Systems

40 pages, 4610 KB

Open AccessArticle

Semantic Priority Navigation for Energy-Aware Mining Robots

by Claudio Urrea, Kevin Valencia-Aragón and John Kern

Systems 2025, 13(9), 799; https://doi.org/10.3390/systems13090799 - 11 Sep 2025

Autonomous navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in a Robot Operating System (ROS). A lightweight Convolutional Neural [...] Read more.

Autonomous navigation in subterranean mines is hindered by deformable terrain, dust-laden visibility, and densely packed, safety-critical machinery. We propose a systems-oriented navigation framework that embeds semantic priorities into reactive planning for energy-aware autonomy in a Robot Operating System (ROS). A lightweight Convolutional Neural Network (CNN) detector fuses RGB-D and LiDAR data to classify obstacles like humans, haul trucks, and debris, writing risk-weighted virtual LaserScans to the local planner so obstacles are evaluated by relevance rather than geometry. By integrating class-specific inflation layers in costmaps within a cyber–physical systems architecture, the system ensures ISO-compliant separation without sacrificing throughput. In Gazebo experiments with three obstacle classes and 60 runs, high-risk clearance increased by 34%, collisions dropped to zero, mission time remained statistically unchanged, and estimated kinematic effort increased by 6% relative to a geometry-only baseline. These results demonstrate effective systems integration and a favorable safety–efficiency trade-off in industrial cyber–physical environments, providing a reproducible reference for scalable deployment in real-world unstructured mining environments. Full article

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48 pages, 5334 KB

Open AccessArticle

Development and Evaluation of an Immersive Metaverse-Based Meditation System for Psychological Well-Being Using LLM-Driven Scenario Generation

by Aihe Yu, Gyuhyuk Lee, Yu Liu, Mingfeng Zhang, Seunga Jung, Jisun Park, Jongtae Rhee and Kyungeun Cho

Systems 2025, 13(9), 798; https://doi.org/10.3390/systems13090798 - 11 Sep 2025

Abstract

The increasing prevalence of mental health disorders highlights the need for innovative and accessible interventions. Although existing digital meditation applications offer valuable basic guidance, they often lack interactivity, real-time personalized feedback, and dynamic simulation of real-life scenarios necessary for comprehensive experiential training applicable [...] Read more.

The increasing prevalence of mental health disorders highlights the need for innovative and accessible interventions. Although existing digital meditation applications offer valuable basic guidance, they often lack interactivity, real-time personalized feedback, and dynamic simulation of real-life scenarios necessary for comprehensive experiential training applicable to daily stressors. To address these limitations, this study developed a novel immersive meditation system specifically designed for deployment within a metaverse environment. The system provides mindfulness practice through two distinct modules within the virtual world. The experience-based module delivers AI-driven social interactions within simulated everyday scenarios, with narrative content dynamically generated by large language models (LLMs), followed by guided inner reflection, thereby forming a scenario–experience–reflection cycle. The breathing-focused module provides real-time feedback through a breath-synchronization interface to enhance respiratory awareness. The feasibility and preliminary effects of this metaverse-based system were explored in a two-week, single-group, pre-test/post-test study involving 31 participants. The participants completed a battery of validated psychological questionnaires assessing psychological distress, mindfulness, acceptance, self-compassion, and self-esteem before and after engaging in the intervention. This study provides exploratory evidence supporting the feasibility and potential of immersive metaverse environments and LLM-based scenario generation for structured mental health interventions, providing initial insights into their psychological impact and user experience. Full article

(This article belongs to the Special Issue Emerging Disruptions in Healthcare Systems: How Digital Innovation Is Reshaping Policy, Practice, and Patient Outcomes)

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26 pages, 9154 KB

Open AccessArticle

Performance and Efficiency Gains of NPU-Based Servers over GPUs for AI Model Inference

by Youngpyo Hong and Dongsoo Kim

Systems 2025, 13(9), 797; https://doi.org/10.3390/systems13090797 - 11 Sep 2025

Abstract

The exponential growth of AI applications has intensified the demand for efficient inference hardware capable of delivering low-latency, high-throughput, and energy-efficient performance. This study presents a systematic, empirical comparison of GPU- and NPU-based server platforms across key AI inference domains: text-to-text, text-to-image, multimodal [...] Read more.

The exponential growth of AI applications has intensified the demand for efficient inference hardware capable of delivering low-latency, high-throughput, and energy-efficient performance. This study presents a systematic, empirical comparison of GPU- and NPU-based server platforms across key AI inference domains: text-to-text, text-to-image, multimodal understanding, and object detection. We configure representative models—LLama-family for text generation, Stable Diffusion variants for image synthesis, LLaVA-NeXT for multimodal tasks, and YOLO11 series for object detection—on a dual NVIDIA A100 GPU server and an eight-chip RBLN-CA12 NPU server. Performance metrics including latency, throughput, power consumption, and energy efficiency are measured under realistic workloads. Results demonstrate that NPUs match or exceed GPU throughput in many inference scenarios while consuming 35–70% less power. Moreover, optimization with the vLLM library on NPUs nearly doubles the tokens-per-second and yields a 92% increase in power efficiency. Our findings validate the potential of NPU-based inference architectures to reduce operational costs and energy footprints, offering a viable alternative to the prevailing GPU-dominated paradigm. Full article

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

26 pages, 11307 KB

Open AccessArticle

Fault Detection and Diagnosis of Rolling Bearings in Automated Container Terminals Using Time–Frequency Domain Filters and CNN-KAN

by Taoying Li, Ruiheng Cheng and Zhiyu Dong

Systems 2025, 13(9), 796; https://doi.org/10.3390/systems13090796 - 10 Sep 2025

Abstract

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production [...] Read more.

In automated container terminals (ACTs), rolling bearings of equipment serve as crucial power transmission components, and their performance directly determines the operational efficiency, reliability, and service life of the entire equipment. Rolling bearing fault detection and diagnosis are key means to improve production efficiency, reduce the safety risks, and achieve sustainable development of equipment in ACTs. However, existing rolling-bearing diagnosis models are vulnerable to environmental noise and interference, depressing accuracy and raising misclassification, and they seldom achieve both noise robustness and a lightweight design; robustness usually increases complexity, while compact networks degrade under low signal-to-noise ratios. Therefore, this paper proposes a noise-robust, lightweight, and interpretable deep learning framework for fault detection and diagnosis of rolling bearings in automated container terminal (ACT) equipment. The framework comprises four coordinated components, including Time-Domain Filter, Frequency-Domain Filter, Physical-Feature Extraction module, and Classification module, whose joint optimization yields complementary time–frequency representations and physics-aligned features, and fuses into robust diagnostic decisions under noisy and non-stationary environments. The first component highlights impulsive transients, the second component emphasizes harmonic and sideband modulation, the third module introduces two differentiable and rolling bearing-signal-informed objectives to align learning with characteristic bearing signatures by weighted-average kurtosis and an Lp/Lq-based envelope-spectral concentration index, and the last module integrates multi-layer convolutional neural networks (CNN) and Deep Kolmogorov–Arnold Networks (DeepKAN). Finally, two public datasets are employed to estimate the model’s performance, and results indicate that the proposed method outperforms others. Full article

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

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25 pages, 5908 KB

Open AccessArticle

Research on Innovation Network Features of Patent-Intensive Industry Clusters and Their Evolution

by Lanqing Ge, Chunyan Li, Deli Cheng and Lei Jiang

Systems 2025, 13(9), 795; https://doi.org/10.3390/systems13090795 - 10 Sep 2025

Abstract

In the contemporary economic landscape shaped by globalization and digital transformation, patent-intensive industries have emerged as critical engines for enhancing national competitiveness. This study analyzed 98,464 collaborative patent application records (2012–2023) from listed companies in patent-intensive sectors, sourced from the China National Intellectual [...] Read more.

