Systems

33 pages, 4987 KB

Open AccessArticle

Analysis of the Driving Mechanism of China’s Provincial Carbon Emission Spatial Correlation Network: Based on the Dual Perspectives of Dynamic Evolution and Static Formation

by Jie-Kun Song, Yang Ding, Hui-Sheng Xiao and Yi-Long Su

Systems 2026, 14(2), 163; https://doi.org/10.3390/systems14020163 - 3 Feb 2026

Abstract

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 [...] Read more.

Against the backdrop of China’s commitment to achieving carbon peaking by 2030 and carbon neutrality by 2060, inter-provincial carbon emissions form a complex interconnected spatial network—clarifying its operational mechanisms is crucial for optimizing regional carbon reduction strategies. Based on 2006–2021 data from 30 Chinese provinces, this study constructs the China Provincial Carbon Emission Spatial Correlation Network (CPCESCN) using a modified gravity model. Social Network Analysis (SNA) explores its structural characteristics, while motif and QAP correlation analyses identify endogenous structural and attribute variables. Innovatively integrating Exponential Random Graph Models (ERGM) and Stochastic Actor-Oriented Models (SAOM), it investigates the network’s static formation mechanisms and dynamic evolution drivers. Results show CPCESCN has a stable multi-threaded structure without isolated nodes, with Jiangsu, Guangdong, Shandong, Zhejiang, Henan, and Sichuan as high-centrality core nodes with high centrality. GDP, green technology innovation, urbanization rate, industrialization rate, energy consumption intensity, and environmental regulations significantly influence network dynamics, with reciprocal relationships as key endogenous drivers. While geographic proximity still facilitates network formation, its impact has weakened notably, and functional complementarity has become the dominant evolutionary driver—based on the findings, policy suggestions are proposed, including deepening inter-provincial functional cooperation, implementing differentiated carbon reduction policies, and optimizing multi-dimensional low-carbon transformation systems. Full article

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20 pages, 872 KB

Open AccessArticle

A Systems Perspective on Ecological Citizenship in Libya: Cultural Mediation of Environmental Attitudes in a Socio-Economic Context

by Ahmed Mohamed Saleh Essayah and Askin Kiraz

Systems 2026, 14(2), 162; https://doi.org/10.3390/systems14020162 - 3 Feb 2026

Abstract

Ecological citizenship offers a framework for understanding how individuals translate environmental knowledge and concern into civic environmental engagement. However, the cultural mechanisms shaping this process remain understudied, particularly in Libya and comparable socio-cultural contexts. This study examines how environmental concern and the New [...] Read more.

Ecological citizenship offers a framework for understanding how individuals translate environmental knowledge and concern into civic environmental engagement. However, the cultural mechanisms shaping this process remain understudied, particularly in Libya and comparable socio-cultural contexts. This study examines how environmental concern and the New Environmental Paradigm (NEP) influence ecological citizenship and the extent to which cultural factors mediate these relationships in Libya. Using a quantitative design, data were collected from 509 adults through validated scales measuring ecological citizenship, environmental concern, the NEP, and cultural values. The statistical analysis results show that environmental concern positively predicts ecological citizenship, with altruistic and biospheric concerns exerting the strongest effects. While the NEP correlates positively with ecological citizenship, its direct impact becomes non-significant when cultural factors are introduced. Cultural values, particularly religiosity, family orientation, generosity, and interdependence, demonstrate strong positive associations with ecological citizenship and mediate the effects of both the NEP and environmental concern, while materialism shows an inverse relationship. The findings of this research highlight that ecological citizenship is shaped not only by environmental attitudes but also by culturally embedded norms and values. Full article

(This article belongs to the Special Issue Systems Thinking and Modelling in Socio-Economic Systems)

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56 pages, 2761 KB

Open AccessArticle

Evolutionary Analysis of Multi-Agent Interactions in the Digital Green Transformation of the Building Materials Industry

by Yonghong Ma and Zihui Wei

Systems 2026, 14(2), 161; https://doi.org/10.3390/systems14020161 - 2 Feb 2026

Abstract

Driven by the “dual carbon” goal and the strategy for cultivating new productive forces, China’s economy is undergoing a crucial transformation from high-speed growth to high-quality development. As a typical high-energy consumption and high-emission sector, the green and low-carbon transformation of the building [...] Read more.

