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Industry-Driven Model-Based Systems Engineering (MBSE) Workforce Competencies—An AI-Based Competency Extraction Framework
Exploring Different Patterns of Perceived Barriers to Sustainable Project Management
The Viable System Model and the Taxonomy of Organizational Pathologies in the Age of Artificial Intelligence (AI)
Introducing the Manufacturing Digital Passport (MDP): A New Concept for Realising Digital Thread Data Sharing in Aerospace and Complex Manufacturing
Degenerative ‘Affordance’ of Social Media in Family Business

Journal Description

Systems

Systems is an international, peer-reviewed, open access journal on systems theory in practice, including fields such as systems engineering management, systems based project planning in urban settings, health systems, environmental management and complex social systems, published monthly online by MDPI. The International Society for the Systems Sciences (ISSS) is affiliated with Systems and its members receive a discount on the article processing charges.

Open Access— free for readers, with article processing charges (APC) paid by authors or their institutions.
High Visibility: indexed within Scopus, SSCI (Web of Science), Ei Compendex, dblp, and other databases.
Journal Rank: JCR - Q1 (Social Sciences, Interdisciplinary) / CiteScore - Q2 (Modeling and Simulation)
Rapid Publication: manuscripts are peer-reviewed and a first decision is provided to authors approximately 18.8 days after submission; acceptance to publication is undertaken in 2.8 days (median values for papers published in this journal in the first half of 2025).
Recognition of Reviewers: reviewers who provide timely, thorough peer-review reports receive vouchers entitling them to a discount on the APC of their next publication in any MDPI journal, in appreciation of the work done.

Impact Factor: 3.1 (2024); 5-Year Impact Factor: 3.1 (2024)

Imprint Information Journal Flyer Open Access ISSN: 2079-8954

Latest Articles

26 pages, 4074 KB

Open AccessArticle

An Integrated Optimization for Resilient Wildfire Evacuation System Design: A Case Study of a Rural County in Korea

by Kyubin Kwon, Yejin Kim and Jinil Han

Systems 2025, 13(12), 1125; https://doi.org/10.3390/systems13121125 - 16 Dec 2025

Wildfires increasingly threaten the operation and stability of regional socio-economic systems, where infrastructure, population, and environmental conditions are tightly interconnected. To enhance operational efficiency and strengthen community resilience, this study develops an integrated optimization framework for wildfire evacuation system design based on mixed-integer [...] Read more.

Wildfires increasingly threaten the operation and stability of regional socio-economic systems, where infrastructure, population, and environmental conditions are tightly interconnected. To enhance operational efficiency and strengthen community resilience, this study develops an integrated optimization framework for wildfire evacuation system design based on mixed-integer programming. The model simultaneously determines the locations of primary and secondary shelters and establishes both main and backup evacuation linkages, forming a dual-stage structure that ensures continuous accessibility even under disrupted conditions such as road blockages or fire spread. Wildfire risk indices derived from topographic and environmental data are incorporated to support risk-aware and balanced shelter allocation. A case study of Uiryeong County, South Korea, demonstrates that the proposed framework effectively improves evacuation efficiency and system reliability, producing spatially coherent and adaptive evacuation plans under diverse disruption scenarios. The findings highlight how operation optimization can enhance socio-economic system resilience and sustainability when facing large-scale environmental disruptions. Full article

(This article belongs to the Special Issue Operation Optimization and Performance Assessment of Complex Social-Economic Systems)

37 pages, 3318 KB

Open AccessArticle

MIRA: An LLM-Driven Dual-Loop Architecture for Metacognitive Reward Design

by Weiying Zhang, Yuhua Xu and Zhixin Sun

Systems 2025, 13(12), 1124; https://doi.org/10.3390/systems13121124 - 16 Dec 2025

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic [...] Read more.

