Systems

11 pages, 235 KiB

Open AccessCommunication

Talking Resilience: Embedded Natural Language Cyber-Organizations by Design

by Andrea Tomassi, Andrea Falegnami and Elpidio Romano

Systems 2025, 13(4), 247; https://doi.org/10.3390/systems13040247 (registering DOI) - 2 Apr 2025

This communication examines the interplay between linguistic mediation and knowledge conversion in cyber-sociotechnical systems (CSTSs) via the WAx framework, which outlines various work representations and eight key conversion activities. Grounded in enactivist principles, we argue that language is a dynamic mechanism that shapes, [...] Read more.

This communication examines the interplay between linguistic mediation and knowledge conversion in cyber-sociotechnical systems (CSTSs) via the WAx framework, which outlines various work representations and eight key conversion activities. Grounded in enactivist principles, we argue that language is a dynamic mechanism that shapes, and is shaped by, human–machine interactions, enhancing system resilience and adaptability. By integrating the concepts of simplexity, complixity, and complexity compression, we illustrate how complex cognitive and operational processes can be selectively condensed into efficient outcomes. A case study of a chatbot-based customer support system demonstrates how the phases of socialization, introspection, externalization, combination, internalization, conceptualization, reification, and influence collaboratively drive the evolution of resilient CSTS designs. Our findings indicate that natural language serves as a bridging tool for effective sense-making, adaptive coordination, and continuous learning, offering novel insights into designing technologically advanced, socially grounded, and evolving sociotechnical systems. Full article

(This article belongs to the Special Issue Cognitive and Practical Perspectives on Resilience, Organization and Entangled Systems)

25 pages, 1505 KiB

Open AccessArticle

Customer-Directed Counterproductive Work Behavior of Gig Workers in Crowdsourced Delivery: A Perspective on Customer Injustice

by Yanfeng Liu, Lanhui Cai, Xueqin Wang and Xueli Tan

Systems 2025, 13(4), 246; https://doi.org/10.3390/systems13040246 (registering DOI) - 2 Apr 2025

Abstract

In the platform economy, customers are the primary interaction partners of gig workers, and their behaviors and attitudes significantly influence gig workers’ work experiences and behavioral responses. Based on the stressor–emotion model and social exchange theory, this paper systematically explores the formation mechanism [...] Read more.

In the platform economy, customers are the primary interaction partners of gig workers, and their behaviors and attitudes significantly influence gig workers’ work experiences and behavioral responses. Based on the stressor–emotion model and social exchange theory, this paper systematically explores the formation mechanism of customer-directed counterproductive work behavior. This study employs structural equation modeling to analyze survey data collected from 385 registered gig workers on crowdsourced delivery platforms in China. The results indicate that customer injustice increases gig workers’ negative emotions, perceived organizational injustice, and customer-directed counterproductive work behavior while decreasing customer commitment. Furthermore, negative emotions, perceived organizational injustice, and customer commitment mediate the relationship between customer injustice and customer-directed counterproductive work behavior. Additionally, job demands act as a buffering mechanism in the occurrence of customer-directed counterproductive work behavior. This study is the first to systematically focus on customer-directed counterproductive work behavior among crowdsourced delivery gig workers, enriching the existing literature. The findings provide practical insights for crowdsourced delivery platforms, aiding in understanding gig workers’ work psychology and optimizing labor management strategies. Full article

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

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20 pages, 819 KiB

Open AccessArticle

Building a Resilient Organization Through Informal Networks: Examining the Role of Individual, Structural, and Attitudinal Factors in Advice-Seeking Tie Formation

by Xiaoyan Jin, Daegyu Yang, Wanlan Sun and Lian Xu

Systems 2025, 13(4), 245; https://doi.org/10.3390/systems13040245 (registering DOI) - 1 Apr 2025

Abstract

Modern organizations operate not only through formal structures but also through informal networks, which play a critical role in fostering a resilient organization. This study focused on informal advice networks within organizations as a key mechanism for strengthening contextual resilience, one of the [...] Read more.