Driven by the “dual carbon” goal and the strategy for cultivating new productive forces, China’s economy is undergoing a crucial transformation from high-speed growth to high-quality development. As a typical high-energy consumption and high-emission sector, the green and low-carbon transformation of the building materials industry directly affects the optimization of the national energy structure and the realization of ecological goals. However, traditional building material enterprises generally face practical challenges such as low resource utilization efficiency, insufficient digitalization and greening integration of the industrial chain, and weak green innovation momentum. The transformation actions of a single entity are difficult to break through systemic bottlenecks, and it is urgently necessary to establish a dynamic evolution mechanism involving multiple entities in collaboration. This paper aims to explore the evolutionary rules and stability of digital green (DG) transformation strategies of building materials enterprises (BMEs) under multi-agent interactions involving government, universities, and consumers. Centering on BMEs, a four-party evolutionary game model among the government, enterprises, universities, and consumers is constructed, and the evolutionary processes of strategic behaviors are characterized through replicator dynamic equations. Using MATLAB R2022 (Version number: 9.13.0.2049777) bnumerical simulations, this study investigates how key parameters, such as government subsidies, penalty intensity, and consumers’ green preferences, affect the transformation pathways of enterprises. The results reveal that the DG transformation behavior of BMEs is significantly influenced by governmental policy incentives and universities’ knowledge innovation. Stronger subsidies and penalties enhance enterprises’ willingness to adopt proactive DG strategies, while consumers’ green preferences further accelerate transformation through market mechanisms. Among multiple strategic combinations, active DG transformation emerges as the main evolutionarily stable strategy. This study provides a systematic multi-agent collaborative analysis framework for the transformation of BME DG, revealing the mechanisms by which policies, knowledge, and market demands influence enterprise decisions. Thus, it offers theoretical and decision-making references for the green and low-carbon transformation of the building materials industry. Full article

(This article belongs to the Special Issue Digital Transformation in the Era of Technological Disruption: The Reshaping and Application of Emerging Technologies on Management Models)

20 pages, 8255 KB

Open AccessArticle

Systematic Risk Assessment of Seawater Intrusion in Ports: Assessment Framework and Empirical Analysis

by Yuan Gao, Zhen Qiao and Longjun He

Systems 2026, 14(2), 160; https://doi.org/10.3390/systems14020160 - 2 Feb 2026

Abstract

Multiple factors, including global climate change and rising sea levels, increase the frequency and intensity of seawater intrusion. However, most previous studies have not regarded seawater intrusion as a typical accident for systematic safety risk assessment. In view of this, this paper comprehensively [...] Read more.

Multiple factors, including global climate change and rising sea levels, increase the frequency and intensity of seawater intrusion. However, most previous studies have not regarded seawater intrusion as a typical accident for systematic safety risk assessment. In view of this, this paper comprehensively employed the fault tree model, the “Man-Machine-Material-Method-Environment” system theory, the Bayesian network model, and the Attack-Defense game theory to conduct qualitative and quantitative analyses concerning seawater intrusion. The main research results were as follows: the fault tree model was applied to sort the hazard-inducing factors from the perspectives of nature and humans, which were further categorized based on the theoretical framework of the “Man-Machine-Material-Method-Environment” (4M1E) theory. Based on the Bayesian network model, and incorporating assessments of the existing defense conditions of the target port, the occurrence probability of seawater intrusion at the port was calculated as 3.05%. Simultaneously, the influence weights of the 4M1E factors on seawater intrusion were quantified; environmental and mechanical factors ranked as the top two contributors, accounting for 57.79% and 29.70% of the total impact, respectively. Utilizing the Attack-Defense game theory, two key risk evolution paths of seawater intrusion were identified, with occurrence probabilities of 7.627‰ and 4.164‰. Typical disaster cases clarified the underlying mechanisms by which these key risk paths triggered seawater intrusion, and targeted prevention and control measures were proposed accordingly. The research findings can not only deepen the systematic understanding of seawater intrusion as a typical marine disaster but also provide technical references for governments and enterprises to optimize their risk management systems. Full article

(This article belongs to the Special Issue Systems Approaches to Risk Management)

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29 pages, 455 KB

Open AccessArticle

Strategic Orientations and Organizational Resilience: The Moderating Role of Digital Technology

by Xiaoya Feng and Xiaobo Wu

Systems 2026, 14(2), 159; https://doi.org/10.3390/systems14020159 - 2 Feb 2026

Abstract

Firms pursuing organizational resilience face competing demands: sustaining growth while reducing performance volatility. This study examines how strategic orientations shape trade-offs between these resilience dimensions and how digital technology breadth and depth moderate these strategy–resilience relationships. Drawing on dynamic capability theory, this study [...] Read more.