In the contemporary economic landscape shaped by globalization and digital transformation, patent-intensive industries have emerged as critical engines for enhancing national competitiveness. This study analyzed 98,464 collaborative patent application records (2012–2023) from listed companies in patent-intensive sectors, sourced from the China National Intellectual Property Administration (CNIPA) database. Through kernel density estimation, social network analysis, and community detection techniques, we examined the evolutionary trajectories of innovation networks and spatial patterns within these industrial clusters. Our findings indicate a notable spatial agglomeration trend in patent-intensive industries, exhibiting a prominent “core-periphery” structural feature. The core nodes of this cluster network closely align with economically developed regions, and the network structure has gradually shifted from a triangular framework supported by Beijing-Tianjin-Hebei, Yangtze River Delta, and Pearl River Delta to a diversified multilateral framework. Moreover, the community structure of the collaborative network within China’s patent-intensive industrial clusters exhibits distinct characteristics driven by technological relevance and strategic synergy, rather than strictly adhering to the principle of geographical proximity. These discoveries not only enrich the application of innovation network theory in the specific context of China, but also provide valuable guidance for cluster enterprises in selecting partners and achieving collaborative innovation. Full article

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25 pages, 1640 KB

Open AccessArticle

Port Investment Optimization and Its Application Under Differentiated Port and Industrial Risks Along the Maritime Silk Road

by Dongxu Chen, Feng Liu, Tong Wu, Xin Xu, Jingyi Wei, Fuyu Lai and Yu Lin

Systems 2025, 13(9), 794; https://doi.org/10.3390/systems13090794 - 9 Sep 2025

Abstract

Since the implementation of the Belt and Road Initiative (BRI) in 2013, Chinese enterprises have expanded port and industrial investments along the Maritime Silk Road (MSR), forming a mutually reinforcing coupled system. Port investments reduce transportation costs and promote the relocation of industries [...] Read more.

Since the implementation of the Belt and Road Initiative (BRI) in 2013, Chinese enterprises have expanded port and industrial investments along the Maritime Silk Road (MSR), forming a mutually reinforcing coupled system. Port investments reduce transportation costs and promote the relocation of industries to host countries. In turn, industrial agglomeration further promotes port investment. However, risks arising from political and economic uncertainties in host countries, as well as fluctuations in international relations, have become increasingly prominent. Due to the differences in the types and levels of risks faced by port and industrial investments, port investment decisions have become more complex and uncertain. To address this issue, this study constructs a bi-level optimization model. The upper model (UM) aims to maximize the total investment profit by optimizing the scale of multiple port investments. The lower model (LM) employs a User Equilibrium (UE) framework to determine the spatial distribution of industries under equilibrium conditions. Using 14 countries along the MSR as a case study, this paper estimates the number of newly constructed berths in each country and the corresponding investment returns. It also finds that local wages and land prices tend to rise after investment. The findings provide valuable references for Chinese enterprises in making overseas investment decisions. Full article

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25 pages, 1499 KB

Open AccessArticle

Digital Transformation and Modeling of Nature-Inspired Systems

by Naira V. Barsegyan, Farida F. Galimulina and Aleksei I. Shinkevich

Systems 2025, 13(9), 793; https://doi.org/10.3390/systems13090793 - 9 Sep 2025

Abstract

With the tightening of environmental regulations, the need to identify tools that foster the development of sustainable systems is growing. The shift toward closed-loop, bio-like systems promotes the creation of nature-inspired systems. However, the transformation processes and toolkits vary across meso-level systems with [...] Read more.

With the tightening of environmental regulations, the need to identify tools that foster the development of sustainable systems is growing. The shift toward closed-loop, bio-like systems promotes the creation of nature-inspired systems. However, the transformation processes and toolkits vary across meso-level systems with differing economic activity. This research reveals the patterns of formation and develops governance models for the evolution of nature-inspired systems, considering the specifics of digital transformation and innovative activity in ensuring environmental security. Methodology includes the following: correlation and regression analysis, factor and cluster analysis, along with automated neural network simulations. The study resulted in the expansion of conceptual frameworks for “nature-inspired system” formation; revealed dependencies between the formation of a nature-inspired macrosystem and mesosystems, while identifying growth hotspots for nature-inspired systems in Russia; identified the priority determinants of nature-inspired mesosystem formation; proposed a composite index (DNIS—Development of a Nature-Inspired System) to assess the cumulative impact of determinants and evaluate ecological performance responses; and developed a typology of regional mesosystems based on economic/ecological performance and “green” technology adoption, enabling differentiated approaches to guiding nature-inspired system development. The findings presented in this study are recommended for applications in improving regional socio-economic development programs. Full article

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25 pages, 6989 KB

Open AccessArticle

Does the Optimal Update Strategy Effectively Promote the Low-Carbon Technology Diffusion Among Manufacturers? An Evolutionary Game of Small-World Network Analysis

by Wanting Chen and Zhi-Hua Hu

Systems 2025, 13(9), 792; https://doi.org/10.3390/systems13090792 - 9 Sep 2025

Abstract

A complex network topology marked by co-competitive relationships between manufacturing enterprises can meaningfully influence low-carbon technology selection, thereby affecting the low-carbon technology diffusion process. This study develops a small-world network game model based on an optimal update strategy involving the government and manufacturers [...] Read more.