Modern organizations operate not only through formal structures but also through informal networks, which play a critical role in fostering a resilient organization. This study focused on informal advice networks within organizations as a key mechanism for strengthening contextual resilience, one of the core components of organizational resilience. By analyzing the activation of informal advice networks, this study conceptualized advice-seeking networks as a critical informal system that enhances contextual resilience and examined the individual, structural, and attitudinal factors influencing their formation. Specifically, we hypothesized that employees with higher levels of Machiavellianism are more likely to engage in advice-seeking behaviors, whereas the relationship between Machiavellianism and advice-seeking behaviors is moderated by betweenness centrality and organizational commitment, such that the positive effect of Machiavellianism on advice-seeking is weaker when betweenness centrality or organizational commitment is high. To empirically test these hypotheses, we conducted a network survey of employees at the headquarters of a life insurance company in Seoul, South Korea, and analyzed the data using an Exponential Random Graph Model (ERGM). The findings provide empirical support for all hypotheses. Based on these results, we discussed the theoretical contributions and practical implications of the study, along with its limitations. Full article

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

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18 pages, 6110 KiB

Open AccessArticle

Application of Systems-of-Systems Theory to Electromagnetic Warfare Intentional Electromagnetic Interference Risk Assessment

by Nigel Davies, Huseyin Dogan and Duncan Ki-Aries

Systems 2025, 13(4), 244; https://doi.org/10.3390/systems13040244 - 1 Apr 2025

Abstract

Battlefields contain complex networks of electromagnetic (EM) systems, owned by adversary/allied military forces and civilians, communicating intentionally or unintentionally. Attacker’s strategies may include Intentional EM Interference (IEMI) to adversary target systems, although transmitted signals may additionally degrade/disrupt allied/civilian systems (called victims). To aid [...] Read more.

Battlefields contain complex networks of electromagnetic (EM) systems, owned by adversary/allied military forces and civilians, communicating intentionally or unintentionally. Attacker’s strategies may include Intentional EM Interference (IEMI) to adversary target systems, although transmitted signals may additionally degrade/disrupt allied/civilian systems (called victims). To aid decision-making processes relating to IEMI attacks, Risk Assessment (RA) is performed to determine whether interference risks to allied/civilian systems are acceptable. Currently, there is no formalized Quantitative RA Method (QRAM) capable of calculating victim risk distributions, so a novel approach is proposed to address this knowledge gap, utilizing an Electromagnetic Warfare (EW) IEMI RA method modeling scenarios consisting of interacting EM systems within complex, dynamic, diverse, and uncertain environments, using Systems-of-Systems (SoS) theory. This paper aims to address this knowledge gap via critical analysis utilizing a case study which demonstrates the use of an Acknowledged SoS-based model as input to a QRAM capable of calculating victim risk distributions within EW IEMI RA-associated scenarios. Transmitter operators possess only uncertain/fuzzy knowledge of victim systems, so it is proposed that a Moot Acknowledged System-of-Fuzzy-Systems applies to EW IEMI RA scenarios. In summary, a novel SoS description feeding a novel QRAM (supported by a systematic literature review of RA mathematical modeling techniques)is proposed to address the knowledge gap. Full article

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

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28 pages, 984 KiB

Open AccessArticle

Data Quality Assessment in Smart Manufacturing: A Review

by Teresa Peixoto, Bruno Oliveira, Óscar Oliveira and Fillipe Ribeiro

Systems 2025, 13(4), 243; https://doi.org/10.3390/systems13040243 (registering DOI) - 31 Mar 2025

Abstract

Data quality in IoT and smart manufacturing environments is essential for optimizing workflows, enabling predictive maintenance, and supporting informed decisions. However, data from sensors present significant challenges due to their real-time nature, diversity of formats, and high susceptibility to faults such as missing [...] Read more.

Data quality in IoT and smart manufacturing environments is essential for optimizing workflows, enabling predictive maintenance, and supporting informed decisions. However, data from sensors present significant challenges due to their real-time nature, diversity of formats, and high susceptibility to faults such as missing values or inconsistencies. Ensuring high-quality data in these environments is crucial to maintaining operational efficiency and process reliability. This paper analyzes some of the data quality metrics presented in the literature, with a focus on adapting them to the context of Industry 4.0. Initially, three models for the classification of the dimensions of data quality are presented, proposed by different authors, which group together dimensions such as accuracy, completeness, consistency, and timeliness in different approaches. Next, a systematic methodology is adopted to evaluate the metrics related to these dimensions, always using a real-time monitoring scenario. This approach combines dynamic thresholds with historical data to assess the quality of incoming data streams and provide relevant insights. The analysis carried out not only facilitates continuous monitoring of data quality but also supports informed decision-making, helping to improve operational efficiency in Industry 4.0 environments. Finally, this paper presents a table summarizing the selected metrics, highlighting the advantages, disadvantages, and potential usage scenarios, and providing a practical basis for implementation in real environments. Full article