Firms pursuing organizational resilience face competing demands: sustaining growth while reducing performance volatility. This study examines how strategic orientations shape trade-offs between these resilience dimensions and how digital technology breadth and depth moderate these strategy–resilience relationships. Drawing on dynamic capability theory, this study analyzes panel data from Chinese high-tech manufacturing firms to investigate how profit-oriented and growth-oriented strategies affect two dimensions of organizational resilience—growth resilience (continuous growth) and volatility resilience (performance stability). Results reveal two distinct mechanisms: a consolidating mechanism whereby profit-oriented strategy reduces performance volatility while constraining growth resilience, and a capitalizing mechanism whereby growth-oriented strategy strengthens growth resilience while increasing performance volatility. Digital technology breadth moderates both strategies but introduces complexity costs that weaken anticipated volatility reduction benefits. Digital technology depth shows limited moderating capacity, amplifying profit-oriented strategy’s growth constraints without significantly reducing performance volatility. This study contributes to organizational resilience literature by demonstrating that organizational resilience dimensions involve systematic trade-offs rather than complementary gains, and extends dynamic capability theory by identifying strategic orientation as a driver that shapes how firms configure and deploy dynamic capabilities toward different organizational resilience outcomes. For managers, these findings suggest aligning strategic orientation with organizational priorities for stability versus growth, recognizing that enhancing one resilience dimension may constrain another, and ensuring digital technology investments match strategic priorities, as broad adoption introduces complexity costs that may offset anticipated benefits. Full article

(This article belongs to the Special Issue Strategic Management Towards Organisational Resilience)

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21 pages, 2203 KB

Open AccessArticle

Toward Demystifying the Missing Links in Model-Based Systems Engineering (MBSE)

by Azad Khandoker, Sabine Sint, Guido Gessl and Klaus Zeman

Systems 2026, 14(2), 158; https://doi.org/10.3390/systems14020158 - 1 Feb 2026

Abstract

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely [...] Read more.

Model-Based Systems Engineering (MBSE) originated in aerospace engineering and has emerged as a promising approach in other fields for designing, analyzing, and managing complex interdisciplinary systems throughout their entire life cycle. While MBSE is applicable to various engineering domains, its applications remain closely tied to software engineering. As software becomes a critical component of physical systems, such as vehicles, appliances, and production plants, bridging the gap between software engineering and other disciplines, such as mechanical, electrical, and civil engineering, becomes essential. Despite its potential, MBSE is still in its early stages when it comes to integrating executable models of physical systems into engineering environments. The purpose of this research is to assess the present capabilities of MBSE by identifying existing missing links, thereby enabling prospective users to make well-informed decisions about its integration into organizational processes. In this analysis, it is important to have a comprehensive view of the complexity of MBSE across different disciplines to obtain an overall picture. In addition to identifying open challenges, we present three critical gaps in the MBSE practice through a comprehensive demonstration case: limited tool interoperability and model integration, modeling language limitations, and dependence on a specialized workforce. Current studies largely view MBSE as the most applicable and effective for the design phase of the system life cycle. Yet, to capture MBSE in its entirety, its principles must be applied throughout the whole system life cycle. Full article

(This article belongs to the Section Systems Engineering)

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21 pages, 437 KB

Open AccessArticle

The Impact of Sequential Cross-Border Mergers and Acquisitions on Innovation Boundaries

by Huili Guo, Pingfeng Liu, Luyao Gao and Deyun Xiao

Systems 2026, 14(2), 157; https://doi.org/10.3390/systems14020157 - 31 Jan 2026

Abstract

In the era of globalization, Chinese firms increasingly leverage sequential cross-border mergers and acquisitions to navigate complex international environments. Understanding the sustained impact of this strategy requires a holistic view beyond isolated transactions. Adopting an open systems perspective, this study examines how sequential [...] Read more.

