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Volume 13
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Systems, Volume 13, Issue 8 (August 2025) – 86 articles

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27 pages, 451 KiB  
Article
Unlocking AI’s Radical Innovation Potential: The Contingent Roles of Digital Foundation and Government Subsidy
by Zongjun Wang, Xian Zhang, Xiying Song and Jinrong Huang
Systems 2025, 13(8), 702; https://doi.org/10.3390/systems13080702 (registering DOI) - 15 Aug 2025
Abstract
Over the past decade, artificial intelligence (AI) has been increasingly used in firm innovation. While AI has contributed to innovation improvement, direct evidence of its effectiveness in radical innovation is limited. This study fills this gap by empirically investigating the impact of AI [...] Read more.
Over the past decade, artificial intelligence (AI) has been increasingly used in firm innovation. While AI has contributed to innovation improvement, direct evidence of its effectiveness in radical innovation is limited. This study fills this gap by empirically investigating the impact of AI on radical innovation and how this relationship is shaped by digital foundation and government subsidy from the perspectives of technological synergy and the external institutional environment. Using panel data from Chinese A-share listed firms from 2007 to 2023, this study empirically tests hypotheses through regression analyses. The findings reveal that AI adoption significantly promotes radical innovation, and this relationship is moderated by the characteristics of a firm’s digital foundation (i.e., degree and rate) as well as government subsidy. Specifically, a high degree of digital foundation hinders AI-driven radical innovation, while a fast rate enhances it. In addition, government subsidy strengthens the positive impact of AI adoption on radical innovation. A heterogeneity analysis further shows that both the timing (early vs. late) and pace (fast vs. slow) of AI adoption exert nuanced impacts: firms that adopt AI later and at a slower pace tend to achieve greater gains in radical innovation. This study advances research on radical innovation in the era of intelligence and provides managerial implications regarding the interplay of AI with internal digital foundation and external government subsidy. Full article
(This article belongs to the Section Systems Practice in Social Science)
27 pages, 1052 KiB  
Article
A Systems Perspective on Customer Segmentation as a Strategic Tool for Sustainable Development Within Slovakia’s Postal Market
by Radovan Madlenak, Pawel Drozdziel, Malgorzata Zysinska and Lucia Madlenakova
Systems 2025, 13(8), 701; https://doi.org/10.3390/systems13080701 (registering DOI) - 15 Aug 2025
Abstract
Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be [...] Read more.
Customer segmentation is a foundation of Customer Relationship Management (CRM) and is widely regarded as a key to business development success. As the principles of sustainable development become increasingly central to business strategy, it is necessary that social, environmental, and economic considerations be incorporated into customer segmentation—even in regulated markets such as the postal market. The article develops and applies a three-dimensional (3D) segmentation model of business customers in the Slovak postal market, utilizing cluster analysis within STATISTICA analytical software for operationalization of the segmentation criteria. The 3D model reacts to the three pillars of sustainable development and is verified under real conditions at Slovak Post, plc. By adopting a systems perspective, the research places customer segmentation as an integral component of the entire socio-technical system, emphasizing the interrelatedness of organizational, social, and environmental considerations. The study illustrates how a systems-based approach to segmentation enables postal operators to uncover key customer segments, optimize resource allocation, and support competitiveness and sustainability goals. The practical applicability of the model is illustrated by its potential for application in other regulated service industries, providing a solid framework for sustainable customer management and strategic decision-making in complex environments. The research underscores the critical role of systems thinking in addressing the complex challenges of sustainable development in regulated industries. Full article
28 pages, 1878 KiB  
Article
Introducing the Manufacturing Digital Passport (MDP): A New Concept for Realising Digital Thread Data Sharing in Aerospace and Complex Manufacturing
by Mohammed M. Mabkhot, Roy S. Kalawsky and Amer Liaqat
Systems 2025, 13(8), 700; https://doi.org/10.3390/systems13080700 (registering DOI) - 15 Aug 2025
Abstract
In the current data-driven era, effective data sharing is set to unlock billions in value for aerospace and complex manufacturing and their supply chains by enhancing product quality, boosting manufacturing and operational efficiency, and generating new value streams. However, current practices are hindered [...] Read more.
In the current data-driven era, effective data sharing is set to unlock billions in value for aerospace and complex manufacturing and their supply chains by enhancing product quality, boosting manufacturing and operational efficiency, and generating new value streams. However, current practices are hindered by fragmented data ecosystems, isolated silos, and reliance on paper-based documentation. Although the Digital Thread (DTh) initiative holds promise, its implementation remains impractical due to interoperability challenges, security and intellectual property risks, and the inherent difficulty of capturing and managing the overwhelming volume of data in such complex products as a holistic thread. This paper introduces the Manufacturing Digital Passport (MDP), a novel industry-driven concept that employs a product-centric, system-independent digital carrier to facilitate targeted, structured sharing of technical product data across the supply chain. The conceptual contribution of this work is the analytical formalisation of the MDP as a value-oriented carrier that shifts DTh thinking from costly, system-wide interoperability toward an incremental, ROI-driven record of lifecycle data. Rooted in real-world challenges and built on foundational principles of modularity, value creation, and model-based structures, the MDP, by design, enhances traceability, security, and trust through a bottom-up, incremental, use case-driven approach. The paper outlines its benefits through core design principles, definition, practical features, and integration strategies with legacy systems, laying the groundwork for a structured adoption roadmap in high-value manufacturing ecosystems. Full article
(This article belongs to the Special Issue Management and Simulation of Digitalized Smart Manufacturing Systems)
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20 pages, 1045 KiB  
Article
Linking Life Satisfaction to Settlement Intention: The Moderating Role of Urban Regeneration Budget Execution in South Korea
by Min-Woo Lee and Kuk-Kyoung Moon
Systems 2025, 13(8), 699; https://doi.org/10.3390/systems13080699 - 15 Aug 2025
Abstract
This study investigates urban life satisfaction and residents’ settlement intention as emergent outcomes of interconnected urban systems and examines the moderating role of urban regeneration budget execution as a systemic policy input. Drawing on the bottom-up spillover perspective and policy feedback theory, this [...] Read more.
