Systems

25 pages, 816 KB

Open AccessArticle

Food Supply Chain Resilience in the Digital Era: The Roles of Supply Chain Security Strategy, Organizational Digital Adaptability, and Industry 4.0 Implementation

by Sabah Abdullah Al-Somali

Systems 2026, 14(3), 303; https://doi.org/10.3390/systems14030303 - 13 Mar 2026

Abstract

In response to escalating global volatility, organizations are prioritizing the adoption of Digital Industry 4.0 Technologies (DI4Ts) to improve efficiency and enhance decision-making capabilities. Refining Organizational Adaptation Theory (OAT), this research examines the factors influencing DI4Ts implementation success and their impact on organizational [...] Read more.

In response to escalating global volatility, organizations are prioritizing the adoption of Digital Industry 4.0 Technologies (DI4Ts) to improve efficiency and enhance decision-making capabilities. Refining Organizational Adaptation Theory (OAT), this research examines the factors influencing DI4Ts implementation success and their impact on organizational performance and resilience within Saudi Arabia’s food logistics firms. Using data from 191 managerial respondents processed through Partial Least Squares Structural Equation Modeling, the findings indicate that dynamic digital capability and effective digital leadership significantly promote the implementation of DI4Ts. Additionally, supply chain security and anti-counterfeiting strategies were shown to exert a stronger influence on operational outcomes and organizational resilience than technology adoption alone. In an institutional environment characterized by a high food import reliance and strict traceability mandates, government policies moderate the relationship between organizational digital adaptability and implementation success. Our research outcomes establish a comprehensive framework for the incorporation of DI4Ts where supply chain security functions as a foundational capability predicating digital effectiveness. Full article

(This article belongs to the Special Issue Emerging Technologies in Supply Chain Management: Enhancing Traceability, Efficiency, Resilience, and Sustainability)

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26 pages, 3163 KB

Open AccessArticle

Identification of Physical Boundary Conditions for Mechatronic Test-Case Generation Using Large Language Models and MBSE System Models

by Matthias May, Georg Jacobs, Simon Dehn, Gregor Höpfner, Thilo Zerwas, Kathrin Boelsen and Sebastian Hacker

Systems 2026, 14(3), 302; https://doi.org/10.3390/systems14030302 - 12 Mar 2026

Abstract

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With [...] Read more.

Future cyber-physical systems (CPSs), integrating subsystems of the mechanical, electrical and software domains, are becoming increasingly interconnected and complex. As complexity grows, testing effort increases as well. This includes the test-case definition step, where the test targets and boundary conditions are specified. With rising system complexity, the effort required to ensure that all relevant conditions for each test target are identified increases. Manual test-case definition remains the norm, creating effort bottlenecks in ensuring systematic coverage and compliance with standards such as ISO 26262 and ISO 29119. This paper explores how large language models (LLMs) can support the identification of complex boundary conditions for CPS test cases through detailed requirement analysis. The impact of performing taxonomy-guided, structured requirement mapping prior to test-case generation was evaluated by comparing it with a version without this guidance. Furthermore, the influence of supplying a Model-Based Systems Engineering (MBSE) system model as context information via Graph RAG is examined. The results show that structured, stepwise reasoning significantly improves reliability and consistency over unguided generation, while system-model information provides valuable contextual insight but has a minor impact in the chosen example. These findings outline a scalable framework for AI-assisted test-case generation. Full article

(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)

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26 pages, 3351 KB

Open AccessArticle

Urban Traffic System Resilience Enhancement Under Rainfall Disturbances Based on Distributed Coordinated Perimeter Control

by Chao Sun, Xinyi Qi, Xiaona Zhang, Huixian Chen, Peng Zhang and Jia Liang

Systems 2026, 14(3), 301; https://doi.org/10.3390/systems14030301 - 12 Mar 2026

Abstract

Urban traffic networks are highly vulnerable to external disturbances such as heavy rainfall, which can induce capacity degradation, non-periodic congestion, and delayed system recovery. To address the limitations of existing perimeter control strategies that primarily focus on demand-side fluctuations and assume fixed network [...] Read more.

