Systems doi: 10.3390/systems14050589

Authors: Weiwei Zhang Renjie Liu Huashuai Chen

As a multi-functional systemic carrier, rural tourism integrates diverse rural resources and serves as a key endogenous driver for sustainable rural development and the enhancement of rural residents’ livelihoods. However, excessive tourism development may lead to environmental pressures and exacerbate inequities in benefit distribution, rendering well-being gains uncertain. This study aims to explore the multidimensional mechanisms through which rural tourism influences rural residents’ well-being by utilizing national data from the 2020 China Rural Revitalization Survey (CRRS). The results indicate that village-level tourism development exerts a positive effect on material and psychological well-being. Effects are particularly strong in eastern and hilly regions and in villages where the party secretary also serves as committee director. Further analysis identifies four channels through which rural tourism enhances well-being: fostering digital financial inclusion, advancing empowerment reforms, reallocating resources, and optimizing governance frameworks. Additionally, tourism development leads to improvements in indicators such as road quality, living environment, and satisfaction with village committee performance—while highlighting policy attention to social security, housing, and income satisfaction.

Systems doi: 10.3390/systems14050588

Authors: Xiaokun Wang Huijuan Zhao Yuming Song

Against the backdrop of the ongoing advancement of the “dual carbon” goals and the carbon emission trading system, green innovation in small and medium-sized manufacturing enterprises faces multiple practical constraints, including financing constraints, technological commercialization risk, and market recognition costs. To examine the mechanism through which government regulation affects firms’ green innovation behavior, this study develops a four-party evolutionary game model involving government, small and medium-sized manufacturing enterprises, consumers, and investment institutions, and analyzes the strategic interactions and dynamic evolution of these actors. The results show that regulatory intensity, consumer green preference, and financial support from investment institutions all exert significant effects on green innovation decisions in small and medium-sized manufacturing enterprises. Whether firms choose substantive green innovation depends primarily on such key factors as financing uncertainty, technological commercialization risk, the intensity of government penalties, and the level of policy incentives. Further stability analysis and numerical simulations indicate that stronger administrative penalties significantly increase the likelihood that firms adopt substantive green innovation and also promote green consumption among consumers. This effect becomes more pronounced when financing uncertainty declines. At the same time, stronger policy incentives for green investment enhance the willingness of investment institutions to participate in green projects, and this effect is further reinforced when technological commercialization risk is reduced. The findings suggest that green innovation in small and medium-sized manufacturing enterprises is characterized by strong multi-actor interdependence. Its evolutionary outcome is shaped not only by regulatory pressure, but also by green financial support, the conditions for technological commercialization, and market demand. Accordingly, sustained green innovation in small and medium-sized manufacturing enterprises requires coordinated efforts to improve regulatory arrangements, strengthen green finance support systems, reduce the cost of technological commercialization, and cultivate green consumer markets.

Systems doi: 10.3390/systems14050587

Authors: Yuan-Wei Du Xiang Wen Yi-Ning Huang

Case-based reasoning (CBR) is an efficient intelligent decision-making approach, but traditional methods often neglect the weight and reliability of decision information and struggle with attribute heterogeneity and missing data. This study proposes a novel CBR method based on the generalized combination (GC) rule to overcome these limitations. We design differentiated similarity calculations for heterogeneous attributes, and construct basic probability assignments (BPAs) by grouping historical cases with identical similarity to handle missing data. Then, Deng entropy and Jousselme distance are used to characterize attribute weight and reliability, respectively. Discounted BPAs are recursively fused via the GC rule, and final decisions are derived through Bayesian approximation. A case study of typhoon disaster emergency decision-making demonstrates the superior performance of the proposed method.

Systems doi: 10.3390/systems14050586

Authors: Heetae Yang Hwansoo Lee

Super apps have emerged as complex digital service ecosystems that integrate multiple heterogeneous services within a unified platform architecture. As artificial intelligence (AI) capabilities become increasingly embedded into these platforms, understanding how AI-enabled features influence user evaluations has become an important research issue. This study develops a new research model by extending the stimulus–organism–response (SOR) framework to examine the determinants of users’ continuance intention toward super apps. Specifically, performance efficacy, service efficiency, and perceived security are conceptualized as stimulus factors. Satisfaction is modeled as the organism variable; and continuance intention represents the behavioral response. In addition, this study conceptualizes AI system capability as a platform-level capability that enables the integration, adaptation, and personalization of heterogeneous services. It examines both its direct effect on user satisfaction and its moderating role in the relationships between functional affordances and satisfaction. Based on survey data collected from 614 super-app users in South Korea, the research model was analyzed using partial least squares structural equation modeling (PLS-SEM). The results reveal that performance efficacy and perceived security significantly influence user satisfaction, whereas service efficiency does not have a significant effect. Furthermore, AI system capability not only directly enhances user satisfaction but also strengthens the relationships between functional affordances and satisfaction. A multi-group analysis comparing financial and non-financial super apps shows that these effects vary depending on the service context. These findings contribute to the literature by conceptualizing AI as a system-level capability that both enables and enhances the realization of functional affordances in complex digital ecosystems.

Systems doi: 10.3390/systems14050585

Authors: Huiru Yu Yanlai Li

Improving customer satisfaction (CS) and gaining competitive advantages are central goals of product improvement, both of which rely on accurate classification of product attributes. Online reviews on e-commerce platforms provide firms with abundant customer feedback, but accurately classifying and prioritizing product attributes remains challenging. To address this issue, we propose an interpretable Kano model. In this method, the Biterm Topic Model (BTM) is first used to identify product attributes from reviews. Then, the Enhanced BERT Model with Attribute-Aware and Convolutional Mechanisms (BERT-A-Conv) is employed to classify the sentiment categories of these attributes. Given the critical role of neutral sentiment, it is incorporated into the Light Gradient Boosting Machine (LightGBM) model to quantify the impact of AS on CS. The Shapley additive explanations (SHAP) method is then adopted to construct the marginal contribution difference (MCD) between adjacent categories, which this study uses to classify product attributes into five Kano categories. On this basis, we calculate the attribute improvement priority score (AIPS) by combining each attribute’s MCD and improvement potential, thereby offering firms a systematic analytical framework to support product iteration and improvement. A case study on smartwatches demonstrates the applicability and feasibility of the proposed method.

Systems doi: 10.3390/systems14050584

Authors: Zhilun (Alan) Huang Linjie Ma Kang-Lin Peng Shanshan Wang Songxue Zhang

The growth of commercial aerospace enterprises (CAEs) has injected new vitality into the entire aerospace system. Nevertheless, there remains a research gap concerning the entrepreneurial behavior of these enterprises, which is primarily driven by commercial demands and technological innovation. Drawing on network embeddedness theory and complex system theory, this study proposes a conceptual framework that links the structural and relational embeddedness of aerospace system subnetworks to entrepreneurial behavior, while examining the mediating roles of perceived organizational resilience and perceived environmental uncertainty. The moderating role of transformational leadership is evaluated using the trait activation theory. A two-phase quantitative design was employed, combining Partial Least Squares Structural Equation Modeling (PLS-SEM) and fuzzy-set Qualitative Comparative Analysis (fsQCA). Empirical analysis using a sample of 265 CAEs in China revealed several key findings: (1) the structural position of CAEs within the aerospace system network, along with informational resources formed through relationships, can enhance perceived organizational resilience and reduce perceived environmental uncertainty, thereby promoting entrepreneurial behavior; (2) entrepreneurs’ transformational leadership can effectively enhance the positive relationship between perceived organizational resilience and their entrepreneurial behavior; (3) two distinct configurations lead to high entrepreneurial behavior among CAEs. The study concludes with corresponding theoretical and practical implications.

Systems doi: 10.3390/systems14050583

Authors: Andreas Frey Cosima Schenk Lara Weiß Christoph König Visvanathan Ramesh Sabine Fabriz Hendrik Drachsler Holger Horz

Artificial intelligence (AI) and digital technology provide extensive possibilities for education. But focusing only on their implementation does not address the challenges associated with them and they may even have a negative impact on learners. So far, the disciplines of psychology and computer science have not provided a theoretical framework for the competence needed to develop and use AI and digital technology in education in order to prepare learners for successful participation in modern societies. The main aim of this paper is to theoretically specify competent use of AI and digital technology in education and to provide a standardized instrument to evaluate courses that teach this competence. Therefore, we (a) combine theoretical contributions from both scientific disciplines to formulate a theoretical framework with four levels for the “Competence to Use Artificial Intelligence and Digital Technology in Educational Processes” (AIEDTEC competence), (b) introduce a questionnaire to evaluate courses that teach AIEDTEC competence, and (c) present results regarding its psychometric properties (N=240). The questionnaire showed good to very good psychometric properties and the assumed factor structure was supported by confirmatory factor analyses. The paper connects research on systems thinking and learning and instruction with recent developments regarding AI and digital technology and thereby provides an essential base for creating effective, modern, and safe learning environments in the future as well as a psychometric evaluation instrument.

Systems doi: 10.3390/systems14050581

Authors: Tatsuto Ito Taisei Hirayama Naoki Hattori Hiroki Sakaji Itsuki Noda

Storage location assignment in automated warehouses strongly affects order-processing efficiency. Existing co-occurrence-based approaches often rely on pointwise mutual information (PMI) statistics or direct frequency co-occurrence. This paper compares two deliberately chosen representation families for storage assignment in automated warehouses operated under Multi-Agent Pickup and Delivery (MAPD) constraints: Pointwise Positive PMI (PPPMI), representing direct pairwise co-occurrence, and Latent Dirichlet Allocation (LDA), representing latent-topic smoothing. The purpose is not to benchmark every possible representation space, but to make the pairwise-versus-latent contrast interpretable under a fixed execution pipeline consisting of task construction, visit-order selection, path planning, and collision avoidance. The broader research setting is motivated by real warehouse order data in which SKU co-occurrence structure is present, but such logs mix latent-topic effects, explicit family-based co-occurrence, noise, and demand variation. We therefore use two controlled abstractions of order structure: one generator with latent-topic mixtures and one generator with more direct family co-occurrence. We embed the proposed LDA representation and the PPPMI baseline in constrained-clustering and simulated-annealing placement methods and evaluate them against frequency-based, load-balancing, and random baselines. Evaluation is conducted in a fixed extended MAPD simulator that explicitly models orientation-aware motion, turning costs, service times, dynamic task release, and collision avoidance. In the latent-topic regime, LDA-based methods tended to form the leading group in average finite-horizon makespan, computed over completed combinations of random seeds and operating conditions. In the supplementary direct-co-occurrence condition, PPPMI was competitive in the plain representation comparison, while LDA-driven local search on top of a frequency-based initial layout remained strong. These results do not imply that LDA is universally superior; rather, they indicate that the relative suitability of PPPMI and LDA depends on the order structure and on how the representation interacts with the placement optimizer. The controlled generators are useful for isolating those effects, but they do not replace external validation on real warehouse logs.

Systems doi: 10.3390/systems14050582

Authors: Lihong Han Runyu Wang

Public data openness refers to the practice whereby governments make the raw data collected, generated, and managed during public administration freely available to society through unified platforms for public development and use. As a government-led reform in digital governance, public data openness offers a new approach to strengthen urban export resilience. Using panel data from 283 cities at the prefecture level and above in China from 2009 to 2023, we construct a comprehensive evaluation system to measure urban export resilience. Taking the launch of local public data platforms as a quasi-natural experiment, we employ a multi-period difference-in-differences (DID) model and a mediating effect model to examine the impact of public data openness on urban export resilience and its underlying mechanisms. The results show that public data openness significantly improves urban export resilience by improving the institutional environment and enhancing information acquisition efficiency. Heterogeneity analysis further shows that the effect is stronger in eastern cities with higher levels of economic development, higher administrative ranks, and larger urban size. These findings enrich the literature on the economic consequences of public data openness and provide policy recommendations for strengthening urban resilience through digital governance.

Systems doi: 10.3390/systems14050580

Authors: Shuyao He Juanqiong Gou Hua Gao Yuhang Xu

In project-based organizations, novices engaged in project-contextualized learning often struggle to balance sustained project learning with immediate task delivery, creating a tension between developmental sustainability and execution sustainability. While informal support mechanisms such as apprenticeship help alleviate this tension, their effectiveness remains limited. To address this issue, this study adopts a Design Science Research approach to develop a generative AI-driven project scaffolding system prototype. The study contributes design knowledge comprising two core elements. First, based on the task execution process, scaffolding support is organized into three dimensions, namely contextualization, cognitive guidance, and cognitive evolution, which correspond to the progression from task understanding to cognitive construction. Second, a cognition-driven scaffolding mechanism is constructed through prompt-driven and knowledge-augmented generation, enabling human-centered intelligent guidance, augmentation, and automation during task execution. Evaluation in a software implementation firm suggests that the system may improve task output quality and support novices’ application of task-relevant strategies in subsequent tasks. These findings indicate the system’s potential to support the sustainability of both project learning and task execution in project practice. This study provides design insights for embedding GenAI-driven scaffolding in project practice, helping organizations in similar project contexts establish sustainable project support approaches.

Systems doi: 10.3390/systems14050579

Authors: Miloš Poliak Laura Škorvánková

The paper analyses the economic impact of the reduction in road traffic accidents in Slovakia between 2000 and 2024 and quantifies both direct and indirect costs of road crashes. Over this period, annual crashes declined from more than 50,000 to approximately 11,500 and fatalities from over 600 to 262, demonstrating the effectiveness of national road safety strategies. The methodology is based on the national road accident database, complemented by macroeconomic and demographic indicators, and follows European recommendations for the valuation of external costs of transport. The study applies the value of a statistical life, the value of a statistical life year, the relative severity index and the critical accident rate, with particular emphasis on comparing the value of a statistical life and the relative severity index. The total VSL-based economic costs of road traffic crashes in 2024 are estimated at approximately €1.25 billion, underscoring the scale of the socioeconomic burden. Building on the forecasted values for 2025, the paper further tests and compares these methodologies on a specific road section, illustrating their practical implications for project appraisal and safety management. The results confirm that VSL-based estimates systematically exceed RSI-based estimates by 21–45% per year, reflecting the broader societal costs captured by the VSL concept. The study shows that investments in safety measures are economically worthwhile and reduce the burden on public finances, while also highlighting the need to harmonize methodologies and improve data quality.

Systems doi: 10.3390/systems14050578

Authors: Cristina Claudia Rotea Mădălina Giorgiana Mangra Claudiu George Bocean Anca Antoaneta Vărzaru Gabriel Ioan Mangra Nițu-Granzulea Silviu Mihai

The transformation of healthcare systems presents a key challenge for EU Member States, as they face growing population needs, resource pressures, and ongoing health disparities among communities. Using a systems-thinking approach provides a suitable framework for analyzing how healthcare resources, behavioral and social health determinants, and overall population outcomes are interconnected. This study aims to explore these relationships from a systemic viewpoint within health communities across the European Union. The research employs a cross-sectional approach, utilizing aggregated data from all 27 EU Member States. It involves descriptive and factor analyses to create composite indices of healthcare resources, positive health determinants, and health outcomes. Furthermore, it uses a univariate Generalized Linear Model (GLM) and multilayer perceptron neural networks to model nonlinear relationships among variables. Cluster analysis is also conducted to classify Member States into different health performance typologies. The results emphasize connections between healthcare system resources and population health outcomes, illustrating how positive determinants impact health status and highlighting structural differences across European communities. The results indicate relevant associations between healthcare system resources and population health outcomes, whereas the analysis of positive health determinants suggests more complex, partly nonlinear patterns. The findings also highlight structural differences across European health communities.

Systems doi: 10.3390/systems14050577

Authors: Imdadullah Hidayat-ur-Rehman Sultan Bader Aljehani Khalid Waleed Ahmed Abdo Mohammad Nurul Alam Mohd Shuaib Siddiqui

Digital payment and settlement markets operate as interconnected financial systems shaped by institutional, technological, and capability-based elements. This study examines how digital transformation and digital financial inclusion interact within this system to influence Sustainable Digital Payment and Settlement Market Performance (SDPSMP), with DeFi adoption capability acting as a structural translation mechanism and AI and big data analytics functioning as adaptive enablers. Integrating the Resource-Based View and Diffusion of Innovation, the study explains why technology diffusion does not consistently produce stable market-level outcomes. Cross-sectional data were collected from 422 professionals in Saudi financial institutions engaged in payment, settlement, and FinTech functions. A dual-stage SEM–ANN approach was employed, using PLS-SEM to test direct, mediating, and moderating effects and Artificial Neural Networks (ANN) to capture nonlinear predictive patterns. Results show that digital transformation and digital financial inclusion enhance DeFi adoption capability and directly improve SDPSMP. DeFi capability partially mediates both relationships. Analytics capability strengthens the effects of inclusion and DeFi capability on system performance but does not moderate the transformation–performance link. ANN findings identify analytics capability and financial inclusion as dominant predictors. The study advances understanding of digital payment markets as complex adaptive systems and provides evidence on how coordinated capability development supports long-term resilience and structural stability.