A central obstacle to the practical deployment of Reinforcement Learning (RL) is the prevalence of sparse rewards, which often necessitates task-specific dense signals crafted through costly trial-and-error. Automated reward decomposition and return–redistribution methods can reduce this burden, but they are largely semantically agnostic and may fail to capture the multifaceted nature of task performance, leading to reward hacking or stalled exploration. Recent work uses Large Language Models (LLMs) to generate reward functions from high-level task descriptions, but these specifications are typically static and may encode biases or inaccuracies from the pretrained model, resulting in a priori reward misspecification. To address this, we propose the Metacognitive Introspective Reward Architecture (MIRA), a closed-loop architecture that treats LLM-generated reward code as a dynamic object refined through empirical feedback. An LLM first produces a set of computable reward factors. A dual-loop design then decouples policy learning from reward revision: an inner loop jointly trains the agent’s policy and a reward-synthesis network to align with sparse ground-truth outcomes, while an outer loop monitors learning dynamics via diagnostic metrics and, upon detecting pathological signatures, invokes the LLM to perform targeted structural edits. Experiments on MuJoCo benchmarks show that MIRA corrects flawed initial specifications and improves asymptotic performance and sample efficiency over strong reward-design baselines. Full article

(This article belongs to the Topic Agents and Multi-Agent Systems)

25 pages, 16135 KB

Open AccessArticle

Environmental Perception Method for Unmanned Surface Vehicles Based on Sea–Sky Line Detection

by Qingze Yu, Ronghua Huang and Guangnian Li

Systems 2025, 13(12), 1123; https://doi.org/10.3390/systems13121123 - 15 Dec 2025

This paper is dedicated to solving the environmental perception system problem of unmanned surface vehicles (USVs) experiencing adverse sea conditions and complex mission scenarios. First, the functionalities and characteristics of each subsystem in the USV environmental perception system under different mission scenarios are [...] Read more.

This paper is dedicated to solving the environmental perception system problem of unmanned surface vehicles (USVs) experiencing adverse sea conditions and complex mission scenarios. First, the functionalities and characteristics of each subsystem in the USV environmental perception system under different mission scenarios are analyzed, and an efficient and stable environmental perception system is designed. Second, the static and dynamic characteristics of the sea–sky line are investigated, along with the impacts on each subsystem of the environmental perception system when the USV experiences six-degree-of-freedom motion on the sea surface. Based on the above analysis, a sea–sky line detection method based on the radar–electro-optical system is designed. This method utilizes the features of the radar and electro-optical subsystems to redefine the region of interest, effectively suppressing interference from non-sea–sky line edges, thereby improving detection efficiency and accuracy. Furthermore, a sea–sky line-based target detection algorithm is proposed, which confines the search area to the vicinity of the detected sea–sky line, significantly reducing false detections caused by sea clutter and noise. Sea trials demonstrate that the proposed method enhances the accuracy and real-time performance of USV environmental perception. The proposed systematic approach offers a practical solution for improving the robustness of USV environmental perception in complex marine environments. Sea trials have shown that the method improves the effectiveness of target information by 3.61%. Full article

(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)

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27 pages, 6271 KB

Open AccessArticle

A Method for Identifying Critical Control Points in Production Scheduling for Crankshaft Production Workshop by Integrating Weighted-ARM with Complex Networks

by Luwen Yuan, Ge Han and Peng Dong

Systems 2025, 13(12), 1122; https://doi.org/10.3390/systems13121122 - 15 Dec 2025

In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. [...] Read more.

In smart manufacturing environments, production scheduling is highly susceptible to multi-source disruptions. However, traditional methods often struggle to accurately characterize the complex interdependencies between control points and disruptions, along with their systemic propagation effects, thereby constraining the proactivity and precision of scheduling optimization. This paper proposes a novel data-driven approach that integrates Weighted Association Rule Mining (WARM) with a two-layer directed weighted complex network to achieve precise identification of critical control points in production scheduling. First, a production loss function integrating delay duration and resource idle cost is constructed, and the max-pooling method is applied to map control point weights, thereby quantifying their intrinsic importance. Subsequently, under the constraint that association rule antecedents are restricted to control points, an improved Apriori algorithm is employed to mine directed “Control Point-Disruption” association rules. These rules are then used to construct a two-layer directed weighted complex network. Furthermore, by combining weighted PageRank and edge betweenness centrality analyses, critical control points and high-risk propagation paths are identified from the dual dimensions of node influence and path propagation capability. A case study conducted in a crankshaft production workshop demonstrates that the proposed method effectively identifies low-frequency yet high-impact hidden nodes often overlooked by traditional rules. The resulting scheduling optimization scheme reduces the occurrence rate of high-impact disruptions by 53% and significantly improves key performance indicators such as on-time delivery rate and equipment utilization. This research provides new theoretical support and a technical pathway for manufacturing enterprises to suppress system disturbances through flexible interventions targeting high-betweenness paths. Full article