(This article belongs to the Special Issue Data Integration and Governance in Business Intelligence Systems)

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30 pages, 590 KiB

Open AccessArticle

Open Government Data Topic Modeling and Taxonomy Development

by Aljaž Ferencek and Mirjana Kljajić Borštnar

Systems 2025, 13(4), 242; https://doi.org/10.3390/systems13040242 - 31 Mar 2025

Abstract

The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where [...] Read more.

The expectations for the (re)use of open government data (OGD) are high. However, measuring their impact remains challenging, as their effects are not solely economic but also long-term and spread across multiple domains. To accurately assess these impacts, we must first understand where they occur. This research presents a structured approach to developing a taxonomy for open government data (OGD) impact areas using machine learning-driven topic modeling and iterative taxonomy refinement. By analyzing a dataset of 697 OGD use cases, we employed various machine learning techniques—including Latent Dirichlet Allocation (LDA), Non-Negative Matrix Factorization (NMF), and Hierarchical Dirichlet Process (HDP)—to extract thematic categories and construct a structured taxonomy. The final taxonomy comprises seven high-level dimensions: Society, Health, Infrastructure, Education, Innovation, Governance, and Environment, each with specific subdomains and characteristics. Our findings reveal that OGD’s impact extends beyond governance and transparency, influencing education, sustainability, and public services. Our approach provides a scalable and data-driven methodology for categorizing OGD impact areas compared to previous research that relies on predefined classifications or manual taxonomies. However, the study has limitations, including a relatively small dataset, brief use cases, and the inherent subjectivity of taxonomic classification, which requires further validation by domain experts. This research contributes to the systematic assessment of OGD initiatives and provides a foundational framework for policymakers and researchers aiming to maximize the benefits of open data. Full article

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

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24 pages, 3681 KiB

Open AccessArticle

Impact of Leadership Relations and Mature Technology on Digital Technology Complementary Innovation

by Juan Chen, Shuang Wei, Qiang Wang and Kunzai Niu

Systems 2025, 13(4), 241; https://doi.org/10.3390/systems13040241 - 31 Mar 2025

Abstract

With the increasing integration of digital technology in supply chains, manufacturers and suppliers are initiating complementary innovations in digital technology. Such digital technology complementary innovations (DTCIs) amplify synergistic effects, fostering cross-domain innovation. This raises significant inquiries about how firms undertake DTCI. In this [...] Read more.

With the increasing integration of digital technology in supply chains, manufacturers and suppliers are initiating complementary innovations in digital technology. Such digital technology complementary innovations (DTCIs) amplify synergistic effects, fostering cross-domain innovation. This raises significant inquiries about how firms undertake DTCI. In this study, we model a supply chain consisting of a buyer, a new supplier, and an existing supplier, exploring the impacts of the presence of mature technology and varying leadership relations on supply chain collaboration patterns. Our findings highlight several key insights: firstly, for the new supplier, their highest level of effort in DTCI is observed when they play a follower role, and yet attaining a leadership position results in heightened profits. Intriguingly, for the buyer, their position as a leader or follower does not impact their DTCI effort level, and they exhibit a preference for a leadership stance. Secondly, when the existing supplier possesses lower bargaining power, the buyer is inclined towards collaborating with them, anticipating higher profits. Under these circumstances, the new supplier’s DTCI effort level is diminished. Thirdly, the probability of DTCI success and the magnitude of digital technology complementarity between parties positively influence the DTCI effort levels of both the buyer and the new supplier but negatively impact the existing supplier. Full article

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

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23 pages, 14629 KiB

Open AccessArticle

Multi-Stage Simulation of Residents’ Disaster Risk Perception and Decision-Making Behavior: An Exploratory Study on Large Language Model-Driven Social–Cognitive Agent Framework

by Xinjie Zhao, Hao Wang, Chengxiao Dai, Jiacheng Tang, Kaixin Deng, Zhihua Zhong, Fanying Kong, Shiyun Wang and So Morikawa

Systems 2025, 13(4), 240; https://doi.org/10.3390/systems13040240 - 31 Mar 2025

Abstract

The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external [...] Read more.