A complex network topology marked by co-competitive relationships between manufacturing enterprises can meaningfully influence low-carbon technology selection, thereby affecting the low-carbon technology diffusion process. This study develops a small-world network game model based on an optimal update strategy involving the government and manufacturers with co-competitive relationships, and then uses it to assess the evolutionary dynamics of low-carbon technology selection and diffusion among manufacturers. The results indicate that the government should identify the critical threshold for subsidies based on the carbon tax to optimize the regulatory and incentivizing effects of government subsidies. The topological structure of manufacturers’ small-world networks is the key to low-carbon technology selection and diffusion. In favorable conditions, when a small-world network approaches a regular network in terms of structure, the extent of low-carbon technology diffusion is maximized; in unfavorable conditions, diffusion is minimal. Thus, the government can tighten or relax market access restrictions on the manufacturing industry and encourage the development of manufacturing clusters to change the structure of market competition. Compared with the random selection, the optimal update strategy can increase the probability density of low-carbon technology diffusion among manufacturers and rapidly achieve a balanced, stable state. Full article

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

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40 pages, 1810 KB

Open AccessArticle

Acceptance of Navigate on Autopilot of New Energy Vehicles in China: An Extended Technology Acceptance Model

by Yi Wang, Tianle Lu, Haojiang Rong, Dong Pan, Wei Luo and Yacong Gao

Systems 2025, 13(9), 791; https://doi.org/10.3390/systems13090791 - 9 Sep 2025

Abstract

This study investigated the factors influencing user acceptance of the Navigate on Autopilot (NOA) functionality in new energy vehicles in China. An extended Technology Acceptance Model (TAM) was developed, incorporating additional factors such as social influence, travel scenarios, price value, perceived trust and [...] Read more.

This study investigated the factors influencing user acceptance of the Navigate on Autopilot (NOA) functionality in new energy vehicles in China. An extended Technology Acceptance Model (TAM) was developed, incorporating additional factors such as social influence, travel scenarios, price value, perceived trust and perceived risk. A questionnaire survey was conducted in Guangzhou, China, and 260 valid responses were obtained. Structural equation modeling (SEM) was used to analyze the relationships between the factors. The results indicated that perceived ease of use, perceived usefulness, travel scenarios, price value, and perceived trust had significant positive effects on attitudes towards NOA, whereas social influence and perceived risk did not. Attitude was the primary determinant of the behavioral intention to use NOA. The findings suggest that to enhance NOA acceptance, new energy vehicle companies should emphasize specific application scenarios, reduce technology costs, provide value-added services, and strengthen user trust in the technology. This study contributes to the understanding of NOA acceptance and provides practical insights into the promotion of driver assistance systems in the context of new energy vehicles in China. Full article

(This article belongs to the Special Issue Modeling, Planning and Management of Sustainable Transport Systems)

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26 pages, 1350 KB

Open AccessArticle

Incentives, Constraints, and Adoption: An Evolutionary Game Analysis on Human–Robot Collaboration Systems in Construction

by Guodong Zhang, Leqi Chen, Xiaowei Luo, Wei Li, Lei Zhang and Qiming Li

Systems 2025, 13(9), 790; https://doi.org/10.3390/systems13090790 - 8 Sep 2025

Abstract

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation [...] Read more.

Addressing the challenges of insufficient incentives, weak constraints, and superficial adoption in promoting human–robot collaboration (HRC) in the construction industry, this study develops a tripartite evolutionary game model among government, contractors, and on-site teams under bounded rationality. Lyapunov stability analysis and numerical simulation are employed to conduct parameter sensitivity analyses. The results show that a strategy profile characterized by flexible regulation, deep adoption, and high-effort collaboration constitutes a stable evolutionary outcome. Moderately increasing government incentives helps accelerate convergence but exhibits diminishing returns under fiscal constraints, indicating that subsidies alone cannot sustain genuine engagement. Reducing penalties for contractors and on-site teams, respectively, induces superficial adoption and low effort, whereas strengthening penalties for bilateral violations simultaneously compresses the space for opportunistic behavior. When the payoff advantage of deep adoption narrows or the payoff from perfunctory adoption rises, convergence toward the preferred steady state slows markedly. Based on the discussion and simulation evidence, we recommend dynamically matching incentives, sanctions, and performance feedback: prioritizing flexible regulation to reduce institutional frictions, configuring differentiated sanctions to maintain a positive payoff differential, reinforcing observable performance to stabilize frontline effort, and adjusting policy weights by project stage and actor characteristics. The study delineates how parameter changes propagate through behavioral choices to shape collaborative performance, providing actionable guidance for policy design and project governance in advancing HRC. Full article

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

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35 pages, 12605 KB

Open AccessArticle

Multi-User Virtual Reality Tool for Remote Communication in Construction Projects: Bridge Maintenance Application

by Sofía Montecinos-Orellana, Felipe Muñoz La Rivera, Javier Mora-Serrano, Pere-Andreu Ubach and María-Jesús Bopp

Systems 2025, 13(9), 789; https://doi.org/10.3390/systems13090789 - 8 Sep 2025

Abstract

Effective communication between construction sites and engineering or architectural offices is critical to the success of construction projects, particularly in the maintenance of critical infrastructure such as bridges. In scenarios where distance limits the physical presence of specialists, Requests for Information (RFIs) are [...] Read more.

Effective communication between construction sites and engineering or architectural offices is critical to the success of construction projects, particularly in the maintenance of critical infrastructure such as bridges. In scenarios where distance limits the physical presence of specialists, Requests for Information (RFIs) are the primary formal exchange tool. However, issues such as incomplete data, poor quality, or delayed responses often lead to significant project delays. This study proposes a multi-user Virtual Reality (VR) platform to optimize communication workflows in these contexts. Using the Design Science Research Methodology (DSRM), an immersive environment was developed to connect up to 20 users simultaneously, integrating BIM models with support for technical details, language, and contextual factors. The tool was validated through a case study focused on the maintenance of a railway bridge, where five real RFIs were simulated. Results show that the immersive experience enhances spatial understanding, improves remote collaboration, and accelerates decision-making. Users highlighted the sense of presence and perceived usefulness, positioning this tool as an effective alternative to overcome communication barriers in geographically distributed infrastructure maintenance. Full article

(This article belongs to the Special Issue Advancing Project Management Through Digital Transformation)

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23 pages, 1303 KB

Open AccessArticle

Building a Governance Reference Model for a Specific Enterprise: Addressing Social Challenges Through Structured Solution

by Jeremy Hilton

Systems 2025, 13(9), 788; https://doi.org/10.3390/systems13090788 - 8 Sep 2025

Abstract

Societal challenges are inherently complex and multi-tiered, arising from the interplay of diverse stakeholders with a spectrum of purposes and different perceptions and expectations, interdependent systems, and dynamic contextual factors that transcend single domains or disciplines. This paper presents a novel approach to [...] Read more.

