Systems

20 pages, 1942 KiB

Open AccessArticle

How Does Points System Facilitate Rural Revitalization? A Case Study of Xinqi Village in Ningxia, China

by Yi Zhou, Ke Tang and Yue Dai

Systems 2025, 13(4), 255; https://doi.org/10.3390/systems13040255 - 3 Apr 2025

China’s rural revitalization strategy demands innovative governance tools to address persistent challenges at the grassroots level. This study investigates how the rural points system contributes to rural revitalization, focusing on Xinqi Village in Ningxia as a case study. Guided by a theoretical framework [...] Read more.

China’s rural revitalization strategy demands innovative governance tools to address persistent challenges at the grassroots level. This study investigates how the rural points system contributes to rural revitalization, focusing on Xinqi Village in Ningxia as a case study. Guided by a theoretical framework that links governance dilemmas, institutional mechanisms, and revitalization outcomes, the paper analyzes the system’s formation, operation, and effects. Theoretically, the points system emerges from the interplay of interest-based competition, face-saving mediation, and social empowerment. It operates through a combination of management tools and incentive structures. Empirical findings indicate that the system improves rural revitalization by enhancing ecological livability, promoting civic behavior, and strengthening governance. However, its impact on industrial development and living standards remains limited. Key challenges include unclear institutional goals, poorly designed indicators, and a lack of material incentives. The paper concludes with policy recommendations to refine the system and argues that its application in underdeveloped rural areas offers valuable lessons for wider implementation in China and beyond. Full article

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

30 pages, 2663 KiB

Open AccessReview

Research Trend Analysis in the Field of Self-Driving Labs Using Network Analysis and Topic Modeling

by Woojun Jung, Insung Hwang and Keuntae Cho

Systems 2025, 13(4), 253; https://doi.org/10.3390/systems13040253 - 3 Apr 2025

Abstract

A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the [...] Read more.

A self-driving lab (SDL) system that automates experimental design, data collection, and analysis using robotics and artificial intelligence (AI) technologies. Its significance has grown substantially in recent years. This study analyzes the overall SDL research trends, examines changes in specific topics, visualizes the relational structure between authors to identify key contributors, and extracts major themes from extensive texts to highlight essential research content. To achieve these objectives, trend analysis, network analysis, and topic modeling were conducted on 352 research papers collected from the Web of Science between 2004 and 2023. To ensure the validity of the topic modeling results, a topic correlation matrix was also performed. The results revealed three key findings. First, SDL research has surged since 2019, driven by advancements in AI technologies, reflecting heightened activity in this field. Second, modern scientific research is advancing with a focus on data-driven approaches, artificial intelligence applications, and experimental optimization through the utilization of SDLs. Third, SDL research exhibits interdisciplinary convergence, encompassing material optimization, biological processes, and AI predictive algorithms. This study underscores the growing importance of SDLs as a research tool across diverse academic disciplines and provides practical insights into sustainable future scientific research directions and strategic approaches. Full article

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

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27 pages, 352 KiB

Open AccessArticle

Investor Attention, Market Dynamics, and Behavioral Insights: A Study Using Google Search Volume

by Shahid Raza, Sun Baiqing, Hassen Soltani and Ousama Ben-Salha

Systems 2025, 13(4), 252; https://doi.org/10.3390/systems13040252 - 3 Apr 2025

Abstract

The rapid advancement of digital technology has transformed how investors gather financial information, with platforms like Google Trends providing valuable insights into investor behavior through the Google Search Volume Index (GSVI). While the relationship between the GSVI and market behavior has been explored [...] Read more.

