Systems

21 pages, 491 KB

Open AccessArticle

Configurations of Sustainable HRM Practices for Organizational Resilience in Japan: A Crisp-Set QCA Study from a Socioformation Perspective

by Haruka Dounishi and Norio Kambayashi

Systems 2026, 14(3), 336; https://doi.org/10.3390/systems14030336 - 23 Mar 2026

Viewed by 389

Abstract

Sustainable human resource management (HRM) has attracted growing attention as a new paradigm for enhancing organizational resilience. However, prior studies mainly examined the effects of individual practices, offering a limited explanation of how organizational resilience emerges as an integrated mechanism. To address this [...] Read more.

Sustainable human resource management (HRM) has attracted growing attention as a new paradigm for enhancing organizational resilience. However, prior studies mainly examined the effects of individual practices, offering a limited explanation of how organizational resilience emerges as an integrated mechanism. To address this theoretical gap, we conceptualize sustainable HRM as an integral talent management process in which multiple practices operate interdependently and investigate the configurational mechanisms through which organizational resilience is generated in Japanese firms and discuss these from the perspective of socioformation. Based on six analytical dimensions derived from a tertiary literature review, we conducted a crisp-set qualitative comparative analysis (csQCA) using securities report data from 36 listed Japanese companies. The results revealed that organizational resilience is not achieved through a single best practice, but rather points to a new form of integrated human resource management aimed at sustainable value creation. From a socioformation perspective, employees are viewed not merely as productive inputs but as agents capable of continuous development through sustained investment in human potential. From this perspective, sustainable social development cannot be reduced to well-being or inclusion indicators alone but also encompasses ethical, collaborative, territorial, and interdisciplinary dimensions of transformation. The findings clarify the theoretical role of integral talent management in sustainable value creation and provide practical implications for human-centred management. Full article

(This article belongs to the Special Issue Exploring the Interrelation of Human Resource Management and Corporate Performance Using Systems Thinking)

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25 pages, 3972 KB

Open AccessArticle

Adaptive Real-Time Speed Control for Automated Smart Manufacturing Systems: A Disturbance-Resilient Solution for Productivity

by Ahmad Attar, Shuya Zhong, Martino Luis and Voicu Ion Sucala

Systems 2026, 14(3), 335; https://doi.org/10.3390/systems14030335 - 23 Mar 2026

Viewed by 280

Abstract

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control [...] Read more.

Manufacturing is going through a significant shift propelled by Industry 4.0 and smart manufacturing infrastructures, requiring sophisticated production control techniques that can adaptively adjust to fluctuating operational situations. This paper presents a novel five-step hybrid simulation framework for adaptive real-time production speed control in smart manufacturing lines, integrating conceptual modelling, hybrid simulation, algorithm redefinition, design of experiments, optimisation, and real-system implementation. The framework transforms the speed management systems into online digital twins capable of optimising system performance and mitigating unforeseen fluctuations, faults, and congestion. A comprehensive case study from the beverage manufacturing sector demonstrates the framework’s effectiveness, utilising a universal simulation platform to model both continuous fluid flow and discrete event processes. The proposed stepwise, multi-threshold algorithm employs multiple distinct logical thresholds evaluated sequentially to optimise both upstream and downstream station speeds, with decision thresholds independently adjustable for each production line segment. The experimental results show significant improvements, including around an 18% increase in overall throughput and a 95.7% reduction in work-in-process inventory. A comprehensive resiliency analysis and statistical tests under various disruption scenarios further validated the approach, demonstrating its superiority. Beyond the studied case, the framework provides a transferable pathway for real-time adaptive control across a wide range of smart manufacturing environments, enabling enhancements to operational efficiency without requiring additional capital investment in new equipment or infrastructure. Full article

(This article belongs to the Special Issue Modeling of Complex Systems and Systems of Systems)

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49 pages, 1088 KB

Open AccessArticle

Correlation Coefficient-Based Group Decision-Making Approach Under Probabilistic Dual Hesitant Fuzzy Linguistic Environment to Resilient Supplier Selection

by Xiao-Wen Qi, Jun-Ling Zhang, Jun-Tao Lai and Chang-Yong Liang

Systems 2026, 14(3), 334; https://doi.org/10.3390/systems14030334 - 23 Mar 2026

Viewed by 230

Abstract

In order to tackle resilient supplier selection (RSS) of high uncertainty in resilient supply chain management, an effective correlation coefficients-based multicriteria group decision-making (MCGDM) methodology has been constructed. The major contribution of the present study is twofold. Firstly, in view of that extant [...] Read more.

In order to tackle resilient supplier selection (RSS) of high uncertainty in resilient supply chain management, an effective correlation coefficients-based multicriteria group decision-making (MCGDM) methodology has been constructed. The major contribution of the present study is twofold. Firstly, in view of that extant criteria systems are all in lack of theoretical rationality, this paper establishes a capabilities-based analytical framework for intensive evaluation of supplier resilience by taking processual viewpoints of dynamic capabilities theory and risk management theory. Secondly, to empower the proposed correlation coefficients-based MCGDM methodology, probabilistic dual hesitant fuzzy uncertain unbalanced linguistic set (PDHF_UUBLS) is employed to capture hybrid uncertainties in decision processes of RSS. Then, theoretically compliant correlation coefficients (CCs) for PDHF_UUBLS are developed, including statistics-based CC, information energy-based CC and their weighted versions. Especially, information energy-based CCs overcome limitations of statistics-based CCs in special cases, thus exhibiting general applicability. In addition, a compatibility-based programming model has also been developed to objectively derive an unknown weighting vector for DMUs. Furthermore, illustrative case studies and comparative experiments have been carried out to verify effectiveness and stability of the proposed methodology. Taken together, this paper satisfies the new normal demand of resilience building in supply chain management and presents an effective MCGDM methodology for handling the key problems of RSS. Full article

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

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20 pages, 1689 KB

Open AccessArticle

Algorithm Brings People Closer: Fairness and Uncertainty in Interpersonal Relationship Closeness Under Algorithmic Management

by Chunyang Huo and Pingqing Liu

Systems 2026, 14(3), 333; https://doi.org/10.3390/systems14030333 - 23 Mar 2026

Viewed by 303

Abstract

With the proliferation of artificial intelligence (AI) products, Algorithmic Management (AM) is becoming an increasingly prevalent option for corporate governance. Meanwhile, the traditional boundaries between human–machine and interpersonal relationships are blurring. While some current studies suggest that AM is alienating interpersonal relationships within [...] Read more.

