Systems

18 pages, 524 KiB

Open AccessArticle

Open-Source Collaboration for Industrial Software Innovation Catch-Up: A Digital–Real Integration Approach

by Xiaohong Chen, Qigang Zhu and Yuntao Long

Systems 2025, 13(9), 733; https://doi.org/10.3390/systems13090733 (registering DOI) - 24 Aug 2025

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic [...] Read more.

In the era of digital–real integration, open-source collaboration has become a strategic pathway for accelerating the innovation catch-up of China’s industrial software. This study employs an exploratory multi-case design, focusing on the China Automotive Operating System open-source project and the FastCAE open-source domestic CAE software integrated development platform to examine how open-source strategies shape collaborative mechanisms and innovation outcomes. The analysis reveals that firms adopt both formal (behavioral and outcome coordination) and informal (relationship and empowerment coordination) strategies, fostering high-level complementary collaboration in data, technology, institution, and human resources. These mechanisms significantly enhance R&D efficiency and quality, drive technological innovation, and create new market innovation, thereby improving collaborative performance. The study contributes to theory by linking open-source-driven digital–real integration with industrial software innovation catch-up and offers practical governance recommendations for strengthening China’s industrial software autonomy and ecosystem sustainability. Full article

(This article belongs to the Special Issue Innovation and Systems Thinking in Operations Management)

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27 pages, 4444 KiB

Open AccessArticle

Understanding Congestion Evolution in Urban TrafficSystems Across Multiple Spatiotemporal Scales: A Causal Emergence Perspective

by Jishun Ou, Jingyuan Li, Weihua Zhang, Pengxiang Yue and Qinghui Nie

Systems 2025, 13(9), 732; https://doi.org/10.3390/systems13090732 (registering DOI) - 24 Aug 2025

Abstract

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion [...] Read more.

Understanding how congestion forms and propagates over space and time is essential for improving the operational efficiency of urban traffic systems. Recent developments in causal emergence theory indicate that the causal structures underlying dynamic models are scale-dependent. Most existing studies on traffic congestion evolution focus on a single, fixed scale, which risks overlooking clearer causal patterns at other scales and thus limiting predictive power and practical applicability. To address this, we develop a multiscale congestion evolution modeling framework grounded in causal emergence theory. Within this framework we build dynamical models at multiple spatiotemporal scales using dynamic Bayesian networks (DBNs) and quantify the causal strength of these models using effective information (EI) and singular value decomposition (SVD)-based diagnostics. Using road networks from three central Kunshan regions, we validate the proposed framework across 24 spatiotemporal scales and five demand scenarios. Across all three regions and the tested scales, we observe evidence of causal emergence in congestion evolution dynamics. When results are pooled across regions and scenarios, models built at the 10 min/150 m scale exhibit stronger and more coherent causal structure than models at other scales. These findings demonstrate that the proposed framework can identify and help build dynamical models of congestion evolution at appropriate spatiotemporal scales, thereby supporting the development of proactive traffic management and effective resilience enhancement strategies for urban transport systems. Full article

31 pages, 1067 KiB

Open AccessArticle

Green Supplier Evaluation in E-Commerce Systems: An Integrated Rough-Dombi BWM-TOPSIS Approach

by Qigan Shao, Simin Liu, Jiaxin Lin, James J. H. Liou and Dan Zhu

Systems 2025, 13(9), 731; https://doi.org/10.3390/systems13090731 (registering DOI) - 23 Aug 2025

Abstract

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. [...] Read more.