Systems doi: 10.3390/systems14050576

Authors: Ninda Nurseha Amalina Heungjo An

Unattended scheduled appointments (“patient no-shows” henceforth) adversely affect healthcare providers and patients’ health, disrupting the continuity of care, operational efficiency, and allocation of medical resources. Therefore, accurate predictive modeling is needed to reduce the impact of patient no-shows. Although machine learning methods, such as logistic regression, random forests, and decision trees, are widely used to predict patient no-shows, they often rely on hard decision splits and static feature importance, limiting adaptability to complex patient behaviors. To address this limitation, we propose a hybrid multi-head attention soft random forest (MHASRF) model that integrates attention mechanisms into a random forest using probabilistic soft splitting. It assigns attention weights across the trees, enabling attention on specific patient behaviors. The MHASRF model exhibited an accuracy of 88.24%, specificity of 91.21%, precision of 81.60%, recall of 82.01%, F1-score of 81.81%, and area under the receiver operating characteristic curve of 94.07%, demonstrating high and balanced performance across metrics. It could also identify key predictors of patient no-shows at two feature-importance levels (tree and attention mechanism), providing deeper insights into patient no-shows. Thus, the proposed MHASRF model is a robust, adaptable, and interpretable method for predicting patient no-shows that can help healthcare providers optimize resources.

Systems doi: 10.3390/systems14050575

Authors: Rino Jo Han-Bin Lee Jihun Han Woong-Kyo Jung Jun-Yong Lee Tae-Young Kang Youngsoo Kim Byung Il Kwak Mee Lan Han Jungmin Kang

The contemporary cyber-threat landscape is becoming increasingly diverse and complex, creating a persistent gap between situational awareness and operational response. This study presents a framework designed to bridge this gap by transforming up-to-date cyber-threat intelligence (CTI) into standardized knowledge structures and actionable defense measures. First, the proposed framework integrates the threat data collected from OpenCTI and normalizes them based on the MITRE ATT&CK tactics and techniques matrix. It then leverages a large language model to automatically generate diverse threat scenarios based on the analyzed intelligence. Each scenario is organized as a tactic sequence, and individual techniques are mapped to MITRE D3FEND defensive categories based on official ATT&CK–D3FEND relationships and structured contextual interpretation. Finally, the framework produces outputs in the form of a Defense Description that includes the corresponding technique IDs, recommended defense strategies, supporting rationales, and prerequisites. An evaluation using several recent cases demonstrates that the proposed framework effectively connects current threat intelligence with practical defense strategies. In summary, the proposed framework strengthens proactive cyber defense by directly linking structured attack flows to actionable context-aware defensive techniques. In addition, this framework provides a structured pipeline that systematizes and automates steps conventionally performed manually, thereby reducing repetitive analyst effort.

Systems doi: 10.3390/systems14050574

Authors: Juan Lin Mengchao Sun Zhen Peng Jianying Niu

High-tech manufacturing is a technology- and knowledge-intensive strategic industry. Its total factor productivity (TFP) directly impacts national competitiveness and economic quality. In China, despite rapid growth, TFP performance varies across sub-sectors and firms. In this study, TFP was adopted as the central outcome variable to capture the comprehensive production and technological efficiency of high-tech manufacturing firms. The Technology–Organization–Environment (TOE) framework was integrated with Dynamic Qualitative Comparative Analysis (Dynamic QCA) to examine the causal complexity, dynamic evolution, and industrial heterogeneity of TFP, using a sample of Chinese A-share-listed companies from 2015 to 2024. The results showed that high TFP depends on configurations rather than on a single factor. Three configurational paths were identified, including “technology–innovation–scale synergy,” “technology–scale dual core,” and “technology-led productivity optimization.” All paths require a strong technological foundation. Conversely, a lack of technology leads to low total factor productivity across all sectors. Moreover, the effectiveness of these pathways evolves over time. The dual-core pathway serves as a stable baseline model. The synergy pathway is reinforced in fast-iteration sectors. Due to weak innovation support, the productivity optimization pathway declined after 2019. Third, different sectors show distinct patterns. Fast-iteration sectors use synergy to handle rapid technical changes. Slow-iteration sectors use the dual-core model to share R&D risks. Productivity-optimized sectors stagnate because they focus on automation instead of innovation. This work reveals deep patterns in TFP growth and provides theoretical support and practical insight for strategic choices of firms, industry resource allocation, and industrial policy optimization.

Systems doi: 10.3390/systems14050573

Authors: Mehdi Saadallah Abbas Shahim Svetlana Khapova

Vulnerability management (VM) is becoming increasingly important as organizations face growing cybersecurity threats. This study examines how organizations adapt their vulnerability management routines in response to evolving vulnerability signals through the integration of artificial intelligence (AI) and automation. Drawing on data from an international fast-moving consumer goods (FMCG) company, we investigate how human expertise and AI interact across the full VM process, from triage to remediation. Using Organizational Routine Theory (ORT), we show that AI does not simply automate tasks but acts as a co-performer, influencing how decisions are made, work is coordinated, and actions are adapted. We develop a three-phase model capturing (1) the integration of AI-enabled automation into strained routines, (2) the manifestation of tensions between human expertise and automation as well as between usability and system complexity, and (3) the stabilization of hybrid routines through iterative adaptation and feedback loops. We identify two key tensions in this process: technology versus human expertise, and usability versus the complexity of multi-vendor tools. These tensions create frictions in practice but also open opportunities for learning and improvement. Rather than treating AI as a technical tool, our findings highlight its role as an active routine participant. Importantly, we show that routine evolution enables organizations to improve how vulnerability signals are interpreted and acted upon, thereby supporting more coordinated and adaptive cybersecurity practices. This has both theoretical implications for understanding how routines evolve with technology and practical relevance for improving adaptive cybersecurity practices. By linking micro-level routine dynamics to broader organizational outcomes, this study contributes to explaining how organizations sustain stable and adaptive operations under conditions of continuous cyber threat exposure.

Systems doi: 10.3390/systems14050572

Authors: Shucheng Fan Shaochuan Fu

Cross-border e-commerce fulfillment depends on coordinated inland container movements across factories, inland container depots (ICDs), and port gateways, yet many container trucking operations still follow synchronous one-truck-one-order execution. This study models the fulfillment network as a platform-enabled socio-technical transportation system in which the ICD acts as a digital–physical coordination node for spatiotemporal decoupling. A drop–buffer–pick task architecture is developed to represent direct execution, relay execution, and delayed dispatch, and a mixed-integer linear programming (MILP) model optimizes task assignment and tractor sequencing under loading-time, port cutoff, inventory, and working-time constraints. In the certified-optimal 10-order instance, gross positive cost decreases from CNY 27,540 to CNY 19,915 (−27.7%); after applying the same post hoc coordination-credit accounting rule, net total fulfillment cost decreases to CNY 18,734 (−32.0%). The 10 orders are served with five tractors under the tested platform configuration, compared with 10 tractors under the restricted benchmark. To address sustainability explicitly, the analysis also reports distance-based emissions and energy-use proxies; the proposed schedule lowers cost and fleet deployment but increases total mileage, showing that economic efficiency and emissions performance do not automatically move together. The evidence is a deterministic baseline for later stochastic, mixed import/export, and collaborative-platform extensions.

Systems doi: 10.3390/systems14050571

Authors: Weijie Chen Yifei Mao Tunan Xu Yibing Wang Zhengfeng Huang Markos Papageorgiou Pengjun Zheng

This study proposes a Behavior-Calibrated Endogenous Choice 2SFCA (BCEC-2SFCA) framework for assessing spatial accessibility to tertiary hospitals. Using large-scale taxi trajectory data from Ningbo, China, we empirically calibrate the Huff model parameters (α =1.1758, β =2.9608) based on observed hospital choices and construct travel time and distance matrices from observed trips. Unlike existing Huff-based FCA approaches that assume parameter values, BCEC-2SFCA jointly estimates the attractiveness elasticity and distance-decay coefficient directly from local healthcare travel behavior and integrates these calibrated probabilities into a 2SFCA structure where hospital catchments are endogenously generated rather than exogenously imposed. Compared with conventional Gaussian 2SFCA, the BCEC-2SFCA model produces a continuously varying and behaviorally plausible accessibility surface and better replicates the relative order of hospital attractiveness (ρ = 0.527, p < 0.05), although its RMSE is slightly higher (0.02700 vs. 0.02211) while MAPE is clearly lower (32.17% vs. 42.12%). Robustness checks using all 22 hospitals confirm stable estimates, and subgroup analyses show consistent advantages across hospital scales. The framework is specifically designed for high-order medical services with strong inter-facility competition—such as tertiary hospitals—and its applicability to proximity-based services is limited.

Systems doi: 10.3390/systems14050570

Authors: Patrick Mbongwa Mhlongo

Universities operate in an environment characterized by complexity, unpredictable challenges, rapid change and stakeholder demands. University employees are a key resource to achieve the strategic goals of the institution, linked to this complexity. Therefore, a conducive environment that fosters employee engagement in the university is critical. Employee engagement as a concept which encompasses employees’ positive attitude towards the organization and its values, whereby employees continuously improve how they perform their duties to improve organizational effectiveness. Organizational effectiveness is the ability of the organization to proactively adapt and adopt new ideas to continuously improve its operations. The purpose of the study was to explore the application of systems thinking as a strategic approach to foster employee engagement across functional boundaries in universities of technology (UoTs). Employee engagement is central to achieving the strategic goals of Universities of Technology. The problem is a lack of an overarching philosophy to foster employee engagement across the institution. To achieve the objectives of this study, a qualitative research methodology was used, underpinned by a constructivism philosophical worldview. A total of 15 participants were purposively selected from the employees of two universities of technology. Semi-structured face-to-face interviews were used to collect data. Thematic analysis was applied to analyze data. The findings revealed that systems thinking would create a conducive environment to foster employee engagement across functional boundaries in the UoTs. In addition, the findings revealed the prevalence of silo practices in universities of technology. Without systems thinking in the institution, departments generally operate in silos and there is no institutionalized philosophy to foster employee engagement, collaboration and knowledge sharing within and beyond functional boundaries.

Systems doi: 10.3390/systems14050569

Authors: Wanliang Lin Mingyu Zhang Wenjia Zhang Can Zhang

Employees’ voice is an important source of organizational learning and adaptive change. As algorithmic management is increasingly applied across organizational management processes, an urgent practical question arises: Does it affect employees’ participation in organizational improvement through voice? To address this challenge, drawing on signaling theory, this study examines the differential effects of distinct dimensions of algorithmic management on voice, while also considering work locus of control as a key moderating variable. We collected one-to-one matched data from 351 employees and their supervisors in a large Chinese platform-based enterprise. We tested the hypothesized theoretical model using structural equation modeling and bootstrapping procedures. The results show that algorithmic feedback enhances employees’ felt responsibility for constructive change, which in turn promotes employees’ voice. In contrast, algorithmic directing, algorithmic scheduling, and algorithmic monitoring undermine employees’ felt responsibility for constructive change and thereby inhibit voice. In addition, work locus of control moderates these relationships: employees with an external work locus of control strengthen the negative effects of algorithmic directing, algorithmic scheduling, and algorithmic monitoring, whereas employees with an internal work locus of control strengthen the positive effect of algorithmic feedback. These findings deepen our understanding of how different dimensions of algorithmic management shape voice and offer practical insights for fostering voice in contexts characterized by algorithmic management.

Systems doi: 10.3390/systems14050568

Authors: Han Zhang Hua Zou Xin Wen

Technology enterprises are leveraging artificial intelligence (AI) to foster disruptive innovation, aiming to seize first-mover advantages in technological catch-up and strategic transformation. Most existing studies adopt static research methods such as empirical analysis to explore corporate disruptive innovation from the dimensions of technology, market, organization and value creation. However, few scholars dynamically investigate the impacts of multi-stakeholder interactions on the disruptive innovation of AI enterprises from the perspective of innovation ecosystem by employing evolutionary game theory. Against this backdrop, this paper adopts the evolutionary game approach to explore how the bounded rational strategic interactions among AI enterprises, incumbent enterprises and governments in the innovation ecosystem affect the evolutionary dynamics of AI enterprises’ disruptive innovation behaviors. It also examines under what conditions of benefits, costs, risks and policies the system can evolve toward a stable strategic equilibrium. The findings reveal that the sustainable advancement of disruptive innovation by AI enterprises is not merely driven by the unilateral willingness of individual firms. Instead, it is jointly shaped by the innovation investment of AI enterprises, cooperative responses of incumbent enterprises, and regulatory and supportive policies of governments, as well as comprehensively influenced by base benefits, R&D investment pressure, technology spillover effects and niche competition risks. This research provides theoretical references for improving the innovation governance and policy support system of the AI industry. Future research can further analyze the influence of strategic interactions among more heterogeneous stakeholders on the evolutionary process of disruptive innovation of AI enterprises.

Systems doi: 10.3390/systems14050566

Authors: Junki Yang Ja-Hee Kim

This study proposes a standards-based Reference AI Business Model Canvas (Reference AI-BMC) that translates the use-case descriptors of ISO/IEC TR 24030 into the nine blocks of the Business Model Canvas, addressing the lack of a structured translation layer between AI standards and business-model design. Using ten selected fields of the ISO/IEC TR 24030 use-case template, a two-round Delphi process derives consensus-based mapping rules from expert judgments; Latent Dirichlet Allocation is used as a field-level semantic analysis to provide interpretive context for the Delphi-derived mappings. Primary mappings are reported as default translation references that met the 80% strict-consensus threshold, secondary mappings as context-dependent relations, and the adjudicated dual-mapping exception A5 (Threats/Challenges → Cost Structure) as a separately documented case. After converting the finalized primary mapping rules into a coding manual, three independent coders applied them to 81 AI use cases; the Layer 1 coding yielded Krippendorff’s α = 1.000, descriptively indicating no observed coder disagreement under the specified coding conditions. The Reference AI-BMC contributes a standards-based, consensus-derived translation layer for systematically organizing AI use cases in business-model terms, offering a structured starting point for early use-case workshops, preliminary portfolio screening, and standards-aware AI service design discussions. Together, these results position the Reference AI-BMC as a standards-based, consensus-derived reference layer for organizing AI use cases in BMC terms, with its applicability bounded by the ISO/IEC TR 24030 descriptor structure and the specified mapping procedure.

Systems doi: 10.3390/systems14050567

Authors: Manru Zhao Yujia Lu

The rapid expansion of e-commerce has transformed market access in developing economies, yet its impact on the spatial structure of market participation remains insufficiently understood. While existing studies primarily examine welfare outcomes such as income growth and consumption smoothing, few investigate how digital platforms reshape the balance of market entry between urban and rural areas. Drawing on New Economic Geography and platform economics theory, this study proposes that e-commerce development rebalances urban–rural market vitality through three associative pathways: alleviating rural capital constraints, improving rural innovation environments, and promoting agricultural-industry agglomeration. Using county-level panel data covering 2725 Chinese counties from 2011 to 2022, we employ a Double Machine Learning (DML) framework to examine the association between designation as an “E-commerce into Rural Comprehensive Demonstration County” and changes in the urban–rural market vitality balance (URMAR). The results indicate that demonstration county designation is associated with a statistically significant reduction in urban–rural market disparity, as measured by both the Theil index and the absolute difference in new enterprise registrations. The directional URMAR indicator further reveals that this convergence is driven primarily by accelerated rural enterprise formation. Subsample analysis confirms that the rebalancing interpretation holds across counties with different baseline market structures. Mechanism analysis provides suggestive evidence consistent with all three proposed associative pathways. Heterogeneity analysis further reveals that these effects are stronger in economically developed eastern regions, in counties linked to higher-tier cities, and in secondary and tertiary industries. These findings advance a market-structure perspective on digital development that complements existing welfare-based approaches and offer policy insights for fostering balanced regional development through targeted digital and complementary investments.

Systems doi: 10.3390/systems14050565

Authors: Kangzheng Huang Weibo Li Shuai Cao Xianping Zhu Peng Li

Accurate identification of key nodes in complex networks is vital for optimizing system robustness and controlling information spread. Existing centrality metrics struggle to balance the continuous extraction of global topological features with the fine-grained perception of local structures, while traditional heuristic algorithms also face severe resolution limitations. To address these issues, this paper proposes a node importance evaluation method based on fractional-order topological propagation and local information entropy (FSEC). This method overcomes the limitations of discrete integer-order propagation inherent in traditional graph walks. It constructs a continuous fractional-order topological propagation operator within the spectral graph theory framework. This enables the smooth projection of node degree features into the global topological space, thereby yielding high-order global impact factors. Furthermore, an information theory mechanism is introduced to quantify the probability distribution of a node’s information contribution within its local neighborhood. The local structural information entropy is then calculated to reflect the node’s asymmetric control over micro-level information flow. Deliberate attack simulations were conducted on nine real-world networks and three types of artificial network models. The results show that the proposed FSEC algorithm significantly outperforms baseline algorithms like Autoencoder and Graph Neural Network (AGNN), Degree Centrality, k-shell, PageRank, and Mixed Degree Decomposition (MDD) in degrading the largest connected component (LCC) and global network efficiency (NE). The proposed method also achieves the minimum Area Under the Curve (AUC) values globally. Its monotonicity is slightly lower than that of AGNN but superior to all other baseline algorithms. In addition, SIR simulations further confirm the effectiveness of the FSEC method. This approach successfully resolves the ranking tie problem among nodes in the same topological layer.