With the proliferation of artificial intelligence (AI) products, Algorithmic Management (AM) is becoming an increasingly prevalent option for corporate governance. Meanwhile, the traditional boundaries between human–machine and interpersonal relationships are blurring. While some current studies suggest that AM is alienating interpersonal relationships within organizations, our research proposes and tests an alternative theoretical perspective, arriving at a contrasting conclusion: interpersonal relationship under AM can be closer. Focusing on employees in organizations utilizing AM, this study investigates how and why AM influences interpersonal relationship closeness (IRC). Grounded in Uncertainty Management Theory, we propose that by fostering a fairer working experience, AM enhances IRC at work by reducing employees’ uncertainty perception. The present research, comprising four studies, empirically establishes that the positive effect of AM on interpersonal closeness is transmitted via fairness perceptions and sequentially mediated by uncertainty reduction. Collectively, these findings challenge the prevailing negative narrative, extend Uncertainty Management Theory to the human–AI interaction context, and offer practical insights for organizations adopting AM. Full article

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

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20 pages, 6053 KB

Open AccessArticle

A Gain-Modulated Max Pressure Control for Port Collection and Distribution Road Networks

by Yifei Mao, Tunan Xu, Nuojia Pan, Weijie Chen, Hang Yang, Manel Grifoll, Markos Papageorgiou and Pengjun Zheng

Systems 2026, 14(3), 332; https://doi.org/10.3390/systems14030332 - 23 Mar 2026

Viewed by 266

Abstract

Freight-dominant port collection and distribution road networks exhibit strong spatial congestion, early spillback, and heterogeneous vehicle dynamics that challenge conventional traffic signal control strategies. Although Max-Pressure (MP) signal control provides strong decentralized stability properties, its classical queue-based formulation lacks sensitivity to incipient spatial [...] Read more.

Freight-dominant port collection and distribution road networks exhibit strong spatial congestion, early spillback, and heterogeneous vehicle dynamics that challenge conventional traffic signal control strategies. Although Max-Pressure (MP) signal control provides strong decentralized stability properties, its classical queue-based formulation lacks sensitivity to incipient spatial congestion and performs poorly when heavy-duty vehicles (HDVs) dominate traffic composition. This paper proposes a gain-modulated Max-Pressure (Gain-MP) control framework, in which conventional pressure computation is augmented by an occupancy-dependent feedback gain that dynamically adjusts phase priorities according to real-time spatial congestion states and current right-of-way conditions. Without altering the decentralized structure of MP, the proposed method introduces a nonlinear feedback mechanism that enhances system responsiveness to congestion formation while suppressing excessive phase switching. The approach is evaluated using microscopic simulation on a signalized grid network representing port access corridors under time-varying demand and high HDV penetration. Results demonstrate that the dynamic Gain-MP controller performs better than classical queue-based MP, PCU-weighted MP, and fixed-time control. Moreover, constant-demand experiments indicate that the dynamic Gain-MP controller maintains bounded vehicle accumulation over a wider empirical demand range than the benchmark MP-based methods under the tested settings. Full article

(This article belongs to the Special Issue Modeling and Simulation Innovations for Advanced Logistics Systems Management)

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25 pages, 3117 KB

Open AccessArticle

Investigating Systems Complexity with the Venus Flytrap (Dionaea muscipula) Using Multiple Models: Introducing High School Students to Approaches in Mechanobiology

by Amanda M. Cottone, Zheng Bian, Jianan Zhao, Susan A. Yoon, Talar Kaloustian, Haowei Li and Rebecca G. Wells

Systems 2026, 14(3), 331; https://doi.org/10.3390/systems14030331 - 23 Mar 2026

Viewed by 342

Abstract

Understanding and developing habits in complex systems thinking using STEM-integrated perspectives is essential in addressing education and workforce needs in society. In this study, we investigated a learning intervention that incorporated multiple models designed to improve engineering students’ understanding of complex systems through [...] Read more.

Understanding and developing habits in complex systems thinking using STEM-integrated perspectives is essential in addressing education and workforce needs in society. In this study, we investigated a learning intervention that incorporated multiple models designed to improve engineering students’ understanding of complex systems through investigating the mechanobiology of the Venus flytrap. Mechanobiology is a transdisciplinary field that integrates biology, engineering, chemistry, and physics to explore how cells and tissues sense and respond to forces in their environment. We used an exploratory, mixed-methods approach to examine the impact of this new curriculum on investigating flytrap closure and prey digestion. We then evaluated students’ understanding of complex systems characteristics (i.e., many interacting parts, decentralization, non-linear interactions, emergence, and adaptation) and in their ability to transfer these principles to other systems. Qualitative analyses demonstrate that students articulated key systems principles in relation to their understanding of flytrap mechanobiology, while descriptive summaries of pre- and post-surveys suggest broader conceptual gains. Furthermore, students demonstrated the transfer of systems thinking to other contexts and reported an enhanced understanding of real-world STEM research. Full article

(This article belongs to the Special Issue Systems Thinking in STEM Education: Pedagogies and Applications)

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34 pages, 701 KB

Open AccessArticle

Developing a Composite Sustainable Smart City Performance Assessment Index: A Novel Indexing Model and Cross-Country Application

by Mert Unal and Mehtap Dursun

Systems 2026, 14(3), 330; https://doi.org/10.3390/systems14030330 - 23 Mar 2026

Viewed by 325

Abstract

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, [...] Read more.

Cities are increasingly expected to address digital transformation and sustainability challenges at the same time. However, existing urban indices generally approach smart city and sustainable city perspectives separately, which limits their ability to capture the integrated nature of contemporary urban development. In addition, many index-based studies rely on similar methodological choices. This study develops a composite Sustainable Smart City (SSC) index supported by a systematic scoring framework that brings smartness and sustainability together. The proposed framework follows a step-by-step procedure covering data preparation, normalization, weighting, aggregation, and final scoring. To address information overlap among indicators, a Redundancy-Penalized Entropy Weighting (RPEW) approach is applied. Then, overall SSC scores are calculated using a soft non-compensatory aggregation to emphasize balanced performance across dimensions. The framework is empirically illustrated through a cross-country case study including 38 OECD (Organization for Economic Co-Operation and Development) countries. A machine-learning-based polynomial forecasting approach is used for a limited number of indicators to deal with data gaps allowing the assessment to reflect more up-to-date conditions. The results highlight clear differences in SSC performance and show that strong outcomes in a single dimension are not sufficient to achieve high overall SSC scores. Instead, balanced progress across economic, digital, environmental, governance, mobility, and social dimensions plays an important role. In addition, the proposed framework provides a practical basis for comparative analysis, benchmarking, and policy-oriented evaluation of smart and sustainable urban development. Full article

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

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33 pages, 575 KB

Open AccessArticle

Sustained Adoption or Abandonment? Unveiling the Factor Configurations for Users’ Continuance Intention Toward Robotaxis

by Tianyi Zhao, Qianyu Deng and Yibao Wang

Systems 2026, 14(3), 329; https://doi.org/10.3390/systems14030329 - 23 Mar 2026

Viewed by 282

Abstract

As robotaxis transition from technological validation to commercial operation, converting first-time tryers into long-term users becomes pivotal for achieving sustainable development. Existing research mainly examines factors affecting initial adoption intention for robotaxis from a net-effect perspective, yet little is known about the factors [...] Read more.