(This article belongs to the Special Issue Scheduling and Optimization in Production and Transportation Systems)

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20 pages, 1274 KB

Open AccessArticle

The Future of ESG in Multinationals: How Digital Twin Technologies Enable Strategic Value Creation

by Eliza Ciobanu

Systems 2025, 13(12), 1121; https://doi.org/10.3390/systems13121121 - 15 Dec 2025

This study examines the role of Digital Twin technologies in advancing Environmental, Social, and Governance performance within multinational corporations. Grounded in socio-technical systems theory and stakeholder theory, the research investigates how digital twins facilitate the integration of organizational capabilities with external accountability mechanisms. [...] Read more.

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

(This article belongs to the Special Issue Digital Transformation and Innovation in Product–Service Systems in Industry 4.0)

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27 pages, 1020 KB

Open AccessArticle

Path Exploration of Artificial Intelligence-Driven Green Supply Chain Management in Manufacturing Enterprises: A Study Based on Random Forest and Dynamic QCA Under the TOE Framework

by Yifei Cao, Lingfeng Hao, Zihan Zhang and Hua Zhang

Systems 2025, 13(12), 1120; https://doi.org/10.3390/systems13121120 - 14 Dec 2025

Artificial intelligence (AI) technology is gradually integrating into the entire process of green supply chain management (GSCM), providing a systematic solution for enterprises to improve productivity and performance. This paper focuses on Chinese manufacturing enterprises, aiming to explore the multi-factor synergistic mechanism influencing [...] Read more.

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

(This article belongs to the Special Issue Innovation Management and Digitalization of Business Models)

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34 pages, 3145 KB

Open AccessReview

Cybersecurity in Smart Grids: A Domain-Centric Review

by Sahithi Angara, Laxima Niure Kandel and Raju Dhakal

Systems 2025, 13(12), 1119; https://doi.org/10.3390/systems13121119 - 14 Dec 2025

The modern power grid is considered a Smart Grid (SG) when it relies extensively on technologies that integrate traditional power infrastructure with Information and Communication Technologies (ICTs). The dependence on Internet of Things (IoT)-based communication systems to operate physical power devices transforms the [...] Read more.

The modern power grid is considered a Smart Grid (SG) when it relies extensively on technologies that integrate traditional power infrastructure with Information and Communication Technologies (ICTs). The dependence on Internet of Things (IoT)-based communication systems to operate physical power devices transforms the grid into a complex system of systems (SoS), introducing cybersecurity vulnerabilities across various SG layers. Several surveys have addressed SG cybersecurity, but none have correlated recent developments with the NIST seven-domain framework, a comprehensive model covering all major SG domains and crucial for domain-level trend analysis. To bridge this gap, we systematically review and classify studies by impacted NIST domain, threat type, and methodology (including tools/platforms used). We note that the scope of applicability of this study is 60 studies (2011–2024) selected exclusively from IEEE Xplore. Unlike prior reviews, this work maps contributions to the NIST domain architecture, examines temporal trends in research, and synthesizes cybersecurity defenses and their limitations. The analysis reveals that research is unevenly distributed: the Operations domain accounts for ~35% of all studies, followed by Generation ~25% and Distribution ~14%, while domains like Transmission (~9%) and Service Provider (5%) are comparatively under-studied. We find a heavy reliance on simulation-based tools (~46% of studies) such as MATLAB/Simulink, and False Data Injection (FDI) attacks are predominantly studied, comprising approximately 36% of analyzed attacks. The broader objective of this work is to guide researchers and SG stakeholders (e.g., utilities, policy-makers) toward understanding and coordinating strategies for improving system-level cyber-resilience, which is crucial for future SGs, while avoiding any overstatement of findings beyond the reviewed evidence. Full article

(This article belongs to the Section Systems Engineering)

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26 pages, 11658 KB

Open AccessArticle

Integrated Subjective–Objective Weighting and Fuzzy Decision Framework for FMEA-Based Risk Assessment of Wind Turbines

by Zhiyong Li, Yihan Wang, Yu Xu, Yunlai Liao, Qijian Liu and Xinlin Qing

Systems 2025, 13(12), 1118; https://doi.org/10.3390/systems13121118 - 12 Dec 2025

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To [...] Read more.