Societal challenges are inherently complex and multi-tiered, arising from the interplay of diverse stakeholders with a spectrum of purposes and different perceptions and expectations, interdependent systems, and dynamic contextual factors that transcend single domains or disciplines. This paper presents a novel approach to developing a Reference Model of Governance tailored to a specific complex, multi-organisational enterprise facing socially complex challenges. Drawing on Angyal’s systems framework, the model introduces a three-dimensional structure with vertical, progression, and transverse dimensions, integrated within a holistic contextual whole. By mapping selected systems methodologies, including Soft Systems Methodology (SSM), Viable System Model (VSM), System Dynamics (SD), and dependency modelling, to these dimensions, the model offers a pragmatic, structured way to explore and regulate complex organisational behaviour. It enables collaborative inquiry, supports adaptive governance, and enhances the enterprise’s ability to address dynamic societal problems such as health, education, and public service delivery. The result is a governance reference model that captures both the operational and contextual realities of the enterprise, providing actionable insight for strategic design or diagnostic intervention. The novel approach is grounded in systemic and critical systems thinking and emphasises the use of methods for understanding to develop a common and shared understanding of the enterprise context and to surface multiple stakeholder perspectives. Full article

(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)

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37 pages, 1279 KB

Open AccessArticle

Examining Investor Interaction with Digital Robo-Advisory Systems: Green Value and Interface Quality in a Socio-Technical Context

by Imdadullah Hidayat-ur-Rehman, Mohammad Nurul Alam, Majed Alsolamy, Saleh Hamed H. Alharbi, Tawfeeq Mohammed B. AlAnazi and Abul Bashar Bhuiyan

Systems 2025, 13(9), 787; https://doi.org/10.3390/systems13090787 - 7 Sep 2025

Viewed by 224

Abstract

The main objective of this paper is to examine the factors influencing investor intention to adopt robo-advisory services in Saudi Arabia, with a particular focus on sustainability and platform interface quality (PIQ) within a socio-technical framework. Drawing on the Diffusion of Innovation (DOI), [...] Read more.

The main objective of this paper is to examine the factors influencing investor intention to adopt robo-advisory services in Saudi Arabia, with a particular focus on sustainability and platform interface quality (PIQ) within a socio-technical framework. Drawing on the Diffusion of Innovation (DOI), Technology Acceptance Model (TAM), Value-Based Adoption Model (VAM), and Trust theory, the research integrates constructs such as Knowledge about Robo-Advisors (KRA), PIQ, Green Perceived Value (GPV), and Perceived Trust (PT). Data were collected through a structured questionnaire targeting financially active individuals, with 387 valid responses analyzed using Partial Least Squares Structural Equation Modelling (PLS-SEM). The findings reveal that KRA significantly influences Intention to Use Robo-Advisors (IURA) both directly and indirectly, through GPV and Relative Advantage (RA), with only marginal support observed for Perceived Usefulness (PU). PIQ strongly influences perceived ease of use (PEOU) and PU, contributing to IURA, while PT significantly moderates the effects of KRA and PIQ. Multi-group analysis (MGA) further highlights heterogeneity across age, education, and investment groups, underscoring the contextual nature of adoption. The study highlights the critical role of PT, PIQ, and GPV alignment in investor decision-making when engaging with robo-advisory platforms. It offers theoretical contributions by extending traditional adoption models through the inclusion of green value and interface quality, and practical implications for FinTech developers and policymakers aiming to build inclusive, trustworthy, and environmentally aligned robo-advisory platforms. Full article

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18 pages, 620 KB

Open AccessArticle

Unveiling the Synergy Between ESG Performance and Digital Transformation

by Feng Yan, Xiongwang Baihui and Yang Su

Systems 2025, 13(9), 786; https://doi.org/10.3390/systems13090786 - 7 Sep 2025

Viewed by 229

Abstract

Against the backdrop of global sustainable development and the fast-growing digital economy, aligning corporate ESG practices with digital transformation is key for enterprises’ high-quality development, yet existing studies have not fully explored ESG’s directional impact on digital transformation. This study examines how corporate [...] Read more.

Against the backdrop of global sustainable development and the fast-growing digital economy, aligning corporate ESG practices with digital transformation is key for enterprises’ high-quality development, yet existing studies have not fully explored ESG’s directional impact on digital transformation. This study examines how corporate ESG performance drives digital transformation and the moderating roles of firm characteristics, industry types, and ownership structures, using 11,109 valid observations from Chinese A-share listed companies (2009–2022); it adopts the causal forest algorithm and supplements with OLS, quantile, and Poisson regressions for robustness tests. The results show that ESG significantly promotes digital transformation—with obvious positive effects from E and S dimensions, while G has no statistical impact—and further analysis reveals nonlinear moderation by firm characteristics and contextual differences: the positive effect is stronger in high-tech and private enterprises but weaker in traditional and state-owned enterprises (due to institutional constraints). These findings offer theoretical insights into ESG–digital synergies and practical guidance for targeted sustainability and digital strategies. Full article

(This article belongs to the Special Issue Sustainable Business Models and Digital Transformation)

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27 pages, 3080 KB

Open AccessArticle

Green Micromobility-Based Last-Mile Logistics from Small-Scale Urban Food Producers

by Ágota Bányai, Ireneusz Kaczmar and Tamás Bányai

Systems 2025, 13(9), 785; https://doi.org/10.3390/systems13090785 - 7 Sep 2025

Viewed by 154

Abstract

The growing demand for sustainable urban logistics highlights the need for innovative, low-emission delivery solutions, particularly in the context of small-scale urban food producers. These producers often face logistical challenges in reaching consumers efficiently while minimizing environmental impacts. Green micro-mobility, such as electric [...] Read more.

The growing demand for sustainable urban logistics highlights the need for innovative, low-emission delivery solutions, particularly in the context of small-scale urban food producers. These producers often face logistical challenges in reaching consumers efficiently while minimizing environmental impacts. Green micro-mobility, such as electric cargo bikes and scooters, offers a promising last-mile delivery alternative that aligns with environmental and economic goals. This study addresses the integration of micromobility into urban food logistics, aiming to enhance both efficiency and sustainability. The authors develop a mathematical optimization model that supports real-time decision-making for last-mile deliveries from multiple local food producers to urban customers using micromobility vehicles. The model considers vehicle capacity constraints, and delivery time windows while minimizing greenhouse gas (GHG) emissions and total operational costs. Optimization results based on realistic urban scenario demonstrate that the proposed model significantly reduces GHG emissions compared to conventional delivery methods. Additionally, it enables a more cost-effective and streamlined delivery operation tailored to the specific needs of small producers. The findings confirm that green micromobility-based logistics, supported by optimized planning, can play a crucial role in building cleaner, more resilient urban food distribution systems. Full article

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23 pages, 534 KB

Open AccessArticle

LLM-Powered, Expert-Refined Causal Loop Diagramming via Pipeline Algebra

by Kirk Reinholtz, Kamran Eftekhari Shahroudi and Svetlana Lawrence

Systems 2025, 13(9), 784; https://doi.org/10.3390/systems13090784 - 7 Sep 2025

Viewed by 845

Abstract

Building a causal-loop diagram (CLD) is central to system-dynamics modeling but demands domain insight, the mastery of CLD notation, and the ability to juggle AI, mathematical, and execution tools. Pipeline Algebra (PA) reduces that burden by treating each step—LLM prompting, symbolic or numeric [...] Read more.