The escalating frequency and complexity of natural disasters highlight the urgent need for deeper insights into how individuals and communities perceive and respond to risk information. Yet, conventional research methods—such as surveys, laboratory experiments, and field observations—often struggle with limited sample sizes, external validity concerns, and difficulties in controlling for confounding variables. These constraints hinder our ability to develop comprehensive models that capture the dynamic, context-sensitive nature of disaster decision-making. To address these challenges, we present a novel multi-stage simulation framework that integrates Large Language Model (LLM)-driven social–cognitive agents with well-established theoretical perspectives from psychology, sociology, and decision science. This framework enables the simulation of three critical phases—information perception, cognitive processing, and decision-making—providing a granular analysis of how demographic attributes, situational factors, and social influences interact to shape behavior under uncertain and evolving disaster conditions. A case study focusing on pre-disaster preventive measures demonstrates its effectiveness. By aligning agent demographics with real-world survey data across 5864 simulated scenarios, we reveal nuanced behavioral patterns closely mirroring human responses, underscoring the potential to overcome longstanding methodological limitations and offer improved ecological validity and flexibility to explore diverse disaster environments and policy interventions. While acknowledging the current constraints, such as the need for enhanced emotional modeling and multimodal inputs, our framework lays a foundation for more nuanced, empirically grounded analyses of risk perception and response patterns. By seamlessly blending theory, advanced LLM capabilities, and empirical alignment strategies, this research not only advances the state of computational social simulation but also provides valuable guidance for developing more context-sensitive and targeted disaster management strategies. Full article

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

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27 pages, 7117 KiB

Open AccessArticle

Integrating Carbon Tax and Subsidies: An Evolutionary Game Theory-Based Shore Power Promotional Strategy Analysis

by Tingwei Zhang, Cheng Hong, Tomaz Kramberger and Yuhong Wang

Systems 2025, 13(4), 239; https://doi.org/10.3390/systems13040239 - 31 Mar 2025

Abstract

Shore power represents one of the principal solutions for the green transformation within the port industry. It significantly aids in the reduction in carbon emissions from vessels while they are berthed in port, yet often necessitates an effective promotional strategy to foster its [...] Read more.

Shore power represents one of the principal solutions for the green transformation within the port industry. It significantly aids in the reduction in carbon emissions from vessels while they are berthed in port, yet often necessitates an effective promotional strategy to foster its installation and utilization. Stakeholders including port authorities, ship operators, and local governments all play a crucial role in achieving this objective. This paper employs a tripartite evolutionary game model in conjunction with a system dynamics model to investigate the evolutionary responses of stakeholders when policy tools are applied, and consequently, to elucidate the dynamics of strategy effectiveness. In this context, six business scenarios are developed to ascertain the potential impacts of implementing subsidies and carbon taxes. The findings demonstrate that any singular strategy, whether a subsidy or a carbon tax, is inadequate for the successful advancement of shore power; on the contrary, a government-led, integrated, and dynamic reward–punishment strategy aids in stabilizing the inherent fluctuations within this game process. Moreover, the initial willingness of ship operators exerts a considerably greater influence than that of the other two stakeholders. Full article

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

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27 pages, 2014 KiB

Open AccessArticle

Research on the Driving Mechanism of the Innovation Ecosystem in China’s Marine Engineering Equipment Manufacturing Industry

by Tuochen Li and Xinyu Zhou

Systems 2025, 13(4), 238; https://doi.org/10.3390/systems13040238 - 30 Mar 2025

Abstract

To enhance the strength of the marine economy, safeguard marine rights and interests, and promote the sustainable development of marine resources, China is actively building an innovation ecosystem in the marine engineering equipment manufacturing industry. Currently, the main challenge facing China’s marine engineering [...] Read more.