Systems doi: 10.3390/systems14050564

Authors: Jiafu Liu Mei Dong

Against the backdrop of concurrent economic transformation, deepening digitalization, and green upgrading, this study aims to uncover the mechanisms through which the system of new quality productive forces drives high-quality economic development. Drawing on a six-element framework comprising new-quality labor, new means of production, new labor objects, new technology, production organization, and data elements, the study uses panel data from 30 Chinese provinces covering the period 2014–2023 and applies dynamic qualitative comparative analysis (dynamic QCA) to examine the relevant configurational pathways and their cross-temporal evolution. The results show that no single condition constitutes a temporally and regionally stable necessary condition for high-quality economic development. Instead, high-quality economic development is primarily realized through three pathways: a technology–organization–tool-dominated pathway, a talent–data–carrier-led pathway, and a technology–organization–carrier-compensation pathway. The explanatory power of these pathways also varies across stages. The findings indicate that high-quality economic development arises from the synergistic configuration and dynamic recombination of multiple elements of new quality productive forces.

Systems doi: 10.3390/systems14050563

Authors: Ming Che Lee

This study proposes an explainable framework for ESG portfolio rebalancing by integrating carbon credit signals, technical indicators, and Transformer-inspired forecasting into a unified decision process. The investable universe consists of six ESG-themed ETFs, namely ESGU, SUSA, ICLN, TAN, KRBN, and KGRN. Carbon-related sustainability information is represented by four S&P carbon indices, including GCC, CCA, EUA, and UCITS. Within the proposed framework, Transformer, Informer, and Temporal Fusion Transformer are used to predict next-day returns, and the forecast outputs are translated into portfolio decisions through threshold filtering, Softmax-based allocation, and inertia smoothing under fixed transaction costs. The empirical results show that the proposed framework remains competitive against Equal Weight, Risk Parity, and Momentum benchmarks, although its advantage is conditional rather than uniformly dominant across all metrics. Informer delivers the strongest Sharpe ratio among the model-based strategies, while Transformer exhibits a more stable risk profile. The ablation results indicate that the smoothing mechanism has the clearest effect on turnover and allocation stability, whereas the incremental value of carbon-related inputs is most visible in Informer. The uncertainty assessment further shows that many benchmark differences are not consistently significant under repeated resampling, but the performance weakening caused by removing carbon inputs in Informer remains identifiable. The subperiod analysis shows that benchmark rules are more competitive in 2024H1, whereas model-based strategies gain relative strength in 2024H2. The explainability analysis indicates that carbon-feature contributions are concentrated more strongly in Intermediate and Carbon-Sensitive asset groups and remain weaker in Broad ESG assets; feature-level and SHAP beeswarm evidence further shows that the three architectures rely on GCC, CCA, EUA, and UCITS in different ways. These findings suggest that carbon-related sustainability signals can provide economically meaningful allocation information in selected settings when they are combined with suitable model architecture and disciplined rebalancing control, thereby supporting a competitive and explainable ESG portfolio rebalancing framework.

Systems doi: 10.3390/systems14050562

Authors: Junda Wu Jiajing Sun Haoyuan Feng Fei Long

In high-inflation economies, stablecoins are increasingly becoming infrastructural channels through which households and firms access U.S.-dollar value outside traditional financial arrangements. We study Argentina as a fragmented USD access system composed of a regulated official channel, an informal parallel channel (the Blue Dollar), and platform-based USDT channels on Binance and Bitso. Using a quantile time-frequency connectedness framework, we estimate reduced-form dynamic dependence and spillover patterns across these interdependent subsystems under normal and extreme market states and across short- and long-term horizons. Four main findings emerge. First, system-wide connectedness is dominated by short-term transmission and rises sharply during policy regime transitions, particularly around the relaxation of capital controls. Second, under normal conditions, stablecoin markets behave as early-moving net spillover transmitters, whereas the Blue Dollar and the official rate primarily absorb shocks. Third, connectedness exhibits a symmetric U-shaped pattern across quantiles, indicating that tail events intensify cross-channel dependence regardless of shock direction. Fourth, under upper-tail extreme market states, the official rate becomes a net transmitter in the long-term frequency band, implying that major devaluation episodes can temporarily reconfigure the system’s transmission architecture, even though stablecoin channels remain important in overall connectedness. These findings should be interpreted as evidence of dynamic dependence rather than structural causality. They suggest that digital dollarization does not simply add another trading venue; it increases boundary permeability, reshapes information hierarchy, and changes the monitoring problem faced by authorities in fragmented financial systems.

Systems doi: 10.3390/systems14050561

Authors: Tongkai Zhang Cheng-Jie Jin Jun Liu

Traffic flow prediction is an important and fundamental task for the operation of Intelligent Transportation Systems. In recent years, most studies on traffic prediction have focused on two-dimensional network traffic flow prediction, while there is still no clear consensus on the study of one-dimensional highway traffic flow prediction, for instance, regarding which model is the most appropriate. To address this gap, we conducted a systematic comparative evaluation of 27 models across five classes, including Statistical models, Machine Learning, Artificial Neural Networks, Deep Neural Networks, and Graph Neural Networks, based on five representative highway traffic datasets. To ensure fairness, evaluations were performed on raw data without signal decomposition or auxiliary modules. Surprisingly, the experimental results reveal that complex deep learning models do not demonstrate advantages in terms of conventional metrics. Instead, simple models, particularly Historical Averaging and tree-based Machine Learning models, exhibit superior performance in most scenarios. And then, we study the underlying reasons for this phenomenon from various perspectives, including the complexity of prediction tasks, the tabular data characteristics, the spectral bias of Neural Networks, and theoretical error bounds. Furthermore, we also analyze why these findings were overlooked in the previous literature, attributing the oversight to the predominant focus on signal decomposition preprocessing, inconsistent prediction settings, and the lack of comprehensive benchmarking. Supported by rich data and extensive information, this work offers valuable references and practical implications for researchers in highway traffic flow prediction. It further advocates that in the era of pursuing sophisticated models, scenario-specific analysis and appropriate simple models still deserve more attention.

Systems doi: 10.3390/systems14050560

Authors: József Blaskó Zoltán Baracskai Tibor Dőry

This conceptual article introduces knowledge-centric recruitment (KCR) as a distinct dynamic capability that reframes recruitment and post-hire socialization as strategic knowledge-development activities. (1) Background: Unlike conventional vacancy-driven approaches, KCR is a proactive process through which firms deliberately access and import external organizational capabilities embodied in senior professionals—termed knowledge-hires—from rival organizations. These knowledge-hires embody tacit, socio-cognitive building blocks of capabilities developed through involvement in their prior employers’ routines and practices. (2) Methods: This article develops a micro-foundational model of KCR comprising four interrelated processes: external capability scanning and prioritization, identification of target capabilities and knowledge-hires, evaluation through the novel lens of contextual capability fit, and expectations of adaptation during onboarding. (3) Results: Contextual capability fit integrates complementary and supplementary quality with knowledge distance to enable firms to forecast both the strategic value of inbound capabilities and the hire’s expected socialization difficulty. (4) Conclusions: The primary theoretical contribution lies in advancing the learning-by-hiring literature by shifting the focus from passive knowledge diffusion to deliberate, calculative capability acquisition. By integrating insights from the knowledge-based view, person–organization fit, absorptive capacity, and strategic recruitment, the KCR model offers a coherent micro-foundational framework for transforming employee mobility into a source of sustained competitive advantage.

Systems doi: 10.3390/systems14050559

Authors: Ruoxi Huang Yong Liu Shiwang Yu

Market-based environmental regulations are increasingly vital for driving green transitions. As a major construction economy and the world’s leading carbon emitter, China launched its Carbon Emission Trading System (CETS) to advance dual-carbon goals and pilot decarbonization in high-emission sectors. Using 2009–2021 data on A-share listed construction enterprises, this study employs a propensity score matching difference-in-differences (PSM-DID) approach to assess CETS’ impact on corporate Environmental, Social, and Governance (ESG) performance. Results show that CETS significantly improves construction enterprises’ ESG performance. Mechanism analysis identifies green technology innovation as a key transmission channel, with government subsidies positively moderating this effect. Heterogeneity analyses reveal stronger policy effects among state-owned enterprises and firms in eastern regions. These findings remain robust under alternative specifications, matching methods, and higher-order fixed effects. This study offers micro-level evidence on how market-based carbon regulations shape corporate sustainability through ESG, informing China’s carbon market refinement and global market-driven decarbonization efforts.

Systems doi: 10.3390/systems14050558

Authors: Shaozhen Hong Yingqi Liu

How non-dominant specialized firms sustain systemic innovation influence in modular service ecosystems without occupying architectural control positions remains theoretically underdeveloped. This study develops a dual-path Resource–Strategy–Capability (R–S–C) mechanism framework to explain how structurally distinct network positions generate divergent innovation trajectories among non-dominant firms. The empirical analysis draws on large-scale patent collaboration network data from China’s autonomous driving industry, covering 26 hidden champion firms and 14 global leading enterprises across 2009–2023. The framework identifies two divergent pathways: firms occupying structural hole positions adopt specialization-deepening strategies that build module-anchoring capabilities, while firms with high betweenness centrality adopt T-shaped strategies that build interface-bridging capabilities—both enabling systemic influence without architectural control. To make the resource construct theoretically precise, the framework distinguishes four categories of network-derived resources operative in the R–S–C mechanism—informational, coordination, reputational, and module-definition resources—and specifies three microfoundational processes through which strategic orientation translates into capability: experiential learning, codification of routines, and legitimation through external recognition. Institutional policy environments moderate these mechanisms by reshaping network structural heterogeneity rather than directly driving firm outcomes. The study challenges the canonical prediction of structural hole theory by demonstrating that brokerage positions generate specialization deepening rather than scope expansion when absorptive capacity constraints are binding, extends service ecosystem theory by introducing non-dominant firm pathways to systemic value co-creation, and reframes institutional policy as a network-structural moderator with transferable implications beyond the Chinese context.

Systems doi: 10.3390/systems14050555

Authors: Ya Li Hua Feng Yihang Sun

Against the backdrop of deep integration of technological innovation and industrial innovation, this study constructs a theoretical model incorporating market and government behavior. Utilizing panel data from 284 prefecture-level cities across China from 2013 to 2023, it empirically analyzes the roles and mechanisms of efficient market and proactive government in facilitating the deep integration. Findings indicate that the market’s “push–pull mechanism” promotes the deep integration of technological and industrial innovation, enhancing output performance. However, market mechanism exhibits diminishing marginal output performance: beyond a certain threshold, the force to drive further performance improvements weakens. The government’s role can positively influence the market mechanism’s ability to drive deep integration of technological innovation and industrial innovation, and growth in output performance. In coordinating government and market efforts, the following principles should be observed: as market mechanism matures, gradually enhance technological innovation by allocating human capital to research activities, particularly basic research; formulate industrial policies that progressively prioritize science and technology innovation activities; and advance the development of public goods, such as technology transfer and commercialization platforms. To advance the deep integration of technological and industrial innovation, policy implications include consistently leveraging market mechanisms, coordinating government and market efforts to design policies for capital and talent mobility, and promoting the construction of conversion platforms like concept-validation centers and science incubators.

Systems doi: 10.3390/systems14050557

Authors: Xiaolei Ma Xiaofei Ye Xingchen Yan Tao Wang Jun Chen

To optimize the freight distribution structure of ports and reduce carbon emissions from freight transportation, this paper develops a bi-level programming model for freight traffic shifting between roadway and waterway networks that incorporates carbon emissions. First, a complex freight network based on the roadway–water transport system is constructed, comprising roadway networks, inland waterway networks, maritime networks, and transshipment nodes. A traffic impedance model is then formulated within this complex network framework, integrating the roadway BPR function, the M/M/1 queuing model for lock passage time on inland waterways, and the M/M/c queuing model for port cargo handling into the impedance function. This allows micro-level congestion effects to be combined with macro-level traffic assignment. Next, a bi-level programming model for freight traffic shifting in the roadway–water network system is established, with carbon emissions incorporated. The NSGA-II algorithm is employed to determine the optimal carbon subsidy level, based on which the traffic distribution in the complex freight network is analyzed. Finally, the proposed model is applied to the roadway–waterway bimodal network in the Hangzhou Bay port area of Cixi. The results indicate that without subsidies, the waterway transport share is only 1.74%. The optimal subsidy efficiency frontier is identified at CNY 350,000/day, where the waterway share increases to 22.7% and carbon emissions decrease by 33.27 tons/day. The subsidy strategy evolves through three stages: first, prioritizing maritime shipping; second, jointly promoting inland and maritime shipping; and finally, shifting focus to infrastructure investment once subsidies reach saturation. This study offers a quantitative analytical tool for designing differentiated carbon subsidy policies to facilitate the road-to-waterway modal shift under fiscal constraints.

Systems doi: 10.3390/systems14050556

Authors: Nana Li Yanwei Hao

Firms that invest similar amounts in digital transformation often end up with very different results: some achieve steady improvement, some remain at a low level, and others experience repeated rises and declines in performance. This study explains why these differences occur by examining technological innovation intensity, absorptive capacity, and operational performance as a connected process over time. Using a nonlinear dynamic model, we show that increasing technological innovation intensity does not guarantee better operational performance. Instead, outcomes depend on whether innovation activities are converted into absorptive capacity. When experience from innovation projects is absorbed and retained, absorptive capacity increases, and operational performance improves and remains stable. When this process is weak, firms may continue increasing innovation intensity, but absorptive capacity does not accumulate, and operational performance remains at a low level. We also find that as technological innovation intensity increases, more innovation activities take place at the same time, which raises coordination pressure. When coordination and management do not improve accordingly, the positive effect of absorptive capacity on operational performance becomes weaker, and performance may begin to rise and fall instead of improving steadily. In addition, the results show that firms need to reach a certain level of absorptive capacity before sustained improvement in operational performance can occur. Below this level, increasing technological innovation intensity alone does not change outcomes. At higher levels of absorptive capacity, operational performance may not always stabilize, but may instead fluctuate over time. These findings show that differences in digital transformation outcomes are not determined by technological innovation intensity alone, but by the joint dynamics of resource reconfiguration, learning accumulation, and coordination complexity, which shape absorptive capacity and operational performance over time.

Systems doi: 10.3390/systems14050554

Authors: Thamir Hamad Alaskar

The integration of Artificial Intelligence (AI) and Big Data is increasingly associated with firms’ resilience in dynamic business environments. This study examines the relationships between AI–Big Data integration, AI capabilities, supply chain agility, and firm resilience, with particular attention paid to the mediating roles of AI capabilities and supply chain agility. Data were collected from 475 experts across firms in Saudi Arabia and analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM). The results indicate that AI–Big Data integration is positively associated with AI capabilities and supply chain agility, both of which, in turn, significantly contribute to firm resilience. In addition, AI capabilities show a direct positive relationship with supply chain agility. The findings further confirm the mediating roles of AI capabilities and supply chain agility in strengthening organizational resilience. This study contributes to the Dynamic Capabilities View (DCV) and Knowledge-Based View (KBV) by empirically examining how integrated AI–Big Data relates to capability development and firm outcomes. The results also provide implications for managers seeking to align AI and Big Data initiatives with supply chain capabilities to support resilience in dynamic environments.

Systems doi: 10.3390/systems14050553

Authors: Yan Zhao Huangyi Gui Shiye Zhang

Creativity relies on dynamic processing within the semantic network system; however, how this system varies under different cognitive strategies remains unclear. Grounded in the Associative Theory of Creativity, this study applied four cognitive strategies (application, analogy, abstraction, and combination) as distinct input constraints to the cognitive system. We tracked the N400 component using event-related potentials (ERPs), which capture brain activity time-locked to specific cognitive events. Specifically, the N400 serves as a reliable neural marker reflecting semantic mismatch and the integration of new information. Repeated-measures analysis of covariance (RM-ANCOVA) revealed that the abstraction strategy yielded the highest level of creativity, while the application strategy yielded the lowest. Neural data indicated that attenuated N400 amplitudes under the application strategy reflected minimal prediction errors within familiar conceptual spaces, whereas pronounced N400 amplitudes under abstraction and combination strategies represent substantial cognitive effort associated with feature extraction and concept integration. Subsequent linear mixed-effects model (LMM) analysis revealed that the N400 component exerted a significant negative moderating effect on individual creativity under the analogy strategy, establishing a boundary condition for transforming semantic updating into final creative output. By exploring associative processes through micro-neural mechanisms, this research provides practical insights for optimizing creative task design and evaluation structures.

Systems doi: 10.3390/systems14050552

Authors: Bo An

Humanities research depends on linking claims to precise evidence, such as juan, pages, passages, or time-stamped media segments. While large language models can assist with extraction and synthesis, their outputs remain difficult to use in scholarly work unless provenance, citation, and verification are explicitly controlled. This paper proposes an evidence-centric knowledge management system for humanities research. The system models sources as stable EvidenceUnits, extracts entities, relations, and events under schema constraints, and admits generated knowledge only after structural validation, evidence-pointer checking, and claim-level verification. Ambiguous or conflicting cases are routed to human review and retained in an audit trail. The main evaluation is a controlled document-modality study on an annotated subset of the Shiji. The system achieves micro-F1 scores of 0.84 for named entity recognition, 0.78 for relation extraction, and 0.82 for event trigger detection. Governance-layer analysis shows that evidence-pointer resolvability rises from 42.3% to 94.2%, while mis-citation and overreach fall to 4.1% and 5.3%. A minimal oral history audio smoke test further demonstrates that timestamped Audio EvidenceUnits can pass through the same governance workflow; though, it is not a full multimodal benchmark.