In the era of globalization, Chinese firms increasingly leverage sequential cross-border mergers and acquisitions to navigate complex international environments. Understanding the sustained impact of this strategy requires a holistic view beyond isolated transactions. Adopting an open systems perspective, this study examines how sequential cross-border M&As influence the evolution of firms’ innovation boundaries as a dynamic system property. Combining grounded theory with textual analysis of Chinese M&A cases, this study develops an integrated systemic process framework and empirically tests it using data from Chinese listed firms (2002–2021) via fixed-effects models. Results reveal that sequential cross-border M&As act as external inputs that trigger internal system reconfiguration, significantly expanding innovation boundaries. This expansion process is mediated by dynamic capabilities, which constitute the firm’s core adaptive mechanism, and is moderated by resource slack that functions as critical system redundancy. These findings contribute to systems science by elucidating how firms, conceptualized as complex adaptive systems, transition from isolated deal-making to sustained capability building through iterative feedback loops in global competition. Full article

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

23 pages, 4797 KB

Open AccessArticle

Surrogate-Based Reconstruction of Structural Damage in Train Collisions: A Systematic Optimization Framework

by Hui Zhao, Dehong Zhang and Ping Xu

Systems 2026, 14(2), 156; https://doi.org/10.3390/systems14020156 - 31 Jan 2026

Abstract

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional [...] Read more.

Accurate reconstruction of train collision accidents is essential for understanding impact conditions, assessing crashworthiness, and supporting safety improvements. This study proposes a surrogate-based optimization framework for reconstructing structural damage in train collisions from post-accident observations. The pre-impact kinematic state, expressed by a six-dimensional vector of relative offsets, rotations, and impact velocity, is formulated as an inverse problem in which a Sum of Squared Relative Deviations (SSRD) between measured and simulated residual deformations serves as the objective function. A reduced two-vehicle finite element (FE) model is developed to capture the dominant impact dynamics, an Optimal Latin Hypercube Design is used to sample the parameter space, and a Kriging surrogate model is constructed to approximate the response. A simulated annealing algorithm is applied to search for the global minimum. The framework is demonstrated on a real high-speed rear-end collision of electric multiple units. The Kriging model achieves a coefficient of determination of about 0.85, and the optimized kinematic state yields FE-predicted residual deformations that agree with field measurements at key locations to within about 5%. The results show that the method can efficiently reconstruct physically plausible collision scenarios and provide insight into parameter sensitivity and identifiability for railway safety analysis. Full article

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

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28 pages, 4659 KB

Open AccessArticle

A Comprehensive Business Intelligence Framework for Diabetes Management in Telemedicine: Advancing Data-Driven Decision Support Through Integrated Visualization and Predictive Analytics

by Emilia-Alexandra Pop, Gabriela Mircea and Claudia-Roxana-Maria Iliescu

Systems 2026, 14(2), 155; https://doi.org/10.3390/systems14020155 - 31 Jan 2026

Abstract

Modern telemedicine requires advanced analytical solutions for efficient management of chronic diseases. This study presents the development of a comprehensive business intelligence (BI) framework using Microsoft Power BI, applied to the optimization of diabetes mellitus management. The methodology integrates Power Query transformations, 35 [...] Read more.

Modern telemedicine requires advanced analytical solutions for efficient management of chronic diseases. This study presents the development of a comprehensive business intelligence (BI) framework using Microsoft Power BI, applied to the optimization of diabetes mellitus management. The methodology integrates Power Query transformations, 35 DAX measures organized into five functional categories, and Python 3.14.2. capabilities for advanced statistical analysis. The framework was implemented and demonstrated using a public clinical dataset of 100,000 patient records, generating five interactive dashboards covering epidemiological, demographic, clinical, geographical, and equity perspectives. A global prevalence of 8.5%, exponential growth with age, gender differences (9.75% males against 7.62% females), and substantial connections between metabolic indicators (BMI, HbA1c, and blood glucose) are all confirmed by the results. Heart disease rates are 6.2 times higher in diabetic people, according to comorbidity research. Complete methodological openness through thorough documentation, Python integration for sophisticated visualizations, and interactive multidimensional drill-down features are some of the major additions. The predictive elements are included as interpretable, exploratory components embedded in the BI environment rather than as clinically validated prediction models. This approach provides an affordable and user-friendly approach that makes advanced analytical capabilities accessible to a broader range of healthcare organizations managing chronic diseases. Full article

(This article belongs to the Special Issue Business Intelligence and Data Analytics in Enterprise Systems)

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25 pages, 1018 KB

Open AccessArticle

Ontology Quality Improvement in the Semantic Web: Evidence from Educational Knowledge Graphs

by Wassim Jaziri and Najla Sassi

Systems 2026, 14(2), 154; https://doi.org/10.3390/systems14020154 - 31 Jan 2026

Abstract

Intelligent systems draw much of their reliability from the quality of their ontologies; however, manual ontology assessment remains patchy, time-consuming, and difficult to scale. To address these limitations, this paper proposes a domain-independent, machine-learning-driven framework for ontology quality assessment and improvement in the [...] Read more.