Urban traffic networks are highly vulnerable to external disturbances such as heavy rainfall, which can induce capacity degradation, non-periodic congestion, and delayed system recovery. To address the limitations of existing perimeter control strategies that primarily focus on demand-side fluctuations and assume fixed network capacity, this study proposes a distributed coordinated perimeter control framework that explicitly incorporates rainfall-induced capacity degradation into system feedback. The proposed framework adopts a two-layer control structure, in which a main controller regulates global network accumulation near the critical macroscopic fundamental diagram (MFD) state, while sub-controllers dynamically adjust perimeter control rates in response to localized traffic conditions and water accumulation. A case study based on real taxi trajectory data from Wuhan City, combined with SUMO-based microscopic traffic simulation, is conducted to evaluate the proposed approach under heavy rainfall conditions. The results show that the distributed coordinated control framework reduces peak network accumulation by 39.6%, increases average vehicle speed by 35.28%, and significantly accelerates post-disturbance recovery. These findings indicate that integrating environmental disturbances into distributed perimeter control can effectively enhance the stability and resilience of urban traffic systems under adverse weather conditions. Full article

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22 pages, 583 KB

Open AccessArticle

Seeing the Unseen: AI Assimilation and Supply–Demand Visibility for Effective Risk Management in Manufacturing Supply Chains

by Jiangmin Ding, Zhaoqi Li and Eon-Seong Lee

Systems 2026, 14(3), 300; https://doi.org/10.3390/systems14030300 - 12 Mar 2026

Abstract

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic [...] Read more.

Artificial intelligence (AI) has become a strategic resource for enhancing supply chain resilience in environments characterized by growing uncertainty and complexity. Building on the resource-based view (RBV) and organizational information processing theory (OIPT), this study examines how AI assimilation as a firm-level strategic capability improves supply–demand visibility and strengthens supply chain risk management (SCRM). Using survey data collected from 129 manufacturing firms in China, the proposed research framework is tested through structural equation modeling. The results show that AI assimilation significantly enhances both supply–demand visibility and SCRM, with visibility playing a partial mediating role in translating AI-enabled capabilities into more effective risk control. These findings indicate that AI contributes to resilience not merely through technological deployment but through its integration into organizational processes that support information processing and coordination. From a managerial perspective, the study suggests that firms should approach AI as an ongoing strategic capability development process rather than a one-time technological investment. By embedding AI into core supply chain functions such as production planning, inventory management, and demand forecasting, firms can improve visibility, anticipate disruptions, and shift toward more proactive and resilient risk management practices. This study advances the literature by integrating RBV and OIPT to explain the strategic mechanisms through which AI assimilation enhances visibility in SCRM, providing empirical evidence from a manufacturing context. Full article

(This article belongs to the Special Issue Artificial Intelligence and Big Data Strategies for Sustainable and Resilient Supply Chain Management)

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18 pages, 816 KB

Open AccessArticle

Can Artificial Intelligence Enhance the Stability of Supply Chain Systems for Sports Enterprises? Insights from Systems Theory and Supply Chain Management Theory

by Zhaoyang Zhao, Biao Wang, Xuan He and Jing Huang

Systems 2026, 14(3), 299; https://doi.org/10.3390/systems14030299 - 12 Mar 2026

Abstract

In the digital economy, the effective use of artificial intelligence (AI) is crucial for maintaining supply chain stability (SCS) in sports enterprises (SEs). Leveraging systems theory and supply chain management theory, we construct a dual machine learning model (DML) to empirically assess the [...] Read more.

In the digital economy, the effective use of artificial intelligence (AI) is crucial for maintaining supply chain stability (SCS) in sports enterprises (SEs). Leveraging systems theory and supply chain management theory, we construct a dual machine learning model (DML) to empirically assess the impact of AI on the SCS of SE. This analysis is based on panel data from 45 Chinese listed SEs over the period 2012–2023. The results indicate that AI significantly enhances supplier stability but notably reduces customer stability in SE. Talent attraction emerges as the primary mechanism, while logistics efficiency fails to fulfill its anticipated role. The impact of AI on SCS in SE exhibits heterogeneity based on enterprise type and profitability status. Our findings offer valuable insights for harnessing the potential of AI and fostering its deeper integration into the supply chains of SE. Full article

