are outlined and discussed, along with important future research directions. The findings highlight the importance of human-centric organizational practices in fostering public value in VUCA-D environments. By enabling proactive behavior, public organizations can enhance adaptability, support value co-creation with citizens, and strengthen trust in public service systems.

Systems doi: 10.3390/systems14070772

Authors: Caicheng Niu Shi He Congying Fang Luyao Niu Yingbo Xiao Wenjia Zhang

Cross-border mobility (CBM) is highly sensitive to policy shocks during public health crises, yet its behavioral and demographic dynamics remain poorly understood. CBM disruptions can be conceptualized as differentiated responses to non-pharmaceutical interventions (NPIs) across demographic groups in integrated cross-border systems. Focusing on the Shenzhen–Hong Kong cross-border mobility corridor, causal evidence shows that NPIs generate both immediate and uneven mobility contractions. These responses are structured along gender and age lines: younger individuals and males exhibit more elastic and rapid behavioral adjustments, whereas females demonstrate greater stability and faster recovery. In contrast, older adults show limited initial responsiveness but prolonged recovery, indicating persistent structural constraints. These findings identify vulnerable populations and provide empirical evidence for designing targeted, equitable border management and transportation policies in highly integrated regions during future public health crises.

Systems doi: 10.3390/systems14070771

Authors: Sanghoon Jung

Image-based generative AI is rapidly entering urban design practice, yet it remains unclear whether its limitations can be addressed through prompt refinement or whether they reflect properties of the generation architecture itself. This study examines this question through a comparative case study of two urban design studio tasks under contrasting constraint conditions, drawing on 151 prompt episodes across 20 team projects. In concept-driven tasks, generative AI primarily supported divergence and visual exploration, while morphological bias was partly managed through curatorial selection and reference anchoring. In site-specific tasks, however, outputs often achieved visual plausibility while failing to preserve relational spatial logic, including circulation hierarchy, adjacency dependencies, and boundary conditions. The findings distinguish element-level errors, recoverable through post-editing, from system-level errors, which tended to resist repair within the observed image-generation workflows. Across cases, a three-stage human–AI workflow emerged as a control mechanism: human-led structuring, AI-driven generation and enrichment, and critical curation and refinement. The study contributes to research on human–AI systems by showing that effective integration in constraint-intensive urban design can depend less on prompt optimisation than on workflow architecture, particularly the allocation of validation, generation, and repair functions between human and AI components.

Systems doi: 10.3390/systems14070770

Authors: Zhenzhen Wang Feifeng Zheng Ming Liu

The electrification of urban transport has made battery electric buses (BEBs) an important option for reducing carbon emissions and improving urban air quality. However, the high investment cost of charging infrastructure and the uncertainty in effective usable battery capacity at the day-ahead scheduling stage—caused by accumulated degradation, heterogeneous operating conditions, and imperfect state estimation—create major challenges for charging infrastructure siting and daily bus operations. This study proposes a joint optimization model for infrastructure siting and BEB charging scheduling, in which effective capacity uncertainty is handled using a distributionally robust optimization (DRO) framework. To solve the resulting mixed-integer nonlinear program efficiently, we develop a matheuristic decomposition method that integrates Adaptive Large Neighborhood Search (ALNS) with small gaps relative to a relaxation-based lower bound. Computational experiments based on real-world bus route data indicate that the proposed framework obtains high-quality solutions with small gaps relative to a relaxation-based lower bound, performs better than representative benchmark heuristics, and scales well to large instances.

Systems doi: 10.3390/systems14070769

Authors: Haixia Feng Renbo Shi Qingjin Wang

The application of artificial intelligence (AI) and the improvement of environmental, social, and governance (ESG) performance have become important concerns for contemporary firms. Understanding whether AI can be effectively integrated into corporate ESG practices has significant implications for sustainable development. Using panel data from Chinese A-share listed firms from 2009 to 2025, this study empirically examines the effect of AI application on corporate ESG performance through a fixed-effects model. The results show that AI application significantly improves corporate ESG performance, indicating that firms with higher levels of AI adoption tend to achieve better ESG outcomes. The heterogeneity analysis further reveals that this effect varies according to firms’ technological intensity, industry pollution characteristics, and the strength of regional environmental regulation. Specifically, the positive effect of AI application is more pronounced among firms with lower technological intensity, firms operating in non-heavily polluting industries, and firms located in regions with stricter environmental regulation. The mediation analysis shows that AI application enhances ESG performance by promoting human capital upgrading and green technological innovation, thereby strengthening firms’ internal capabilities and technological foundations for sustainable development. This study contributes to the literature by integrating AI application and ESG-oriented sustainable development within a unified analytical framework.