Intelligent systems draw much of their reliability from the quality of their ontologies; however, manual ontology assessment remains patchy, time-consuming, and difficult to scale. To address these limitations, this paper proposes a domain-independent, machine-learning-driven framework for ontology quality assessment and improvement in the Semantic Web. The framework combines structural, semantic, and documentation metrics with supervised learning models to predict quality issues and recommend targeted refinements through a four-phase workflow comprising ML model development, metric definition, automated improvement, and empirical evaluation. The approach is validated on educational knowledge graphs using 1500 ontology modules from the EDUKG repository, including a 100-module expert-annotated gold set (κ = 0.82). Experimental results show structural precision of 93.5% and semantic precision of 90.2%, with overall F1-scores close to 90%, while reducing ontology development time by 42% and quality assessment time by 65%. These findings demonstrate that coupling ML with structured quality metrics substantially enhances ontology reliability while preserving pedagogical and operational relevance in educational settings. Although empirical validation is conducted in the education domain, the modular and ontology-agnostic architecture can be adapted to other knowledge-intensive domains through retraining and domain-specific calibration, offering a reproducible foundation for continuous ontology quality improvement in Semantic Web applications. Full article

(This article belongs to the Special Issue Digital Engineering: Transformational Tools and Strategies)

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15 pages, 712 KB

Open AccessArticle

Stage-Aware Governance of Large Language Models: Managing Uncertainty and Human Oversight in AI-Assisted Literature Review Systems

by Junic Kim and Haeyong Shin

Systems 2026, 14(2), 153; https://doi.org/10.3390/systems14020153 - 31 Jan 2026

Abstract

This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and [...] Read more.

This study proposes a stage-aware governance framework for large language models (LLMs) that structures human oversight and accountability across different decision stages in AI-assisted literature review systems. Large language models (LLMs) are increasingly embedded in systematic review workflows, yet how human oversight and accountability should be structured across different decision stages remains unclear. This study evaluates three LLMs in a controlled two-stage literature review workflow—title-and-abstract screening and eligibility assessment—using identical evidence inputs and fixed inclusion criteria, with outputs benchmarked against expert consensus under fully reproducible conditions with standardized prompts and comprehensive logging. While LLMs closely matched expert decisions during screening (precision 0.83–0.91; F1 up to 0.89; Cohen’s κ 0.65–0.85), performance degraded substantially at the eligibility stage (F1 0.58–0.65; κ 0.52–0.62), indicating increased epistemic uncertainty when fine-grained criteria must be inferred from abstract-level information. Importantly, disagreements clustered in borderline cases rather than random error, supporting a stage-aware governance approach in which LLMs automate high-throughput screening while inter-model disagreement is operationalized as an actionable uncertainty signal that triggers human oversight in more consequential decision stages. These findings highlight the need for explicit oversight thresholds, responsibility allocation, and auditability in the responsible deployment of AI-assisted decision systems for evidence synthesis. Full article

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

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38 pages, 1612 KB

Open AccessArticle

The Mechanism and Spatiotemporal Variations in Digital Economy in Enhancing Resilience of the Cotton Industry Chain

by Muhabaiti Pareti, Sixue Qin, Yang Su, Jiao Zhang and Jiangtao Zhang

Systems 2026, 14(2), 152; https://doi.org/10.3390/systems14020152 - 31 Jan 2026

Abstract

In the era of the digital economy, enhancing the resilience of industrial chains is a core task in building a modern industrial system. This paper views the cotton industrial chain as a system composed of multiple segments and entities, aiming to explore how [...] Read more.