To enhance the strength of the marine economy, safeguard marine rights and interests, and promote the sustainable development of marine resources, China is actively building an innovation ecosystem in the marine engineering equipment manufacturing industry. Currently, the main challenge facing China’s marine engineering equipment manufacturing industry innovation ecosystem is a lack of driving forces. Therefore, this paper focuses on the driving mechanism of China’s marine engineering equipment manufacturing industry innovation ecosystem. Through a literature-coding analysis and interpretive structural modeling (ISM), 17 driving factors of the innovation ecosystem in China’s marine engineering equipment manufacturing industry were identified, and an analytical model was constructed to explore the relationships among these driving factors. Combining data from industry experts, the paper reveals the driving mechanism of China’s marine engineering equipment manufacturing industry innovation ecosystem. The results show that the management level, the risk-resilience capability of marine engineering equipment manufacturing enterprises, and the guidance capacity of universities and research institutions are key driving factors of the innovation ecosystem in China’s marine engineering equipment manufacturing industry. Strengthening these driving factors can enhance the system’s overall driving force, contributing to the high-quality development of China’s marine engineering equipment manufacturing industry. The significance of this study lies in providing a theoretical basis for optimizing the allocation of driving factors in China’s marine engineering equipment manufacturing industry innovation ecosystem and offering important pathways for innovation in and the development of the global marine engineering equipment manufacturing industry. Full article

(This article belongs to the Special Issue Research and Practices in Technological Innovation Management Systems)

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42 pages, 4296 KiB

Open AccessArticle

Sustainable Operations: Risk Evolution and Diversification Strategies Throughout the Lifecycle of Wind Energy Public–Private Partnership Projects

by Rongji Lai, Shiying Liu and Yinglin Wang

Systems 2025, 13(4), 237; https://doi.org/10.3390/systems13040237 - 30 Mar 2025

Abstract

As global energy demand grows and the focus on environmental sustainability intensifies, wind energy, as a form of clean energy, plays a pivotal role in the global energy transition. The public–private partnership (PPP) model, by integrating resources from both the public and private [...] Read more.

As global energy demand grows and the focus on environmental sustainability intensifies, wind energy, as a form of clean energy, plays a pivotal role in the global energy transition. The public–private partnership (PPP) model, by integrating resources from both the public and private sectors, effectively propels the implementation of wind energy projects. However, these projects face a myriad of risks during both development and operation, making effective risk management crucial to project success. This paper, through literature analysis and System Dynamics methodology, develops a risk diversification indicator system that covers the entire project lifecycle. In addition, by combining the improved G1 weighting method and the entropy method, a dynamic risk model is established. Furthermore, through numerical simulation and sensitivity analysis, the risk levels of each subsystem and the key boundary risk factors are identified, and a set of highly adaptable risk diversification strategies is proposed. These strategies will enhance the resilience of wind energy PPP projects, foster trust among stakeholders, help participants effectively respond to and predict risk evolution, improve the project’s risk tolerance, and ensure its long-term sustainable operation. Full article

(This article belongs to the Special Issue Risk Management in Project Management—How Two Major Dimensions (Relational and Processual) Influence Risk Management in Project Management)

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23 pages, 1121 KiB

Open AccessArticle

A Systematic Mapping-Driven Framework for Vetting Participation in Business Ecosystems

by Margaret Mastropetrou, Konstadinos Kutsikos and George Bithas

Systems 2025, 13(4), 236; https://doi.org/10.3390/systems13040236 - 29 Mar 2025

Abstract

A key strategic option for many organizations across the globe is to examine whether and how business ecosystems can help them survive and thrive amidst continuous changes in business realities. Joining a business ecosystem, though, is not a straightforward decision. Current research efforts [...] Read more.

A key strategic option for many organizations across the globe is to examine whether and how business ecosystems can help them survive and thrive amidst continuous changes in business realities. Joining a business ecosystem, though, is not a straightforward decision. Current research efforts are falling short of fully identifying a concise and practical set of decision-making factors that potential ecosystem participants can meaningfully use. To address this limitation, the authors developed a framework of decision-making factors (motivations, prerequisites, ecosystem attractiveness), based on (a) their findings of a systematic mapping study they conducted and (b) their parallel research efforts in business ecosystems operations. The proposed framework encompasses a concrete “vocabulary” of decision-making factors that can enable complex “dialogs” between existing and new business ecosystem stakeholders. As a result, this research effort (a) offers a clear and unambiguous categorization of previously overloaded and ambiguous decision-making factors; (b) captures relationships between the three core components of the proposed framework, thus considering upfront any synergies or conflicts among them; and (c) makes the candidate organization’s decision-making process pragmatic, i.e., misalignment among the proposed factors should be considered a ‘red flag’ that may drive the candidate organization to pivot its decision-making process towards another business ecosystem. Full article