This study investigates urban life satisfaction and residents’ settlement intention as emergent outcomes of interconnected urban systems and examines the moderating role of urban regeneration budget execution as a systemic policy input. Drawing on the bottom-up spillover perspective and policy feedback theory, this study posits that satisfaction with core aspects of urban living—such as housing, transportation, and public safety—reflects the functioning of multiple interrelated urban subsystems, which accumulate into a global sense of well-being that influences settlement intention. Furthermore, when urban regeneration budgets are visibly and fully executed, they operate as institutional feedback mechanisms, leading residents to attribute their life satisfaction to effective system performance and reinforcing their desire to stay. Using survey data from Incheon Metropolitan City and Gyeonggi Province in South Korea, the study employs stereotype logistic regression to test the proposed model. The findings reveal that urban life satisfaction significantly predicts stronger settlement intention, and this effect is amplified in municipalities with higher levels of budget execution. These results contribute to theoretical understanding by linking subjective well-being with institutional performance and offer practical guidance for South Korean local governments seeking to strengthen community resilience through transparent and outcome-driven urban policy delivery. Full article
(This article belongs to the Section Systems Practice in Social Science)
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20 pages, 848 KiB  
Article
Research on the Dynamic Relationship Between the Growth of Innovation Activity and Entrepreneurial Activity in China
by Song Lin and Haiyao Liu
Systems 2025, 13(8), 698; https://doi.org/10.3390/systems13080698 - 14 Aug 2025
Abstract
This study aims to empirically investigate the contemporaneous, bidirectional causal relationship between innovation and entrepreneurial activities in China by constructing a dynamic simultaneous equation system. Using panel data from 31 provincial administrative regions from 2000 to 2022, our empirical results demonstrate a robust [...] Read more.
This study aims to empirically investigate the contemporaneous, bidirectional causal relationship between innovation and entrepreneurial activities in China by constructing a dynamic simultaneous equation system. Using panel data from 31 provincial administrative regions from 2000 to 2022, our empirical results demonstrate a robust two-way causal relationship: vigorous innovation activities significantly stimulate the emergence and subsequent growth of entrepreneurial ventures, while entrepreneurial dynamism similarly promotes regional innovation. These findings remain stable and consistent after rigorous robustness checks. Further, employing a Panel Vector Autoregression (PVAR) approach in extended analyses, we find clear evidence of a stable positive feedback loop between innovation and entrepreneurship, characterized by progressive and cumulative effects. Additionally, regional heterogeneity analysis indicates that macroeconomic disparities significantly influence the bidirectional relationship between innovation and entrepreneurship. Specifically, differences in regional resource endowments and economic conditions largely account for variations in innovation–entrepreneurship dynamics across regions. Consequently, local governments should tailor innovation and entrepreneurship policies to regional contexts to maximize economic outcomes effectively under China’s current development paradigm. Full article
(This article belongs to the Section Systems Practice in Social Science)
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23 pages, 1506 KiB  
Article
Dynamic Risk Assessment Framework for Tanker Cargo Operations: Integrating Game-Theoretic Weighting and Grey Cloud Modelling with Port-Specific Empirical Validation
by Lihe Feng, Binyue Xu, Chaojun Ding, Hongxiang Feng and Tianshou Liu
Systems 2025, 13(8), 697; https://doi.org/10.3390/systems13080697 - 14 Aug 2025
Abstract
The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, [...] Read more.
The complex interdependencies among numerous safety risk factors influencing oil tanker loading/unloading operations constitute a focal point in academic research. To enhance safety management in oil port operations, this study conducts a risk analysis of oil tanker berthing and cargo transfer safety. Initially, safety risk factors are identified based on the Wu-li Shi-li Ren-li (WSR) systems methodology. Subsequently, a hybrid weighting approach integrating the Fuzzy Analytic Hierarchy Process (FAHP), G2 method, and modified CRITIC technique is employed to calculate indicator weights. These weights are then synthesised into a combined weight (GVW) using cooperative game theory and variable weight theory. Further, by integrating grey theory with the cloud model (GCM), a risk assessment is performed using Tianjin Port as a case study. Results indicate that the higher-risk indicators for Tianjin Port include vessel traffic density, safety of berthing/unberthing operations, safety of cargo transfer operations, safety of pipeline transfer operations, psychological resilience, proficiency of pilots and captains, and emergency management capability. The overall comprehensive risk evaluation value for Tianjin Port is 0.403, corresponding to a “Moderate Risk” level. Comparative experiments demonstrate that the results generated by this model align with those obtained through Fuzzy Comprehensive Evaluation Methods. However, the proposed GVW-GCM framework provides a more objective and accurate reflection of safety risks during tanker operations. Based on the computational outcomes, targeted recommendations for risk mitigation are presented. The integrated weighting model—incorporating game theory and variable weight concepts—coupled with the grey cloud methodology, establishes an interpretable and reusable analytical framework for the safety assessment of oil port operations under diverse port conditions. This approach provides critical decision support for constructing comprehensive management systems governing oil tanker loading/unloading operations. Full article
(This article belongs to the Special Issue Modeling and Simulation Innovations for Advanced Logistics Systems Management)
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27 pages, 1955 KiB  
Article
How Industry–University–Research Integration Promotes Green Technology Innovation in Chinese Enterprises: The Dual Mediating Pathways and Nonlinear Effects
by Chuang Li, Xin Zhang and Liping Wang
Systems 2025, 13(8), 696; https://doi.org/10.3390/systems13080696 - 14 Aug 2025
Abstract
This study examines 3256 Chinese A-share-listed companies from 2011 to 2022 to investigate the facilitative role and impact mechanism of industry–university–research (IUR) integration on corporate green technology innovation (GTI). The findings indicate that (1) the collaboration among IUR substantially enhances enterprises’ GTI, and [...] Read more.
This study examines 3256 Chinese A-share-listed companies from 2011 to 2022 to investigate the facilitative role and impact mechanism of industry–university–research (IUR) integration on corporate green technology innovation (GTI). The findings indicate that (1) the collaboration among IUR substantially enhances enterprises’ GTI, and this conclusion remains robust following various tests; (2) the integration of IUR can enhance GTI by mitigating managerial myopia and augmenting media attention; (3) integrating IUR into state-owned enterprises (SOEs) and large enterprises (LEs) has a stronger role in promoting GTI, according to a heterogeneity test; (4) further research shows that the impact of the depth and breadth of IUR cooperation on GTI presents an inverted U-shaped relationship from the promotion effect to the inhibition effect. Full article
(This article belongs to the Section Systems Practice in Social Science)
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14 pages, 550 KiB  
Article
Systemic Governance of Rural Revitalization: Social Capital Transfer Through State-Owned Enterprise Interventions in China
by Xinhui Wu, Minsheng Li and Yaofu Huang
Systems 2025, 13(8), 695; https://doi.org/10.3390/systems13080695 - 14 Aug 2025
Abstract
This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize [...] Read more.