Building a causal-loop diagram (CLD) is central to system-dynamics modeling but demands domain insight, the mastery of CLD notation, and the ability to juggle AI, mathematical, and execution tools. Pipeline Algebra (PA) reduces that burden by treating each step—LLM prompting, symbolic or numeric computation, algorithmic transforms, and cloud execution—as a typed, idempotent operator in one algebraic expression. Operators are intrinsically idempotent (implemented through memoization), so every intermediate result is re-used verbatim, yielding bit-level reproducibility even when individual components are stochastic. Unlike DAG (directed acyclic graph) frameworks such as Airflow or Snakemake, which force analysts to wire heterogeneous APIs together with glue code, PA’s compact notation lets them think in the problem space, rather than in workflow plumbing—echoing Iverson’s dictum that “notation is a tool of thought.” We demonstrated PA on a peer-reviewed study of novel-energy commercialization. Starting only from the article’s abstract, an AI-extracted problem statement, and an AI-assisted web search, PA produced an initial CLD. A senior system-dynamics practitioner identified two shortcomings: missing best-practice patterns and lingering dependence on the problem statement. A one-hour rewrite that embedded best-practice rules, used iterative prompting, and removed the problem statement yielded a diagram that conformed to accepted conventions and better captured the system. The results suggest that earlier gaps were implementation artifacts, not flaws in PA’s design; quantitative validation will be the subject of future work. Full article

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22 pages, 735 KB

Open AccessArticle

Enhancing ESG Risk Assessment with Litigation Signals: A Legal-AI Hybrid Approach for Detecting Latent Risks

by Minjung Park

Systems 2025, 13(9), 783; https://doi.org/10.3390/systems13090783 - 5 Sep 2025

Viewed by 270

Abstract

Environmental, Social, and Governance (ESG) ratings are widely used for investment and regulatory decision-making, yet they often suffer from symbolic compliance and information asymmetry. To address these limitations, this study introduces a hybrid ESG risk assessment model that integrates court ruling data with [...] Read more.

Environmental, Social, and Governance (ESG) ratings are widely used for investment and regulatory decision-making, yet they often suffer from symbolic compliance and information asymmetry. To address these limitations, this study introduces a hybrid ESG risk assessment model that integrates court ruling data with traditional ESG ratings to detect latent sustainability risks. Using a dataset of 213 ESG-related U.S. court rulings from January 2023 to May 2025, we apply natural language processing (TF-IDF, Legal-BERT) and explainable AI (SHAP) techniques to extract structured features from legal texts. We construct and compare classification models—including Random Forest, XGBoost, and a Legal-BERT-based hybrid model—to predict firms’ litigation risk. The hybrid model significantly outperforms the baseline ESG-only model in all key metrics: F1-score (0.81), precision (0.79), recall (0.84), and AUC-ROC (0.87). SHAP analysis reveals that legal features such as regulatory sanctions and governance violations are the most influential predictors. This study demonstrates the empirical value of integrating adjudicated legal evidence into ESG modeling and offers a transparent, verifiable framework to enhance ESG risk evaluation and reduce information asymmetry in sustainability assessments. Full article

(This article belongs to the Special Issue Systems Analysis of Enterprise Sustainability: Second Edition)

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30 pages, 1744 KB

Open AccessArticle

Efficiency in High-Rise Building Design: A Lean Approach to Waste Identification and Reduction

by Nicolás Morales-Caballero, Karen Castañeda, Eric Forcael and Rodrigo F. Herrera

Systems 2025, 13(9), 782; https://doi.org/10.3390/systems13090782 - 5 Sep 2025

Viewed by 274

Abstract

The design phase of buildings represents a dynamic and complex process, constantly evolving with modifications and feedback. It involves numerous professionals from various specialties, resulting in a fragmented and iterative trial-and-error process. Analyzing waste is the first step towards increasing the efficiency of [...] Read more.

The design phase of buildings represents a dynamic and complex process, constantly evolving with modifications and feedback. It involves numerous professionals from various specialties, resulting in a fragmented and iterative trial-and-error process. Analyzing waste is the first step towards increasing the efficiency of the design process for high-rise buildings using Lean methodology. Initially, the design phase was characterized, and processes were classified into productive, contributory, and non-contributory work. Typical waste in building design was identified, analyzed, and ranked based on frequency and impact to facilitate understanding and elimination. Three traditional design stages were identified: Schematic Design (SD), Design Development (DD), and Construction Documentation (CD). A total of 33 typical wastes were classified into the eight Lean categories. Key waste ranked by the Frequency-Adjusted Importance Index (FAII) for cost, schedule, and quality metrics were late-stage design changes, waiting for resources and information, rework, and late-stage clarification of requirements. Full article

(This article belongs to the Special Issue Systems Approach to Innovation in Construction Projects)

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20 pages, 1623 KB

Open AccessArticle

Industry-Driven Model-Based Systems Engineering (MBSE) Workforce Competencies—An AI-Based Competency Extraction Framework

by Aditya Akundi, Phani Ram Teja Ravipati, Sergio A. Luna Fong and Wilkistar Otieno

Systems 2025, 13(9), 781; https://doi.org/10.3390/systems13090781 - 5 Sep 2025

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Abstract

Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in [...] Read more.

Model-based systems engineering (MBSE) is being rapidly adopted in U.S. industries across various sectors. While practitioners and academics recognize many benefits of adopting MBSE, industries also report challenges such as limited tool expertise and a shortage of skilled personnel. Highlighting the difficulties in industry adoption of MBSE, prior research by the authors identified challenges such as tool limitations, knowledge gaps, cultural and political barriers, costs, and the level of customer understanding and acceptance of MBSE practices. Additionally, another study by the authors points out a gap between industry demands for MBSE skills in new hires and the current academic training programs. To further assess the MBSE industry’s workforce needs, this paper introduces a two-phase method for the Structured Extraction of MBSE competencies using large language models based on current workforce demands from LinkedIn job postings. Phase 1 involved extracting 1960 job descriptions from LinkedIn using the term “model-based systems engineer.” In phase 2, large language models (LLMs) employing deep transformer architectures were used to transform unstructured text into structured data. An AI agent was used as an autonomous software layer to manage every interaction between the raw dataset from Phase 1 and the LLM. Supported by the analyzed data, a competency framework is proposed that summarizes the tools, technical skills, and soft skills expected of a model-based systems engineer by the industry. The framework is designed to include core competencies shared across all MBSE roles, with specific competencies tailored for aerospace & defense, manufacturing and automotive, and software and IT sectors. Full article

(This article belongs to the Special Issue Advancing Digital Engineering: Transforming Industries with Model-Based Systems Engineering and Digital Twin Technology)

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60 pages, 12559 KB

Open AccessArticle

A Decade of Studies in Smart Cities and Urban Planning Through Big Data Analytics

by Florin Dobre, Andra Sandu, George-Cristian Tătaru and Liviu-Adrian Cotfas

Systems 2025, 13(9), 780; https://doi.org/10.3390/systems13090780 - 5 Sep 2025

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Abstract

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in [...] Read more.