(This article belongs to the Special Issue Business Model Innovation in the Digital Era)

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30 pages, 6435 KiB

Open AccessArticle

Digital Transformation, Enterprise Niche Resilience, and Substantive Innovation in Manufacturing Single Champion Enterprises

by Renyan Mu, Yang Xu and Jingshu Zhang

Systems 2025, 13(4), 235; https://doi.org/10.3390/systems13040235 - 28 Mar 2025

Abstract

This study investigates the relationship between digital transformation and the substantive innovation of single champion manufacturing enterprises (SCMEs). Using panel data from listed SCMEs between 2017 and 2022, we applied a double fixed-effects model to analyze the effects of digital transformation on substantive [...] Read more.

This study investigates the relationship between digital transformation and the substantive innovation of single champion manufacturing enterprises (SCMEs). Using panel data from listed SCMEs between 2017 and 2022, we applied a double fixed-effects model to analyze the effects of digital transformation on substantive innovation performance. The findings indicate that digital transformation significantly enhances SCMEs’ innovation performance, exhibiting a positive linear relationship. However, as the degree of transformation increases, the effect gradually diminishes, following an inverted U-shaped pattern. Furthermore, we introduced a theoretical framework of enterprise niche resilience and examined the moderating roles of niche resource resilience and niche structural resilience in the relationship between digital transformation and innovation performance. The results show that factors such as human resource resilience, capital resource resilience, supply chain resilience, and shareholder governance resilience play critical roles in enhancing innovation capabilities and supporting the digital transformation process. Finally, from the perspectives of macro-, meso-, and microenterprise niche positioning, we further discussed the heterogeneity across different regions, industrial chains, and lifecycle stages. This research provides new insights into innovation theory, niche theory, and resilience theory, offering valuable practical implications for policymakers and SCME managers to respond to global risks and drive domestic industrial upgrades. Full article

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

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33 pages, 2689 KiB

Open AccessReview

Management Frameworks and Management System Standards in the Context of Integration and Unification: A Review and Classification of Core Building Blocks for Consilience

by Yalcin Gerek and Mehmet Nafiz Aydin

Systems 2025, 13(4), 234; https://doi.org/10.3390/systems13040234 - 28 Mar 2025

Abstract

Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified [...] Read more.

Management frameworks (MFs) and management system standards (MSSs) are essential tools for improving organisational management practises. They inherently include a range of fundamental building blocks that facilitate the creation of structured management systems. However, these building blocks have not yet been holistically identified or unified into a consilient taxonomy. Addressing this research gap, this study conducts a comprehensive review of 415 academic papers and theses, 47 ISO MSSs, and 79 MFs sourced from scholarly databases and official publications. Utilising a novel heuristic methodology, this study integrates a literature review, clustering, text mining analytics, and an expert review to develop a Consilient Building Block Taxonomy (CBBT). This taxonomy categorises the foundational components of MFs and MSSs, presenting them as a structured framework that unifies these elements into a cohesive system. By providing a systematic classification, the CBBT serves as a foundation for the development of a Unified Singular Management System (USMS). The proposed taxonomy enhances operational coherence, strategic alignment, and efficiency by consolidating the core aspects of diverse management systems. This study concludes with insights into how the CBBT can be leveraged to achieve integration and unification in management practises, offering significant potential for both research and practical applications. Full article

(This article belongs to the Section Systems Theory and Methodology)

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29 pages, 10164 KiB

Open AccessArticle

A Novel Management Approach for Optimal Operation of Hybrid AC-DC Microgrid in the Presence of Wind and Load Uncertainties

by Hamed Zeinoddini-Meymand, Reza Safipour and Farhad Namdari

Systems 2025, 13(4), 233; https://doi.org/10.3390/systems13040233 - 28 Mar 2025

Abstract

The optimal operation of a hybrid AC-DC microgrid is investigated in this study. The operation of an AC microgrid connected to the main grid and an islanded DC microgrid has been examined under three management approaches. In the first approach, two microgrids are [...] Read more.