This study investigates how state-owned enterprises (SOEs) contribute to rural revitalization in China through systemic interventions that enable the transfer of social capital. Addressing the gap between external resource inputs and internal development needs, the study adopts a systems thinking framework to conceptualize social capital as comprising structural, relational, and cognitive components. Drawing on multi-case evidence from assistance projects led by China Southern Power Grid, this study selects 11 assistance projects from a broader pool of 199 cases, to demonstrate how SOEs act as institutional nodes to reshape rural governance systems. They rebuild local organizational networks (structural capital), establish long-term trust through “strong commitment–weak contract” mechanisms (relational capital), and localize technical knowledge to align with rural contexts (cognitive capital). These interlinked processes form an integrated system that enhances rural governance capacity and promotes sustainable development. The findings highlight that SOEs are not merely resource providers but systemic catalysts that support cross-scalar collaboration and social infrastructure building. The study contributes a novel perspective by integrating social capital theory with a systemic governance lens and offer a actionable insights into the institutional design of assistance models for the future interventions by SOEs and similar entities in underdeveloped areas. Full article
(This article belongs to the Special Issue Systems Thinking: Insights and Solutions to Complex Societal Challenges)
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26 pages, 819 KiB  
Article
Critical Success Factors in Agile-Based Digital Transformation Projects
by Meiying Chen, Xinyu Sun and Meixi Liu
Systems 2025, 13(8), 694; https://doi.org/10.3390/systems13080694 - 13 Aug 2025
Abstract
Digital transformation (DT) requires organizations to navigate complex technological and organizational changes, often under conditions of uncertainty. While agile methodologies are widely adopted to address the iterative and cross-functional nature of DT, limited attention has been paid to identifying critical success factors (CSFs) [...] Read more.
Digital transformation (DT) requires organizations to navigate complex technological and organizational changes, often under conditions of uncertainty. While agile methodologies are widely adopted to address the iterative and cross-functional nature of DT, limited attention has been paid to identifying critical success factors (CSFs) from a socio-technical systems (STS) perspective. This study addresses that gap by integrating and prioritizing CSFs as interdependent elements within a layered socio-technical framework. Drawing on a systematic review of 17 empirical and conceptual studies, we adapt Chow and Cao’s agile success model and validate a set of 14 CSFs across five domains—organizational, people, process, technical, and project—through a Delphi-informed Analytic Hierarchy Process (AHP). The findings reveal that organizational and people-related enablers, particularly management commitment, team capability, and organizational environment, carry the greatest weight in agile-based DT contexts. These results inform a three-layered framework—comprising organizational readiness, agile delivery, and project artefacts—which reflects how social, technical, and procedural factors interact systemically. The study contributes both theoretically, by operationalizing STS theory in the agile DT domain, and practically, by providing a prioritized CSF model to guide strategic planning and resource allocation in transformation initiatives. Full article
(This article belongs to the Special Issue Advancing Project Management Through Digital Transformation)
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33 pages, 7126 KiB  
Article
An Adaptive Lag Trap in Socio-Technical Systems: The Paradoxical Effect of Digitalization and Labor on Logistics Investment in China
by Keming Chen, Chunxiao Huang, Ting Wang, Tianqi Zhu, Tingting Li and Dan Zhao
Systems 2025, 13(8), 693; https://doi.org/10.3390/systems13080693 - 13 Aug 2025
Abstract
The economic efficacy of logistics infrastructure is being reshaped by the dual forces of digitalization and the labor market. However, a new-era “investment return paradox” has emerged. Digitalization and an abundant labor force are theoretically positive forces, so why does their combination, when [...] Read more.
The economic efficacy of logistics infrastructure is being reshaped by the dual forces of digitalization and the labor market. However, a new-era “investment return paradox” has emerged. Digitalization and an abundant labor force are theoretically positive forces, so why does their combination, when coupled with capital investment, paradoxically engender negative emergence that suppresses growth? Conceptualizing the regional economy as a Socio-Technical System (STS), this paper unravels this paradox by identifying and theorizing an “adaptive lag trap”. Using provincial panel data from China, we first provide empirical validation for this trap, identifying a significant negative three-way interaction involving labor quantity (coef. = −0.218, p < 0.05). We then demonstrate that high-skilled labor quality is the key to mitigating this trap. While its direct interactive effects are not statistically significant, our analysis uncovers a robust and theoretically potent pattern: a higher-skilled workforce systematically reverses the negative trend of the interaction effect. The split-sample test provides the clearest evidence of this pattern, showing the coefficient pivoting from negative (−0.0572) in the low-skill subsample to positive (+0.109) in its high-skill counterpart. Our findings establish that high-skill human capital is a necessary condition to circumvent the “adaptive lag trap”, underscoring the imperative for a policy shift from investing in the scale of labor to cultivating its skill structure within a co-evolutionary framework. Full article
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19 pages, 2493 KiB  
Article
Harnessing Generative Artificial Intelligence to Construct Multimodal Resources for Chinese Character Learning
by Jinglei Yu, Jiachen Song and Yu Lu
Systems 2025, 13(8), 692; https://doi.org/10.3390/systems13080692 - 13 Aug 2025
Abstract
In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. [...] Read more.
In Chinese character learning, distinguishing similar characters is challenging for learners regardless of their proficiency. This is due to the complex orthography (visual word form) linking symbol, pronunciation, and meaning. Multimedia learning is a promising approach to implement learning strategies for Chinese characters. However, the availability of multimodal resources specifically designed for distinguishing similar Chinese characters is limited. With the advanced development of generative artificial intelligence (GenAI), we propose a practical framework for constructing multimodal resources, enabling flexible and semi-automated resource generation for Chinese character learning. The framework first constructs image illustrations due to their broad applicability across various learning contexts. After that, other four types of multimodal resources implementing learning strategies for similar character learning can be developed in the future, including summary slide, micro-video, self-test question, and basic information. An experiment was conducted with one group receiving the constructed multimodal resources and the other receiving the traditional text-based resources for similar character learning. We explored the participants’ learning performance, motivation, satisfaction, and attitudes. The results showed that the multimodal resources significantly improved performance on distinguishing simple characters, but were not suitable for non-homophones, i.e., visually similar characters with different pronunciations. Micro-videos introducing character formation knowledge significantly increased students’ learning motivation for character evolution and calligraphy. Overall, the resources received high satisfaction, especially for micro-videos and image illustrations. The findings regarding the effective design of multimodal resources for implementing learning strategies (e.g., using visual mnemonics, character formation knowledge, and group reviews) and implications for different Chinese character types are also discussed. Full article
(This article belongs to the Special Issue The Application of a Large Language Model (LLM) in Education Reform and Innovation)
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20 pages, 1477 KiB  
Article
AI-Powered Insights: How Digital Supply Networks and Public–Private Alliances Shape Socio-Economic Paths to Sustainability
by Khayriyah Almuammari, Kolawole Iyiola, Ahmad Alzubi and Hasan Yousef Aljuhmani
Systems 2025, 13(8), 691; https://doi.org/10.3390/systems13080691 - 13 Aug 2025
Abstract
By weaving together cutting-edge AI robotics, resilient global supply chains, universal school enrollment, and dynamic public–private energy investments, this study unveils a powerful, integrated blueprint for driving environmental sustainability in the 21st century. In doing so, the study employed advanced machine-learning techniques—specifically, it [...] Read more.