The rapid growth of e-commerce has created substantial environmental impacts, driving the need for advanced optimization models to enhance supply chain sustainability. As consumer preferences shift toward environmental responsibility, organizations must adopt robust quantitative methods to reduce ecological footprints while ensuring operational efficiency. This study develops a novel hybrid multi-criteria decision-making (MCDM) model to evaluate and prioritize green suppliers under uncertainty, integrating the rough-Dombi best–worst method (BWM) and an improved Technique for Order Preference by Similarity to Ideal Solution (TOPSIS). The proposed model addresses two key challenges: (1) inconsistency in expert judgments through rough set theory and Dombi aggregation operators and (2) ranking instability via an enhanced TOPSIS formulation that mitigates rank reversal. Mathematically, the rough-Dombi BWM leverages interval-valued rough numbers to model subjective expert preferences, while the Dombi operator ensures flexible and precise weight aggregation. The modified TOPSIS incorporates a dynamic distance metric to strengthen ranking robustness. A case study of five e-commerce suppliers validates the model’s effectiveness, with results identifying cost, green competitiveness, and external environmental management as the dominant evaluation dimensions. Key indicators—such as product price, pollution control, and green design—are rigorously prioritized using the proposed framework. Theoretical contributions include (1) a new rough-Dombi fusion for criteria weighting under uncertainty and (2) a stabilized TOPSIS variant with reduced sensitivity to data perturbations. Practically, the model provides e-commerce enterprises with a computationally efficient tool for sustainable supplier selection, enhancing resource allocation and green innovation. This study advances the intersection of uncertainty modeling, operational research, and sustainability analytics, offering scalable methodologies for mathematical decision-making in supply chain contexts. Full article

(This article belongs to the Section Supply Chain Management)

22 pages, 622 KiB

Open AccessArticle

Leveraging Big Data Analytics Capability for Firm Innovativeness: The Role of Sustained Innovation and Organizational Slack

by Chunjia Hu, Yitong Xu and Pengbin Gao

Systems 2025, 13(9), 730; https://doi.org/10.3390/systems13090730 - 22 Aug 2025

Abstract

In the era of digital transformation and data-driven decision-making, big data analytics capability (BDAC) is crucial for firms to enhance innovation and sustainable competitive advantage in highly dynamic markets. Grounded in dynamic capability theory, this study used a moderated mediation model to explore [...] Read more.

In the era of digital transformation and data-driven decision-making, big data analytics capability (BDAC) is crucial for firms to enhance innovation and sustainable competitive advantage in highly dynamic markets. Grounded in dynamic capability theory, this study used a moderated mediation model to explore the impact of BDAC on innovativeness. Empirical analysis was conducted by using survey data from 270 enterprises to test the hypotheses. The results reveal that BDAC significantly and positively influences innovativeness, and sustained innovation mediates this relationship. Moreover, organizational slack positively moderates the effect of BDAC on innovativeness, both the direct effect and indirect effect. These findings provide theoretical support and practical implications for understanding how BDAC enhances firm innovativeness. Full article

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

40 pages, 2364 KiB

Open AccessArticle

Cascading Failure Modeling and Resilience Analysis of Coupled Centralized Supply Chain Networks Under Hybrid Loads

by Ziqiang Zeng, Ning Wang, Dongyu Xu and Rui Chen

Systems 2025, 13(9), 729; https://doi.org/10.3390/systems13090729 - 22 Aug 2025

Abstract

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of [...] Read more.

As manufacturing and logistics-oriented supply chains continue to expand in scale and complexity, and the coupling between their physical execution layers and information–decision layers deepens, the resulting high interdependence within the system significantly increases overall fragility. Driven by key technological barriers, economies of scale, and the trend toward resource centralization, supply chains have increasingly evolved into centralized structures, with critical functions such as decision-making highly concentrated in a few focal firms. While this configuration may enhance coordination under normal conditions, it also significantly increases dependency on focal nodes. Once a focal node is disrupted, the intense task, information, and risk loads it carries cannot be effectively dispersed across the network, thereby amplifying load spillovers, coordination imbalances, and information delays, and ultimately triggering large-scale cascading failures. To capture this phenomenon, this study develops a coupled network model comprising a Physical Network and an Information and Decision Risk Network. The Physical Network incorporates a tri-load coordination mechanism that distinguishes among theoretical operational load (capacity), actual production load (production output), and actual delivery load (order fulfillment), using a load sensitivity coefficient to describe the asymmetric propagation among them. The Information and Decision Risk Network is further divided into a communication subnetwork, which represents transmission efficiency and delay, and a decision risk subnetwork, which reflects the diffusion of uncertainty and risk contagion caused by information delays. A discrete-event simulation approach is employed to evaluate system resilience under various failure modes and parametric conditions. The results reveal the following: (1) under a centralized structure, poorly allocated redundancy can worsen local imbalances and amplify disruptions; (2) the failure of a focal firm is more likely to cause a full network collapse; and (3) node failures in the Communication System Network have a greater destabilizing effect than those in the Physical Network. Full article