Systems doi: 10.3390/systems14070768

Authors: Qiang Wang Yanyu Zhang Qiguo Gong

This study takes human–machine task allocation as its entry point and systematically examines production-line configuration and combination decisions in multi-product, small-batch manufacturing settings. By considering the trade-offs in cost and flexibility among three production-line types—fully automated intelligent lines, flexible production lines, and manual production lines—the study develops a cost-comparison framework to investigate how firms should choose optimal configuration strategies under different market demand environments to achieve total cost minimization. The results show that production batch size is a key factor determining strategy selection. When the batch size is small, a two-type configuration (combining automated lines and manual lines) achieves cost advantages. When the batch size falls within a medium range, a three-type configuration (integrating automated lines, flexible production lines, and manual lines) exhibits distinct advantages. When the batch size is large, a configuration dominated by flexible production lines yields the lowest cost. Furthermore, when the improvement investment cost is sufficiently low, the investment can significantly reduce production cost and lower the critical boundary for making small-batch production economically feasible, thereby enhancing the practicality of small-batch flexible manufacturing. The study provides a decision-making approach for production-line configuration selection based on production batch-size characteristics.

Systems doi: 10.3390/systems14070767

Authors: Qi Xu Tianwei Ma Gaosi Li Yuan Luo Zhu Yao Tao Wang

Transportation cost plays a central role in total logistics costs, yet freight rates vary significantly across different scenarios due to multiple factors such as region, cargo type, and vehicle type. Existing prediction models mostly focus on a single dimension or simple linear relationships, making it difficult to effectively capture geographic heterogeneity, nonlinearity, and multi-factor interactions. To address these issues, this paper constructs four predictive models—Random Forest, CNN, LightGBM, and a geographically enhanced LightGBM by constructing city-level average freight rates as well as inter-city freight rate differences and ratios to explicitly encode spatial dependencies into the model and conducts an in-depth analysis of prediction results from the perspectives of cargo type and vehicle length in Guangxi, China. Experimental results show that the geographically enhanced LightGBM model outperforms the other three benchmark models across key evaluation metrics, verifying the effectiveness of incorporating geographic information for improving freight rate prediction accuracy. This study not only provides multiple model solutions and a multi-dimensional evaluation perspective for national freight rate prediction but also offers practical insights for optimizing logistics resource allocation and formulating differentiated pricing strategies.

Systems doi: 10.3390/systems14070766

Authors: Dongming Gu Tianfei Zhang Ke Xu Shenghui Li

Hydrogen has become a strategic component of the energy transition and decarbonization. In China, province-level administrative regions have issued many hydrogen-related policies, but existing research has paid limited attention to how these policy texts are organized into coherent policy systems. This study evaluates the text-based structural coherence, design completeness, and textual coordination of China’s provincial hydrogen industry policies. Using 315 hydrogen industry policy documents issued across 31 province-level administrative regions, it constructs a “themes–instruments–text-based coherence” analytical framework that integrates the latent Dirichlet allocation topic model, content analysis, and the policy modeling consistency index model. The results show that China’s provincial hydrogen industry policy systems contain five themes: hydrogen industry development, technological innovation, policy support, industry regulation, and operational management. The thematic structure is strongly oriented toward industrial development and technological innovation. The policy instrument analysis indicates that capacity-building instruments dominate the instrument configuration, accounting for 55.87%, while instruments related to government demand, institutional supply, and safety regulation are less prominently represented. The PMC-based evaluation shows that the average text-level coherence score is 6.75, indicating a generally good level of structural coverage and design completeness as reflected in policy texts, but with regional and dimensional differences. Issuing levels constitute the weakest textual dimension, while thematic coverage and instrument configuration show room for improvement, suggesting less systematic textual representation of vertical linkage and uneven instrument complementarity. This study contributes to hydrogen policy research by shifting the analytical focus from individual policy documents to provincial policy systems and by clarifying how policy themes, instruments, administrative arrangements, governance levels, and temporal structures shape the structural coverage and design completeness of hydrogen industry policy at the textual design level.