In the era of the digital economy, enhancing the resilience of industrial chains is a core task in building a modern industrial system. This paper views the cotton industrial chain as a system composed of multiple segments and entities, aiming to explore how the digital economy drives the collaborative evolution of the chain’s constituent elements, organizational structure, and overall functions, ultimately enhancing its resilience to respond to shocks and adapt to changes. The study focuses on the cotton industrial chain, systematically analyzing the mechanisms and spatiotemporal characteristics of the digital economy’s impact on its resilience, aiming to provide theoretical support and practical pathways for constructing a secure, efficient, and sustainable cotton industrial chain. Based on panel data from nine provinces in China’s three major cotton-producing regions from 2013 to 2022, the study uses the entropy method to measure the technological innovation vitality and the resilience of the cotton industrial chain, employing a semi-parametric panel model to empirically test the systemic association between them, and utilizing a mediation effect model to identify the roles of market information utilization and the scale of planting in this relationship. The findings indicate the following: (1) The development of the digital economy significantly enhances the resilience of the cotton industrial chain and exhibits an inverted U-shaped nonlinear relationship. (2) The digital economy enhances the overall resilience and synergy of the cotton industrial chain through two key pathways: improving the technological innovation vitality and increasing the level of planting scale. (3) The influence of the digital economy on the resilience of the cotton industrial chain shows geographical heterogeneity, with the order being “Yangtze River Basin cotton areas > Northwest Inland cotton areas > Yellow River Basin cotton areas.” The impact of the digital economy on the resilience of the cotton industrial chain also exhibits temporal heterogeneity, with “2013–2017 > 2018–2022.” From the perspective of system optimization, future efforts should focus on constructing regionally differentiated collaborative mechanisms, improving the integrated platform for market information services, strengthening incentives for large-scale planting policies, enhancing the digital literacy of practitioners, and conducting skills training, in order to strengthen the overall resilience and sustainable evolution of China’s cotton industrial chain. Full article

(This article belongs to the Section Supply Chain Management)

15 pages, 756 KB

Open AccessArticle

Modelling Cyber Resilience in SMEs as a Socio-Technical System: A Systemic Approach to Adaptive Digital Risk Management

by Alona Bahmanova and Natalja Lace

Systems 2026, 14(2), 151; https://doi.org/10.3390/systems14020151 - 31 Jan 2026

Abstract

Small and medium-sized enterprises (SMEs) increasingly rely on digital technologies in everyday operations, often without having sufficient resources or structured mechanisms to manage the cyber risks that accompany this dependence. As digitalization deepens, cyber incidents in SMEs are shaped not only by technical [...] Read more.

Small and medium-sized enterprises (SMEs) increasingly rely on digital technologies in everyday operations, often without having sufficient resources or structured mechanisms to manage the cyber risks that accompany this dependence. As digitalization deepens, cyber incidents in SMEs are shaped not only by technical vulnerabilities but also by human behavior and organizational practices. However, much of the existing research still approaches cyber resilience through fragmented technological or managerial lenses. This study takes a conceptual and theory-driven approach to examine cyber resilience in SMEs as a socio-technical system. Building on systems theory and adaptive management, the analysis draws on a structured synthesis of interdisciplinary literature to develop a systemic model of adaptive digital risk management. The model is developed through a structured conceptual process combining systematic exploration of interdisciplinary literature, analytical synthesis of recurring conceptual patterns, and system-level model construction informed by systems theory and adaptive management principles. Cyber resilience is therefore interpreted as a dynamic capability that develops over time, especially in digital environments characterized by increasing automation and evolving forms of human–technology interaction. The study contributes to cyber resilience research by offering a system-oriented perspective and provides SMEs with a conceptual basis for strengthening adaptive approaches to digital risk management. Full article

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

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49 pages, 2085 KB

Open AccessArticle

A Domain-Specific Modeling Language for Production Systems in Early Engineering Phases

by Lasse Beers, Hamied Nabizada, Maximilian Weigand, Alain Chahine, Felix Gehlhoff and Alexander Fay

Systems 2026, 14(2), 150; https://doi.org/10.3390/systems14020150 - 30 Jan 2026

Abstract

The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engineering (MBSE) provides a promising approach to manage this complexity by enabling consistent and structured [...] Read more.

The development of modern production systems involves numerous interdependent disciplines, heterogeneous data sources, and frequent design iterations, making the conceptual design phase particularly complex and error-prone. Model-Based Systems Engineering (MBSE) provides a promising approach to manage this complexity by enabling consistent and structured system representations. While domain-specific modeling languages (DSMLs) can tailor MBSE methods to specific domains, existing approaches often lack standardized semantics, user guidance, and tool support to ensure consistent model creation and verification. This paper introduces a DSML framework tailored for the conceptual design of production systems, integrating both methodological guidance and standard-based domain knowledge. The approach builds upon the Software Platform Embedded Systems (SPES) framework and extends Systems Modeling Language (SysML) through the Unified Modeling Language (UML) profile mechanism, providing clear modeling constructs, viewpoint-specific diagram types, and automated consistency checks. To enhance comprehensibility and domain alignment, the framework incorporates supplementary DSMLs that capture structures and semantics from established industrial standards. The proposed method is evaluated using an aircraft production case study, demonstrating improved applicability of MBSE for the conceptual design of complex production systems. Full article