Systems doi: 10.3390/systems14050551

Authors: Mouhamed Bayane Bouraima Jakub Więckowski

This paper assesses the challenges and policy responses for the adoption of electric vehicles (EVs) in Africa. We applied a decision support system framework comprising a new integration of the RANking COMparison Method (RANCOM) and Root Assessment Method (RAM) for the first time in the literature to address the multi-criteria decision analysis (MCDA) problems based on expert opinions. Six experts evaluated five criteria along with ten policy responses. While the weights of criteria are computed via the RANCOM method, the RAM approach ranks the policy responses. Moreover, the Compromise Fuzzy Ranking (CFR) method defines the consensus rankings via both positional ranks and preference scores. Furthermore, a three-stage comparative analysis is carried out for criteria weighting, policy responses ranking, and alternative consensus ranking. A sensitivity analysis is carried out including the consideration of experts’ significance according to their experience and their omission. The findings indicated the most critical challenges were the scarcity in charging infrastructure and the affordability and accessibility issues. The resilient charging infrastructure is the most appropriate policy response. The findings direct planners and EVs policymakers across the continent toward a policy that will ensure a clean and sustainable transportation system.

Systems doi: 10.3390/systems14050550

Authors: Yanping Chen Yunfei Shao

Low-carbon service outsourcing creates a governance problem in which manufacturers must address hidden emission-reduction capability before contracting and hidden effort after contracting. Consumer low-carbon preference does not automatically translate into market returns, because consumers rely on information disclosure, certification, carbon labeling, and traceability to perceive actual emission-reduction performance. This study develops a principal–agent model for a low-carbon service supply chain composed of a manufacturer and a low-carbon service provider. The baseline model examines screening and effort incentives under dual information asymmetry, the extended static model introduces heterogeneous consumer preferences and information perception, and the dynamic model incorporates consumer trust evolution. The results show that menu contracts enable manufacturers to distinguish service-provider types and induce emission-reduction effort, but truthful self-selection requires information rent. Consumer low-carbon preference strengthens incentive intensity only when disclosure converts actual emission-reduction performance into perceived low-carbon value. Disclosure investment improves the market return of emission-reduction effort, but its effectiveness is constrained by disclosure cost, provider risk aversion, and output uncertainty. Consumer low-carbon trust converges to a steady state supported by sustained emission-reduction effort and credible disclosure. The conclusions apply primarily to low-carbon service outsourcing settings in which provider capability and effort are difficult to observe and market response depends on consumers’ perception of low-carbon information. This study extends principal–agent analysis to low-carbon service supply chains and shows that effective low-carbon governance depends on the coordination of contract incentives, information disclosure, and trust accumulation.

Systems doi: 10.3390/systems14050549

Authors: Pavel-Cristian Crăciun Andreea-Maria Trăistaru Oana-Alexandra Dragomirescu Ana-Ramona Bologa Robert-Cristian Necula

Financial systems operate under strict requirements for availability, low latency, resilience, and regulatory compliance, yet infrastructure management in these environments remains largely reactive. This paper addresses that limitation by proposing a DevOps–Machine Learning framework for predictive resource and operational optimization in adaptive financial infrastructure. The study follows an artefact-oriented approach inspired by Design Science Research: the framework is defined conceptually, instantiated as a containerized Proof of Concept, and evaluated through controlled benchmarking. The proposed architecture integrates observability, workload forecasting, decision support, and automated actuation to support proactive scaling and more adaptive operational control in cloud-based financial environments. The experimental setup uses synthetic financial-like workloads with cyclical demand, stochastic variation, and sudden spikes to compare conventional reactive scaling with forecast-enhanced strategies. The results indicate that embedding predictive intelligence into infrastructure operations improves the ability to anticipate workload changes and offers a more structured basis for balancing responsiveness, operational control, and resource efficiency than purely threshold-based mechanisms. The study concludes that predictive resource and operational optimization in financial systems should not be treated as an isolated autoscaling problem, but as part of a broader DevOps–Machine Learning architecture for adaptive financial infrastructure.

Systems doi: 10.3390/systems14050548

Authors: Zewei Li Siyu Cao Tao Miao Bin Fang Yun Ye

Accurately predicting traffic crash severity is critical for intelligent transportation systems, where outcomes emerge from the interaction of infrastructure, environment, traffic control, and human behavior. However, existing approaches face three key challenges: severe class imbalance, computational inefficiency, and limited support for system-level risk understanding. To address these issues, this study proposes a unified and system-aware framework integrating Conditional Tabular Generative Adversarial Network (CTGAN), Tabular Prior-data Fitted Network (TabPFN), and eXplainable Artificial Intelligence (XAI) methods for data augmentation, efficient prediction, and interpretable analysis. CTGAN enhances rare but critical crash states while preserving feature dependencies; TabPFN enables accurate multi-class prediction with limited dataset-specific tuning; and XAI methods quantify the influence of key factors and their interactions. Experiments on a real-world crash dataset from Boston show that the proposed framework achieves competitive predictive performance with less reliance on dataset-specific hyperparameter tuning, while also providing complementary interpretability results from multiple perspectives. The results further reveal that crash severity is jointly shaped by visibility, traffic control, roadside features, and temporal dynamics, highlighting the interconnected nature of risk within the transportation system. By integrating predictive modeling with complementary interpretability analysis, the framework provides a systems-oriented basis for examining how environmental, infrastructural, and temporal conditions jointly relate to crash severity in the studied urban crash data, while offering a methodological reference for broader safety applications that require further validation.

Systems doi: 10.3390/systems14050547

Authors: Aleksandar Ivanović Miloš Radenković Sergei Prokhorov Aleksandra Labus Božidar Radenković

Adaptive and evolutive software systems are characterized by ontologically defined non-determinism—not a defect but the primary force of their evolution. Non-determinism arises from recursion, interaction, and selection between abstract components and can only be resolved as the execution sequence grows sufficiently for one outcome to become determinate. In adaptive systems, managing this non-determinism through structural adaptation of abstract components during execution is the defining operational characteristic—one that no existing execution substrate formally supports. The problems of evolutive AI systems—inconsistency, non-reproducibility, absence of causal traceability, and an inability to enforce purpose-constrained autonomy—cannot be resolved within AI architectures alone. Resolving them requires a formal execution substrate in which causal context growth, resolution-moment detection, and structural adaptation of abstract components are first-class properties. This paper introduces the Zero Tier Execution Substrate (ZTES), grounded in a foundational model that defines execution as a sequence generated by recursive invocations of abstract components with ontologically specified purpose, in which non-determinism is resolved within the causal context of the sequence before commitment. ZTES is a homomorphic specification of this model, achieved through disciplined composition of the Mesarović–Takahara system ontology, Lamport-consistent causal ordering, P-DEVS transition semantics, and the Three-Phase execution kernel—mechanisms individually proven at a global scale. System execution is formally identified with the causal evolution of knowledge: Execution(Σ) ≡ Evolution(K). The historical knowledge base K has a two-dimensional orthogonal structure—the eschatological dimension encoding purpose lineage through recursive specialization, and the sequential dimension encoding event order through iterative mapping—which establishes purpose integrity as a substrate-level property of evolutive execution. The semantic closure of ZTES establishes deterministic reproducibility, governance–execution equivalence, and purpose-constrained autonomy as structural consequences of substrate closure rather than as additional architectural layers.

Systems doi: 10.3390/systems14050545

Authors: Junyuan Li Wendong Yang

Accurate carbon price forecasting is essential for market stability, regulatory assessment, and risk management in emission trading systems. However, existing approaches find it challenging to extract informative signals from high-dimensional multi-source data, integrate mixed-frequency influencing factors, and maintain robustness in multistep forecasting. This study proposes a unified multi-step interval-valued carbon price forecasting system that integrates a multi-objective reinforcement learning (MORL)-based mechanism for feature selection, a multi-source mixed-frequency data sampling (M-MIDAS) module, and a leading-expert dynamic residual correction network (LE-DRCN). MORL performs adaptive feature selection by jointly considering multiscale temporal characteristics and feature redundancy. M-MIDAS aligns multi-source heterogeneous factors into a unified representation, whereas LE-DRCN captures structured residual patterns and dynamically adjusts expert contributions through attention-based integration, thereby improving forecasting accuracy and stability across horizons. The proposed system consistently outperforms benchmark models in multistep forecasting on China Emission Allowance market data. Ablation analysis confirmed the importance of mixed-frequency representation and dynamic residual correction, and statistical tests further verified the robustness of the performance gains. Interpretability analysis revealed horizon-dependent expert contributions, reflecting adaptive model behavior under varying market conditions. Overall, the proposed system provides a robust and interpretable solution for multi-step interval-valued carbon price forecasting under complex multi-source and mixed-frequency environments.

Systems doi: 10.3390/systems14050546

Authors: Samson Quaye Maurice Dawson Ahmed Ben Ayed

Modern machine learning approaches for vulnerability detection achieve strong performance within specific datasets, yet their reliability degrades under domain shift, limiting their effectiveness for real-world secure software development lifecycle (SDLC) decision-making. In particular, probabilistic vulnerability predictions, while well-calibrated, exhibit instability across heterogeneous codebases, reducing their suitability as standalone risk indicators. This paper introduces Intelligent Risk-Adaptive Secure SDLC (IRAS-SDLC), a lifecycle risk aggregation framework for Secure AI-Augmented Software Assurance under the Risk Management Framework (RMF) and Zero Trust. The proposed framework integrates model-derived vulnerability likelihood with structured security metrics, specifically exploitability and impact derived from standardized Common Vulnerability Scoring System (CVSS) data, to construct a unified and interpretable risk representation. This formulation enables consistent prioritization across SDLC phases while aligning with RMF control families and Zero Trust continuous verification principles. By combining learned semantic signals with domain-independent security factors, IRAS mitigates the instability of vulnerability likelihood under distributional shifts and provides a more robust basis for cross-domain risk assessment. The framework embeds risk evaluation early in the SDLC, enabling proactive identification of vulnerabilities during the requirements and design phases rather than post-implementation detection. Empirical evaluation demonstrates that IRAS-SDLC maintains meaningful risk estimation under domain shift and significantly improves lifecycle outcomes. In particular, early risk identification yields negative detection latency relative to conventional methods and reduces simulated remediation costs by up to an order of magnitude. IRAS-SDLC bridges the gap between machine learning-based vulnerability prediction and governance-aligned security assurance by providing a stable, interpretable, and lifecycle-aware risk assessment mechanism that is directly compatible with RMF-based compliance workflows and Zero Trust architectures.

Systems doi: 10.3390/systems14050544

Authors: Huabin Wu Simiao Tong Benyan Ren Yu Mao

The fragmentation of resources, ecological imbalance, and the disintegration of essential factors in modern urban–rural development have become core bottlenecks constraining the achievement of global sustainable development goals. Existing research often focuses on static analysis or single-dimensional exploration, making it difficult to fully reveal the evolutionary patterns of urban–rural sustainable development systems and the core mechanisms of digital empowerment. This study adopts ecological wisdom as a theoretical perspective, introduces system dynamics methods, and constructs a three-dimensional linkage system simulation model of digital technology, factor circulation, and ecological wisdom capital. Based on model simulation data, a 70-year long-cycle simulation is conducted to explore the leverage effect of digital technology and the self-organizing evolutionary mechanisms of the system. The findings are as follows: First, the transformation of urban–rural sustainable development from fragmentation to synergy is essentially a self-organizing phase transition process. Within the system, digital technology, factor circulation, and ecological wisdom capital form a mutually beneficial symbiotic relationship through positive feedback mechanisms, driving the system toward higher-order evolution. Second, key variables such as community participation, urban–rural ecological resilience, and development pressure exhibit significant threshold effects. Only when the investment scale and institutional guarantees for these variables reach critical values can the system’s path dependence be broken, significantly driving synergistic transformation. Third, as an exogenous lever, digital technology can break down barriers to factor segmentation, reduce energy loss during system transformation, and achieve systematic integration of urban–rural resources, public services, and ecological capital with lightweight investment, serving as the core breakthrough for promoting synergistic transformation. This study integrates ecological wisdom and digital technology into the system dynamics analysis framework of urban–rural synergistic transformation, clarifies the dynamic transmission pathways of digital technology empowering urban–rural synergy, overcomes the limitations of traditional static research, enriches the cross-disciplinary application of ecological wisdom and system dynamics in the field of urban–rural development, and provides a scientific policy basis for the mutual construction of the digital economy, ecological protection, and urban–rural integration.

Systems doi: 10.3390/systems14050542

Authors: Nagwa Amin Abdelkawy

Oil-dependent economies function as complex socio-economic systems in which resource revenues, institutional structures, and investment outcomes are dynamically interlinked. When governance is weak, oil windfalls can trigger reinforcing feedback loops that erode institutional capacity, reduce investment quality, and deepen resource dependence. This paper examines whether governance may function as a system-level regulating mechanism that weakens these self-reinforcing dynamics. We study 13 oil-dependent economies over 2000–2023 using the Incremental Capital–Output Ratio (ICOR)—the investment required to produce one unit of output—as a proxy for system-level capital efficiency. Panel ARDL–PMG estimation, which separates short-run perturbations from long-run equilibrium, shows that oil rents are associated with a higher ICOR in the long run, indicating declining system efficiency. A composite governance index, and anti-corruption capacity in particular, are associated with a substantially lower ICOR and weaken the oil–inefficiency transmission. Notably, anti-corruption is the only governance dimension operating across both temporal scales, functioning as both an immediate corrective mechanism (preventing procurement fraud) and a structural stabilizer (shaping the institutional environment over time). Results are robust across alternative ICOR specifications, supported by Granger causality tests, and stable when any single country is dropped. The findings identify governance—especially anti-corruption capacity—as a critical leverage point for improving system-wide capital efficiency in resource-dependent developing economies.

Systems doi: 10.3390/systems14050543

Authors: Liuyi Liu Yu Cheng Lijie Wei Yue Zhang Xibing Han

Green technology transfer is a vital pathway for optimizing innovation resources and advancing regional low-carbon transformation. Using green patent transfer data from 92 cities in China’s five major urban agglomerations during 2006–2023, this study employs two-way fixed-effects and mediation models to examine the spatiotemporal evolution of green technology transfer and its impact on carbon emission efficiency. The results show that: (1) Green technology transfer has expanded steadily. While local transfers remain dominant, inter-city transfers are rising, and the spatial pattern has evolved into a core–periphery structure with gradient diffusion and multi-center linkage. (2) Such transfer significantly improves carbon emission efficiency, with heterogeneous effects across regions. The Yangtze River Delta, Pearl River Delta, Middle Yangtze, and Chengdu–Chongqing agglomerations show the strongest effects. Within these regions, intra-agglomeration and inter-city transfers produce greater emission-reduction outcomes than intra-city transfers. (3) Green technology transfer indirectly improves carbon emission efficiency by upgrading industrial structures, strengthening urban innovation capacity, and enhancing resource allocation efficiency. This study explores the multidimensional mechanisms through which green technology transfer influences carbon emission efficiency at the urban-agglomeration scale and provides empirical evidence for optimizing regional green technology transfer patterns, promoting collaborative low-carbon and high-quality development, and supporting China’s dual-carbon goals.

Systems doi: 10.3390/systems14050541

Authors: Shengshai Zhang Shiping Chen Jianhui Jiang Xiaodong Yu

To effectively alleviate the common problems of information noise and information loss in personalized recommendation systems, as well as to address data sparsity and cold-start issues, this paper proposes a collaborative filtering recommendation model that integrates user attributes and association rules, named Impoved_UARCF. The model introduces a user attribute-sensitive module and a user-item rating-sensitive module to perform deep feature modeling from the perspectives of multi-dimensional user attributes and user-item rating interactions, respectively. The user attribute-sensitive module employs a similarity computation mechanism based on user attributes to mine and decouple deep attribute features among users, enhancing the discriminability and generalization ability of feature representations, thereby effectively resolving information noise and information loss. The user-item rating-sensitive module utilizes association rule mining technology to learn the relationship weights between users in real time, enabling accurate aggregation and propagation of user-item rating features, thus effectively addressing data sparsity and cold-start problems. Extensive experiments conducted on three public datasets verify the superiority of Impoved_UARCF in recommendation performance, as well as the effectiveness, scalability, and robustness of each module design.