Systems doi: 10.3390/systems14070765

Authors: Kaiyang Song Junli Zhao

Against the backdrop of China’s new development paradigm, this study examines how the coupling coordination between the national value chain (NVC) and the global value chain (GVC) affects regional green development and the mechanisms through which it does so. Focusing on China’s manufacturing sector, we employ a coupling coordination model, a high-dimensional fixed effects estimator, two-stage least squares, and bootstrap mediation tests on pooled cross-sectional data covering 31 provincial-level administrative units. The results indicate that greater NVC–GVC coupling coordination exerts a significant and positive effect on green development. Three transmission mechanisms are identified. First, NVC participation, environmental protection investment, and industrial structure upgrading jointly promote green development through a resource allocation and scale effect channel (NVC-led channel). Second, GVC participation operates through a green knowledge spillover and learning paths channel, which on net suppresses green development. Third, GVC participation also operates through an environmental regulations and standards transmission channel, which on net suppresses green development. The aggregate effect is nonetheless positive. Across the 2012, 2015, and 2017 sample years, the resource allocation and scale effect mechanism mediated by NVC participation dominates the knowledge spillover and regulatory transmission mechanism mediated by GVC participation, yielding a significant net stimulus to regional green development.

Systems doi: 10.3390/systems14070764

Authors: Filipe P. Ramos Arnaldo Santos Tania Rocha Fernando Petronilho Rui Pereira

Healthcare systems are complex adaptive environments in which clinical work, digital technologies, and organizational routines interact continuously, often challenging the integration of artificial intelligence (AI) into everyday practice. Although explainable AI (xAI) has been proposed to address concerns related to algorithmic opacity and professional trust, explainability is still frequently approached. Grounded in General Systems Theory, sociotechnical systems theory, and complexity science, this study conceptualizes explainability as an emergent system-level property of healthcare systems. Using Design Science Research as a systems-oriented inquiry methodology, a human-centered conceptual framework for AI-supported clinical decision-making was developed through iterative cycles of problem framing, design, demonstration, and evaluation. The framework was explored in rehabilitation nursing, a domain characterized by multidimensional patient data, longitudinal decision processes, and close professional–patient interaction. Iterative engagement with Rehabilitation Nursing Specialists informed design principles related to user participation, contextualized explanations, and workflow alignment. An exploratory evaluation with 144 Specialists assessed perceived usefulness, comprehensibility of explanations, and acceptance of AI-supported recommendations in realistic scenarios. The findings indicate that explainability is experienced not as a property of the algorithm alone, but as an outcome emerging from interactions between AI behavior, human interpretation, and organizational context. The framework shows potential to meaningfully support clinical decision-making in Rehabilitation Nursing by providing contextually aligned, human-centered explanatory outputs.

Systems doi: 10.3390/systems14070763

Authors: Marian Pompiliu Cristescu

Citizen-reporting platforms generate high-volume, multilingual streams of service requests, yet operational triage often relies on coarse category labels and manual inspection. This study develops an explainable analytics pipeline with probability calibration for FixMyStreet Brussels reports, combining text-based urgency modeling, topic discovery, and spatio-temporal hotspot scoring to inform municipal analytic review. From 522,132 raw reports, we build an English-normalized text field for modeling, derive resolution-time outcomes from closed cases, and curate a 1000-item gold standard with an explicit high-urgency class. A TF-IDF logistic regression baseline achieves reasonable classification performance on the labeled split and, after probability calibration, yields confidence estimates that are more suitable for risk-aware prioritization than uncalibrated scores. Topic-level analyses reveal dominant themes related to sidewalks, road damage, and bulky waste, and hotspot scores highlight persistent, high-impact issue clusters. Event detection on aggregated signals did not identify events above the predefined z-score threshold during the analysis window, suggesting that the observed dynamics are more visible as chronic, recurring problems than as abrupt threshold-level anomalies. Explainability audits via Shapley Additive Explanations (SHAP) expose linguistically intuitive drivers for urgent cases (e.g., dangerous, risk, and accident) and complaint-oriented terms (e.g., abandoned, illegal, and dirty), providing transparent hooks for governance review. The analysis is therefore presented as an open-data, English-normalized decision-support prototype rather than as a validated native multilingual triage system. The labeled evidence base contains 2200 distinct human-reviewed reports. It comprises the 1000-report gold standard, a 200-report model-ranked high-urgency candidate set, and a 1000-report expanded validation subset. The expanded validation subset contains 439 high-urgency cases, including 42 cases from a random 500-report corpus sample. To avoid overstating language evidence, this study makes no native multilingual claim. The empirical claim is limited to English-normalized text analytics over reports that were originally submitted in a multilingual civic-reporting setting.