By weaving together cutting-edge AI robotics, resilient global supply chains, universal school enrollment, and dynamic public–private energy investments, this study unveils a powerful, integrated blueprint for driving environmental sustainability in the 21st century. In doing so, the study employed advanced machine-learning techniques—specifically, it introduced an ANN-enhanced wavelet quantile regression framework to uncover the multiscale determinants of China’s ecological footprint. Leveraging quarterly data from 2011/Q1 through 2024/Q4, it reveals dynamic, quantile-specific relationships that conventional approaches often miss. The result from the study demonstrates that robotics, supply-chain integration, public–private energy investments, gender-parity enrolment, and economic growth each exert a positive—and often escalating—upward pressure on the nation’s ecological footprint over short, medium, and long horizons, with the strongest effects in high ecological footprint contexts. The study proposes a significant, tailor-made policy based on these findings. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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20 pages, 623 KiB  
Article
Failure Analysis and SME Growth: The Role of Dynamic Capabilities and Environmental Dynamism
by Xiaoshu Ma, Luqian Chen and Xiaoyu Yu
Systems 2025, 13(8), 690; https://doi.org/10.3390/systems13080690 - 13 Aug 2025
Abstract
Although prior research acknowledges that small and medium-sized enterprises (SMEs) can turn failures into growth opportunities, the mechanisms through which failure analysis contributes to such growth remain underexplored. Grounded in organizational learning and dynamic capabilities theory, this study explores how failure analysis facilitates [...] Read more.
Although prior research acknowledges that small and medium-sized enterprises (SMEs) can turn failures into growth opportunities, the mechanisms through which failure analysis contributes to such growth remain underexplored. Grounded in organizational learning and dynamic capabilities theory, this study explores how failure analysis facilitates SME growth through the mediating role of dynamic capabilities and the moderating role of environmental dynamism. Drawing on survey data from 207 managers of China SMEs, the study employs linear regression and bootstrapping techniques to empirically test the proposed hypotheses. The results reveal that failure analysis significantly promotes SME growth, with dynamic capabilities—specifically, sensing, seizing, and reconfiguring—serving as key mediators. Furthermore, environmental dynamism positively moderates both the relationship between failure analysis and dynamic capabilities, and the indirect effect of failure analysis on growth via dynamic capabilities. Unlike previous research that focuses primarily on innovation or resilience, this study uniquely highlights the role of failure analysis in cultivating dynamic capabilities to drive SME growth. Full article
(This article belongs to the Section Systems Practice in Social Science)
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33 pages, 2448 KiB  
Article
Collaborative Causal Inference and Multi-Agent Dynamic Intervention for “Dual Carbon” Public Opinion Driven by Reinforced Large Language Models and Diffusion Models
by Xin Chen
Systems 2025, 13(8), 689; https://doi.org/10.3390/systems13080689 - 12 Aug 2025
Viewed by 191
Abstract
Under the “Dual Carbon” goal, public opinion analysis is crucial for optimizing policy implementation and enhancing social consensus, yet it faces challenges such as insufficient multi-source data integration, limited causal modeling, and delayed interventions. This study proposes a collaborative framework integrating reinforcement learning-enhanced [...] Read more.
Under the “Dual Carbon” goal, public opinion analysis is crucial for optimizing policy implementation and enhancing social consensus, yet it faces challenges such as insufficient multi-source data integration, limited causal modeling, and delayed interventions. This study proposes a collaborative framework integrating reinforcement learning-enhanced large language models (LLMs), diffusion models, and multi-agent systems (MASs). By constructing a four-dimensional causal network of “policy–technology–economy–public sentiment”, it analyzes multi-source data and simulates multi-agent interactions. The experimental results show that this framework outperforms Latent Dirichlet Allocation (LDA), Bidirectional Encoder Representations from Transformers (BERT), and Susceptible Infected Recovered (SIR) models in causal inference, dynamic intervention, and multi-agent collaboration. Reinforcement Learning from Human Feedback (RLHF) optimizes LLM outputs for reliable policy recommendations, with pass@10 showing strong correlations. This study provides scientific support for “Dual Carbon” policymaking and public opinion guidance, facilitating the green and low-carbon transition. Full article
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17 pages, 1534 KiB  
Article
TSGformer: A Unified Temporal–Spatial Graph Transformer with Adaptive Cross-Scale Modeling for Multivariate Time Series
by Yan Chen, Cheng Li and Xiaoli Zhao
Systems 2025, 13(8), 688; https://doi.org/10.3390/systems13080688 - 12 Aug 2025
Viewed by 136
Abstract
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-series correlations and disentangle relevant spectral components, leading to limited forecasting accuracy and robustness under non-stationary [...] Read more.
Multivariate time series forecasting requires modeling complex and evolving spatio-temporal dependencies as well as frequency-domain patterns; however, the existing Transformer-based approaches often struggle to effectively capture dynamic inter-series correlations and disentangle relevant spectral components, leading to limited forecasting accuracy and robustness under non-stationary conditions. To address these challenges, we propose TSGformer, a Transformer-based architecture that integrates multi-scale adaptive graph learning, adaptive spectral decomposition, and cross-scale interactive fusion modules to jointly model temporal, spatial, and spectral dynamics in multivariate time series data. Specifically, TSGformer constructs dynamic graphs at multiple temporal scales to adaptively learn evolving inter-variable relationships, applies an adaptive spectral enhancement module to emphasize critical frequency components while suppressing noise, and employs interactive convolution blocks to fuse multi-domain features effectively. Extensive experiments across eight benchmark datasets show that TSGformer achieves the best results on five datasets, with an MSE of 0.354 on Exchange, improving upon the best baselisnes by 2.4%. Ablation studies further verify the effectiveness of each proposed component, and visualization analyses reveal that TSGformer captures meaningful dynamic correlations aligned with real-world patterns. Full article
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16 pages, 507 KiB  
Article
Identifying and Prioritising Factors for Effective Decision-Making in Data-Driven Organisations: A DEMATEL Approach
by Roxana-Mariana Nechita, Flavia-Petruța-Georgiana Stochioiu and Iuliana Grecu
Systems 2025, 13(8), 687; https://doi.org/10.3390/systems13080687 - 12 Aug 2025
Viewed by 120
Abstract
The strategic transformation of increasing data volumes into managerial decisions is critical for organisational performance and sustainability; yet, it faces hurdles like poor data quality, technological deficiencies, and skill gaps. This study investigates the causal interdependencies among key factors influencing data-driven decision-making within [...] Read more.