(This article belongs to the Special Issue Model-Based Systems Engineering (MBSE) for Complex Systems)

26 pages, 2129 KB

Open AccessArticle

Restructuring of the Global Chip Trade Network: Characteristic Evolution and Driving Factors

by Lei Fu and Xiangyi Ding

Systems 2026, 14(2), 149; https://doi.org/10.3390/systems14020149 - 30 Jan 2026

Abstract

As the “brain” of the information industry and modern manufacturing, chips have emerged as a focal point in global competition over critical technologies. Based on global chip trade data from 2010 to 2023, this study employs social network analysis to investigate the structural [...] Read more.

As the “brain” of the information industry and modern manufacturing, chips have emerged as a focal point in global competition over critical technologies. Based on global chip trade data from 2010 to 2023, this study employs social network analysis to investigate the structural evolution of the chip trade network and applies the quadratic assignment procedure (QAP) to examine the driving mechanisms of network reconstruction. The findings are as follows: First, the global chip trade network exhibits a loosely connected core-periphery structure, characterized by clustering and polarization, with a pronounced short-term deglobalization trend. Second, China, the United States, Germany, France, South Korea, and Singapore have long dominated central positions in competitive dynamics, while developing economies such as Mexico, Malaysia, and the Philippines have significantly risen in prominence in recent years. Third, the network takes on a core–subcore–periphery configuration with clearly delineated trade communities, reflecting a community-based, multi-centric, and hierarchical pattern. Fourth, political relations serve as a key driver of network restructuring, with their promotional effect on chip trade being negatively moderated by technological distance yet positively moderated by economic-complexity distance. Full article

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

20 pages, 335 KB

Open AccessArticle

Performance Expectation Gap and Risk-Taking of Agricultural Enterprises: The Moderating Effect of Institutional Environment

by Xiaonan Fan, Jiayi Wang, Qing Li, Mei Zhou and Youran Gao

Systems 2026, 14(2), 148; https://doi.org/10.3390/systems14020148 - 30 Jan 2026

Abstract

In recent years, the operational performance of agricultural enterprises has been influenced by both natural conditions and market environments, resulting in high uncertainty and volatility. When performance falls below expectations, agricultural enterprises consciously engage in strategic change and proactive risk-taking to alleviate performance [...] Read more.

In recent years, the operational performance of agricultural enterprises has been influenced by both natural conditions and market environments, resulting in high uncertainty and volatility. When performance falls below expectations, agricultural enterprises consciously engage in strategic change and proactive risk-taking to alleviate performance pressures. Based on Firm Behavioral Theory, Performance Feedback Theory, and Prospect Theory, we examine how performance expectation gap affects risk-taking of agricultural enterprises by using panel data of Chinese A-share listed agricultural firms from 2007 to 2023. The results show that performance expectation gap has a positive effect on risk-taking, which means the greater the gap, the higher the level of risk-taking. And the better developed the institutional environment, the greater the tendency for risk-taking. Further analysis shows that performance expectation gap promotes risk-taking by driving strategic change within agricultural enterprises. This research enriches the study on the influencing factors of risk-taking in agricultural enterprises, offering decision-making insights for them to prudently assess and manage risks. Full article

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

28 pages, 1496 KB

Open AccessArticle

Investigating the Structural Dynamics of Terminal Operating System Selection: A Holistic Framework from Automation to Intelligence in Container Terminals

by Serdar Alnıpak

Systems 2026, 14(2), 147; https://doi.org/10.3390/systems14020147 - 30 Jan 2026

Abstract

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is [...] Read more.