Systems doi: 10.3390/systems14070762

Authors: Pengju Wang Zhixiang Yin Zhuang Xiong

By constructing a system dynamics (SD) model of how mission-oriented innovation consortia empower the development of future industries, this study reveals the internal causal structure and dynamic evolution mechanism of the system in the process of multi-actor collaborative innovation. The results show that, throughout the simulation period, innovation consortia exert a sustained positive enabling effect on the development of future industries, mainly reflected in the expansion of industrial market demand, the accumulation of core technology breakthroughs, and the increase in the added value of the future industrial chain. Value co-creation intensity, patent resource complementarity, and technological adaptability jointly constitute the key mechanisms through which innovation consortia empower future industries. These mechanisms are coupled through multiple feedback paths, thereby maintaining the continuous evolutionary momentum of the system. From a system dynamics perspective, this study characterizes the dynamic interaction process and nonlinear evolutionary features between innovation consortia and future industry development, enriches the relevant theoretical framework, and provides a dynamic simulation basis for policy design related to future industries.

Systems doi: 10.3390/systems14070761

Authors: Jia Di Chunhui Yuan Xiaolong Li

This study examines how digital technology development affects entrepreneurial activity from a spatial perspective. Existing studies have mainly emphasized the local entrepreneurial effects of digital technology, while paying relatively limited attention to whether such effects extend across cities through spatial linkages. Using manually compiled large-scale business registration data, this study constructs a balanced panel dataset covering 281 prefecture-level and above cities in China from 2006 to 2020. Spatial autocorrelation tests and spatial econometric models are applied to identify the local and spatial spillover effects of digital technology. The findings suggest that urban entrepreneurial activity exhibits significant spatial dependence. After controlling for spatial correlation, digital technology development significantly promotes local entrepreneurial activity and generates positive spillover effects on surrounding cities. Effect decomposition results show that the indirect effect of digital technology is stronger than its direct effect, suggesting that spatial spillover is an important channel through which digital technology affects entrepreneurial activity. Further analysis finds that this effect differs across technology types, city size, urban hierarchy, and geographical location. Distance-band tests indicate that the positive spillover effect weakens as geographical distance increases. Overall, digital technology can promote entrepreneurial activity through regional linkages, but its spatial influence depends on actual intercity connections.

Systems doi: 10.3390/systems14070760

Authors: Lulu Hu Wei Chen Dong Wu Feng Han

The global trade of tin-containing products has become increasingly complex due to supply–demand imbalances, geopolitical risks, and high trade concentration. Ensuring supply chain stability is critical for sectors such as electronics. This study constructed a global tin trade network (2000–2024), applied complex network analysis, and developed a cascading failure model to assess structural vulnerability and simulate supply disruptions. Results showed a highly concentrated network, with China, the United States, and Germany acting as key hubs. China emerged as the largest exporter of tin-containing final products in 2024 (84.70 kt), while the United States was the largest importer (27.82 kt) in 2024. The electronics and machinery sectors were particularly vulnerable, exhibiting large avalanche sizes and deep propagation hierarchies, while home appliances and food packaging showed comparatively lower risks. Simulations further revealed that disruptions in major supplier countries, particularly China, could trigger cascading failures affecting 193 economies (80.1% of all trading partners). To improve resilience, this study highlighted the importance of supply diversification and inventory buffers, industry differentiation management, and real-time monitoring systems, which are essential for building a more robust and sustainable global tin trade network.