The strategic transformation of increasing data volumes into managerial decisions is critical for organisational performance and sustainability; yet, it faces hurdles like poor data quality, technological deficiencies, and skill gaps. This study investigates the causal interdependencies among key factors influencing data-driven decision-making within data-driven organisations. Utilising the Decision-Making Trial and Evaluation Laboratory (DEMATEL) method, a robust structural and Multi-Attribute Decision-Making (MADM) technique, expert judgments from five management-level professionals were analysed to construct direct and total-relation matrices. The results classify Data Analytics Literacy (DAL) and Business-Strategy Alignment (BSA) as primary causal factors, while Data Quality (DQ), Data Infrastructure & Technology (DIT), and Data Culture & Governance (DCG) emerge as effect factors. These findings provide a structured framework for prioritising managerial interventions, suggesting that strengthening foundational elements (DAL and BSA) will significantly enhance analytical capabilities and strategic alignment. A limitation is the small, expert-based sample, indicating the potential for future validation with larger, more diverse panels or Fuzzy-DEMATEL applications. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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33 pages, 7985 KiB  
Article
Spatiotemporal Characteristics of Land Use Carbon Budget and Carbon Balance Capacity in Karst Mountainous Areas: A Case Study Using Social Network Analysis
by Bo Chen, Jiayi Zhao, Yongli Yao and Wenjin Chen
Systems 2025, 13(8), 686; https://doi.org/10.3390/systems13080686 - 12 Aug 2025
Viewed by 178
Abstract
Collaborative carbon regulation in Karst mountains critically reconciles socio-ecological conflicts. While intercity linkages drive spatial carbon heterogeneity, prior studies have focused on administrative-scale accounting, neglecting systematic spatial association network (SAN) analysis. Integrating SAN and geospatial detector models, we reveal county-level carbon balance dynamics [...] Read more.
Collaborative carbon regulation in Karst mountains critically reconciles socio-ecological conflicts. While intercity linkages drive spatial carbon heterogeneity, prior studies have focused on administrative-scale accounting, neglecting systematic spatial association network (SAN) analysis. Integrating SAN and geospatial detector models, we reveal county-level carbon balance dynamics in Guizhou, China (2000–2020). The key findings show the following: provincial carbon emissions rose 53% (0.96 to 1.47 × 108 t) against a 15% sequestration decline (0.67 to 0.57 × 108 t); emission networks shifted from single-core clustering to the axial Liupanshui–Guiyang–Tongren corridor, while sequestration networks retained peripheral ecological dominance; carbon balance capacity (CBC) exhibited an inverted C-shaped pattern (higher in the southeast, lower in the central–west) with westward centroid migration; and electricity consumption dominated spatial heterogeneity, with synergistic nighttime light–PM2.5 interactions showing strongest nonlinear enhancement. Notably, Jianhe County maintained peak CBC (16.5) via forest carbon sinks, whereas Shiqian County suffered the steepest decline due to industrial encroachment. This work pioneers dynamic carbon coupling analysis in fragile ecosystems, offering transdisciplinary tools for global “dual-carbon” governance. Full article
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24 pages, 3281 KiB  
Study Protocol
Standard Revision Project Scheduling Problem Considering Coordination Degree of Standards Systems
by Yunping Wang, Dan Xu, Lijun Zhou and Zhe Li
Systems 2025, 13(8), 685; https://doi.org/10.3390/systems13080685 - 12 Aug 2025
Viewed by 136
Abstract
Standards undergo periodic review to ensure their alignment with technological advancements and market trends. However, this process can lead to incompatibilities between standards. A major challenge for standards development organizations (SDOs) is ensuring the coordination of standards systems through effective scheduling. Traditional project [...] Read more.
Standards undergo periodic review to ensure their alignment with technological advancements and market trends. However, this process can lead to incompatibilities between standards. A major challenge for standards development organizations (SDOs) is ensuring the coordination of standards systems through effective scheduling. Traditional project scheduling models focused on minimizing the duration or cost do not meet the unique management needs of standards. This study introduces the Standard Revision Project Scheduling Problem (SRPSP), which considers revision dependencies in a standard citation network. A new objective function, the Coordination Index of Standard Systems (CISS), is proposed to quantify the coordination degree among standards within a citation network. To solve this problem, a Particle Swarm Optimization (PSO) algorithm is employed. Computational experiments using real-world data from TC544 demonstrate the framework’s superiority, achieving a 12% higher CISS than traditional makespan-centric models. Scenarios characterized by three key parameters of standard citation networks—network topology, scale, and average node degree—are analyzed. The results provide a benchmark for researchers to compare and improve upon. This research contributes to the development of a practical data-driven decision support system for SDOs to evaluate standards revision programs and enhance the systematic effects of standards systems during the revision process. Full article
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28 pages, 1873 KiB  
Article
Optimizing Innovation Decisions with Deep Learning: An Attention–Utility Enhanced IPA–Kano Framework for Customer-Centric Product Development
by Xuehui Wu and Zhong Wu
Systems 2025, 13(8), 684; https://doi.org/10.3390/systems13080684 - 12 Aug 2025
Viewed by 158
Abstract
This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit [...] Read more.
This study employs deep learning techniques, specifically BERT and Latent Dirichlet Allocation (LDA), to analyze customer satisfaction and attribute-level attention from user-generated content. By integrating these insights with Kano model surveys, we systematically rank attribute preferences and enhance decision-making accuracy. Addressing the explicit attention–implicit utility discrepancy, we extend the traditional IPA–Kano model by incorporating an attention dimension, thereby constructing a three-dimensional optimization framework with eight decision spaces. This enhanced framework enables the following: (1) fine-grained classification of customer requirements by distinguishing between an attribute’s perceived salience and its actual impact on satisfaction; (2) strategic resource allocation, differentiating between quality enhancement priorities and cognitive expectation management to maximize innovation impact under resource constraints. To validate the model, we conducted a case study on wearable watches for the elderly, analyzing 12,527 online reviews to extract 41 functional attributes. Among these, 14 were identified as improvement priorities, 9 as maintenance attributes, and 7 as low-priority features. Additionally, six cognitive management strategies were formulated to address attention–utility mismatches. Comparative validation involving domain experts and consumer interviews confirmed that the proposed IPAA–Kano model, leveraging deep learning, outperforms the traditional IPA–Kano model in classification accuracy and decision relevance. By integrating deep learning with optimization-based decision models, this research offers a practical and systematic methodology for translating customer attention and satisfaction data into actionable innovation strategies, thus providing a robust, data-driven approach to resource-efficient product development and technological innovation. Full article
(This article belongs to the Special Issue Data-Driven Methods in Business Process Management)
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15 pages, 733 KiB  
Article
The Impact of Artificial Intelligence Adoption on Organizational Decision-Making: An Empirical Study Based on the Technology Acceptance Model in Business Management
by Yanshuo Song, Xiaodong Qiu and Jiatong Liu
Systems 2025, 13(8), 683; https://doi.org/10.3390/systems13080683 - 11 Aug 2025
Viewed by 182
Abstract
With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational [...] Read more.