Accurate fault risk assessment is essential for maintaining wind turbine reliability. Traditional failure modes and effects analysis (FMEA)-based approaches struggle to handle the fuzziness, uncertainty, and conflicting nature of multi-criteria evaluations, which may lead to delayed fault detection and increased maintenance risks. To address these limitations, this paper proposes an enhanced risk assessment framework that integrates subjective-objective weighting and fuzzy decision-making. First, a combined subjective–objective weighting (CSOW) model with adaptive fusion is developed by integrating the analytic hierarchy process (AHP) and the entropy weight method (EWM). The CSOW model optimizes the weighting of severity (S), occurrence (O), and detection (D) indicators by balancing expert knowledge and data-driven information. Second, a fuzzy decision-making model based on interval-valued intuitionistic fuzzy numbers and VIKOR (IVIFN-VIKOR) is established to represent expert evaluations and determine risk rankings. Notably, the overlap rate between the top 10 failure modes identified by the proposed method and a fault-tree-based Monte Carlo simulation incorporating mean time between failures (MTBF) and mean time to repair (MTTR) reaches 90%, substantially higher than other methods. This confirms the superior performance of the framework and provides enterprises with a systematic approach for risk assessment and maintenance planning. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

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33 pages, 3370 KB

Open AccessArticle

AIP-Urban: Edge-Enabled Deep Learning Framework for Predictive Maintenance and Anomaly Detection in Urban Traffic Infrastructure

by Wajih Abdallah and Mansoor Alghamdi

Systems 2025, 13(12), 1117; https://doi.org/10.3390/systems13121117 - 11 Dec 2025

Urban traffic infrastructures like traffic signals, surveillance cameras, and embedded sensors play an essential role in providing sustainable mobility but are also susceptible to malfunctions, data drift, and degradation from environmental conditions. In this study, we propose AIP-Urban, an edge AI-enabled predictive maintenance [...] Read more.

Urban traffic infrastructures like traffic signals, surveillance cameras, and embedded sensors play an essential role in providing sustainable mobility but are also susceptible to malfunctions, data drift, and degradation from environmental conditions. In this study, we propose AIP-Urban, an edge AI-enabled predictive maintenance framework that employs deep spatio-temporal learning with continuous anomaly detection for smart transportation systems. Our framework integrates IoT sensing, computer vision, and time-series analytics to identify and forecast infrastructure failures before they occur. For visual and numerical anomalies (e.g., traffic signal outage, abrupt congestion, sensor disconnection), we employ a hybrid CNN–Transformer model, while we utilise a Temporal LSTM predictor to estimate a degradation trend to predict maintenance events within 24 h. The models are deployed on Jetson Nano edge devices to enable real-time processing under energy constraints. Extensive simulation studies using datasets from SUMO, CityCam, and UA-DETRAC show that AIP-Urban achieved 94% accuracy for anomaly detection (F1 = 0.94), with RMSE = 0.11 for failure prediction and an edge inference latency of 72 ms, while power consumption remained below 7.8 W. Statistical tests (Wilcoxon p < 0.05) show goodness-of-fit compared to baseline models of CNN, LSTM, and Transformer only. This study shows promise in improving the reliability, safety, and sustainability of urban traffic using proactive, explainable, and energy-aware AI at the edge. AIP-Urban serves as a reproducible reference architecture for future AI-driven transportation maintenance systems that is aligned with intelligent and resilient smart cities principles. Full article

(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)

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35 pages, 2751 KB

Open AccessArticle

Research on the Configurational Paths of Collaborative Performance in the Innovation Ecosystem from the Perspective of Complex Systems

by Xin Li, Haiyun Xu, Robin Haunschild, Zehua Tong and Chunjiang Liu

Systems 2025, 13(12), 1116; https://doi.org/10.3390/systems13121116 - 11 Dec 2025

This study integrates complex systems theory and innovation ecosystem theory to develop a unified framework encompassing the innovation environment, innovation actors, and innovation networks. Using fsQCA and NCA, it examines the impact of cross-layer interactions and the coupling of multiple factors on collaborative [...] Read more.