Systems doi: 10.3390/systems14070759

Authors: Marzena Kramarz Mariusz Kmiecik

Medical and pharmaceutical last-mile deliveries are simultaneously expected to be fast, reliable and temperature-safe for patients and to become measurably greener, yet these objectives often pull transport operations in opposite directions. Third-party logistics (3PL) providers are therefore increasingly required not only to report transport CO2 emissions, but also to learn from them; however, it remains unclear whether the routine operational data they collect are sufficiently informative to enable such emission-based learning in this regulated and service-critical setting. This study examines the predictive limits of machine learning models in estimating CO2 emissions in medical and pharmaceutical last-mile deliveries performed by a 3PL operator. Using operational data from six customers, we compare global and customer-specific models for the following two dependent variables: total CO2 emissions per transport operation and CO2 emissions per pallet. Linear and non-linear models, including linear regression, ElasticNet, Random Forest, HistGradientBoosting and XGBoost, are evaluated using chronological train-test splitting and cross-validation. The results show that global models fail to outperform a naïve benchmark, with negative R2 values for both emission measures. Customer-level models reveal substantial heterogeneity as follows: for selected customers, especially those with more regular operational patterns, moderate predictive performance is achieved, while for others, emissions remain largely unpredictable using the available variables. The findings suggest that routine shipment-level data are insufficient for robust emission prediction in 3PL last-mile operations. Emission-based learning requires richer contextual, vehicle, route, traffic and telematics data, as well as customer-sensitive modelling approaches. The study contributes by identifying the data and modelling limits of sustainability intelligence in medical and pharmaceutical last-mile logistics.

Systems doi: 10.3390/systems14070758

Authors: Zhang Cheng Lei Zhou Zhiyu Chen

Frequent introductions and revisions of climate policy instruments constitute a salient exogenous shock to firms’ strategic decisions. From a socio-technical systems perspective, climate policy uncertainty (CPU) represents an external institutional disturbance that reshapes the interaction between firms’ technological upgrading and organizational governance. Using panel data on 1783 Chinese listed firms from 2011–2024, we examined how CPU affects firms’ industrial intelligence. We employed fixed-effects models, mediation, and moderation analyses, supplemented by robustness tests. We found that, first, CPU significantly promotes firms’ industrial intelligence transformation. Second, board governance plays a key moderating role: higher board educational attainment, stronger innovation orientation, and an environmental or risk committee significantly strengthen CPU’s positive effect on industrial intelligence. Third, CPU promotes industrial intelligence mainly through two channels: an opportunity effect via increased R&D investment, and a pressure effect via reduced total factor productivity, pushing firms to adopt intelligent transformation to address productivity pressure. Moreover, this effect is stronger for firms in high-pollution industries, larger firms, and long-established firms. These findings suggest that corporate industrial intelligence is not merely a technological response, but a socio-technical adaptation process shaped by climate policy uncertainty, board governance, and resource reconfiguration. This study provides evidence on firms’ digital, intelligent, and green transformation under climate policy uncertainty and offers implications for board governance and sustainable adaptation.

Systems doi: 10.3390/systems14070757

Authors: Zhiwei Wang Shengkang Gao Peng Lin Guannan Qu Die Hu

In the fuzzy front end of innovation, firms often lack sufficient citation, market, and performance data, which limits the usefulness of outcome-based approaches to screening early-stage product innovation opportunities. To address this problem, this study develops a text co-occurrence network-based measurement system for assessing early-stage product innovation opportunities in new product development. We first preprocess idea texts through concept extraction and semantic cleaning, and then construct an integrated semantic network by combining market-related texts with ideation data. The Leiden algorithm is applied to detect latent knowledge communities in the network. Building on this structure, we assess early-stage product innovation opportunities along two complementary dimensions: cross-domain knowledge recombination, capturing the extent to which an idea draws on concept communities that are otherwise weakly connected, and network structural perturbation, capturing the degree to which an idea reconfigures existing semantic boundaries and connection patterns. Based on community entropy and modularity change, we construct a composite indicator for the ex ante assessment of early-stage ideas with stronger product innovation potential. Compared with traditional approaches relying on patent citations, market outcomes, or expert judgments, the proposed method enables earlier screening of ideas that deviate from dominant semantic trajectories and may warrant further development attention. The framework is explicitly positioned as an ex ante screening and attention-allocation tool for early-stage product innovation opportunities, not as a deterministic predictor of later market success.