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

29 pages, 2158 KiB

Open AccessArticle

An Integrated Task Decomposition Framework Considering Knowledge Reuse and Resource Availability for Complex Task Crowdsourcing

by Biyu Yang, Shixin Xie and Longxiao Li

Systems 2025, 13(9), 728; https://doi.org/10.3390/systems13090728 - 22 Aug 2025

Abstract

Complex task crowdsourcing (CTC) integrates distributed talent, knowledge, and ideas into innovation via the web; however, task decomposition remains a critical challenge. While existing studies focus primarily on workflow management for specific tasks, they leave a gap in decomposing more complex, creative tasks, [...] Read more.

Complex task crowdsourcing (CTC) integrates distributed talent, knowledge, and ideas into innovation via the web; however, task decomposition remains a critical challenge. While existing studies focus primarily on workflow management for specific tasks, they leave a gap in decomposing more complex, creative tasks, which are characterized by the absence of objective ground truths, nonlinear dependencies, and non-sequential processes. To address this gap, we propose a novel integrated task decomposition framework for CTC that comprises three interconnected components. First, primary decomposition considers knowledge reuse by identifying similar past task decomposition schemes to inform the initial breakdown. Second, modifications to the scheme are guided by work breakdown structure (WBS)-based principles, which also serve as a foundation when no prior knowledge is available. Third, to enhance executability, a task package model is proposed to combine subtasks that share common resources, thereby reducing coordination costs and avoiding waste of workers’ capabilities. To solve this model, we develop an improved non-dominated sorting genetic algorithm (NSGA-II) to generate the final decomposition scheme. A case study from ZBJ.COM validates the feasibility and effectiveness of the proposed framework. Experimental results demonstrate that, compared to baseline algorithms, the improved NSGA-II better balances conflicting objectives and generates non-dominated solution sets with higher diversity and more uniform distribution. Full article

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17 pages, 6197 KiB

Open AccessArticle

Carbon, Climate, and Collapse: Coupling Climate Feedbacks and Resource Dynamics to Predict Societal Collapse

by Greta Savitsky, Grace Burnett and Brian Beckage

Systems 2025, 13(9), 727; https://doi.org/10.3390/systems13090727 - 22 Aug 2025

Viewed by 104

Abstract

Anthropogenic climate change threatens production of essential natural resources, such as food, fiber, fresh water, and provisioning of ecosystem services such as carbon sequestration, increasing the risk of societal collapse. The Human and Nature Dynamics (HANDY) model simulates the effect of resource overexploitation [...] Read more.

Anthropogenic climate change threatens production of essential natural resources, such as food, fiber, fresh water, and provisioning of ecosystem services such as carbon sequestration, increasing the risk of societal collapse. The Human and Nature Dynamics (HANDY) model simulates the effect of resource overexploitation on societal collapse but lacks representation of feedbacks between climate change and resource regeneration in ecological systems. We extend the HANDY model by integrating models of climate change and ecological function to examine the risk of societal collapse. We conducted a sensitivity analysis of our expanded model by systematically varying key parameters to examine the range of plausible socio-ecological conditions and evaluate model uncertainty. We find that lowered greenhouse gas emissions and resilient ecosystems can delay societal collapse by up to approximately 500 years, but that any scenario with greater than net-zero greenhouse gas emissions ultimately leads to societal collapse driven by climate-induced loss of ecosystem function. Reductions in greenhouse gas emissions are the most effective intervention to delay or prevent societal collapse, followed by the conservation and management of resilient ecological systems to sequester atmospheric carbon. Full article

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20 pages, 1110 KiB

Open AccessArticle

A New Time-Sensitive Graph Model for Conflict Resolution with Simultaneous Decision-Maker Moves

by He Wang, Xinhang Zhang, Yuming Huang, Bingfeng Ge, Zeqiang Hou and Jianghan Zhu

Systems 2025, 13(9), 726; https://doi.org/10.3390/systems13090726 - 22 Aug 2025

Viewed by 30

Abstract

The time dimension critically shapes decision-making and conflict evolution in real-world scenarios. This paper extends the Graph Model for Conflict Resolution (GMCR) framework by integrating time attributes, proposing a novel Time-Sensitive GMCR (TSGMCR) methodology that supports concurrent moves by multiple decision-makers (DMs). Within [...] Read more.