Systems doi: 10.3390/systems14050540

Authors: Franziska Wieja Georg Jacobs Philipp Waigand Kathrin Boelsen Thilo Zerwas

The development of patient-specific medical systems poses challenges for requirements engineering and their traceable integration into system models, as regulatory frameworks require transparent and traceable links between stakeholder needs and product requirements. Document-based approaches lack the formal structure required to derive verifiable product requirements and integrate patient-individual parameters into testing processes. This study investigates how Model-Based Systems Engineering (MBSE) can formalize user needs, user requirements, and patient context parameters to enable a traceable, model-based testing of patient-specific systems. A SysML-based formalization approach is developed that extends the motego method. New stereotypes for user needs and user requirements are introduced and linked via SysML relations to stakeholders, use scenarios, and product requirements. A patient context model is proposed to represent patient-individual parameters and integrate them into a model-based test framework. The approach is demonstrated using an exemplary use case by instantiating representative user needs, user requirements, and patient context parameters and linking them to functions, solutions, and domain model elements within the test framework. An exemplary use case demonstrates the integration and transfer of context parameters. The results show a traceable chain between stakeholders and product requirements, as well as the consistent transfer of context parameters into domain model elements. The simulation is used exclusively as an executable component to demonstrate data integration and model consistency; no validation of physical accuracy is performed.

Systems doi: 10.3390/systems14050538

Authors: Carlos J. Mármol Aurelio Luna Isabel Legaz

Computer fraud has become a rapidly expanding form of cybercrime linked to the growth of digital infrastructures and socioeconomic development. This study adopts a socio-technical systems perspective to examine the temporal evolution, regional disparities, structural determinants, and future trends of computer fraud in Spain (2011–2022). Official data from the Spanish Ministry of the Interior were used to calculate incidence rates per 100,000 inhabitants. Temporal trends were analyzed using linear regression, regional patterns using clustering analysis, and structural associations using correlation models. Projections were developed to estimate trends up to 2035. Computer fraud increased sharply from 44.7 to 707.7 cases per 100,000 inhabitants, with the strongest growth observed in card and bank fraud. Higher rates were found in economically developed and highly digitalized regions. Fraud incidence was positively associated with broadband access, mobile connectivity, and income levels, whereas traditional technologies were negatively associated. These findings indicate that computer fraud should be understood as a system-level phenomenon driven by the interplay of digital, economic, and territorial factors. Effective prevention requires integrated strategies that combine technological, regulatory, and educational measures, adapted to regional vulnerability profiles.

Systems doi: 10.3390/systems14050539

Authors: Bill Deng Pan Yujing Zhou Dahai Liu Thomas Yang Hongyun Chen Yongxin Liu Jian Wang Yunpeng Zhang

Urban transportation intrusion detection is difficult because many compromised messages remain individually credible until they are checked against surrounding road, sensor, and signal states. This study investigated this problem by formulating it as a five-way message classification task over one benign class and four attack families, and by evaluating three detector families under matched access to transportation state: a local-rule baseline, a flat-feature multiclass logistic model, and a knowledge-graph detector with explicit graph reasoning. This study presents a two-part evaluation that combined a controlled simulator with a real-city analysis built from OpenStreetMap and Texas Department of Transportation (TxDOT) data for downtown Austin, Houston, and Dallas. In the fully observed configuration, both the flat-feature logistic and knowledge-graph detectors perform well, while the knowledge-graph detector preserves an explicit rule structure. In the three-city configuration, the knowledge-graph detector shows better portability across cities and lower inference latency. The ablation results further show that roadside sensing and topology account for most of the graph-based detector’s performance.

Systems doi: 10.3390/systems14050537

Authors: Can Wang Xiaofei Ye Xingchen Yan Tao Wang Jun Chen

Parking reservation systems (PRS) are promoted as smart urban parking tools, yet their continued use remains limited because users face both technological uncertainty and schedule-related uncertainty. This study develops a behavioral analysis framework that combines structural equation modeling (SEM), a stated-preference binary logit model, and Random Forest learning. SEM examines how perceived usefulness, perceived ease of use, perceived risk, social influence, and behavioral attitude shape intention to reuse. The binary logit model examines whether users retain their reserved lot under 10 reservation mechanisms and three arrival scenarios. Random Forest is then used to test nonlinear prediction and interaction effects, with intention to reuse measured as the average of the two reuse-intention items and model performance evaluated by the conventional coefficient of determination (R2), mean squared error, and mean absolute error. The results show that perceived risk suppresses perceived usefulness and behavioral attitude, early and especially late arrival sharply reduce reservation retention, and discount intensity is the strongest positive operational lever. Random Forest additionally shows that the effect of perceived risk depends on perceived ease of use: a more intuitive interface buffers the negative effect of risk on predicted reuse intention. These findings indicate that behavioral uncertainty in PRS is simultaneously perceptual, situational, and interactive. PRS design should therefore combine flexible time management, transparent real-time information, and low-friction user interfaces.

Systems doi: 10.3390/systems14050536

Authors: Kyung-Yul Lee Juho Bai

Ship trajectory prediction from Automatic Identification System (AIS) data has been predominantly approached as a time-series forecasting problem, where sequential models operate on coordinate sequences to predict future positions. This paradigm, while effective, neglects a key observation: the spatial layout of multiple vessel trajectories on a chart-like plane carries rich interaction information that is difficult to capture through sequential processing alone. To address this, Mapex (Map Exploitation) is proposed as a vision-based framework that rasterizes multi-vessel AIS trajectories into chart-like multi-channel images and processes them with a visual encoder, treating trajectory prediction as a map-reading task. Each vessel contributes three image channels encoding its trajectory heatmap, speed field, and heading field, converting raw coordinates into a spatial representation where physical movement patterns become visually apparent. A parallel coordinate branch supplies the course-over-ground information that the raster does not encode explicitly, and a fusion module combines both streams for autoregressive five-channel trajectory generation. Unlike coordinate-domain models that process position sequences numerically, Mapex understands vessel motion through its spatial layout, capturing relative positions, trajectory shapes, and kinematic patterns as visual features rather than abstract number sequences. Experiments on the Piraeus AIS dataset demonstrate that Mapex reduces the average displacement error (ADE) by approximately 68% compared to the best coordinate-domain baseline and the mean squared error (MSE) by over 80% compared to the strongest prior method, while requiring significantly fewer parameters than recent LLM-based approaches. These results suggest that spatial visualization of trajectories provides a fundamentally richer representation than coordinate sequences for multi-vessel trajectory prediction.

Systems doi: 10.3390/systems14050535

Authors: Chenyi Xie Yuan Zheng Qingchao Liu Jian Wang Wenping Duan Yu Tang Bin Ran

Accurate trajectory data are fundamental to traffic modeling and autonomous vehicle development. However, reconstructing trajectories in cut-in scenarios is challenging due to complex multi-vehicle interactions and frequently sparse, noisy observations. Existing model-based methods require extensive parameter tuning, while purely data-driven methods depend on densely labeled trajectory datasets and may violate physical consistency. To address these limitations, this paper proposes CI-PINN (cut-in physics-informed neural network), a self-supervised framework for trajectory reconstruction under severe data degradation. By integrating a longitudinal interaction model that captures anticipation and relaxation behaviors, CI-PINN ensures kinematic plausibility by jointly minimizing data-fitting and physics residual losses. Experiments on the NGSIM dataset demonstrate robust performance across missing rates of 80–90%, achieving a mean absolute error of 0.91 m and a mean squared error of 2.17 m2, which are 63.2% and 78.1% lower than the best baseline method, respectively. These results demonstrate a label-efficient and physically consistent framework for trajectory reconstruction in cut-in scenarios. Beyond improving microscopic trajectory fidelity, the proposed method preserves system-level traffic metrics more reliably, facilitating more accurate safety assessments and intelligent transportation applications.

Systems doi: 10.3390/systems14050534

Authors: Meijiao Sun Cheng Pan Renyan Mu

Whether industry–university–research (IUR) collaboration improves firm-level productivity, and under what conditions, remains insufficiently understood. Existing studies often examine the effects of IUR collaboration in isolation, overlooking the fact that productivity outcomes emerge from the interaction of internal capabilities, external environments, and institutional support. This study develops a systems-oriented framework to examine how IUR collaboration affects firm-level total factor productivity (TFP) and how this effect depends on multiple contingencies. Using an unbalanced panel of 24,227 firm-year observations for 3540 Chinese A-share non-financial listed firms from 2011 to 2024, we embed IUR collaboration into an augmented production function that integrates internal research and development (R&D) with externally acquired knowledge. The results show that IUR collaboration is positively associated with firm TFP, with estimated productivity gains of approximately 2.3% in the baseline specification and 3.5% in the instrumental-variable specification. More importantly, this effect is conditional rather than automatic: it is significantly stronger for firms with higher absorptive capacity, firms operating in more dynamic environments, and firms receiving greater innovation subsidies. Additional analyses further show that the effect is concentrated in non-state-owned enterprises and high-technology industries. Overall, the findings suggest that the productivity gains from external knowledge sourcing are system-dependent and shaped by the joint configuration of internal, external, and institutional factors. This study contributes by providing a system-level explanation of how IUR collaboration translates into productivity improvement and by highlighting the importance of complementary mechanisms in innovation systems.

Systems doi: 10.3390/systems14050533

Authors: Jeongho Kim Seonmin Lee Sehee Kim Jonghyuk Park Young Soo Park

Detecting non-empty bicycle baskets in shared mobility services is a low-frequency yet operationally important task for maintenance efficiency. However, the rarity of such events makes it difficult to secure sufficient training data, while conventional manual inspection imposes a substantial operational burden. To address data scarcity and class imbalance, this study proposes an AI-based basket state monitoring framework with a crop-based synthetic data generation pipeline. The proposed method first detects basket regions from scene images using YOLOv8n and then edits cropped basket ROIs, rather than full images, to generate synthetic non-empty samples. This approach reduces structural distortion and improves the reliability of training data under limited real-data conditions. The same basket-centered ROI workflow is also applied to basket status recognition, where MobileNetV3-small is used as the classification model. Experimental results showed that all settings using synthetic data outperformed the real-only baseline. Specifically, the real-only setting achieved a Recall of 0.235 and an F1-score of 0.339, whereas the proposed framework improved Recall to 0.797 and F1-score to 0.788. These results suggest that the proposed system improves monitoring reliability and may help reduce operational burden in real-world settings.

Systems doi: 10.3390/systems14050527

Authors: Liang Nie Huaixia Shi Qinglei Zhang Jiyun Qin

Accurate demand forecasting across multiple aggregation levels is essential for managing complex supply networks, where operations must balance inventory costs, service levels, and resource coordination under non-stationary and heterogeneous demand patterns. Existing spatiotemporal models typically treat all forecasting units at a single resolution, obscuring inherent hierarchical structures and often producing inconsistent predictions across levels. This study proposes a Hierarchical Hybrid Spatio-Temporal Demand Forecasting (H2SDF) architecture that formulates multi-granularity forecasting as a coupled system-of-systems problem. H2SDF decomposes the task into three coordinated layers. At the macro layer, a frequency-aware model extracts global trends and multi-scale periodicities from aggregate demand, providing a stable system-level reference. At the meso layer, a Transformer-based multi-task learner disaggregates the macro signal into location-specific forecasts while learning dynamic inter-location dependencies via self-attention, avoiding reliance on predefined static graphs. At the micro layer, gradient-boosted tree models refine category-level predictions by fusing upstream signals with contextual covariates to correct residual errors. A top-down coupling mechanism propagates forecasts and consistency constraints across layers. Experiments on a 2976 h real-world dataset with 18 locations and 8 product categories demonstrate that H2SDF reduces RMSE and improves R2 compared with state-of-the-art baselines across all three granularities. The results confirm that hierarchical decomposition with heterogeneous model synergy effectively mitigates demand uncertainty and strengthens decision support for inventory, logistics, and workforce planning.

Systems doi: 10.3390/systems14050532

Authors: Batuhan Kocaoglu

Digital maturity models are widely used to support transformation; however, many remain organization-level, lack transparency, and are only weakly linked to implementation prioritization. These limitations are especially consequential in purchasing, where maturity may vary substantially across activities and sub-processes. This study develops a process-level digital maturity assessment-and-roadmapping (DEMA) artifact for purchasing. Within the broader DEMA architecture, this study develops and evaluates only the Smart Business Processes component, while retaining Digital Strategy and Infrastructure as a contextual architectural layer. Drawing on design science research and maturity model development guidance, DEMA was developed through literature synthesis, iterative expert involvement over approximately 18 months, and structured refinement conducted in approximately 20 sessions. The artifact was refined through three anonymized pilot applications in electronics manufacturing SMEs and then demonstrated through a focal case application in an electronics SME that used an ERP system but lacked a purchasing-specific digital transformation roadmap. Evaluation was utility-oriented rather than psychometric, focusing on whether the artifact could (i) generate differentiated capability profiles across purchasing subprocesses, (ii) improve item clarity, stage interpretation, and scoring logic through pilot-based refinement, and (iii) translate assessment results into feasible targets, priorities, and sequenced roadmap actions under facilitated conditions. To provide bounded but direct validation evidence, the study also included a lightweight two-rater consistency and interpretability check on a representative subset of 24 items, together with a structured diagnosis-to-roadmap traceability review of six representative items. The results showed moderate exact agreement, perfect adjacent agreement, positive weighted inter-rater agreement for ordinal ratings, and favorable interpretability scores. Together, these findings provide bounded empirical support for the artifact’s practical consistency and usability within the study’s development-oriented scope. Unlike reflective survey scales, the DEMA is evaluated here as a staged, prescriptive maturity grid. Accordingly, the methodological emphasis is on interpretability, traceability, and assessment-to-action usefulness in facilitated use rather than psychometric scale validation. The DEMA integrates a fully disclosed 70-item staged instrument with explicit scoring, dual-target setting, dependency-aware prioritization, and a structured implementation methodology. In the focal case, the artifact revealed uneven maturity profiles across capabilities, distinguished between current and target capability states, and supported the prioritization of concrete intervention areas, such as data-entry automation/RPA, digital tool budgeting, remote-access improvement, and analytics-related training. Rather than pursuing psychometric scale validation, this study presents a transparent, implementation-oriented artifact for purchasing and shows how process-level maturity diagnosis can be translated into roadmap development in guided-application settings. Therefore, the contribution is design-oriented and practice-facing rather than a claim of broad theoretical advancement or comparative superiority over existing maturity frameworks.

Systems doi: 10.3390/systems14050531

Authors: Li Zheng Yu Xiao

This study conceptualizes universities as socio-technical systems and investigates the efficiency dynamics of student cultivation processes in Chinese universities under the external shock of the COVID-19 pandemic. Employing DEA-Malmquist and panel regression analysis of 40 Chinese universities from 2018 to 2022, we treat each institution as an integrated system of interrelated elements that collectively determine cultivation performance. Results indicate that while most institutions achieved optimal scale, their cultivation systems require improvement in pure technical efficiency and technological progress, suggesting performance depends more on internal configurations than resource volume. Regional analysis reveals that a higher proportion of eastern universities demonstrate lower systemic efficiency compared to central and western institutions, challenging assumptions about resource-rich environments automatically yielding superior system performance. Notably, no statistically significant direct pandemic impact on cultivation efficiency was identified, suggesting that efficiency patterns remained relatively stable during the pandemic period, which may reflect certain adaptive responses rather than direct evidence of systemic resilience through rapid reconfiguration of teaching and management processes. These findings imply that enhancing university cultivation systems requires targeted interventions in internal management structures and systemic integration of technological innovations, contributing to understanding how higher education systems respond to environmental perturbations and informing the design of resilient educational institutions.

Systems doi: 10.3390/systems14050530

Authors: Amirhossein Nafei Rong-Ho Lin Hsien-Ming Chen Shu-Chuan Chen Seyed Mohammadtaghi Azimi

Computer Numerical Control (CNC) machining centers are critical assets in discrete manufacturing, yet many shop floors still rely on periodic expert judgment for machine selection and workload allocation. This practice is unsuitable for high-mix production because machine condition and risk can change rapidly due to tool wear, thermal drift, coolant variation, and alarms. Moreover, decision evidence is fragmented and often incomplete across controller and programmable logic controller signals, production records, and inspection results, making manual evaluation time-consuming and prone to misjudgment. Static rankings can also break down under unforeseen shop-floor disruptions, requiring rapid event-driven re-prioritization and rescheduling. To address these challenges, this research proposes a shop-floor decision intelligence pipeline that executes a rolling-window, uncertainty-aware ranking-and-dispatch loop directly on the shop floor. The industrial compute node continuously collects multi-source operational evidence, normalizes it into a unified event representation, and aggregates rolling-window indicators for each machine. A mapping structure then converts these indicators into neutrosophic triplets that separate performance from evidence credibility. Using this representation, a shop-floor decision procedure continuously updates machine priority scores using a TOPSIS procedure, which are further translated into workload allocation and persistence-confirmed protective action requests. A case study demonstrates end-to-end operation. It shows that the top-ranked machines remain stable under risk-aversion and weight-uncertainty analyses, while the protective logic prevents unsafe dispatching when reject-level conditions persist under reliable evidence. Overall, the proposed pipeline reframes CNC machine selection as a rolling-window, evidence-driven decision process and provides a pathway toward near-real-time and safety-aware shop-floor coordination.