Systems doi: 10.3390/systems14070756

Authors: Zhengang Zhang Defei Wang

Background and Purpose: Contemporary digital government initiatives in China face a well-documented real-world paradox: massive fiscal outlays on digital governance coexist with marked disequilibrium in regional innovation returns. Two structural mismatches define this paradox. First, local governments overwhelmingly prioritize high-visibility hardware investments such as data centers and large AI models, while neglecting deep-seated institutional reforms including cross-departmental business process reengineering and factor market liberalization. The pervasive phenomenon of “aggregated but non-interoperable data, and interoperable data left unused” reflects a severe asynchrony between rapid technological deployment and lagging institutional restructuring. Second, comparable digital investments yield vastly divergent innovation dividends across eastern, central, and western regions, with regional divergence entrenching into a rigid “higher in the east lower in the west, higher in the south lower in the north” pattern. Extant literature, largely confined to the lens of “instrumental rationality,” reduces digital government to an exogenous technological variable, leaving it unable to explain this core practical puzzle of “homogeneous inputs generating heterogeneous returns.” Moving beyond the narrow “technology-enabled governance” narrative, this study draws on the Digital-Era Governance (DEG) paradigm to investigate the actual impact of institutional restructuring on regional scientific and technological innovation capacity, aiming to provide empirical evidence to unlock the inefficiency lock-in prevalent in digital governance practices. Research Methods: This study uses 280 prefecture-level cities and above in China from 2018 to 2023 as the research sample and constructs a two-way fixed-effects model for benchmark regression analysis. To address endogeneity, the average level of digital government development in other cities within the same province is used as an instrumental variable, and the 2SLS method is employed to identify the causal effect. On this basis, a series of robustness checks are conducted, including excluding the special impact of the COVID-19 pandemic, substituting core variable measures, and decomposing the dimensions of the core explanatory variables, to ensure the reliability of the research conclusions. For mechanism identification, the Bootstrap sampling method is used to test the dual mediating effects of “digital industry agglomeration” and “resource misallocation alleviation”; furthermore, moderating effects and heterogeneity analysis models are introduced to reveal the boundary constraints of regional economic development levels and city types on the empowerment effect. Main Findings: Empirical results show that: (1) Digital government construction significantly improves regional scientific and technological innovation capacity, and this conclusion remains valid after endogeneity treatment and robustness checks. (2) Mechanism analysis demonstrates that digital government drives innovation through the dual paths of “promoting digital industry agglomeration” and “alleviating resource misallocation,” with the marginal contribution of alleviating resource misallocation being significantly higher than that of industrial agglomeration. This suggests that, in transitional economies, eliminating institutional frictions in factor mobility brings greater innovation dividends than simply building physical spatial clusters. (3) Moderating effects indicate that the higher the level of regional economic development, the stronger the innovation empowerment effect of digital government. (4) Heterogeneity analysis further reveals that the innovation dividends of digital government are significant only in non-resource-based cities, non-central cities, and large and medium-sized cities, while in resource-based cities, central cities, and small cities, the effects are systematically absorbed and not significant. Research Conclusions and Contributions: This study breaks through the ontological limitations of existing research that views digital government as a technological tool, grounding it within the DEG theoretical framework and confirming that digital government is an institutional force in the reconstruction of regional innovation ecosystems. The findings suggest to policymakers that digital government construction should promote a shift from a “technology-oriented” to an “institution-oriented” approach. The policy focus should shift from mere infrastructure expansion to the elimination of deep-seated institutional frictions, the improvement of factor allocation efficiency, and the advancement of gradients and the implementation of classified governance, all guided by regional economic foundations and heterogeneity characteristics.

Systems doi: 10.3390/systems14070755

Authors: Bogdan Tiganoaia Ionut-Petrisor Anghel Constantin Bratianu Doina Banciu

This paper proposes, implements and tests a resilient and decentralized system based on blockchain and quantum-secure communication for cyber, scientific, and strategic diplomacy. By introducing a dynamic post-quantum cryptographic binding layer, the proposed architecture significantly amplifies organizational resilience against sovereign-state data disruption and harvesting attempts within highly adversarial environments. The system guarantees end-to-end transparency and confidentiality in the sharing of resources, and it can be used in resilient organizations and for cyber, scientific, and strategic diplomacy. It integrates the following technologies and modules: blockchain technology with a semantic distribution of quantum keys, a frontend interface built exclusively with React, a backend module implemented in Python Flask 3.1.2 and served via Gunicorn 25.0.3, and a set of modular components dedicated to blockchain, quantum simulation, and distributed storage. Moving past theoretical bounds, this work presents structural Threat Models, real-world deployment parameters for Eastern European research infrastructures, and multi-variable empirical performance evaluations under authentic institutional workloads. Moreover, the proposed architecture offers an extensible foundation for the development of future resilient ecosystems supported by blockchain and quantum technologies.