Smart cities and urban planning have succeeded in gathering the attention of researchers worldwide, especially in the last decade, as a result of a series of technological, social and economic developments that have shaped the need for evolution from the traditional way in which the cities were viewed. Technology has been incorporated in many sectors associated with smart cities, such as communications, transportation, energy, and water, resulting in increasing people’s quality of life and satisfying the needs of a society in continuous change. Furthermore, with the rise in machine learning (ML) and artificial intelligence (AI), as well as Geographic Information Systems (GIS), the applications of big data analytics in the context of smart cities and urban planning have diversified, covering a wide range of applications starting with traffic management, environmental monitoring, public safety, and adjusting power distribution based on consumption patterns. In this context, the present paper brings to the fore the papers written in the 2015–2024 period and indexed in Clarivate Analytics’ Web of Science Core Collection and analyzes them from a bibliometric point of view. As a result, an annual growth rate of 10.72% has been observed, showing an increased interest from the scientific community in this area. Through the use of specific bibliometric analyses, key themes, trends, prominent authors and institutions, preferred journals, and collaboration networks among authors, data are extracted and discussed in depth. Thematic maps and topic discovery through Latent Dirichlet Allocation (LDA) and doubled by a BERTopic analysis, n-gram analysis, factorial analysis, and a review of the most cited papers complete the picture on the research carried on in the last decade in this area. The importance of big data analytics in the area of urban planning and smart cities is underlined, resulting in an increase in their ability to enhance urban living by providing personalized and efficient solutions to everyday life situations. Full article

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23 pages, 1085 KB

Open AccessArticle

Configurations Driving High Performance in Hydrogen Fuel Cell Vehicle Enterprises

by Wei Li, Mengxin Wang, Xiaoguang Liu and Shizheng Tan

Systems 2025, 13(9), 779; https://doi.org/10.3390/systems13090779 - 4 Sep 2025

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Abstract

The hydrogen fuel cell vehicle (HFCV) market is growing rapidly, but technological limitations, high costs, and market constraints are hindering enterprise performance. Existing studies often analyze isolated factors, overlooking their configurational interactions. This study applies the Technology–Organization–Environment (TOE) framework and fuzzy-set Qualitative Comparative [...] Read more.

The hydrogen fuel cell vehicle (HFCV) market is growing rapidly, but technological limitations, high costs, and market constraints are hindering enterprise performance. Existing studies often analyze isolated factors, overlooking their configurational interactions. This study applies the Technology–Organization–Environment (TOE) framework and fuzzy-set Qualitative Comparative Analysis (fsQCA) to examine how R&D capability, human capital level, scale of enterprise, attention allocation, and government support shape high performance in 40 Chinese HFCV enterprises. The consistency of all antecedents does not exceed 0.9, indicating that high performance does not depend on any single factor. The sufficiency analysis identifies three effective configurations: technology-driven, internal–external synergy, and organization–policy-driven, with an overall solution consistency of 0.9206 and a coverage of 0.4167. Without adequate government support and human capital, achieving high performance in HFCV enterprises appears improbable. These findings reveal multiple pathways toward high performance and highlight the importance of condition combinations over isolated effects, offering theoretical and practical insights into sustainable development strategies for emerging green industries. Full article

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20 pages, 1389 KB

Open AccessArticle

Catalyzing the Transition to a Green Economy: A Systemic Analysis of China’s Agricultural Socialized Services and Their Mechanization Pathways

by Xiuyan Su, Xueqi Wang, Yuefei Zhuo, Guan Li and Zhongguo Xu

Systems 2025, 13(9), 778; https://doi.org/10.3390/systems13090778 - 4 Sep 2025

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Abstract

The green transformation of agricultural systems is crucial for environmental protection and food security, yet smallholder-dominated systems face immense structural barriers. This study investigates whether agricultural socialized services (ASSs)—an emerging institutional innovation—can serve as a catalyst for this transition. Using household survey data [...] Read more.

The green transformation of agricultural systems is crucial for environmental protection and food security, yet smallholder-dominated systems face immense structural barriers. This study investigates whether agricultural socialized services (ASSs)—an emerging institutional innovation—can serve as a catalyst for this transition. Using household survey data from the China Land Economy Survey (CLES), this study examines the direct impact and mediating pathways of ASSs on farmers’ adoption of green production behaviors. We also reveal the heterogeneity effects of household operating scale. The results show the following: (1) Agricultural socialized services positively impact farmers’ adoption of green production behaviors, which can contribute to advancing sustainable agricultural development. (2) ASSs do not simply increase the quantity of machines. Instead, they facilitate a shift from costly asset ownership to efficient mechanization-as-a-service. (3) Furthermore, a heterogeneity analysis reveals that the positive impacts of ASSs are heterogenous at different levels. ASSs more significantly influence farmers’ adoption of green practices for small-scale farms (operating at a size less than 4.8 mu). It provides robust empirical evidence that ASSs can effectively “decouple” green modernization from large-scale farmers to overcome structural barriers. These findings help to provide policy implications for promoting ASSs and sustainable agriculture production. Full article

(This article belongs to the Special Issue Sustainable Strategies and Organizational Transitions for Green Economy)

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41 pages, 4345 KB

Open AccessReview

Structuring Multi-Criteria Decision Approaches for Public Procurement: Methods, Standards and Applications

by Debora Anelli, Pierluigi Morano, Tiziana Acquafredda and Francesco Tajani

Systems 2025, 13(9), 777; https://doi.org/10.3390/systems13090777 - 4 Sep 2025

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Abstract

The selection of the most economically advantageous tender (MEAT) in public procurement procedures requires transparent evaluation systems capable of integrating heterogeneous criteria, including qualitative ones, to reconcile quality and cost. This systematic review analyzes 74 studies published between 1998 and 2023 to explore [...] Read more.