The optimal operation of a hybrid AC-DC microgrid is investigated in this study. The operation of an AC microgrid connected to the main grid and an islanded DC microgrid has been examined under three management approaches. In the first approach, two microgrids are not connected, and the DC microgrid is operated in the islanded mode. In the second and third approaches, AC and DC microgrids are connected. The main difference between these two approaches is the energy management framework. In the second approach, each microgrid has its own management system, while the third approach integrates both into a single energy management system to form an AC-DC microgrid that minimizes overall operational costs. The main goal of the proposed model is to minimize the operating costs of two microgrids over a 24 h period. The investigated AC microgrid includes a microturbine, wind turbine and diesel generator in order to supply the residential load profile, and the DC microgrid includes an energy storage system, fuel cell, wind turbine and solar panel in order to supply the commercial load profile. Simulations are performed first with a wind and load scenario in order to show and compare the optimal points of using the decision variables in three approaches. Finally, in order to prove the effectiveness of the proposed method in the presence of uncertainties, the cost distribution function for the three approaches is presented by means of Monte Carlo simulation. Applying the proposed model results in the following the cost reduction: 67.9% in the DC microgrid, 14.2% in the AC microgrid and 24.4% overall. This reduction is primarily attributed to the microgrid central energy management system, which decreases reliance on the main grid and instead utilizes alternative sources such as fuel cells. Comparing the first and third approaches, the fuel cell’s contribution to supplying microgrid loads increased by 29%, while the main grid’s participation decreased by 26%. Full article

(This article belongs to the Section Systems Engineering)

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26 pages, 1882 KiB

Open AccessArticle

Exploring the Mechanism of Sustainable Innovation in the Complex System: A Case Study

by Yuanyuan Chu

Systems 2025, 13(4), 232; https://doi.org/10.3390/systems13040232 - 28 Mar 2025

Abstract

The construction of complex systems is of great significance in enhancing national competitiveness and promoting social development. However, the academic community currently lacks a systematic understanding of its sustainable innovation mechanism. This study selected the China Manned Space Engineering Application System (CMSEAS) as [...] Read more.

The construction of complex systems is of great significance in enhancing national competitiveness and promoting social development. However, the academic community currently lacks a systematic understanding of its sustainable innovation mechanism. This study selected the China Manned Space Engineering Application System (CMSEAS) as a representative case of a complex system. Research data were collected by a multi-method approach including document literature, internal data, field research, and interviews. Through the lens of grounded theory, the study delves into how the complex system achieves local innovation and how to maintain the sustainability of innovation. Findings indicate that, firstly, late-mover advantage and spiritual strength jointly contribute to the knowledge accumulation of national major task-oriented complex systems, and this knowledge accumulation significantly improves the innovation ability of complex systems. Secondly, while emphasizing the enhancement of innovation capabilities, it is imperative for complex systems to implement holistic risk management, which is an important guarantee for successfully achieving the goal. Thirdly, in the context of market failure, the whole nation system provides strong support for the national major task-oriented complex system. The overall institution and overall capacity constitute the backbone for ensuring sustainable innovation. Full article

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17 pages, 1790 KiB

Open AccessArticle

Advancing Artificial Intelligence of Things Security: Integrating Feature Selection and Deep Learning for Real-Time Intrusion Detection

by Faisal Albalwy and Muhannad Almohaimeed

Systems 2025, 13(4), 231; https://doi.org/10.3390/systems13040231 - 28 Mar 2025

Abstract

The size of data transmitted through various communication systems has recently increased due to technological advancements in the Artificial Intelligence of Things (AIoT) and the industrial Internet of Things (IoT). IoT communications rely on intrusion detection systems (IDS) to ensure secure and reliable [...] Read more.

The size of data transmitted through various communication systems has recently increased due to technological advancements in the Artificial Intelligence of Things (AIoT) and the industrial Internet of Things (IoT). IoT communications rely on intrusion detection systems (IDS) to ensure secure and reliable data transmission, as traditional security mechanisms, such as firewalls and encryption, remain susceptible to attacks. An effective IDS is crucial as evolving threats continue to expose new security vulnerabilities. This study proposes an integrated approach combining feature selection methods and principal component analysis (PCA) with advanced deep learning (DL) models for real-time intrusion detection, significantly improving both computational efficiency and accuracy compared to previous methods. Specifically, five feature selection methods (correlation-based feature subset selection (CFS), Pearson analysis, gain ratio (GR), information gain (IG) and symmetrical uncertainty (SU)) were integrated with PCA to optimise feature dimensionality and enhance predictive performance. Three classifiers—artificial neural networks (ANNs), deep neural networks (DNNs), and TabNet–were evaluated on the RT-IoT2022 dataset. The ANN classifier combined with Pearson analysis and PCA achieved the highest intrusion detection accuracy of 99.7%, demonstrating substantial performance improvements over ANN alone (92%) and TabNet (94%) without feature selection. Key features identified by Pearson analysis included id.resp_p, service, fwd_init_window_size and flow_SYN_flag_count, which significantly contributed to the performance gains. These results indicate that combining Pearson analysis with PCA consistently improves classification performance across multiple models. Furthermore, the deployment of classifiers directly on the original dataset decreased the accuracy, emphasising the importance of feature selection in enhancing AIoT and IoT security. This predictive model strengthens IDS capabilities, enabling early threat detection and proactive mitigation strategies against cyberattacks in real-time AIoT environments. Full article