The time dimension critically shapes decision-making and conflict evolution in real-world scenarios. This paper extends the Graph Model for Conflict Resolution (GMCR) framework by integrating time attributes, proposing a novel Time-Sensitive GMCR (TSGMCR) methodology that supports concurrent moves by multiple decision-makers (DMs). Within TSGMCR, we define new stability concepts and implement comparative analysis. The methodology is applied to the Jakarta–Bandung high-speed railway project conflict, demonstrating its effectiveness in resolving complex real-world conflicts and identifying beneficial coalition formations. Full article

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23 pages, 5486 KiB

Open AccessArticle

Do Supply Chain Management, ESG Sustainability Practices, and ICT Have an Impact on Environmental Sustainability?

by Abdurahim Ben Salem, Kolawole Iyiola and Ahmad Alzubi

Systems 2025, 13(9), 725; https://doi.org/10.3390/systems13090725 - 22 Aug 2025

Viewed by 114

Abstract

Can supply chain strategies, ESG practices, and digital innovations be the game-changers the planet needs for a sustainable future? Motivated by this question, this study investigates the drivers of CO₂ emissions, focusing on supply chain management (GSC), ESG sustainability practices, and Information [...] Read more.

Can supply chain strategies, ESG practices, and digital innovations be the game-changers the planet needs for a sustainable future? Motivated by this question, this study investigates the drivers of CO₂ emissions, focusing on supply chain management (GSC), ESG sustainability practices, and Information and Communication Technology (ICT) in China from 2002Q4 to 2024Q4. Utilizing a series of wavelet tools—including wavelet coherence (WTC), partial wavelet coherence (PWC), and multiple wavelet coherence (MWC)—the study uncovers associations across time and frequency domains. To the best of the authors’ knowledge, this is the first study to examine these dynamics within the Chinese context using advanced wavelet techniques. The WTC results reveal that GSC, ICT, and patents are positively associated with CO₂ emissions, particularly during 2008–2016 and 2018–2024, while ESG practices reduced emissions before 2016 but became positively linked to emissions afterward. MWC and PWC analyses confirm that these drivers influence CO₂ within 1–4-year bands, while wavelet Granger causality tests indicate weak short-term but strong medium- to long-term causal relationships among ESG, GSC, PAT, ICT, and CO₂ emissions. Based on these results, policy recommendations are formulated. Full article

(This article belongs to the Special Issue Systemic Optimization in Sustainable Business Operations: Theory and Practice)

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20 pages, 474 KiB

Open AccessArticle

Artificial Intelligence Usage and Supply Chain Resilience: An Organizational Information Processing Theory Perspective

by Heng Pan, Ning Zou, Rouyue Wang, Jingchen Ma and Danping Liu

Systems 2025, 13(9), 724; https://doi.org/10.3390/systems13090724 - 22 Aug 2025

Viewed by 215

Abstract

Frequent disruptions to global supply chains, driven by factors such as trade restrictions and geopolitical conflicts, brought supply chain resilience to the forefront of both academic research and industry practice. Concurrently, the rapid advancement of artificial intelligence (AI) technologies in supply chain management [...] Read more.

Frequent disruptions to global supply chains, driven by factors such as trade restrictions and geopolitical conflicts, brought supply chain resilience to the forefront of both academic research and industry practice. Concurrently, the rapid advancement of artificial intelligence (AI) technologies in supply chain management in recent years offers new perspectives for researching resilience. Based on the Organizational Information Processing Theory (OIPT), this study explores the direct and indirect mechanisms through which AI usage impacts supply chain resilience from an information processing perspective. Within the OIPT framework, we develop a theoretical model incorporating AI usage, supply chain resilience, supply chain efficiency, supply chain collaboration, and digital information technology capability. We empirically test the model using survey data collected from 231 Chinese manufacturing senior executives and supply chain managers, employing partial least squares structural equation modeling (PLS-SEM). The findings reveal that AI usage has a significant direct positive effect on supply chain resilience. Additionally, supply chain efficiency and collaboration act as mediators in this relationship. Furthermore, we examined the moderating role of a firm’s digital information technology capability and found that it positively moderates the impact of AI usage on supply chain resilience. Full article

(This article belongs to the Section Supply Chain Management)

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