The selection of the most economically advantageous tender (MEAT) in public procurement procedures requires transparent evaluation systems capable of integrating heterogeneous criteria, including qualitative ones, to reconcile quality and cost. This systematic review analyzes 74 studies published between 1998 and 2023 to explore the application of multi-criteria decision analysis (MCDA) methods in public construction procurement. The vast majority of MCDA applications focus on the award phase, with constant growth over the last 10 years. However, applications in the prequalification and verification phases are much less frequent and remain under-represented. Geographically, Europe is the most active area in terms of publications, followed by China and some countries in the Asia-Pacific area. In these regions, MCDA has been employed more systematically over time, while in other areas (e.g., Africa, Latin America), applications are sporadic or absent. Analytic Hierarchy Process (AHP) is confirmed as the most widely used technique. Emerging techniques (such as BWM, MABAC, EDAS, VIKOR, advanced TOPSIS) show greater computational rigor and in some cases better theoretical properties, but are less used due to complexity, less practical familiarity and the lack of accessible software tools. The operationalization of environmental and social criteria is still poorly standardized: clear indications on metrics, measurement scales and data sources are often lacking. In most cases, the criteria are treated in a generic or qualitative way, without common standards. Furthermore, the use of sensitivity analyses and procedures for aggregating judgments between evaluators is limited, with a consequent risk of poor robustness and transparency in the evaluation. In order to consider proposing a framework or guidelines based on the review findings, a six-step operational framework that connects selection of criteria and their operationalization, choice of method based on the context, robustness checks and standard minimum reporting, with clear assignment of roles and deliverables, is provided. The framework summarizes and makes the review evidence applicable. Full article

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31 pages, 8682 KB

Open AccessArticle

The Spatiotemporal Characteristics and Spatial Linkages of the Coupling Coordination Between Economic Development and Ecological Resilience in the Guizhou Central Urban Agglomeration

by Zhi Liu, Jiayi Zhao, Bo Chen, Yongli Yao and Min Zhao

Systems 2025, 13(9), 776; https://doi.org/10.3390/systems13090776 - 4 Sep 2025

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Abstract

Exploring the spatiotemporal characteristics and spatial correlation structure of the coupling and coordination relationship between urban economic development and ecological resilience is of great significance for optimizing the regional coordinated development strategies of urban agglomerations and building high-quality economic development regions. Taking 33 [...] Read more.

Exploring the spatiotemporal characteristics and spatial correlation structure of the coupling and coordination relationship between urban economic development and ecological resilience is of great significance for optimizing the regional coordinated development strategies of urban agglomerations and building high-quality economic development regions. Taking 33 counties (cities, districts) in the Qianzhong Urban Agglomeration as the research objects, this study adopts the analytical paradigm of “mechanism exploration—level measurement—relationship evolution—spatial correlation”, expands and constructs a four-dimensional ecological resilience evaluation index system based on the “risk resistance—adaptation—recovery” framework, and systematically analyzes the spatiotemporal dynamics and spatial correlation characteristics of the coupling and coordination between economic development and ecological resilience from 2005 to 2020 by combining the coupling coordination model, trend surface analysis, and spatial gravity model. The research results show that the overall coupling coordination degree between economic development and ecological resilience in the Qianzhong Urban Agglomeration presents an upward trend, and the key to optimizing the coupling coordination lies in improving the level of urban economic development. The spatial correlation of regional coupling coordination degree is increasingly close, and its spatial connection structure shows the characteristics of “core polarization, edge collapse and multi-center germination”. The research results provide important enlightenment for formulating differentiated sustainable development strategies for urban agglomerations in ecologically fragile areas. Full article

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

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30 pages, 515 KB

Open AccessArticle

Executive Cognitive Styles and Enterprise Digital Strategic Change Under Environmental Dynamism: The Mediating Role of Absorptive Capacity in a Complex Adaptive System

by Xiaochuan Guo, Chunyun Fan and You Chen

Systems 2025, 13(9), 775; https://doi.org/10.3390/systems13090775 - 4 Sep 2025

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Abstract

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring [...] Read more.

Driven by the new wave of technological revolution and industrial transformation, firms are accelerating strategic change to gain new competitive advantages. Situated within a complex adaptive system, firms must adapt to highly dynamic and uncertain external environments by adjusting executive cognitive structures, reconfiguring resources and capabilities, and strengthening collaboration with industrial ecosystem elements; hence, digital strategic change is characterized by continuous evolution. Using a sample of Chinese A-share listed firms from 2015 to 2023, this study develops a “cognition–capability–strategy” pathway model grounded in upper echelons theory and dynamic capabilities theory to examine how executive cognitive styles, i.e., cognitive flexibility and cognitive complexity, drive digital strategic change via absorptive capacity and how environmental dynamism moderates these relationships. The findings show that executive cognition, as a decision node in strategic change, can dynamically adjust firms’ strategic paths by activating absorptive capacity in rapidly changing external information environments; environmental dynamism differentially affects the two cognitive styles. Heterogeneity tests further indicate that the role of executive cognition varies significantly with regional digital economy development levels, firm life cycle, and industry factor intensities. The study reveals how firms can respond to high environmental uncertainty through cognition–strategy alignment and resource capability reconfiguration in a complex adaptive system, providing theoretical references and practical insights for emerging economies to advance digital transformation and enhance competitiveness. Full article

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

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29 pages, 2308 KB

Open AccessArticle

Drone-Assisted Order Picking Problem: Adaptive Genetic Algorithm

by Esra Boz and Erfan Babaee Tirkolaee

Systems 2025, 13(9), 774; https://doi.org/10.3390/systems13090774 - 4 Sep 2025

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Abstract

This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. [...] Read more.

This study tries to make some improvements in the order picking operations by offering a novel mathematical model and efficient solution algorithm. Accordingly, the order picking policies are examined to allow for picking more orders by reducing the collection time/distance of order pickers. Batching orders for the pick are included in the order picking process as it could enable the order picker to collect more orders. Since the most labor-intensive movement in the order picking function in a high-level shelf layout is the retrieval of products from upper shelves and placing them onto the collection vehicle in the picker-to-part system, the use of drones is preferred to eliminate this costly movement. Drones assist humans in the order picking process by retrieving products from upper levels, thus reducing the order picking time. Here, a Vehicle Routing Problem (VRP) is formulated to deal with drone routing which is then solved based on the Order Picking Problem (OPP) framework. Consequently, an integrated OPP involving both order pickers and drones is addressed and formulated using a Mixed-Integer Linear Programming (MILP) model. To cope with the complexity of the problem, an Adaptive Genetic Algorithm (AGA) is designed which is able to yield superior results compared to the classical Genetic Algorithm (GA). Finally, a sensitivity analysis is performed to assess the behavior of the model against real-world fluctuations. The main reason for this research is to speed up the order picking process in warehouses by taking advantage of the tools brought by the technology age. According to the research results, when the results of the drone-assisted order picking process are compared to the order picking process without drone support, an improvement of 29.68% is observed. The theoretical contribution of this work is that it initially mathematically defines the drone-aided OPP in the literature and proposes a solution with the help of the AGA. As a practical contribution, it provides a solution with the capacity to reduce operational costs by accelerating the order picking operation in warehouses and a practical optimization framework for logistics managers. In addition, warehouse managers, senior company managers, and researchers working on order picking processes can benefit from this study. Full article

(This article belongs to the Section Supply Chain Management)

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25 pages, 505 KB

Open AccessArticle

Dual-Dimensional Digital Transformation Systematically Reshapes the U-Curve of Knowledge and Political Distance on Subsidiary Exit

by Zhengyuan Zhou and Lei Wang

Systems 2025, 13(9), 773; https://doi.org/10.3390/systems13090773 - 3 Sep 2025

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Abstract

In the era of digital business model innovation, multinational corporations face a dual challenge: leveraging digital technologies to overcome institutional barriers while reconfiguring value creation in cross-border operations. Grounded in institutional theory and the digital transformation literature, this study investigates how knowledge distance [...] Read more.