With the rapid development of artificial intelligence technology, its widespread application in the field of business management has become a significant issue faced by contemporary enterprises. Based on the Technology Acceptance Model, this study explores the impact of AI technology acceptance on organizational decision-making efficiency, performance, and the depth of technology application. It also reveals the driving mechanisms of top management support, perceived usefulness, and perceived ease of use on AI technology adoption through path analysis. To validate the research hypotheses, the study employed structural equation modeling (SEM) based on survey data collected from 420 respondents across various industries. The study found that top management support significantly enhances technology acceptance through perceived variables, while perceived usefulness is the core factor driving technology adoption. Although perceived ease of use has a weaker effect, it is equally important in lowering the psychological barriers during the initial stages of technology adoption. The adoption of AI technology has significantly improved organizational decision efficiency and overall performance, promoting the deep application of technology by optimizing resource allocation and enhancing scientific decision-making capabilities. This study further validates the applicability of the TAM theory in the context of AI technology, expanding its theoretical explanatory power in complex technology-adoption mechanisms. At the same time, the research provides practical guidance for enterprises in the introduction and application of technology, emphasizing that managers need to shape an open and innovative organizational culture at a strategic level and enhance employees’ willingness to accept technology through technical training and value transmission. Future research can incorporate cross-cultural and multi-level analytical frameworks to explore the dynamic adaptation paths of AI technology adoption and its potential risks in sustainable development. Full article
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31 pages, 3266 KiB  
Article
Context-Driven Recommendation via Heterogeneous Temporal Modeling and Large Language Model in the Takeout System
by Wei Deng, Dongyi Hu, Zilong Jiang, Peng Zhang and Yong Shi
Systems 2025, 13(8), 682; https://doi.org/10.3390/systems13080682 - 11 Aug 2025
Viewed by 128
Abstract
On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, [...] Read more.
On food delivery platforms, user decisions are often driven by dynamic contextual factors such as time, intent, and lifestyle patterns. Traditional context-aware recommender systems struggle to capture such implicit signals, especially when user behavior spans heterogeneous long- and short-term patterns. To address this, we propose a context-driven recommendation framework that integrates a hybrid sequence modeling architecture with a Large Language Model for post hoc reasoning and reranking. Specifically, the solution tackles several key issues: (1) integration of multimodal features to achieve explicit context fusion through a hybrid fusion strategy; (2) introduction of a context capture layer and a context propagation layer to enable effective encoding of implicit contextual states hidden in the heterogeneous long and short term; (3) cross attention mechanisms to facilitate context retrospection, which allows implicit contexts to be uncovered; and (4) leveraging the reasoning capabilities of DeepSeek-R1 as a post-processing step to perform open knowledge-enhanced reranking. Extensive experiments on a real-world dataset show that our approach significantly outperforms strong baselines in both prediction accuracy and Top-K recommendation quality. Case studies further demonstrate the model’s ability to uncover nuanced, implicit contextual cues—such as family roles and holiday-specific behaviors—making it particularly effective for personalized, dynamic recommendations in high-frequency scenes. Full article
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25 pages, 3364 KiB  
Article
Multi-Region Taxi Pick-Up Demand Prediction Based on Edge-GATv2-LSTM
by Jiawen Li, Zhengfeng Huang, Jinliang Li and Pengjun Zheng
Systems 2025, 13(8), 681; https://doi.org/10.3390/systems13080681 - 11 Aug 2025
Viewed by 89
Abstract
Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid [...] Read more.
Currently, the short-term accurate prediction of multi-region taxi pick-up demand often adopts methods that integrate graph neural networks with temporal modeling. However, most models focus solely on node features during the learning process, neglecting or simplifying edge features. This study adopts a hybrid prediction framework, Edge-GATv2-LSTM, which integrates an edge-aware attention-based graph neural network (Edge-GATv2) with a temporal modeling component (LSTM). The framework not only models spatial interactions among regions via GATv2 and temporal evolution via LSTM but also incorporates edge features into the attention computation structure, jointly representing them with node features. This enables the model to perceive both node attributes and the strength of inter-regional relationships during attention weight calculation. Experiments are conducted based on real-world taxi order data from Ningbo City, and the results demonstrate that the adopted Edge-GATv2-LSTM model exhibits favorable performance in terms of pick-up demand prediction accuracy. Specifically, the model achieves the lowest RMSE and MAE of 3.85 and 2.86, respectively, outperforming all baseline methods and confirming its effectiveness in capturing spatiotemporal demand patterns. This research can provide decision-making support for taxi drivers, platform operators, and traffic management departments—for example, by offering a reference basis for optimizing taxi pick-up route planning when vehicles are unoccupied. Full article
(This article belongs to the Special Issue AI-Driven Transportation Systems: Innovations, Challenges, and Future Mobility)
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27 pages, 859 KiB  
Article
Performance Enhancement Pathways for Electric Vehicle Manufacturing Companies Driven by Digital Transformation—A Configuration Analysis Based on the TOE Framework
by Yiqi Zhao, Qingfeng Meng and Zhen Li
Systems 2025, 13(8), 680; https://doi.org/10.3390/systems13080680 - 10 Aug 2025
Viewed by 390
Abstract
Digital transformation has brought unprecedented transformation and opportunities in manufacturing enterprises. Focusing on 65 listed companies in the electric vehicle sector as the research objects and drawing on the “Technology–Organization–Environment” (TOE) framework, this study selects three dimensions—technology, organization, and environment—and six antecedent conditions. [...] Read more.