This study integrates complex systems theory and innovation ecosystem theory to develop a unified framework encompassing the innovation environment, innovation actors, and innovation networks. Using fsQCA and NCA, it examines the impact of cross-layer interactions and the coupling of multiple factors on collaborative performance. Empirical analysis in the field of natural language processing (NLP) demonstrates that no single factor is sufficient to serve as a necessary condition for achieving high-innovation collaboration performance. Innovation actors, as endogenous evolutionary drivers, play a central and catalytic role in the collaboration process. Moreover, under specific conditions, the relationship between the innovation environment and innovation networks exhibits a substitutive effect, with certain capabilities enabling this dynamic. This study extends the theoretical understanding of collaboration pathways within innovation ecosystems and offers practical recommendations for fostering innovation cooperation across different industries and organizations. It achieves this by constructing a “situational type–configuration path” matrix, decision tree, and innovation collaboration performance realization model. Full article

(This article belongs to the Section Complex Systems and Cybernetics)

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31 pages, 2597 KB

Open AccessArticle

Dark Markets for Bright Futures? Unveiling the Shadow Economy’s Influence on Economic Development

by Oana-Ramona Lobonț, Andreea-Florentina Crăciun, Sorana Vătavu, Ana-Cristina Nicolescu and Marian Pompiliu Cristescu

Systems 2025, 13(12), 1115; https://doi.org/10.3390/systems13121115 - 11 Dec 2025

This paper examines the changes in the level of informal and shadow economy, mapping their evolution within the EU and measuring their implications on economic growth. The study also addresses the issue of conceptual differences in the methodology for measuring these phenomena. We [...] Read more.

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

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

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38 pages, 3361 KB

Open AccessSystematic Review

Data-Driven Decision-Making in Marketing: A Systematic Literature Review of Emerging Themes and Research Gaps

by Rui Nunes Cruz and Albérico Travassos Rosário

Systems 2025, 13(12), 1114; https://doi.org/10.3390/systems13121114 - 10 Dec 2025

This study assesses how Data-Driven Decision-Making (DDDM) impacts marketing practices and research. Using the PRISMA 2020 protocol, this research conducted systematic reviews of 94 peer-reviewed articles and utilized bibliometric and thematic analyses. From this, four major themes emerged: improvement in the customer experience [...] Read more.

This study assesses how Data-Driven Decision-Making (DDDM) impacts marketing practices and research. Using the PRISMA 2020 protocol, this research conducted systematic reviews of 94 peer-reviewed articles and utilized bibliometric and thematic analyses. From this, four major themes emerged: improvement in the customer experience via the personalization of marketing; marketing driven by innovation through data resource versatility, Machine Learning, analytics, and Artificial Intelligence; performance enhancement through the optimal allocation of resources; and the data governance and ethical use of such resources, and the use of such data resources. This study illustrates how the combination of multi-level theory and methodical stricture accounts for the systemic influence of DDDM in marketing. This study adds to these theories by proposing a cohesive and synthesized understanding of the interplay of the technological, organizational, and governance elements in data-driven marketing. This research provides organizations with actionable guidance aimed at increasing effective analytics-driven decision-making, while also ensuring the responsible use of data. Full article

(This article belongs to the Special Issue Data-Driven Insights with Predictive Marketing Analysis)

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29 pages, 1944 KB

Open AccessArticle

Towards Governance of Socio-Technical System of Systems: Leveraging Lessons from Proven Engineering Principles

by Mohamed Mogahed and Mo Mansouri

Systems 2025, 13(12), 1113; https://doi.org/10.3390/systems13121113 - 10 Dec 2025

Healthcare delivery systems operate as complex socio-technical Systems-of-Systems (SoS), where autonomous entities—hospitals, insurers, laboratories, and technology vendors—must coordinate to achieve collective outcomes that exceed individual capabilities. Despite substantial investment in interoperability standards and regulatory frameworks, persistent fragmentation undermines care quality, operational efficiency, and [...] Read more.