(This article belongs to the Topic Digital Technologies in Supply Chain Risk Management)

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25 pages, 701 KB

Open AccessArticle

Spatial Interplay Between Digital–Real Integration and New Quality Productive Forces: Evidence from China

by Xiao Li and Xiaoxuan Liu

Systems 2026, 14(3), 298; https://doi.org/10.3390/systems14030298 - 12 Mar 2026

Abstract

The deep integration of the digital and real economies is a critical force reshaping the global economic landscape. At the same time, new high-quality productive forces that are distinguished by their high level of efficiency, quality, and technology mark a new phase in [...] Read more.

The deep integration of the digital and real economies is a critical force reshaping the global economic landscape. At the same time, new high-quality productive forces that are distinguished by their high level of efficiency, quality, and technology mark a new phase in productivity evolution. Understanding the spatial interplay between these two phenomena is crucial for coordinated development. This study empirically investigates their bidirectional relationship and spatial spillover effects. With panel data from 30 provinces in China (2011–2022), we use the Generalized Spatial Three-Stage Least Squares (GS3SLS) approach to estimate a spatial simultaneous equations model. The results reveal a significant bidirectional positive correlation, with the promotional effect of digital–real integration on new quality productive forces being slightly stronger. However, we also identify significantly negative spatial spillover effects between them. These findings underscore the necessity of strengthening their interactive development, fostering interregional cooperation, and optimizing source allocation to alleviate adverse spillovers. This study systematically examines their spatial dynamics and proposes policy recommendations to foster the coordinated advancement of both digital–real integration and New Quality Productive Forces. Full article

(This article belongs to the Special Issue Sustainable Business Models and Digital Transformation)

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10 pages, 730 KB

Open AccessCommentary

The Need for Digital Architecture in Operationalizing Digital Engineering Strategies for Smart Production Systems

by Paul Grefen and Anna Wilbik

Systems 2026, 14(3), 297; https://doi.org/10.3390/systems14030297 - 11 Mar 2026

Abstract

The global market for production organizations is becoming increasingly dynamic and complex. To address this development, production organizations develop strategies to become smarter in their operations, aiming at more flexibility and just-in-time mechanisms. These strategies imply advanced levels of digitization of the tactical [...] Read more.

The global market for production organizations is becoming increasingly dynamic and complex. To address this development, production organizations develop strategies to become smarter in their operations, aiming at more flexibility and just-in-time mechanisms. These strategies imply advanced levels of digitization of the tactical decision making for and operational control of the primary production processes, both within individual organizations and at the level of supply networks. To embody this digitization, a spectrum of advanced digital technologies is available, such as artificial intelligence, blockchain, end-to-end process control, internet-of-things, and augmented reality. The realization of the strategies through digitization with these technologies is, however, severely hindered by two typical problems. Firstly, a large gap often exists between the high-level, abstract intentions formulated in strategies and the detailed, concrete embodiment in digital technology. Secondly, classes of digital technologies often are considered in isolation in projects resulting from a digital strategy, making synergies between technologies almost impossible and thus heavily reducing the added value of digitization efforts. This short commentary paper argues that the effective use of digital architecture in smart production system design is the path to effectively addressing these two problems. The observations and suggested architecture approach are illustrated by experiences with large international research and development projects in the smart production domain. Full article

(This article belongs to the Special Issue Digital Engineering Strategies of Smart Production Systems)

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26 pages, 636 KB

Open AccessArticle

How Platform Participants Drive Digital Innovation? A Configuration Analysis Based on the TOE Framework

by Jun Liu, Kang Ren, Jing Lv and Jing Yang

Systems 2026, 14(3), 296; https://doi.org/10.3390/systems14030296 - 11 Mar 2026

Abstract

As industrial internet platforms increasingly play a central role in the digital transformation of manufacturing, they have become crucial areas for manufacturing enterprises to pursue digital innovation. Current academic research has paid relatively little attention to the digital innovation of participating enterprises within [...] Read more.