In the face of mounting complexity in container terminal operations, the selection of an effective information system is paramount. The TOS (Terminal Operating System) is the most significant of all the information systems in existence for terminals. The objective of this study is to establish a set of criteria for selecting container TOS, determine the priority weights of these criteria and investigate their interactions. To the author’s knowledge, this is the first study to address this topic in such a detailed context. The hybrid FAHP (Fuzzy Analytic Hierarchy Process) and F-DEMATEL (Fuzzy Decision-Making Trial and Evaluation Laboratory) methodology was employed for the 18 criteria that were identified through the academic literature and expert views. The findings demonstrated that container terminal operators have expressed an expectation for a TOS structure that integrates complex business processes, provides effective decision support, increases traceability, works in harmony with advanced technologies, supports smart port transformation processes, enhances digital maturity and enables rapid intervention in bottlenecks. Furthermore, the fact that TOSs should support integration with external stakeholders is also critical in terms of collaboration and transparency, which are of great importance in supply chain management. It is hoped that the present study will contribute to the relevant literature and also provide a structural framework for terminal operators to select the most suitable TOS and for providers to design the most effective product. Full article

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23 pages, 5636 KB

Open AccessArticle

Research on Interpretable Tourism Demand Forecasting Based on VSN–xLSTM Model

by Hanpo Hou and Haiying Wang

Systems 2026, 14(2), 146; https://doi.org/10.3390/systems14020146 - 30 Jan 2026

Abstract

To address the limitations of traditional tourism demand forecasting models in leveraging multi-source data and their lack of interpretability, this study proposes an integrated multi-data-driven interpretable forecasting framework incorporating historical visitor volumes, social media activities, holiday schedules, weather conditions, and seasonal indicators. This [...] Read more.

To address the limitations of traditional tourism demand forecasting models in leveraging multi-source data and their lack of interpretability, this study proposes an integrated multi-data-driven interpretable forecasting framework incorporating historical visitor volumes, social media activities, holiday schedules, weather conditions, and seasonal indicators. This study develops a system-oriented tourism demand forecasting framework that integrates a Variable Selection Network (VSN) and an enhanced long short-term memory (xLSTM) architecture to jointly model and interpret multi-source demand drivers. The VSN module employs a dynamic feature weighting mechanism to automatically discern distribution characteristics and relevance variations across heterogeneous data sources, thereby assigning adaptive weights to input variables. The xLSTM model incorporates innovative exponential gating and matrix memory structures, enabling rapid adaptation to sudden tourist flow fluctuations while effectively capturing long-term cyclical dependencies. By combining VSN-derived feature importance weights with SHAP-based prediction attribution analysis, this framework offers dual-level interpretability—in both input feature selection and output explanation. Experimental results demonstrate that social media data significantly reflect tourist attention and travel intention and reveal distinctive demand-driving mechanisms for various types of tourism destinations. The study provides theoretical insights and empirical support for advancing tourism demand forecasting and management strategies. Full article

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23 pages, 662 KB

Open AccessArticle

When Digital Power Backfires: A Systems Perspective on Technology-Enacted Abusive Supervision, Defensive Silence, and Counterproductive Work Behavior

by Hong Chen and Zhaoqi Li

Systems 2026, 14(2), 145; https://doi.org/10.3390/systems14020145 - 30 Jan 2026

Abstract

Based on Conservation of Resources (COR) theory and a socio-technical systems perspective, this study examines how technology-enacted abusive supervision (TAS) influences employees’ counterproductive work behavior (CWB) in digitalized organizational contexts. Conceptualizing TAS as a system-embedded form of digitally mediated control, we argue that [...] Read more.

Based on Conservation of Resources (COR) theory and a socio-technical systems perspective, this study examines how technology-enacted abusive supervision (TAS) influences employees’ counterproductive work behavior (CWB) in digitalized organizational contexts. Conceptualizing TAS as a system-embedded form of digitally mediated control, we argue that technology-amplified supervisory power constitutes a persistent resource threat that reshapes employees’ behavioral regulation strategies. Using three-wave time-lagged survey data from 428 employees working in digital-intensive enterprises in China, we develop and test a moderated mediation model. The results indicate that TAS is positively associated with CWB, with defensive silence serving as a critical mediating mechanism. Although defensive silence may temporarily reduce interpersonal risk, it disrupts feedback and resource replenishment processes, leading to cumulative resource depletion and a higher likelihood of counterproductive behavior over time. Moreover, power distance significantly moderates this indirect effect, such that the mediating role of defensive silence is stronger among employees with higher-power-distance orientations. By integrating leadership research, COR theory, cultural value orientations, and a socio-technical systems perspective, this study advances our understanding of covert resistance and behavioral risk in technology-driven work systems and offers important implications for digital governance and sustainable organizational performance. Full article

(This article belongs to the Special Issue Digital Transformation in the Era of Technological Disruption: The Reshaping and Application of Emerging Technologies on Management Models)

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