Healthcare delivery systems operate as complex socio-technical Systems-of-Systems (SoS), where autonomous entities—hospitals, insurers, laboratories, and technology vendors—must coordinate to achieve collective outcomes that exceed individual capabilities. Despite substantial investment in interoperability standards and regulatory frameworks, persistent fragmentation undermines care quality, operational efficiency, and systemic adaptability. This fragmentation stems from a fundamental governance paradox: how can independent systems retain operational autonomy while adhering to shared rules that ensure systemic resilience? This paper addresses this challenge by advancing a governance-oriented architecture grounded in Object-Oriented Programming (OOP) principles. We reinterpret core OOP constructs—encapsulation, modularity, inheritance, polymorphism, and interface definition—as governance mechanisms that enable autonomy through principled constraints while fostering structured coordination across heterogeneous systems. Central to this framework is the Confluence Interoperability Covenant (CIC), a socio-technical governance artifact that functions as an adaptive interface mechanism, codifying integrated legal, procedural, and technical standards without dictating internal system architectures. To validate this approach, we develop a functional proof-of-concept simulation using Petri Nets, modeling constituent healthcare systems as autonomous entities interacting through CIC-governed transitions. Comparative simulation results demonstrate that CIC-based governance significantly reduces fragmentation (from 0.8077 to 0.1538) while increasing successful interactions fivefold (from 68 to 339 over 400 steps). This work contributes foundational principles for SoS Engineering and offers practical guidance for designing scalable, interoperable governance architectures in mission-critical socio-technical domains. Full article

(This article belongs to the Special Issue Governance of System of Systems (SoS))

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21 pages, 956 KB

Open AccessArticle

How to Harness LLMs in Project-Based Learning: Empirical Evidence for Individual Autonomy and Moderate Constraints in Engineering Education

by Xiaoyu Yi, Wenkai Feng, Yali He and Fei Wang

Systems 2025, 13(12), 1112; https://doi.org/10.3390/systems13121112 - 10 Dec 2025

The integration of large language models (LLMs) into project-based learning (PBL) holds significant potential for addressing enduring pedagogical challenges in engineering education, such as providing scalable, personalized support during complex problem-solving. Grounded in Self-Determination Theory (SDT), this study investigates how different LLM usage [...] Read more.

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

(This article belongs to the Special Issue The Application of a Large Language Model (LLM) in Education Reform and Innovation)

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38 pages, 916 KB

Open AccessSystematic Review

Integrating Business Intelligence and Operations Research for Sustainable Supply Chain Systems: A Systematic Review

by Rui Pedro Marques and Dorabella Santos

Systems 2025, 13(12), 1111; https://doi.org/10.3390/systems13121111 - 10 Dec 2025

This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable [...] Read more.

This systematic review explores how business intelligence (BI) and operations research (OR) help organizations ensure sustainable practices in supply chain management (SCM). Drawing on 56 peer-reviewed studies, this review synthesizes how BI tools support sustainability by transforming large and complex datasets into actionable insights, enhancing transparency, improving forecasting, optimizing production and inventory, reducing waste, and enabling circular economy practices. Complementarily, OR provides methodological rigor through optimization models, simulation, and multicriteria decision-making, enabling organizations to balance economic, environmental, and social objectives in supply chain design and operations. The findings reveal that BI and OR jointly contribute to 11 of the 17 United Nations Sustainable Development Goals (SDGs), demonstrating their strategic relevance for global sustainable development. This paper’s contribution is twofold: it consolidates fragmented academic research through an integrative framework clarifying how BI and OR reinforce sustainability within SCM, and it provides practitioners with evidence of how these tools can generate both operational efficiency and a competitive advantage while meeting environmental and social responsibilities. Future research should focus on bridging existing gaps in the literature and advancing the practical applications of these technologies. 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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30 pages, 3843 KB

Open AccessArticle

Structure and Evolution of the Global Financial Services Greenfield FDI Network: Complex System Analysis Based on the TERGM Model

by Guoli Zhang, Ruxiao Qu, Lujian Wang and Fang Lu

Systems 2025, 13(12), 1110; https://doi.org/10.3390/systems13121110 - 9 Dec 2025

Cross-border greenfield investment in the financial services sector is increasingly understood not as isolated flows, but as a complex, dynamic global system. This systemic perspective is essential for understanding its holistic structure and evolution amidst globalisation and digital transformation. This paper utilises financial [...] Read more.