As robotaxis transition from technological validation to commercial operation, converting first-time tryers into long-term users becomes pivotal for achieving sustainable development. Existing research mainly examines factors affecting initial adoption intention for robotaxis from a net-effect perspective, yet little is known about the factors affecting continuance intention and their nonlinear causal mechanisms. This study integrates the Expectation–Confirmation Model (ECM) and the Unified Theory of Acceptance and Use of Technology (UTAUT) to construct a systematic analytical framework and employs fuzzy-set Qualitative Comparative Analysis (fsQCA) for configurational analysis. Using survey data from 327 users in China with actual robotaxi experiences, the findings unveil four factor configurations driving high continuance intention and two causing non-high continuance intention. Regarding the interplay of factors driving high continuance intention, post-usage usefulness, satisfaction, and perceived safety constitute a complementary mechanism, whereas expectation confirmation and personal innovativeness form a substitutive mechanism that depends on the specific patterns of factor configurations. This study contributes to the robotaxi adoption literature by extending the research context to the post-adoption phase, developing a tailored theoretical framework, and applying a configurational approach rooted in complex systems analysis paradigms. The findings offer implications for governments to formulate synergistic policy mixes and for robotaxi companies to design user retention strategies. Full article

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

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22 pages, 6671 KB

Open AccessArticle

Evaluating the Influence of Alert Modalities on Driver Attention Transitions Under Visual Distraction: A Sequence Analysis Approach

by Niloufar Shirani, Elena Orlova, Manmohan Joshi, Paul (Young Joun) Ha, Yu Song, Anshu Bamney, Kai Wang and Eric Jackson

Systems 2026, 14(3), 328; https://doi.org/10.3390/systems14030328 - 20 Mar 2026

Viewed by 333

Abstract

This study evaluates how different alert conditions influence driver attention transitions under conditions of visual distraction using sequence analysis. Employing a within-subject experimental design, 13 participants underwent trials in a driving simulator, experiencing three distinct alert conditions: face-tracking auditory alerts, steering wheel auditory [...] Read more.

This study evaluates how different alert conditions influence driver attention transitions under conditions of visual distraction using sequence analysis. Employing a within-subject experimental design, 13 participants underwent trials in a driving simulator, experiencing three distinct alert conditions: face-tracking auditory alerts, steering wheel auditory torque alerts, and a control scenario without alerts. An eye-tracking system was used to capture drivers’ gaze durations and sequences across three key areas of interest: road, dashboard, and tablet-based infotainment system. Analysis involved computation of transition probabilities, Markov chain modeling for long-term attentional distributions, and entropy analyses to quantify the randomness of gaze transitions. Results showed that face-tracking alerts significantly increased the likelihood of gaze redirection to the road compared to the other conditions, enhancing both immediate and sustained attention. Steering wheel torque alerts demonstrated minimal effectiveness, sometimes performing worse than the no-alert condition due to their passive nature, allowing drivers to bypass attention redirection. Steady-state analyses confirmed that face alerts notably improved sustained driver focus on the road by approximately 3.6%, reinforcing their utility for prolonged attentional control. Entropy analyses further revealed that face alerts provided an optimal balance between structured attention shifts and behavioral flexibility, enhancing attentional predictability. Findings are consistent with previous literature, emphasizing the superior effectiveness of active, gaze-based interventions over passive mechanisms. This research underscores the importance of designing proactive alert systems in vehicle safety technology to effectively mitigate visual distraction-related risks. Full article

(This article belongs to the Special Issue Safe Systems for Road Safety: A Human Factors Perspective)

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39 pages, 5529 KB

Open AccessArticle

An Interpretable Credit Default Risk Prediction Framework Integrating Causal Feature Selection and Double Machine Learning

by Tinggui Chen, Rui Zhang and Jian Hou

Systems 2026, 14(3), 327; https://doi.org/10.3390/systems14030327 - 19 Mar 2026

Viewed by 316

Abstract

In the context of the rapid advancement of financial technology, the issue of credit card default has become increasingly salient, emerging as one of the crucial risks that financial institutions are eagerly addressing. Traditional credit card default risk prediction models predominantly rely on [...] Read more.

In the context of the rapid advancement of financial technology, the issue of credit card default has become increasingly salient, emerging as one of the crucial risks that financial institutions are eagerly addressing. Traditional credit card default risk prediction models predominantly rely on statistical correlations for feature selection. This approach not only makes it challenging to uncover the genuine causal relationships between variables but also leads to limitations in prediction accuracy and interpretability. To overcome these limitations, this paper presents a novel credit card default risk prediction model that integrates causal feature screening, interaction feature construction, and interpretability enhancement. Initially, by leveraging the information value (IV) and eXtreme gradient boosting (XGBoost), we perform initial feature dimensionality reduction. Subsequently, we introduce the Peter Clark algorithm (PC) augmented with perturbation enhancement and bootstrap sampling to identify a stable set of causal features. Building on this foundation, we proceed to construct higher-order interaction features to bolster the model’s nonlinear modeling capacity. These causal features and their interaction counterparts are then fed into a variety of mainstream machine learning models for training and evaluation purposes. Furthermore, on the basis of the causal feature set identified via the PC algorithm, we construct a causal path diagram. We also incorporate the causal forest double machine learning (causal forest DML) method to estimate the causal effects of features. Additionally, we design a counterfactual explanation mechanism to aid in analyzing the direction and magnitude of the impact of variable interventions on default probability. Empirical tests conducted using four typical credit datasets reveal the following findings: (1) the introduction of causal features generally enhances the model’s performance in terms of the F1 score, area under the curve (AUC), and geometric mean (G-mean). This improvement is especially pronounced in models that are highly reliant on feature quality, such as logistic regression (LR). (2) Causal features offer significant advantages in terms of model interpretability, stability, and compliance, thereby presenting a new research paradigm for credit risk prevention and control in high-risk financial scenarios. Full article

(This article belongs to the Special Issue Data Analytics for Social, Economic and Environmental Issues)

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26 pages, 1791 KB

Open AccessArticle

A Configurational Analysis of Risk-Taking in Intelligent Manufacturing Firms Under Multiple Institutional Logics

by Zixin Dou, Jianfeng Shi and Shaoshuai Tang

Systems 2026, 14(3), 326; https://doi.org/10.3390/systems14030326 - 19 Mar 2026

Viewed by 264

Abstract

Corporate risk-taking, crucial for sustainable development, is shaped by the interplay of multiple institutional logics. However, existing research lacks a systematic understanding of how government, market, and technology logics collectively drive corporate risk-taking. This study addresses this gap by employing fuzzy-set Qualitative Comparative [...] Read more.