Systems doi: 10.3390/systems14050529

Authors: Jiaxiang Hu Qi Chen

Urban e-commerce fulfillment involves multiple operational stages, including goods receipt, storage, picking, packaging, dispatching, and delivery to customers. This study focuses on one strategic component of this broader fulfillment process: the location–allocation design of urban distribution centers. We develop a socio-technical decision support system for bi-objective urban distribution center planning, in which ex ante location decisions determine which candidate facilities should be opened, whereas ex post allocation decisions assign demand points to the selected facilities under service-time constraints. The model jointly minimizes total logistics cost and population-weighted delivery time, seeking a synergistic balance between cost efficiency and service responsiveness rather than optimizing either objective in isolation. We further embed a large language model into NSGA-II as a bounded supervisory controller that periodically diagnoses search states, adjusts operators and probabilities, and records structured adaptation logs. Experiments on the ZDT benchmark suite and a real urban case demonstrate that this approach improves optimization performance while producing reviewable intervention records. The study contributes to systems research by organizing adaptive AI supervision and organizational oversight into an integrated urban logistics planning system.

Systems doi: 10.3390/systems14050528

Authors: Heng Chen Qian Li Bo Zhou Jiaqi Tang

Based on panel data from 30 Chinese provinces (2008–2022), this study employs fixed-effects, spatial econometric, and moderation effect models to analyze how logistics transport structure adjustment affects carbon emissions. The findings reveal that: (1) Carbon emissions from the logistics industry show a fluctuating upward trend, with a spatial pattern of “higher in the south and lower in the north”; economically developed provinces and major transportation hubs exhibit higher emission levels. (2) The increasing share of railway transport has a more pronounced effect on carbon reduction in provinces with higher industrialization levels. (3) Transport restructuring generates negative spatial spillover effects whereas railway substitution reduces emissions both locally and in neighboring regions, though with regional heterogeneity: most effective in eastern China, weaker in western China, and insignificant in central China. These findings support developing differentiated, regionally coordinated low-carbon logistics policies.

Systems doi: 10.3390/systems14050526

Authors: Zhiyu Chen Jianzu Wu Chongchong Lyu

Digital technologies are increasingly reshaping how top management teams (TMTs) make strategic decisions regarding green innovation. Although prior research has examined the roles of TMT characteristics and artificial intelligence (AI), it remains unclear how TMT internal structures influence green innovation through organizational attention and how AI shapes this process. In particular, CEO–TMT faultlines, reflecting divisions in experiences, roles, and authority, may affect how environmental issues are recognized and prioritized, especially in AI-enabled contexts where complex information processing can amplify internal divisions. Drawing on the attention-based view (ABV), this study examines the cognitive mechanism linking CEO–TMT faultlines to green innovation. Using a panel dataset of 35,347 firm-year observations from 2010 to 2023, we find that CEO–TMT faultlines negatively affect green innovation through reduced eco-attention. Moreover, AI technology strengthens the negative relationship between CEO–TMT faultlines and eco-attention, thereby deepening the negative indirect effect on green innovation. These findings highlight the role of organizational attention in linking TMT structures to strategic outcomes and suggest that AI adoption may reinforce, rather than mitigate, the challenges arising from internal divisions.

Systems doi: 10.3390/systems14050525

Authors: Hang Yang Tianjiao Qi Ying Wang Tao Hong

Amid the restructuring of the global knowledge economy, the structural imbalance of “high input, low commercialization” and the “valley of death” trap have emerged as core bottlenecks restricting the high-quality development of regional innovation. This study aims to explore the collaborative pathway connecting knowledge production to value creation by constructing a ternary Innovation–Platform–Commercialization (IPC) system, which covers front-end research and development (R&D), mid-end integration, and back-end commercialization. Based on China’s provincial panel data from 2013 to 2023, this paper comprehensively employs the coupling coordination model, spatial disparity analysis, and machine learning methods to quantitatively evaluate the coordinated evolutionary pattern of the system and deconstruct its core driving factors. The results indicate the following: First, the overall coordination of the IPC system has steadily improved, yet it exhibits significant spatial agglomeration and local lock-in risks. Second, the underlying cause of spatial disequilibrium has evolved from an absolute scale gap to cross-regional structural fractures. Third, the current core constraint on system development is no longer front-end R&D investment but rather high-value technology trading barriers and the insufficient empowerment of intermediary networks. Finally, the driving effects of traditional elements such as R&D funds and platform scale significantly diminish or even stagnate after crossing specific thresholds, whereas agile organizational structures exhibit superior potential for coordination empowerment. This research breaks through the limitations of traditional unidirectional linear transfer models, confirming that the constraint logic of regional coordination has substantially shifted from “insufficient macro-resource investment” to “micro-organizational operational friction.” Consequently, it provides a theoretical and decision-making basis for local governments to implement precise governance tailored to regional endowments.

Systems doi: 10.3390/systems14050524

Authors: Yingyao Zhang Shuai Lei Xiao Lai Kuo Zhao Xianhui Liu Xingbo Su Yimeng Zhang Chao Li

Cross-organizational collaboration in digital manufacturing systems is often hindered by fragmented data handoff, inconsistent versions, and delayed downstream collaboration across lifecycle stages and heterogeneous systems. To address this issue, a digital thread (DT)-based business process collaboration paradigm is proposed, and its corresponding modeling and execution method is developed in this paper. Firstly, a digital thread-based business collaboration architecture is constructed, in which the unified digital model (UDM) and the authoritative source of truth (ASoT) are integrated to support consistent referencing and authoritative state control of shared data objects. Subsequently, a business process collaboration model and a data-state-driven business collaboration mechanism are established by integrating data state visibility with dual resource constraints. Finally, the proposed method is formalized and evaluated through timed colored Petri net (TCPN) simulation using production business data from a civil aircraft manufacturing enterprise. The results indicate that, under identical resource constraints, the nominal makespan is reduced by approximately 11.3% as collaboration triggering is shifted from document-level handoff to data-state-driven collaboration, thereby improving process parallelism without increasing physical resource inputs.

Systems doi: 10.3390/systems14050523

Authors: Jun Wang Shuangying Chen

Mixed-ownership state-owned enterprises (SOEs) make strategic choices within a governance system shaped by competing shareholders, resource dependence, and institutional legitimacy. Drawing on an attention-based explanation, we argue that relative ownership balance is associated with digitalization–strategic orientation in mixed-ownership SOEs. We further argue that this association depends on system conditions, particularly firms’ dependence on state-linked resources and the broader legitimacy of digitalization in the institutional environment. Using panel data from Chinese listed manufacturing SOEs from 2009 to 2019, we find that greater equity balance is associated with stronger digitalization–strategic orientation. This association is strengthened when digitalization is more institutionally legitimate within the industry, whereas the evidence for the moderating role of subsidy is weaker and suggestive. This study contributes to research on minority shareholder influence, offers evidence consistent with an attention-based explanation of strategic prioritization, and shows that digitalization in SOEs is shaped not only by capabilities, incentives, or external pressure, but also by governance contestation within a broader institutional environment.

Systems doi: 10.3390/systems14050522

Authors: Khalid Mehmood Muhammad Mohsin Hakeem Sangheon Han Gyung Yeol Yang Nourah O. Alshaghdali Irma Potháczky Rácz

This study investigates the influence of artificial intelligence-driven sustainable human resource management (AI-SHRM) on employee job engagement and resilience, with a particular focus on the mediating role of relational contracts. Anchored in the social exchange paradigm, the study examines how the fulfillment of relational contracts fosters positive work outcomes, thereby enhancing overall organizational performance. Additionally, this research explores the role of perceived artificial intelligence decision-making (PAIDM) in amplifying these outcomes by promoting fairness and transparency within HR practices. Utilizing data from a three-wave field survey of 481 respondents in China, the findings reveal that AI-SHRM significantly enhances relational contracts, leading to increased job engagement and resilience. Moreover, PAIDM serves as a significant moderating factor, intensifying the positive effects by strengthening perceptions of equitable treatment. This research advances both theoretical and practical perspectives on AISHRM, relational contracts, and the role of AI-driven decisions in contemporary workplace dynamics, offering critical insights for future organizational studies.

Systems doi: 10.3390/systems14050521

Authors: Dohoon Kim

Ours study focuses on the Last mile Distribution (LD) sector, which has been significantly affected by digital transformation, to examine changes in the Distribution and Logistics (DL) industry from the perspective of the national industrial system. We employed the Input–Output (IO) framework and delineated the LD and other DL industries by reconfiguring the generic IO data, which do not specify these sectors. We also constructed industrial relational networks based on the IO analysis outcomes to examine their structural properties further. Our analysis found that the forward linkages of the LD sector have been more significant than its backward linkages, and that the forward linkages tend to strengthen with digital transformation. However, the backward linkages were not strengthened by digital transformation. Network analysis also confirmed the structural hole characteristics of the LD sector and its role as a powerful authority upon which major industries depend. In particular, the latter provides structural support for the strong forward linkage effect of the LD sector. Our findings provide insights into effective policies to digitalize the DL industries. For example, if the LD sector were deregulated in an innovative manner to support the co-evolution of other industries, the national supply chain would be further enhanced.

Systems doi: 10.3390/systems14050520

Authors: Xu Fan Benye Wang

The rapid advancement of artificial intelligence (AI) has reshaped corporate innovation, yet the existing literature has largely overlooked the non-linear boundary conditions of AI’s innovation effects. This study asks: what is the functional form of the AI–innovation relationship, and through which mechanisms does it operate? Using a sample of 25,204 firm-year observations from Chinese A-share manufacturing companies (2010–2023), we employ fixed-effects models, U-tests, bootstrap mediation, and text similarity analysis. The findings reveal an inverted-U-shaped relationship with a turning point at 2.948. Absorptive capacity partially mediates this relationship, while industry concentration negatively moderates it. Patent text similarity analysis confirms the “homogenization trap.” Heterogeneity analysis shows AI’s enabling effect is more sustainable in non-state-owned and high-tech firms. This study extends the TOE framework by identifying the optimal AI adoption range and empirically validating the homogenization trap, offering guidance for firms to invest in proprietary AI models and for governments to promote open data initiatives. Future research should test these findings across different institutional contexts, particularly European economies.

Systems doi: 10.3390/systems14050519

Authors: Esteban Concha Eduardo Espinosa Guillermo Ramírez Silvia E. Restrepo Ricardo Lizana Fuentes Ricardo León Mauricio Arenas Jesús C. Hernández Federico M. Serra Carmen Luisa Vásquez

This paper presents an initial estimate of the energy demand and charging power requirements for the initial replacement of internal combustion public transport buses by battery electric buses in Chillán and Chillán Viejo, Ñuble Region, Chile. The analysis is based on an operational baseline derived from route lengths, service frequencies, departures, and fleet data, combined with official energy and transport demand projections. A case study is conducted for the introduction of 10 battery electric buses on Line 13 of public transport, comparing 100% overnight depot charging and 80% overnight charging complemented by opportunity charging. Results show that the initial 10-bus deployment would require an installed depot charging power between 300 and 450 kW, depending on the charging strategy, with an annual delivered energy of 1149.75 MWh. Long-term scaling scenarios suggest that bus-dedicated charging infrastructure could require between 4.52 MW and 22.35 MW by 2050. Although the numerical results are specific to the case study, the main contribution of this study lies in an operationally grounded planning framework that links the sizing of pilot routes with long-term charging demand scenarios and charging infrastructure projections, providing a transferable basis for preliminary electric bus planning in other mid-size or emerging regions facing similar infrastructure constraints.

Systems doi: 10.3390/systems14050516

Authors: Ali Asghar Bataleblu John Arthur Poirier Julia Taylor

Key challenges for the future Systems Engineering (SE) include practically converting concepts into actions that solve complex problems and implementing formalized Transdisciplinary Integration (TDI) approaches to achieve this. This paper starts with a literature review, which exposes specific gaps: Transdisciplinarity in SE as a mindset is not well established and Transdisciplinary Integration Processes (TDIP) are not formalized and rigorously applied. Challenges are collected with a focus on “Transdisciplinarity in SE” as a meta-discipline in supporting TDI meta-model development. To this end, a TDI conceptual capability model comprising ends, ways, and means that coalesce transdisciplinary knowledge, methods, and stakeholder perspectives, and a TDIP model applicable across wide-ranging problem spaces and dimensions are proposed. A Disciplinarity Index (DI) is introduced to analyze a spectrum from Intra- to Alter-Disciplinarity; determine proportionality of problem dimensions, factors, and bounding aspects; and define a solution approach. The models are validated by applying the DI to four socio-technical problem domains ranging in scope and scale. This study concludes that the diverse challenges are approachable using the proposed conceptual foundation and models, which are unique in establishing a mechanism to blend disciplines, meet the challenges, and achieve Transdisciplinarity in the SE community while forming relations in the broader systems and transdisciplinary communities.

Systems doi: 10.3390/systems14050518

Authors: Naomi Bennett-Steele Sophie Morris Sarah J. Moller James Hall Jenny Stocker Xinfang Wang Suzanne Bartington Zongbo Shi Alexandra Penn

The UK has a legally binding commitment to achieve net-zero greenhouse gas emissions by 2050, and multiple statutory responsibilities related to mitigation actions are held at local or regional authority levels. However, local authorities face multiple political, financial and service pressures. Adopting a “whole-systems approach” which addresses complexity across interrelated sectors is necessary to achieve significant progress and to optimise co-benefits, but systems thinking is not currently formally embedded in local government. Using the West Midlands’ journey to net zero as a case study, this research explores the use of participatory systems mapping with local and regional authority officers to examine the system that would enable ‘thriving, net zero communities in the West Midlands’. Analysis of the systems map, constructed across a workshop and online follow-up sessions, identified opportunities for local and regional authorities to create local benefit whilst delivering on both statutory functions and net zero, by disrupting reinforcing poverty cycles, improving the quality of housing, and increasing skills and employment support. This whole-system approach also highlights the need for different ways of working in local government to facilitate greater cross-team collaboration to address ‘wicked’ problems such as poverty, climate adaptation and resilience.

Systems doi: 10.3390/systems14050517

Authors: Jianan Zhou Weijun Zhong Shue Mei

The commissioning of private suppliers to conduct technological research and development (R&D) has become a central instrument for the public sector to promote technological innovation in public goods. However, information asymmetry and goal divergence between public principals and suppliers create moral hazard problems that can undermine innovation efficiency. Purely output-based incentive contracts are often insufficient to curb suppliers’ opportunistic behavior, especially when R&D outputs are uncertain and difficult to measure ex ante. This raises the need to complement incentive contracts with supervision mechanisms and to jointly optimize the structure of incentives and monitoring efforts. Building on principal–agent theory, this paper develops an incentive model that explicitly incorporates both supervision intensity and regulatory difficulty and analyzes how these factors shape suppliers’ R&D efforts and the public sector’s benefit levels. The results show, first, that appropriately designed supervision can increase suppliers’ willingness to invest in R&D and thereby help to strengthen the effectiveness of incentives. Second, incentive contracts need to be adjusted in line with supervision intensity: by reallocating rewards based on both observed outputs and supervision results, the public principal can induce higher effort levels. Third, as regulatory difficulty rises, the marginal effectiveness of supervision changes; under high regulatory difficulty, excessive supervision may even weaken incentive effects, implying that supervision intensity should be kept within a moderate range. Fourth, there exists an interior level of supervision intensity that balances monitoring costs against incentive benefits and thus maximizes the principal’s overall expected payoff. Viewed from a system engineering perspective, commissioned public goods R&D constitutes a complex multi-actor system subject to external disturbances in which incentive and supervision mechanisms operate as joint control structures regulating system behavior. The findings provide analytical support and policy-relevant insights for designing and calibrating supervision and incentive mechanisms in technological innovation projects for public goods.

Systems doi: 10.3390/systems14050514

Authors: Zhibo Zhao Zhe Huang Yufei Qiao Jiamin Ren

The New Energy Industry (NEI) is a strategic sector for harmonizing the energy-economy-environment nexus, yet it faces challenges from high technological complexity and industrial fragmentation. DIT enables enterprises to transition from passive response to proactive implementation of systematic adjustments. This study explores how Digital Intelligence Technology (DIT) drives integrated innovation within the sector. Utilizing a panel dataset of 750 listed firms from 2007 to 2023, we examine the influence of DIT through mediation, threshold, and moderation models. Our findings indicate that DIT significantly facilitates integrated innovation by expanding corporate capabilities and reducing information asymmetries. This impact is primarily contingent upon R&D intensity and government subsidies. Notably, R&D intensity exhibits a triple-threshold effect (3.82%, 7.47%, and 7.88%); specifically, once R&D investment surpasses 7.88%, DIT generates a multiplier effect with internal capabilities, substantially accelerating the integration of internal and external innovation elements. Furthermore, State-Owned Enterprises (SOEs) demonstrate superior efficiency in leveraging DIT for innovative outcomes compared to non-SOEs, bolstered by policy advantages. Government subsidies further mitigate resource constraints and enhance risk-bearing capacity. These results provide critical strategic insights for NEI to refine R&D management and bolster human capital, ultimately helping the industry achieve a balance between economic growth and environmental sustainability.