As industrial internet platforms increasingly play a central role in the digital transformation of manufacturing, they have become crucial areas for manufacturing enterprises to pursue digital innovation. Current academic research has paid relatively little attention to the digital innovation of participating enterprises within industrial internet platforms, failing to fully reveal the driving mechanisms of such innovation in this context. Based on the TOE framework and adopting a platform participant perspective, this study employs fuzzy set qualitative comparative analysis (fsQCA). By surveying 169 manufacturing enterprises participating in industrial internet platforms, it integrates seven key antecedents—technology availability, technology fit, digital leadership, organizational structural flexibility, resource orchestration, policy support, and competitive pressure—to systematically explore the complex influence pathways of multi-factor concurrent interactions on digital innovation. The research results show that the high digital innovation of manufacturing enterprises on the industrial internet platform includes precise implementation type, exploration-oriented type and co-evolution type, while the non-high digital innovation paths include technology blocking type, dual-core absence type and system disorder type. These conclusions expand the theoretical framework for digital innovation in manufacturing enterprises within industrial internet platforms and offer practical recommendations for their digital innovation practices. Full article

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40 pages, 4057 KB

Open AccessArticle

A Sustainable Workforce Scheduling System for County-Level Logistics Centers Under Uncertain Demand: Integrating Human-Centered Objectives and Change Management Perspectives

by Yixuan Wu, Yuhan Gong, Zhenheng Hu, Yiwen Gao and Junchi Ma

Systems 2026, 14(3), 295; https://doi.org/10.3390/systems14030295 - 10 Mar 2026

Abstract

For logistics facilities at the county level, workforce scheduling is a basic operational concern. Although these facilities are developing rapidly, they still mostly rely on human and semi-automated work. Significant differences in employee productivity and skill levels, along with regular changes in demand, [...] Read more.

For logistics facilities at the county level, workforce scheduling is a basic operational concern. Although these facilities are developing rapidly, they still mostly rely on human and semi-automated work. Significant differences in employee productivity and skill levels, along with regular changes in demand, exacerbate this challenge. This study proposes a sustainability-oriented dual-objective optimization model to coordinate operational cost control with employee well-being enhancement. To address this issue, we designed an improved Genetic Algorithm that combines heuristic initialization with specialized repair operators, forming a systematic optimization framework. The effectiveness of the proposed system design and algorithm has been validated through real-world case studies. Experimental results demonstrate that this model not only achieves a balance between cost and employee satisfaction under uncertain demand conditions but also provides county-level logistics centers with sustainable scheduling solutions adaptable to business changes. Management recommendations based on the experimental results are proposed, such as implementing differentiated scheduling strategies, easing restrictions on maximum working hour variations, establishing a progressive optimization mechanism, and optimizing staffing and employee structure in accordance with corporate characteristics. This study provides scientific decision support for county-level logistics systems to achieve sustainable operations and human resource management transformation. Full article

(This article belongs to the Topic Sustainability Efforts and Importance of Change Management, 2nd Edition)

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31 pages, 1969 KB

Open AccessArticle

MORL-SGF: A Governance-Aware Multi-Objective Reinforcement Learning Framework with Digital Twin Policy Validation for Sustainable Smart Cities

by Saad Alharbi

Systems 2026, 14(3), 294; https://doi.org/10.3390/systems14030294 - 10 Mar 2026

Abstract

Smart city decision systems must balance conflicting objectives including efficiency, sustainability, equity, safety, and public accountability. Existing AI and reinforcement learning approaches often optimize isolated objectives and rarely provide integrated mechanisms for sustainability alignment, transparency, and pre-deployment validation. This paper introduces MORL-SGF, a [...] Read more.

Smart city decision systems must balance conflicting objectives including efficiency, sustainability, equity, safety, and public accountability. Existing AI and reinforcement learning approaches often optimize isolated objectives and rarely provide integrated mechanisms for sustainability alignment, transparency, and pre-deployment validation. This paper introduces MORL-SGF, a governance-aware framework that integrates ESG/SDG-aligned multi-objective reinforcement learning, Digital Twin (DT)-based policy validation, and Pareto-based policy auditing within a single learning pipeline. The framework preserves vector-valued rewards to avoid hidden scalarization bias and supports auditable policy selection from a portfolio of Pareto-optimal candidates. MORL-SGF is validated analytically and conceptually through formal modeling and structured evidence synthesis rather than empirical deployment, providing a blueprint for subsequent simulation-based and real-world implementation studies. Future work will focus on large-scale Digital Twin benchmarking, stakeholder preference modeling, and deployment-oriented evaluation. Full article