Corporate risk-taking, crucial for sustainable development, is shaped by the interplay of multiple institutional logics. However, existing research lacks a systematic understanding of how government, market, and technology logics collectively drive corporate risk-taking. This study addresses this gap by employing fuzzy-set Qualitative Comparative Analysis on data from Chinese intelligent manufacturing firms to explore the configurational pathways leading to high risk-taking. Our analysis reveals three distinct pathways: (1) An innovation-driven transformation pathway, characterized by a strong synergy between government and technology logics, with market logic playing a supplementary role. (2) A green transformation pathway, where government logic dominates, supported by market and technology logics in a hierarchical structure. (3) A resource synergy pathway, marked by the high-level integration of all three logics for strategic breakthroughs. Theoretically, this study advances institutional theory by developing an integrative framework that moves beyond a single-logic perspective, revealing the synergistic and substitutive relationships among multiple logics. Practically, our findings provide managers with a configurational roadmap for strategically aligning with institutional forces to enhance risk-taking capacity. Full article

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

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20 pages, 2306 KB

Open AccessArticle

Core Ontology Usability: From a Formalized Knowledge Base to the Development of a System of Systems Domain Understanding

by Joyce Martin, Jakob Axelsson and Jan Carlson

Systems 2026, 14(3), 325; https://doi.org/10.3390/systems14030325 - 19 Mar 2026

Viewed by 280

Abstract

This paper describes the step-by-step processes towards the formalization of a core ontology for missions and capabilities in systems of systems, and the development of a specific system of systems domain ontology from the formalized ontology. The study traces the ontology development process [...] Read more.

This paper describes the step-by-step processes towards the formalization of a core ontology for missions and capabilities in systems of systems, and the development of a specific system of systems domain ontology from the formalized ontology. The study traces the ontology development process through the SABIOx methodology’s requirements, setup, capture, design, and implementation phases. In this process, we demonstrate the core ontology’s usability and reusability. Usability refers to the ontology’s adequacy for specific use as a reference point for SoS knowledge exploration for development and operational purposes, and reusability refers to the ontology’s adequacy for several uses, such as facilitating the understanding of different domain-specific systems of systems. This demonstration is done in three steps: formalization of the core ontology, exploration of the usefulness of this formalization, and development of a domain ontology from the core ontology. These result in: the incorporation of systematic ontology development processes; the application of ontology tools for machine readability; coherence and consistency checking of the ontology artifact; querying support for the ontology knowledge base; and testing of the core knowledge with a domain-specific system of systems. An alignment of these aspects provides different points of view of how a system of systems can be formulated, how the concepts collectively describe the development of an SoS emergent behavior, and how the ontology knowledge base can be explored to support decision frameworks guiding a system of systems. Full article

(This article belongs to the Section Systems Engineering)

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35 pages, 1076 KB

Open AccessArticle

Digital Transformation in SMEs: Governance Performance Mediated by AI-Enabled Analytics and Process Integration

by Sultan Bader Aljehani, Khalid Waleed Ahmed Abdo, Imdadullah Hidayat-ur-Rehman, Doaa Mohamed Ibrahim Badran and Mahmoud Abdelgawwad Abdelhady

Systems 2026, 14(3), 324; https://doi.org/10.3390/systems14030324 - 18 Mar 2026

Viewed by 487

Abstract

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits [...] Read more.

Digital transformation has become important for SMEs that want better control, transparency, and coordinated operations. Yet, many studies treat digital tools in isolation and do not explain how AI and big data capabilities, together with process integration, drive governance outcomes. This gap limits a clear understanding of how digital transformation supports governance performance in SMEs. This study examines how digital transformation (DT) influences digital governance performance (DGP) in SMEs, with AI and big data analytical capability (AIBDAC) and process integration capability (PIC) as mediators. The research is grounded in the Resource-Based View, Dynamic Capabilities Theory, and the Technology Organization Environment framework. Data were collected from SMEs across five regions of Saudi Arabia using cluster and purposive sampling to target employees and managers involved in digital, analytical, and process integration work. A total of 396 valid responses were included in the analysis. Partial Least Squares Structural Equation Modelling (PLS SEM) was used to assess the measurement model, test the hypothesized paths, and evaluate mediation and moderation effects. The findings show that DT, AIBDAC, PIC, and top management support (TMS) have significant direct effects on DGP. AIBDAC and PIC act as key mediators, fully transmitting the effects of digital innovation capability and strategic readiness and partially mediating the effects of DT and TMS. Multi-group analysis shows that small and medium-large firms rely on different capability combinations. The study contributes by explaining how SMEs strengthen governance through capability development and offers practical guidance for improving governance through digital transformation. Full article

(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)

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20 pages, 854 KB

Open AccessArticle

Replacement vs. Augmentation: An Analysis of Romanian Students and Faculty Views of the Impact of AI on the Labor Market

by Kamer-Ainur Aivaz, Daniel Teodorescu and Oana Roxana Radu

Systems 2026, 14(3), 323; https://doi.org/10.3390/systems14030323 - 18 Mar 2026

Viewed by 274

Abstract

The rapid development of artificial intelligence (AI) has intensified debates regarding its impact on the labor market, specifically concerning the potential for replacement versus the augmentation of human labor. While the existing literature highlights both the opportunities and risks associated with AI, research [...] Read more.

The rapid development of artificial intelligence (AI) has intensified debates regarding its impact on the labor market, specifically concerning the potential for replacement versus the augmentation of human labor. While the existing literature highlights both the opportunities and risks associated with AI, research conducted by faculty in academic settings focuses predominantly on academic integrity, paying limited attention to AI readiness and/or anxiety related to labor market entry. This study aims to compare the perceptions of students and faculty in Romania regarding the impact of AI on employment, exploring the role of personal and organizational readiness in shaping these attitudes. The research is based on an empirical approach utilizing a questionnaire applied to a sample of 271 respondents, consisting of 197 students and 74 faculty members. Data analysis included descriptive and inferential methods, such as Chi-square tests and binary logistic regression, and was theoretically grounded in the Unified Theory of Acceptance and Use of Technology (UTAUT) and Social Cognitive Theory (SCT). The results indicate significant differences between students and faculty regarding general attitudes toward AI, with students manifesting higher levels of concern regarding job replacement. However, both groups converge in their functional definition of AI as a major factor in labor transformation, suggesting an evaluative rather than a cognitive difference. Multivariate analyses show that personal readiness and the perception of organizational readiness are the primary predictors of a positive attitude toward AI, while demographic variables lose statistical significance when these dimensions are controlled. This study contributes to the literature by highlighting that AI-related anxiety is not inherently determined by demographic characteristics but represents a malleable state shaped by individual competencies and institutional conditions. The findings underscore the strategic role of universities in reducing perceptions of replacement and facilitating the transition to an AI-augmented labor market through training policies, adequate infrastructure, and transparent institutional communication. Full article

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

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27 pages, 2944 KB

Open AccessArticle

Presale Strategies for Fresh Agricultural Products Considering Option Ordering

by Zhong Zhao and Chunyu Dai

Systems 2026, 14(3), 322; https://doi.org/10.3390/systems14030322 - 18 Mar 2026

Viewed by 276

Abstract

Under traditional spot-sale strategies, the perishability and demand uncertainty of fresh agricultural products often result in market share erosion and profit losses for retailers. To address this challenge, this study constructs and compares decision models under different combinations of ordering modes and sales [...] Read more.