Digital transformation has brought unprecedented transformation and opportunities in manufacturing enterprises. Focusing on 65 listed companies in the electric vehicle sector as the research objects and drawing on the “Technology–Organization–Environment” (TOE) framework, this study selects three dimensions—technology, organization, and environment—and six antecedent conditions. Using fsQCA configurational analysis, this research explores diverse paths to improving corporate performance, identifying five pathways. Among these, digital transformation and operational efficiency consistently serve as pivotal bridging conditions across multiple configurations. Furthermore, when enterprises demonstrate strong capabilities in both the technological and organizational dimensions, other conditions tend to act as substitutes, interacting synergistically with these core strengths to enhance overall firm performance. This study organically combines the TOE framework and fsQCA, deepening the application of the TOE theory in the field of electric vehicle manufacturing enterprises. Additionally, based on the configurational paths derived from the research, it provides differentiated countermeasure suggestions for electric vehicle manufacturing enterprises, offering practical guidance for enhancing their performance in the context of digital transformation. Full article
(This article belongs to the Section Complex Systems and Cybernetics)
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21 pages, 2314 KiB  
Article
An Explainable Machine-Learning Framework Based on XGBoost–SHAP and Big Data for Revealing the Socioeconomic Drivers of Population Urbanization in China
by Ziheng Shangguan
Systems 2025, 13(8), 679; https://doi.org/10.3390/systems13080679 - 9 Aug 2025
Viewed by 340
Abstract
The global acceleration of population urbanization has transformed cities into primary spatial hubs of human activity. As urban populations continue to expand, identifying the socioeconomic drivers of urbanization and elucidating their underlying mechanisms are essential for achieving Sustainable Development Goal 11, established by [...] Read more.
The global acceleration of population urbanization has transformed cities into primary spatial hubs of human activity. As urban populations continue to expand, identifying the socioeconomic drivers of urbanization and elucidating their underlying mechanisms are essential for achieving Sustainable Development Goal 11, established by the United Nations. This study leverages machine learning and big data to investigate the determinants of population urbanization in China over the period 1991–2023. Utilizing the XGBoost algorithm combined with SHAP (Shapley Additive Explanations), the analysis reveals a tripartite structure of key drivers encompassing industrial support, employment orientation, and infrastructure accessibility. Regional assessments indicate distinct urbanization patterns: Eastern coastal areas are predominantly driven by finance and service industries; central inland regions follow an investment-led trajectory anchored in infrastructure development and real estate expansion, while the western interior relies mainly on employment-centered strategies. Partial Dependence Plots (PDPs) highlighted spatial variations in the effects of sensitive factors, with interaction analyses revealing synergistic effects between tertiary sector shares and the working-age share in eastern coastlands, structural amplification by real estate investment with appropriate working-age population shares in the central inlands, and balancing interactions between GDP growth rates and tertiary sector shares in the western interior. These findings contribute to a more nuanced understanding of the socioeconomic forces shaping urbanization and offer evidence-based recommendations for policymakers in other developing countries seeking to foster sustainable urban growth. Full article
(This article belongs to the Section Systems Practice in Social Science)
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24 pages, 6986 KiB  
Article
Deciphering the Impact of COVID-19 on Korean Sector ETFs: Insights from an ARIMAX and Granger Causality
by Insu Choi, Tae Kyoung Lee, Sungsu Park, Kyeong Soo Shin, Suin Lee and Woo Chang Kim
Systems 2025, 13(8), 678; https://doi.org/10.3390/systems13080678 - 9 Aug 2025
Viewed by 179
Abstract
The COVID-19 pandemic caused major disruptions to worldwide financial markets, which resulted in market instability and unpredictability. South Korean investors used sector-specific exchange-traded funds (ETFs) to handle the market challenges. This research examines the connection between COVID-19 statistics, including total confirmed cases and [...] Read more.
The COVID-19 pandemic caused major disruptions to worldwide financial markets, which resulted in market instability and unpredictability. South Korean investors used sector-specific exchange-traded funds (ETFs) to handle the market challenges. This research examines the connection between COVID-19 statistics, including total confirmed cases and deaths, and Korean sector ETF market performance. The research uses the ARIMAX model to evaluate how external variables affect ETF price volatility. The research uses Granger causality tests to determine the direction of relationships between pandemic metrics and sectoral performance, while K-means clustering identifies patterns across different sectors. The analysis reveals significant statistical connections between pandemic disruptions and three sectors, including communication services, healthcare, and IT. The research shows that COVID-19 metrics strongly affected the performance of sector-specific ETFs throughout the analyzed time period. The research establishes a basis for additional studies about external shock effects on financial instruments and delivers valuable information to investors and policymakers who need to manage global crisis risks. Full article
(This article belongs to the Special Issue Data Analytics for Social, Economic and Environmental Issues)
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28 pages, 4637 KiB  
Article
Identification and Prediction Methods for Frontier Interdisciplinary Fields Integrating Large Language Models
by Yu Wu, Qiao Lin, Jinming Wu, Ru Yao and Xuefu Zhang
Systems 2025, 13(8), 677; https://doi.org/10.3390/systems13080677 - 8 Aug 2025
Viewed by 395
Abstract
Identifying frontier interdisciplinary domains is essential for tracking scientific evolution and informing strategic research planning. This study proposes a comprehensive framework that integrates (1) semantic disciplinary classification using a large language model (GPT-3.5-Turbo), (2) quantitative metrics for interdisciplinarity (degree and integration strength) and [...] Read more.
Identifying frontier interdisciplinary domains is essential for tracking scientific evolution and informing strategic research planning. This study proposes a comprehensive framework that integrates (1) semantic disciplinary classification using a large language model (GPT-3.5-Turbo), (2) quantitative metrics for interdisciplinarity (degree and integration strength) and frontierness (novelty, growth, and impact), and (3) trend prediction using time series models, including Transformer, LSTM, GRU, Random Forest, and Linear Regression. The framework systematically captures both structural and temporal dimensions of emerging research fields. Compared to conventional citation-based or topic modeling approaches, it enhances semantic precision, supports multi-label classification, and enables forward-looking forecasts. Empirical validation shows that the Transformer model achieved the highest predictive performance, outperforming other deep learning and baseline models. As an illustrative example, the framework was applied to synthetic biology, which demonstrated high interdisciplinarity, strong novelty, and growing academic influence. These results underscore the field’s strategic position as a frontier interdisciplinary domain. Beyond this case, the proposed framework is generalizable to other domains and provides a scalable, data-driven solution for dynamic monitoring of emerging interdisciplinary areas. It holds promise for applications in science and technology intelligence, research evaluation, and policy support. Full article
(This article belongs to the Topic Data Science and Intelligent Management)
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30 pages, 6103 KiB  
Article
Security and Resilience of a Data Space Based Manufacturing Supply Chain
by Yoshihiro Norikane and Hidekazu Nishimura
Systems 2025, 13(8), 676; https://doi.org/10.3390/systems13080676 - 8 Aug 2025
Viewed by 197
Abstract
The manufacturing supply chain has been exposed to natural disasters and geopolitical risks whose impacts, such as disruptions in the supply of materials and parts, can be devastating. In recent years, the data space has become more widely implemented, and it is expected [...] Read more.