(This article belongs to the Special Issue Artificial Intelligence and Data-Driven Analytics in Smart City Systems)

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27 pages, 2463 KB

Open AccessArticle

County-Level Spatial Mismatch and Spatio-Temporal Dynamics of Population and Economic Distribution in the Yellow River Basin

by Wenxin Yu and Yu Gao

Systems 2026, 14(3), 293; https://doi.org/10.3390/systems14030293 - 10 Mar 2026

Abstract

Understanding the spatial mismatch between population distribution and economic activities is central to regional development, particularly in large river basins experiencing rapid demographic change. Existing studies often rely on slow-moving structural variables and give limited attention to dynamic population processes. This study examines [...] Read more.

Understanding the spatial mismatch between population distribution and economic activities is central to regional development, particularly in large river basins experiencing rapid demographic change. Existing studies often rely on slow-moving structural variables and give limited attention to dynamic population processes. This study examines the spatiotemporal patterns, demographic mechanisms, and regional heterogeneity of population–economy mismatch in the Yellow River Basin from 2000 to 2020. We hypothesize that population–economy mismatch exhibits pronounced spatial heterogeneity across the upper, middle, and lower reaches of the basin, and that demographic structure and migration dynamics play a decisive role in shaping these patterns. Using county-level data, we construct a Population–Economy Distribution Disparity Ratio R, apply decomposable Theil indices, and estimate two-way fixed-effects panel models incorporating demographic and migration indicators. Spatial econometric models are further employed as robustness checks. The results show that intra-regional disparities account for more than 97% of total population–economy mismatch, while inter-regional differences remain limited. Population migration intensity and age structure significantly influence mismatch dynamics, with effects varying systematically along the basin gradient. These findings underscore the importance of integrating dynamic demographic processes into spatial mismatch analysis and support regionally differentiated and systemically coordinated policy interventions for high-quality development in the Yellow River Basin. Full article

(This article belongs to the Section Systems Practice in Social Science)

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23 pages, 2631 KB

Open AccessArticle

A Novel Portfolio Selection Method via Deep Reinforcement Learning

by Ni Gao, Yan Liu, Yiyue He, Juan Zhang and Lefang Zhang

Systems 2026, 14(3), 292; https://doi.org/10.3390/systems14030292 - 9 Mar 2026

Abstract

Portfolio selection is a fundamental task in quantitative finance that aims to allocate capital across assets to balance risk and return. While deep learning has shown great promise in this field, extracting reliable feature representations from non-stationary and noisy financial data remains a [...] Read more.

Portfolio selection is a fundamental task in quantitative finance that aims to allocate capital across assets to balance risk and return. While deep learning has shown great promise in this field, extracting reliable feature representations from non-stationary and noisy financial data remains a significant challenge. The existing models often fail to simultaneously capture the temporal dynamics of price series and complex inter-asset correlations, which limits their trading performance. To address these issues, we propose Denoising-Sequence-Correlation Reinforcement Learning (DSCRL), a novel portfolio selection framework based on deep reinforcement learning. DSCRL employs a dual-stream feature extraction network, where one stream aims to learn temporal market dynamics and the other aims to capture asset correlations, enabling more informative representations. A denoising module is further integrated to mitigate the impact of noise, ensuring stability and robustness in the learning process. Furthermore, a deterministic policy gradient (DPG)-based decision network is designed to directly optimize continuous portfolio weights and normalize them to satisfy budget constraints while preserving the importance. Extensive experiments conducted on multiple benchmark datasets demonstrate that DSCRL consistently outperforms both traditional financial heuristics and advanced deep reinforcement approaches. The results highlight its superior ability to achieve higher cumulative returns with lower volatility. Overall, DSCRL provides an effective and robust solution that strikes a better trade-off between pursuing profits and managing risks in dynamic financial markets. Full article

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22 pages, 2816 KB

Open AccessArticle

Digital Economy, Green Technology Innovation and Urban Carbon Emissions: Evidence from Chinese Cities

by Ran Wu, Shimao Su, Jiyun Hou and Xiaolei Wang

Systems 2026, 14(3), 291; https://doi.org/10.3390/systems14030291 - 9 Mar 2026

Abstract

Based on 2011–2022 panel data covering 278 Chinese cities, a panel fixed-effects model, a mediating effect model, and a threshold regression model are used to conduct an empirical analysis of the influence of the digital economy (DE) on urban carbon emission performance from [...] Read more.