Under traditional spot-sale strategies, the perishability and demand uncertainty of fresh agricultural products often result in market share erosion and profit losses for retailers. To address this challenge, this study constructs and compares decision models under different combinations of ordering modes and sales strategies. Specifically, for ordering modes, retailers can choose between wholesale ordering and option ordering as their ordering mode, while for sales strategies, they can select either presale or spot sale based on consumer presale preference. The study aims to identify the conditions for implementing presales, examine the impact mechanism of option ordering on presales, and analyze differences in market share and expected profit across various ordering–sales strategy combinations. The results reveal the following: (1) presales outperform spot sales in market share and expected profit only when consumer presale preference exceeds a critical threshold, which is higher under option ordering; (2) compared to wholesale ordering, option ordering reduces the incremental market share and profit gains from presales but allows retailers adopting presales to achieve higher expected profits; (3) once the critical threshold for presale implementation is met, the presale strategy under wholesale ordering facilitates faster market share capture, whereas the presale strategy under option ordering maximizes retailer profits. Furthermore, retailers can lower the threshold for implementing presales and expand their applicability by optimizing freshness-keeping efforts or adjusting option contract parameters. Full article

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23 pages, 787 KB

Open AccessArticle

How Do Supply Chain Risks Inhibit Manufacturing Firms’ Global Expansion? A System Theory Perspective on Transmission Mechanisms and Mitigation Strategies

by Mingrong Wang, Xiaohui Yuan and Hanshen Li

Systems 2026, 14(3), 321; https://doi.org/10.3390/systems14030321 - 18 Mar 2026

Viewed by 290

Abstract

Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected [...] Read more.

Managing supply chain risks is a core pillar of operational and supply chain resilience building in the global industrial chain system, which is essential for the high-quality and sustainable development of manufacturing firms. Against the backdrop of escalating global economic uncertainties and interconnected supply chain vulnerabilities, mitigating the adverse impact of supply chain risks on firms’ overseas market expansion has become a critical research and practical issue in the field of operational and supply chain risk management. Based on the textual analysis of annual reports of listed firms, this study constructs a systematic supply chain risk measurement indicator system through standardized text preprocessing, multi-dimensional feature keyword lexicon construction, context co-occurrence frequency calculation and so on. We further validate the effectiveness of the indicator system by comparing its trend with the global economic uncertainty index, confirming that it can capture firm-specific supply chain risk information effectively. Employing text analysis, this study constructs a systematic supply chain risk measurement indicator system for A-share manufacturing firms and empirically verifies that elevated supply chain risks significantly constrain their overseas market expansion. Three interrelated operational mechanisms, namely surging operating costs, tightened financing constraints, and slumping R&D investments, drive this inhibitory effect. Notably, firms can effectively offset this negative effect by broadening overseas operational scope and intensifying overseas digital and technological innovation. Heterogeneity analyses further reveal that the inhibitory effect is more pronounced for five types of firms: those with lower overseas revenue, located in less market-oriented regions, operating in upstream value chain sectors, with lower current liabilities, and with a lower degree of digital transformation. Full article

(This article belongs to the Special Issue Operation and Supply Chain Risk Management)

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21 pages, 4941 KB

Open AccessArticle

A Physics-Informed Multimodal Deep Learning Framework for City-Scale Air-Quality and Health-Risk Prediction

by Khaled M. Alhawiti

Systems 2026, 14(3), 320; https://doi.org/10.3390/systems14030320 - 18 Mar 2026

Viewed by 260

Abstract

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health [...] Read more.

Accurate and interpretable air quality prediction remains a critical challenge for environmental health management due to complex, nonlinear interactions among emissions, meteorology, and atmospheric chemistry. This study presents a hybrid physics informed and multimodal deep learning framework for city-scale air quality and health risk prediction. The framework combines a Gaussian plume dispersion model with a residual CNN-LSTM network that learns data driven corrections while preserving physical consistency. Multimodal open datasets, including ground based pollutant sensors, meteorological records, and satellite derived aerosol and temperature features, are jointly fused to improve spatiotemporal fidelity. An Exposure Health Index module further links predicted pollutant fields with respiratory morbidity indicators, providing a quantitative bridge between atmospheric variability and health outcomes. Using open source datasets from Riyadh, Jeddah, and Dammam, the proposed approach achieves up to 25% lower mean absolute error and

R^{2}

values above 0.85 compared with physics only and purely data driven baselines. Explainability analyses using SHAP and spatial attention highlight physically plausible drivers and confirm feature relevance. The results demonstrate that physics guided residual learning can unify deterministic dispersion modeling and multimodal inference, providing a transparent, scalable, and reproducible foundation for air quality forecasting and health risk assessment. Full article

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26 pages, 12179 KB

Open AccessArticle

Analysis of Influencing Factors and Prediction of Provincial Energy Poverty in China Based on Explainable Deep Learning

by Zihao Fan, Pengying Fan and Yile Wang

Systems 2026, 14(3), 319; https://doi.org/10.3390/systems14030319 - 17 Mar 2026

Viewed by 334

Abstract

Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based [...] Read more.

Energy poverty remains an important challenge for sustainable development in China, with pronounced regional disparities and evolving temporal dynamics that require accurate and interpretable prediction tools. This study develops a provincial panel-based framework that combines Energy Poverty Index (EPI) construction, SSA-LSTM prediction, SHAP-based model interpretation, and two-way fixed effects (TWFE) regression analysis. Using provincial data for China (2003–2022), we first construct a composite EPI with the entropy weight method, then apply a Sparrow Search Algorithm (SSA) to optimize LSTM hyperparameters for EPI forecasting. SHAP is used to interpret feature contributions to model-predicted EPI, and TWFE regression is used to provide complementary panel-data evidence on factor–EPI associations. The results show that the SSA-LSTM model outperforms benchmark machine learning and deep learning models in out-of-sample prediction performance. SHAP-based interpretation indicates that variables such as GDP, energy intensity, and power generation per capita contribute strongly to prediction variation, with notable regional heterogeneity. TWFE results are broadly consistent with several key patterns identified in the SHAP analysis. Overall, the proposed framework provides an accurate and interpretable provincial energy poverty prediction approach and offers a useful empirical reference for energy poverty monitoring and policy discussion. Full article

(This article belongs to the Special Issue Advancing Open Innovation in the Age of AI and Digital Transformation)

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21 pages, 2518 KB

Open AccessArticle

Synergy of Low-Carbon City Pilot and Carbon Emissions Trading in Reducing Pollution and CO₂ Emissions: Quasi-Natural Experimental Evidence from Chinese Cities

by Yanfang Cui, Yilin Hu, Zengchuan Wang, Li Li, Yalin Lei and Sanmang Wu

Systems 2026, 14(3), 318; https://doi.org/10.3390/systems14030318 - 17 Mar 2026

Viewed by 353

Abstract

The low-carbon city pilot (LCCP) and carbon emissions trading (CET) represent two critical policies for reducing carbon emissions. Accurately evaluating their synergistic effects on the reduction in pollution and carbon emissions (RPCE) is of utmost importance for advancing China’s low-carbon economic growth and [...] Read more.