Systems doi: 10.3390/systems14070754

Authors: Ioannis Valachis Sofoklis Skoultsos

Tourism destinations operate as multi-actor governance environments in which stakeholders interpret sustainability initiatives differently, reflecting their distinct institutional roles. This study applies a diagnostic system dynamics perspective to examine perception asymmetries among governance actors, tourism and hospitality professionals, and local community members across Greek tourism destinations. Drawing on survey data from 466 respondents, one-way Analysis of Variance (ANOVA) comparisons across four perception domains reveal a consistent pattern: stakeholder evaluations differ significantly for HR sustainability practices (F = 114.60, p < 0.001) and organisational support conditions (F = 21.29, p < 0.001), while remaining broadly aligned in assessments of overall sustainability outcomes (F = 0.15, p = 0.861). Interpreted through causal loop reasoning, this is consistent with divergence at the implementation level alongside shared strategic orientations. This combination may be interpreted as indicative of feedback asymmetry together with alignment in outcomes, and carries implications for coordination and institutional trust. The study positions stakeholder perception analysis within the problem-structuring stage of the system dynamics modelling cycle, showing how observed perception patterns may be used to identify areas warranting subsequent system dynamics modelling. In this way, it advances a diagnostic framework applicable to multi-actor governance contexts beyond tourism.

Systems doi: 10.3390/systems14070753

Authors: Yanxin Guo Zhaojun Kong

Public health emergencies create demand environments in which routine demand can be estimated from historical observations, whereas emergency demand is often characterized by limited data and expert assessments. To address this challenge, this study develops an uncertain-random newsvendor model for emergency supply chains based on chance theory. Routine demand is modeled as a random variable, while emergency demand is represented as an uncertain variable, enabling both stochastic and epistemic uncertainties to be incorporated within a unified analytical framework. The model is analyzed under decentralized and centralized decision-making modes, and closed-form optimal ordering policies are derived. The results show that the proposed framework generalizes both stochastic and uncertain newsvendor models as special cases. Residual value, shortage cost, expected emergency demand, and belief degree significantly affect inventory decisions and supply chain performance. Higher residual values and larger emergency demand expectations encourage inventory expansion, while centralized decision-making consistently generates higher order quantities and expected profits than decentralized decision-making. Moreover, the efficiency loss associated with decentralized decision-making increases with the belief degree, indicating that supply chain coordination becomes increasingly important when decision-makers place greater confidence in emergency demand forecasts. The findings highlight the importance of inventory incentives, demand forecasting, and coordinated decision-making in emergency operations. This study provides a theoretical foundation for emergency procurement and inventory planning when historical data are limited and demonstrates the value of integrating chance theory into emergency supply chain management under uncertain-random demand environments.

Systems doi: 10.3390/systems14070752

Authors: Carlos Hernandez-Santos Gloria A. Martinez-Malacara Nain de la Cruz Luis Alejandro Reynoso-Guajardo Jose Isidro Hernandez-Vega Mario Carlos Gallardo-Morales Francisco Fabian Macias-Tobias Amadeo Hernandez Roxana Garcia-Andrade

Recent global disruptions have exposed the fragility of tightly coordinated supply chains, particularly under transportation and information delays, motivating the need for analytical tools to assess their stability limits. This study analyzes a two-echelon supply chain system to determine how delays affect stability and performance, with an emphasis on the role of feedback coordination. A continuous-time delay-differential modeling framework was developed to examine both uncoupled and coupled configurations. Stability is analyzed through characteristic equations, and explicit closed-form expressions for the critical delay threshold are derived as functions of the coupling gain and shipment rate. The uncoupled system is shown to exhibit delay-independent marginal stability but lacks the ability to regulate downstream inventory. In contrast, the coupled system achieves inventory regulation but introduces delay-dependent stability with a critical delay, beyond which oscillations grow unbounded. A key result revealed an inverse relationship between coupling strength and delay tolerance, highlighting a trade-off between responsiveness and robustness. An optimal control formulation further demonstrates that the stability constraints limit the achievable performance. These findings provide a theoretical explanation for the vulnerability of just-in-time systems and offer practical guidelines for resilient logistics design, enabling supply chain practitioners to quantify stability margins and balance coordination efficiency with robustness to transportation delays.