The manufacturing supply chain has been exposed to natural disasters and geopolitical risks whose impacts, such as disruptions in the supply of materials and parts, can be devastating. In recent years, the data space has become more widely implemented, and it is expected to be used as a platform for widespread collaboration between companies. This article discusses how companies participating in the manufacturing supply chain cooperate to recover from disruption and mitigate risks using a data space platform and a flexible manufacturing system. Employing enterprise architecture modeling, we explore a comprehensive strategy for enhancing the resilience of a data space-based manufacturing supply chain. The proposed strategy adopts a comprehensive approach to addressing physical security and cybersecurity risks from a security perspective. By combining enterprise architecture modeling with the Unified Architecture Framework and conducting a scenario-based simulation, we discovered that an alternative manufacturing process with a flexible method in the data space can be a key security control measure for mitigating the risk associated with parts supply. The results of the alternative manufacturing simulation show that flexible manufacturing using BJT and MIM methods elicits better performance in terms of parts production volume and cost compared with conventional methods. The proposed method and the findings of this study contribute to consolidating a profound understanding of security and the mitigation of disruptive situations in a data space-based manufacturing supply chain. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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17 pages, 507 KiB  
Article
The Impact of Rural Energy Poverty on Primary Health Services Efficiency: The Case of China
by Xiangdong Sun, Xinyi Zheng, Shulei Li, Jinhao Zhang and Hongxu Shi
Systems 2025, 13(8), 675; https://doi.org/10.3390/systems13080675 - 8 Aug 2025
Viewed by 204
Abstract
Primary healthcare is vital to achieving universal health coverage, as emphasized by Sustainable Development Goal 3 (SDG 3). However, energy poverty remains a critical yet overlooked barrier to the efficiency of primary healthcare services in rural China—precisely the focus of this study. It [...] Read more.
Primary healthcare is vital to achieving universal health coverage, as emphasized by Sustainable Development Goal 3 (SDG 3). However, energy poverty remains a critical yet overlooked barrier to the efficiency of primary healthcare services in rural China—precisely the focus of this study. It employs panel regression models and threshold analysis methods using data from 31 Chinese provinces for the period 2014–2021, sourced from the EPSDATA data platform. Robustness checks are performed using bootstrap procedures, accompanied by detailed mechanism analyses. The empirical results demonstrate that rural energy poverty significantly reduces primary healthcare efficiency, particularly in provinces initially characterized by lower healthcare performance. The mechanism analysis identifies four critical transmission channels—off-farm employment, internet intensity, food safety, and health education—through which rural energy poverty undermines healthcare outcomes. Furthermore, threshold regressions uncover nonlinear relationships, indicating that the negative impacts of rural energy poverty intensify when household medical expenditures exceed 10.9%, the old-age dependency ratio surpasses 22.61%, and the rural energy poverty index is higher than 0.641. In theoretical terms, this study identifies rural energy poverty as a critical determinant of primary healthcare efficiency, thereby addressing an important gap in the existing literature. At the policy level, the findings emphasize the necessity for integrated measures targeting both rural energy poverty and primary healthcare inefficiencies to achieve SDG 3 and sustainably promote equitable, high-quality healthcare access in rural China. Full article
(This article belongs to the Section Systems Practice in Social Science)
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39 pages, 3212 KiB  
Article
Handling Preliminary Engineering Information: An Interview Study and Practical Approach for Clarifying Information Maturity
by Jens T. Brinkmann and David C. Wynn
Systems 2025, 13(8), 674; https://doi.org/10.3390/systems13080674 - 8 Aug 2025
Viewed by 237
Abstract
Handling preliminary information appropriately is a critical challenge for many aspects of systems engineering design. The topic is gaining renewed visibility due to the expanding possibilities to apply AI to preliminary information to support systems design, engineering, and management. However, there are few [...] Read more.
Handling preliminary information appropriately is a critical challenge for many aspects of systems engineering design. The topic is gaining renewed visibility due to the expanding possibilities to apply AI to preliminary information to support systems design, engineering, and management. However, there are few empirical studies of the practicalities of handling immature information and there is a lack of concretely developed, empirically evaluated, and practical approaches for clarifying information maturity levels, needed to ensure such information is appropriately used. This article addresses the gap, contributing new insight into how immature information is handled in industrial practice that is derived from interviews with 15 engineering and product development professionals from 5 companies. Thematic analysis reveals how practitioners work with preliminary information and where they require support. A solution was developed to address the empirically identified needs. In 5 follow-up interviews, practitioner feedback on this concept demonstrator was supportive. The main result of this research, in addition to the insights into practice, is a practical maturity grid-based assessment system that can help the providers of preliminary information self-assess and communicate information maturity levels. The assessments may be stored alongside the information and may be aggregated and visualised in CAD, augmented reality, or a range of charts to make information maturity visible and hence allow it to be more deliberately considered and managed. Implications of this research include that managers should promote greater awareness and discussion of preliminary information’s maturity and should introduce structured processes to track and manage the maturity of key information as it is progressively developed. The detailed maturity grids presented in this article may provide a foundation for such processes and can be adapted for particular situations. Full article
(This article belongs to the Section Systems Engineering)
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24 pages, 1640 KiB  
Article
Digital Innovation, Business Models Transformations, and Agricultural SMEs: A PRISMA-Based Review of Challenges and Prospects
by Bingfeng Sun, Jianping Yu, Shoukat Iqbal Khattak, Sadia Tariq and Muhammad Zahid
Systems 2025, 13(8), 673; https://doi.org/10.3390/systems13080673 - 8 Aug 2025
Viewed by 514
Abstract
Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a [...] Read more.
Digital innovation is rapidly transforming the agriculture sector, drawing attention from global development institutions, policymakers, tech firms, and scholars aimed at aligning food systems with international goals like Zero Hunger and the FAO agendas. Small and medium enterprises in agriculture (Agri-SMEs) represent a significant portion of processing and production units but face challenges in digital transformation despite their importance. Technologies such as Artificial Intelligence (AI), blockchain, cloud services, IoT, and mobile platforms offer tools to improve efficiency, access, value creation, and traceability. However, the patterns and applications of these transformations in Agri-SMEs remain fragmented and under-theorized. This paper presents a systematic review of interactions between digital transformation and innovation in Agri-SMEs based on findings from ninety-five peer-reviewed studies. Key themes identified include AI-based decision support, blockchain traceability, cloud platforms, IoT precision agriculture, and mobile technologies for financial integration. The review maps these themes against business model values and highlights barriers like capacity gaps and infrastructure deficiencies that hinder scalable adoption. It concludes with recommendations for future research, policy, and ecosystem coordination to promote the sustainable development of digitally robust Agri-SMEs. Full article
(This article belongs to the Special Issue Digital Innovation and Business Model Transformation in Small- and Medium-Sized Enterprises)
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