The low-carbon city pilot (LCCP) and carbon emissions trading (CET) represent two critical policies for reducing carbon emissions. Accurately evaluating their synergistic effects on the reduction in pollution and carbon emissions (RPCE) is of utmost importance for advancing China’s low-carbon economic growth and achieving the dual-carbon objectives. Utilizing data from 279 prefecture-level cities during 2008 to 2021, this study employed a multi-phase differences-in-differences model to investigate the synergistic effects of the concurrent implementation of LCCP and CET (referred to as the “dual pilot” policy) on RPCE. The findings revealed that (1) the dual pilot policies reduced per capita CO₂ emissions by 0.644% and PM_2.5 concentration by 0.114%, with the dual effect being significantly superior to that of single pilot policies; (2) through mechanism analysis, it was found that technological innovation and clean energy transition served as the principal channels through which the “dual pilot” policy exerted its influence on RPCE; and (3) heterogeneity analysis demonstrated that the “dual pilot” policy was particularly effective in the RPCE in big cities, non-resource-based cities, and highly urbanized cities. This study provides novel empirical evidence supporting the integration of active government intervention with effective market mechanisms to maximize synergies in carbon emission reduction policies and achieve RPCE. Full article

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

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19 pages, 1423 KB

Open AccessArticle

Shipping Market Sentiment Shocks and BDI Volatility: Evidence from News-Based Indicators

by Lili Qu, Nan Hong and Yutong Han

Systems 2026, 14(3), 317; https://doi.org/10.3390/systems14030317 - 17 Mar 2026

Viewed by 304

Abstract

To address the lag and limited sensitivity of conventional shipping freight indicators, this study develops a news-based sentiment measure for the shipping market and examines its association with BDI dynamics. Using shipping-related news headlines from 2019 to 2025, a RoBERTa classifier fine-tuned on [...] Read more.

To address the lag and limited sensitivity of conventional shipping freight indicators, this study develops a news-based sentiment measure for the shipping market and examines its association with BDI dynamics. Using shipping-related news headlines from 2019 to 2025, a RoBERTa classifier fine-tuned on manually annotated data is used to quantify headline sentiment, and a daily Cumulative Sentiment Index (CSI) is constructed using an event-smoothing window with exponential decay. A higher CSI indicates more positive market sentiment, while a lower CSI reflects more negative sentiment. Empirical evidence shows that CSI exhibits pronounced responses around major market events and is closely linked to BDI behavior in event windows. In addition, an EGARCH specification augmented with CSI indicates that sentiment is significantly associated with conditional volatility, suggesting that news-based sentiment contains incremental information relevant to BDI risk dynamics. Overall, the proposed CSI provides a quantitative approach to tracking shipping market sentiment using publicly available news headlines and offers a complementary perspective to transaction-based freight indices. Full article

(This article belongs to the Topic Data Science and Intelligent Management)

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26 pages, 1215 KB

Open AccessArticle

Pressure Wave Propagation Optimization Models for Supply Chain Risk Mitigation

by Ming Liu, Jiawei Zhang and Yueyu Ding

Systems 2026, 14(3), 316; https://doi.org/10.3390/systems14030316 - 17 Mar 2026

Viewed by 311

Abstract

Supply chain (SC) disruption risk assessment and mitigation have attracted significant attention in both academia and practice. However, existing research predominantly focuses on unidirectional disruption propagation, either forward or backward, despite the reality that risks can propagate bi-directionally in complex supply chain networks. [...] Read more.

Supply chain (SC) disruption risk assessment and mitigation have attracted significant attention in both academia and practice. However, existing research predominantly focuses on unidirectional disruption propagation, either forward or backward, despite the reality that risks can propagate bi-directionally in complex supply chain networks. Furthermore, conventional assessment tools often concentrate on conceptualizing and quantifying risks, while risk mitigation requires mathematical optimization approaches. To bridge these gaps, this paper proposes a novel pressure wave-based approach inspired by fluid mechanics to assess bi-directional disruption propagation in cluster supply chain networks (CSCNs). The method conceptualizes disruptions as pressure signals that transmit between SC partners and explicitly quantifies disruption severity through wave intensity. By employing mathematical optimization, we develop a framework that assists managers in optimizing risk mitigation strategies, including inventory buffering and cross-chain cooperation. Numerical experiments demonstrate the effectiveness of the proposed method in explaining risk influencing factors, mitigating disruption risks, and achieving dynamic restructuring of SC structures. The results show that our approach reduces the Cluster Propagation Vulnerability Index (CPVI) by up to 40% compared to baseline models without optimization decisions. Full article

(This article belongs to the Special Issue Uncertainty-Aware Decision Making in Supply Chain and Logistics: Integration of Fuzzy, Stochastic, and Analytical Approaches)

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30 pages, 1715 KB

Open AccessArticle

AI-Based Model for Maintaining Good Healthcare Quality Against Cybersecurity Risks

by Abdullah M. Algarni and Vijey Thayananthan

Systems 2026, 14(3), 315; https://doi.org/10.3390/systems14030315 - 17 Mar 2026

Viewed by 477

Abstract

Artificial Intelligence (AI) has strong potential in health monitoring systems to support high-quality healthcare while mitigating cybersecurity risks. AI-based solutions for health and wellness applications, particularly for cardiovascular disease monitoring, are being explored to address complex healthcare challenges and improve patient outcomes. The [...] Read more.

Artificial Intelligence (AI) has strong potential in health monitoring systems to support high-quality healthcare while mitigating cybersecurity risks. AI-based solutions for health and wellness applications, particularly for cardiovascular disease monitoring, are being explored to address complex healthcare challenges and improve patient outcomes. The integration of quantum and AI-based techniques is also gaining attention for enhancing future healthcare applications and communication technologies. Purpose: The primary objective is to improve cardiac care by accurately predicting symptoms and mitigating cyber-risks that threaten digital health integrity. By leveraging Integrated Quantum Networks (IQNs) and AI-driven protocols, this research aims to reduce the prevalence/incidence of non-communicable diseases by 50% by 2035 through proactive prevention and superior treatment management. Method: The framework utilizes AI-based techniques and AI-quantum-enhanced sensors and IQN to build a secure, proactive monitoring system. This theoretical framework integrates high-precision data collection with robust risk management systems to protect against vulnerabilities in digital health infrastructure. These components work in tandem to ensure that sensitive medical data remain resilient against emerging cyber threats. Anticipated Results and Conclusions: The system is expected to improve cybersecurity resilience, system performance, and energy efficiency (EE), supporting the development of secure and advanced future healthcare applications. Full article

(This article belongs to the Section Artificial Intelligence and Digital Systems Engineering)

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21 pages, 1159 KB

Open AccessArticle

Low-Carbon Production Strategies of Manufacturing Firms Under Free-Riding and Technology Spillovers: A Moran Process Analysis

by Jingfei Ding and Keyong Zhang

Systems 2026, 14(3), 314; https://doi.org/10.3390/systems14030314 - 17 Mar 2026

Viewed by 221

Abstract

Against the backdrop of China’s dual-carbon goals and the global green transition, low-carbon production in the manufacturing sector is crucial to achieving high-quality development. Based on the dual mechanisms of the free-riding effect and technology spillovers, this paper develops a Moran stochastic evolutionary [...] Read more.