Based on 2011–2022 panel data covering 278 Chinese cities, a panel fixed-effects model, a mediating effect model, and a threshold regression model are used to conduct an empirical analysis of the influence of the digital economy (DE) on urban carbon emission performance from the quantitative and efficiency perspectives. The key findings include the following: (1) An inverted U-relationship is observed between the DE development and urban per capita carbon emissions (PCE), while the nexus between the DE and carbon emission efficiency (CEE) follows a U-shaped pattern. (2) The DE yields a stronger carbon reduction effect once green technology innovation attains elevated levels; conversely, under conditions of nascent green innovation, its principal impact manifests through improvements in CEE. Only when green technology innovation surpasses a critical threshold does the DE begin to reduce carbon emissions. (3) Heterogeneity analysis indicates that, in optimization and upgrading agglomerations, carbon emissions are reduced by DE at a later time point. In growth and expansion agglomerations, the impact of DE on CEE is more evident. Moreover, policy priorities should include fostering innovation-driven digitalization, expanding green technology diffusion, and optimizing regional mechanisms for coordinated low-carbon growth. Full article

(This article belongs to the Topic Sustainable Development and Coordinated Governance of Urban and Rural Areas Under the Guidance of Ecological Wisdom—2nd Edition)

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30 pages, 2902 KB

Open AccessArticle

Self-Organizing Skill Networks in Emerging Work Systems: Evidence from the Platform-Mediated Digital Nomad Economy

by Tianhe Jiang

Systems 2026, 14(3), 290; https://doi.org/10.3390/systems14030290 - 9 Mar 2026

Abstract

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent [...] Read more.

The digital nomad economy—the ecosystem in which professional skills are traded through online platforms independent of geographic co-location—dynamically recombines skills into project-based portfolios with absent firm-level hierarchy. Yet it remains shaped by platform taxonomies, interfaces, and ranking/recommendation incentives. This study examines the emergent structure within this setting using the Semantic-Structural Systems Analysis (S²SA) framework, which integrates LLM-assisted skill extraction, transformer-based semantic embeddings, and multi-layer network analysis. We analyze a dual-source dataset comprising approximately 50,000 public Upwork profiles from a top-rated/high-earning segment (January–March 2023) and 2.0 million Reddit posts and comments (2018–2023) from remote-work and digital-nomad communities. The resulting skill network exhibits a pronounced core–periphery organization and modular “skill ecotopes” corresponding to coherent functional specializations. In predictive models of skill-level effective hourly rates, semantic brokerage and semantic diversity function as robust predictors of higher rates, significantly outperforming popularity-only baselines. Longitudinal discourse analyses surrounding the COVID-19 pandemic and the generative AI shock reveal rapid attentional shifts followed by the emergence and recombination of new skill clusters. We interpret these results as evidence consistent with constrained self-organization in platform-mediated labor markets. To support replication, prompts, parameters, and robustness checks are fully reported. Full article

(This article belongs to the Special Issue Digital Transformation of Business Ecosystems)

45 pages, 2031 KB

Open AccessArticle

Green Transition Decisions of Manufacturing Enterprises: A Systemic–Synergistic Perspective on Decentralized Governance and Green Credit

by Yuyuan Song, Hengjun Huang and Xuewei Gan

Systems 2026, 14(3), 289; https://doi.org/10.3390/systems14030289 - 9 Mar 2026

Abstract

Global, industrialization-driven environmental bottlenecks push manufacturing enterprises toward green transitions; yet, the information asymmetry between central and local governments, and between enterprises and banks, hinders this process. Adopting a systemic–synergistic perspective integrating decentralized governance and green credit, in this study, we investigate the [...] Read more.