The rapid advancement of digital technology has transformed how investors gather financial information, with platforms like Google Trends providing valuable insights into investor behavior through the Google Search Volume Index (GSVI). While the relationship between the GSVI and market behavior has been explored in developed markets, its application in emerging markets like Pakistan remains underexplored. This study investigates how investor attention, measured by the GSVI, influences market volatility, liquidity, and stock price movements in the Pakistan Stock Exchange (PSX), using weekly data from the KSE-100 Index between 2019 and 2024. The findings reveal that the GSVI significantly impacts market volatility and liquidity, particularly in retail-driven markets with high information asymmetry. Additionally, this research shows that the GSVI is a reliable predictor for stock price fluctuations, with heightened investor attention correlating with increased market activity. Despite the limitations of the GSVI in fully capturing investor sentiment, this study contributes to behavioral finance literature by demonstrating the role of digital information flows in shaping market behavior in emerging markets. It offers actionable insights for investors, financial institutions, and policymakers in Pakistan while suggesting areas for future research in applying the GSVI to global contexts and exploring alternative proxies for investor sentiment in emerging economies. Full article

(This article belongs to the Special Issue Decision Making in Uncertain Environments via Advanced Analytical Methods)

26 pages, 2613 KiB

Open AccessArticle

System Elements Identification Method for Heat Transfer Modelling in MBSE

by Patrick Jagla, Georg Jacobs, Vincent Derpa, Lukas Irnich, Gregor Höpfner, Stefan Wischmann and Joerg Berroth

Systems 2025, 13(4), 251; https://doi.org/10.3390/systems13040251 - 3 Apr 2025

Abstract

Today’s systems are becoming increasingly complex due to the multitude of interactions between subsystems. This is also true for the electromechanical drivetrain and its physically interacting cooling system. In order to provide a virtual representation of such systems, including system architecture and product [...] Read more.

Today’s systems are becoming increasingly complex due to the multitude of interactions between subsystems. This is also true for the electromechanical drivetrain and its physically interacting cooling system. In order to provide a virtual representation of such systems, including system architecture and product behaviour, model-based systems engineering (MBSE) introduces system models. System models are built using system elements and reoccurring models. MBSE, therefore, enhances the efficient development of complex systems by promoting model reuse in interdisciplinary architectural modelling. The reuse of models, such as calculation models, reduces redundancy, accelerates development iterations, and streamlines consistency. However, there is a lack of standardised and reusable model libraries to facilitate this reuse. In the approach in this paper, the reusability of those models is facilitated by the system elements, referred to as “solution elements”. MBSE system elements enable the structuring, reuse, and organization of models within model libraries. The identification of these system elements for heat-exchanging systems, however, remains an open challenge. Consequently, the aim of this paper is to develop a method for systematically identifying system elements in heat-exchanging systems, providing a formalized approach to reusing thermal models. The method focuses on functional and heat-transfer processes at the contact level referred to here as thermal contacts. The developed method is demonstrated through a case study of a thermal management system (TMS) of an electric truck. It is shown that a small set of recurring system elements can be used to represent a large number of individual thermal interactions, within TMS components and, therefore, streamline modelling efficiency significantly. Full article

(This article belongs to the Section Systems Engineering)

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18 pages, 961 KiB

Open AccessArticle

Barriers to the Adoption of Big Data Analytics in Saudi Arabia’s Manufacturing Sector: An Interpretive Structural Modeling Approach

by Almuhannad S. Alorfi and Naif Alsaadi

Systems 2025, 13(4), 250; https://doi.org/10.3390/systems13040250 - 3 Apr 2025

Abstract

Big data analytics has the potential to greatly improve the operations of manufacturing industries, aid in decision making, and foster innovation. However, there exist several barriers that undermine the successful adoption of big data analytics in these industries. This paper presents a structural [...] Read more.

Big data analytics has the potential to greatly improve the operations of manufacturing industries, aid in decision making, and foster innovation. However, there exist several barriers that undermine the successful adoption of big data analytics in these industries. This paper presents a structural analysis of the barrier to big data analytics adoption in manufacturing industries. Through an extensive literature review and expert analysis, a compilation of the various barriers was made. The interpretive structure modeling (ISM) technique was then used to analyze the interplay between the barriers: this technique was used to build a hierarchy whose respective objective functions indicated how each barrier influenced the other. These findings help in the understanding of the hierarchical relationships between the various barriers and can thus help organizations in prioritizing strategies to mitigate these barriers. The results depict some barriers which do have a high-power influence over others and, as such, depict critical points that manufacturing industries need to address when adopting big data analytics. This paper also elaborates the relationships between the barriers, which will help the decision makers create strategies to mitigate them effectively. This study’s findings contribute to the existing body of knowledge on barriers to adopting big data analytics in manufacturing industries and provides an efficient approach for organizations to systematically address barriers. Full article