Against the backdrop of China’s dual-carbon goals and the global green transition, low-carbon production in the manufacturing sector is crucial to achieving high-quality development. Based on the dual mechanisms of the free-riding effect and technology spillovers, this paper develops a Moran stochastic evolutionary game model of manufacturing firms’ low-carbon production strategies under government regulation. We analyze the dynamic evolution and stability of low-carbon versus conventional production strategies under strong- and weak-selection conditions. The results show that under strong selection, a low free-riding payoff promotes the diffusion of the low-carbon strategy and the formation of a stable equilibrium; a moderate free-riding payoff makes population size the key factor shaping evolutionary outcomes; and a high free-riding payoff leads the system to degenerate into a steady state dominated by conventional production. Under weak selection, government subsidies and fines increase the fixation probability and stability of the low-carbon strategy, whereas excessive free-riding payoffs undermine the persistence of the transition. Numerical simulations validate the theoretical analysis and indicate that government regulation, technology spillovers, and population structure jointly shape the long-term evolution of low-carbon behavior, providing a theoretical basis and decision-making reference for optimizing policy mechanisms and promoting the low-carbon transition of the manufacturing sector. Full article

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

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18 pages, 421 KB

Open AccessArticle

Embrace LLM-Based Cognitive Architecture to Boost Team Problem-Solving in Open-Ended Tasks

by Hashmath Shaik, Gnaneswar Villuri and Alex Doboli

Systems 2026, 14(3), 313; https://doi.org/10.3390/systems14030313 - 16 Mar 2026

Viewed by 385

Abstract

Open-ended, team-based problem solving demands (i) a bridge between stochastic language models and symbolic control, (ii) mechanisms for idea elaboration, (iii) feature-level concept combination, and (iv) internal representations that support understanding beyond mere association. We present a cognitive architecture (CA) that couples an [...] Read more.

Open-ended, team-based problem solving demands (i) a bridge between stochastic language models and symbolic control, (ii) mechanisms for idea elaboration, (iii) feature-level concept combination, and (iv) internal representations that support understanding beyond mere association. We present a cognitive architecture (CA) that couples an LLM with an editable knowledge-graph (KG) scaffold and a controller that adaptively schedules five reasoning strategies. Elaborations are cast as graph updates validated against coverage and consistency checks; combinations produce property- and relation-level recompositions. On 30 collaborative programming dialogs (nine representative scenarios), adaptive prompting improves solution completeness by 19.1% and reduces required turns by 18.5% over a CoT baseline; explicit concept combinations increase Distinct-3 by 12.4 points with a +0.7 gain in human-rated creativity. Ablations show that Soft→Pruning scaffolds best support early elaboration, while Hard partitioning helps under ambiguity. The CA demonstrates a practical route to aligning LLMs with team intent in open-ended tasks. Full article

(This article belongs to the Special Issue Human-AI (H-AI) Teams: Designing for Human-AI Interactions)

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38 pages, 4516 KB

Open AccessArticle

A Formal Modeling Framework for Time-Aware Cyber–Physical Systems of Systems

by Riad Helal, Faiza Belala, Nabil Hameurlain and Akram Seghiri

Systems 2026, 14(3), 312; https://doi.org/10.3390/systems14030312 - 16 Mar 2026

Viewed by 306

Abstract

Cyber–Physical Systems of Systems (CPSoS) integrate autonomous constituent systems to accomplish complex missions. Nonetheless, decentralized coordination and continuous evolution create intricate dependencies that make behavior difficult to analyze. Current semi-formal modeling approaches, despite being easy to understand and widely accessible, lack semantic precision [...] Read more.

Cyber–Physical Systems of Systems (CPSoS) integrate autonomous constituent systems to accomplish complex missions. Nonetheless, decentralized coordination and continuous evolution create intricate dependencies that make behavior difficult to analyze. Current semi-formal modeling approaches, despite being easy to understand and widely accessible, lack semantic precision and are not computationally checkable to guarantee time-critical properties. Furthermore, current formal methods are often fragmented: they analyze behavior either at the individual CPS level or the collective CPSoS level, failing to provide a multi-level specification. To address these limitations, we propose an integrated framework combining SysML and Maude rewriting logic. SysML provides structural and behavioral specification capabilities, while Maude enables rigorous semantics, executable models, and formal verification. First, our approach proposes MM-CPSoS, a meta-model that unifies CPS and CPSoS entities with explicit temporal constraints. Dynamic behavior is captured through evolution patterns governing mission progression across both levels. Then, we encode SysML models into Maude as object-oriented configurations and conditional rewrite rules, enabling linear temporal logic (LTL) model checking of temporal properties. Finally, we demonstrate our approach through a Time-Aware Road Crisis Management System (TaRCiMaS²). Full article

(This article belongs to the Section Systems Engineering)

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27 pages, 900 KB

Open AccessArticle

Enhancing Student Systems Thinking in Generative Artificial Intelligence-Supported Logistics Management Education in China: An Integrated Model with PLS-SEM and FsQCA

by Jing Liang, Yuxiang Zhang, Huyang Xu, Ming Zeng and Yuyan Luo

Systems 2026, 14(3), 311; https://doi.org/10.3390/systems14030311 - 16 Mar 2026

Viewed by 288

Abstract

Systems thinking is a core competence in logistics management, as decisions across transportation, warehousing, and delivery functions are highly interconnected and often generate delayed, trade-off, or system-wide consequences. Despite the increasing integration of generative artificial intelligence (GenAI) tools into logistics education, limited research [...] Read more.

Systems thinking is a core competence in logistics management, as decisions across transportation, warehousing, and delivery functions are highly interconnected and often generate delayed, trade-off, or system-wide consequences. Despite the increasing integration of generative artificial intelligence (GenAI) tools into logistics education, limited research has examined how to enhance systems thinking in such contexts. Drawing on triadic reciprocal determinism, this study conceptualizes systems thinking enhancement as an emergent outcome of interactions among behavioral regulation, cognitive conditions, and environmental scaffolding. Using survey data from 236 logistics management students in Chinese universities, we integrate Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine both net effects and configurational mechanisms. Results show that self-regulated learning exhibits the strongest positive association with systems thinking, while germane cognitive load is positively associated and extraneous cognitive load is negatively associated with systems thinking. Teacher GenAI scaffolding is linked to more favorable cognitive load allocation. fsQCA findings further reveal that high-level systems thinking emerges from specific combinations where self-regulated learning and germane cognitive load are fundamental conditions, whereas the absence of self-regulated learning consistently leads to low-level systems thinking. These findings provide guidance for the design of GenAI-supported curricula and scaffolding strategies. Full article

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

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27 pages, 3039 KB

Open AccessArticle

A Sociological Model of Political Regimes in the Parisi–Talagrand and Sherrington–Kirkpatrick Framework: Imposed vs. Natural Replica Symmetry in Totalitarian Systems

by Kostadin Yotov, Emil Hadzhikolev, Stanka Hadzhikoleva and Todor Rachovski

Systems 2026, 14(3), 310; https://doi.org/10.3390/systems14030310 - 16 Mar 2026

Viewed by 270

Abstract

This study proposes a theoretical–empirical framework for analyzing political regimes based on a structural analogy between electoral behavior and spin-glass systems in statistical physics. Society is modeled as a system of interacting agents (voters) influenced by both interpersonal interactions and external factors such [...] Read more.