Global, industrialization-driven environmental bottlenecks push manufacturing enterprises toward green transitions; yet, the information asymmetry between central and local governments, and between enterprises and banks, hinders this process. Adopting a systemic–synergistic perspective integrating decentralized governance and green credit, in this study, we investigate the green transition decisions of manufacturing enterprises. We construct a quadrilateral evolutionary game model involving the central government, local governments, enterprises, and banks, employing MATLAB R2022b to simulate the effects of the key parameters. Subject to the model’s structural assumptions and parameter boundaries, three core findings emerge: first, we find that punitive environmental policies outperform incentive-based instruments in driving enterprise emission reduction; second, we find that the adaptive adjustments made by decentralized governance can effectively facilitate green practices among enterprises; third, within this framework, we find that green credit exerts a non-monotonic impact on enterprises’ green transition behaviors; meanwhile banks’ assessments of enterprises’ environmental risks can indirectly promote enterprise abatement by motivating local governments through signal transmission. This study underscores the systemic synergy of decentralized governance and green credit, offering insights for multistakeholder coordination and policy optimization to advance organizational sustainability transitions for the green economy. Full article

(This article belongs to the Special Issue Sustainable Strategies and Organizational Transitions for Green Economy)

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36 pages, 762 KB

Open AccessArticle

Communication and Information Systems User Support as a Governance Mechanism in a High-Security Public Organization

by Mihael Plevnik

Systems 2026, 14(3), 288; https://doi.org/10.3390/systems14030288 - 9 Mar 2026

Abstract

Reliable internal communication and information systems (CISs) constitute a foundational yet often under-examined layer of contemporary digital government and organizational resilience. While existing research has predominantly focused on citizen-facing services and external platforms, comparatively limited attention has been devoted to how internal CIS [...] Read more.

Reliable internal communication and information systems (CISs) constitute a foundational yet often under-examined layer of contemporary digital government and organizational resilience. While existing research has predominantly focused on citizen-facing services and external platforms, comparatively limited attention has been devoted to how internal CIS user support structures function as governance mechanisms that sustain institutional continuity, authority, and operational control. This article reconceptualises CIS user support as a governance mechanism and empirically examines its implementation within a high-security public-sector organization, the Slovenian Armed Forces. The study integrates perspectives from information systems governance and IT service management with an in-depth case study based on legal and organizational framework analysis, structured user interviews (n = 24), and longitudinal operational data on CIS incidents and service performance (2022–2024). The findings demonstrate that a multi-tier CIS user support architecture, informed by IT service management principles and integrated with cybersecurity and cryptographic control functions, contributes to institutional reliability, process accountability, and operational resilience. Building on these results, the article develops a five-dimensional governance model of CIS user support encompassing organizational authority, process formalization, security integration, communication legitimacy, and data-driven oversight. By conceptualizing CIS user support as an embedded governance structure rather than merely an operational IT function, the study extends information systems governance and digital government scholarship and provides analytically transferable insights for high-security public organizations operating under conditions of elevated risk and institutional constraint. Full article

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25 pages, 1804 KB

Open AccessArticle

Data Asset Quality Evaluation Model Considering the Requirements of Circulation Scenarios

by Tao Xu, Lu Jiang, Jianxin You and Hengjia Zhang

Systems 2026, 14(3), 287; https://doi.org/10.3390/systems14030287 - 9 Mar 2026

Abstract

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation [...] Read more.

High-quality datasets are increasingly recognized as foundational inputs to economic development, industrial upgrading, and public governance. A rigorous evaluation system for data asset quality is therefore needed to improve data governance and to enable value realization in circulation. Focusing on three representative circulation scenarios—data interaction, data exchange, and data trading—this study develops an indicator system from technical, business, and benefit-oriented dimensions. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) method is used to identify causal relationships among indicators and key drivers. To integrate multi-expert judgments under uncertainty, hesitant linguistic variables and evidence theory are adopted, and the Best–Worst Method (BWM) is applied to derive more consistent indicator weights. The resulting weights are combined with the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) to obtain a comprehensive ranking of data asset quality with scenario-adjustable emphasis. A traffic-flow dataset from a data technology enterprise is used to demonstrate applicability and effectiveness. The proposed framework advances scenario-adaptive data quality evaluation and supports enterprise data governance, data transaction pricing, and the implementation of high-quality dataset policies. Full article