Cross-border greenfield investment in the financial services sector is increasingly understood not as isolated flows, but as a complex, dynamic global system. This systemic perspective is essential for understanding its holistic structure and evolution amidst globalisation and digital transformation. This paper utilises financial services greenfield investment projects from 100 major economies from 2003 to 2021 to construct the Global Financial Services Greenfield FDI Network (GFS-GFN). By combining Social Network Analysis (SNA) and Temporal Exponential Random Graph Models (TERGMs), we systematically investigate its dynamic evolutionary features and endogenous mechanisms. The findings reveal the following: (1) System-wide, the network exhibits persistent expansion, “small-world” properties, and a pronounced “rich club” effect among source countries. (2) Nodally, the structure has evolved from a US-UK “dual-core” to a multipolar configuration, as emerging hubs like China, the UAE, and Singapore rapidly approach the traditional centres. (3) Structurally, the network has fragmented from Euro-American dominance into five major communities, forming a diverse, complementary pattern. Network evolution is primarily driven by endogenous mechanisms. Investment relationships widely exhibit reciprocity, preferential attachment, transitive closure, and marked path dependence. Full article

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

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15 pages, 720 KB

Open AccessArticle

Validation of a Patient Prioritization Tool: Addressing Decision-Support Tools’ Development in Complex Systems

by Ana Tereza Lopes Pécora, Marie-Eve Lamontagne, Angel Ruiz, José Roberto Frega, Julien Déry, José Eduardo Pécora Junior and Rogério de Fraga

Systems 2025, 13(12), 1109; https://doi.org/10.3390/systems13121109 - 9 Dec 2025

As times to access health services have significantly increased worldwide in recent years, strategies and tools to better manage patients’ waiting lists have gained research interest. Computer-Based Patient Prioritization Tools (PPT) aim to manage access to care by ranking patients on waiting lists [...] Read more.

As times to access health services have significantly increased worldwide in recent years, strategies and tools to better manage patients’ waiting lists have gained research interest. Computer-Based Patient Prioritization Tools (PPT) aim to manage access to care by ranking patients on waiting lists equitably and rigorously so that higher-priority patients are treated ahead of those with lower priority, regardless of when they were added to the list. However, healthcare systems are inherently complex, involving multiple stakeholders, dynamic interactions, and contextual constraints that make the implementation of such tools challenging. The development of decision-support tools in such environments follows an iterative life cycle that includes design, implementation, verification, validation, and deployment. Among these stages, validation is critical to ensure that the tool not only meets its intended specifications but also produces improved outcomes without unintended consequences when integrated into real-world workflows. Although the literature devoted to PPT is rich, works describing the transition of research prototypes to real-world applications within these complex systems are relatively scarce. This paper presents and discusses the validation process of a PPT, illustrating how this step contributes to improving the tool, building future users’ confidence, and providing insights into the challenges and difficulties related to expert evaluation in complex healthcare environments. Full article

(This article belongs to the Special Issue Project Management of Complex Systems (Manufacturing and Services))

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31 pages, 3544 KB

Open AccessArticle

Strategic Architecture of Sustainable System Development for ESG Transformation in Large Multi-Purpose Sports Venues

by Min-Ren Yan, Chien-Heng Chou and Hui-Lan Chi

Systems 2025, 13(12), 1108; https://doi.org/10.3390/systems13121108 - 9 Dec 2025

This paper proposes a strategic architecture of sustainable system development (SSD) for ESG enterprise transformation, demonstrating its application through a real-world case study on large multi-purpose sports venues (LMPSVs). The research integrates systems thinking, computer-aided dynamic business modeling and digital technologies to support [...] Read more.