This study proposes a theoretical–empirical framework for analyzing political regimes based on a structural analogy between electoral behavior and spin-glass systems in statistical physics. Society is modeled as a system of interacting agents (voters) influenced by both interpersonal interactions and external factors such as media and institutions, formalized through a social Hamiltonian. By introducing a partition function and free energy, political regimes are interpreted as distinct macroscopic phases governed by four effective macro-parameters: external field, conformism, interaction heterogeneity, and inverse social temperature. Democratic societies correspond to a multistable regime characterized by sensitivity to initial conditions and replica symmetry breaking (RSB), reflecting the coexistence of competing social configurations. Authoritarian regimes, in contrast, arise when a strong unidirectional external field, high conformism, and low effective social temperature stabilize a single dominant macroscopic state, producing a regime analogous to replica symmetry (RS). A central result of the model is the distinction between the predictability of macroscopic outcomes and structural social multistability, as well as between natural and externally imposed homogenization of collective behavior. To illustrate the empirical relevance of the framework, the model is applied to the transition from the Weimar Republic to the National Socialist regime (1919–1933), using aggregated electoral data to construct proxy indicators for the effective parameters governing social interactions. The proposed approach enables structural identification of early signals of authoritarian transition through changes in the parameters of social dynamics. Full article

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

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19 pages, 637 KB

Open AccessArticle

Examining the Relationship Between Organizational Ambidexterity and Firm Performance in New Technology-Based Firms

by Julio César Acosta-Prado, Elías Aburto-Camacllanqui, José Ever Castellanos Narciso and Ricardo Mora Pabón

Systems 2026, 14(3), 309; https://doi.org/10.3390/systems14030309 - 16 Mar 2026

Viewed by 260

Abstract

Organizational ambidexterity is an essential topic in management research. A growing number of studies argue that organizational ambidexterity is increasingly critical to the sustained competitive advantage of firms. However, there is less research on ambidexterity in new technology-based firms, despite the significant impact [...] Read more.

Organizational ambidexterity is an essential topic in management research. A growing number of studies argue that organizational ambidexterity is increasingly critical to the sustained competitive advantage of firms. However, there is less research on ambidexterity in new technology-based firms, despite the significant impact it has on local and national economies. The study examined the relationship between organizational ambidexterity and firm performance (non-economic and economic). The sample consists of 102 Colombian new technology-based firms. A latent variable design or structural equation modeling was followed. The statistical method was Partial Least Squares Structural Equation Modelling (PLS-SEM). According to the results, organizational ambidexterity is positively related to both non-economic and economic performance. Organizational ambidexterity explained 10% of the variance of the economic performance and 56% of the variance of the non-economic performance. These findings highlight the importance of organizational ambidexterity to obtain better firm performance, especially non-economic performance related to customer perception, employee satisfaction, and improvement in the quality of products and services in new technology-based firms. Full article

(This article belongs to the Special Issue Systems Approaches in Knowledge Management and Organizational Innovation)

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24 pages, 9551 KB

Open AccessArticle

An Optimization Framework for Manned–Unmanned Squad Equipment System Design and Collocation Scheme Oriented to Micro-Scenarios and Operation Loops

by Cancan Hu, Yaping Wang, Yu Zhang, Fan Yang and Shuocan Zhu

Systems 2026, 14(3), 308; https://doi.org/10.3390/systems14030308 - 16 Mar 2026

Viewed by 211

Abstract

In the design of Infantry Squad Weapon Equipment System-of-Systems (ISWES), traditional text-based systems engineering primarily relies on empirical methods derived from historical designs. This approach suffers from low design efficiency, protracted development cycles, and incomplete system requirements analysis. In addition, determining effective equipment [...] Read more.

In the design of Infantry Squad Weapon Equipment System-of-Systems (ISWES), traditional text-based systems engineering primarily relies on empirical methods derived from historical designs. This approach suffers from low design efficiency, protracted development cycles, and incomplete system requirements analysis. In addition, determining effective equipment configurations to maximize the integrated operational capabilities of weapon systems has garnered increasing attention. This study proposes a micro-scenario-oriented squad equipment system design framework featuring manned–unmanned teaming collocation optimization. First, an MBSE method applicable to ISWES modeling is proposed, and the initial allocation scheme of ISWES is obtained. Subsequently, a multi-objective optimization model for an allocation scheme is established based on operation loop theory with the objectives of maximizing combat effectiveness and network robustness while minimizing weapon costs, and the CCMO algorithm is employed to obtain the Pareto set. Then, a multi-attribute scheme selection method leveraging Successive Elimination of Alternatives Based on Order and Degree of Efficiency (SEABODE)-improved TOPSIS is proposed to identify the optimal collocation. Finally, a case study on infantry squad-level equipment system design validates the framework’s feasibility and effectiveness. Full article

(This article belongs to the Section Systems Engineering)

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36 pages, 1027 KB

Open AccessArticle

Governing Human–AI Co-Evolution: Intelligentization Capability and Dynamic Cognitive Advantage

by Tianchi Lu

Systems 2026, 14(3), 307; https://doi.org/10.3390/systems14030307 - 15 Mar 2026

Viewed by 566

Abstract

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the [...] Read more.

This research addresses a structural cybernetic anomaly within strategic management precipitated by the integration of artificial intelligence into the organizational core. Traditional paradigms, specifically the resource-based view and the dynamic capabilities framework, operate under closed-system, first-order cybernetic assumptions that fail to capture the dissipative nature of algorithmic agents. By conceptualizing the enterprise as a complex adaptive system operating far from thermodynamic equilibrium, this study introduces the theory of dynamic cognitive advantage. Grounded in second-order cybernetics, the framework posits that competitive differentiation emerges from the historical, recursive, structural coupling of human semantic intent and machine syntactic processing. This research formalizes this co-evolutionary dynamic utilizing coupled non-linear differential equations and time decay integrals. Furthermore, it operationalizes the central mechanism of this capability—the cognitive flywheel—and proposes a fractal governance architecture to mitigate systemic vulnerabilities such as automation bias. To transition these propositions into management science, a proposed mixed-methods empirical research agenda is presented. It outlines a future partial least squares–structural equation modeling (PLS-SEM) approach to test the mediating role of the cognitive flywheel and the moderating effect of fractal governance on organizational resilience. This research provides a mathematically formalized, empirically testable architecture for navigating the artificial intelligence economy. Full article

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

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