Systems doi: 10.3390/systems14070751

Authors: Manuel Mennicken Niklas Baglikow Georg Jacobs Stefan Wischmann

Today, the development of electrified commercial vehicles requires efficient validation methods that enable early system testing while maintaining consistency between development and testing models. Hardware-in-the-Loop (HiL) testing provides a suitable approach by combining real hardware with executable real-time simulation models. However, HiL models are typically created manually for specific test cases, resulting in high modeling effort and limited reuse of development knowledge. This paper presents a methodology for the automated synthesis of executable, test-specific system-level HiL simulation models from predefined and reusable simulation components based on function-oriented SysML system architecture models. The approach combines filtering of architectural data, automated analysis of model availability and parameter completeness and configurable selection of the desired model fidelity. Reusable simulation components aligned with the system architecture are automatically selected, parametrized, connected, and aggregated into executable Simulink HiL models including test-bench interfaces. The methodology was validated for three electrified vehicle architectures and deployed on a dSPACE-based HiL test bench for drivetrain functionality and range analyses. The generated system-level HiL models were created in a few seconds while maintaining consistency with the central MBSE system architecture.

Systems doi: 10.3390/systems14070750

Authors: Xihao Lin Xu Xin

Train fires in railway bridge–tunnel connection sections (BTCSs) create severe evacuation challenges because tunnel–bridge spatial transitions interact with heat, smoke, visibility loss, and constrained rescue conditions. Existing evacuation management methods remain limited in coupling quantitative risk assessment with adaptive route guidance under evolving fire hazards. To address this issue, this paper proposes a large language model (LLM)-enhanced intelligent decision-support system (IDSS) framework for quantitative risk assessment and dynamic evacuation routing in BTCS fire scenarios. First, a multi-dimensional risk assessment model is established using the analytic hierarchy process and fuzzy comprehensive evaluation to quantify post-stop evacuation risk from the perspectives of evacuation organization, structural damage, and line recovery. Second, a dynamic topology-based routing method is developed to prune fire-threatened nodes and identify safer evacuation paths under evolving hazard conditions. The risk assessment model and routing algorithm are further embedded as callable tools into an LLM-enhanced evacuation IDSS under a perception–reasoning–recommendation architecture, in which an LLM orchestrates tool invocation, situational reasoning, and recommendation generation, thereby enabling autonomous risk interpretation, dynamic route replanning, and cross-regional collaborative decision support. The proposed framework is validated through a representative real-world railway engineering case. The results show that the IDSS-recommended routes achieved higher comprehensive safety scores (80.44 and 79.56) than routes involving fire-affected areas did (77.00 and 77.88). Workflow analysis further indicates that the proposed IDSS reduces the manual route-derivation workload by integrating risk assessment, topology pruning, and route allocation into structured, human-reviewable evacuation recommendations. Expert evaluations further confirm the rationality and compliance of the outputs, with review scores ranging from 1.76 to 1.92 out of 2.00. Overall, the proposed framework offers a feasible decision-support approach for intelligent evacuation management in complex railway fire emergencies.

Systems doi: 10.3390/systems14070748

Authors: Ming Chen Huijiao Liu Ming Jiang Shasha Guo

Promoting the coordinated development of digitalization and greening has become an important pathway for firms to achieve high-quality growth. Using panel data for A-share listed firms in China’s Yangtze River Basin from 2010 to 2022, this study examines the effect of supply chain innovation policy on firms’ digital–green development. We measure the synergy between digitalization and greening using a composite system synergy approach and identify the policy effect through a quasi-natural experiment based on the supply chain innovation policy, combined with a synthetic difference-in-differences model. The results show that the policy significantly improves the coordinated development of firm digitalization and greening, and the findings remain robust across a series of tests. Mechanism analysis indicates that this effect operates through three channels: easing financing constraints, increasing supply chain diversification, and promoting industrial chain modernization. Moderating effect tests further show that supply chain efficiency, supply chain resilience, and entrepreneurship strengthen the policy’s positive effect on digital–green development. Heterogeneity analysis suggests that the policy effect varies systematically with firm size, market competitiveness, and information asymmetry. This study provides micro-level evidence on how supply chain innovation policy can promote firms’ digital–green transformation and offers useful implications for policies aimed at improving firm competitiveness and supporting sustainable development.