This paper proposes a strategic architecture of sustainable system development (SSD) for ESG enterprise transformation, demonstrating its application through a real-world case study on large multi-purpose sports venues (LMPSVs). The research integrates systems thinking, computer-aided dynamic business modeling and digital technologies to support ESG enterprise transformation strategies and business operations. An ESGI (environmental, social, governance, and innovation) framework is proposed to demonstrate the transformation process through empirical data and scenario analysis. Furthermore, this study develops an integrated strategic enterprise architecture (ISEA) to integrate strategic planning with enterprise execution, wherein strategic architecture (SA) takes precedence over enterprise architecture (EA). The SSD-driven SA provides directional strategic guidance integrated with EA. This study prioritizes the construction of SA required for enterprise ESG transformation, serving as a research blueprint for future construction of EA at the technical implementation level. The findings indicate that LMPSVs must adopt integrated transformation strategies, strengthen digital capabilities, and embed ESGI as a core concept to sustain a competitive advantage in volatile environments. The framework offers a systematic and dynamic approach emphasizing inclusive growth, operational resilience, value creation, and sustainable development goals. The study’s originality lies in the SSD-based ESGI framework, which translates conceptual ideas into advanced operational models for scientific management and performance improvement. By integrating cross-disciplinary knowledge, the framework provides a systematic, dynamic, and multi-value-oriented analytical tool that bridges the gap in the existing literature, offering significant value for both theoretical development and practical application. Full article

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32 pages, 3530 KB

Open AccessArticle

Empowering Service Designers with Integrated Modelling Tools: A Model-Driven Approach

by Francisco Javier Pérez-Blanco, Juan Manuel Vara, Cristian Gómez-Macías, David Granada and Carlos Villarrubia

Systems 2025, 13(12), 1107; https://doi.org/10.3390/systems13121107 - 8 Dec 2025

Service design often involves using diverse business and process modelling notations to represent strategic and operational aspects of services. Although complementary, no modelling environment currently enables integrated use of these notations. This paper addresses this gap by proposing a model-driven solution that supports [...] Read more.

Service design often involves using diverse business and process modelling notations to represent strategic and operational aspects of services. Although complementary, no modelling environment currently enables integrated use of these notations. This paper addresses this gap by proposing a model-driven solution that supports multiple modelling notations within a unified environment. The research is guided by the following question: To what extent can a modelling environment that integrates multiple business and process modelling notations benefit service designers? To answer it, the study adopts Design Science Research (DSR) methodology and develops a prototype integrating several graphical Domain-Specific Languages (DSLs), along with mechanisms for model transformation, traceability, and validation. The prototype was evaluated through a two-phase process: (1) a laboratory case study applying the double diamond model of service design to a real-world scenario, and (2) an empirical study involving nine service design professionals who assessed the usability of the tool, efficiency, and completeness of generated models. Results show that integrating heterogeneous modelling notations through Model-Driven Engineering (MDE) can reduce modelling effort by up to 36.4% and generate models with up to 97.7% completeness, demonstrating not only technical benefits but also contributions to the well-being of designers by reducing cognitive load, fostering consistency, and improving communication among the stakeholders involved in the designing process. Full article

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

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20 pages, 523 KB

Open AccessArticle

From Pathology to Purchase: Compulsive Short Video Use and Socio-Technical Moderation in E-Commerce

by Rob Kim Marjerison, Jin Young Jun and Jong Min Kim

Systems 2025, 13(12), 1106; https://doi.org/10.3390/systems13121106 - 8 Dec 2025

Short-video platforms such as TikTok, Douyin, and Instagram Reels have transformed digital consumption into an immersive, algorithmically mediated commerce ecosystem. This study examines how compulsive short video use (CSV), a maladaptive pattern linked to diminished self-regulation, shapes purchase intention (PI). Drawing on compulsive [...] Read more.

Short-video platforms such as TikTok, Douyin, and Instagram Reels have transformed digital consumption into an immersive, algorithmically mediated commerce ecosystem. This study examines how compulsive short video use (CSV), a maladaptive pattern linked to diminished self-regulation, shapes purchase intention (PI). Drawing on compulsive consumption theory, dual-process perspectives, and socio-technical systems theory (STST), we estimate a structural equation model using survey data from 542 active short-video users. The results show that CSV exerts a strong and consistent positive effect on PI, indicating that compulsive engagement functions as a commercially consequential psychological state. This relationship is contingent on socio-technical conditions: technical support and platform familiarity substantially amplify the CSV–PI pathway, social belonging provides weaker but positive reinforcement, and social interaction attenuates the effect by redirecting attention away from transactional cues. These findings position CSV as both a form of digital pathology and a commercially activating mechanism within socio-technical environments. The study also offers guidance for platform managers seeking to balance monetization with ethical responsibility in short-video commerce ecosystems. Full article

(This article belongs to the Special Issue Systems Approaches in E-Commerce and Business Intelligence: Innovations and Applications)

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