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

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21 pages, 2464 KiB

Open AccessArticle

Analyzing the Impact of Government Subsidies on Carbon Emission Mitigation Considering Carriers’ Price-and-Service Competition and Green Shippers

by Lijuan Yang, Duanyu Chen, Youyuan Chen and Zhifeng Zhang

Systems 2025, 13(4), 249; https://doi.org/10.3390/systems13040249 - 3 Apr 2025

Abstract

High operational costs discourage shipping carriers from adopting green technologies, thereby exacerbating carbon emissions. Government subsidies can address the financial predicament faced by carriers. However, previous studies have overlooked whether governmental subsidies can help mitigate carbon emissions with intensified competition in both price [...] Read more.

High operational costs discourage shipping carriers from adopting green technologies, thereby exacerbating carbon emissions. Government subsidies can address the financial predicament faced by carriers. However, previous studies have overlooked whether governmental subsidies can help mitigate carbon emissions with intensified competition in both price and service between carriers, alongside rising environmental consciousness from shippers. To fill in this gap, game-theoretic models have been developed to explore optimal strategies for each partner of a shipping supply chain under three scenarios. Optimal solutions are derived through model analysis, followed by numerical analysis. Our findings are as follows: (1) the provision of governmental subsidization is conducive to a significant decrease in carbon emission with carriers’ price-and-service competition and shippers’ green awareness; (2) freight prices, profits and social welfare are all negatively related to government subsidies in a certain price-competitive environment; (3) price competition intensity is not conducive to carbon emission reduction but can benefit prices and social welfare; and (4) both low-carbon preference and intensified service competition jointly benefit profits and social welfare but are detrimental to carbon emission reduction. Our paper provides several meaningful insights for governments and shipping companies in formulating emission reduction strategies, contributing to environmental benefits and supporting the achievement of sustainability goals. Full article

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

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20 pages, 2779 KiB

Open AccessArticle

Effects of Policy Communication Changes on Social Media: Before and After Policy Adjustment

by Zenglei Yue and Guang Yu

Systems 2025, 13(4), 248; https://doi.org/10.3390/systems13040248 - 2 Apr 2025

Abstract

The structure of a policy communication network shows the effect of policy communication on social media. Policies need to be dynamically adjusted during the implementation process, which may affect the policy’s interaction on social media. Based on the Policy Network Theory, this study [...] Read more.

The structure of a policy communication network shows the effect of policy communication on social media. Policies need to be dynamically adjusted during the implementation process, which may affect the policy’s interaction on social media. Based on the Policy Network Theory, this study explores the effects of policy communication changes on social media before and after the adjustment of China’s Mass Entrepreneurship and Innovation (MEI) Policy using Exponential Random Graph Models (ERGMs) analysis and community analysis. The study reveals that after the policy adjustment, the communication network structure indicated a significant increase in triangular configurations, yet the formation of edges remained constrained. Meanwhile, cross-community connections in the communication network decreased, with communities exhibiting localized contraction, and emotional polarization becoming more pronounced. These phenomena occurred because policy adjustments have boosted interaction levels through new incentive mechanisms, whereas the content and delivery methods of policy communication remain insufficiently engaging, which constrains relationship-building. Additionally, the policy’s evolution from a mobilization–participation model to a vertical governance paradigm has systematically reconfigured inter-community interaction patterns, resulting in structural transformations in cross-group information flows. To enhance the dissemination of policies on social media, it is recommended to intervene in the policy communication network structure through role embedding, shift from a reactive public sentiment management paradigm to proactive emotional governance, and strengthen policy communication strategies that emphasize emotional resonance. These measures can improve the effectiveness of policy communication and help address the challenges posed by emotional polarization and network fragmentation. Full article

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

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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 - 2 Apr 2025

Abstract

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 - 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 - 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 - 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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