Systems doi: 10.3390/systems14050515

Authors: Yoonseok Kang Dongchul Park

Smart manufacturing depends on operational data that remain continuous, interpretable, and reusable in practice. In constrained small- and medium-sized enterprise (SME) factories, however, the main bottleneck often lies not in later-stage analytics or AI applications but in securing an operationally viable data foundation under real deployment conditions. A lifecycle-based analysis of smart manufacturing data pipelines, together with recurrent SME deployment constraints identified in prior studies, led this study to derive six recurring operational risks. On this basis, this study proposes an Operational Data Foundation Framework structured around core requirements of continuity, governance, diagnosability, operability, reprocessability, and evolvability. These requirements are further articulated through design principles and assessable operational invariants. The framework was instantiated in a real SME factory, where heterogeneous field sources were integrated into a coherent operational data foundation for smart manufacturing through constrained communication paths, durable edge-side capture, cloud-side stream processing, controlled data normalization, and monitoring and alerting functions. Requirement-based evidence from the field implementation showed that the system preserved stable semantics across the pipeline, made failures traceable to specific lifecycle segments, preserved historical records for later reprocessing, and remained manageable under constrained deployment conditions. A representative field case further demonstrated the framework’s practical value: severe communication instability was diagnosed through lifecycle-segment discrepancy analysis and improved from approximately 33% to 95% packet reception after targeted intervention. This study contributes a field-grounded and assessable design logic for operational data foundations, with field evidence from a single food manufacturing SME supporting its feasibility and operational relevance.

Systems doi: 10.3390/systems14050513

Authors: Junaid Khan Yuping Deng Hira Jan Shah Mir Mowahed

This study rigorously examines the impact of political instability (POI) on firm performance (FPER) using high-dimensional panel data from 2006 to 2022, drawn from the World Bank Enterprise Survey (WBES) for 60 economies. Using advanced machine learning-based econometric techniques, including Double-Selection LASSO Regression (DSLR) and Partialing-Out LASSO Regression (POLR), the analysis reveals that POI significantly reduces FPER across the sampled countries. These findings remain robust across a series of validation tests, including alternative estimation approaches such as Cross-Fit Partialing-Out LASSO Regression (CF-POLR) and Bayesian Model Averaging (BMA), the use of alternative FPER proxies—employment growth (FEMG), innovation (IINN), and labor productivity (FLP)—and the substitution of POI with government regulation (REG). Mediation analysis further indicates that operational costs (OCOST) and firm investment (FINV) significantly and partially mediate the total effect of POI on FPER. In contrast, financial constraints (FCST) do not emerge as a significant mediator. The moderation analysis shows that political connections (PC) substantially attenuate the negative impact of POI on FPER. Heterogeneity analyses demonstrate that small, young, and capital-intensive firms are more severely affected by POI than medium and large, older, technology-intensive, and labor-intensive firms. Additionally, firm ownership-based heterogeneity indicates that state-owned enterprises experience slightly stronger adverse effects from fluctuations in POI than non-state-owned firms. Based on these empirical insights, policymakers need to promote institutional stability and provide direct support for vulnerable young and small firms to reduce the adverse effects of POI on FPER. Ultimately, this boosts economic flexibility in politically unstable markets by managing key growth drivers.

Systems doi: 10.3390/systems14050512

Authors: Lizhuo Cui Jiarui Feng Yuge Zhang Zhifei Li Feiyu Hao Junran Zhao Anzhe Shao Lizhi Xing

The U.S. strategies of “friend-shoring” and “near-shoring,” aimed at enhancing supply chain autonomy, are profoundly restructuring global production networks. This study empirically evaluates the impact of these strategies on China’s factor-intensive industries. Utilizing the Asian Development Bank Multi-Regional Input-Output database, we constructed a Global Industrial Value Chain Backbone Network and applied the X-index Filtering Algorithm to identify core trade relationships. Policy impacts were quantified by comparing degree, betweenness, and closeness centralities between null and counterfactual models. The results indicate that “friend-shoring” exerts a significant “squeeze effect” on China, with resource-intensive industries facing severe decoupling risks that cascade into supporting services. Conversely, the impact of “near-shoring” remains limited, as Chinese firms mitigate trade diversion through strategic overseas investment. Scenario analysis further reveals that while new trade remedies targeting re-exports may bolster emerging hubs like Vietnam and Mexico in the short term, they increase the topological distance of global production networks, leading to a systemic decline in efficiency. These findings provide critical quantitative evidence regarding the evolution and systemic risks of global value chains under geopolitical intervention.

Systems doi: 10.3390/systems14050511

Authors: Ying Zhao Zhengyang Qin Zhaoyu Wang Wenbing Wu

In volatile environments, teams must rapidly adapt to complex internal and external pressures. Although paradoxical leadership (PL) has been linked to enhanced team adaptability, the motivational pathways driving this systemic outcome remain underexplored. To address this gap, we examine how collective goal orientations—treated as resource-allocation rules—mediate the relationship between PL and team adaptability. Grounded in Conservation of Resources theory, we tested the model in a three-wave, multi-source study of 114 high-tech specialist teams, where team members rated PL at T1 and team goal orientations at T2, while supervisors rated team adaptability at T3. Our findings reveal that, within this dataset and context, PL’s indirect association with team adaptability operates largely through a performance approach orientation, an agentic strategy that mobilizes resources for visible competence in the short term. In contrast, while a team learning orientation predicts adaptability when assessed alone, its unique mediating effect becomes non-significant when performance approach orientation is taken into account, consistent with conceptual and empirical overlap between the two constructs. Moreover, although PL reduces performance avoidance orientation, this reduction does not significantly enhance team adaptability. In the present dataset and context, these findings indicate performance approach orientation as the more robust motivational pathway linking PL to team adaptability, thereby providing a foundation for further inquiry into how systemic levers might selectively shape distinct team motivational states.

Systems doi: 10.3390/systems14050510

Authors: Yating Li Lu Liu

The cooperation and integration of channels has promoted the evolution of the traditional dual-channel supply chain into an omni-channel supply chain. The omni-retailing modes can be divided into M mode and R mode depending on the difference in the online channel openers (which are the manufacturer and the retailer, respectively). This paper explores the inter-chain competition equilibrium and intra-chain coordination strategy of two different omni-channel supply chains (which adopt M mode and R mode, respectively). First, the EPEC (Equilibrium Problems with Equilibrium Constraints), MPEC (Mathematical Program with Equilibrium Constraints), and Nash decision models are used to depict the competition equilibrium of the two omni-channel supply chains for three cases: Both supply chains are decentralized, one is decentralized, and the other is integrated, and both are integrated, respectively. Then, the supply chain contracts are designed to coordinate the different kinds of omni-retailing modes. Last, this study verifies and extends the relevant conclusions by using numerical analysis. The results show that: service cooperation is the dominant strategy of omni-channel supply chains when there exists inter-chain competition; R mode is gradually dominating with the deepening of the service cooperation; there exists the parameter interval to make it so that M mode and R mode each becomes the dominant mode in supply chain competition; supply chain coordination is the dominant strategy of inter-chain competition; and the wholesale price contract with subsidy designed in this paper can realize the coordination of the omni-channel supply chain, effectively. But when the two omni-channel supply chains all adopt the coordinative strategy, the supply chain system may fall into the “prisoner’s dilemma”.

Systems doi: 10.3390/systems14050509

Authors: Yehudit Chassida Vered Elimelech Sarit Schussheim

Educational systems increasingly function as complex social environments in which schools, families, and communities interact across institutional boundaries. Within these interconnected systems, teachers frequently operate at the boundary between schools and parents, negotiating expectations regarding trust, authority, and parental participation. Although parental involvement has been widely studied, relatively little attention has been given to how teachers themselves perceive and navigate these relationships within everyday school practice. This study examines novice teachers’ perceptions of parent–teacher relations through a multi-level systemic perspective. Based on a quantitative survey of approximately 200 novice ultra-Orthodox teachers, the research explores teachers’ perceptions of parental trust and active parental involvement across three interconnected levels: micro-level teacher–parent interactions, meso-level school organizational contexts, and macro-level systemic transformations in educational governance. The findings reveal that teachers’ perceptions of parental trust and involvement are shaped by both organizational conditions and broader systemic changes in school–family relations. Conceptualizing teacher–parent relations through a multi-level lens, the study contributes an empirical application of this perspective to the examination of parent–teacher relations. It demonstrates how teachers interpret and navigate these relationships across different levels of the educational system within a distinct socio-cultural context. Focusing on teachers’ perceptions, the study further extends existing conceptualizations of teachers as boundary actors.

Systems doi: 10.3390/systems14050508

Authors: Natansh Vyas Kristin Falk Omid Razbani

Systems capability is the cognitive ability to plan and execute actions using systems-oriented reasoning. While essential for the design and development of complex systems, it manifests in large organizations primarily through communication and technical documentation, making it difficult to observe and measure at scale. This paper presents an automated method for inferring systems capability from natural language artifacts. We construct a feature-based representation that captures linguistic indicators of systems-oriented reasoning and use these features to train an ordinal logistic regression model on 75 graded systems engineering essay-type case reports, with expert-assigned quality grades serving as proxies for capability levels. The model is evaluated using a 60–20–20 train–validation–test split, achieving 53.3% test accuracy and a mean absolute error of 0.73 grade levels. Notably, 93.3% of predictions fall within ±1 grade level of the true assessments, indicating that specific linguistic patterns and systems attributes are strongly associated with higher systems capability. The proposed approach enables scalable assessment of systems capability with applications in engineering education, workforce development, and enterprise planning. Future work will refine the model and evaluate its performance in industrial settings.

Systems doi: 10.3390/systems14050507

Authors: Qi Xu Jiahao Wang Hongcheng Li Song Wu Qiang Yan

In response to the increasing operational complexity of inland waterway systems, this study develops a multi-objective optimization framework for ship lock scheduling under energy-consumption and resource constraints. The model evaluates five operational dimensions, namely average waiting time, lock utilization, total energy consumption, arrival rescheduling rate, and berth-overcapacity penalty. Based on historical lockage records from the Da Teng Gorge Ship Lock Hub, four representative multi-objective algorithms—NSGA-II, NSGA-III, MOEA/D, and SPEA-II—are comparatively examined. The revised analysis emphasizes trade-off performance rather than unsupported absolute dominance claims: NSGA-III shows the most balanced overall behavior on the preserved empirical instance, MOEA/D remains competitive in time-sensitive scenarios, and SPEA-II performs well in some overcapacity-control settings. To improve methodological transparency, the paper clarifies the physical meaning and source of major parameters, distinguishes measured quantities from scenario settings, and reports carbon impact as a derived indicator linked to energy consumption. These revisions provide a more transparent and practically interpretable basis for intelligent ship lock scheduling under congestion, energy, and resource constraints.

Systems doi: 10.3390/systems14050506

Authors: Marietta Balázsné Lendvai András Schlett Judit Beke

The increasing vulnerability of global food systems—exacerbated by the pandemic, climate change, and disruptions to international supply chains—has highlighted the importance of local food production for sustainability, food security, and rural resilience. At the same time, the LOHAS (Lifestyles of Health and Sustainability) value system is gaining prominence, shaping consumer demand for locally produced, environmentally responsible, and health-oriented products. While the existing literature predominantly addresses LOHAS consumers and local food systems as separate research domains, limited empirical attention has been paid to the value-based alignment between LOHAS principles and local food producers, particularly from a territorial and place-based perspective. This study seeks to address this gap by examining how LOHAS value dimensions are reflected in the self-identification and operational practices of local food producers, and by analyzing how such value alignment may be interpreted as contributing to the sustainability and resilience of territorially embedded rural production systems. From a systems perspective, LOHAS-related value alignment may be interpreted as a potential coordination mechanism that may contribute to strengthening feedback loops between producers and consumers and may enhance the adaptive capacity of short food supply chains as socio-ecological systems. The empirical analysis draws on an online survey conducted in the second quarter of 2024 among 73 local producers operating in Zala and Vas counties in Western Hungary. Factor analysis and cluster analysis were applied to identify underlying value structures and producer typologies. The results reveal two distinct producer clusters, one of which exhibits a strong alignment with LOHAS values. Producers within this cluster place particular emphasis on sustainability, environmental responsibility, health consciousness, and authenticity, alongside a pronounced commitment to local embeddedness and community-oriented practices. Overall, the findings demonstrate that LOHAS-related values are not confined to the consumer side but are increasingly embedded in territorially grounded local production models. This value alignment may contribute to strengthening short food supply chains rooted in specific geographical contexts, thereby contributing to the long-term socio-economic and environmental sustainability of rural regions.

Systems doi: 10.3390/systems14050505

Authors: Zhengrui Li Qingjin Wang Shuai Huang Tian Lan

Driven by the rapid proliferation of generative artificial intelligence, the computing power industry is undergoing a paradigm shift from traditional linear supply chains toward complex, interdependent innovation ecosystems. This study investigates the evolutionary dynamics of the computing power ecosystem, specifically examining the strategic interplay between antitrust regulation and vertical integration. We construct a tripartite evolutionary game framework involving the government regulators, leading computing power incumbents, and downstream AI innovators. By deriving evolutionarily stable strategies, we analyze the underlying mechanisms of system transitions and employ numerical simulations to explore key parametric sensitivities. The theoretical analysis suggests that the evolution of the AI computing power innovation ecosystem manifests distinct stage-based progressions and threshold-driven bifurcation characteristics—potentially transitioning from an initial efficiency-based state of “natural monopoly and passive dependence” during the industry’s emergence, through transitionary states such as the “comfort zone trap” or “regulatory stalemate” during the expansion phase, and ultimately converging toward a mature configuration of “co-opetition and endogenous growth.” The model suggests that downstream AI firms may benefit from advancing vertical integration, achieving hardware–software co-optimization through self-developed domain-specific architectures, The analysis further implies that the leading computing power firm could strengthen its ecological niche by opening its underlying interfaces and software stacks to maintain its ecological niche as the industry cornerstone in integrated form. For the government, it is necessary to establish precise dynamic intervention and orderly exit mechanisms.

Systems doi: 10.3390/systems14050504

Authors: Faed Mahmoud Buojaylah Fayid Askin Kiraz

Higher education institutions are under increasing pressure to strengthen environmental education (EE) due to critical environmental challenges, while also addressing learner support, engagement, and instructional resource constraints. Recent advances in conversational artificial intelligence (AI), particularly generative AI systems based on large language models such as ChatGPT, enable new forms of human–machine cooperation and provide opportunities for interactive guidelines and individualized feedback. This study evaluates AI-supported EE compared with conventional classroom instruction using a quasi-experimental pre-test/post-test research design. Forty undergraduate students from a Libyan university were recruited and assigned to either the AI-supported EE group (n = 20) or a conventional classroom control group (n = 20). Both groups followed the same EE curriculum over eight weeks. Learning outcomes were assessed across environmental knowledge, attitudes, and environmentally responsible behavior using structured instruments. Paired-samples t-tests indicated statistically significant improvements within the AI-supported group across all outcomes (p < 0.05). However, between-group comparisons did not show statistically significant differences. Analysis controlling for baseline differences indicated a statistically significant group effect for knowledge (p < 0.05), while attitudes and behavior remained non-significant. These findings suggest that AI-supported learning may support EE learning for higher education.

Systems doi: 10.3390/systems14050503

Authors: Bolu Wei Pan Liu Chen Su

Against the backdrop of broadly similar safety regulation, firms in high-risk industries continue to differ in day-to-day safety governance. This study draws on distributed-cognition and socio-technical systems perspectives to examine how formal, material, and relational carriers help senior-management safety prioritization acquire a recorded organizational footprint under performance pressure. We conceptualize institutionalization, digital safety system support, and team safety climate as interdependent carriers and analyze three-wave, multi-source data from 183 firms and 667 teams in Chinese high-risk industries. Firm-level operational leading indicators (OLI) from safety-management records serve as the primary recorded-outcome layer. OLI analyses show that institutionalization and digital safety system support are robustly associated with the overall recorded-indicator composite; team safety climate, by contrast, aligns most clearly with proactive reporting. The three carriers leave carrier-specific signatures across recorded-indicator bundles: institutionalization in closed-loop rectification, digital safety system support in digital-system activity, and team safety climate in proactive reporting. Performance pressure operates mainly as an additive contextual force in the recorded layer. The findings contribute to Systems scholarship on entangled socio-technical resilience by identifying how managerial priorities acquire organizational reach through differentiated carrier coupling in the recorded organizational layer.

Systems doi: 10.3390/systems14050502

Authors: Homero Malagón Alfonso Ávila Robinson Aida Huerta Barrientos

Innovation system decision-making is a core component in promoting incentives and conditions necessary for the emergence of innovation. It also plays a critical role in guiding policy and modeling strategies that aim to promote science, technology, and entrepreneurship at national, regional, and local levels. Decision-makers often select innovation system models that do not align with contextual scope, data accessibility, or institutional conditions, undermining their implementation. The lack of alignment between innovation system model assumptions and contextual realities undermines analysis and policy design, particularly when trying to implement a regional model on a national scale without any sort of adaptation. This study presents a framework that aligns innovation system models to specific contexts by providing a decision-making system based on structural analysis. Using a comprehensive collection of relevant previous studies related to the theoretical evolution of innovation system models, this research provides insights regarding the most used types and techniques to compare innovation systems comprising national and regional ISs, helix models, and innovation and entrepreneurship ecosystems. For each model, explanatory potential via structural analysis is operationalized through five indicators derived from multilevel graphs: geopolitical scope, number of actors, vertical and horizontal density, and Shannon’s entropy. These indicators are then systematized into dimensions comprising two feasibility filters and three mechanism-related dimensions, forming the basis for a minimum viable innovation system model selection heuristic. This structural analysis shows that ecosystem lenses capture distributive and adaptive interaction structures; helix models emphasize coordination and governance; and national or regional innovation systems underscore policy reach and institutional boundaries. The results provide a numerical analysis of three different contexts—a national mission, a city entrepreneurship program, and a regional coordination upgrading effort—highlighting areas for improvement in planning, project implementation, and public policy design.