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28 pages, 2250 KB

Open AccessArticle

Occupational Gender Bias in Chinese Generative AI Models: Cross-Model Evidence of Stereotypical Amplification and Systematic Underrepresentation

by Yunhong Liu, Aijun Lin, Sui Peng and Zelong Cai

Systems 2026, 14(3), 286; https://doi.org/10.3390/systems14030286 - 9 Mar 2026

Abstract

Occupational gender stereotypes are widely embedded in social cognition and increasingly reproduced through generative artificial intelligence (AI). Two mainstream Chinese generative AI models (DeepSeek V3 and Qwen 2.5) were audited by eliciting occupation–gender pronoun associations for 72 census-anchored occupations using a standardized questionnaire [...] Read more.

Occupational gender stereotypes are widely embedded in social cognition and increasingly reproduced through generative artificial intelligence (AI). Two mainstream Chinese generative AI models (DeepSeek V3 and Qwen 2.5) were audited by eliciting occupation–gender pronoun associations for 72 census-anchored occupations using a standardized questionnaire and an automated testing pipeline. Each occupation was queried in 1000 independent rounds, yielding 2,880,000 item-level observations. The results show that, for both models, the fitted relationship between census female shares and model-implied female pronoun associations follows an S-shaped pattern. This pattern is consistent with a dominance-amplifying mapping that pushes male-dominated occupations toward lower female attribution and female-dominated occupations toward higher female attribution. Meanwhile, women’s overall visibility is consistently shifted downward: when the census benchmark is 50% female, the predicted female proportion remains below parity at 48% in DeepSeek and 43% in Qwen. Cross-model comparisons reveal substantial heterogeneity in bias profiles: DeepSeek primarily compresses female attribution in male-dominated occupations, whereas Qwen amplifies female dominance in occupations where women already predominate. Overall, these findings characterize a multi-layered output-level bias pattern combining structural amplification with a system-wide downward shift in women’s aggregate visibility. Full article

(This article belongs to the Special Issue Ethics and Governance of Artificial Intelligence (AI) Systems)

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18 pages, 363 KB

Open AccessArticle

Enterprise Digital Transformation, Green Innovation, and Carbon Emissions: Evidence from China’s A-Share Listed Companies

by Xuan Yu, Yinglong Wu and Qi Chen

Systems 2026, 14(3), 285; https://doi.org/10.3390/systems14030285 - 8 Mar 2026

Abstract

Drawing on the ecological economic system theory, this paper constructs a theoretical model to analyze the impact of enterprise digital transformation on carbon emissions and the critical mechanism of green innovation therein. Empirical evidence based on data from China’s A-share listed companies from [...] Read more.

Drawing on the ecological economic system theory, this paper constructs a theoretical model to analyze the impact of enterprise digital transformation on carbon emissions and the critical mechanism of green innovation therein. Empirical evidence based on data from China’s A-share listed companies from 2007 to 2024 indicates the following: First, enterprise digital transformation significantly reduces corporate carbon emissions. This conclusion remains robust after a series of robustness checks and endogeneity treatments. Second, digital transformation promotes carbon reduction primarily by increasing the quantity of green innovation, whereas the mediating role of green innovation quality has not yet manifested. Third, heterogeneity analysis confirms our theoretical deductions, revealing that this carbon reduction effect is significantly stronger in regions with high environmental regulation intensity and is predominantly manifested in highly polluting industries. Fourth, descriptive spatial analysis indicates that digital transformation is associated with reduced carbon emissions in surrounding areas, exhibiting broader regional environmental correlations as the geographic range expands. Finally, the implementation of policy instruments, represented by carbon trading and low-carbon city construction, reinforces the carbon reduction effect of digital transformation. By integrating internal technological processes, contextual heterogeneities, descriptive spatial observations, and macro-policy environments, this paper provides holistic insights into the synergistic evolution of enterprise digitalization and green transition. Full article

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