(This article belongs to the Special Issue Integration of Cybersecurity, AI, and IoT Technologies)

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28 pages, 2802 KiB

Open AccessArticle

Critical Success Factors for Enhancing Intelligent Loading and Unloading in Urban Supply Chains: A Comprehensive Approach Based on Fuzzy DEMATEL-AISM-MICMAC

by Xiaoteng Wang, Meihui Zhou and Miao Su

Systems 2025, 13(4), 230; https://doi.org/10.3390/systems13040230 - 27 Mar 2025

Abstract

With the development of the smart logistics industry, the demand for intelligent loading and unloading (ILU) within urban supply chains (USCs) is increasing. However, few studies have examined the critical success factors (CSFs) for enhancing ILU in USCs. This study establishes a CSF [...] Read more.

With the development of the smart logistics industry, the demand for intelligent loading and unloading (ILU) within urban supply chains (USCs) is increasing. However, few studies have examined the critical success factors (CSFs) for enhancing ILU in USCs. This study establishes a CSF model to support ILU improvement. Specifically, it integrates stakeholder theory, resource-based view theory, and innovation diffusion theory. Through research conducted in collaboration with 16 logistics industry specialists in Korea, 19 critical factors were identified. Fuzzy DEMATEL and the Adversarial Interpretive Structure Model (AISM) were then applied to analyze the identified factors. The results indicate that stakeholder collaboration, government support, and regulatory compliance are the most important factors affecting ILU improvement within USCs. Finally, cross-impact matrix multiplication applied to classification (MICMAC) analysis further verifies that these factors have a high driving power and low dependence, making them independent driving factors of the entire system. Furthermore, this study emphasizes the role of market research and automated system design. This work contributes to the knowledge on the intelligent logistics management of supply chains. Full article

(This article belongs to the Section Supply Chain Management)

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26 pages, 5652 KiB

Open AccessArticle

Reliable Process Tracking Under Incomplete Event Logs Using Timed Genetic-Inductive Process Mining

by Yutika Amelia Effendi and Minsoo Kim

Systems 2025, 13(4), 229; https://doi.org/10.3390/systems13040229 - 27 Mar 2025

Abstract

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper [...] Read more.

Process mining facilitates the discovery, conformance, and enhancement of business processes using event logs. However, incomplete event logs and the complexities of concurrent activities present significant challenges in achieving accurate process models that fulfill the completeness condition required in process mining. This paper introduces a Timed Genetic-Inductive Process Mining (TGIPM) algorithm, a novel approach that integrates the strengths of Timed Genetic Process Mining (TGPM) and Inductive Mining (IM). TGPM extends traditional Genetic Process Mining (GPM) by incorporating time-based analysis, while the IM is widely recognized for producing sound and precise process models. For the first time, these two algorithms are combined into a unified framework to address both missing activity recovery and structural correctness in process discovery. This study evaluates two scenarios: a sequential approach, in which TGPM and IM are executed independently and sequentially, and the TGIPM approach, where both algorithms are integrated into a unified framework. Experimental results using real-world event logs from a health service in Indonesia demonstrate that TGIPM achieves higher fitness, precision, and generalization compared to the sequential approach, while slightly compromising simplicity. Moreover, the TGIPM algorithm exhibits lower computational cost and more effectively captures parallelism, making it particularly suitable for large and incomplete datasets. This research underscores the potential of TGIPM to enhance process mining outcomes, offering a robust framework for accurate and efficient process discovery while driving process innovation across industries. Full article

(This article belongs to the Special Issue Data-Driven Analysis of Industrial Systems Using AI)

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