In the era of digital business model innovation, multinational corporations face a dual challenge: leveraging digital technologies to overcome institutional barriers while reconfiguring value creation in cross-border operations. Grounded in institutional theory and the digital transformation literature, this study investigates how knowledge distance and political distance shape subsidiary exits through a U-shaped relationship, and how digital transformation breadth and depth differentially reconfigure these effects. We conduct empirical research on 1203 Chinese multinational enterprises from 2015 to 2019. The results indicate that both knowledge distance and political distance exhibit a U-shaped relationship with the subsidiary exit. The breadth of digital transformation strengthens the U-shaped relationship between knowledge distance and subsidiary exit but weakens the relationship between political distance and subsidiary exit. The depth of digital transformation mitigates the effects of both knowledge distance and political distance on subsidiary exit. These findings provide a novel explanatory perspective on the ‘Distance Paradox’ in internationalization theory, address a critical gap in the multinational enterprise (MNE) exit literature, and propose a modular governance blueprint for MNEs. Full article

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

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22 pages, 557 KB

Open AccessArticle

Resilience Through Integration: The Synergistic Role of National and Organizational Culture in Enhancing Market Responsiveness

by Hyojin Kim, Daesik Hur and Jaeyoung Oh

Systems 2025, 13(9), 772; https://doi.org/10.3390/systems13090772 - 3 Sep 2025

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Abstract

This study explains how integrating with foreign suppliers fortifies a buying firm’s supply-chain resilience, captured here as heightened market responsiveness. Drawing on information-processing theory, we argue that supplier integration equips buyers with richer, faster information flows that enable timely adaptation to market shocks. [...] Read more.

This study explains how integrating with foreign suppliers fortifies a buying firm’s supply-chain resilience, captured here as heightened market responsiveness. Drawing on information-processing theory, we argue that supplier integration equips buyers with richer, faster information flows that enable timely adaptation to market shocks. Extending value-congruence theory, we posit that this resilience dividend depends on simultaneous cultural alignment at two levels—national and organizational. Survey data from 174 manufacturing firms engaged in international buyer–supplier relationships across East Asia, North America, Latin America and Europe were analyzed via hierarchical regression. Results confirm that foreign supplier integration has a positive main effect on market responsiveness. Crucially, a significant three-way interaction (integration × national-culture congruence × organizational-culture congruence) reveals that the responsiveness—and thus resilience—payoff materializes only when both cultural layers are highly congruent; congruence at just one layer is insufficient. By demonstrating the contingent, multilevel nature of resilience benefits, this research advances the global supply-chain literature in three ways: (1) it unites information-processing and value-congruence perspectives to clarify when integration generates adaptive capability; (2) it positions dual-level cultural fit as a prerequisite for resilient performance; and (3) it offers region-spanning evidence that guides managers in designing culturally attuned integration strategies to withstand market turbulence. Full article

(This article belongs to the Special Issue Navigating Complexity in a Changing World: Challenges and Opportunities for Sustainable and Resilient Supply Chains)

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29 pages, 1088 KB

Open AccessArticle

Defining Nanostores: Cybernetic Insights on Independent Grocery Micro-Retailers’ Identity and Transformations

by David Ernesto Salinas-Navarro, Eliseo Vilalta-Perdomo, Rebecca Michell Herron and Christopher Mejía-Argueta

Systems 2025, 13(9), 771; https://doi.org/10.3390/systems13090771 - 3 Sep 2025

Viewed by 364

Abstract

Nanostores—micro, independent grocery retailers—are often defined overlooking their socioeconomic roles and relational significance in favour of their primary functional aspects. To close this gap, this study adopts a systemic perspective to examine how multiple stakeholders (owners, customers, and suppliers) shape nanostore identity. Accordingly, [...] Read more.

Nanostores—micro, independent grocery retailers—are often defined overlooking their socioeconomic roles and relational significance in favour of their primary functional aspects. To close this gap, this study adopts a systemic perspective to examine how multiple stakeholders (owners, customers, and suppliers) shape nanostore identity. Accordingly, this study proposes a framework of X-Y-Z identity statements, along with the use of the TASCOI tool, to examine nanostore descriptions and map their roles, expectations, and transformation processes. This systemic framework, rooted in management cybernetics, enabled the collection and analysis of 168 survey responses from 34 stores in Mexico City. The results show that nanostore identities are varied and context-dependent, operating as grocery stores, family projects, community anchors, economic lifelines, and competitors. This diversity influences stakeholder engagement, resource utilisation, and operational decisions. Overall, this study provides a transferable framework for analysing micro-business identity and transformation, with implications for problem-solving, decision-making, and policy development. Future research should address the current limitations of this study, including its geographical cross-sectional design, limited sampling method, reliance on self-reported perceptions, and lack of generalisability to other populations. Future work will involve exploring other urban contexts, utilising longitudinal data, expanding the sample, and adopting a participatory research approach to gain a deeper understanding of identity dynamics and their implications for nanostore resilience and survivability. Full article

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

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25 pages, 5491 KB

Open AccessArticle

When BIM Meets MBSE: Building a Semantic Bridge for Infrastructure Data Integration

by Joseph Murphy, Siyuan Ji, Charles Dickerson, Chris Goodier, Sonia Zahiroddiny and Tony Thorpe

Systems 2025, 13(9), 770; https://doi.org/10.3390/systems13090770 - 2 Sep 2025

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Abstract

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient [...] Read more.

The global infrastructure industry is faced with increasing system complexity and requirements driven by the Sustainable Development Goals, technological advancements, and the shift from Industry 4.0 to human-centric 5.0 principles. Coupled with persistent infrastructure investment deficits, these pressures necessitate improved methods for efficient requirements management and validation. While digital twins promise transformative real-time decision-making, reliance on static unstructured data formats inhibits progress. This paper presents a novel framework that integrates Building Information Modelling (BIM) and Model-Based Systems Engineering (MBSE), using Linked Data principles to preserve semantic meaning during information exchange between physical abstractions and requirements. The proposed approach automates a step of compliance validation against regulatory standards explored through a case study, utilising requirements from a high-speed railway station fire safety system and a modified duplex apartment digital model. The workflow (i) digitises static documents into machine-readable MBSE formats, (ii) integrates structured data into dynamic digital models, and (iii) creates foundations for data exchange to enable compliance validation. These findings highlight the framework’s ability to enhance traceability, bridge static and dynamic data gaps, and provide decision-making support in digital twin environments. This study advances the application of Linked Data in infrastructure, enabling broader integration of ontologies required for dynamic decision-making trade-offs. Full article

(This article belongs to the Special Issue Digital and Data-Driven Systems Engineering: Bridging Theory and Practice)

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