Systems doi: 10.3390/systems14050501

Authors: Yue Yu Li Jia Xuemao Guo

The common root characteristic of CO2 and air pollutants in the power industry, both derived from fossil fuel combustion, provides a natural basis for their synergistic emission reduction. However, existing studies suffer from the lack of a multi-pollutant synergistic evaluation system and an imperfect emission reduction technology database, which hinder their ability to support low-cost and high-efficiency emission reduction practices in the industry. Targeting the minimization of synergistic emission reduction costs and the maximization of emission reduction effects, this study integrated the process and economic parameters of 11 power generation technologies and 55 pollutant control technologies to establish a full-chain energy conservation and emission reduction technology database for the power industry, through literature research, industry surveys, and data mining. Based on the definition of pollution equivalent in the Environmental Protection Tax Law, we innovatively developed an air pollutant equivalent normalization evaluation method and constructed a two-dimensional coordinate system comprehensive evaluation system for CO2 and air pollutants, enabling quantitative analysis and visual evaluation of the synergistic emission reduction effects of various technologies. The results show that new energy power generation technologies such as nuclear power and wind power, as well as O2/CO2 cycle combustion, ammonia-based desulfurization, and SNCR-SCR combined reduction technologies, exhibit excellent synergistic emission reduction performance for CO2 and multiple pollutants. In contrast, some conventional pollutant control technologies, such as the limestone-gypsum method and traditional electrostatic precipitation, have significant CO2 emission increase antagonistic effects. This study also completed the two-dimensional classification of 66 emission reduction technologies based on “emission reduction efficiency-economic cost”, identified application scenarios for different types of technologies, and proposed optimized paths for synergistic emission reduction adapted to the development of the power industry. The research findings fill the gap in quantitative standards for multi-pollutant synergistic emission reduction, provide theoretical support and detailed technical references for emission reduction technology selection and environmental policy formulation in the power industry, and help the industry achieve the dual development requirements of the “double carbon” goal and air quality improvement.

Systems doi: 10.3390/systems14050500

Authors: Xueqing Pei Chunlin Li

Digital governance in higher education represents a complex systemic challenge, shaped by the intricate interplay of socio–economic–political contexts, technological infrastructures, and multiple stakeholders. Yet existing scholarship tends to examine these factors in isolation, lacking an integrated theoretical lens capable of capturing their systemic interdependencies and dynamic interactions. This study addresses this gap by drawing on the Social–Ecological Systems (SES) framework—a well-established systems theory for analyzing coupled social and ecological dynamics—to construct an integrated analytical framework for university digital governance. The framework organizes governance into three interconnected dimensions: external contexts, internal systems, and interaction effects. External contexts—including technological ecosystems and socio–economic–political factors—shape opportunities and constraints for universities. Internal systems, comprising resource systems, resource units, governance structures, and actors, form a complex network through information flows, resource flows, and institutional arrangements. Interaction effects emerge from these networks and are observed in both social outcomes and ecological outcomes, encompassing both positive and negative dimensions. The framework advances theory by extending the SES perspective to higher education, integrating multiple governance elements, and operationalizing core variables for measurement. Practically, it provides universities with a systematic tool for diagnosing digital governance performance, identifying gaps, and guiding optimization, while also supporting cross-institutional benchmarking and longitudinal monitoring. Future research should empirically test the framework, refine the operational indicators, and explore its applicability across diverse institutional and cultural contexts.

Systems doi: 10.3390/systems14050499

Authors: Ersin Acikgoz Gulden Oner

This study analyzes the econometric correlation between resale prices and CO2 emissions of 832 bulk carriers sold from 2018 to 2025. It uses a cross-sectional hedonic pricing model to look at how environmental performance affects the value of sub-types of dry bulk vessels (Capesize, Panamax, Supramax, and Handysize) and age groups (0–5, 6–10, 11–15, and 16+). The findings show that emission efficiency has a statistically significant and negative effect on second-hand prices for all models. Results indicate that higher emission intensity (higher technical efficiency values) reduces vessel values. The magnitude of this effect varies by ship type and age group. Based on the Technical Efficiency Indicator (TEI), refers to Energy Efficiency Existing Ship Index (EEXI) or Energy Efficiency Design Index (EEDI) coefficients, the Supramax segment appears to be the most price-sensitive, followed by Panamax, Capesize, and Handysize. Age has a consistently negative and significant effect on prices, while vessel size positively affects asset values. Further analysis shows that TEI levels increase with vessel age, whereas they decrease with larger vessel size and more recent measurement years. These results are consistent with tightening regulatory pressures under the International Maritime Organization (IMO) frameworks. The economic implications of IMO’s environmental regulations on carbon intensity indicate that compliance with regulation standards creates a measurable price differential in the second-hand ship market. These findings have important implications for shipowners’ investment strategies, regulatory policy design, and the decarbonization path of the maritime sector. This study contributes to the growing research on environmental economics in maritime transport by providing empirical evidence on how carbon regulations translate into tangible asset value impacts.

Systems doi: 10.3390/systems14050498

Authors: Yan Li Jiafei Yue Qingbo Huang

Port competition is increasingly shaped by network structures and differentiated functional roles rather than isolated capacity-based comparisons. This study investigates the structural evolution and functional differentiation of the global container port network from a systems perspective by integrating port-cluster identification with role-based functional evaluation. A CONCOR-based approach is employed to delineate structurally cohesive port clusters, while the rank-sum ratio (RSR) method is used to assess ports’ dominant functional roles, including High-Efficiency core, Bridge-Control, and free-form bridging functions. Based on a comparative analysis of network data for 2014 and 2024, the results reveal a transition from a relatively dispersed and multi-polar configuration toward a more concentrated and hierarchical system. Three recurrent spatial structures are identified, reflecting differentiated patterns of trunk connectivity, corridor organisation, and adaptive network flexibility. Functionally, core hubs have expanded their coverage of mainline services, Bridge-Control ports have become increasingly concentrated at strategic chokepoints and transition zones, and free-form bridging ports have enhanced routing flexibility by linking structurally non-overlapping subnetworks. These findings advance understanding of the evolving structure and interdependence of global port competition and provide insights for system-level coordination, cluster-based governance, and coordinated infrastructure planning.

Systems doi: 10.3390/systems14050496

Authors: Maurice Yolles

This paper extends the metacybernetic framework by grounding its conceptual descriptions in first principles of information physics. We demonstrate that for living systems to organise efficiently under uncertainty, they must adhere to a strict recursive pattern, a “fractal seed” originating in the third-order interaction between potential and action. By utilising Fisher Information Field Theory (FIFT) within an Informational Realism paradigm, we formalise this process through variational analysis on an implicate–explicate manifold. Under a rigorous informational parsimony constraint (a functional analogue of the holographic principle), we treat the J-field as the dispositional reservoir of latent potential and the I-field as the operative field of structured configurations, and show how their autopoietic coupling generates the system’s Potential–Actuation trait poles as a scale-invariant viability structure This coupling reveals that the boundary substructure, which encodes the holographic content, directly conditions the emergent superstructure through a deterministic parity rule inherited from the dyadic logic of the minimal generic living system represented by θ^2. Drawing on the application of Fisher Information, we show that maintaining informational parsimony requires the system’s architecture to oscillate: odd-numbered orders express two traits (dyads), whereas even-numbered orders express three (triads). This produces a canonical 2–3–2–3–2 sequence, preventing a combinatorial explosion of traits as systemic depth increases. We present the Cogitor5 model as a complete fifth-order exemplar of this rule, demonstrating how this rhythmic structural pattern enables self-evolution, systemic coherence, and collective intelligence in both biological and artificial agencies.

Systems doi: 10.3390/systems14050497

Authors: Kaixuan Zhu Lirong Song Xuejie Yang Wenxing Lu Dongxiao Gu

We systematically analyze the evolutionary characteristics of China’s public health policies, focusing on the dynamic changes in policy content, stage-specific differences, and inter-subject collaborative relationships. Based on 137 public health policy documents issued by the central government, the analysis is conducted from a dual perspective: first, the BERTopic model is employed to identify prominent policy themes and track their evolutionary paths; second, Social Network Analysis (SNA) is utilized to deconstruct the collaborative mechanisms and network structural characteristics among policy actors, goals, and tools. The findings indicate: (1) Collaboration among core policy actors is close, yet inter-departmental transparency and collaborative inclusivity remain limited for certain organizations. (2) Policy goals show a diversifying trend, with the strategic focus shifting from infectious disease prevention and control to comprehensive public health services. (3) There are significant preferences in the selection of policy tools for balancing rapid emergency response with sustainable long-term health governance. These findings reveal the evolutionary laws of the public health policy system and provide a theoretical basis for optimizing the policy framework and enhancing governance efficacy.

Systems doi: 10.3390/systems14050495

Authors: Guojie Xie Yu Tian Ruilin Zhang

With the in-depth advancement of the “Digital China” initiative, policies and research discourses related to the digital economy have continued evolved, making it necessary to systematically examine their stage-specific characteristics and underlying logic from a long-term perspective. Accordingly, this study adopts information society theory as the analytical framework and selects the annual series of reports on China’s digital economy development published by the China Academy of Information and Communications Technology (CAICT) from 2015 to 2024 as the research corpus. Using text mining techniques and Latent Dirichlet Allocation (LDA) topic modeling, this paper conducts a longitudinal examination of the stage-wise systemic evolution of key topics in China’s digital economy development. The findings indicate that over the past decade, the topic structure of China’s digital economy has followed a clear evolutionary trajectory, progressing from “informatization-driven development” to “platform expansion,” and subsequently to “data factors and institutional governance.” In the early stage, the focus was on information infrastructure development and industrial integration; the middle stage shifted toward the platform economy and enterprise growth; more recently, the emphasis has increasingly been placed on the construction of data factor markets and the improvement of governance frameworks. This process of topic evolution not only reflects changes in the practical forms of the digital economy but also reveals the ongoing adjustment of the state’s cognitive framework and governance logic regarding digital economy development. These findings provide empirical evidence for understanding the systemic evolution of China’s digital economy over time. By identifying the stage-specific pathways of China’s digital economy, this study extends the application of information society theory within this context and provides new empirical evidence for understanding the evolutionary logic underlying high-quality digital economy development.

Systems doi: 10.3390/systems14050494

Authors: Taisei Hirayama Kohei Yoshida Hiroki Sakaji Itsuki Noda

Automated warehouses must coordinate fleets of mobile robots online while meeting order deadlines. In online Multi-Agent Pickup and Delivery with Deadlines (MAPD-D), committing to a feasible task immediately may restrict flexibility and increase downstream tardiness through congestion and reservation interactions. We propose Deliberate Assignment Deferral (DAD-θ), a one-parameter gate on top of deadline-aware Token Passing baselines (D-TP and D-TPTS). At each token turn, the token holder evaluates the baseline-defined assignable tasks using the baseline score (lower is better); it commits only if the best score is at most θ, and otherwise follows the baseline fallback. A safety override forces assignment once any assignable task reaches non-positive pickup slack. We also introduce a scale-normalized score that makes θ dimensionless for transfer across maps and deadline scales. In 100-seed paired simulations across four arrival/deadline regimes on a benchmark map, scenario-calibrated DAD-θ reduces cumulative tardiness by 9–58% and increases on-time completion by 1.3–10.0 percentage points relative to always-assign. We discuss how θ can be calibrated offline in a digital twin and monitored online via deferral and safety-trigger rates for service-level control.

Systems doi: 10.3390/systems14050493

Authors: Liu Liu Jianxin Zhu

Low-carbon technology innovation serves as the core driver for multiple countries striving to achieve their dual-carbon goals. Therefore, building efficient low-carbon technology innovation networks and accelerating low-carbon technological innovation have become key focuses of academic research. Leveraging patent and publication data, this study constructs dual networks for low-carbon basic and applied research. It employs Exponential Random Graph Models (ERGMs) and Multilevel Exponential Random Graph Models (MERGMs) to explain the different formation factors and dependency relationships within dual networks. Building on this, this study introduces the NK model to analyze the order of effects of these network formation factors and dependencies. The findings reveal the following: (1) The formation factors of dual low-carbon innovation networks differ significantly. For the basic research network (BRN), the key formation factors—in order of effect—are collaboration stability, transitive closure, partner addition, the Matthew effect, and knowledge siphoning. For the applied research network (ARN), the key formation factors—in order of effect—are historical collaboration, collaboration stability, partner addition, cognitive proximity, and knowledge siphoning. (2) The BRN and ARN exhibit an asymmetric dependency. The dependence of the BRN on the ARN is manifested as structural symbiosis, whereas the ARN, guided by the BRN, demonstrates the transmission of collaborative relationships. This study elucidates the complex formation mechanisms and dependency patterns of low-carbon technology innovation networks, providing a theoretical foundation and decision-making support for the differentiated governance of network structures and the optimized allocation of innovation resources.

Systems doi: 10.3390/systems14050490

Authors: Shuju Hu Jinjing Wan Jinxiu Hou Xiaohan Hu Yongsheng Sun

The shift from the central place paradigm to the network paradigm in regional relation research emphasizes the need to elucidate the factors and mechanisms driving urban network dynamics. Leveraging firm-level big data—including a headquarters–branch relationships database (29,359 headquarters and 114,679 branches) and an investment relationships database (21,843 investing firms and 69,733 recipients)—this study constructs an urban network integrating both vertical and horizontal enterprise connections. Using exponential random graph models (ERGMs), it analyzes the influencing factors and driving mechanisms of urban network dynamics in the Yellow River Basin (YRB). This study found that the urban network in the YRB is characterized by multiple isolated “core–periphery” radial networks. Strong connections are concentrated within each province’s major cities and their immediate surroundings, while horizontal connections across provincial borders are weaker. From 2000 to 2020, the urban network has evolved from isolated “core–periphery” radial networks to corridor networks where some core nodes are interconnected. The urban network dynamics in the YRB result from the combined influences of the preferential attachment mechanism, the network self-organization mechanism, the multi-dimensional proximity mechanisms, and the geographical boundary effect. Enterprises tend to establish branches or investments in cities with spatial proximity and larger economic scales. Reciprocal and transitive structures significantly facilitate urban network formation. Additionally, institutional proximity, geographical proximity, cultural proximity, cognitive proximity, and geomorphological division all exert varying degrees of influence on enterprise connections between cities.

Systems doi: 10.3390/systems14050492

Authors: Yao Huang Fubin Liu Dingli Liu Weijun Liu Rongwei Bu

Traditional cognitive practices in the Engineering Management Major (EMM) are often constrained by safety risks, high costs, and geographical limitations. This study proposes a novel Virtual Cognitive Practice (VCP) mode that integrates ubiquitous learning (U-learning) with web-based panoramic maps to overcome these challenges. We developed a VCP system leveraging panoramic data of roads, bridges, and tunnels from commercial web mapping platforms to provide high-fidelity, interactive observation environments. To evaluate its effectiveness, 147 undergraduate students participated in a virtual practice course and subsequently completed a structured questionnaire. The results demonstrate that the accuracy on objective knowledge tests exceeded 80%, alongside a high mean score of 4.27/5 for visualization satisfaction. Statistical analysis using Chi-square tests indicates that students with prior on-site experience are significantly more confident in the VCP mode’s potential as a pedagogical alternative. This research bridges the technical gap in EMM practical education by providing a flexible, ubiquitous learning ecosystem.

Systems doi: 10.3390/systems14050491

Authors: Weiquan Kong Senlai Zhu Gaoming Yu

With the rapid development of e-commerce and the increasing complexity of urban logistics, traditional delivery methods face significant challenges due to regional traffic restrictions and congestion. This paper presents a two-stage optimization approach for urban delivery routing, integrating the Split Delivery Vehicle Routing Problem (SDVRP) and truck-drone collaboration to address these challenges. In the first stage, a transportation route optimization model based on SDVRP is proposed, which accounts for regional constraints and vehicle capacity limitations. The model allows for demand splitting, reducing the number of vehicles required and minimizing transportation costs. In the second stage, a truck-drone collaborative delivery model is introduced to handle the “last mile” distribution, where drones complement trucks by delivering to areas with restricted vehicle access. The optimization model aims to minimize overall delivery costs while ensuring timely service. An enhanced genetic algorithm is further developed to solve this complex, multi-constrained model. Experimental results show that the proposed collaborative strategy reduces delivery costs by over 10% compared to truck-only delivery, and the improved algorithm achieves a 4.77% average cost reduction over traditional approaches. This study provides valuable insights for optimizing urban logistics systems under regional constraints, offering both theoretical and practical contributions to smart logistics development.